From 9cc0ce64537c22191f796935d10b00c0cb54af99 Mon Sep 17 00:00:00 2001 From: nicksxs Date: Tue, 27 Aug 2024 22:11:44 +0800 Subject: [PATCH] Site updated: 2024-08-27 22:11:43 --- 2019/12/18/1Q84读后感/index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../07/关于读书打卡与分享/index.html | 2 +- .../聊聊-Linux-下的-top-命令/index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../《长安的荔枝》读后感/index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- .../index.html | 2 +- atom.xml | 4 +- baidusitemap.xml | 54 +- categories/Java/GC/index.html | 2 +- categories/Java/page/3/index.html | 2 +- .../leetcode/java/linked-list/index.html | 2 +- leancloud_counter_security_urls.json | 2 +- page/38/index.html | 2 +- page/39/index.html | 2 +- page/40/index.html | 2 +- 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What hurts more, the pain of hard work or the pain of regret?

村上春树《1Q84》读后感

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看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

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What hurts more, the pain of hard work or the pain of regret?

村上春树《1Q84》读后感

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看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}Linux 下 grep 命令的一点小技巧 | Nicksxs's Blog

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What hurts more, the pain of hard work or the pain of regret?

Linux 下 grep 命令的一点小技巧

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

用了比较久的 grep 命令，其实都只是用了最最基本的功能来查日志，

譬如

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grep 'xxx' xxxx.log

然后有挺多情况比如想要找日志里带一些符号什么的，就需要用到一些特殊的

比如这样\"userId\":\"123456\"，因为比如用户 ID 有时候会跟其他的 id 一样，只用具体的值 123456 来查的话干扰信息太多了，如果直接这样

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grep '\"userId\":\"123456\"' xxxx.log

好像不行，盲猜就是符号的问题，特别是\和"这两个，

之前一直是想试一下，但是没成功，昨天在排查一个问题的时候发现了，只要把这些都转义了就行了

grep '\\\"userId\\\":\\\"123456\\\"' xxxx.log

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}Linux 下 grep 命令的一点小技巧 | Nicksxs's Blog

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What hurts more, the pain of hard work or the pain of regret?

Linux 下 grep 命令的一点小技巧

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

用了比较久的 grep 命令，其实都只是用了最最基本的功能来查日志，

譬如

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grep 'xxx' xxxx.log

然后有挺多情况比如想要找日志里带一些符号什么的，就需要用到一些特殊的

比如这样\"userId\":\"123456\"，因为比如用户 ID 有时候会跟其他的 id 一样，只用具体的值 123456 来查的话干扰信息太多了，如果直接这样

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grep '\"userId\":\"123456\"' xxxx.log

好像不行，盲猜就是符号的问题，特别是\和"这两个，

之前一直是想试一下，但是没成功，昨天在排查一个问题的时候发现了，只要把这些都转义了就行了

grep '\\\"userId\\\":\\\"123456\\\"' xxxx.log

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Leetcode 25 Reverse Nodes in k-Group 题解分析

发表于 更新于 分类于 ， 阅读次数： 阅读次数： Disqus：

Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

Example:

Given this linked list: 1->2->3->4->5

For k = 2, you should return: 2->1->4->3->5

For k = 3, you should return: 3->2->1->4->5

Note:

  • Only constant extra memory is allowed.
  • You may not alter the values in the list’s nodes, only nodes itself may be changed.

这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
简单来看下

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public ListNode reverseKGroup(ListNode head, int k) {
    if (k <= 1) {
        return head;
    }
    if (head == null) {
        return null;
    }
    ListNode tempEnd = head;
    ListNode tempHead = head;
    ListNode lastHead = null;
    boolean isHeadSet = false;
    while (true) {
        // k 个一组获取头尾
        for (int i = 0; i < k - 1; i++) {
            System.out.println(tempEnd.val);
            if (tempEnd.next != null) {
                tempEnd  = tempEnd.next;
            } else {
                tempEnd = null;
                break;
            }
        }
        if (tempEnd != null) {
            if (!isHeadSet) {
                // 只有在第一组返回的时候需要赋值给 head
                head = reverse(null, tempHead, tempEnd);
                isHeadSet = !isHeadSet;
            } else {
                // 后面的需要将前一组的尾巴传进去，作为后一组头
                reverse(lastHead, tempHead, tempEnd);
            }
            if (tempHead.next != null) {
                // 如果后续节点还有
                lastHead = tempHead;
                tempEnd = tempHead.next;
                tempHead = tempHead.next;
            } else {
                break;
            }
        } else {
            break;
        }
    }

    return head;
}
public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
    // k 个一组的头尾传进来
    ListNode tail = null;
    while (tail != tempEnd) {
        // tail 表示是将头往后换时，把后面那个先拿着
        tail = tempHead.next;
        // 然后就是交换
        tempHead.next = tempEnd.next;
        tempEnd.next = tempHead;
        tempHead = tail;
    }
    if (lastHead !=null) {
        // 如果不是第一组，则需要接上前一组的尾巴
        lastHead.next = tail;
    }
    return tail;
}
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Leetcode 25 Reverse Nodes in k-Group 题解分析

发表于 更新于 分类于 ， 阅读次数： 阅读次数： Disqus：

Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

Example:

Given this linked list: 1->2->3->4->5

For k = 2, you should return: 2->1->4->3->5

For k = 3, you should return: 3->2->1->4->5

Note:

  • Only constant extra memory is allowed.
  • You may not alter the values in the list’s nodes, only nodes itself may be changed.

这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
简单来看下

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public ListNode reverseKGroup(ListNode head, int k) {
    if (k <= 1) {
        return head;
    }
    if (head == null) {
        return null;
    }
    ListNode tempEnd = head;
    ListNode tempHead = head;
    ListNode lastHead = null;
    boolean isHeadSet = false;
    while (true) {
        // k 个一组获取头尾
        for (int i = 0; i < k - 1; i++) {
            System.out.println(tempEnd.val);
            if (tempEnd.next != null) {
                tempEnd  = tempEnd.next;
            } else {
                tempEnd = null;
                break;
            }
        }
        if (tempEnd != null) {
            if (!isHeadSet) {
                // 只有在第一组返回的时候需要赋值给 head
                head = reverse(null, tempHead, tempEnd);
                isHeadSet = !isHeadSet;
            } else {
                // 后面的需要将前一组的尾巴传进去，作为后一组头
                reverse(lastHead, tempHead, tempEnd);
            }
            if (tempHead.next != null) {
                // 如果后续节点还有
                lastHead = tempHead;
                tempEnd = tempHead.next;
                tempHead = tempHead.next;
            } else {
                break;
            }
        } else {
            break;
        }
    }

    return head;
}
public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
    // k 个一组的头尾传进来
    ListNode tail = null;
    while (tail != tempEnd) {
        // tail 表示是将头往后换时，把后面那个先拿着
        tail = tempHead.next;
        // 然后就是交换
        tempHead.next = tempEnd.next;
        tempEnd.next = tempHead;
        tempHead = tail;
    }
    if (lastHead !=null) {
        // 如果不是第一组，则需要接上前一组的尾巴
        lastHead.next = tail;
    }
    return tail;
}
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Leetcode 3 Longest Substring Without Repeating Characters 题解分析

发表于 分类于 ， ， ， ， 阅读次数： 阅读次数： Disqus：

又做了个题，看记录是以前用 C++写过的，现在捋一捋思路，用 Java 再写了一下，思路还是比较清晰的，但是边界细节处理得比较差

简要介绍

Given a string s, find the length of the longest substring without repeating characters.

样例

Example 1:

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Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.

Example 2:

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Input: s = "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.

Example 3:

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Input: s = "pwwkew"
Output: 3
Explanation: The answer is "wke", with the length of 3.
Notice that the answer must be a substring, "pwke" is a subsequence and not a substring.

Example 4:

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Input: s = ""
Output: 0

就是一个最长不重复的字符串长度，因为也是中等难度的题，不太需要特别复杂的思考，最基本的就是O(N*N)两重循环，不过显然不太好，万一超时间，还有一种就是线性复杂度的了，这个就是需要搞定一个思路，比如字符串时 abcdefgaqwrty，比如遍历到第二个a的时候其实不用再从头去遍历了，只要把前面那个a给排除掉，继续往下算就好了

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class Solution {
    Map<String, Integer> counter = new HashMap<>();
    public int lengthOfLongestSubstring(String s) {
        int length = s.length();
        // 当前的长度
        int subStringLength = 0;
        // 最长的长度
        int maxSubStringLength = 0;
        // 考虑到重复的位置已经被跳过的情况，即已经在当前长度的字符串范围之前的重复字符不需要回溯
        int lastDuplicatePos = -1;
        for (int i = 0; i < length; i++) {
            // 使用 map 存储字符和上一次出现的位置，如果存在并且大于上一次重复位置
            if (counter.get(String.valueOf(s.charAt(i))) != null && counter.get(String.valueOf(s.charAt(i))) > lastDuplicatePos) {
                // 记录重复位置
                lastDuplicatePos = counter.get(String.valueOf(s.charAt(i)));
                // 重置不重复子串的长度，减去重复起点
                subStringLength = i - counter.get(String.valueOf(s.charAt(i))) - 1;
                // 替换当前位置
                counter.replace(String.valueOf(s.charAt(i)), i);
            } else {
                // 如果不存在就直接 put
                counter.put(String.valueOf(s.charAt(i)), i);
            }
            // 长度累加
            subStringLength++;
            if (subStringLength > maxSubStringLength) {
                // 简单替换
                maxSubStringLength = subStringLength;
            }
        }
        return maxSubStringLength;
    }
}

注释应该写的比较清楚了。

0%
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What hurts more, the pain of hard work or the pain of regret?

Leetcode 3 Longest Substring Without Repeating Characters 题解分析

发表于 分类于 ， ， ， ， 阅读次数： 阅读次数： Disqus：

又做了个题，看记录是以前用 C++写过的，现在捋一捋思路，用 Java 再写了一下，思路还是比较清晰的，但是边界细节处理得比较差

简要介绍

Given a string s, find the length of the longest substring without repeating characters.

样例

Example 1:

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Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.

Example 2:

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Input: s = "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.

Example 3:

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Input: s = "pwwkew"
Output: 3
Explanation: The answer is "wke", with the length of 3.
Notice that the answer must be a substring, "pwke" is a subsequence and not a substring.

Example 4:

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Input: s = ""
Output: 0

就是一个最长不重复的字符串长度，因为也是中等难度的题，不太需要特别复杂的思考，最基本的就是O(N*N)两重循环，不过显然不太好，万一超时间，还有一种就是线性复杂度的了，这个就是需要搞定一个思路，比如字符串时 abcdefgaqwrty，比如遍历到第二个a的时候其实不用再从头去遍历了，只要把前面那个a给排除掉，继续往下算就好了

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class Solution {
    Map<String, Integer> counter = new HashMap<>();
    public int lengthOfLongestSubstring(String s) {
        int length = s.length();
        // 当前的长度
        int subStringLength = 0;
        // 最长的长度
        int maxSubStringLength = 0;
        // 考虑到重复的位置已经被跳过的情况，即已经在当前长度的字符串范围之前的重复字符不需要回溯
        int lastDuplicatePos = -1;
        for (int i = 0; i < length; i++) {
            // 使用 map 存储字符和上一次出现的位置，如果存在并且大于上一次重复位置
            if (counter.get(String.valueOf(s.charAt(i))) != null && counter.get(String.valueOf(s.charAt(i))) > lastDuplicatePos) {
                // 记录重复位置
                lastDuplicatePos = counter.get(String.valueOf(s.charAt(i)));
                // 重置不重复子串的长度，减去重复起点
                subStringLength = i - counter.get(String.valueOf(s.charAt(i))) - 1;
                // 替换当前位置
                counter.replace(String.valueOf(s.charAt(i)), i);
            } else {
                // 如果不存在就直接 put
                counter.put(String.valueOf(s.charAt(i)), i);
            }
            // 长度累加
            subStringLength++;
            if (subStringLength > maxSubStringLength) {
                // 简单替换
                maxSubStringLength = subStringLength;
            }
        }
        return maxSubStringLength;
    }
}

注释应该写的比较清楚了。

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Leetcode 104 二叉树的最大深度(Maximum Depth of Binary Tree) 题解分析

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题目介绍

给定一个二叉树，找出其最大深度。

二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。

说明: 叶子节点是指没有子节点的节点。

示例：
给定二叉树 [3,9,20,null,null,15,7]，

1
2
3
4
5
  3
 / \
9  20
  /  \
 15   7

返回它的最大深度 3 。

代码

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// 主体是个递归的应用
public int maxDepth(TreeNode root) {
    // 节点的退出条件之一
    if (root == null) {
        return 0;
    }
    int left = 0;
    int right = 0;
    // 存在左子树，就递归左子树
    if (root.left != null) {
        left = maxDepth(root.left);
    }
    // 存在右子树，就递归右子树
    if (root.right != null) {
        right = maxDepth(root.right);
    }
    // 前面返回后，左右取大者
    return Math.max(left + 1, right + 1);
}

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

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Leetcode 104 二叉树的最大深度(Maximum Depth of Binary Tree) 题解分析

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题目介绍

给定一个二叉树，找出其最大深度。

二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。

说明: 叶子节点是指没有子节点的节点。

示例：
给定二叉树 [3,9,20,null,null,15,7]，

1
2
3
4
5
  3
 / \
9  20
  /  \
 15   7

返回它的最大深度 3 。

代码

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// 主体是个递归的应用
public int maxDepth(TreeNode root) {
    // 节点的退出条件之一
    if (root == null) {
        return 0;
    }
    int left = 0;
    int right = 0;
    // 存在左子树，就递归左子树
    if (root.left != null) {
        left = maxDepth(root.left);
    }
    // 存在右子树，就递归右子树
    if (root.right != null) {
        right = maxDepth(root.right);
    }
    // 前面返回后，左右取大者
    return Math.max(left + 1, right + 1);
}

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

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Leetcode 234 回文链表(Palindrome Linked List) 题解分析

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题目介绍

Given a singly linked list, determine if it is a palindrome.
给定一个单向链表，判断是否是回文链表

例一 Example 1:

Input: 1->2
Output: false

例二 Example 2:

Input: 1->2->2->1
Output: true

挑战下自己

Follow up:
Could you do it in O(n) time and O(1) space?

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

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/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
0%
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Leetcode 234 回文链表(Palindrome Linked List) 题解分析

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题目介绍

Given a singly linked list, determine if it is a palindrome.
给定一个单向链表，判断是否是回文链表

例一 Example 1:

Input: 1->2
Output: false

例二 Example 2:

Input: 1->2->2->1
Output: true

挑战下自己

Follow up:
Could you do it in O(n) time and O(1) space?

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

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/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
0%
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Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析

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题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

例子:

1
2
preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]

返回的二叉树

1
2
3
4
5
  3
 / \
9  20
  /  \
 15   7

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

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class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}
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Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析

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题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

例子:

1
2
preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]

返回的二叉树

1
2
3
4
5
  3
 / \
9  20
  /  \
 15   7

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

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class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}
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What hurts more, the pain of hard work or the pain of regret?

Leetcode 160 相交链表(intersection-of-two-linked-lists) 题解分析

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题目介绍

写一个程序找出两个单向链表的交叉起始点，可能是我英语不好，图里画的其实还有一点是交叉以后所有节点都是相同的
Write a program to find the node at which the intersection of two singly linked lists begins.

For example, the following two linked lists:

begin to intersect at node c1.

Example 1:

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Input: intersectVal = 8, listA = [4,1,8,4,5], listB = [5,6,1,8,4,5], skipA = 2, skipB = 3
Output: Reference of the node with value = 8
Input Explanation: The intersected node's value is 8 (note that this must not be 0 if the two lists intersect). From the head of A, it reads as [4,1,8,4,5]. From the head of B, it reads as [5,6,1,8,4,5]. There are 2 nodes before the intersected node in A; There are 3 nodes before the intersected node in B.

分析题解

一开始没什么头绪，感觉只能最原始的遍历，后来看了一些文章，发现比较简单的方式就是先找两个链表的长度差，因为从相交点开始肯定是长度一致的，这是个很好的解题突破口，找到长度差以后就是先跳过长链表的较长部分，然后开始同步遍历比较 A，B 链表；

代码

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public ListNode getIntersectionNode(ListNode headA, ListNode headB) {
        if (headA == null || headB == null) {
            return null;
        }
        // 算 A 的长度
        int countA = 0;
        ListNode tailA = headA;
        while (tailA != null) {
            tailA = tailA.next;
            countA++;
        }
        // 算 B 的长度
        int countB = 0;
        ListNode tailB = headB;
        while (tailB != null) {
            tailB = tailB.next;
            countB++;
        }
        tailA = headA;
        tailB = headB;
        // 依据长度差，先让长的链表 tail 指针往后移
        if (countA > countB) {
            while (countA > countB) {
                tailA = tailA.next;
                countA--;
            }
        } else if (countA < countB) {
            while (countA < countB) {
                tailB = tailB.next;
                countB--;
            }
        }
        // 然后以相同速度遍历两个链表比较
        while (tailA != null) {
            if (tailA == tailB) {
                return tailA;
            } else {
                tailA = tailA.next;
                tailB = tailB.next;
            }
        }
        return null;
    }

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

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Leetcode 160 相交链表(intersection-of-two-linked-lists) 题解分析

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题目介绍

写一个程序找出两个单向链表的交叉起始点，可能是我英语不好，图里画的其实还有一点是交叉以后所有节点都是相同的
Write a program to find the node at which the intersection of two singly linked lists begins.

For example, the following two linked lists:

begin to intersect at node c1.

Example 1:

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Input: intersectVal = 8, listA = [4,1,8,4,5], listB = [5,6,1,8,4,5], skipA = 2, skipB = 3
Output: Reference of the node with value = 8
Input Explanation: The intersected node's value is 8 (note that this must not be 0 if the two lists intersect). From the head of A, it reads as [4,1,8,4,5]. From the head of B, it reads as [5,6,1,8,4,5]. There are 2 nodes before the intersected node in A; There are 3 nodes before the intersected node in B.

分析题解

一开始没什么头绪，感觉只能最原始的遍历，后来看了一些文章，发现比较简单的方式就是先找两个链表的长度差，因为从相交点开始肯定是长度一致的，这是个很好的解题突破口，找到长度差以后就是先跳过长链表的较长部分，然后开始同步遍历比较 A，B 链表；

代码

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public ListNode getIntersectionNode(ListNode headA, ListNode headB) {
        if (headA == null || headB == null) {
            return null;
        }
        // 算 A 的长度
        int countA = 0;
        ListNode tailA = headA;
        while (tailA != null) {
            tailA = tailA.next;
            countA++;
        }
        // 算 B 的长度
        int countB = 0;
        ListNode tailB = headB;
        while (tailB != null) {
            tailB = tailB.next;
            countB++;
        }
        tailA = headA;
        tailB = headB;
        // 依据长度差，先让长的链表 tail 指针往后移
        if (countA > countB) {
            while (countA > countB) {
                tailA = tailA.next;
                countA--;
            }
        } else if (countA < countB) {
            while (countA < countB) {
                tailB = tailB.next;
                countB--;
            }
        }
        // 然后以相同速度遍历两个链表比较
        while (tailA != null) {
            if (tailA == tailB) {
                return tailA;
            } else {
                tailA = tailA.next;
                tailB = tailB.next;
            }
        }
        return null;
    }

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

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Leetcode 124 二叉树中的最大路径和(Binary Tree Maximum Path Sum) 题解分析

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题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

The path sum of a path is the sum of the node’s values in the path.

Given the root of a binary tree, return the maximum path sum of any path.

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

路径和 是路径中各节点值的总和。

给你一个二叉树的根节点 root ，返回其 最大路径和

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

代码

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int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

结果图

难得有个 100%，贴个图哈哈

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Leetcode 124 二叉树中的最大路径和(Binary Tree Maximum Path Sum) 题解分析

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题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

The path sum of a path is the sum of the node’s values in the path.

Given the root of a binary tree, return the maximum path sum of any path.

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

路径和 是路径中各节点值的总和。

给你一个二叉树的根节点 root ，返回其 最大路径和

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

代码

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int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

结果图

难得有个 100%，贴个图哈哈

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What hurts more, the pain of hard work or the pain of regret?

关于读书打卡与分享

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最近群里大佬发起了一个读书打卡活动，需要每天读一会书，在群里打卡分享感悟，争取一个月能读完一本书，说实话一天十分钟的读书时间倒是问题不大，不过每天都要打卡，而且一个月要读完一本书，其实难度还是有点大的，不过也想试试看。
之前某某老大给自己立了个 flag，说要读一百本书，这对我来说挺难实现的，一则我也不喜欢书只读一小半，二则感觉对于喜欢看的内容范围还是比较有限制，可能也算是比较矫情，不爱追热门的各类东西，因为往往会有一些跟大众不一致的观点看法，显得格格不入。所以还是这个打卡活动可能会比较适合我，书是人类进步的阶梯。
到现在是打卡了三天了，读的主要是白岩松的《幸福了吗》，对于白岩松，我们这一代人是比较熟悉，并且整体印象比较不错的一个央视主持人，从《焦点访谈》开始，到疫情期间的各类一线节目，可能对我来说是个三观比较正，敢于说一些真话的主持人，这中间其实是有个空档期，没怎么看电视，也不太关注了，只是在疫情期间的节目，还是一如既往地给人一种可靠的感觉，正好有一次偶然微信读书推荐了白岩松的这本书，就看了一部分，正好这次继续往下看，因为相对来讲不会很晦涩，并且从这位知名央视主持人的角度分享他的过往和想法看法，还是比较有意思的。
从对汶川地震，08 年奥运等往事的回忆和一些细节的呈现，也让我了解比较多当时所不知道的，特别是汶川地震，那时的我还在读高中，真的是看着电视，作为“猛男”都忍不住泪目了，共和国之殇，多难兴邦，但是这对于当事人来说，都是一场醒不过来的噩梦。
然后是对于足球的热爱，其实光这个就能掰扯很多，因为我不爱足球，只爱篮球，其中原因有的没的也挺多可以说的，但是看了他的书，才能比较深入的了解一个足球迷，对足球，对中国足球，对世界杯，对阿根廷的感情。
接下去还是想能继续坚持下去，加油！

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What hurts more, the pain of hard work or the pain of regret?

关于读书打卡与分享

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最近群里大佬发起了一个读书打卡活动，需要每天读一会书，在群里打卡分享感悟，争取一个月能读完一本书，说实话一天十分钟的读书时间倒是问题不大，不过每天都要打卡，而且一个月要读完一本书，其实难度还是有点大的，不过也想试试看。
之前某某老大给自己立了个 flag，说要读一百本书，这对我来说挺难实现的，一则我也不喜欢书只读一小半，二则感觉对于喜欢看的内容范围还是比较有限制，可能也算是比较矫情，不爱追热门的各类东西，因为往往会有一些跟大众不一致的观点看法，显得格格不入。所以还是这个打卡活动可能会比较适合我，书是人类进步的阶梯。
到现在是打卡了三天了，读的主要是白岩松的《幸福了吗》，对于白岩松，我们这一代人是比较熟悉，并且整体印象比较不错的一个央视主持人，从《焦点访谈》开始，到疫情期间的各类一线节目，可能对我来说是个三观比较正，敢于说一些真话的主持人，这中间其实是有个空档期，没怎么看电视，也不太关注了，只是在疫情期间的节目，还是一如既往地给人一种可靠的感觉，正好有一次偶然微信读书推荐了白岩松的这本书，就看了一部分，正好这次继续往下看，因为相对来讲不会很晦涩，并且从这位知名央视主持人的角度分享他的过往和想法看法，还是比较有意思的。
从对汶川地震，08 年奥运等往事的回忆和一些细节的呈现，也让我了解比较多当时所不知道的，特别是汶川地震，那时的我还在读高中，真的是看着电视，作为“猛男”都忍不住泪目了，共和国之殇，多难兴邦，但是这对于当事人来说，都是一场醒不过来的噩梦。
然后是对于足球的热爱，其实光这个就能掰扯很多，因为我不爱足球，只爱篮球，其中原因有的没的也挺多可以说的，但是看了他的书，才能比较深入的了解一个足球迷，对足球，对中国足球，对世界杯，对阿根廷的感情。
接下去还是想能继续坚持下去，加油！

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}聊聊 Linux 下的 top 命令 | Nicksxs's Blog

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What hurts more, the pain of hard work or the pain of regret?

聊聊 Linux 下的 top 命令

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

top 命令在日常的 Linux 使用中，特别是做一些服务器的简单状态查看，排查故障都起了比较大的作用，但是由于这个命令看到的东西比较多，一般只会看部分，或者说像我这样就会比较片面地看一些信息，比如默认是进程维度的，可以在启动命令的时候加-H进入线程模式

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-H  :Threads-mode operation
            Instructs top to display individual threads.  Without this command-line option a summation of all threads in each process  is  shown.   Later
            this can be changed with the `H' interactive command.

这样就能用在 Java 中去 jstack 中找到对应的线程
其实还有比较重要的两个操作，
一个是在 top 启动状态下，按c键，这样能把比如说是一个 Java 进程，具体的进程命令显示出来
像这样
执行前是这样

执行后是这样

第二个就是排序了

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SORTING of task window

          For  compatibility,  this top supports most of the former top sort keys.  Since this is primarily a service to former top users, these commands
          do not appear on any help screen.
                command   sorted-field                  supported
                A         start time (non-display)      No
                M         %MEM                          Yes
                N         PID                           Yes
                P         %CPU                          Yes
                T         TIME+                         Yes

          Before using any of the following sort provisions, top suggests that you temporarily turn on column highlighting using the `x' interactive com‐
          mand.  That will help ensure that the actual sort environment matches your intent.

          The following interactive commands will only be honored when the current sort field is visible.  The sort field might not be visible because:
                1) there is insufficient Screen Width
                2) the `f' interactive command turned it Off

             <  :Move-Sort-Field-Left
                 Moves the sort column to the left unless the current sort field is the first field being displayed.

             >  :Move-Sort-Field-Right
                 Moves the sort column to the right unless the current sort field is the last field being displayed.

查看 man page 可以找到这一段，其实一般 man page 都是最细致的，只不过因为太多了，有时候懒得看，这里可以通过大写 M 和大写 P 分别按内存和 CPU 排序，下面还有两个小技巧，通过按 x 可以将当前活跃的排序列用不同颜色标出来，然后可以通过<和>直接左右移动排序列

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}聊聊 Linux 下的 top 命令 | Nicksxs's Blog

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What hurts more, the pain of hard work or the pain of regret?

聊聊 Linux 下的 top 命令

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

top 命令在日常的 Linux 使用中，特别是做一些服务器的简单状态查看，排查故障都起了比较大的作用，但是由于这个命令看到的东西比较多，一般只会看部分，或者说像我这样就会比较片面地看一些信息，比如默认是进程维度的，可以在启动命令的时候加-H进入线程模式

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-H  :Threads-mode operation
            Instructs top to display individual threads.  Without this command-line option a summation of all threads in each process  is  shown.   Later
            this can be changed with the `H' interactive command.

这样就能用在 Java 中去 jstack 中找到对应的线程
其实还有比较重要的两个操作，
一个是在 top 启动状态下，按c键，这样能把比如说是一个 Java 进程，具体的进程命令显示出来
像这样
执行前是这样

执行后是这样

第二个就是排序了

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SORTING of task window

          For  compatibility,  this top supports most of the former top sort keys.  Since this is primarily a service to former top users, these commands
          do not appear on any help screen.
                command   sorted-field                  supported
                A         start time (non-display)      No
                M         %MEM                          Yes
                N         PID                           Yes
                P         %CPU                          Yes
                T         TIME+                         Yes

          Before using any of the following sort provisions, top suggests that you temporarily turn on column highlighting using the `x' interactive com‐
          mand.  That will help ensure that the actual sort environment matches your intent.

          The following interactive commands will only be honored when the current sort field is visible.  The sort field might not be visible because:
                1) there is insufficient Screen Width
                2) the `f' interactive command turned it Off

             <  :Move-Sort-Field-Left
                 Moves the sort column to the left unless the current sort field is the first field being displayed.

             >  :Move-Sort-Field-Right
                 Moves the sort column to the right unless the current sort field is the last field being displayed.

查看 man page 可以找到这一段，其实一般 man page 都是最细致的，只不过因为太多了，有时候懒得看，这里可以通过大写 M 和大写 P 分别按内存和 CPU 排序，下面还有两个小技巧，通过按 x 可以将当前活跃的排序列用不同颜色标出来，然后可以通过<和>直接左右移动排序列

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Leetcode 42 接雨水 (Trapping Rain Water) 题解分析

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题目介绍

给定 n 个非负整数表示每个宽度为 1 的柱子的高度图，计算按此排列的柱子，下雨之后能接多少雨水。

示例


输入：height = [0,1,0,2,1,0,1,3,2,1,2,1]
输出：6
解释：上面是由数组 [0,1,0,2,1,0,1,3,2,1,2,1] 表示的高度图，在这种情况下，可以接 6 个单位的雨水（蓝色部分表示雨水）。

简单分析

其实最开始的想法是从左到右扫区间，就是示例中的第一个水槽跟第二个水槽都可以用这个办法解决

前面这种是属于右侧比左侧高的情况，对于左侧高右侧低的就不行了，（写这篇的时候想起来可以再反着扫一遍可能可以）

所以这个方案不好，贴一下这个方案的代码

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public int trap(int[] height) {
    int lastLeft = -1;
    int sum = 0;
    int tempSum = 0;
    boolean startFlag = true;
    for (int j : height) {
        if (startFlag && j <= 0) {
            startFlag = false;
            continue;
        }
        if (j >= lastLeft) {
            sum += tempSum;
            tempSum = 0;
            lastLeft = j;
        } else {
            tempSum += lastLeft - j;
        }
    }
    return sum;
}

后面结合网上的解法，其实可以反过来，对于每个格子找左右侧的最大值，取小的那个和当前格子的差值就是这一个的储水量了

理解了这种想法，代码其实就不难了

代码

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int n = height.length;
if (n <= 2) {
    return 0;
}
// 思路转变下，其实可以对于每一格算储水量，算法就是找到这一格左边的最高点跟这一格右边的最高点，
// 比较两侧的最高点，取小的那个，然后再跟当前格子的高度对比，差值就是当前格的储水量
int maxL[] = new int[n];
int maxR[] = new int[n];
int max = height[0];
maxL[0] = 0;
// 计算左侧的最高点
for (int i = 1; i < n - 1; i++) {
    maxL[i] = max;
    if (max < height[i]) {
        max = height[i];
    }
}
max = height[n - 1];
maxR[n - 1] = 0;
int tempSum, sum = 0;
// 计算右侧的最高点，并且同步算出来储水量，节省一个循环
for (int i = n - 2; i > 0; i--) {
    maxR[i] = max;
    if (height[i] > max) {
        max = height[i];
    }
    tempSum = Math.min(maxL[i], maxR[i]) - height[i];
    if (tempSum > 0) {
        sum += tempSum;
    }
}
return sum;
0%
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Leetcode 42 接雨水 (Trapping Rain Water) 题解分析

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题目介绍

给定 n 个非负整数表示每个宽度为 1 的柱子的高度图，计算按此排列的柱子，下雨之后能接多少雨水。

示例


输入：height = [0,1,0,2,1,0,1,3,2,1,2,1]
输出：6
解释：上面是由数组 [0,1,0,2,1,0,1,3,2,1,2,1] 表示的高度图，在这种情况下，可以接 6 个单位的雨水（蓝色部分表示雨水）。

简单分析

其实最开始的想法是从左到右扫区间，就是示例中的第一个水槽跟第二个水槽都可以用这个办法解决

前面这种是属于右侧比左侧高的情况，对于左侧高右侧低的就不行了，（写这篇的时候想起来可以再反着扫一遍可能可以）

所以这个方案不好，贴一下这个方案的代码

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public int trap(int[] height) {
    int lastLeft = -1;
    int sum = 0;
    int tempSum = 0;
    boolean startFlag = true;
    for (int j : height) {
        if (startFlag && j <= 0) {
            startFlag = false;
            continue;
        }
        if (j >= lastLeft) {
            sum += tempSum;
            tempSum = 0;
            lastLeft = j;
        } else {
            tempSum += lastLeft - j;
        }
    }
    return sum;
}

后面结合网上的解法，其实可以反过来，对于每个格子找左右侧的最大值，取小的那个和当前格子的差值就是这一个的储水量了

理解了这种想法，代码其实就不难了

代码

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int n = height.length;
if (n <= 2) {
    return 0;
}
// 思路转变下，其实可以对于每一格算储水量，算法就是找到这一格左边的最高点跟这一格右边的最高点，
// 比较两侧的最高点，取小的那个，然后再跟当前格子的高度对比，差值就是当前格的储水量
int maxL[] = new int[n];
int maxR[] = new int[n];
int max = height[0];
maxL[0] = 0;
// 计算左侧的最高点
for (int i = 1; i < n - 1; i++) {
    maxL[i] = max;
    if (max < height[i]) {
        max = height[i];
    }
}
max = height[n - 1];
maxR[n - 1] = 0;
int tempSum, sum = 0;
// 计算右侧的最高点，并且同步算出来储水量，节省一个循环
for (int i = n - 2; i > 0; i--) {
    maxR[i] = max;
    if (height[i] > max) {
        max = height[i];
    }
    tempSum = Math.min(maxL[i], maxR[i]) - height[i];
    if (tempSum > 0) {
        sum += tempSum;
    }
}
return sum;
0%
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Leetcode 028 实现 strStr() ( Implement strStr() ) 题解分析

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：

题目介绍

Implement strStr().

Return the index of the first occurrence of needle in haystack, or -1 if needle is not part of haystack.

Clarification:

What should we return when needle is an empty string? This is a great question to ask during an interview.

For the purpose of this problem, we will return 0 when needle is an empty string. This is consistent to C’s strstr() and Java’s indexOf().

示例

Example 1:

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Input: haystack = "hello", needle = "ll"
Output: 2

Example 2:

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Input: haystack = "aaaaa", needle = "bba"
Output: -1

Example 3:

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Input: haystack = "", needle = ""
Output: 0

题解

字符串比较其实是写代码里永恒的主题，底层的编译器等处理肯定需要字符串对比，像 kmp 算法也是很厉害

code

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public int strStr(String haystack, String needle) {
        // 如果两个字符串都为空，返回 -1
        if (haystack == null || needle == null) {
            return -1;
        }
        // 如果 haystack 长度小于 needle 长度，返回 -1
        if (haystack.length() < needle.length()) {
            return -1;
        }
        // 如果 needle 为空字符串，返回 0
        if (needle.equals("")) {
            return 0;
        }
        // 如果两者相等，返回 0
        if (haystack.equals(needle)) {
            return 0;
        }
        int needleLength = needle.length();
        int haystackLength = haystack.length();
        for (int i = needleLength - 1; i <= haystackLength - 1; i++) {
            // 比较 needle 最后一个字符，倒着比较稍微节省点时间
            if (needle.charAt(needleLength - 1) == haystack.charAt(i)) {
                // 如果needle 是 1 的话直接可以返回 i 作为位置了
                if (needle.length() == 1) {
                    return i;
                }
                boolean flag = true;
                // 原来比的是 needle 的最后一个位置，然后这边从倒数第二个位置开始
                int j = needle.length() - 2;
                for (; j >= 0; j--) {
                    // 这里的 i- (needleLength - j) + 1 ) 比较绕，其实是外循环的 i 表示当前 i 位置的字符跟 needle 最后一个字符
                    // 相同，j 在上面的循环中--，对应的 haystack 也要在 i 这个位置 -- ，对应的位置就是 i - (needleLength - j) + 1
                    if (needle.charAt(j) != haystack.charAt(i - (needleLength - j) + 1)) {
                        flag = false;
                        break;
                    }
                }
                // 循环完了之后，如果 flag 为 true 说明 从 i 开始倒着对比都相同，但是这里需要起始位置，就需要
                // i - needleLength + 1
                if (flag) {
                    return i - needleLength + 1;
                }
            }
        }
        // 这里表示未找到
        return  -1;
    }
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Leetcode 028 实现 strStr() ( Implement strStr() ) 题解分析

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题目介绍

Implement strStr().

Return the index of the first occurrence of needle in haystack, or -1 if needle is not part of haystack.

Clarification:

What should we return when needle is an empty string? This is a great question to ask during an interview.

For the purpose of this problem, we will return 0 when needle is an empty string. This is consistent to C’s strstr() and Java’s indexOf().

示例

Example 1:

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Input: haystack = "hello", needle = "ll"
Output: 2

Example 2:

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Input: haystack = "aaaaa", needle = "bba"
Output: -1

Example 3:

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Input: haystack = "", needle = ""
Output: 0

题解

字符串比较其实是写代码里永恒的主题，底层的编译器等处理肯定需要字符串对比，像 kmp 算法也是很厉害

code

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public int strStr(String haystack, String needle) {
        // 如果两个字符串都为空，返回 -1
        if (haystack == null || needle == null) {
            return -1;
        }
        // 如果 haystack 长度小于 needle 长度，返回 -1
        if (haystack.length() < needle.length()) {
            return -1;
        }
        // 如果 needle 为空字符串，返回 0
        if (needle.equals("")) {
            return 0;
        }
        // 如果两者相等，返回 0
        if (haystack.equals(needle)) {
            return 0;
        }
        int needleLength = needle.length();
        int haystackLength = haystack.length();
        for (int i = needleLength - 1; i <= haystackLength - 1; i++) {
            // 比较 needle 最后一个字符，倒着比较稍微节省点时间
            if (needle.charAt(needleLength - 1) == haystack.charAt(i)) {
                // 如果needle 是 1 的话直接可以返回 i 作为位置了
                if (needle.length() == 1) {
                    return i;
                }
                boolean flag = true;
                // 原来比的是 needle 的最后一个位置，然后这边从倒数第二个位置开始
                int j = needle.length() - 2;
                for (; j >= 0; j--) {
                    // 这里的 i- (needleLength - j) + 1 ) 比较绕，其实是外循环的 i 表示当前 i 位置的字符跟 needle 最后一个字符
                    // 相同，j 在上面的循环中--，对应的 haystack 也要在 i 这个位置 -- ，对应的位置就是 i - (needleLength - j) + 1
                    if (needle.charAt(j) != haystack.charAt(i - (needleLength - j) + 1)) {
                        flag = false;
                        break;
                    }
                }
                // 循环完了之后，如果 flag 为 true 说明 从 i 开始倒着对比都相同，但是这里需要起始位置，就需要
                // i - needleLength + 1
                if (flag) {
                    return i - needleLength + 1;
                }
            }
        }
        // 这里表示未找到
        return  -1;
    }
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Leetcode 053 最大子序和 ( Maximum Subarray ) 题解分析

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题目介绍

Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.

A subarray is a contiguous part of an array.

示例

Example 1:

Input: nums = [-2,1,-3,4,-1,2,1,-5,4]
Output: 6
Explanation: [4,-1,2,1] has the largest sum = 6.

Example 2:

Input: nums = [1]
Output: 1

Example 3:

Input: nums = [5,4,-1,7,8]
Output: 23

说起来这个题其实非常有渊源，大学数据结构的第一个题就是这个，而最佳的算法就是传说中的 online 算法，就是遍历一次就完了，最基本的做法就是记下来所有的连续子数组，然后求出最大的那个。

代码

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public int maxSubArray(int[] nums) {
        int max = nums[0];
        int sum = nums[0];
        for (int i = 1; i < nums.length; i++) {
            // 这里最重要的就是这一行了，其实就是如果前面的 sum 是小于 0 的，那么就不需要前面的 sum，反正加上了还不如不加大
            sum = Math.max(nums[i], sum + nums[i]);
            // max 是用来承载最大值的
            max = Math.max(max, sum);
        }
        return max;
    }
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Leetcode 053 最大子序和 ( Maximum Subarray ) 题解分析

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题目介绍

Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.

A subarray is a contiguous part of an array.

示例

Example 1:

Input: nums = [-2,1,-3,4,-1,2,1,-5,4]
Output: 6
Explanation: [4,-1,2,1] has the largest sum = 6.

Example 2:

Input: nums = [1]
Output: 1

Example 3:

Input: nums = [5,4,-1,7,8]
Output: 23

说起来这个题其实非常有渊源，大学数据结构的第一个题就是这个，而最佳的算法就是传说中的 online 算法，就是遍历一次就完了，最基本的做法就是记下来所有的连续子数组，然后求出最大的那个。

代码

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public int maxSubArray(int[] nums) {
        int max = nums[0];
        int sum = nums[0];
        for (int i = 1; i < nums.length; i++) {
            // 这里最重要的就是这一行了，其实就是如果前面的 sum 是小于 0 的，那么就不需要前面的 sum，反正加上了还不如不加大
            sum = Math.max(nums[i], sum + nums[i]);
            // max 是用来承载最大值的
            max = Math.max(max, sum);
        }
        return max;
    }
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Leetcode 83 删除排序链表中的重复元素 ( Remove Duplicates from Sorted List *Easy* ) 题解分析

发表于 更新于 分类于 ， 阅读次数： 阅读次数： Disqus：
给定一个已排序的链表的头 `head` ， 删除所有重复的元素，使每个元素只出现一次 。返回 已排序的链表 。

题目介绍

给定一个已排序的链表的头 head ， 删除所有重复的元素，使每个元素只出现一次 。返回 已排序的链表 。
PS：注意已排序，还有返回也要已排序

示例 1：

输入：head = [1,1,2]
输出：[1,2]

示例 2：

输入：head = [1,1,2,3,3]
输出：[1,2,3]

提示：

  • 链表中节点数目在范围 [0, 300] 内
  • -100 <= Node.val <= 100
  • 题目数据保证链表已经按 升序 排列

分析与题解

这题其实是比较正常的 easy 级别的题目，链表已经排好序了，如果还带一个排序就更复杂一点，
只需要前后项做个对比，如果一致则移除后项，因为可能存在多个重复项，所以只有在前后项不同
时才会更新被比较项

code

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public ListNode deleteDuplicates(ListNode head) {
    // 链表头是空的或者只有一个头结点，就不用处理了
    if (head == null || head.next == null) {
        return head;
    }
    ListNode tail = head;
    // 以处理节点还有后续节点作为循环边界条件
    while (tail.next != null) {
        ListNode temp = tail.next;
        // 如果前后相同，那么可以跳过这个节点，将 Tail  ---->   temp  ---> temp.next 
        // 更新成  Tail ---->  temp.next
        if (temp.val == tail.val) {
            tail.next = temp.next;
        } else {
            // 不相同，则更新 tail
            tail = tail.next;
        }
    }
    // 最后返回头结点
    return head;
}

链表应该是个需要反复的训练的数据结构，因为涉及到前后指针，然后更新操作，判空等，
我在这块也是掌握的不太好，需要多练习。

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Leetcode 83 删除排序链表中的重复元素 ( Remove Duplicates from Sorted List *Easy* ) 题解分析

发表于 更新于 分类于 ， 阅读次数： 阅读次数： Disqus：
给定一个已排序的链表的头 `head` ， 删除所有重复的元素，使每个元素只出现一次 。返回 已排序的链表 。

题目介绍

给定一个已排序的链表的头 head ， 删除所有重复的元素，使每个元素只出现一次 。返回 已排序的链表 。
PS：注意已排序，还有返回也要已排序

示例 1：

输入：head = [1,1,2]
输出：[1,2]

示例 2：

输入：head = [1,1,2,3,3]
输出：[1,2,3]

提示：

  • 链表中节点数目在范围 [0, 300] 内
  • -100 <= Node.val <= 100
  • 题目数据保证链表已经按 升序 排列

分析与题解

这题其实是比较正常的 easy 级别的题目，链表已经排好序了，如果还带一个排序就更复杂一点，
只需要前后项做个对比，如果一致则移除后项，因为可能存在多个重复项，所以只有在前后项不同
时才会更新被比较项

code

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public ListNode deleteDuplicates(ListNode head) {
    // 链表头是空的或者只有一个头结点，就不用处理了
    if (head == null || head.next == null) {
        return head;
    }
    ListNode tail = head;
    // 以处理节点还有后续节点作为循环边界条件
    while (tail.next != null) {
        ListNode temp = tail.next;
        // 如果前后相同，那么可以跳过这个节点，将 Tail  ---->   temp  ---> temp.next 
        // 更新成  Tail ---->  temp.next
        if (temp.val == tail.val) {
            tail.next = temp.next;
        } else {
            // 不相同，则更新 tail
            tail = tail.next;
        }
    }
    // 最后返回头结点
    return head;
}

链表应该是个需要反复的训练的数据结构，因为涉及到前后指针，然后更新操作，判空等，
我在这块也是掌握的不太好，需要多练习。

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Leetcode 1115 交替打印 FooBar ( Print FooBar Alternately *Medium* ) 题解分析

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：
无聊随机了道并发题，还是比较明显的用信号量就可以解决的题，不过第一次做还是有点不适应，要加油多练练

无聊想去 roll 一题就看到了有并发题，就找到了这题，其实一眼看我的想法也是用信号量，但是用 condition 应该也是可以处理的，不过这类问题好像本地有点难调，因为它好像是抽取代码执行的，跟直观的逻辑比较不一样
Suppose you are given the following code:

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class FooBar {
  public void foo() {
    for (int i = 0; i < n; i++) {
      print("foo");
    }
  }

  public void bar() {
    for (int i = 0; i < n; i++) {
      print("bar");
    }
  }
}

The same instance of FooBar will be passed to two different threads:

  • thread A will call foo(), while
  • thread B will call bar().
    Modify the given program to output "foobar" n times.

示例

Example 1:

Input: n = 1
Output: “foobar”
Explanation: There are two threads being fired asynchronously. One of them calls foo(), while the other calls bar().
“foobar” is being output 1 time.

Example 2:

Input: n = 2
Output: “foobarfoobar”
Explanation: “foobar” is being output 2 times.

题解

简析

其实用信号量是很直观的，就是让打印 foo 的线程先拥有信号量，打印后就等待，给 bar 信号量 + 1，然后 bar 线程运行打印消耗 bar 信号量，再给 foo 信号量 + 1

code

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class FooBar {
    
    private final Semaphore foo = new Semaphore(1);
    private final Semaphore bar = new Semaphore(0);
    private int n;

    public FooBar(int n) {
        this.n = n;
    }

    public void foo(Runnable printFoo) throws InterruptedException {
        
        for (int i = 0; i < n; i++) {
            foo.acquire();
        	// printFoo.run() outputs "foo". Do not change or remove this line.
        	printFoo.run();
            bar.release();
        }
    }

    public void bar(Runnable printBar) throws InterruptedException {
        
        for (int i = 0; i < n; i++) {
            bar.acquire();
            // printBar.run() outputs "bar". Do not change or remove this line.
        	printBar.run();
            foo.release();
        }
    }
}
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Leetcode 1115 交替打印 FooBar ( Print FooBar Alternately *Medium* ) 题解分析

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：
无聊随机了道并发题，还是比较明显的用信号量就可以解决的题，不过第一次做还是有点不适应，要加油多练练

无聊想去 roll 一题就看到了有并发题，就找到了这题，其实一眼看我的想法也是用信号量，但是用 condition 应该也是可以处理的，不过这类问题好像本地有点难调，因为它好像是抽取代码执行的，跟直观的逻辑比较不一样
Suppose you are given the following code:

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class FooBar {
  public void foo() {
    for (int i = 0; i < n; i++) {
      print("foo");
    }
  }

  public void bar() {
    for (int i = 0; i < n; i++) {
      print("bar");
    }
  }
}

The same instance of FooBar will be passed to two different threads:

  • thread A will call foo(), while
  • thread B will call bar().
    Modify the given program to output "foobar" n times.

示例

Example 1:

Input: n = 1
Output: “foobar”
Explanation: There are two threads being fired asynchronously. One of them calls foo(), while the other calls bar().
“foobar” is being output 1 time.

Example 2:

Input: n = 2
Output: “foobarfoobar”
Explanation: “foobar” is being output 2 times.

题解

简析

其实用信号量是很直观的，就是让打印 foo 的线程先拥有信号量，打印后就等待，给 bar 信号量 + 1，然后 bar 线程运行打印消耗 bar 信号量，再给 foo 信号量 + 1

code

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class FooBar {
    
    private final Semaphore foo = new Semaphore(1);
    private final Semaphore bar = new Semaphore(0);
    private int n;

    public FooBar(int n) {
        this.n = n;
    }

    public void foo(Runnable printFoo) throws InterruptedException {
        
        for (int i = 0; i < n; i++) {
            foo.acquire();
        	// printFoo.run() outputs "foo". Do not change or remove this line.
        	printFoo.run();
            bar.release();
        }
    }

    public void bar(Runnable printBar) throws InterruptedException {
        
        for (int i = 0; i < n; i++) {
            bar.acquire();
            // printBar.run() outputs "bar". Do not change or remove this line.
        	printBar.run();
            foo.release();
        }
    }
}
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Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析

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题目介绍

Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.

An input string is valid if:

  1. Open brackets must be closed by the same type of brackets.
  2. Open brackets must be closed in the correct order.

示例

Example 1:

Input: s = “()”
Output: true

Example 2:

Input: s = “()[]{}”
Output: true

Example 3:

Input: s = “(]”
Output: false

Constraints:

  • 1 <= s.length <= 10^4
  • s consists of parentheses only '()[]{}'.

解析

easy题，并且看起来也是比较简单的，三种括号按对匹配，直接用栈来做，栈里面存的是括号的类型，如果是左括号，就放入栈中，如果是右括号，就把栈顶的元素弹出，如果弹出的元素不是左括号，就返回false，如果弹出的元素是左括号，就继续往下走，如果遍历完了，如果栈里面还有元素，就返回false，如果遍历完了，如果栈里面没有元素，就返回true

代码

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class Solution {
    public boolean isValid(String s) {

        if (s.length() % 2 != 0) {
            return false;
        }
        Stack<String> stk = new Stack<>();
        for (int i = 0; i < s.length(); i++) {
            if (s.charAt(i) == '{' || s.charAt(i) == '(' || s.charAt(i) == '[') {
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                continue;
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                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }

        }
        return stk.size() == 0;
    }
}
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Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析

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题目介绍

Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.

An input string is valid if:

  1. Open brackets must be closed by the same type of brackets.
  2. Open brackets must be closed in the correct order.

示例

Example 1:

Input: s = “()”
Output: true

Example 2:

Input: s = “()[]{}”
Output: true

Example 3:

Input: s = “(]”
Output: false

Constraints:

  • 1 <= s.length <= 10^4
  • s consists of parentheses only '()[]{}'.

解析

easy题，并且看起来也是比较简单的，三种括号按对匹配，直接用栈来做，栈里面存的是括号的类型，如果是左括号，就放入栈中，如果是右括号，就把栈顶的元素弹出，如果弹出的元素不是左括号，就返回false，如果弹出的元素是左括号，就继续往下走，如果遍历完了，如果栈里面还有元素，就返回false，如果遍历完了，如果栈里面没有元素，就返回true

代码

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class Solution {
    public boolean isValid(String s) {

        if (s.length() % 2 != 0) {
            return false;
        }
        Stack<String> stk = new Stack<>();
        for (int i = 0; i < s.length(); i++) {
            if (s.charAt(i) == '{' || s.charAt(i) == '(' || s.charAt(i) == '[') {
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}《长安的荔枝》读后感 | Nicksxs's Blog

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What hurts more, the pain of hard work or the pain of regret?

《长安的荔枝》读后感

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断断续续地看完了马伯庸老师的《长安的荔枝》，一开始是看这本书在排行榜排得很高，又是马伯庸的，之前看过他的《古董局中局》，还是很有意思的，而且正好是比较短的，不过前后也拖了蛮久才看完，看完后读了下马老师自己写的后记，就特别有感触。
整个故事是围绕一个上林署监事李善德被委任一项给贵妃送荔枝的差事展开，“长安回望绣成堆，山顶千门次第开，一骑红尘妃子笑，无人知是荔枝来”，以前没细究过这个送荔枝的过程，但是以以前的运输速度和保鲜条件，感觉也不是太现实，所以主人公一开始就以为只是像以往一样是送荔枝干这种，能比较方便运输，不容易变质的，结果发现其实是同僚在坑他，这次是要在贵妃生辰的时候给贵妃送来新鲜的岭南荔枝，用比较时兴的词来说，这就是个送命题啊，鲜荔枝一日色变，两日香变，三日味变，同僚的还有杜甫跟韩承，都觉得老李可以直接写休书了，保全家人，不然就是全家送命，李善德也觉得基本算是判刑了，而且其实是这事被转了几次，最后到老李所在的上林署，主管为了骗他接下这个活还特意在文书上把荔枝鲜的“鲜”字贴住，那会叫做“贴黄”，变成了荔枝“煎”，所以说官场险恶，大家都想把这烫手山芋丢出去，结果丢到了我们老实的老李头上，但是从接到这个通知到贵妃的生辰六月初一还有挺长的时间，其实这个活虽然送命，但是在前期这个“荔枝使”也基本就是类似带着尚方宝剑，御赐黄马褂的职位，随便申请经费，不必像常规的部门费用需要定预算，申请后再层层审批，而是特事特批特办的耍赖做法，所以在这段时间是能够潇洒挥霍一下的。其实可以好好地捞一波给妻女，然后写下和离，在自己死后能让她们过的好一些，但最后还是在杜甫的一番劝导下做出了尝试一番的决定，因为也没其他办法，既是退无可退，何不向前拼死一搏，其实说到这，我觉得看这本书感觉有所收获的第一点，有时候总觉得事情没戏了，想躺平放弃了，但是这样其实这个结果是不会变好的，尝试努力，拼尽全力搏一搏，说不定会有所改观，至少不会变更坏了。

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What hurts more, the pain of hard work or the pain of regret?

《长安的荔枝》读后感

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断断续续地看完了马伯庸老师的《长安的荔枝》，一开始是看这本书在排行榜排得很高，又是马伯庸的，之前看过他的《古董局中局》，还是很有意思的，而且正好是比较短的，不过前后也拖了蛮久才看完，看完后读了下马老师自己写的后记，就特别有感触。
整个故事是围绕一个上林署监事李善德被委任一项给贵妃送荔枝的差事展开，“长安回望绣成堆，山顶千门次第开，一骑红尘妃子笑，无人知是荔枝来”，以前没细究过这个送荔枝的过程，但是以以前的运输速度和保鲜条件，感觉也不是太现实，所以主人公一开始就以为只是像以往一样是送荔枝干这种，能比较方便运输，不容易变质的，结果发现其实是同僚在坑他，这次是要在贵妃生辰的时候给贵妃送来新鲜的岭南荔枝，用比较时兴的词来说，这就是个送命题啊，鲜荔枝一日色变，两日香变，三日味变，同僚的还有杜甫跟韩承，都觉得老李可以直接写休书了，保全家人，不然就是全家送命，李善德也觉得基本算是判刑了，而且其实是这事被转了几次，最后到老李所在的上林署，主管为了骗他接下这个活还特意在文书上把荔枝鲜的“鲜”字贴住，那会叫做“贴黄”，变成了荔枝“煎”，所以说官场险恶，大家都想把这烫手山芋丢出去，结果丢到了我们老实的老李头上，但是从接到这个通知到贵妃的生辰六月初一还有挺长的时间，其实这个活虽然送命，但是在前期这个“荔枝使”也基本就是类似带着尚方宝剑，御赐黄马褂的职位，随便申请经费，不必像常规的部门费用需要定预算，申请后再层层审批，而是特事特批特办的耍赖做法，所以在这段时间是能够潇洒挥霍一下的。其实可以好好地捞一波给妻女，然后写下和离，在自己死后能让她们过的好一些，但最后还是在杜甫的一番劝导下做出了尝试一番的决定，因为也没其他办法，既是退无可退，何不向前拼死一搏，其实说到这，我觉得看这本书感觉有所收获的第一点，有时候总觉得事情没戏了，想躺平放弃了，但是这样其实这个结果是不会变好的，尝试努力，拼尽全力搏一搏，说不定会有所改观，至少不会变更坏了。

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Leetcode 1260 二维网格迁移 ( Shift 2D Grid *Easy* ) 题解分析

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题目介绍

Given a 2D grid of size m x n and an integer k. You need to shift the grid k times.

In one shift operation:

Element at grid[i][j] moves to grid[i][j + 1].
Element at grid[i][n - 1] moves to grid[i + 1][0].
Element at grid[m - 1][n - 1] moves to grid[0][0].
Return the 2D grid after applying shift operation k times.

示例

Example 1:

Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 1
Output: [[9,1,2],[3,4,5],[6,7,8]]

Example 2:

Input: grid = [[3,8,1,9],[19,7,2,5],[4,6,11,10],[12,0,21,13]], k = 4
Output: [[12,0,21,13],[3,8,1,9],[19,7,2,5],[4,6,11,10]]

Example 3:

Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 9
Output: [[1,2,3],[4,5,6],[7,8,9]]

提示

  • m == grid.length
  • n == grid[i].length
  • 1 <= m <= 50
  • 1 <= n <= 50
  • -1000 <= grid[i][j] <= 1000
  • 0 <= k <= 100

解析

这个题主要是矩阵或者说数组的操作，并且题目要返回的是个 List，所以也不用原地操作，只需要找对位置就可以了，k 是多少就相当于让这个二维数组头尾衔接移动 k 个元素

代码

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public List<List<Integer>> shiftGrid(int[][] grid, int k) {
        // 行数
        int m = grid.length;
        // 列数
        int n = grid[0].length;
        // 偏移值，取下模
        k = k % (m * n);
        // 反向取下数量，因为我打算直接从头填充新的矩阵
        /*
         *    比如
         *    1 2 3
         *    4 5 6
         *    7 8 9
         *    需要变成
         *    9 1 2
         *    3 4 5
         *    6 7 8
         *    就要从 9 开始填充
         */
        int reverseK = m * n - k;
        List<List<Integer>> matrix = new ArrayList<>();
        // 这类就是两层循环
        for (int i = 0; i < m; i++) {
            List<Integer> line = new ArrayList<>();
            for (int j = 0; j < n; j++) {
                // 数量会随着循环迭代增长, 确认是第几个
                int currentNum = reverseK + i * n +  (j + 1);
                // 这里处理下到达矩阵末尾后减掉 m * n
                if (currentNum > m * n) {
                    currentNum -= m * n;
                }
                // 根据矩阵列数 n 算出在原来矩阵的位置
                int last = (currentNum - 1) % n;
                int passLine = (currentNum - 1) / n;

                line.add(grid[passLine][last]);
            }
            matrix.add(line);
        }
        return matrix;
    }

结果数据


比较慢

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Leetcode 1260 二维网格迁移 ( Shift 2D Grid *Easy* ) 题解分析

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题目介绍

Given a 2D grid of size m x n and an integer k. You need to shift the grid k times.

In one shift operation:

Element at grid[i][j] moves to grid[i][j + 1].
Element at grid[i][n - 1] moves to grid[i + 1][0].
Element at grid[m - 1][n - 1] moves to grid[0][0].
Return the 2D grid after applying shift operation k times.

示例

Example 1:

Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 1
Output: [[9,1,2],[3,4,5],[6,7,8]]

Example 2:

Input: grid = [[3,8,1,9],[19,7,2,5],[4,6,11,10],[12,0,21,13]], k = 4
Output: [[12,0,21,13],[3,8,1,9],[19,7,2,5],[4,6,11,10]]

Example 3:

Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 9
Output: [[1,2,3],[4,5,6],[7,8,9]]

提示

  • m == grid.length
  • n == grid[i].length
  • 1 <= m <= 50
  • 1 <= n <= 50
  • -1000 <= grid[i][j] <= 1000
  • 0 <= k <= 100

解析

这个题主要是矩阵或者说数组的操作，并且题目要返回的是个 List，所以也不用原地操作，只需要找对位置就可以了，k 是多少就相当于让这个二维数组头尾衔接移动 k 个元素

代码

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public List<List<Integer>> shiftGrid(int[][] grid, int k) {
        // 行数
        int m = grid.length;
        // 列数
        int n = grid[0].length;
        // 偏移值，取下模
        k = k % (m * n);
        // 反向取下数量，因为我打算直接从头填充新的矩阵
        /*
         *    比如
         *    1 2 3
         *    4 5 6
         *    7 8 9
         *    需要变成
         *    9 1 2
         *    3 4 5
         *    6 7 8
         *    就要从 9 开始填充
         */
        int reverseK = m * n - k;
        List<List<Integer>> matrix = new ArrayList<>();
        // 这类就是两层循环
        for (int i = 0; i < m; i++) {
            List<Integer> line = new ArrayList<>();
            for (int j = 0; j < n; j++) {
                // 数量会随着循环迭代增长, 确认是第几个
                int currentNum = reverseK + i * n +  (j + 1);
                // 这里处理下到达矩阵末尾后减掉 m * n
                if (currentNum > m * n) {
                    currentNum -= m * n;
                }
                // 根据矩阵列数 n 算出在原来矩阵的位置
                int last = (currentNum - 1) % n;
                int passLine = (currentNum - 1) / n;

                line.add(grid[passLine][last]);
            }
            matrix.add(line);
        }
        return matrix;
    }

结果数据


比较慢

0%
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Leetcode 16 最接近的三数之和 ( 3Sum Closest *Medium* ) 题解分析

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：

题目介绍

Given an integer array nums of length n and an integer target, find three integers in nums such that the sum is closest to target.

Return the sum of the three integers.

You may assume that each input would have exactly one solution.

简单解释下就是之前是要三数之和等于目标值，现在是找到最接近的三数之和。

示例

Example 1:

Input: nums = [-1,2,1,-4], target = 1
Output: 2
Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = 2).

Example 2:

Input: nums = [0,0,0], target = 1
Output: 0

Constraints:

  • 3 <= nums.length <= 1000
  • -1000 <= nums[i] <= 1000
  • -10^4 <= target <= 10^4

简单解析

这个题思路上来讲不难，也是用原来三数之和的方式去做，利用”双指针法”或者其它描述法，但是需要简化逻辑

code

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public int threeSumClosest(int[] nums, int target) {
        Arrays.sort(nums);
        // 当前最近的和
        int closestSum = nums[0] + nums[1] + nums[nums.length - 1];
        for (int i = 0; i < nums.length - 2; i++) {
            if (i == 0 || nums[i] != nums[i - 1]) {
                // 左指针
                int left = i + 1;
                // 右指针
                int right = nums.length - 1;
                // 判断是否遍历完了
                while (left < right) {
                    // 当前的和
                    int sum = nums[i] + nums[left] + nums[right];
                    // 小优化，相等就略过了
                    while (left < right && nums[left] == nums[left + 1]) {
                        left++;
                    }
                    while (left < right && nums[right] == nums[right - 1]) {
                        right--;
                    }
                    // 这里判断，其实也还是希望趋近目标值
                    if (sum < target) {
                        left++;
                    } else {
                        right--;
                    }
                    // 判断是否需要替换
                    if (Math.abs(sum - target) < Math.abs(closestSum - target)) {
                        closestSum = sum;
                    }
                }
            }
        }
        return closestSum;
    }

结果

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Leetcode 16 最接近的三数之和 ( 3Sum Closest *Medium* ) 题解分析

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：

题目介绍

Given an integer array nums of length n and an integer target, find three integers in nums such that the sum is closest to target.

Return the sum of the three integers.

You may assume that each input would have exactly one solution.

简单解释下就是之前是要三数之和等于目标值，现在是找到最接近的三数之和。

示例

Example 1:

Input: nums = [-1,2,1,-4], target = 1
Output: 2
Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = 2).

Example 2:

Input: nums = [0,0,0], target = 1
Output: 0

Constraints:

  • 3 <= nums.length <= 1000
  • -1000 <= nums[i] <= 1000
  • -10^4 <= target <= 10^4

简单解析

这个题思路上来讲不难，也是用原来三数之和的方式去做，利用”双指针法”或者其它描述法，但是需要简化逻辑

code

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public int threeSumClosest(int[] nums, int target) {
        Arrays.sort(nums);
        // 当前最近的和
        int closestSum = nums[0] + nums[1] + nums[nums.length - 1];
        for (int i = 0; i < nums.length - 2; i++) {
            if (i == 0 || nums[i] != nums[i - 1]) {
                // 左指针
                int left = i + 1;
                // 右指针
                int right = nums.length - 1;
                // 判断是否遍历完了
                while (left < right) {
                    // 当前的和
                    int sum = nums[i] + nums[left] + nums[right];
                    // 小优化，相等就略过了
                    while (left < right && nums[left] == nums[left + 1]) {
                        left++;
                    }
                    while (left < right && nums[right] == nums[right - 1]) {
                        right--;
                    }
                    // 这里判断，其实也还是希望趋近目标值
                    if (sum < target) {
                        left++;
                    } else {
                        right--;
                    }
                    // 判断是否需要替换
                    if (Math.abs(sum - target) < Math.abs(closestSum - target)) {
                        closestSum = sum;
                    }
                }
            }
        }
        return closestSum;
    }

结果

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What hurts more, the pain of hard work or the pain of regret?

Leetcode 278 第一个错误的版本 ( First Bad Version *Easy* ) 题解分析

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题目介绍

You are a product manager and currently leading a team to develop a new product. Unfortunately, the latest version of your product fails the quality check. Since each version is developed based on the previous version, all the versions after a bad version are also bad.

Suppose you have n versions [1, 2, ..., n] and you want to find out the first bad one, which causes all the following ones to be bad.

You are given an API bool isBadVersion(version) which returns whether version is bad. Implement a function to find the first bad version. You should minimize the number of calls to the API.

示例

Example 1:

Input: n = 5, bad = 4
Output: 4
Explanation:
call isBadVersion(3) -> false
call isBadVersion(5) -> true
call isBadVersion(4) -> true
Then 4 is the first bad version.

Example 2:

Input: n = 1, bad = 1
Output: 1

简析

简单来说就是一个二分查找，但是这个问题其实处理起来还是需要搞清楚一些边界问题

代码

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public int firstBadVersion(int n) {
    // 类似于双指针法
    int left = 1, right = n, mid;
    while (left < right) {
        // 取中点
        mid = left + (right - left) / 2;
        // 如果不是错误版本，就往右找
        if (!isBadVersion(mid)) {
            left = mid + 1;
        } else {
        // 如果是的话就往左查找
            right = mid;
        }
    }
    // 这里考虑交界情况是，在上面循环中如果 left 是好的，right 是坏的，那进入循环的时候 mid == left
    // 然后 left = mid + 1 就会等于 right，循环条件就跳出了，此时 left 就是那个起始的错误点了
    // 其实这两个是同一个值
    return left;
}

往右移动示例

往左移动示例

结果

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What hurts more, the pain of hard work or the pain of regret?

Leetcode 278 第一个错误的版本 ( First Bad Version *Easy* ) 题解分析

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题目介绍

You are a product manager and currently leading a team to develop a new product. Unfortunately, the latest version of your product fails the quality check. Since each version is developed based on the previous version, all the versions after a bad version are also bad.

Suppose you have n versions [1, 2, ..., n] and you want to find out the first bad one, which causes all the following ones to be bad.

You are given an API bool isBadVersion(version) which returns whether version is bad. Implement a function to find the first bad version. You should minimize the number of calls to the API.

示例

Example 1:

Input: n = 5, bad = 4
Output: 4
Explanation:
call isBadVersion(3) -> false
call isBadVersion(5) -> true
call isBadVersion(4) -> true
Then 4 is the first bad version.

Example 2:

Input: n = 1, bad = 1
Output: 1

简析

简单来说就是一个二分查找，但是这个问题其实处理起来还是需要搞清楚一些边界问题

代码

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public int firstBadVersion(int n) {
    // 类似于双指针法
    int left = 1, right = n, mid;
    while (left < right) {
        // 取中点
        mid = left + (right - left) / 2;
        // 如果不是错误版本，就往右找
        if (!isBadVersion(mid)) {
            left = mid + 1;
        } else {
        // 如果是的话就往左查找
            right = mid;
        }
    }
    // 这里考虑交界情况是，在上面循环中如果 left 是好的，right 是坏的，那进入循环的时候 mid == left
    // 然后 left = mid + 1 就会等于 right，循环条件就跳出了，此时 left 就是那个起始的错误点了
    // 其实这两个是同一个值
    return left;
}

往右移动示例

往左移动示例

结果

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}分类: GC | Nicksxs's Blog

Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

GC 分类

2020
2019
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}分类: gc | Nicksxs's Blog

Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

gc 分类

2021
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}分类: java | Nicksxs's Blog

Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

java 分类

2021
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What hurts more, the pain of hard work or the pain of regret?

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}分类: Linked List | Nicksxs's Blog

Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

Linked List 分类

2021
2020
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cs.js> \ No newline at end of file diff --git a/leancloud_counter_security_urls.json b/leancloud_counter_security_urls.json index 3aa97df3db..d805592d14 100644 --- a/leancloud_counter_security_urls.json +++ b/leancloud_counter_security_urls.json @@ -1 +1 @@ -[{"title":"2019年终总结","url":"/2020/02/01/2019年终总结/"},{"title":"村上春树《1Q84》读后感","url":"/2019/12/18/1Q84读后感/"},{"title":"2020 年终总结","url":"/2021/03/31/2020-年终总结/"},{"title":"2020年中总结","url":"/2020/07/11/2020年中总结/"},{"title":"2021 年中总结","url":"/2021/07/18/2021-年中总结/"},{"title":"2021 年终总结","url":"/2022/01/22/2021-年终总结/"},{"title":"2022 年终总结","url":"/2023/01/15/2022-年终总结/"},{"title":"AQS篇二 之 Condition 浅析笔记","url":"/2021/02/21/AQS-之-Condition-浅析笔记/"},{"title":"34_Search_for_a_Range","url":"/2016/08/14/34-Search-for-a-Range/"},{"title":"add-two-number","url":"/2015/04/14/Add-Two-Number/"},{"title":"Apollo 如何获取当前环境","url":"/2022/09/04/Apollo-如何获取当前环境/"},{"title":"Apollo 客户端启动过程分析","url":"/2022/09/18/Apollo-客户端启动过程分析/"},{"title":"Apollo 的 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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

top 命令在日常的 Linux 使用中，特别是做一些服务器的简单状态查看，排查故障都起了比较大的作用，但是由于这个命令看到的东西比较多，一般只会看部分，或者说像我这样就会比较片面地看一些信息，比如默认是进程维度的，可以在启动命令的时候加-H进入线程模式

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-H  :Threads-mode operation
            Instructs top to display individual threads.  Without this command-line option a summation of all threads in each process  is  shown.   Later
            this can be changed with the `H' interactive command.

这样就能用在 Java 中去 jstack 中找到对应的线程
其实还有比较重要的两个操作，
一个是在 top 启动状态下，按c键，这样能把比如说是一个 Java 进程，具体的进程命令显示出来
像这样
执行前是这样

执行后是这样

第二个就是排序了

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SORTING of task window

          For  compatibility,  this top supports most of the former top sort keys.  Since this is primarily a service to former top users, these commands
          do not appear on any help screen.
                command   sorted-field                  supported
                A         start time (non-display)      No
                M         %MEM                          Yes
                N         PID                           Yes
                P         %CPU                          Yes
                T         TIME+                         Yes

          Before using any of the following sort provisions, top suggests that you temporarily turn on column highlighting using the `x' interactive com‐
          mand.  That will help ensure that the actual sort environment matches your intent.

          The following interactive commands will only be honored when the current sort field is visible.  The sort field might not be visible because:
                1) there is insufficient Screen Width
                2) the `f' interactive command turned it Off

             <  :Move-Sort-Field-Left
                 Moves the sort column to the left unless the current sort field is the first field being displayed.

             >  :Move-Sort-Field-Right
                 Moves the sort column to the right unless the current sort field is the last field being displayed.

查看 man page 可以找到这一段，其实一般 man page 都是最细致的，只不过因为太多了，有时候懒得看，这里可以通过大写 M 和大写 P 分别按内存和 CPU 排序，下面还有两个小技巧，通过按 x 可以将当前活跃的排序列用不同颜色标出来，然后可以通过<和>直接左右移动排序列

发表于 分类于 ， 阅读次数： Disqus：

事情源于周末来回家发生的两件事情，先是回去的时候从高铁下车要坐公交，现在算是有个比较好的临时候车点了，但是可能由于疫情好转，晚上都不用检查健康码就可以进候车点，但是上公交的时候还是需要看健康码，一般情况下从高铁下来的，各个地方的人都有，而且也不太清楚这边上公交车需要查验健康码，我的一个看法是候车的时候就应该放个横幅告示，或者再配合喇叭循环播放，请提前准备好健康码，上车前需要查验，因为像这周的情况，我乘坐的那辆车是间隔时间比较长，而且终点是工业开发区，可能是比较多外来务工人员的目的地，正好这部分人可能对于操作手机检验健康码这个事情不太熟悉，所以结果就是头上几个不知道怎么搞出来健康码，然后让几乎有满满一整车的人在后面堵着，司机又非常厌烦那些没有提前出示健康码的，有位乘客搞得久了点，还被误以为没刷卡买票，差点吵起来，其实公共交通或者高铁站负责的在公交指引路线上多写一句上车前需要查验健康码，可能就能改善比较多，还有就是那个积水的路，这个吐槽起来就一大坨了，整个绍兴像 dayuejin 一样到处都是破路。
第二个就是来杭州的时候，经过人行横道，远处车道的公交车停下来等我们了，为了少添麻烦总想快点穿过去，但是这时靠近我们的车道（晚上光线较暗，可见度不佳）有一辆从远处来的奥迪 A4 还是 A5 这种的车反而想加速冲过去，如果少看一下可能我已经残了，交规考的靠近人行道要减速好像基本都是个摆设了，杭州也只有公交车因为一些考核指标原因会主动礼让，人其实需要有同理心，虽然可能有些人是开车多于骑车走路的，但是总也不可能永远不穿人行道吧，甚至这些人可能还会在人行道红灯的时候走过去。这个事情不是吐槽公共交通的，只是也有些许关系，想起来还有一件事也是刚才来杭州的时候看到的，等公交的时候看到有辆路虎要加塞，而目标车道刚好是辆大货车，大货车看到按了喇叭，路虎犹豫了下还是挤进去了，可能是对路虎的扛撞性能非常自信吧，反正我是挺后怕的，这种级别的车，被撞了的话估计还是鸡蛋撞石头，吨位惯性在那，这里再延伸下，挺多开豪车的人好像都觉得这路上路权更大一些，谁谁都得让着他，可能实际吃亏的不多，所以越加巩固了这种思维，当真的碰到不管的可能就会明白了，路权这个事情在天朝也基本没啥人重视，也没想说个结论，就到这吧

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

You are given an array prices where prices[i] is the price of a given stock on the ith day.

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。

你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。

返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。

简单分析

其实这个跟二叉树的最长路径和有点类似，需要找到整体的最大收益，但是在迭代过程中需要一个当前的值

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int maxSofar = 0;
public int maxProfit(int[] prices) {
    if (prices.length <= 1) {
        return 0;
    }
    int maxIn = prices[0];
    int maxOut = prices[0];
    for (int i = 1; i < prices.length; i++) {
        if (maxIn > prices[i]) {
            // 当循环当前值小于之前的买入值时就当成买入值，同时卖出也要更新
            maxIn = prices[i];
            maxOut = prices[i];
        }
        if (prices[i] > maxOut) {
            // 表示一个可卖出点，即比买入值高时
            maxOut = prices[i];
            // 需要设置一个历史值
            maxSofar = Math.max(maxSofar, maxOut - maxIn);
        }
    }
    return maxSofar;
}

总结下

一开始看到 easy 就觉得是很简单，就没有 maxSofar ，但是一提交就出现问题了
对于[2, 4, 1]这种就会变成 0，所以还是需要一个历史值来存放历史最大值，这题有点动态规划的意思

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

最近看了下这本垃圾回收算法手册，看到了第三章的标记-整理回收算法，做个简单的读书笔记

双指针整理算法

对于一块待整理区域，通过两个指针，free 在区域的起始端，scan 指针在区域的末端，free 指针从前往后知道找到空闲区域，scan 从后往前一直找到存活对象，当 free 指针未与 scan 指针交叉时，会给 scan 位置的对象特定位置标记上 free 的地址，即将要转移的地址，不过这里有个限制，这种整理算法一般会用于对象大小统一的情况，否则 free 指针扫描时还需要匹配scan 指针扫描到的存活对象的大小。

Lisp 2 整理算法

需要三次完整遍历堆区域
第一遍是遍历后将计算出所有对象的最终地址（转发地址）
第二遍是使用转发地址更新赋值器线程根以及被标记对象中的引用，该操作将确保它们指向对象的新位置
第三次遍历是relocate最终将存活对象移动到其新的目标位置

引线整理算法

这个真的长见识了，

可以看到，原来是 A，B，C 对象引用了 N，这里会在第一次遍历的时候把这种引用反过来，让 N 的对象头部保存下 A 的地址，表示这类引用，然后在遍历到 B 的时候在链起来，到最后就会把所有引用了 N 对象的所有对象通过引线链起来，在第二次遍历的时候就把更新A，B，C 对象引用的 N 地址，并且移动 N 对象

单次遍历算法

这个一直提到过位图的实现方式，

可以看到在第一步会先通过位图标记，标记的方式是位图的每一位对应的堆内存的一个字（这里可能指的是 byte 吧），然后将一个存活对象的内存区域的第一个字跟最后一个字标记，这里如果在通过普通的方式就还需要一个地方在存转发地址，但是因为具体的位置可以通过位图算出来，也就不需要额外记录了

发表于 分类于 ， 阅读次数： Disqus：

新年开工开车来杭州，因为没有车位加限行今天来就没开车来了，从东站做公交回住的地方，这班神奇的车我之前也吐槽过了，有神奇的乘客和神奇的司机，因为基本上这班车是从我毕业就开始乘了，所以也算是比较熟悉了，以前总体感觉不太好的是乘坐时间太长了，不过这个也不能怪车，是我自己住得远（离东站），后来住到了现在的地方，也算是直达，并且 LD 比较喜欢直达的，不爱更快却要换乘的地铁，所以坐的频率比较高，也说过前面那些比较气人的乘客，自己不好好戴口罩，反而联合一起上车的乘客诽谤司机，说他要吃人了要打人了，也正是这个司机比较有意思，上车就让戴好口罩，还给大家讲，哪里哪里又有疫情了，我觉得其实这个司机还是不错的，特殊时期，对于这种公共交通，这样的确是比较负责任的做法，只是说话方式，语气这个因人而异，他也不是来伺候人的，而且这么一大车人，说了一遍不行，再说一遍，三遍以上了，嗓门大一点也属于正常的人的行为。
还是说回今天要说的，今天这位司机我看着跟前面说的那位有点像，因为上车的时候比较暗没看清脸，主要原因是这位司机开车比较猛，比较急，然后车上因为这个时间点，比较多大学开学来的学生，拎着个行李箱，一开始是前面已经都站满了人，后面还有很多空位，因为后面没地方放行李箱，就因为这样前面站着的有几个就在说司机开慢点，结果司机貌似也没听进去，还是我行我素，过了会又有人说司机开稳一点，就在这个人说完没一会，停在红绿灯路口的车里，就有人问有没有垃圾桶，接着又让司机开门，说晕车太严重了，要下车，司机开了门，我望出去两个妹子下了车，好像在路边草丛吐了，前面开门下车的时候就有人说她们第一次来杭州，可能有点责怪司机开的不稳，也影响了杭州交通给新来杭州的人的感受，说完了事情经过，其实我有蛮多感触，对于杭州公交司机，我大概是大一来了没多久，陪室友去文三路买电脑就晕车，下车的时候在公交车站吐了，可能是从大学开始缺乏锻炼，又饮食不规律，更加容易晕车，大部分晕车我觉得都是我自己的原因，有时候是上车前吃太多了，或者早上起太早，没睡好，没吃东西，反正自己也是挺多原因的，说到司机的原因的话，我觉得可能这班车还算好的，最让我难受的还是上下班高峰的时候，因为经过的那条路是比较重要的主干道，路比较老比较窄，并且还有很多人行道，所以经常一脚油门连带着一脚刹车，真的很难受，这种算是我觉得真的是公交体验比较差的一点，但是这一点呢也不能完全怪公交司机，杭州的路政规划是很垃圾，没看错，是垃圾，所以总体结论是公交还行，主要是路政规划就是垃圾，包括这条主干道这么多人行道，并且两边都是老小区，老年人在上班高峰可能要买菜送娃或者其他事情，在通畅的情况下可能只需要六分钟的路程，有时候因为各种原因，半小时都开不完，扯开去一点，杭州的路，核心的高速说封就封，本来是高架可以直接通到城西，结果没造，到了路本已经很拥挤的时候开始来造隧道，各种破坏，隧道接高架的地方，无尽的加塞，对于我这样的小白司机来说真的是太恶心了，所以我一直想说的就是杭州这个地方房价领先基础设施十年，地铁，高架，高速通通不行，地面道路就更不行了。
总结下，其实杭州的真正的公交体验差，应该还是路造成的，对于前面的那两位妹子来说，有可能是她们来自于公交司机都是开的特别稳，并且路况也很好的地方，也或者是我被虐习惯了🤦‍♂️

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

top 命令在日常的 Linux 使用中，特别是做一些服务器的简单状态查看，排查故障都起了比较大的作用，但是由于这个命令看到的东西比较多，一般只会看部分，或者说像我这样就会比较片面地看一些信息，比如默认是进程维度的，可以在启动命令的时候加-H进入线程模式

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-H  :Threads-mode operation
            Instructs top to display individual threads.  Without this command-line option a summation of all threads in each process  is  shown.   Later
            this can be changed with the `H' interactive command.

这样就能用在 Java 中去 jstack 中找到对应的线程
其实还有比较重要的两个操作，
一个是在 top 启动状态下，按c键，这样能把比如说是一个 Java 进程，具体的进程命令显示出来
像这样
执行前是这样

执行后是这样

第二个就是排序了

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SORTING of task window

          For  compatibility,  this top supports most of the former top sort keys.  Since this is primarily a service to former top users, these commands
          do not appear on any help screen.
                command   sorted-field                  supported
                A         start time (non-display)      No
                M         %MEM                          Yes
                N         PID                           Yes
                P         %CPU                          Yes
                T         TIME+                         Yes

          Before using any of the following sort provisions, top suggests that you temporarily turn on column highlighting using the `x' interactive com‐
          mand.  That will help ensure that the actual sort environment matches your intent.

          The following interactive commands will only be honored when the current sort field is visible.  The sort field might not be visible because:
                1) there is insufficient Screen Width
                2) the `f' interactive command turned it Off

             <  :Move-Sort-Field-Left
                 Moves the sort column to the left unless the current sort field is the first field being displayed.

             >  :Move-Sort-Field-Right
                 Moves the sort column to the right unless the current sort field is the last field being displayed.

查看 man page 可以找到这一段，其实一般 man page 都是最细致的，只不过因为太多了，有时候懒得看，这里可以通过大写 M 和大写 P 分别按内存和 CPU 排序，下面还有两个小技巧，通过按 x 可以将当前活跃的排序列用不同颜色标出来，然后可以通过<和>直接左右移动排序列

发表于 分类于 ， 阅读次数： Disqus：

事情源于周末来回家发生的两件事情，先是回去的时候从高铁下车要坐公交，现在算是有个比较好的临时候车点了，但是可能由于疫情好转，晚上都不用检查健康码就可以进候车点，但是上公交的时候还是需要看健康码，一般情况下从高铁下来的，各个地方的人都有，而且也不太清楚这边上公交车需要查验健康码，我的一个看法是候车的时候就应该放个横幅告示，或者再配合喇叭循环播放，请提前准备好健康码，上车前需要查验，因为像这周的情况，我乘坐的那辆车是间隔时间比较长，而且终点是工业开发区，可能是比较多外来务工人员的目的地，正好这部分人可能对于操作手机检验健康码这个事情不太熟悉，所以结果就是头上几个不知道怎么搞出来健康码，然后让几乎有满满一整车的人在后面堵着，司机又非常厌烦那些没有提前出示健康码的，有位乘客搞得久了点，还被误以为没刷卡买票，差点吵起来，其实公共交通或者高铁站负责的在公交指引路线上多写一句上车前需要查验健康码，可能就能改善比较多，还有就是那个积水的路，这个吐槽起来就一大坨了，整个绍兴像 dayuejin 一样到处都是破路。
第二个就是来杭州的时候，经过人行横道，远处车道的公交车停下来等我们了，为了少添麻烦总想快点穿过去，但是这时靠近我们的车道（晚上光线较暗，可见度不佳）有一辆从远处来的奥迪 A4 还是 A5 这种的车反而想加速冲过去，如果少看一下可能我已经残了，交规考的靠近人行道要减速好像基本都是个摆设了，杭州也只有公交车因为一些考核指标原因会主动礼让，人其实需要有同理心，虽然可能有些人是开车多于骑车走路的，但是总也不可能永远不穿人行道吧，甚至这些人可能还会在人行道红灯的时候走过去。这个事情不是吐槽公共交通的，只是也有些许关系，想起来还有一件事也是刚才来杭州的时候看到的，等公交的时候看到有辆路虎要加塞，而目标车道刚好是辆大货车，大货车看到按了喇叭，路虎犹豫了下还是挤进去了，可能是对路虎的扛撞性能非常自信吧，反正我是挺后怕的，这种级别的车，被撞了的话估计还是鸡蛋撞石头，吨位惯性在那，这里再延伸下，挺多开豪车的人好像都觉得这路上路权更大一些，谁谁都得让着他，可能实际吃亏的不多，所以越加巩固了这种思维，当真的碰到不管的可能就会明白了，路权这个事情在天朝也基本没啥人重视，也没想说个结论，就到这吧

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

You are given an array prices where prices[i] is the price of a given stock on the ith day.

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。

你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。

返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。

简单分析

其实这个跟二叉树的最长路径和有点类似，需要找到整体的最大收益，但是在迭代过程中需要一个当前的值

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int maxSofar = 0;
public int maxProfit(int[] prices) {
    if (prices.length <= 1) {
        return 0;
    }
    int maxIn = prices[0];
    int maxOut = prices[0];
    for (int i = 1; i < prices.length; i++) {
        if (maxIn > prices[i]) {
            // 当循环当前值小于之前的买入值时就当成买入值，同时卖出也要更新
            maxIn = prices[i];
            maxOut = prices[i];
        }
        if (prices[i] > maxOut) {
            // 表示一个可卖出点，即比买入值高时
            maxOut = prices[i];
            // 需要设置一个历史值
            maxSofar = Math.max(maxSofar, maxOut - maxIn);
        }
    }
    return maxSofar;
}

总结下

一开始看到 easy 就觉得是很简单，就没有 maxSofar ，但是一提交就出现问题了
对于[2, 4, 1]这种就会变成 0，所以还是需要一个历史值来存放历史最大值，这题有点动态规划的意思

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

最近看了下这本垃圾回收算法手册，看到了第三章的标记-整理回收算法，做个简单的读书笔记

双指针整理算法

对于一块待整理区域，通过两个指针，free 在区域的起始端，scan 指针在区域的末端，free 指针从前往后知道找到空闲区域，scan 从后往前一直找到存活对象，当 free 指针未与 scan 指针交叉时，会给 scan 位置的对象特定位置标记上 free 的地址，即将要转移的地址，不过这里有个限制，这种整理算法一般会用于对象大小统一的情况，否则 free 指针扫描时还需要匹配scan 指针扫描到的存活对象的大小。

Lisp 2 整理算法

需要三次完整遍历堆区域
第一遍是遍历后将计算出所有对象的最终地址（转发地址）
第二遍是使用转发地址更新赋值器线程根以及被标记对象中的引用，该操作将确保它们指向对象的新位置
第三次遍历是relocate最终将存活对象移动到其新的目标位置

引线整理算法

这个真的长见识了，

可以看到，原来是 A，B，C 对象引用了 N，这里会在第一次遍历的时候把这种引用反过来，让 N 的对象头部保存下 A 的地址，表示这类引用，然后在遍历到 B 的时候在链起来，到最后就会把所有引用了 N 对象的所有对象通过引线链起来，在第二次遍历的时候就把更新A，B，C 对象引用的 N 地址，并且移动 N 对象

单次遍历算法

这个一直提到过位图的实现方式，

可以看到在第一步会先通过位图标记，标记的方式是位图的每一位对应的堆内存的一个字（这里可能指的是 byte 吧），然后将一个存活对象的内存区域的第一个字跟最后一个字标记，这里如果在通过普通的方式就还需要一个地方在存转发地址，但是因为具体的位置可以通过位图算出来，也就不需要额外记录了

发表于 分类于 ， 阅读次数： Disqus：

新年开工开车来杭州，因为没有车位加限行今天来就没开车来了，从东站做公交回住的地方，这班神奇的车我之前也吐槽过了，有神奇的乘客和神奇的司机，因为基本上这班车是从我毕业就开始乘了，所以也算是比较熟悉了，以前总体感觉不太好的是乘坐时间太长了，不过这个也不能怪车，是我自己住得远（离东站），后来住到了现在的地方，也算是直达，并且 LD 比较喜欢直达的，不爱更快却要换乘的地铁，所以坐的频率比较高，也说过前面那些比较气人的乘客，自己不好好戴口罩，反而联合一起上车的乘客诽谤司机，说他要吃人了要打人了，也正是这个司机比较有意思，上车就让戴好口罩，还给大家讲，哪里哪里又有疫情了，我觉得其实这个司机还是不错的，特殊时期，对于这种公共交通，这样的确是比较负责任的做法，只是说话方式，语气这个因人而异，他也不是来伺候人的，而且这么一大车人，说了一遍不行，再说一遍，三遍以上了，嗓门大一点也属于正常的人的行为。
还是说回今天要说的，今天这位司机我看着跟前面说的那位有点像，因为上车的时候比较暗没看清脸，主要原因是这位司机开车比较猛，比较急，然后车上因为这个时间点，比较多大学开学来的学生，拎着个行李箱，一开始是前面已经都站满了人，后面还有很多空位，因为后面没地方放行李箱，就因为这样前面站着的有几个就在说司机开慢点，结果司机貌似也没听进去，还是我行我素，过了会又有人说司机开稳一点，就在这个人说完没一会，停在红绿灯路口的车里，就有人问有没有垃圾桶，接着又让司机开门，说晕车太严重了，要下车，司机开了门，我望出去两个妹子下了车，好像在路边草丛吐了，前面开门下车的时候就有人说她们第一次来杭州，可能有点责怪司机开的不稳，也影响了杭州交通给新来杭州的人的感受，说完了事情经过，其实我有蛮多感触，对于杭州公交司机，我大概是大一来了没多久，陪室友去文三路买电脑就晕车，下车的时候在公交车站吐了，可能是从大学开始缺乏锻炼，又饮食不规律，更加容易晕车，大部分晕车我觉得都是我自己的原因，有时候是上车前吃太多了，或者早上起太早，没睡好，没吃东西，反正自己也是挺多原因的，说到司机的原因的话，我觉得可能这班车还算好的，最让我难受的还是上下班高峰的时候，因为经过的那条路是比较重要的主干道，路比较老比较窄，并且还有很多人行道，所以经常一脚油门连带着一脚刹车，真的很难受，这种算是我觉得真的是公交体验比较差的一点，但是这一点呢也不能完全怪公交司机，杭州的路政规划是很垃圾，没看错，是垃圾，所以总体结论是公交还行，主要是路政规划就是垃圾，包括这条主干道这么多人行道，并且两边都是老小区，老年人在上班高峰可能要买菜送娃或者其他事情，在通畅的情况下可能只需要六分钟的路程，有时候因为各种原因，半小时都开不完，扯开去一点，杭州的路，核心的高速说封就封，本来是高架可以直接通到城西，结果没造，到了路本已经很拥挤的时候开始来造隧道，各种破坏，隧道接高架的地方，无尽的加塞，对于我这样的小白司机来说真的是太恶心了，所以我一直想说的就是杭州这个地方房价领先基础设施十年，地铁，高架，高速通通不行，地面道路就更不行了。
总结下，其实杭州的真正的公交体验差，应该还是路造成的，对于前面的那两位妹子来说，有可能是她们来自于公交司机都是开的特别稳，并且路况也很好的地方，也或者是我被虐习惯了🤦‍♂️

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， 阅读次数： Disqus：

Condition也是 AQS 中很重要的一块内容，可以先看段示例代码，这段代码应该来自于Doug Lea大大，可以在 javadoc 中的 condition 部分找到，其实大大原来写过基于 synchronized 实现的，后面我也贴下代码

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import java.util.concurrent.locks.Condition;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;

class BoundedBuffer {
    final Lock lock = new ReentrantLock();
    // condition 依赖于 lock 来产生
    final Condition notFull = lock.newCondition();
    final Condition notEmpty = lock.newCondition();

    // 对象池子，put 跟 take 的就是这里的
    final Object[] items = new Object[100];
    int putptr, takeptr, count;

    // 生产
    public void put(Object x) throws InterruptedException {
        // 这里也说明了，需要先拥有锁
        lock.lock();
        try {
            while (count == items.length)
                notFull.await();  // 队列已满，等待，直到 not full 才能继续生产
            items[putptr] = x;
            if (++putptr == items.length) putptr = 0;
            ++count;
            notEmpty.signal(); // 生产成功，队列已经 not empty 了，发个通知出去
        } finally {
            lock.unlock();
        }
    }

    // 消费
    public Object take() throws InterruptedException {
        lock.lock();
        try {
            while (count == 0)
                notEmpty.await(); // 队列为空，等待，直到队列 not empty，才能继续消费
            Object x = items[takeptr];
            if (++takeptr == items.length) takeptr = 0;
            --count;
            notFull.signal(); // 被我消费掉一个，队列 not full 了，发个通知出去
            return x;
        } finally {
            lock.unlock();
        }
    }
}

介绍下 Condition 的结构

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public class ConditionObject implements Condition, java.io.Serializable {
        private static final long serialVersionUID = 1173984872572414699L;
        /** First node of condition queue. */
        private transient Node firstWaiter;
        /** Last node of condition queue. */
        private transient Node lastWaiter;

主要的就这么点，而且也复用了 AQS 阻塞队列或者大大叫 lock queue中同样的 Node 节点，只不过它没有使用其中的双向队列，也就是prev 和 next，而是在 Node 中的 nextWaiter，所以只是个单向的队列，没使用 next 其实还有个用处，后面会提到，看下结构的示意图

然后主要是看两个方法，await 和 signal,
先来看下 await

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/**
 * Implements interruptible condition wait.
 * <ol>
 * <li> If current thread is interrupted, throw InterruptedException.
 * <li> Save lock state returned by {@link #getState}.
 * <li> Invoke {@link #release} with saved state as argument,
 *      throwing IllegalMonitorStateException if it fails.
 * <li> Block until signalled or interrupted.
 * <li> Reacquire by invoking specialized version of
 *      {@link #acquire} with saved state as argument.
 * <li> If interrupted while blocked in step 4, throw InterruptedException.
 * </ol>
 */
public final void await() throws InterruptedException {
    if (Thread.interrupted())
        throw new InterruptedException();

    // 将当前节点包装成一个  condition waiter node 节点
    Node node = addConditionWaiter();

    // 完全释放占有的锁，这里需要是占有锁的线程
    int savedState = fullyRelease(node);
    int interruptMode = 0;

    // 判断下是否在阻塞队列中，因为有可能被其他节点从等待队列移动到阻塞队列
    while (!isOnSyncQueue(node)) {
        // park等待，等待被唤醒
        LockSupport.park(this);
        if ((interruptMode = checkInterruptWhileWaiting(node)) != 0)
            break;
    }

    // 被唤醒后进入阻塞队列，等待获取锁，这里继续用了fullyRelease返回的 state
    if (acquireQueued(node, savedState) && interruptMode != THROW_IE)
        interruptMode = REINTERRUPT;
    if (node.nextWaiter != null) // clean up if cancelled
        unlinkCancelledWaiters();
    if (interruptMode != 0)
        reportInterruptAfterWait(interruptMode);
}

添加条件队列节点

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/**
 * Adds a new waiter to wait queue.
 * @return its new wait node
 */
private Node addConditionWaiter() {
    Node t = lastWaiter;
    // If lastWaiter is cancelled, clean out.
    // 如果节点已经不是 CONDITION 状态了，表示已经取消了
    if (t != null && t.waitStatus != Node.CONDITION) {
        // 把等待队列中取消的节点清理出去
        unlinkCancelledWaiters();
        t = lastWaiter;
    }
    // 把当前线程包装成waitStatus=CONDITION 的节点
    Node node = new Node(Thread.currentThread(), Node.CONDITION);
    // 没有 lastWaiter 节点，直接是 firstWaiter
    if (t == null)
        firstWaiter = node;
    else
    // 不然就接在 lastWaiter 后面
        t.nextWaiter = node;
    // 当前节点就会变成新的 lastWaiter
    lastWaiter = node;
    return node;
}

清理取消的节点

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/**
 * Unlinks cancelled waiter nodes from condition queue.
 * Called only while holding lock. This is called when
 * cancellation occurred during condition wait, and upon
 * insertion of a new waiter when lastWaiter is seen to have
 * been cancelled. This method is needed to avoid garbage
 * retention in the absence of signals. So even though it may
 * require a full traversal, it comes into play only when
 * timeouts or cancellations occur in the absence of
 * signals. It traverses all nodes rather than stopping at a
 * particular target to unlink all pointers to garbage nodes
 * without requiring many re-traversals during cancellation
 * storms.
 */
private void unlinkCancelledWaiters() {
    Node t = firstWaiter;
    Node trail = null;
    // 循环遍历单向链表的节点，如果状态不是 CONDITION 就清出去
    while (t != null) {
        Node next = t.nextWaiter;
        // 循环链表操作，清掉取消的节点
        if (t.waitStatus != Node.CONDITION) {
            t.nextWaiter = null;
            if (trail == null)
                firstWaiter = next;
            else
                trail.nextWaiter = next;
            if (next == null)
                lastWaiter = trail;
        }
        else
            trail = t;
        t = next;
    }
}

完全释放锁

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/**
 * Invokes release with current state value; returns saved state.
 * Cancels node and throws exception on failure.
 * @param node the condition node for this wait
 * @return previous sync state
 */
final int fullyRelease(Node node) {
    boolean failed = true;
    try {
        // 获取下当前的 state 值，因为是可重入的，所以这个值要保存下来
        int savedState = getState();
        // 这里还包含比较多操作，不过跟前面分析 AQS 的释放比较类似，不深入了
        if (release(savedState)) {
            failed = false;
            // 返回这个值
            return savedState;
        } else {
            throw new IllegalMonitorStateException();
        }
    } finally {
        if (failed)
            node.waitStatus = Node.CANCELLED;
    }
}

判断是否在阻塞队列中

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/**
 * Returns true if a node, always one that was initially placed on
 * a condition queue, is now waiting to reacquire on sync queue.
 * @param node the node
 * @return true if is reacquiring
 */
final boolean isOnSyncQueue(Node node) {
    // 如果waitStatus 是 CONDITION 或者没有 prev 前置节点肯定就不在
    if (node.waitStatus == Node.CONDITION || node.prev == null)
        return false;
    // 这里就是我前面提到的 next 的作用
    if (node.next != null) // If has successor, it must be on queue
        return true;
    // 从 tail 开始找，是否在阻塞队列中
    /*
     * node.prev can be non-null, but not yet on queue because
     * the CAS to place it on queue can fail. So we have to
     * traverse from tail to make sure it actually made it.  It
     * will always be near the tail in calls to this method, and
     * unless the CAS failed (which is unlikely), it will be
     * there, so we hardly ever traverse much.
     */
    return findNodeFromTail(node);
}
/**
 * Returns true if node is on sync queue by searching backwards from tail.
 * Called only when needed by isOnSyncQueue.
 * @return true if present
 */
private boolean findNodeFromTail(Node node) {
    Node t = tail;
    // 从 tail 开始，从后往前找
    for (;;) {
        if (t == node)
            return true;
        if (t == null)
            return false;
        t = t.prev;
    }
}

await 的逻辑差不多就是这样子，主要的就是把自己包成一个 Node 节点，waitStatus 的状态是 CONDITION，挂在等待队列的最后，然后完全释放锁，park 等待

signal

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/**
 * Moves the longest-waiting thread, if one exists, from the
 * wait queue for this condition to the wait queue for the
 * owning lock.
 *
 * @throws IllegalMonitorStateException if {@link #isHeldExclusively}
 *         returns {@code false}
 */
public final void signal() {
    if (!isHeldExclusively())
        throw new IllegalMonitorStateException();
    // firstWaiter 肯定是最早开始等待的
    Node first = firstWaiter;
    // 如果不为空就唤醒
    if (first != null)
        doSignal(first);
}
/**
 * Removes and transfers nodes until hit non-cancelled one or
 * null. Split out from signal in part to encourage compilers
 * to inline the case of no waiters.
 * @param first (non-null) the first node on condition queue
 */
private void doSignal(Node first) {
    do {
        // 因为要去唤醒 first 节点了，firstWaiter 需要再从后面找一个
        // 并且判断是否为空，如果是空的话就直接可以把 lastWaiter 设置成空了
        if ( (firstWaiter = first.nextWaiter) == null)
            lastWaiter = null;
        //  first 不需要继续保存后面的 waiter 了，因为 firstWaiter 已经是 first 的后置节点了
        first.nextWaiter = null;
    // 如果 first 节点转移不成功，并且 firstWaiter 节点不为空，则继续进入循环
    } while (!transferForSignal(first) &&
             (first = firstWaiter) != null);
}
/**
 * Transfers a node from a condition queue onto sync queue.
 * Returns true if successful.
 * @param node the node
 * @return true if successfully transferred (else the node was
 * cancelled before signal)
 */
final boolean transferForSignal(Node node) {
    /*
     * If cannot change waitStatus, the node has been cancelled.
     */
    // 如果状态已经不是 CONDITION 就不会设置成功，返回 false
    if (!compareAndSetWaitStatus(node, Node.CONDITION, 0))
        return false;
    /*
     * Splice onto queue and try to set waitStatus of predecessor to
     * indicate that thread is (probably) waiting. If cancelled or
     * attempt to set waitStatus fails, wake up to resync (in which
     * case the waitStatus can be transiently and harmlessly wrong).
     */
    // 调用跟aqs 第一篇中一样的 enq 方法进入阻塞队列，返回入队后的前一节点
    Node p = enq(node);
    int ws = p.waitStatus;
    // 将前置节点状态设置成SIGNAL，表示后面有节点在等了
    if (ws > 0 || !compareAndSetWaitStatus(p, ws, Node.SIGNAL))
        LockSupport.unpark(node.thread);
    // 返回 true，上一个方法的循环就退出了
    return true;
}

这里其实就是把 condition 等待队列的第一个未取消的节点入队到阻塞队列去争锁

附录

synchronized 版的 BoundedBuffer

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/*
  File: BoundedBuffer.java

  Originally written by Doug Lea and released into the public domain.
  This may be used for any purposes whatsoever without acknowledgment.
  Thanks for the assistance and support of Sun Microsystems Labs,
  and everyone contributing, testing, and using this code.

  History:
  Date       Who                What
  11Jun1998  dl               Create public version
  17Jul1998  dl               Simplified by eliminating wait counts
  25aug1998  dl               added peek
   5May1999  dl               replace % with conditional (slightly faster)
*/

package EDU.oswego.cs.dl.util.concurrent;

/**
 * Efficient array-based bounded buffer class.
 * Adapted from CPJ, chapter 8, which describes design.
 * <p>[<a href="http://gee.cs.oswego.edu/dl/classes/EDU/oswego/cs/dl/util/concurrent/intro.html"> Introduction to this package. </a>] <p>
 **/

public class BoundedBuffer implements BoundedChannel {

  protected final Object[]  array_;      // the elements

  protected int takePtr_ = 0;            // circular indices
  protected int putPtr_ = 0;       

  protected int usedSlots_ = 0;          // length
  protected int emptySlots_;             // capacity - length

  /**
   * Helper monitor to handle puts. 
   **/
  protected final Object putMonitor_ = new Object();

  /**
   * Create a BoundedBuffer with the given capacity.
   * @exception IllegalArgumentException if capacity less or equal to zero
   **/
  public BoundedBuffer(int capacity) throws IllegalArgumentException {
    if (capacity <= 0) throw new IllegalArgumentException();
    array_ = new Object[capacity];
    emptySlots_ = capacity;
  }

  /**
   * Create a buffer with the current default capacity
   **/

  public BoundedBuffer() { 
    this(DefaultChannelCapacity.get()); 
  }

  /** 
   * Return the number of elements in the buffer.
   * This is only a snapshot value, that may change
   * immediately after returning.
   **/
  public synchronized int size() { return usedSlots_; }

  public int capacity() { return array_.length; }

  protected void incEmptySlots() {
    synchronized(putMonitor_) {
      ++emptySlots_;
      putMonitor_.notify();
    }
  }

  protected synchronized void incUsedSlots() {
    ++usedSlots_;
    notify();
  }

  protected final void insert(Object x) { // mechanics of put
    --emptySlots_;
    array_[putPtr_] = x;
    if (++putPtr_ >= array_.length) putPtr_ = 0;
  }

  protected final Object extract() { // mechanics of take
    --usedSlots_;
    Object old = array_[takePtr_];
    array_[takePtr_] = null;
    if (++takePtr_ >= array_.length) takePtr_ = 0;
    return old;
  }

  public Object peek() {
    synchronized(this) {
      if (usedSlots_ > 0)
        return array_[takePtr_];
      else
        return null;
    }
  }


  public void put(Object x) throws InterruptedException {
    if (x == null) throw new IllegalArgumentException();
    if (Thread.interrupted()) throw new InterruptedException();

    synchronized(putMonitor_) {
      while (emptySlots_ <= 0) {
	try { putMonitor_.wait(); }
        catch (InterruptedException ex) {
          putMonitor_.notify();
          throw ex;
        }
      }
      insert(x);
    }
    incUsedSlots();
  }

  public boolean offer(Object x, long msecs) throws InterruptedException {
    if (x == null) throw new IllegalArgumentException();
    if (Thread.interrupted()) throw new InterruptedException();

    synchronized(putMonitor_) {
      long start = (msecs <= 0)? 0 : System.currentTimeMillis();
      long waitTime = msecs;
      while (emptySlots_ <= 0) {
        if (waitTime <= 0) return false;
	try { putMonitor_.wait(waitTime); }
        catch (InterruptedException ex) {
          putMonitor_.notify();
          throw ex;
        }
        waitTime = msecs - (System.currentTimeMillis() - start);
      }
      insert(x);
    }
    incUsedSlots();
    return true;
  }



  public  Object take() throws InterruptedException { 
    if (Thread.interrupted()) throw new InterruptedException();
    Object old = null; 
    synchronized(this) { 
      while (usedSlots_ <= 0) {
        try { wait(); }
        catch (InterruptedException ex) {
          notify();
          throw ex; 
        }
      }
      old = extract();
    }
    incEmptySlots();
    return old;
  }

  public  Object poll(long msecs) throws InterruptedException { 
    if (Thread.interrupted()) throw new InterruptedException();
    Object old = null; 
    synchronized(this) { 
      long start = (msecs <= 0)? 0 : System.currentTimeMillis();
      long waitTime = msecs;
      
      while (usedSlots_ <= 0) {
        if (waitTime <= 0) return null;
        try { wait(waitTime); }
        catch (InterruptedException ex) {
          notify();
          throw ex; 
        }
        waitTime = msecs - (System.currentTimeMillis() - start);

      }
      old = extract();
    }
    incEmptySlots();
    return old;
  }

}

发表于 更新于 分类于 ， 阅读次数： Disqus：

很多东西都是时看时新，而且时间长了也会忘，所以再来复习下，也会有一些新的角度看法这次来聊下AQS的内容，主要是这几个点，

第一个线程

第一个线程抢到锁了，此时state跟阻塞队列是怎么样的，其实这里是之前没理解对的地方

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/**
         * Fair version of tryAcquire.  Don't grant access unless
         * recursive call or no waiters or is first.
         */
        protected final boolean tryAcquire(int acquires) {
            final Thread current = Thread.currentThread();
            int c = getState();
            // 这里如果state还是0说明锁还空着
            if (c == 0) {
                // 因为是公平锁版本的，先去看下是否阻塞队列里有排着队的
                if (!hasQueuedPredecessors() &&
                    compareAndSetState(0, acquires)) {
                    // 没有排队的，并且state使用cas设置成功的就标记当前占有锁的线程是我
                    setExclusiveOwnerThread(current);
                    // 然后其实就返回了，包括阻塞队列的head和tail节点和waitStatus都没有设置
                    return true;
                }
            }
            else if (current == getExclusiveOwnerThread()) {
                int nextc = c + acquires;
                if (nextc < 0)
                    throw new Error("Maximum lock count exceeded");
                setState(nextc);
                return true;
            }
            // 这里就是第二个线程会返回false
            return false;
        }
    }

第二个线程

当第二个线程进来的时候应该是怎么样，结合代码来看

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/**
     * Acquires in exclusive mode, ignoring interrupts.  Implemented
     * by invoking at least once {@link #tryAcquire},
     * returning on success.  Otherwise the thread is queued, possibly
     * repeatedly blocking and unblocking, invoking {@link
     * #tryAcquire} until success.  This method can be used
     * to implement method {@link Lock#lock}.
     *
     * @param arg the acquire argument.  This value is conveyed to
     *        {@link #tryAcquire} but is otherwise uninterpreted and
     *        can represent anything you like.
     */
    public final void acquire(int arg) {
        // 前面第一种情况是tryAcquire直接成功了，这个if判断第一个条件就是false，就不往下执行了
        // 如果是第二个线程，第一个条件获取锁不成功，条件判断!tryAcquire(arg) == true，就会走
        // acquireQueued(addWaiter(Node.EXCLUSIVE), arg)
        if (!tryAcquire(arg) &&
            acquireQueued(addWaiter(Node.EXCLUSIVE), arg))
            selfInterrupt();
    }

然后来看下addWaiter的逻辑

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/**
     * Creates and enqueues node for current thread and given mode.
     *
     * @param mode Node.EXCLUSIVE for exclusive, Node.SHARED for shared
     * @return the new node
     */
    private Node addWaiter(Node mode) {
        // 这里是包装成一个node
        Node node = new Node(Thread.currentThread(), mode);
        // Try the fast path of enq; backup to full enq on failure
        // 最快的方式就是把当前线程的节点放在阻塞队列的最后
        Node pred = tail;
        // 只有当tail，也就是pred不为空的时候可以直接接上
        if (pred != null) {
            node.prev = pred;
            // 如果这里cas成功了，就直接接上返回了
            if (compareAndSetTail(pred, node)) {
                pred.next = node;
                return node;
            }
        }
        // 不然就会继续走到这里
        enq(node);
        return node;
    }

然后就是enq的逻辑了

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/**
     * Inserts node into queue, initializing if necessary. See picture above.
     * @param node the node to insert
     * @return node's predecessor
     */
    private Node enq(final Node node) {
        for (;;) {
            // 如果状态没变化的话，tail这时还是null的
            Node t = tail;
            if (t == null) { // Must initialize
                // 这里就会初始化头结点，就是个空节点
                if (compareAndSetHead(new Node()))
                    // tail也赋值成head
                    tail = head;
            } else {
                // 这里就设置tail了
                node.prev = t;
                if (compareAndSetTail(t, node)) {
                    t.next = node;
                    return t;
                }
            }
        }
    }

所以从这里可以看出来，其实head头结点不是个真实的带有线程的节点，并且不是在第一个线程进来的时候设置的

解锁

通过代码来看下

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/**
     * Attempts to release this lock.
     *
     * <p>If the current thread is the holder of this lock then the hold
     * count is decremented.  If the hold count is now zero then the lock
     * is released.  If the current thread is not the holder of this
     * lock then {@link IllegalMonitorStateException} is thrown.
     *
     * @throws IllegalMonitorStateException if the current thread does not
     *         hold this lock
     */
    public void unlock() {
        // 释放锁
        sync.release(1);
    }
/**
     * Releases in exclusive mode.  Implemented by unblocking one or
     * more threads if {@link #tryRelease} returns true.
     * This method can be used to implement method {@link Lock#unlock}.
     *
     * @param arg the release argument.  This value is conveyed to
     *        {@link #tryRelease} but is otherwise uninterpreted and
     *        can represent anything you like.
     * @return the value returned from {@link #tryRelease}
     */
    public final boolean release(int arg) {
        // 尝试去释放
        if (tryRelease(arg)) {
            Node h = head;
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }
protected final boolean tryRelease(int releases) {
            int c = getState() - releases;
            if (Thread.currentThread() != getExclusiveOwnerThread())
                throw new IllegalMonitorStateException();
            boolean free = false;
    		// 判断是否完全释放锁，因为可重入
            if (c == 0) {
                free = true;
                setExclusiveOwnerThread(null);
            }
            setState(c);
            return free;
        }
// 这段代码和上面的一致，只是为了顺序性，又拷下来看下

public final boolean release(int arg) {
        // 尝试去释放，如果是完全释放，返回的就是true，否则是false
        if (tryRelease(arg)) {
            Node h = head;
            // 这里判断头结点是否为空以及waitStatus的状态，前面说了head节点其实是
            // 在第二个线程进来的时候初始化的，如果是空的话说明没后续节点，并且waitStatus
            // 也表示了后续的等待状态
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }

/**
     * Wakes up node's successor, if one exists.
     *
     * @param node the node
     */
// 唤醒后继节点
    private void unparkSuccessor(Node node) {
        /*
         * If status is negative (i.e., possibly needing signal) try
         * to clear in anticipation of signalling.  It is OK if this
         * fails or if status is changed by waiting thread.
         */
        int ws = node.waitStatus;
        if (ws < 0)
            compareAndSetWaitStatus(node, ws, 0);

        /*
         * Thread to unpark is held in successor, which is normally
         * just the next node.  But if cancelled or apparently null,
         * traverse backwards from tail to find the actual
         * non-cancelled successor.
         */
        Node s = node.next;
        // 如果后继节点是空或者当前节点取消等待了
        if (s == null || s.waitStatus > 0) {
            s = null;
            // 从后往前找，找到非取消的节点，注意这里不是找到就退出，而是一直找到头
            // 所以不必担心中间有取消的
            for (Node t = tail; t != null && t != node; t = t.prev)
                if (t.waitStatus <= 0)
                    s = t;
        }
        if (s != null)
            // 将其唤醒
            LockSupport.unpark(s.thread);
    }

发表于 分类于 ， ， ， 阅读次数： Disqus：

最近群里大佬发起了一个读书打卡活动，需要每天读一会书，在群里打卡分享感悟，争取一个月能读完一本书，说实话一天十分钟的读书时间倒是问题不大，不过每天都要打卡，而且一个月要读完一本书，其实难度还是有点大的，不过也想试试看。
之前某某老大给自己立了个 flag，说要读一百本书，这对我来说挺难实现的，一则我也不喜欢书只读一小半，二则感觉对于喜欢看的内容范围还是比较有限制，可能也算是比较矫情，不爱追热门的各类东西，因为往往会有一些跟大众不一致的观点看法，显得格格不入。所以还是这个打卡活动可能会比较适合我，书是人类进步的阶梯。
到现在是打卡了三天了，读的主要是白岩松的《幸福了吗》，对于白岩松，我们这一代人是比较熟悉，并且整体印象比较不错的一个央视主持人，从《焦点访谈》开始，到疫情期间的各类一线节目，可能对我来说是个三观比较正，敢于说一些真话的主持人，这中间其实是有个空档期，没怎么看电视，也不太关注了，只是在疫情期间的节目，还是一如既往地给人一种可靠的感觉，正好有一次偶然微信读书推荐了白岩松的这本书，就看了一部分，正好这次继续往下看，因为相对来讲不会很晦涩，并且从这位知名央视主持人的角度分享他的过往和想法看法，还是比较有意思的。
从对汶川地震，08 年奥运等往事的回忆和一些细节的呈现，也让我了解比较多当时所不知道的，特别是汶川地震，那时的我还在读高中，真的是看着电视，作为“猛男”都忍不住泪目了，共和国之殇，多难兴邦，但是这对于当事人来说，都是一场醒不过来的噩梦。
然后是对于足球的热爱，其实光这个就能掰扯很多，因为我不爱足球，只爱篮球，其中原因有的没的也挺多可以说的，但是看了他的书，才能比较深入的了解一个足球迷，对足球，对中国足球，对世界杯，对阿根廷的感情。
接下去还是想能继续坚持下去，加油！

发表于 分类于 ， ， ， ， ， ， 阅读次数： Disqus：

前面写过一系列的 redis 源码分析的，但是实际上很多的问题还是需要结合实际的使用，然后其实就避不开缓存使用的三个著名问题，穿透，击穿和雪崩，这三个概念也是有着千丝万缕的关系，

缓存穿透

缓存穿透是指当数据库中本身就不存在这个数据的时候，使用一般的缓存策略时访问不到缓存后就访问数据库，但是因为数据库也没数据，所以如果不做任何策略优化的话，这类数据就每次都会访问一次数据库，对数据库压力也会比较大。

缓存击穿

缓存击穿跟穿透比较类似的，都是访问缓存不在，然后去访问数据库，与穿透不一样的是击穿是在数据库中存在数据，但是可能由于第一次访问，或者缓存过期了，需要访问到数据库，这对于访问量小的情况其实算是个正常情况，但是随着请求量变高就会引发一些性能隐患。

缓存雪崩

缓存雪崩就是击穿的大规模集群效应，当大量的缓存过期失效的时候，这些请求都是直接访问到数据库了，会对数据库造成很大的压力。

对于以上三种场景也有一些比较常见的解决方案，但也不能说是万无一失的，需要随着业务去寻找合适的方案

解决缓存穿透

对于数据库中就没这个数据的时候，一种是可以对这个 key 设置下空值，即以一个特定的表示是数据库不存在的，这种情况需要合理地调整过期时间，当这个 key 在数据库中有数据了的话，也需要有策略去更新这个值，并且如果这类 key 非常多，这个方法就会不太合适，就可以使用第二种方法，就是布隆过滤器，bloom filter，前置一个布隆过滤器，当这个 key 在数据库不存在的话，先用布隆过滤器挡一道，如果不在的话就直接返回了，当然布隆过滤器不是绝对的准确的

解决缓存击穿

当一个 key 的缓存过期了，如果大量请求过来访问这个 key，请求都会落在数据库里，这个时候就可以使用一些类似于互斥锁的方式去让一个线程去访问数据库，更新缓存，但是这里其实也有个问题，就是如果是热点 key 其实这种方式也比较危险，万一更新失败，或者更新操作的时候耗时比较久，就会有一大堆请求卡在那，这种情况可能需要有一些异步提前刷新缓存，可以结合具体场景选择方式

解决缓存雪崩

雪崩的情况是指大批量的 key 都一起过期了，击穿的放大版，大批量的请求都打到数据库上了，一方面有可能直接缓存不可用了，就需要用集群化高可用的缓存服务，然后对于实际使用中也可以使用本地缓存结合 redis 缓存，去提高可用性，再配合一些限流措施，然后就是缓存使用过程总的过期时间最好能加一些随机值，防止在同一时间过期而导致雪崩，结合互斥锁防止大量请求打到数据库。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

The path sum of a path is the sum of the node’s values in the path.

Given the root of a binary tree, return the maximum path sum of any path.

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

路径和 是路径中各节点值的总和。

给你一个二叉树的根节点 root ，返回其 最大路径和

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

代码

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int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

结果图

难得有个 100%，贴个图哈哈

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What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， 阅读次数： Disqus：

Condition也是 AQS 中很重要的一块内容，可以先看段示例代码，这段代码应该来自于Doug Lea大大，可以在 javadoc 中的 condition 部分找到，其实大大原来写过基于 synchronized 实现的，后面我也贴下代码

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import java.util.concurrent.locks.Condition;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;

class BoundedBuffer {
    final Lock lock = new ReentrantLock();
    // condition 依赖于 lock 来产生
    final Condition notFull = lock.newCondition();
    final Condition notEmpty = lock.newCondition();

    // 对象池子，put 跟 take 的就是这里的
    final Object[] items = new Object[100];
    int putptr, takeptr, count;

    // 生产
    public void put(Object x) throws InterruptedException {
        // 这里也说明了，需要先拥有锁
        lock.lock();
        try {
            while (count == items.length)
                notFull.await();  // 队列已满，等待，直到 not full 才能继续生产
            items[putptr] = x;
            if (++putptr == items.length) putptr = 0;
            ++count;
            notEmpty.signal(); // 生产成功，队列已经 not empty 了，发个通知出去
        } finally {
            lock.unlock();
        }
    }

    // 消费
    public Object take() throws InterruptedException {
        lock.lock();
        try {
            while (count == 0)
                notEmpty.await(); // 队列为空，等待，直到队列 not empty，才能继续消费
            Object x = items[takeptr];
            if (++takeptr == items.length) takeptr = 0;
            --count;
            notFull.signal(); // 被我消费掉一个，队列 not full 了，发个通知出去
            return x;
        } finally {
            lock.unlock();
        }
    }
}

介绍下 Condition 的结构

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public class ConditionObject implements Condition, java.io.Serializable {
        private static final long serialVersionUID = 1173984872572414699L;
        /** First node of condition queue. */
        private transient Node firstWaiter;
        /** Last node of condition queue. */
        private transient Node lastWaiter;

主要的就这么点，而且也复用了 AQS 阻塞队列或者大大叫 lock queue中同样的 Node 节点，只不过它没有使用其中的双向队列，也就是prev 和 next，而是在 Node 中的 nextWaiter，所以只是个单向的队列，没使用 next 其实还有个用处，后面会提到，看下结构的示意图

然后主要是看两个方法，await 和 signal,
先来看下 await

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/**
 * Implements interruptible condition wait.
 * <ol>
 * <li> If current thread is interrupted, throw InterruptedException.
 * <li> Save lock state returned by {@link #getState}.
 * <li> Invoke {@link #release} with saved state as argument,
 *      throwing IllegalMonitorStateException if it fails.
 * <li> Block until signalled or interrupted.
 * <li> Reacquire by invoking specialized version of
 *      {@link #acquire} with saved state as argument.
 * <li> If interrupted while blocked in step 4, throw InterruptedException.
 * </ol>
 */
public final void await() throws InterruptedException {
    if (Thread.interrupted())
        throw new InterruptedException();

    // 将当前节点包装成一个  condition waiter node 节点
    Node node = addConditionWaiter();

    // 完全释放占有的锁，这里需要是占有锁的线程
    int savedState = fullyRelease(node);
    int interruptMode = 0;

    // 判断下是否在阻塞队列中，因为有可能被其他节点从等待队列移动到阻塞队列
    while (!isOnSyncQueue(node)) {
        // park等待，等待被唤醒
        LockSupport.park(this);
        if ((interruptMode = checkInterruptWhileWaiting(node)) != 0)
            break;
    }

    // 被唤醒后进入阻塞队列，等待获取锁，这里继续用了fullyRelease返回的 state
    if (acquireQueued(node, savedState) && interruptMode != THROW_IE)
        interruptMode = REINTERRUPT;
    if (node.nextWaiter != null) // clean up if cancelled
        unlinkCancelledWaiters();
    if (interruptMode != 0)
        reportInterruptAfterWait(interruptMode);
}

添加条件队列节点

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/**
 * Adds a new waiter to wait queue.
 * @return its new wait node
 */
private Node addConditionWaiter() {
    Node t = lastWaiter;
    // If lastWaiter is cancelled, clean out.
    // 如果节点已经不是 CONDITION 状态了，表示已经取消了
    if (t != null && t.waitStatus != Node.CONDITION) {
        // 把等待队列中取消的节点清理出去
        unlinkCancelledWaiters();
        t = lastWaiter;
    }
    // 把当前线程包装成waitStatus=CONDITION 的节点
    Node node = new Node(Thread.currentThread(), Node.CONDITION);
    // 没有 lastWaiter 节点，直接是 firstWaiter
    if (t == null)
        firstWaiter = node;
    else
    // 不然就接在 lastWaiter 后面
        t.nextWaiter = node;
    // 当前节点就会变成新的 lastWaiter
    lastWaiter = node;
    return node;
}

清理取消的节点

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/**
 * Unlinks cancelled waiter nodes from condition queue.
 * Called only while holding lock. This is called when
 * cancellation occurred during condition wait, and upon
 * insertion of a new waiter when lastWaiter is seen to have
 * been cancelled. This method is needed to avoid garbage
 * retention in the absence of signals. So even though it may
 * require a full traversal, it comes into play only when
 * timeouts or cancellations occur in the absence of
 * signals. It traverses all nodes rather than stopping at a
 * particular target to unlink all pointers to garbage nodes
 * without requiring many re-traversals during cancellation
 * storms.
 */
private void unlinkCancelledWaiters() {
    Node t = firstWaiter;
    Node trail = null;
    // 循环遍历单向链表的节点，如果状态不是 CONDITION 就清出去
    while (t != null) {
        Node next = t.nextWaiter;
        // 循环链表操作，清掉取消的节点
        if (t.waitStatus != Node.CONDITION) {
            t.nextWaiter = null;
            if (trail == null)
                firstWaiter = next;
            else
                trail.nextWaiter = next;
            if (next == null)
                lastWaiter = trail;
        }
        else
            trail = t;
        t = next;
    }
}

完全释放锁

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/**
 * Invokes release with current state value; returns saved state.
 * Cancels node and throws exception on failure.
 * @param node the condition node for this wait
 * @return previous sync state
 */
final int fullyRelease(Node node) {
    boolean failed = true;
    try {
        // 获取下当前的 state 值，因为是可重入的，所以这个值要保存下来
        int savedState = getState();
        // 这里还包含比较多操作，不过跟前面分析 AQS 的释放比较类似，不深入了
        if (release(savedState)) {
            failed = false;
            // 返回这个值
            return savedState;
        } else {
            throw new IllegalMonitorStateException();
        }
    } finally {
        if (failed)
            node.waitStatus = Node.CANCELLED;
    }
}

判断是否在阻塞队列中

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/**
 * Returns true if a node, always one that was initially placed on
 * a condition queue, is now waiting to reacquire on sync queue.
 * @param node the node
 * @return true if is reacquiring
 */
final boolean isOnSyncQueue(Node node) {
    // 如果waitStatus 是 CONDITION 或者没有 prev 前置节点肯定就不在
    if (node.waitStatus == Node.CONDITION || node.prev == null)
        return false;
    // 这里就是我前面提到的 next 的作用
    if (node.next != null) // If has successor, it must be on queue
        return true;
    // 从 tail 开始找，是否在阻塞队列中
    /*
     * node.prev can be non-null, but not yet on queue because
     * the CAS to place it on queue can fail. So we have to
     * traverse from tail to make sure it actually made it.  It
     * will always be near the tail in calls to this method, and
     * unless the CAS failed (which is unlikely), it will be
     * there, so we hardly ever traverse much.
     */
    return findNodeFromTail(node);
}
/**
 * Returns true if node is on sync queue by searching backwards from tail.
 * Called only when needed by isOnSyncQueue.
 * @return true if present
 */
private boolean findNodeFromTail(Node node) {
    Node t = tail;
    // 从 tail 开始，从后往前找
    for (;;) {
        if (t == node)
            return true;
        if (t == null)
            return false;
        t = t.prev;
    }
}

await 的逻辑差不多就是这样子，主要的就是把自己包成一个 Node 节点，waitStatus 的状态是 CONDITION，挂在等待队列的最后，然后完全释放锁，park 等待

signal

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/**
 * Moves the longest-waiting thread, if one exists, from the
 * wait queue for this condition to the wait queue for the
 * owning lock.
 *
 * @throws IllegalMonitorStateException if {@link #isHeldExclusively}
 *         returns {@code false}
 */
public final void signal() {
    if (!isHeldExclusively())
        throw new IllegalMonitorStateException();
    // firstWaiter 肯定是最早开始等待的
    Node first = firstWaiter;
    // 如果不为空就唤醒
    if (first != null)
        doSignal(first);
}
/**
 * Removes and transfers nodes until hit non-cancelled one or
 * null. Split out from signal in part to encourage compilers
 * to inline the case of no waiters.
 * @param first (non-null) the first node on condition queue
 */
private void doSignal(Node first) {
    do {
        // 因为要去唤醒 first 节点了，firstWaiter 需要再从后面找一个
        // 并且判断是否为空，如果是空的话就直接可以把 lastWaiter 设置成空了
        if ( (firstWaiter = first.nextWaiter) == null)
            lastWaiter = null;
        //  first 不需要继续保存后面的 waiter 了，因为 firstWaiter 已经是 first 的后置节点了
        first.nextWaiter = null;
    // 如果 first 节点转移不成功，并且 firstWaiter 节点不为空，则继续进入循环
    } while (!transferForSignal(first) &&
             (first = firstWaiter) != null);
}
/**
 * Transfers a node from a condition queue onto sync queue.
 * Returns true if successful.
 * @param node the node
 * @return true if successfully transferred (else the node was
 * cancelled before signal)
 */
final boolean transferForSignal(Node node) {
    /*
     * If cannot change waitStatus, the node has been cancelled.
     */
    // 如果状态已经不是 CONDITION 就不会设置成功，返回 false
    if (!compareAndSetWaitStatus(node, Node.CONDITION, 0))
        return false;
    /*
     * Splice onto queue and try to set waitStatus of predecessor to
     * indicate that thread is (probably) waiting. If cancelled or
     * attempt to set waitStatus fails, wake up to resync (in which
     * case the waitStatus can be transiently and harmlessly wrong).
     */
    // 调用跟aqs 第一篇中一样的 enq 方法进入阻塞队列，返回入队后的前一节点
    Node p = enq(node);
    int ws = p.waitStatus;
    // 将前置节点状态设置成SIGNAL，表示后面有节点在等了
    if (ws > 0 || !compareAndSetWaitStatus(p, ws, Node.SIGNAL))
        LockSupport.unpark(node.thread);
    // 返回 true，上一个方法的循环就退出了
    return true;
}

这里其实就是把 condition 等待队列的第一个未取消的节点入队到阻塞队列去争锁

附录

synchronized 版的 BoundedBuffer

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/*
  File: BoundedBuffer.java

  Originally written by Doug Lea and released into the public domain.
  This may be used for any purposes whatsoever without acknowledgment.
  Thanks for the assistance and support of Sun Microsystems Labs,
  and everyone contributing, testing, and using this code.

  History:
  Date       Who                What
  11Jun1998  dl               Create public version
  17Jul1998  dl               Simplified by eliminating wait counts
  25aug1998  dl               added peek
   5May1999  dl               replace % with conditional (slightly faster)
*/

package EDU.oswego.cs.dl.util.concurrent;

/**
 * Efficient array-based bounded buffer class.
 * Adapted from CPJ, chapter 8, which describes design.
 * <p>[<a href="http://gee.cs.oswego.edu/dl/classes/EDU/oswego/cs/dl/util/concurrent/intro.html"> Introduction to this package. </a>] <p>
 **/

public class BoundedBuffer implements BoundedChannel {

  protected final Object[]  array_;      // the elements

  protected int takePtr_ = 0;            // circular indices
  protected int putPtr_ = 0;       

  protected int usedSlots_ = 0;          // length
  protected int emptySlots_;             // capacity - length

  /**
   * Helper monitor to handle puts. 
   **/
  protected final Object putMonitor_ = new Object();

  /**
   * Create a BoundedBuffer with the given capacity.
   * @exception IllegalArgumentException if capacity less or equal to zero
   **/
  public BoundedBuffer(int capacity) throws IllegalArgumentException {
    if (capacity <= 0) throw new IllegalArgumentException();
    array_ = new Object[capacity];
    emptySlots_ = capacity;
  }

  /**
   * Create a buffer with the current default capacity
   **/

  public BoundedBuffer() { 
    this(DefaultChannelCapacity.get()); 
  }

  /** 
   * Return the number of elements in the buffer.
   * This is only a snapshot value, that may change
   * immediately after returning.
   **/
  public synchronized int size() { return usedSlots_; }

  public int capacity() { return array_.length; }

  protected void incEmptySlots() {
    synchronized(putMonitor_) {
      ++emptySlots_;
      putMonitor_.notify();
    }
  }

  protected synchronized void incUsedSlots() {
    ++usedSlots_;
    notify();
  }

  protected final void insert(Object x) { // mechanics of put
    --emptySlots_;
    array_[putPtr_] = x;
    if (++putPtr_ >= array_.length) putPtr_ = 0;
  }

  protected final Object extract() { // mechanics of take
    --usedSlots_;
    Object old = array_[takePtr_];
    array_[takePtr_] = null;
    if (++takePtr_ >= array_.length) takePtr_ = 0;
    return old;
  }

  public Object peek() {
    synchronized(this) {
      if (usedSlots_ > 0)
        return array_[takePtr_];
      else
        return null;
    }
  }


  public void put(Object x) throws InterruptedException {
    if (x == null) throw new IllegalArgumentException();
    if (Thread.interrupted()) throw new InterruptedException();

    synchronized(putMonitor_) {
      while (emptySlots_ <= 0) {
	try { putMonitor_.wait(); }
        catch (InterruptedException ex) {
          putMonitor_.notify();
          throw ex;
        }
      }
      insert(x);
    }
    incUsedSlots();
  }

  public boolean offer(Object x, long msecs) throws InterruptedException {
    if (x == null) throw new IllegalArgumentException();
    if (Thread.interrupted()) throw new InterruptedException();

    synchronized(putMonitor_) {
      long start = (msecs <= 0)? 0 : System.currentTimeMillis();
      long waitTime = msecs;
      while (emptySlots_ <= 0) {
        if (waitTime <= 0) return false;
	try { putMonitor_.wait(waitTime); }
        catch (InterruptedException ex) {
          putMonitor_.notify();
          throw ex;
        }
        waitTime = msecs - (System.currentTimeMillis() - start);
      }
      insert(x);
    }
    incUsedSlots();
    return true;
  }



  public  Object take() throws InterruptedException { 
    if (Thread.interrupted()) throw new InterruptedException();
    Object old = null; 
    synchronized(this) { 
      while (usedSlots_ <= 0) {
        try { wait(); }
        catch (InterruptedException ex) {
          notify();
          throw ex; 
        }
      }
      old = extract();
    }
    incEmptySlots();
    return old;
  }

  public  Object poll(long msecs) throws InterruptedException { 
    if (Thread.interrupted()) throw new InterruptedException();
    Object old = null; 
    synchronized(this) { 
      long start = (msecs <= 0)? 0 : System.currentTimeMillis();
      long waitTime = msecs;
      
      while (usedSlots_ <= 0) {
        if (waitTime <= 0) return null;
        try { wait(waitTime); }
        catch (InterruptedException ex) {
          notify();
          throw ex; 
        }
        waitTime = msecs - (System.currentTimeMillis() - start);

      }
      old = extract();
    }
    incEmptySlots();
    return old;
  }

}

发表于 更新于 分类于 ， 阅读次数： Disqus：

很多东西都是时看时新，而且时间长了也会忘，所以再来复习下，也会有一些新的角度看法这次来聊下AQS的内容，主要是这几个点，

第一个线程

第一个线程抢到锁了，此时state跟阻塞队列是怎么样的，其实这里是之前没理解对的地方

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/**
         * Fair version of tryAcquire.  Don't grant access unless
         * recursive call or no waiters or is first.
         */
        protected final boolean tryAcquire(int acquires) {
            final Thread current = Thread.currentThread();
            int c = getState();
            // 这里如果state还是0说明锁还空着
            if (c == 0) {
                // 因为是公平锁版本的，先去看下是否阻塞队列里有排着队的
                if (!hasQueuedPredecessors() &&
                    compareAndSetState(0, acquires)) {
                    // 没有排队的，并且state使用cas设置成功的就标记当前占有锁的线程是我
                    setExclusiveOwnerThread(current);
                    // 然后其实就返回了，包括阻塞队列的head和tail节点和waitStatus都没有设置
                    return true;
                }
            }
            else if (current == getExclusiveOwnerThread()) {
                int nextc = c + acquires;
                if (nextc < 0)
                    throw new Error("Maximum lock count exceeded");
                setState(nextc);
                return true;
            }
            // 这里就是第二个线程会返回false
            return false;
        }
    }

第二个线程

当第二个线程进来的时候应该是怎么样，结合代码来看

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/**
     * Acquires in exclusive mode, ignoring interrupts.  Implemented
     * by invoking at least once {@link #tryAcquire},
     * returning on success.  Otherwise the thread is queued, possibly
     * repeatedly blocking and unblocking, invoking {@link
     * #tryAcquire} until success.  This method can be used
     * to implement method {@link Lock#lock}.
     *
     * @param arg the acquire argument.  This value is conveyed to
     *        {@link #tryAcquire} but is otherwise uninterpreted and
     *        can represent anything you like.
     */
    public final void acquire(int arg) {
        // 前面第一种情况是tryAcquire直接成功了，这个if判断第一个条件就是false，就不往下执行了
        // 如果是第二个线程，第一个条件获取锁不成功，条件判断!tryAcquire(arg) == true，就会走
        // acquireQueued(addWaiter(Node.EXCLUSIVE), arg)
        if (!tryAcquire(arg) &&
            acquireQueued(addWaiter(Node.EXCLUSIVE), arg))
            selfInterrupt();
    }

然后来看下addWaiter的逻辑

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/**
     * Creates and enqueues node for current thread and given mode.
     *
     * @param mode Node.EXCLUSIVE for exclusive, Node.SHARED for shared
     * @return the new node
     */
    private Node addWaiter(Node mode) {
        // 这里是包装成一个node
        Node node = new Node(Thread.currentThread(), mode);
        // Try the fast path of enq; backup to full enq on failure
        // 最快的方式就是把当前线程的节点放在阻塞队列的最后
        Node pred = tail;
        // 只有当tail，也就是pred不为空的时候可以直接接上
        if (pred != null) {
            node.prev = pred;
            // 如果这里cas成功了，就直接接上返回了
            if (compareAndSetTail(pred, node)) {
                pred.next = node;
                return node;
            }
        }
        // 不然就会继续走到这里
        enq(node);
        return node;
    }

然后就是enq的逻辑了

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/**
     * Inserts node into queue, initializing if necessary. See picture above.
     * @param node the node to insert
     * @return node's predecessor
     */
    private Node enq(final Node node) {
        for (;;) {
            // 如果状态没变化的话，tail这时还是null的
            Node t = tail;
            if (t == null) { // Must initialize
                // 这里就会初始化头结点，就是个空节点
                if (compareAndSetHead(new Node()))
                    // tail也赋值成head
                    tail = head;
            } else {
                // 这里就设置tail了
                node.prev = t;
                if (compareAndSetTail(t, node)) {
                    t.next = node;
                    return t;
                }
            }
        }
    }

所以从这里可以看出来，其实head头结点不是个真实的带有线程的节点，并且不是在第一个线程进来的时候设置的

解锁

通过代码来看下

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/**
     * Attempts to release this lock.
     *
     * <p>If the current thread is the holder of this lock then the hold
     * count is decremented.  If the hold count is now zero then the lock
     * is released.  If the current thread is not the holder of this
     * lock then {@link IllegalMonitorStateException} is thrown.
     *
     * @throws IllegalMonitorStateException if the current thread does not
     *         hold this lock
     */
    public void unlock() {
        // 释放锁
        sync.release(1);
    }
/**
     * Releases in exclusive mode.  Implemented by unblocking one or
     * more threads if {@link #tryRelease} returns true.
     * This method can be used to implement method {@link Lock#unlock}.
     *
     * @param arg the release argument.  This value is conveyed to
     *        {@link #tryRelease} but is otherwise uninterpreted and
     *        can represent anything you like.
     * @return the value returned from {@link #tryRelease}
     */
    public final boolean release(int arg) {
        // 尝试去释放
        if (tryRelease(arg)) {
            Node h = head;
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }
protected final boolean tryRelease(int releases) {
            int c = getState() - releases;
            if (Thread.currentThread() != getExclusiveOwnerThread())
                throw new IllegalMonitorStateException();
            boolean free = false;
    		// 判断是否完全释放锁，因为可重入
            if (c == 0) {
                free = true;
                setExclusiveOwnerThread(null);
            }
            setState(c);
            return free;
        }
// 这段代码和上面的一致，只是为了顺序性，又拷下来看下

public final boolean release(int arg) {
        // 尝试去释放，如果是完全释放，返回的就是true，否则是false
        if (tryRelease(arg)) {
            Node h = head;
            // 这里判断头结点是否为空以及waitStatus的状态，前面说了head节点其实是
            // 在第二个线程进来的时候初始化的，如果是空的话说明没后续节点，并且waitStatus
            // 也表示了后续的等待状态
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }

/**
     * Wakes up node's successor, if one exists.
     *
     * @param node the node
     */
// 唤醒后继节点
    private void unparkSuccessor(Node node) {
        /*
         * If status is negative (i.e., possibly needing signal) try
         * to clear in anticipation of signalling.  It is OK if this
         * fails or if status is changed by waiting thread.
         */
        int ws = node.waitStatus;
        if (ws < 0)
            compareAndSetWaitStatus(node, ws, 0);

        /*
         * Thread to unpark is held in successor, which is normally
         * just the next node.  But if cancelled or apparently null,
         * traverse backwards from tail to find the actual
         * non-cancelled successor.
         */
        Node s = node.next;
        // 如果后继节点是空或者当前节点取消等待了
        if (s == null || s.waitStatus > 0) {
            s = null;
            // 从后往前找，找到非取消的节点，注意这里不是找到就退出，而是一直找到头
            // 所以不必担心中间有取消的
            for (Node t = tail; t != null && t != node; t = t.prev)
                if (t.waitStatus <= 0)
                    s = t;
        }
        if (s != null)
            // 将其唤醒
            LockSupport.unpark(s.thread);
    }

发表于 分类于 ， ， ， 阅读次数： Disqus：

最近群里大佬发起了一个读书打卡活动，需要每天读一会书，在群里打卡分享感悟，争取一个月能读完一本书，说实话一天十分钟的读书时间倒是问题不大，不过每天都要打卡，而且一个月要读完一本书，其实难度还是有点大的，不过也想试试看。
之前某某老大给自己立了个 flag，说要读一百本书，这对我来说挺难实现的，一则我也不喜欢书只读一小半，二则感觉对于喜欢看的内容范围还是比较有限制，可能也算是比较矫情，不爱追热门的各类东西，因为往往会有一些跟大众不一致的观点看法，显得格格不入。所以还是这个打卡活动可能会比较适合我，书是人类进步的阶梯。
到现在是打卡了三天了，读的主要是白岩松的《幸福了吗》，对于白岩松，我们这一代人是比较熟悉，并且整体印象比较不错的一个央视主持人，从《焦点访谈》开始，到疫情期间的各类一线节目，可能对我来说是个三观比较正，敢于说一些真话的主持人，这中间其实是有个空档期，没怎么看电视，也不太关注了，只是在疫情期间的节目，还是一如既往地给人一种可靠的感觉，正好有一次偶然微信读书推荐了白岩松的这本书，就看了一部分，正好这次继续往下看，因为相对来讲不会很晦涩，并且从这位知名央视主持人的角度分享他的过往和想法看法，还是比较有意思的。
从对汶川地震，08 年奥运等往事的回忆和一些细节的呈现，也让我了解比较多当时所不知道的，特别是汶川地震，那时的我还在读高中，真的是看着电视，作为“猛男”都忍不住泪目了，共和国之殇，多难兴邦，但是这对于当事人来说，都是一场醒不过来的噩梦。
然后是对于足球的热爱，其实光这个就能掰扯很多，因为我不爱足球，只爱篮球，其中原因有的没的也挺多可以说的，但是看了他的书，才能比较深入的了解一个足球迷，对足球，对中国足球，对世界杯，对阿根廷的感情。
接下去还是想能继续坚持下去，加油！

发表于 分类于 ， ， ， ， ， ， 阅读次数： Disqus：

前面写过一系列的 redis 源码分析的，但是实际上很多的问题还是需要结合实际的使用，然后其实就避不开缓存使用的三个著名问题，穿透，击穿和雪崩，这三个概念也是有着千丝万缕的关系，

缓存穿透

缓存穿透是指当数据库中本身就不存在这个数据的时候，使用一般的缓存策略时访问不到缓存后就访问数据库，但是因为数据库也没数据，所以如果不做任何策略优化的话，这类数据就每次都会访问一次数据库，对数据库压力也会比较大。

缓存击穿

缓存击穿跟穿透比较类似的，都是访问缓存不在，然后去访问数据库，与穿透不一样的是击穿是在数据库中存在数据，但是可能由于第一次访问，或者缓存过期了，需要访问到数据库，这对于访问量小的情况其实算是个正常情况，但是随着请求量变高就会引发一些性能隐患。

缓存雪崩

缓存雪崩就是击穿的大规模集群效应，当大量的缓存过期失效的时候，这些请求都是直接访问到数据库了，会对数据库造成很大的压力。

对于以上三种场景也有一些比较常见的解决方案，但也不能说是万无一失的，需要随着业务去寻找合适的方案

解决缓存穿透

对于数据库中就没这个数据的时候，一种是可以对这个 key 设置下空值，即以一个特定的表示是数据库不存在的，这种情况需要合理地调整过期时间，当这个 key 在数据库中有数据了的话，也需要有策略去更新这个值，并且如果这类 key 非常多，这个方法就会不太合适，就可以使用第二种方法，就是布隆过滤器，bloom filter，前置一个布隆过滤器，当这个 key 在数据库不存在的话，先用布隆过滤器挡一道，如果不在的话就直接返回了，当然布隆过滤器不是绝对的准确的

解决缓存击穿

当一个 key 的缓存过期了，如果大量请求过来访问这个 key，请求都会落在数据库里，这个时候就可以使用一些类似于互斥锁的方式去让一个线程去访问数据库，更新缓存，但是这里其实也有个问题，就是如果是热点 key 其实这种方式也比较危险，万一更新失败，或者更新操作的时候耗时比较久，就会有一大堆请求卡在那，这种情况可能需要有一些异步提前刷新缓存，可以结合具体场景选择方式

解决缓存雪崩

雪崩的情况是指大批量的 key 都一起过期了，击穿的放大版，大批量的请求都打到数据库上了，一方面有可能直接缓存不可用了，就需要用集群化高可用的缓存服务，然后对于实际使用中也可以使用本地缓存结合 redis 缓存，去提高可用性，再配合一些限流措施，然后就是缓存使用过程总的过期时间最好能加一些随机值，防止在同一时间过期而导致雪崩，结合互斥锁防止大量请求打到数据库。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

The path sum of a path is the sum of the node’s values in the path.

Given the root of a binary tree, return the maximum path sum of any path.

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

路径和 是路径中各节点值的总和。

给你一个二叉树的根节点 root ，返回其 最大路径和

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

代码

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int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

结果图

难得有个 100%，贴个图哈哈

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， 阅读次数： Disqus：

今天真的是被气得不轻，情况是碰到一个有 70 多秒的直行红灯，然后直行就排了很长的队，但是左转车道没车，就有好几辆车占着左转车道，准备往直行车道插队加塞，一般这种加塞的，会挑个不太计较的，如果前面一辆不让的话就再等等，我因为赶着回家，就不想让，结果那辆车几次车头直接往里冲，当时怒气值基本已经蓄满了，我真的是分毫都不想让，如果路上都是让着这种人的，那么这种情况只会越来越严重，我理解的这种心态，就赌你怕麻烦，多一事不如少一事，结果就是每次都能顺利插队加塞，其实延伸到我们社会中的种种实质性的排队或者等同于排队的情况，都已经有这种惯有思维，一方面这种不符合规则，可能在严重程度上容易被很多人所忽视，基本上已经被很多人当成是“合理”行为，另一方面，对于这些“微小”的违规行为，本身管理层面也基本没有想要管的意思，就更多的成为了纵容这些行为的导火索，并且大多数人都是想着如果不让，发生点小剐小蹭的要浪费很多时间精力来处理，甚至会觉得会被别人觉得自己太小气等等，诸多内外成本结合起来，会真的去硬刚的可能少之又少了，这样也就让更多的人觉得这种行为是被默许的，再举个非常小的例子，以我们公司疫情期间的盒饭发放为例，有两个比较“有意思”的事情，第一个就是因为疫情，本来是让排队要间隔一米，但是可能除了我比较怕死会跟前面的人保持点距离基本没别人会不挨着前面的人，甚至我跟我前面的人保持点距离，后面的同学会推着我让我上去；第二个是关于拿饭，这么多人排着队拿饭，然后有部分同学，一个人拿好几份，帮组里其他人的都拿了，有些甚至一个人拿十份，假如这个盒饭发放是说明了可以按部门直接全领了那就没啥问题，但是当时的状况是个人排队领自己的那一份，如果一个同学直接帮着组里十几个人都拿了，后面排队的人是什么感受呢，甚至有些是看到队伍排长了，就找队伍里自己认识的比较靠前的人说你帮我也拿一份，其实作为我这个比较按规矩办事的“愣头青”来说，我是比较不能接受这两件小事里的行为的，再往下说可能就有点偏激了，先说到这~

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

写一个程序找出两个单向链表的交叉起始点，可能是我英语不好，图里画的其实还有一点是交叉以后所有节点都是相同的
Write a program to find the node at which the intersection of two singly linked lists begins.

For example, the following two linked lists:

begin to intersect at node c1.

Example 1:

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Input: intersectVal = 8, listA = [4,1,8,4,5], listB = [5,6,1,8,4,5], skipA = 2, skipB = 3
Output: Reference of the node with value = 8
Input Explanation: The intersected node's value is 8 (note that this must not be 0 if the two lists intersect). From the head of A, it reads as [4,1,8,4,5]. From the head of B, it reads as [5,6,1,8,4,5]. There are 2 nodes before the intersected node in A; There are 3 nodes before the intersected node in B.

分析题解

一开始没什么头绪，感觉只能最原始的遍历，后来看了一些文章，发现比较简单的方式就是先找两个链表的长度差，因为从相交点开始肯定是长度一致的，这是个很好的解题突破口，找到长度差以后就是先跳过长链表的较长部分，然后开始同步遍历比较 A，B 链表；

代码

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public ListNode getIntersectionNode(ListNode headA, ListNode headB) {
        if (headA == null || headB == null) {
            return null;
        }
        // 算 A 的长度
        int countA = 0;
        ListNode tailA = headA;
        while (tailA != null) {
            tailA = tailA.next;
            countA++;
        }
        // 算 B 的长度
        int countB = 0;
        ListNode tailB = headB;
        while (tailB != null) {
            tailB = tailB.next;
            countB++;
        }
        tailA = headA;
        tailB = headB;
        // 依据长度差，先让长的链表 tail 指针往后移
        if (countA > countB) {
            while (countA > countB) {
                tailA = tailA.next;
                countA--;
            }
        } else if (countA < countB) {
            while (countA < countB) {
                tailB = tailB.next;
                countB--;
            }
        }
        // 然后以相同速度遍历两个链表比较
        while (tailA != null) {
            if (tailA == tailB) {
                return tailA;
            } else {
                tailA = tailA.next;
                tailB = tailB.next;
            }
        }
        return null;
    }

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

发表于 更新于 分类于 阅读次数： Disqus：

Java 中的这个话题也是比较常遇到的，关于这块原先也是比较忽略的，但是仔细想想又有点遗忘了就在这里记一下
简单看下代码
java.lang.Object#equals

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public boolean equals(Object obj) {
        return (this == obj);
    }

对于所有对象的父类，equals 方法其实对比的就是对象的地址，也就是是否是同一个对象，试想如果像 Integer 或者 String 这种，我们没有重写 equals，那其实就等于是在用==，可能就没法达到我们的目的，所以像 String 这种常用的 jdk 类都是默认重写了
java.lang.String#equals

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public boolean equals(Object anObject) {
        if (this == anObject) {
            return true;
        }
        if (anObject instanceof String) {
            String anotherString = (String)anObject;
            int n = value.length;
            if (n == anotherString.value.length) {
                char v1[] = value;
                char v2[] = anotherString.value;
                int i = 0;
                while (n-- != 0) {
                    if (v1[i] != v2[i])
                        return false;
                    i++;
                }
                return true;
            }
        }
        return false;
    }

然后呢就是为啥一些书或者 effective java 中写了 equals 跟 hashCode 要一起重写，这里涉及到当对象作为 HashMap 的 key 的时候
首先 HashMap 会使用 hashCode 去判断是否在同一个槽里，然后在通过 equals 去判断是否是同一个 key，是的话就替换，不是的话就链表接下去，如果不重写 hashCode 的话，默认的 object 的hashCode 是 native 方法，根据对象的地址生成的，这样其实对象的值相同的话，因为地址不同，HashMap 也会出现异常，所以需要重写，同时也需要重写 equals 方法，才能确认是同一个 key，而不是落在同一个槽的不同 key.

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

前几天同事问了我个 mysql 索引的问题，虽然大概知道，但是还是想来实践下，就是 is null，is not null 这类查询是否能用索引，可能之前有些网上的文章说都是不能用索引，但是其实不是，我们来看个小试验

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CREATE TABLE `null_index_t` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `null_key` varchar(255) DEFAULT NULL,
  `null_key1` varchar(255) DEFAULT NULL,
  `null_key2` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `idx_1` (`null_key`) USING BTREE,
  KEY `idx_2` (`null_key1`) USING BTREE,
  KEY `idx_3` (`null_key2`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

用个存储过程来插入数据

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delimiter $	#以delimiter来标记用$表示存储过程结束
create procedure nullIndex1()
begin
declare i int;	
declare j int;	
set i=1;
set j=1;
while(i<=100) do	
	while(j<=100) do	
		IF (i % 3 = 0) THEN
	     INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (null , LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8));
    ELSEIF (i % 3 = 1) THEN
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), NULL, LEFT(MD5(RAND()), 8));
	  ELSE
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8), NULL);
    END IF;
		set j=j+1;
	end while;
	set i=i+1;
	set j=1;	
end while;
end 
$
call nullIndex1();

然后看下我们的 is null 查询

1
EXPLAIN select * from null_index_t WHERE null_key is null;


再来看看另一个

1
EXPLAIN select * from null_index_t WHERE null_key is not null;


从这里能看出来啥呢，可以思考下

从上面可以发现，is null应该是用上了索引了，所以至少不是一刀切不能用，但是看着is not null好像不太行额
我们在做一点小改动，把这个表里的数据改成 9100 条是 null，剩下 900 条是有值的，然后再执行下

然后再来看看执行结果

1
EXPLAIN select * from null_index_t WHERE null_key is null;

1
EXPLAIN select * from null_index_t WHERE null_key is not null;


是不是不一样了，这里再补充下我试验使用的 mysql 是 5.7 的，不保证在其他版本的一致性，
其实可以看出随着数据量的变化，mysql 会不会使用索引是会变化的，不是说 is not null 一定会使用，也不是一定不会使用，而是优化器会根据查询成本做个预判，这个预判尽可能会减小查询成本，主要包括回表啥的，但是也不一定完全准确。

发表于 分类于 ， ， ， 阅读次数： Disqus：

几年前看了柴静的《穹顶之下》觉得这个记者调查得很深入，挺有水平，然后再看到了她跟丁仲礼的采访，其实没看完整，也没试着去理解，就觉得环境问题挺严重的，为啥柴静这个对面的这位好像对这个很不屑的样子，最近因为丁仲礼上了美国制裁名单，B 站又有人把这个视频发了出来，就完整看了下，就觉得自己挺惭愧的，就抱着对柴静的好感而没来由的否定了丁老的看法和说法，所以人也需要不断地学习，改正之前错误的观点，当然不是说我现在说的就是百分百正确，只是个人的一些浅显的见解

先聊聊这个事情，整体看下来我的一些理解，IPCC给中国的方案其实是个很大的陷阱，它里面有几个隐藏的点是容易被我们外行忽略的，第一点是基数，首先发达国家目前（指2010年采访或者IPCC方案时间）的人均碳排放量已经是远高于发展中国家的了，这也就导致了所谓的发达国家承诺减排80%是个非常有诚意的承诺其实就是忽悠；第二点是碳排放是个累计过程，从1900年开始到2050年，或者说到2010年，发达国家已经排的量是远超过发展中国家的，这是非常不公平的；第三点其实是通过前两点推导出来的，也就是即使发达国家这么有诚意地说减排80%，扒开这层虚伪的外衣，其实是给他们11亿人分走了48%的碳排放量，留给发展中国家55亿人口的只剩下了52%；第四点，人是否因为国家的发达与否而应受到不平等待遇，如果按国家维度，丁老说的，摩纳哥要跟中国分同样的排放量么，中国人还算不算人；第五点，这点算是我自己想的，也可能是柴静屁股决定脑袋想不到的点，她作为一个物质生活条件已经足够好了，那么对于那些生活在物质条件平均线以下的，他们是否能像城里人那样有空调地暖，洗澡有热水器浴霸，上下班能开车，这些其实都直接或者间接地导致了碳排放；他们有没有改善物质生活条件地权利呢，并且再说回来，其实丁老也给了我们觉得合理地方案，我们保证不管发达国家不管减排多少，我们都不会超过他们的80%，我觉得这是真正的诚意，而不是接着减排80%的噱头来忽悠人，也是像丁老这样的专家才能看破这个陷阱，碳排放权其实就是发展权，就是人权，中国人就不是人了么，或者说站在贫困线以下的人民是否有改善物质条件的权利，而不是说像柴静这样，只是管她自己，可能觉得小孩因为空气污染导致身体不好，所以做了穹顶之下这个纪录片，想去改善这个事情，空气污染不是说对的，只是每个国家都有这个过程，如果不发展，哪里有资源去让人活得好，活得好了是前提，然后再去各方面都改善。

对于这个问题其实更想说的是人的认知偏差，之前总觉得美帝是更自由民主，公平啥的，或者说觉得美帝啥都好，有种无脑愤青的感觉，外国的月亮比较圆，但是经历了像川普当选美国总统以来的各种魔幻操作，还有对于疫情的种种不可思议的美国民众的反应，其实更让人明白第一是外国的月亮没比较圆，第二是事情总是没那么简单粗暴非黑即白，美国不像原先设想地那么领先优秀，但是的确有很多方面是全球领先的，天朝也有体制所带来的优势，不可妄自菲薄，也不能忙不自大，还是要多学习知识，提升认知水平。

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， 阅读次数： Disqus：

今天真的是被气得不轻，情况是碰到一个有 70 多秒的直行红灯，然后直行就排了很长的队，但是左转车道没车，就有好几辆车占着左转车道，准备往直行车道插队加塞，一般这种加塞的，会挑个不太计较的，如果前面一辆不让的话就再等等，我因为赶着回家，就不想让，结果那辆车几次车头直接往里冲，当时怒气值基本已经蓄满了，我真的是分毫都不想让，如果路上都是让着这种人的，那么这种情况只会越来越严重，我理解的这种心态，就赌你怕麻烦，多一事不如少一事，结果就是每次都能顺利插队加塞，其实延伸到我们社会中的种种实质性的排队或者等同于排队的情况，都已经有这种惯有思维，一方面这种不符合规则，可能在严重程度上容易被很多人所忽视，基本上已经被很多人当成是“合理”行为，另一方面，对于这些“微小”的违规行为，本身管理层面也基本没有想要管的意思，就更多的成为了纵容这些行为的导火索，并且大多数人都是想着如果不让，发生点小剐小蹭的要浪费很多时间精力来处理，甚至会觉得会被别人觉得自己太小气等等，诸多内外成本结合起来，会真的去硬刚的可能少之又少了，这样也就让更多的人觉得这种行为是被默许的，再举个非常小的例子，以我们公司疫情期间的盒饭发放为例，有两个比较“有意思”的事情，第一个就是因为疫情，本来是让排队要间隔一米，但是可能除了我比较怕死会跟前面的人保持点距离基本没别人会不挨着前面的人，甚至我跟我前面的人保持点距离，后面的同学会推着我让我上去；第二个是关于拿饭，这么多人排着队拿饭，然后有部分同学，一个人拿好几份，帮组里其他人的都拿了，有些甚至一个人拿十份，假如这个盒饭发放是说明了可以按部门直接全领了那就没啥问题，但是当时的状况是个人排队领自己的那一份，如果一个同学直接帮着组里十几个人都拿了，后面排队的人是什么感受呢，甚至有些是看到队伍排长了，就找队伍里自己认识的比较靠前的人说你帮我也拿一份，其实作为我这个比较按规矩办事的“愣头青”来说，我是比较不能接受这两件小事里的行为的，再往下说可能就有点偏激了，先说到这~

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

写一个程序找出两个单向链表的交叉起始点，可能是我英语不好，图里画的其实还有一点是交叉以后所有节点都是相同的
Write a program to find the node at which the intersection of two singly linked lists begins.

For example, the following two linked lists:

begin to intersect at node c1.

Example 1:

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Input: intersectVal = 8, listA = [4,1,8,4,5], listB = [5,6,1,8,4,5], skipA = 2, skipB = 3
Output: Reference of the node with value = 8
Input Explanation: The intersected node's value is 8 (note that this must not be 0 if the two lists intersect). From the head of A, it reads as [4,1,8,4,5]. From the head of B, it reads as [5,6,1,8,4,5]. There are 2 nodes before the intersected node in A; There are 3 nodes before the intersected node in B.

分析题解

一开始没什么头绪，感觉只能最原始的遍历，后来看了一些文章，发现比较简单的方式就是先找两个链表的长度差，因为从相交点开始肯定是长度一致的，这是个很好的解题突破口，找到长度差以后就是先跳过长链表的较长部分，然后开始同步遍历比较 A，B 链表；

代码

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public ListNode getIntersectionNode(ListNode headA, ListNode headB) {
        if (headA == null || headB == null) {
            return null;
        }
        // 算 A 的长度
        int countA = 0;
        ListNode tailA = headA;
        while (tailA != null) {
            tailA = tailA.next;
            countA++;
        }
        // 算 B 的长度
        int countB = 0;
        ListNode tailB = headB;
        while (tailB != null) {
            tailB = tailB.next;
            countB++;
        }
        tailA = headA;
        tailB = headB;
        // 依据长度差，先让长的链表 tail 指针往后移
        if (countA > countB) {
            while (countA > countB) {
                tailA = tailA.next;
                countA--;
            }
        } else if (countA < countB) {
            while (countA < countB) {
                tailB = tailB.next;
                countB--;
            }
        }
        // 然后以相同速度遍历两个链表比较
        while (tailA != null) {
            if (tailA == tailB) {
                return tailA;
            } else {
                tailA = tailA.next;
                tailB = tailB.next;
            }
        }
        return null;
    }

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

发表于 更新于 分类于 阅读次数： Disqus：

Java 中的这个话题也是比较常遇到的，关于这块原先也是比较忽略的，但是仔细想想又有点遗忘了就在这里记一下
简单看下代码
java.lang.Object#equals

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public boolean equals(Object obj) {
        return (this == obj);
    }

对于所有对象的父类，equals 方法其实对比的就是对象的地址，也就是是否是同一个对象，试想如果像 Integer 或者 String 这种，我们没有重写 equals，那其实就等于是在用==，可能就没法达到我们的目的，所以像 String 这种常用的 jdk 类都是默认重写了
java.lang.String#equals

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public boolean equals(Object anObject) {
        if (this == anObject) {
            return true;
        }
        if (anObject instanceof String) {
            String anotherString = (String)anObject;
            int n = value.length;
            if (n == anotherString.value.length) {
                char v1[] = value;
                char v2[] = anotherString.value;
                int i = 0;
                while (n-- != 0) {
                    if (v1[i] != v2[i])
                        return false;
                    i++;
                }
                return true;
            }
        }
        return false;
    }

然后呢就是为啥一些书或者 effective java 中写了 equals 跟 hashCode 要一起重写，这里涉及到当对象作为 HashMap 的 key 的时候
首先 HashMap 会使用 hashCode 去判断是否在同一个槽里，然后在通过 equals 去判断是否是同一个 key，是的话就替换，不是的话就链表接下去，如果不重写 hashCode 的话，默认的 object 的hashCode 是 native 方法，根据对象的地址生成的，这样其实对象的值相同的话，因为地址不同，HashMap 也会出现异常，所以需要重写，同时也需要重写 equals 方法，才能确认是同一个 key，而不是落在同一个槽的不同 key.

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

前几天同事问了我个 mysql 索引的问题，虽然大概知道，但是还是想来实践下，就是 is null，is not null 这类查询是否能用索引，可能之前有些网上的文章说都是不能用索引，但是其实不是，我们来看个小试验

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CREATE TABLE `null_index_t` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `null_key` varchar(255) DEFAULT NULL,
  `null_key1` varchar(255) DEFAULT NULL,
  `null_key2` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `idx_1` (`null_key`) USING BTREE,
  KEY `idx_2` (`null_key1`) USING BTREE,
  KEY `idx_3` (`null_key2`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

用个存储过程来插入数据

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delimiter $	#以delimiter来标记用$表示存储过程结束
create procedure nullIndex1()
begin
declare i int;	
declare j int;	
set i=1;
set j=1;
while(i<=100) do	
	while(j<=100) do	
		IF (i % 3 = 0) THEN
	     INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (null , LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8));
    ELSEIF (i % 3 = 1) THEN
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), NULL, LEFT(MD5(RAND()), 8));
	  ELSE
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8), NULL);
    END IF;
		set j=j+1;
	end while;
	set i=i+1;
	set j=1;	
end while;
end 
$
call nullIndex1();

然后看下我们的 is null 查询

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EXPLAIN select * from null_index_t WHERE null_key is null;


再来看看另一个

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EXPLAIN select * from null_index_t WHERE null_key is not null;


从这里能看出来啥呢，可以思考下

从上面可以发现，is null应该是用上了索引了，所以至少不是一刀切不能用，但是看着is not null好像不太行额
我们在做一点小改动，把这个表里的数据改成 9100 条是 null，剩下 900 条是有值的，然后再执行下

然后再来看看执行结果

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EXPLAIN select * from null_index_t WHERE null_key is null;

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EXPLAIN select * from null_index_t WHERE null_key is not null;


是不是不一样了，这里再补充下我试验使用的 mysql 是 5.7 的，不保证在其他版本的一致性，
其实可以看出随着数据量的变化，mysql 会不会使用索引是会变化的，不是说 is not null 一定会使用，也不是一定不会使用，而是优化器会根据查询成本做个预判，这个预判尽可能会减小查询成本，主要包括回表啥的，但是也不一定完全准确。

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几年前看了柴静的《穹顶之下》觉得这个记者调查得很深入，挺有水平，然后再看到了她跟丁仲礼的采访，其实没看完整，也没试着去理解，就觉得环境问题挺严重的，为啥柴静这个对面的这位好像对这个很不屑的样子，最近因为丁仲礼上了美国制裁名单，B 站又有人把这个视频发了出来，就完整看了下，就觉得自己挺惭愧的，就抱着对柴静的好感而没来由的否定了丁老的看法和说法，所以人也需要不断地学习，改正之前错误的观点，当然不是说我现在说的就是百分百正确，只是个人的一些浅显的见解

先聊聊这个事情，整体看下来我的一些理解，IPCC给中国的方案其实是个很大的陷阱，它里面有几个隐藏的点是容易被我们外行忽略的，第一点是基数，首先发达国家目前（指2010年采访或者IPCC方案时间）的人均碳排放量已经是远高于发展中国家的了，这也就导致了所谓的发达国家承诺减排80%是个非常有诚意的承诺其实就是忽悠；第二点是碳排放是个累计过程，从1900年开始到2050年，或者说到2010年，发达国家已经排的量是远超过发展中国家的，这是非常不公平的；第三点其实是通过前两点推导出来的，也就是即使发达国家这么有诚意地说减排80%，扒开这层虚伪的外衣，其实是给他们11亿人分走了48%的碳排放量，留给发展中国家55亿人口的只剩下了52%；第四点，人是否因为国家的发达与否而应受到不平等待遇，如果按国家维度，丁老说的，摩纳哥要跟中国分同样的排放量么，中国人还算不算人；第五点，这点算是我自己想的，也可能是柴静屁股决定脑袋想不到的点，她作为一个物质生活条件已经足够好了，那么对于那些生活在物质条件平均线以下的，他们是否能像城里人那样有空调地暖，洗澡有热水器浴霸，上下班能开车，这些其实都直接或者间接地导致了碳排放；他们有没有改善物质生活条件地权利呢，并且再说回来，其实丁老也给了我们觉得合理地方案，我们保证不管发达国家不管减排多少，我们都不会超过他们的80%，我觉得这是真正的诚意，而不是接着减排80%的噱头来忽悠人，也是像丁老这样的专家才能看破这个陷阱，碳排放权其实就是发展权，就是人权，中国人就不是人了么，或者说站在贫困线以下的人民是否有改善物质条件的权利，而不是说像柴静这样，只是管她自己，可能觉得小孩因为空气污染导致身体不好，所以做了穹顶之下这个纪录片，想去改善这个事情，空气污染不是说对的，只是每个国家都有这个过程，如果不发展，哪里有资源去让人活得好，活得好了是前提，然后再去各方面都改善。

对于这个问题其实更想说的是人的认知偏差，之前总觉得美帝是更自由民主，公平啥的，或者说觉得美帝啥都好，有种无脑愤青的感觉，外国的月亮比较圆，但是经历了像川普当选美国总统以来的各种魔幻操作，还有对于疫情的种种不可思议的美国民众的反应，其实更让人明白第一是外国的月亮没比较圆，第二是事情总是没那么简单粗暴非黑即白，美国不像原先设想地那么领先优秀，但是的确有很多方面是全球领先的，天朝也有体制所带来的优势，不可妄自菲薄，也不能忙不自大，还是要多学习知识，提升认知水平。

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

例子:

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preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]

返回的二叉树

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2
3
4
5
  3
 / \
9  20
  /  \
 15   7

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

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class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

Design a stack that supports push, pop, top, and retrieving the minimum element in constant time.
设计一个栈，支持压栈，出站，获取栈顶元素，通过常数级复杂度获取栈中的最小元素

  • push(x) – Push element x onto stack.
  • pop() – Removes the element on top of the stack.
  • top() – Get the top element.
  • getMin() – Retrieve the minimum element in the stack.

示例

Example 1:

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Input
["MinStack","push","push","push","getMin","pop","top","getMin"]
[[],[-2],[0],[-3],[],[],[],[]]

Output
[null,null,null,null,-3,null,0,-2]

Explanation
MinStack minStack = new MinStack();
minStack.push(-2);
minStack.push(0);
minStack.push(-3);
minStack.getMin(); // return -3
minStack.pop();
minStack.top();    // return 0
minStack.getMin(); // return -2

简要分析

其实现在大部分语言都自带类栈的数据结构，Java 也自带 stack 这个数据结构，所以这个题的主要难点的就是常数级的获取最小元素，最开始的想法是就一个栈外加一个记录最小值的变量就行了，但是仔细一想是不行的，因为随着元素被 pop 出去，这个最小值也可能需要梗着变化，就不太好判断了，所以后面是用了一个辅助栈。

代码

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class MinStack {
        // 这个作为主栈
        Stack<Integer> s1 = new Stack<>();
        // 这个作为辅助栈，放最小值的栈
        Stack<Integer> s2 = new Stack<>();
        /** initialize your data structure here. */
        public MinStack() {

        }

        public void push(int x) {
            // 放入主栈
            s1.push(x);
            // 当 s2 是空或者当前值是小于"等于" s2 栈顶时，压入辅助最小值的栈
            // 注意这里的"等于"非常必要，因为当最小值有多个的情况下，也需要压入栈，否则在 pop 的时候就会不对等
            if (s2.isEmpty() || x <= s2.peek()) {
                s2.push(x);
            }
        }

        public void pop() {
            // 首先就是主栈要 pop，然后就是第二个了，跟上面的"等于"很有关系，
            // 因为如果有两个最小值，如果前面等于的情况没有压栈，那这边相等的时候 pop 就会少一个了，可能就导致最小值不对了
            int x = s1.pop();
            if (x == s2.peek())  {
                s2.pop();
            }
        }

        public int top() {
            // 栈顶的元素
            return s1.peek();
        }

        public int getMin() {
            // 辅助最小栈的栈顶
            return s2.peek();
        }
    }

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

前几天清华美院学姐的热点火了，然后仔细看了下，其实是个学姐诬陷以为其貌不扬的男同学摸她屁股

然后还在朋友圈发文想让他社死，我也是挺晚才知道这个词什么意思，然后后面我看到了这个图片，挺有意思的

本来其实也没什么想聊这个的，是在 B 站看了个吐槽这个的，然后刚好晚上乘公交的时候又碰到了有点类似的问题
故事描述下，我们从始发站做了公交，这辆公交司机上次碰到过一回，就是会比较关注乘客的佩戴情况，主要考虑到目前国内疫情，然后这次在差不多人都坐满的情况下，可能在提示了三次让车内乘客戴好口罩，但是他指的那个中年女性还是没有反应，司机就转头比较大声指着这个乘客（中年女性）让戴好口罩，然后这个乘客（中年女性）就大声的说“我口罩是滑下来了，你指着我干嘛，你态度这么差，要吃了我一样，我要投诉你”等等，然后可能跟她一块的一个中年女性也是这么帮腔指责司机，比较基本的理解，车子里这么多乘客，假如是处于这位乘客口罩滑下来了而不自知的情况下，司机在提示了三次以后回头指着她说，我想的是没什么问题的，但是这位却反而指责这位司机指着她，并且说是态度差，要吃了她，完全是不可理喻的，并且一直喋喋不休说她口罩滑掉了有什么错，要投诉这个司机，让他可以提前退休了，在其他乘客的劝说下司机准备继续开车时，又口吐芬芳“你个傻，你来打我呀”，真的是让我再次体会到了所谓的恶人先告状的又一完美呈现，后面还有个乘客还是表示要打死司机这个傻，让我有点不明所以，俗话说有人是得理不饶人，前提是得理，这种理亏不饶人真的是挺让人长见识的，试想下，司机在提示三次后，这位乘客还是没有把口罩戴好，如何在不指着这位乘客的情况下能准确的提示到她呢，并且觉得语气态度不好，司机要载着一车的人，因为你这一个乘客不戴好口罩而不能正常出发，有些着急应该很正常吧，可能是平时自己在家里耀武扬威的使唤别人习惯了吧，别人不敢这么大声跟她说话，其实想想这位中年女性应该年纪不会很大，还比较时髦的吧，像一些常见的中年杭州本地人可能是不会说傻*这个词的吧。
杭州的公交可能是在二月份疫情还比较严重的时候是要求上车出示健康码，后面比较缓和以后只要求佩戴好口罩，但是在我们小绍兴，目前还是一律要求检验健康码和佩戴口罩，对于疫情中，并且目前阶段国内也时有报出小范围的疫情的情况下，司机尽职要求佩戴好口罩其实也是为了乘客着想，另一种情况如果司机不严格要求，万一车上有个感染者，这位中年女性被传染了，如果能找到这个司机的话，是不是想“打死”这个司机，竟然让感染者上了车，反正她自己是不可能有错的，上来就是对方态度差，要投诉，自己不戴好口罩啥都没错，我就想知道如果因为自己没戴好口罩被感染了，是不是也是司机的错，毕竟没有像仆人那样点头哈腰求着她戴好口罩。
再说回来，整个车就她一个人没戴好口罩，并且还有个细节，其实这个乘客是上了车之后就没戴好了，本来上车的时候是戴好的，这种比较有可能是觉得上车的时候司机那看一眼就好了，如果好好戴着口罩，一点事情都没有，唉，纯粹是太气愤了，调理逻辑什么的就忽略吧

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

之前看了 dubbo 的一些代码，在学习过程中，主要关注那些比较“高级”的内容，SPI，自适应扩展等，却忘了一些作为一个 rpc 框架最核心需要的部分，比如如何通信，序列化，网络，容错机制等等，因为其实这个最核心的就是远程调用，自适应扩展其实就是让代码可扩展性，可读性，更优雅等，写的搓一点其实也问题不大，但是一个合适的通信协议，序列化方法，如何容错等却是真正保证是一个 rpc 框架最重要的要素。
首先来看这张图
cluster
在集群调用失败时，Dubbo 提供了多种容错方案，缺省为 failover 重试。
各节点关系：

  • 这里的 Invoker 是 Provider 的一个可调用 Service 的抽象，Invoker 封装了 Provider 地址及 Service 接口信息
  • Directory 代表多个 Invoker，可以把它看成 List<Invoker> ，但与 List 不同的是，它的值可能是动态变化的，比如注册中心推送变更
  • Cluster 将 Directory 中的多个 Invoker 伪装成一个 Invoker，对上层透明，伪装过程包含了容错逻辑，调用失败后，重试另一个
  • Router 负责从多个 Invoker 中按路由规则选出子集，比如读写分离，应用隔离等
  • LoadBalance 负责从多个 Invoker 中选出具体的一个用于本次调用，选的过程包含了负载均衡算法，调用失败后，需要重选

集群容错模式

Failover Cluster

失败自动切换，当出现失败，重试其它服务器 1。通常用于读操作，但重试会带来更长延迟。可通过 retries=”2” 来设置重试次数(不含第一次)。

重试次数配置如下：

<dubbo:service retries=”2” />
这里重点看下 Failover Cluster集群模式的实现

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public class FailoverCluster implements Cluster {

    public final static String NAME = "failover";

    public <T> Invoker<T> join(Directory<T> directory) throws RpcException {
        return new FailoverClusterInvoker<T>(directory);
    }

}

这个代码就非常简单，重点需要看FailoverClusterInvoker里的代码，FailoverClusterInvoker继承了AbstractClusterInvoker类，其中invoke 方法是在抽象类里实现的

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@Override
public Result invoke(final Invocation invocation) throws RpcException {
    checkWhetherDestroyed();
    // binding attachments into invocation.
    // 绑定 attachments 到 invocation 中.
    Map<String, Object> contextAttachments = RpcContext.getContext().getObjectAttachments();
    if (contextAttachments != null && contextAttachments.size() != 0) {
        ((RpcInvocation) invocation).addObjectAttachments(contextAttachments);
    }
    // 列举 Invoker
    List<Invoker<T>> invokers = list(invocation);
    // 加载 LoadBalance 负载均衡器
    LoadBalance loadbalance = initLoadBalance(invokers, invocation);
    RpcUtils.attachInvocationIdIfAsync(getUrl(), invocation);
    // 调用 实际的 doInvoke 进行后续操作
    return doInvoke(invocation, invokers, loadbalance);
}
// 这是个抽象方法，实际是由子类实现的
 protected abstract Result doInvoke(Invocation invocation, List<Invoker<T>> invokers,
                                       LoadBalance loadbalance) throws RpcException;

然后重点就是FailoverClusterInvoker中的doInvoke方法了，其实它里面也就这么一个方法

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@Override
    @SuppressWarnings({"unchecked", "rawtypes"})
    public Result doInvoke(Invocation invocation, final List<Invoker<T>> invokers, LoadBalance loadbalance) throws RpcException {
        List<Invoker<T>> copyInvokers = invokers;
        checkInvokers(copyInvokers, invocation);
        String methodName = RpcUtils.getMethodName(invocation);
        // 获取重试次数，这里默认是 2 次，还有可以注意下后面的+1
        int len = getUrl().getMethodParameter(methodName, RETRIES_KEY, DEFAULT_RETRIES) + 1;
        if (len <= 0) {
            len = 1;
        }
        // retry loop.
        RpcException le = null; // last exception.
        List<Invoker<T>> invoked = new ArrayList<Invoker<T>>(copyInvokers.size()); // invoked invokers.
        Set<String> providers = new HashSet<String>(len);
        // 循环调用，失败重试
        for (int i = 0; i < len; i++) {
            //Reselect before retry to avoid a change of candidate `invokers`.
            //NOTE: if `invokers` changed, then `invoked` also lose accuracy.
            if (i > 0) {
                checkWhetherDestroyed();
                // 在进行重试前重新列举 Invoker，这样做的好处是，如果某个服务挂了，
                // 通过调用 list 可得到最新可用的 Invoker 列表
                copyInvokers = list(invocation);
                // check again
                // 对 copyinvokers 进行判空检查
                checkInvokers(copyInvokers, invocation);
            }
            // 通过负载均衡来选择 invoker
            Invoker<T> invoker = select(loadbalance, invocation, copyInvokers, invoked);
            // 将其添加到 invoker 到 invoked 列表中
            invoked.add(invoker);
            // 设置上下文
            RpcContext.getContext().setInvokers((List) invoked);
            try {
                // 正式调用
                Result result = invoker.invoke(invocation);
                if (le != null && logger.isWarnEnabled()) {
                    logger.warn("Although retry the method " + methodName
                            + " in the service " + getInterface().getName()
                            + " was successful by the provider " + invoker.getUrl().getAddress()
                            + ", but there have been failed providers " + providers
                            + " (" + providers.size() + "/" + copyInvokers.size()
                            + ") from the registry " + directory.getUrl().getAddress()
                            + " on the consumer " + NetUtils.getLocalHost()
                            + " using the dubbo version " + Version.getVersion() + ". Last error is: "
                            + le.getMessage(), le);
                }
                return result;
            } catch (RpcException e) {
                if (e.isBiz()) { // biz exception.
                    throw e;
                }
                le = e;
            } catch (Throwable e) {
                le = new RpcException(e.getMessage(), e);
            } finally {
                providers.add(invoker.getUrl().getAddress());
            }
        }
        throw new RpcException(le.getCode(), "Failed to invoke the method "
                + methodName + " in the service " + getInterface().getName()
                + ". Tried " + len + " times of the providers " + providers
                + " (" + providers.size() + "/" + copyInvokers.size()
                + ") from the registry " + directory.getUrl().getAddress()
                + " on the consumer " + NetUtils.getLocalHost() + " using the dubbo version "
                + Version.getVersion() + ". Last error is: "
                + le.getMessage(), le.getCause() != null ? le.getCause() : le);
    }

Failfast Cluster

快速失败，只发起一次调用，失败立即报错。通常用于非幂等性的写操作，比如新增记录。

Failsafe Cluster

失败安全，出现异常时，直接忽略。通常用于写入审计日志等操作。

Failback Cluster

失败自动恢复，后台记录失败请求，定时重发。通常用于消息通知操作。

Forking Cluster

并行调用多个服务器，只要一个成功即返回。通常用于实时性要求较高的读操作，但需要浪费更多服务资源。可通过 forks=”2” 来设置最大并行数。

Broadcast Cluster

广播调用所有提供者，逐个调用，任意一台报错则报错 2。通常用于通知所有提供者更新缓存或日志等本地资源信息。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

Given a singly linked list, determine if it is a palindrome.
给定一个单向链表，判断是否是回文链表

例一 Example 1:

Input: 1->2
Output: false

例二 Example 2:

Input: 1->2->2->1
Output: true

挑战下自己

Follow up:
Could you do it in O(n) time and O(1) space?

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

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/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

例子:

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preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]

返回的二叉树

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3
4
5
  3
 / \
9  20
  /  \
 15   7

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

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class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

Design a stack that supports push, pop, top, and retrieving the minimum element in constant time.
设计一个栈，支持压栈，出站，获取栈顶元素，通过常数级复杂度获取栈中的最小元素

  • push(x) – Push element x onto stack.
  • pop() – Removes the element on top of the stack.
  • top() – Get the top element.
  • getMin() – Retrieve the minimum element in the stack.

示例

Example 1:

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Input
["MinStack","push","push","push","getMin","pop","top","getMin"]
[[],[-2],[0],[-3],[],[],[],[]]

Output
[null,null,null,null,-3,null,0,-2]

Explanation
MinStack minStack = new MinStack();
minStack.push(-2);
minStack.push(0);
minStack.push(-3);
minStack.getMin(); // return -3
minStack.pop();
minStack.top();    // return 0
minStack.getMin(); // return -2

简要分析

其实现在大部分语言都自带类栈的数据结构，Java 也自带 stack 这个数据结构，所以这个题的主要难点的就是常数级的获取最小元素，最开始的想法是就一个栈外加一个记录最小值的变量就行了，但是仔细一想是不行的，因为随着元素被 pop 出去，这个最小值也可能需要梗着变化，就不太好判断了，所以后面是用了一个辅助栈。

代码

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class MinStack {
        // 这个作为主栈
        Stack<Integer> s1 = new Stack<>();
        // 这个作为辅助栈，放最小值的栈
        Stack<Integer> s2 = new Stack<>();
        /** initialize your data structure here. */
        public MinStack() {

        }

        public void push(int x) {
            // 放入主栈
            s1.push(x);
            // 当 s2 是空或者当前值是小于"等于" s2 栈顶时，压入辅助最小值的栈
            // 注意这里的"等于"非常必要，因为当最小值有多个的情况下，也需要压入栈，否则在 pop 的时候就会不对等
            if (s2.isEmpty() || x <= s2.peek()) {
                s2.push(x);
            }
        }

        public void pop() {
            // 首先就是主栈要 pop，然后就是第二个了，跟上面的"等于"很有关系，
            // 因为如果有两个最小值，如果前面等于的情况没有压栈，那这边相等的时候 pop 就会少一个了，可能就导致最小值不对了
            int x = s1.pop();
            if (x == s2.peek())  {
                s2.pop();
            }
        }

        public int top() {
            // 栈顶的元素
            return s1.peek();
        }

        public int getMin() {
            // 辅助最小栈的栈顶
            return s2.peek();
        }
    }

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

前几天清华美院学姐的热点火了，然后仔细看了下，其实是个学姐诬陷以为其貌不扬的男同学摸她屁股

然后还在朋友圈发文想让他社死，我也是挺晚才知道这个词什么意思，然后后面我看到了这个图片，挺有意思的

本来其实也没什么想聊这个的，是在 B 站看了个吐槽这个的，然后刚好晚上乘公交的时候又碰到了有点类似的问题
故事描述下，我们从始发站做了公交，这辆公交司机上次碰到过一回，就是会比较关注乘客的佩戴情况，主要考虑到目前国内疫情，然后这次在差不多人都坐满的情况下，可能在提示了三次让车内乘客戴好口罩，但是他指的那个中年女性还是没有反应，司机就转头比较大声指着这个乘客（中年女性）让戴好口罩，然后这个乘客（中年女性）就大声的说“我口罩是滑下来了，你指着我干嘛，你态度这么差，要吃了我一样，我要投诉你”等等，然后可能跟她一块的一个中年女性也是这么帮腔指责司机，比较基本的理解，车子里这么多乘客，假如是处于这位乘客口罩滑下来了而不自知的情况下，司机在提示了三次以后回头指着她说，我想的是没什么问题的，但是这位却反而指责这位司机指着她，并且说是态度差，要吃了她，完全是不可理喻的，并且一直喋喋不休说她口罩滑掉了有什么错，要投诉这个司机，让他可以提前退休了，在其他乘客的劝说下司机准备继续开车时，又口吐芬芳“你个傻，你来打我呀”，真的是让我再次体会到了所谓的恶人先告状的又一完美呈现，后面还有个乘客还是表示要打死司机这个傻，让我有点不明所以，俗话说有人是得理不饶人，前提是得理，这种理亏不饶人真的是挺让人长见识的，试想下，司机在提示三次后，这位乘客还是没有把口罩戴好，如何在不指着这位乘客的情况下能准确的提示到她呢，并且觉得语气态度不好，司机要载着一车的人，因为你这一个乘客不戴好口罩而不能正常出发，有些着急应该很正常吧，可能是平时自己在家里耀武扬威的使唤别人习惯了吧，别人不敢这么大声跟她说话，其实想想这位中年女性应该年纪不会很大，还比较时髦的吧，像一些常见的中年杭州本地人可能是不会说傻*这个词的吧。
杭州的公交可能是在二月份疫情还比较严重的时候是要求上车出示健康码，后面比较缓和以后只要求佩戴好口罩，但是在我们小绍兴，目前还是一律要求检验健康码和佩戴口罩，对于疫情中，并且目前阶段国内也时有报出小范围的疫情的情况下，司机尽职要求佩戴好口罩其实也是为了乘客着想，另一种情况如果司机不严格要求，万一车上有个感染者，这位中年女性被传染了，如果能找到这个司机的话，是不是想“打死”这个司机，竟然让感染者上了车，反正她自己是不可能有错的，上来就是对方态度差，要投诉，自己不戴好口罩啥都没错，我就想知道如果因为自己没戴好口罩被感染了，是不是也是司机的错，毕竟没有像仆人那样点头哈腰求着她戴好口罩。
再说回来，整个车就她一个人没戴好口罩，并且还有个细节，其实这个乘客是上了车之后就没戴好了，本来上车的时候是戴好的，这种比较有可能是觉得上车的时候司机那看一眼就好了，如果好好戴着口罩，一点事情都没有，唉，纯粹是太气愤了，调理逻辑什么的就忽略吧

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

之前看了 dubbo 的一些代码，在学习过程中，主要关注那些比较“高级”的内容，SPI，自适应扩展等，却忘了一些作为一个 rpc 框架最核心需要的部分，比如如何通信，序列化，网络，容错机制等等，因为其实这个最核心的就是远程调用，自适应扩展其实就是让代码可扩展性，可读性，更优雅等，写的搓一点其实也问题不大，但是一个合适的通信协议，序列化方法，如何容错等却是真正保证是一个 rpc 框架最重要的要素。
首先来看这张图
cluster
在集群调用失败时，Dubbo 提供了多种容错方案，缺省为 failover 重试。
各节点关系：

  • 这里的 Invoker 是 Provider 的一个可调用 Service 的抽象，Invoker 封装了 Provider 地址及 Service 接口信息
  • Directory 代表多个 Invoker，可以把它看成 List<Invoker> ，但与 List 不同的是，它的值可能是动态变化的，比如注册中心推送变更
  • Cluster 将 Directory 中的多个 Invoker 伪装成一个 Invoker，对上层透明，伪装过程包含了容错逻辑，调用失败后，重试另一个
  • Router 负责从多个 Invoker 中按路由规则选出子集，比如读写分离，应用隔离等
  • LoadBalance 负责从多个 Invoker 中选出具体的一个用于本次调用，选的过程包含了负载均衡算法，调用失败后，需要重选

集群容错模式

Failover Cluster

失败自动切换，当出现失败，重试其它服务器 1。通常用于读操作，但重试会带来更长延迟。可通过 retries=”2” 来设置重试次数(不含第一次)。

重试次数配置如下：

<dubbo:service retries=”2” />
这里重点看下 Failover Cluster集群模式的实现

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public class FailoverCluster implements Cluster {

    public final static String NAME = "failover";

    public <T> Invoker<T> join(Directory<T> directory) throws RpcException {
        return new FailoverClusterInvoker<T>(directory);
    }

}

这个代码就非常简单，重点需要看FailoverClusterInvoker里的代码，FailoverClusterInvoker继承了AbstractClusterInvoker类，其中invoke 方法是在抽象类里实现的

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@Override
public Result invoke(final Invocation invocation) throws RpcException {
    checkWhetherDestroyed();
    // binding attachments into invocation.
    // 绑定 attachments 到 invocation 中.
    Map<String, Object> contextAttachments = RpcContext.getContext().getObjectAttachments();
    if (contextAttachments != null && contextAttachments.size() != 0) {
        ((RpcInvocation) invocation).addObjectAttachments(contextAttachments);
    }
    // 列举 Invoker
    List<Invoker<T>> invokers = list(invocation);
    // 加载 LoadBalance 负载均衡器
    LoadBalance loadbalance = initLoadBalance(invokers, invocation);
    RpcUtils.attachInvocationIdIfAsync(getUrl(), invocation);
    // 调用 实际的 doInvoke 进行后续操作
    return doInvoke(invocation, invokers, loadbalance);
}
// 这是个抽象方法，实际是由子类实现的
 protected abstract Result doInvoke(Invocation invocation, List<Invoker<T>> invokers,
                                       LoadBalance loadbalance) throws RpcException;

然后重点就是FailoverClusterInvoker中的doInvoke方法了，其实它里面也就这么一个方法

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@Override
    @SuppressWarnings({"unchecked", "rawtypes"})
    public Result doInvoke(Invocation invocation, final List<Invoker<T>> invokers, LoadBalance loadbalance) throws RpcException {
        List<Invoker<T>> copyInvokers = invokers;
        checkInvokers(copyInvokers, invocation);
        String methodName = RpcUtils.getMethodName(invocation);
        // 获取重试次数，这里默认是 2 次，还有可以注意下后面的+1
        int len = getUrl().getMethodParameter(methodName, RETRIES_KEY, DEFAULT_RETRIES) + 1;
        if (len <= 0) {
            len = 1;
        }
        // retry loop.
        RpcException le = null; // last exception.
        List<Invoker<T>> invoked = new ArrayList<Invoker<T>>(copyInvokers.size()); // invoked invokers.
        Set<String> providers = new HashSet<String>(len);
        // 循环调用，失败重试
        for (int i = 0; i < len; i++) {
            //Reselect before retry to avoid a change of candidate `invokers`.
            //NOTE: if `invokers` changed, then `invoked` also lose accuracy.
            if (i > 0) {
                checkWhetherDestroyed();
                // 在进行重试前重新列举 Invoker，这样做的好处是，如果某个服务挂了，
                // 通过调用 list 可得到最新可用的 Invoker 列表
                copyInvokers = list(invocation);
                // check again
                // 对 copyinvokers 进行判空检查
                checkInvokers(copyInvokers, invocation);
            }
            // 通过负载均衡来选择 invoker
            Invoker<T> invoker = select(loadbalance, invocation, copyInvokers, invoked);
            // 将其添加到 invoker 到 invoked 列表中
            invoked.add(invoker);
            // 设置上下文
            RpcContext.getContext().setInvokers((List) invoked);
            try {
                // 正式调用
                Result result = invoker.invoke(invocation);
                if (le != null && logger.isWarnEnabled()) {
                    logger.warn("Although retry the method " + methodName
                            + " in the service " + getInterface().getName()
                            + " was successful by the provider " + invoker.getUrl().getAddress()
                            + ", but there have been failed providers " + providers
                            + " (" + providers.size() + "/" + copyInvokers.size()
                            + ") from the registry " + directory.getUrl().getAddress()
                            + " on the consumer " + NetUtils.getLocalHost()
                            + " using the dubbo version " + Version.getVersion() + ". Last error is: "
                            + le.getMessage(), le);
                }
                return result;
            } catch (RpcException e) {
                if (e.isBiz()) { // biz exception.
                    throw e;
                }
                le = e;
            } catch (Throwable e) {
                le = new RpcException(e.getMessage(), e);
            } finally {
                providers.add(invoker.getUrl().getAddress());
            }
        }
        throw new RpcException(le.getCode(), "Failed to invoke the method "
                + methodName + " in the service " + getInterface().getName()
                + ". Tried " + len + " times of the providers " + providers
                + " (" + providers.size() + "/" + copyInvokers.size()
                + ") from the registry " + directory.getUrl().getAddress()
                + " on the consumer " + NetUtils.getLocalHost() + " using the dubbo version "
                + Version.getVersion() + ". Last error is: "
                + le.getMessage(), le.getCause() != null ? le.getCause() : le);
    }

Failfast Cluster

快速失败，只发起一次调用，失败立即报错。通常用于非幂等性的写操作，比如新增记录。

Failsafe Cluster

失败安全，出现异常时，直接忽略。通常用于写入审计日志等操作。

Failback Cluster

失败自动恢复，后台记录失败请求，定时重发。通常用于消息通知操作。

Forking Cluster

并行调用多个服务器，只要一个成功即返回。通常用于实时性要求较高的读操作，但需要浪费更多服务资源。可通过 forks=”2” 来设置最大并行数。

Broadcast Cluster

广播调用所有提供者，逐个调用，任意一台报错则报错 2。通常用于通知所有提供者更新缓存或日志等本地资源信息。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

Given a singly linked list, determine if it is a palindrome.
给定一个单向链表，判断是否是回文链表

例一 Example 1:

Input: 1->2
Output: false

例二 Example 2:

Input: 1->2->2->1
Output: true

挑战下自己

Follow up:
Could you do it in O(n) time and O(1) space?

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

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/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， 阅读次数： Disqus：

一说到这个主题，想到的应该是双亲委派模型，不过讲的包括但不限于这个，主要内容是参考深入理解 Java 虚拟机书中的介绍，
一个类型的生命周期包含了七个阶段，加载，验证，准备，解析，初始化，使用，卸载。

  • 加载

  1. 通过一个类的全限定名来获取定义此类的二进制字节流
  2. 将这个字节流代表的静态存储结构转化为方法区的运行时数据结构
  3. 在内存中生成了一个代表这个类的 java.lang.Class 对象，作为方法区这个类的各种数据的访问入口

  • 验证

  1. 文件格式验证
  2. 元数据验证
  3. 字节码验证
  4. 符号引用验证

  • 准备

准备阶段是正式为类中定义的变量（即静态变量，被static修饰的变量）分配内存并设置类变量初始值的阶段

  • 解析

解析阶段是 Java 虚拟机将常量池内的符号引用替换为直接引用的过程

以上验证、准备、解析 三个阶段又合称为链接阶段，链接阶段要做的是将加载到JVM中的二进制字节流的类数据信息合并到JVM的运行时状态中。

  • 初始化

类的初始化阶段是类加载过程的最后一个步骤，也是除了自定义类加载器之外将主动权交给了应用程序，其实就是执行类构造器()方法的过程，()并不是我们在 Java 代码中直接编写的方法，它是 Javac编译器的自动生成物，()方法是由编译器自动收集类中的所有类变量的复制动作和静态句块(static{}块)中的语句合并产生的，编译器收集的顺序是由语句在原文件中出现的顺序决定的，静态语句块中只能访问定义在静态语句块之前的变量，定义在它之后的变量，在前面的静态语句块可以复制，但是不能访问，同时还要保证父类的执行先于子类，然后保证多线程下的并发问题

最终，方法区会存储当前类类信息，包括类的静态变量、类初始化代码（定义静态变量时的赋值语句 和 静态初始化代码块）、实例变量定义、实例初始化代码（定义实例变量时的赋值语句实例代码块和构造方法）和实例方法，还有父类的类信息引用。

发表于 分类于 ， ， 阅读次数： Disqus：

在前司和目前公司，用的配置中心都是使用的 Apollo，经过了业界验证，比较强大的配置管理系统，特别是在0.10 后开始支持对使用 value 注解的配置值进行自动更新，今天刚好有个同学问到我，就顺便写篇文章记录下，其实也是借助于 spring 强大的 bean 生命周期管理，可以实现BeanPostProcessor接口，使用postProcessBeforeInitialization方法，来对bean 内部的属性和方法进行判断，是否有 value 注解，如果有就是将它注册到一个 map 中，可以看到这个方法com.ctrip.framework.apollo.spring.annotation.SpringValueProcessor#processField

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@Override
  protected void processField(Object bean, String beanName, Field field) {
    // register @Value on field
    Value value = field.getAnnotation(Value.class);
    if (value == null) {
      return;
    }
    Set<String> keys = placeholderHelper.extractPlaceholderKeys(value.value());

    if (keys.isEmpty()) {
      return;
    }

    for (String key : keys) {
      SpringValue springValue = new SpringValue(key, value.value(), bean, beanName, field, false);
      springValueRegistry.register(beanFactory, key, springValue);
      logger.debug("Monitoring {}", springValue);
    }
  }

然后我们看下这个springValueRegistry是啥玩意

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public class SpringValueRegistry {
  private static final long CLEAN_INTERVAL_IN_SECONDS = 5;
  private final Map<BeanFactory, Multimap<String, SpringValue>> registry = Maps.newConcurrentMap();
  private final AtomicBoolean initialized = new AtomicBoolean(false);
  private final Object LOCK = new Object();

  public void register(BeanFactory beanFactory, String key, SpringValue springValue) {
    if (!registry.containsKey(beanFactory)) {
      synchronized (LOCK) {
        if (!registry.containsKey(beanFactory)) {
          registry.put(beanFactory, LinkedListMultimap.<String, SpringValue>create());
        }
      }
    }

    registry.get(beanFactory).put(key, springValue);

    // lazy initialize
    if (initialized.compareAndSet(false, true)) {
      initialize();
    }
  }

这类其实就是个 map 来存放 springvalue，然后有com.ctrip.framework.apollo.spring.property.AutoUpdateConfigChangeListener来监听更新操作，当有变更时

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@Override
 public void onChange(ConfigChangeEvent changeEvent) {
   Set<String> keys = changeEvent.changedKeys();
   if (CollectionUtils.isEmpty(keys)) {
     return;
   }
   for (String key : keys) {
     // 1. check whether the changed key is relevant
     Collection<SpringValue> targetValues = springValueRegistry.get(beanFactory, key);
     if (targetValues == null || targetValues.isEmpty()) {
       continue;
     }

     // 2. check whether the value is really changed or not (since spring property sources have hierarchies)
     // 这里其实有一点比较绕，是因为 Apollo 里的 namespace 划分，会出现 key 相同，但是 namespace 不同的情况，所以会有个优先级存在，所以需要去校验 environment 里面的是否已经更新，如果未更新则表示不需要更新
     if (!shouldTriggerAutoUpdate(changeEvent, key)) {
       continue;
     }

     // 3. update the value
     for (SpringValue val : targetValues) {
       updateSpringValue(val);
     }
   }
 }

其实原理很简单，就是得了解知道下

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

题目介绍

给定一个二叉树，找出其最大深度。

二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。

说明: 叶子节点是指没有子节点的节点。

示例：
给定二叉树 [3,9,20,null,null,15,7]，

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  3
 / \
9  20
  /  \
 15   7

返回它的最大深度 3 。

代码

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// 主体是个递归的应用
public int maxDepth(TreeNode root) {
    // 节点的退出条件之一
    if (root == null) {
        return 0;
    }
    int left = 0;
    int right = 0;
    // 存在左子树，就递归左子树
    if (root.left != null) {
        left = maxDepth(root.left);
    }
    // 存在右子树，就递归右子树
    if (root.right != null) {
        right = maxDepth(root.right);
    }
    // 前面返回后，左右取大者
    return Math.max(left + 1, right + 1);
}

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

发表于 分类于 ， ， ， 阅读次数： Disqus：

终于回忆起来了，年纪大了写这种东西真的要立马写，不然很容易想不起来，那天应该是 9 月 12 日，也就是上周六，因为我爸也去了，而且娘亲（丈母娘，LD 这么叫，我也就随了她这么叫，当然是背后，当面就叫妈）也在那，早上一到那二爹就给我爸指挥了活，要挖一条院子的出水道，自己想出来的词，因为觉得下水道是竖的，在那稍稍帮了一会会忙，然后我还是比较惯例的跟着 LD 还有娘亲去住的家里，主要是老丈人可能也不太想让我干太累的活，因为上次已经差不多把三楼都整理干净了，然后就是二楼了，二楼说实话我也帮不上什么忙，主要是衣服被子什么的，正好是有张以前小孩子睡过的那种摇篮床，看上去虽然有一些破损，整体还是不错的，所以打算拿过去，我就负责把它拆掉了，比较简单的是只要拧螺丝就行了，但是其实是用了好多好多工具才搞定的，一开始只要螺丝刀就行了，但是因为年代久了，后面的螺帽也有点锈住或者本身就会串着会一起动，所以拿来了个扳手，大部分的其实都被这两个工具给搞定了，但是后期大概还剩下四分之一的时候，有一颗完全锈住，并且螺纹跟之前那些都不一样，但是这个已经是最大的螺丝刀了，也没办法换个大的了，所以又去找来个一字的，因为十字的不是也可以用一字的拧嘛，结果可能是我买的工具箱里的一字螺丝刀太新了，口子那很锋利，直接把螺丝花纹给划掉了，大的小的都划掉，然后真的变成凹进去一个圆柱体了，然后就想能不能隔一层布去拧，然而因为的确是已经变成圆柱体了，布也不太给力，不放弃的我又去找来了个老虎钳，妄图把划掉的螺丝用老虎钳钳住，另一端用扳手拧开螺帽，但是这个螺丝跟螺帽真的是生锈的太严重了，外加上钳不太牢，完全是两边一起转，实在是没办法了，在征得同意之后，直接掰断了，火死了，一颗螺丝折腾得比我拆一张床还久，那天因为早上去的也比较晚了，然后就快吃午饭了，正好想着带一点东西过去，就把一些脸盆，泡脚桶啥的拿过去了，先是去吃了饭，还是在那家快餐店，菜的口味还是依然不错，就是人比较多，我爸旁边都是素菜，都没怎么吃远一点的荤菜，下次要早点去，把荤菜放我爸旁边😄（PS：他们家饭还是依然尴尬，需要等），吃完就开到在修的房子那把东西拿了出来，我爸已经动作很快的打了一小半的地沟了，说实话那玩意真的是很重，我之前把它从三楼拿下来，就觉得这个太重了，这次还要用起来，感觉我的手会分分钟废掉，不过一开始我还是跟着LD去了住的家里，惯例睡了午觉，那天睡得比较踏实，竟然睡了一个小时，醒了想了下，其实LD她们收拾也用不上我（没啥力气活），我还是去帮我爸他们，跟LD说了下就去了在修的老房子那，两位老爹在一起钻地，看着就很累，我连忙上去想换一会他们，因为刚好是钻到混凝土地线，特别难，力道不够就会滑开，用蛮力就是钻进去拔不出来，原理是因为本身浇的时候就是很紧实的，需要边钻边动，那家伙实在是太重了，真的是汗如雨下，基本是三个人轮流来，我是个添乱的，经常卡住，然后把地线，其实就是一条混凝土横梁，里面还有14跟18的钢筋，需要割断，这个割断也是很有技巧，钢筋本身在里面是受到挤压的，直接用切割的，到快断掉的时候就会崩一下，非常危险，还是老丈人比较有经验，要留一点点，然后直接用榔头敲断就好了，本来以为这个是最难的了，结果下面是一块非常大的青基石，而且也是石头跟石头挤一块，边上一点点打钻有点杯水车薪，后来是用那种螺旋的钻，钻四个洞，相对位置大概是个长方形，这样子把中间这个长方形钻出来就比较容易地能拿出来了，后面的也容易搞出来了，后面的其实难度不是特别大了，主要是地沟打好之后得看看高低是不是符合要求的，不能本来是往外排水的反而外面高，这个怎么看就又很有技巧了，一般在地上的只要侧着看一下就好了，考究点就用下水平尺，但是在地下的，不用水平尺，其实可以借助于地沟里正要放进去的水管，放点水进去，看水往哪流就行了，铺好水管后，就剩填埋的活了，不是太麻烦了，那天真的是累到了，打那个混凝土的时候我真的是把我整个人压上去了，不过也挺爽的，有点把平时无处发泄的蛮力发泄出去了。

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

又 roll 到了一个以前做过的题，不过现在用 Java 也来写一下，是 easy 级别的，所以就简单说下

简要介绍

You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order, and each of their nodes contains a single digit. Add the two numbers and return the sum as a linked list.

You may assume the two numbers do not contain any leading zero, except the number 0 itself.
就是给了两个链表，用来表示两个非负的整数，在链表中倒序放着，每个节点包含一位的数字，把他们加起来以后也按照原来的链表结构输出

样例

example 1

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Input: l1 = [2,4,3], l2 = [5,6,4]
Output: [7,0,8]
Explanation: 342 + 465 = 807.

example 2

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Input: l1 = [0], l2 = [0]
Output: [0]

example 3

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Input: l1 = [9,9,9,9,9,9,9], l2 = [9,9,9,9]
Output: [8,9,9,9,0,0,0,1]

题解

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public ListNode addTwoNumbers(ListNode l1, ListNode l2) {
        ListNode root = new ListNode();
        if (l1 == null && l2 == null) {
            return root;
        }
        ListNode tail = root;
        int entered = 0;
        // 这个条件加了 entered，就是还有进位的数
        while (l1 != null || l2 != null || entered != 0) {
            int temp = entered;
            if (l1 != null) {
                temp += l1.val;
                l1 = l1.next;
            }
            if (l2 != null) {
                temp += l2.val;
                l2 = l2.next;
            }
            entered = (temp - temp % 10) / 10;
            tail.val = temp % 10;
            // 循环内部的控制是为了排除最后的空节点
            if (l1 != null || l2 != null || entered != 0) {
                tail.next = new ListNode();
                tail = tail.next;
            }
        }
//        tail = null;
        return root;
    }

这里唯二需要注意的就是两个点，一个是循环条件需要包含进位值还存在的情况，还有一个是最后一个节点，如果是空的了，就不要在 new 一个出来了，写的比较挫

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， 阅读次数： Disqus：

一说到这个主题，想到的应该是双亲委派模型，不过讲的包括但不限于这个，主要内容是参考深入理解 Java 虚拟机书中的介绍，
一个类型的生命周期包含了七个阶段，加载，验证，准备，解析，初始化，使用，卸载。

  • 加载

  1. 通过一个类的全限定名来获取定义此类的二进制字节流
  2. 将这个字节流代表的静态存储结构转化为方法区的运行时数据结构
  3. 在内存中生成了一个代表这个类的 java.lang.Class 对象，作为方法区这个类的各种数据的访问入口

  • 验证

  1. 文件格式验证
  2. 元数据验证
  3. 字节码验证
  4. 符号引用验证

  • 准备

准备阶段是正式为类中定义的变量（即静态变量，被static修饰的变量）分配内存并设置类变量初始值的阶段

  • 解析

解析阶段是 Java 虚拟机将常量池内的符号引用替换为直接引用的过程

以上验证、准备、解析 三个阶段又合称为链接阶段，链接阶段要做的是将加载到JVM中的二进制字节流的类数据信息合并到JVM的运行时状态中。

  • 初始化

类的初始化阶段是类加载过程的最后一个步骤，也是除了自定义类加载器之外将主动权交给了应用程序，其实就是执行类构造器()方法的过程，()并不是我们在 Java 代码中直接编写的方法，它是 Javac编译器的自动生成物，()方法是由编译器自动收集类中的所有类变量的复制动作和静态句块(static{}块)中的语句合并产生的，编译器收集的顺序是由语句在原文件中出现的顺序决定的，静态语句块中只能访问定义在静态语句块之前的变量，定义在它之后的变量，在前面的静态语句块可以复制，但是不能访问，同时还要保证父类的执行先于子类，然后保证多线程下的并发问题

最终，方法区会存储当前类类信息，包括类的静态变量、类初始化代码（定义静态变量时的赋值语句 和 静态初始化代码块）、实例变量定义、实例初始化代码（定义实例变量时的赋值语句实例代码块和构造方法）和实例方法，还有父类的类信息引用。

发表于 分类于 ， ， 阅读次数： Disqus：

在前司和目前公司，用的配置中心都是使用的 Apollo，经过了业界验证，比较强大的配置管理系统，特别是在0.10 后开始支持对使用 value 注解的配置值进行自动更新，今天刚好有个同学问到我，就顺便写篇文章记录下，其实也是借助于 spring 强大的 bean 生命周期管理，可以实现BeanPostProcessor接口，使用postProcessBeforeInitialization方法，来对bean 内部的属性和方法进行判断，是否有 value 注解，如果有就是将它注册到一个 map 中，可以看到这个方法com.ctrip.framework.apollo.spring.annotation.SpringValueProcessor#processField

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@Override
  protected void processField(Object bean, String beanName, Field field) {
    // register @Value on field
    Value value = field.getAnnotation(Value.class);
    if (value == null) {
      return;
    }
    Set<String> keys = placeholderHelper.extractPlaceholderKeys(value.value());

    if (keys.isEmpty()) {
      return;
    }

    for (String key : keys) {
      SpringValue springValue = new SpringValue(key, value.value(), bean, beanName, field, false);
      springValueRegistry.register(beanFactory, key, springValue);
      logger.debug("Monitoring {}", springValue);
    }
  }

然后我们看下这个springValueRegistry是啥玩意

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public class SpringValueRegistry {
  private static final long CLEAN_INTERVAL_IN_SECONDS = 5;
  private final Map<BeanFactory, Multimap<String, SpringValue>> registry = Maps.newConcurrentMap();
  private final AtomicBoolean initialized = new AtomicBoolean(false);
  private final Object LOCK = new Object();

  public void register(BeanFactory beanFactory, String key, SpringValue springValue) {
    if (!registry.containsKey(beanFactory)) {
      synchronized (LOCK) {
        if (!registry.containsKey(beanFactory)) {
          registry.put(beanFactory, LinkedListMultimap.<String, SpringValue>create());
        }
      }
    }

    registry.get(beanFactory).put(key, springValue);

    // lazy initialize
    if (initialized.compareAndSet(false, true)) {
      initialize();
    }
  }

这类其实就是个 map 来存放 springvalue，然后有com.ctrip.framework.apollo.spring.property.AutoUpdateConfigChangeListener来监听更新操作，当有变更时

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@Override
 public void onChange(ConfigChangeEvent changeEvent) {
   Set<String> keys = changeEvent.changedKeys();
   if (CollectionUtils.isEmpty(keys)) {
     return;
   }
   for (String key : keys) {
     // 1. check whether the changed key is relevant
     Collection<SpringValue> targetValues = springValueRegistry.get(beanFactory, key);
     if (targetValues == null || targetValues.isEmpty()) {
       continue;
     }

     // 2. check whether the value is really changed or not (since spring property sources have hierarchies)
     // 这里其实有一点比较绕，是因为 Apollo 里的 namespace 划分，会出现 key 相同，但是 namespace 不同的情况，所以会有个优先级存在，所以需要去校验 environment 里面的是否已经更新，如果未更新则表示不需要更新
     if (!shouldTriggerAutoUpdate(changeEvent, key)) {
       continue;
     }

     // 3. update the value
     for (SpringValue val : targetValues) {
       updateSpringValue(val);
     }
   }
 }

其实原理很简单，就是得了解知道下

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

题目介绍

给定一个二叉树，找出其最大深度。

二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。

说明: 叶子节点是指没有子节点的节点。

示例：
给定二叉树 [3,9,20,null,null,15,7]，

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  3
 / \
9  20
  /  \
 15   7

返回它的最大深度 3 。

代码

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// 主体是个递归的应用
public int maxDepth(TreeNode root) {
    // 节点的退出条件之一
    if (root == null) {
        return 0;
    }
    int left = 0;
    int right = 0;
    // 存在左子树，就递归左子树
    if (root.left != null) {
        left = maxDepth(root.left);
    }
    // 存在右子树，就递归右子树
    if (root.right != null) {
        right = maxDepth(root.right);
    }
    // 前面返回后，左右取大者
    return Math.max(left + 1, right + 1);
}

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

发表于 分类于 ， ， ， 阅读次数： Disqus：

终于回忆起来了，年纪大了写这种东西真的要立马写，不然很容易想不起来，那天应该是 9 月 12 日，也就是上周六，因为我爸也去了，而且娘亲（丈母娘，LD 这么叫，我也就随了她这么叫，当然是背后，当面就叫妈）也在那，早上一到那二爹就给我爸指挥了活，要挖一条院子的出水道，自己想出来的词，因为觉得下水道是竖的，在那稍稍帮了一会会忙，然后我还是比较惯例的跟着 LD 还有娘亲去住的家里，主要是老丈人可能也不太想让我干太累的活，因为上次已经差不多把三楼都整理干净了，然后就是二楼了，二楼说实话我也帮不上什么忙，主要是衣服被子什么的，正好是有张以前小孩子睡过的那种摇篮床，看上去虽然有一些破损，整体还是不错的，所以打算拿过去，我就负责把它拆掉了，比较简单的是只要拧螺丝就行了，但是其实是用了好多好多工具才搞定的，一开始只要螺丝刀就行了，但是因为年代久了，后面的螺帽也有点锈住或者本身就会串着会一起动，所以拿来了个扳手，大部分的其实都被这两个工具给搞定了，但是后期大概还剩下四分之一的时候，有一颗完全锈住，并且螺纹跟之前那些都不一样，但是这个已经是最大的螺丝刀了，也没办法换个大的了，所以又去找来个一字的，因为十字的不是也可以用一字的拧嘛，结果可能是我买的工具箱里的一字螺丝刀太新了，口子那很锋利，直接把螺丝花纹给划掉了，大的小的都划掉，然后真的变成凹进去一个圆柱体了，然后就想能不能隔一层布去拧，然而因为的确是已经变成圆柱体了，布也不太给力，不放弃的我又去找来了个老虎钳，妄图把划掉的螺丝用老虎钳钳住，另一端用扳手拧开螺帽，但是这个螺丝跟螺帽真的是生锈的太严重了，外加上钳不太牢，完全是两边一起转，实在是没办法了，在征得同意之后，直接掰断了，火死了，一颗螺丝折腾得比我拆一张床还久，那天因为早上去的也比较晚了，然后就快吃午饭了，正好想着带一点东西过去，就把一些脸盆，泡脚桶啥的拿过去了，先是去吃了饭，还是在那家快餐店，菜的口味还是依然不错，就是人比较多，我爸旁边都是素菜，都没怎么吃远一点的荤菜，下次要早点去，把荤菜放我爸旁边😄（PS：他们家饭还是依然尴尬，需要等），吃完就开到在修的房子那把东西拿了出来，我爸已经动作很快的打了一小半的地沟了，说实话那玩意真的是很重，我之前把它从三楼拿下来，就觉得这个太重了，这次还要用起来，感觉我的手会分分钟废掉，不过一开始我还是跟着LD去了住的家里，惯例睡了午觉，那天睡得比较踏实，竟然睡了一个小时，醒了想了下，其实LD她们收拾也用不上我（没啥力气活），我还是去帮我爸他们，跟LD说了下就去了在修的老房子那，两位老爹在一起钻地，看着就很累，我连忙上去想换一会他们，因为刚好是钻到混凝土地线，特别难，力道不够就会滑开，用蛮力就是钻进去拔不出来，原理是因为本身浇的时候就是很紧实的，需要边钻边动，那家伙实在是太重了，真的是汗如雨下，基本是三个人轮流来，我是个添乱的，经常卡住，然后把地线，其实就是一条混凝土横梁，里面还有14跟18的钢筋，需要割断，这个割断也是很有技巧，钢筋本身在里面是受到挤压的，直接用切割的，到快断掉的时候就会崩一下，非常危险，还是老丈人比较有经验，要留一点点，然后直接用榔头敲断就好了，本来以为这个是最难的了，结果下面是一块非常大的青基石，而且也是石头跟石头挤一块，边上一点点打钻有点杯水车薪，后来是用那种螺旋的钻，钻四个洞，相对位置大概是个长方形，这样子把中间这个长方形钻出来就比较容易地能拿出来了，后面的也容易搞出来了，后面的其实难度不是特别大了，主要是地沟打好之后得看看高低是不是符合要求的，不能本来是往外排水的反而外面高，这个怎么看就又很有技巧了，一般在地上的只要侧着看一下就好了，考究点就用下水平尺，但是在地下的，不用水平尺，其实可以借助于地沟里正要放进去的水管，放点水进去，看水往哪流就行了，铺好水管后，就剩填埋的活了，不是太麻烦了，那天真的是累到了，打那个混凝土的时候我真的是把我整个人压上去了，不过也挺爽的，有点把平时无处发泄的蛮力发泄出去了。

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

又 roll 到了一个以前做过的题，不过现在用 Java 也来写一下，是 easy 级别的，所以就简单说下

简要介绍

You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order, and each of their nodes contains a single digit. Add the two numbers and return the sum as a linked list.

You may assume the two numbers do not contain any leading zero, except the number 0 itself.
就是给了两个链表，用来表示两个非负的整数，在链表中倒序放着，每个节点包含一位的数字，把他们加起来以后也按照原来的链表结构输出

样例

example 1

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Input: l1 = [2,4,3], l2 = [5,6,4]
Output: [7,0,8]
Explanation: 342 + 465 = 807.

example 2

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Input: l1 = [0], l2 = [0]
Output: [0]

example 3

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Input: l1 = [9,9,9,9,9,9,9], l2 = [9,9,9,9]
Output: [8,9,9,9,0,0,0,1]

题解

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public ListNode addTwoNumbers(ListNode l1, ListNode l2) {
        ListNode root = new ListNode();
        if (l1 == null && l2 == null) {
            return root;
        }
        ListNode tail = root;
        int entered = 0;
        // 这个条件加了 entered，就是还有进位的数
        while (l1 != null || l2 != null || entered != 0) {
            int temp = entered;
            if (l1 != null) {
                temp += l1.val;
                l1 = l1.next;
            }
            if (l2 != null) {
                temp += l2.val;
                l2 = l2.next;
            }
            entered = (temp - temp % 10) / 10;
            tail.val = temp % 10;
            // 循环内部的控制是为了排除最后的空节点
            if (l1 != null || l2 != null || entered != 0) {
                tail.next = new ListNode();
                tail = tail.next;
            }
        }
//        tail = null;
        return root;
    }

这里唯二需要注意的就是两个点，一个是循环条件需要包含进位值还存在的情况，还有一个是最后一个节点，如果是空的了，就不要在 new 一个出来了，写的比较挫

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}Nicksxs's Blog - What hurts more, the pain of hard work or the pain of regret?

Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

Java 真的是任何一个中间件，比较常用的那种，都有很多内容值得深挖，比如这个缓存，慢慢有过一些感悟，比如如何提升性能，缓存无疑是一大重要手段，最底层开始 CPU 就有缓存，而且又小又贵，再往上一点内存一般作为硬盘存储在运行时的存储，一般在代码里也会用内存作为一些本地缓存，譬如数据库，像 mysql 这种也是有innodb_buffer_pool来提升查询效率，本质上理解就是用更快的存储作为相对慢存储的缓存，减少查询直接访问较慢的存储，并且这个都是相对的，比起 cpu 的缓存，那内存也是渣，但是与普通机械硬盘相比，那也是两个次元的水平。

闲扯这么多来说说 mybatis 的缓存，mybatis 一般作为一个轻量级的 orm 使用，相对应的就是比较重量级的 hibernate，不过不在这次讨论范围，上一次是主要讲了 mybatis 在解析 sql 过程中，对于两种占位符的不同替换实现策略，这次主要聊下 mybatis 的缓存，前面其实得了解下前置的东西，比如 sqlsession，先当做我们知道 sqlsession 是个什么玩意，可能或多或少的知道 mybatis 是有两级缓存，

一级缓存

第一级的缓存是在 BaseExecutor 中的 PerpetualCache，它是个最基本的缓存实现类，使用了 HashMap 实现缓存功能，代码其实没几十行

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public class PerpetualCache implements Cache {

  private final String id;

  private final Map<Object, Object> cache = new HashMap<>();

  public PerpetualCache(String id) {
    this.id = id;
  }

  @Override
  public String getId() {
    return id;
  }

  @Override
  public int getSize() {
    return cache.size();
  }

  @Override
  public void putObject(Object key, Object value) {
    cache.put(key, value);
  }

  @Override
  public Object getObject(Object key) {
    return cache.get(key);
  }

  @Override
  public Object removeObject(Object key) {
    return cache.remove(key);
  }

  @Override
  public void clear() {
    cache.clear();
  }

  @Override
  public boolean equals(Object o) {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    if (this == o) {
      return true;
    }
    if (!(o instanceof Cache)) {
      return false;
    }

    Cache otherCache = (Cache) o;
    return getId().equals(otherCache.getId());
  }

  @Override
  public int hashCode() {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    return getId().hashCode();
  }

}

可以看一下BaseExecutor 的构造函数

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protected BaseExecutor(Configuration configuration, Transaction transaction) {
    this.transaction = transaction;
    this.deferredLoads = new ConcurrentLinkedQueue<>();
    this.localCache = new PerpetualCache("LocalCache");
    this.localOutputParameterCache = new PerpetualCache("LocalOutputParameterCache");
    this.closed = false;
    this.configuration = configuration;
    this.wrapper = this;
  }

就是把 PerpetualCache 作为 localCache，然后怎么使用我看简单看一下，BaseExecutor 的查询首先是调用这个函数

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameter);
    CacheKey key = createCacheKey(ms, parameter, rowBounds, boundSql);
    return query(ms, parameter, rowBounds, resultHandler, key, boundSql);
  }

可以看到首先是调用了 createCacheKey 方法，这个方法呢，先不看怎么写的，如果我们自己要实现这么个缓存，首先这个缓存 key 的设计也是个问题，如果是以表名加主键作为 key，那么分页查询，或者没有主键的时候就不行，来看看 mybatis 是怎么设计的

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@Override
  public CacheKey createCacheKey(MappedStatement ms, Object parameterObject, RowBounds rowBounds, BoundSql boundSql) {
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    CacheKey cacheKey = new CacheKey();
    cacheKey.update(ms.getId());
    cacheKey.update(rowBounds.getOffset());
    cacheKey.update(rowBounds.getLimit());
    cacheKey.update(boundSql.getSql());
    List<ParameterMapping> parameterMappings = boundSql.getParameterMappings();
    TypeHandlerRegistry typeHandlerRegistry = ms.getConfiguration().getTypeHandlerRegistry();
    // mimic DefaultParameterHandler logic
    for (ParameterMapping parameterMapping : parameterMappings) {
      if (parameterMapping.getMode() != ParameterMode.OUT) {
        Object value;
        String propertyName = parameterMapping.getProperty();
        if (boundSql.hasAdditionalParameter(propertyName)) {
          value = boundSql.getAdditionalParameter(propertyName);
        } else if (parameterObject == null) {
          value = null;
        } else if (typeHandlerRegistry.hasTypeHandler(parameterObject.getClass())) {
          value = parameterObject;
        } else {
          MetaObject metaObject = configuration.newMetaObject(parameterObject);
          value = metaObject.getValue(propertyName);
        }
        cacheKey.update(value);
      }
    }
    if (configuration.getEnvironment() != null) {
      // issue #176
      cacheKey.update(configuration.getEnvironment().getId());
    }
    return cacheKey;
  }

首先需要 id，这个 id 是 mapper 里方法的 id， 然后是偏移量跟返回行数，再就是 sql，然后是参数，基本上是会有影响的都加进去了，在这个 update 里面

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public void update(Object object) {
    int baseHashCode = object == null ? 1 : ArrayUtil.hashCode(object);

    count++;
    checksum += baseHashCode;
    baseHashCode *= count;

    hashcode = multiplier * hashcode + baseHashCode;

    updateList.add(object);
  }

其实是一个 hash 转换，具体不纠结，就是提高特异性，然后回来就是继续调用 query

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql) throws SQLException {
    ErrorContext.instance().resource(ms.getResource()).activity("executing a query").object(ms.getId());
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    if (queryStack == 0 && ms.isFlushCacheRequired()) {
      clearLocalCache();
    }
    List<E> list;
    try {
      queryStack++;
      list = resultHandler == null ? (List<E>) localCache.getObject(key) : null;
      if (list != null) {
        handleLocallyCachedOutputParameters(ms, key, parameter, boundSql);
      } else {
        list = queryFromDatabase(ms, parameter, rowBounds, resultHandler, key, boundSql);
      }
    } finally {
      queryStack--;
    }
    if (queryStack == 0) {
      for (DeferredLoad deferredLoad : deferredLoads) {
        deferredLoad.load();
      }
      // issue #601
      deferredLoads.clear();
      if (configuration.getLocalCacheScope() == LocalCacheScope.STATEMENT) {
        // issue #482
        clearLocalCache();
      }
    }
    return list;
  }

可以看到是先从 localCache 里取，取不到再 queryFromDatabase，其实比较简单，这是一级缓存，考虑到 sqlsession 跟 BaseExecutor 的关系，其实是随着 sqlsession 来保证这个缓存不会出现脏数据幻读的情况，当然事务相关的后面可能再单独聊。

二级缓存

其实这个一级二级顺序有点反过来，其实查询的是先走的二级缓存，当然二级的需要配置开启，默认不开，
需要通过

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<setting name="cacheEnabled" value="true"/>

来开启，然后我们的查询就会走到

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public class CachingExecutor implements Executor {

  private final Executor delegate;
  private final TransactionalCacheManager tcm = new TransactionalCacheManager();

这个 Executor 中，这里我放了类里面的元素，发现没有一个 Cache 类，这就是一个特点了，往下看查询过程

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameterObject);
    CacheKey key = createCacheKey(ms, parameterObject, rowBounds, boundSql);
    return query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

  @Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql)
      throws SQLException {
    Cache cache = ms.getCache();
    if (cache != null) {
      flushCacheIfRequired(ms);
      if (ms.isUseCache() && resultHandler == null) {
        ensureNoOutParams(ms, boundSql);
        @SuppressWarnings("unchecked")
        List<E> list = (List<E>) tcm.getObject(cache, key);
        if (list == null) {
          list = delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
          tcm.putObject(cache, key, list); // issue #578 and #116
        }
        return list;
      }
    }
    return delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

看到没，其实缓存是从 tcm 这个成员变量里取，而这个是什么呢，事务性缓存(直译下)，因为这个其实是用 MappedStatement 里的 Cache 作为key 从 tcm 的 map 取出来的

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public class TransactionalCacheManager {

  private final Map<Cache, TransactionalCache> transactionalCaches = new HashMap<>();

MappedStatement是被全局使用的，所以其实二级缓存是跟着 mapper 的 namespace 走的，可以被多个 CachingExecutor 获取到，就会出现线程安全问题，线程安全问题可以用SynchronizedCache来解决，就是加锁，但是对于事务中的脏读，使用了TransactionalCache来解决这个问题，

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public class TransactionalCache implements Cache {

  private static final Log log = LogFactory.getLog(TransactionalCache.class);

  private final Cache delegate;
  private boolean clearOnCommit;
  private final Map<Object, Object> entriesToAddOnCommit;
  private final Set<Object> entriesMissedInCache;

在事务还没提交的时候，会把中间状态的数据放在 entriesToAddOnCommit 中，只有在提交后会放进共享缓存中，

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public void commit() {
    if (clearOnCommit) {
      delegate.clear();
    }
    flushPendingEntries();
    reset();
  }

发表于 分类于 ， ， ， 阅读次数： Disqus：

小工记四

第四周去的时候让我们去了现在在住的房子里，去三楼整理东西了，蛮多的东西需要收拾整理，有些需要丢一下，以前往往是把不太要用的东西就放三楼了，但是后面就不会再去收拾整理，LD 跟丈母娘负责收拾，我不太知道哪些还要的，哪些不要了，而且本来也不擅长这种收拾🤦‍♂️，然后就变成她们收拾出来废纸箱，我负责拆掉，压平，这时候终于觉得体重还算是有点作用，总体来说这个事情我其实也不擅长，不擅长的主要是捆起来，可能我总是小题大做，因为纸箱大小不一，如果不做一下分类，然后把大的折小一些的话，直接绑起来，容易拎起来就散掉了，而且一些鞋盒子这种小件的纸盒会比较薄，冰箱这种大件的比较厚，厚的比较不容易变形，需要大力踩踏，而且扎的时候需要用体重压住捆实了之后那样子才是真的捆实的，不然待会又是松松垮垮容易滑出来散架，因为压住了捆好后，下来了之后箱子就会弹开了把绳子崩紧实，感觉又是掌握到生活小技巧了😃，我这里其实比较单调无聊，然后 LD 那可以说非常厉害了，一共理出来 11 把旧电扇，还有好多没用过全新的不锈钢脸盆大大小小的，感觉比店里在卖的还多，还有是有比较多小时候的东西，特别多小时候的衣服，其实这种对我来说最难了，可能需要读一下断舍离，蛮多东西都舍不得扔，但是其实是没啥用了，然后还占地方，这天应该算是比较轻松的一天了，上午主要是把收拾出来要的和不要的搬下楼，然后下午要去把纸板给卖掉。中午还是去小快餐店吃的，在住的家里理东西还有个好处就是中午吃完饭可以小憩一下，因为我个人是非常依赖午休的，不然下午完全没精神，而且心态也会比较烦躁，一方面是客观的的确比较疲惫，另一方面应该主观心理作用也有点影响，就像上班的时候也是觉得不午睡就会很难受，心理作用也有一点，不过总之能睡还是睡一会，真的没办法就心态好点，吃完午饭之后我们就推着小平板车去收废品的地方卖掉了上午我收拾捆起来的纸板，好像卖了一百多，都是直接过地磅了，不用一捆一捆地称，不过有个小插曲，那里另外一个大爷在倒他的三轮车的时候撞了我一下，还好车速慢，屁股上肉垫后，接下来就比较麻烦了，是LD 她们两姐妹从小到大的书，也要去卖掉，小平板车就载不下了，而且着实也不太好推，轮子不太平，导致推着很累，书有好多箱，本来是想去亲戚家借电动三轮车，因为不会开摩托的那种，摩托的那种 LD 邻居家就有，可是到了那发现那个也是很大，而且刹车是用脚踩的那种，俺爹不太放心，就说第二天周日他有空会帮忙去载了卖掉的，然后比较搞笑的来了，丈母娘看错了时间，以为已经快五点了，就让我们顺便在车里带点东西去在修的房子，放到那边三楼去，到了那还跟老丈人说已经这么迟了要赶紧去菜场买菜了，结果我们回来以后才发现看错了一个小时🤦‍♂️。
前面可以没提，前三周去的我们一般就周六去一天，然后周日因为要早点回杭州，而且可能想让我们周日能休息下，但是这周就因为周日的时候我爸要去帮忙载书，然后 LD 姐姐也会过来收拾东西，我们周日就又去整理收拾了，周日由于俺爹去的很早，我过去的时候书已经木有了，主要是去收拾东西了，把一些有用没用的继续整理，基本上三楼的就处理完毕了，舒了一大口气，毕竟让丈母娘一个人收拾实在是太累了，但是要扔掉的衣服比较棘手，附近知道的青蛙回收桶被推倒了，其他地方也不知道哪里有，我们就先载了一些东西去在修的房子那，然后去找青蛙桶，结果一个小区可以进，但是已经满了，另一个不让进，后来只能让 LD 姐姐带去她们小区扔了，塞了满满一车。因为要赶回杭州的车就没有等我爸一起回来，他还在那帮忙搞卫生间的墙缝。
虽然这两天不太热，活也不算很吃力，不过我这个体重和易出汗的体质，还是让短袖不知道湿透了多少次，灌了好多水和冰红茶（下午能提提神），回来周一早上称体重也比较喜人，差一点就达到阶段目标，可以想想去哪里吃之前想好的烤肉跟火锅了（估计吃完立马回到解放前）。

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

又做了个题，看记录是以前用 C++写过的，现在捋一捋思路，用 Java 再写了一下，思路还是比较清晰的，但是边界细节处理得比较差

简要介绍

Given a string s, find the length of the longest substring without repeating characters.

样例

Example 1:

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Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.

Example 2:

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Input: s = "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.

Example 3:

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Input: s = "pwwkew"
Output: 3
Explanation: The answer is "wke", with the length of 3.
Notice that the answer must be a substring, "pwke" is a subsequence and not a substring.

Example 4:

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Input: s = ""
Output: 0

就是一个最长不重复的字符串长度，因为也是中等难度的题，不太需要特别复杂的思考，最基本的就是O(N*N)两重循环，不过显然不太好，万一超时间，还有一种就是线性复杂度的了，这个就是需要搞定一个思路，比如字符串时 abcdefgaqwrty，比如遍历到第二个a的时候其实不用再从头去遍历了，只要把前面那个a给排除掉，继续往下算就好了

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class Solution {
    Map<String, Integer> counter = new HashMap<>();
    public int lengthOfLongestSubstring(String s) {
        int length = s.length();
        // 当前的长度
        int subStringLength = 0;
        // 最长的长度
        int maxSubStringLength = 0;
        // 考虑到重复的位置已经被跳过的情况，即已经在当前长度的字符串范围之前的重复字符不需要回溯
        int lastDuplicatePos = -1;
        for (int i = 0; i < length; i++) {
            // 使用 map 存储字符和上一次出现的位置，如果存在并且大于上一次重复位置
            if (counter.get(String.valueOf(s.charAt(i))) != null && counter.get(String.valueOf(s.charAt(i))) > lastDuplicatePos) {
                // 记录重复位置
                lastDuplicatePos = counter.get(String.valueOf(s.charAt(i)));
                // 重置不重复子串的长度，减去重复起点
                subStringLength = i - counter.get(String.valueOf(s.charAt(i))) - 1;
                // 替换当前位置
                counter.replace(String.valueOf(s.charAt(i)), i);
            } else {
                // 如果不存在就直接 put
                counter.put(String.valueOf(s.charAt(i)), i);
            }
            // 长度累加
            subStringLength++;
            if (subStringLength > maxSubStringLength) {
                // 简单替换
                maxSubStringLength = subStringLength;
            }
        }
        return maxSubStringLength;
    }
}

注释应该写的比较清楚了。

发表于 分类于 ， ， ， 阅读次数： Disqus：

小工记三

前面这两周周末也都去老丈人家帮忙了，上上周周六先是去了那个在装修的旧房子那，把三楼收拾了下，因为要搬进来住，来不及等二楼装修好，就要把三楼里的东西都整理干净，这个活感觉是比较 easy，原来是就准备把三楼当放东西仓储的地方了，我们乡下大部分三层楼都是这么用的，这次也是没办法，之前搬进来的木头什么的都搬出去，主要是这上面灰尘太多，后面清理鼻孔的时候都是黑色的了，把东西都搬出去以后主要是地还是很脏，就扫了地拖了地，因为是水泥地，灰尘又太多了，拖起来都是会灰尘扬起来，整个脱完了的确干净很多，然而这会就出了个大乌龙，我们清理的是三楼的西边一间，结果老丈人上来说要住东边那间的🤦‍♂️，不过其实西边的也得清理，因为还是要放被子什么的，不算是白费功夫，接着清理东边那间，之前这个房子做过群租房，里面有个高低铺的床，当时觉得可以用在放被子什么的就没扔，只是拆掉了放旁边，我们就把它擦干净了又装好，发现螺丝🔩少了几个，亘古不变的真理，拆了以后装要不就多几个要不就少几个，不是很牢靠，不过用来放放被子省得放地上总还是可以的，对了前面还做了个事情就是铺地毯，其实也不是地毯，就是类似于墙布雨篷布那种，别人不用了送给我们的，三楼水泥地也不会铺瓷砖地板了就放一下，干净好看点，不过大小不合适要裁一下，那把剪刀是真的太难用了，我手都要抽筋了，它就是刀口只有一小个点是能剪下来的，其他都是钝的，后来还是用刀片直接裁，铺好以后，真的感觉也不太一样了，焕然一新的感觉
差不多中午了就去吃饭了，之前两次是去了一家小饭店，还是还比较干净，但是店里菜不好吃，还死贵，这次去了一家小快餐店，口味好，便宜，味道是真的不错，带鱼跟黄鱼都好吃，一点都不腥，我对这类比较腥的鱼真的是很挑剔的，基本上除了家里做的很少吃外面的，那天抱着试试的态度吃了下，真的还不错，后来丈母娘说好像这家老板是给别人结婚喜事酒席当厨师的，怪不得做的好吃，其实本来是有一点小抗拒，怕不干净什么的，后来发现菜很好吃，而且可能是老丈人跟干活的师傅去吃的比较多，老板很客气，我们吃完饭，还给我们买了葡萄吃，不过这家店有一个槽点，就是饭比较不好吃，有时候会夹生，不过后面聊起来其实是这种小菜馆饭点的通病，烧的太早太多容易多出来浪费，烧的迟了不够吃，而且大的电饭锅比较不容易烧好。
下午前面还是在处理三楼的，窗户上各种钉子，实在是太多了，我后面在走廊上排了一排🤦‍♂️，有些是直接断了，有些是就撬了出来，感觉我在杭州租房也没有这样子各种钉钉子，挂下衣服什么的也不用这么多吧，比较不能理解，搞得到处都是钉子。那天我爸也去帮忙了，主要是在卫生间里做白缝，其实也是个技术活，印象中好像我小时候自己家里也做过这个事情，但是比较模糊了，后面我们三楼搞完了就去帮我爸了，前面是我老婆二爹在那先刷上白缝，这里叫白缝，有些考究的也叫美缝，就是瓷砖铺完之后的缝，如果不去弄的话，里面水泥的颜色就露出来了，而且容易渗水，所以就要用白水泥加胶水搅拌之后糊在缝上，但是也不是直接糊，先要把缝抠一抠，因为铺瓷砖的还不会仔细到每个缝里的水泥都是一样满，而且也需要一些空间糊上去，不然就太表面的一层很容易被水直接冲掉了，然后这次其实也不是用的白水泥，而是直接现成买来就已经配好的用来填缝的，兑水搅拌均匀就好了，后面就主要是我跟我爸在搞，那个时候真的觉得我实在是太胖了，蹲下去真的没一会就受不了了，膝盖什么的太难受了，后面我跪着刷，然后膝盖又疼，也是比较不容易，不过我爸动作很快，我中间跪累了休息一会，我爸就能搞一大片，后面其实我也没做多少（谦虚一下），总体来讲这次不是很累，就是蹲着跪着腿有点受不了，是应该好好减肥了。

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

这个问题也是面试中常被问到的，就抽空来了解下这个，跳过一大段前面初始化的逻辑，
对于一条select * from t1 where id = #{id}这样的 sql，在初始化扫描 mapper 的xml文件的时候会根据是否是 dynamic 来判断生成 DynamicSqlSource 还是 RawSqlSource，这里它是一条 RawSqlSource，
在这里做了替换，将#{}替换成了?

前面说的是否 dynamic 就是在这里进行判断

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// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseScriptNode
public SqlSource parseScriptNode() {
    MixedSqlNode rootSqlNode = parseDynamicTags(context);
    SqlSource sqlSource;
    if (isDynamic) {
      sqlSource = new DynamicSqlSource(configuration, rootSqlNode);
    } else {
      sqlSource = new RawSqlSource(configuration, rootSqlNode, parameterType);
    }
    return sqlSource;
  }
// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseDynamicTags
protected MixedSqlNode parseDynamicTags(XNode node) {
    List<SqlNode> contents = new ArrayList<>();
    NodeList children = node.getNode().getChildNodes();
    for (int i = 0; i < children.getLength(); i++) {
      XNode child = node.newXNode(children.item(i));
      if (child.getNode().getNodeType() == Node.CDATA_SECTION_NODE || child.getNode().getNodeType() == Node.TEXT_NODE) {
        String data = child.getStringBody("");
        TextSqlNode textSqlNode = new TextSqlNode(data);
        if (textSqlNode.isDynamic()) {
          contents.add(textSqlNode);
          isDynamic = true;
        } else {
          contents.add(new StaticTextSqlNode(data));
        }
      } else if (child.getNode().getNodeType() == Node.ELEMENT_NODE) { // issue #628
        String nodeName = child.getNode().getNodeName();
        NodeHandler handler = nodeHandlerMap.get(nodeName);
        if (handler == null) {
          throw new BuilderException("Unknown element <" + nodeName + "> in SQL statement.");
        }
        handler.handleNode(child, contents);
        isDynamic = true;
      }
    }
    return new MixedSqlNode(contents);
  }
// org.apache.ibatis.scripting.xmltags.TextSqlNode#isDynamic
  public boolean isDynamic() {
    DynamicCheckerTokenParser checker = new DynamicCheckerTokenParser();
    GenericTokenParser parser = createParser(checker);
    parser.parse(text);
    return checker.isDynamic();
  }
    private GenericTokenParser createParser(TokenHandler handler) {
    return new GenericTokenParser("${", "}", handler);
  }

可以看到其中一个条件就是是否有${}这种占位符，假如说上面的 sql 换成 ${}，那么可以看到它会在这里创建一个 dynamicSqlSource,

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// org.apache.ibatis.scripting.xmltags.DynamicSqlSource
public class DynamicSqlSource implements SqlSource {

  private final Configuration configuration;
  private final SqlNode rootSqlNode;

  public DynamicSqlSource(Configuration configuration, SqlNode rootSqlNode) {
    this.configuration = configuration;
    this.rootSqlNode = rootSqlNode;
  }

  @Override
  public BoundSql getBoundSql(Object parameterObject) {
    DynamicContext context = new DynamicContext(configuration, parameterObject);
    rootSqlNode.apply(context);
    SqlSourceBuilder sqlSourceParser = new SqlSourceBuilder(configuration);
    Class<?> parameterType = parameterObject == null ? Object.class : parameterObject.getClass();
    SqlSource sqlSource = sqlSourceParser.parse(context.getSql(), parameterType, context.getBindings());
    BoundSql boundSql = sqlSource.getBoundSql(parameterObject);
    context.getBindings().forEach(boundSql::setAdditionalParameter);
    return boundSql;
  }

}

这里眼尖的同学可能就可以看出来了，RawSqlSource 在初始化的时候已经经过了 parse，把#{}替换成了?占位符，但是 DynamicSqlSource 并没有
再看这个图，我们发现在这的时候还没有进行替换
然后往里跟
好像是这里了

这里 rootSqlNode.apply 其实是一个对原来 sql 的解析结果的一个循环调用，不同类型的标签会构成不同的 node，像这里就是一个 textSqlNode

可以发现到这我们的 sql 已经被替换了，而且是直接作为 string 类型替换的，所以可以明白了这个问题所在，就是注入，不过细心的同学发现其实这里是有个

理论上还是可以做过滤的，不过好像现在没用起来。
我们前面可以发现对于#{}是在启动扫描 mapper的 xml 文件就替换成了 ?,然后是在什么时候变成实际的值的呢

发现到这的时候还是没有替换，其实说白了也就是 prepareStatement 那一套，

在这里进行替换，会拿到 org.apache.ibatis.mapping.ParameterMapping，然后进行替换，因为会带着类型信息，所以不用担心注入咯

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

Java 真的是任何一个中间件，比较常用的那种，都有很多内容值得深挖，比如这个缓存，慢慢有过一些感悟，比如如何提升性能，缓存无疑是一大重要手段，最底层开始 CPU 就有缓存，而且又小又贵，再往上一点内存一般作为硬盘存储在运行时的存储，一般在代码里也会用内存作为一些本地缓存，譬如数据库，像 mysql 这种也是有innodb_buffer_pool来提升查询效率，本质上理解就是用更快的存储作为相对慢存储的缓存，减少查询直接访问较慢的存储，并且这个都是相对的，比起 cpu 的缓存，那内存也是渣，但是与普通机械硬盘相比，那也是两个次元的水平。

闲扯这么多来说说 mybatis 的缓存，mybatis 一般作为一个轻量级的 orm 使用，相对应的就是比较重量级的 hibernate，不过不在这次讨论范围，上一次是主要讲了 mybatis 在解析 sql 过程中，对于两种占位符的不同替换实现策略，这次主要聊下 mybatis 的缓存，前面其实得了解下前置的东西，比如 sqlsession，先当做我们知道 sqlsession 是个什么玩意，可能或多或少的知道 mybatis 是有两级缓存，

一级缓存

第一级的缓存是在 BaseExecutor 中的 PerpetualCache，它是个最基本的缓存实现类，使用了 HashMap 实现缓存功能，代码其实没几十行

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public class PerpetualCache implements Cache {

  private final String id;

  private final Map<Object, Object> cache = new HashMap<>();

  public PerpetualCache(String id) {
    this.id = id;
  }

  @Override
  public String getId() {
    return id;
  }

  @Override
  public int getSize() {
    return cache.size();
  }

  @Override
  public void putObject(Object key, Object value) {
    cache.put(key, value);
  }

  @Override
  public Object getObject(Object key) {
    return cache.get(key);
  }

  @Override
  public Object removeObject(Object key) {
    return cache.remove(key);
  }

  @Override
  public void clear() {
    cache.clear();
  }

  @Override
  public boolean equals(Object o) {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    if (this == o) {
      return true;
    }
    if (!(o instanceof Cache)) {
      return false;
    }

    Cache otherCache = (Cache) o;
    return getId().equals(otherCache.getId());
  }

  @Override
  public int hashCode() {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    return getId().hashCode();
  }

}

可以看一下BaseExecutor 的构造函数

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protected BaseExecutor(Configuration configuration, Transaction transaction) {
    this.transaction = transaction;
    this.deferredLoads = new ConcurrentLinkedQueue<>();
    this.localCache = new PerpetualCache("LocalCache");
    this.localOutputParameterCache = new PerpetualCache("LocalOutputParameterCache");
    this.closed = false;
    this.configuration = configuration;
    this.wrapper = this;
  }

就是把 PerpetualCache 作为 localCache，然后怎么使用我看简单看一下，BaseExecutor 的查询首先是调用这个函数

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameter);
    CacheKey key = createCacheKey(ms, parameter, rowBounds, boundSql);
    return query(ms, parameter, rowBounds, resultHandler, key, boundSql);
  }

可以看到首先是调用了 createCacheKey 方法，这个方法呢，先不看怎么写的，如果我们自己要实现这么个缓存，首先这个缓存 key 的设计也是个问题，如果是以表名加主键作为 key，那么分页查询，或者没有主键的时候就不行，来看看 mybatis 是怎么设计的

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@Override
  public CacheKey createCacheKey(MappedStatement ms, Object parameterObject, RowBounds rowBounds, BoundSql boundSql) {
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    CacheKey cacheKey = new CacheKey();
    cacheKey.update(ms.getId());
    cacheKey.update(rowBounds.getOffset());
    cacheKey.update(rowBounds.getLimit());
    cacheKey.update(boundSql.getSql());
    List<ParameterMapping> parameterMappings = boundSql.getParameterMappings();
    TypeHandlerRegistry typeHandlerRegistry = ms.getConfiguration().getTypeHandlerRegistry();
    // mimic DefaultParameterHandler logic
    for (ParameterMapping parameterMapping : parameterMappings) {
      if (parameterMapping.getMode() != ParameterMode.OUT) {
        Object value;
        String propertyName = parameterMapping.getProperty();
        if (boundSql.hasAdditionalParameter(propertyName)) {
          value = boundSql.getAdditionalParameter(propertyName);
        } else if (parameterObject == null) {
          value = null;
        } else if (typeHandlerRegistry.hasTypeHandler(parameterObject.getClass())) {
          value = parameterObject;
        } else {
          MetaObject metaObject = configuration.newMetaObject(parameterObject);
          value = metaObject.getValue(propertyName);
        }
        cacheKey.update(value);
      }
    }
    if (configuration.getEnvironment() != null) {
      // issue #176
      cacheKey.update(configuration.getEnvironment().getId());
    }
    return cacheKey;
  }

首先需要 id，这个 id 是 mapper 里方法的 id， 然后是偏移量跟返回行数，再就是 sql，然后是参数，基本上是会有影响的都加进去了，在这个 update 里面

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public void update(Object object) {
    int baseHashCode = object == null ? 1 : ArrayUtil.hashCode(object);

    count++;
    checksum += baseHashCode;
    baseHashCode *= count;

    hashcode = multiplier * hashcode + baseHashCode;

    updateList.add(object);
  }

其实是一个 hash 转换，具体不纠结，就是提高特异性，然后回来就是继续调用 query

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql) throws SQLException {
    ErrorContext.instance().resource(ms.getResource()).activity("executing a query").object(ms.getId());
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    if (queryStack == 0 && ms.isFlushCacheRequired()) {
      clearLocalCache();
    }
    List<E> list;
    try {
      queryStack++;
      list = resultHandler == null ? (List<E>) localCache.getObject(key) : null;
      if (list != null) {
        handleLocallyCachedOutputParameters(ms, key, parameter, boundSql);
      } else {
        list = queryFromDatabase(ms, parameter, rowBounds, resultHandler, key, boundSql);
      }
    } finally {
      queryStack--;
    }
    if (queryStack == 0) {
      for (DeferredLoad deferredLoad : deferredLoads) {
        deferredLoad.load();
      }
      // issue #601
      deferredLoads.clear();
      if (configuration.getLocalCacheScope() == LocalCacheScope.STATEMENT) {
        // issue #482
        clearLocalCache();
      }
    }
    return list;
  }

可以看到是先从 localCache 里取，取不到再 queryFromDatabase，其实比较简单，这是一级缓存，考虑到 sqlsession 跟 BaseExecutor 的关系，其实是随着 sqlsession 来保证这个缓存不会出现脏数据幻读的情况，当然事务相关的后面可能再单独聊。

二级缓存

其实这个一级二级顺序有点反过来，其实查询的是先走的二级缓存，当然二级的需要配置开启，默认不开，
需要通过

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<setting name="cacheEnabled" value="true"/>

来开启，然后我们的查询就会走到

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public class CachingExecutor implements Executor {

  private final Executor delegate;
  private final TransactionalCacheManager tcm = new TransactionalCacheManager();

这个 Executor 中，这里我放了类里面的元素，发现没有一个 Cache 类，这就是一个特点了，往下看查询过程

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameterObject);
    CacheKey key = createCacheKey(ms, parameterObject, rowBounds, boundSql);
    return query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

  @Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql)
      throws SQLException {
    Cache cache = ms.getCache();
    if (cache != null) {
      flushCacheIfRequired(ms);
      if (ms.isUseCache() && resultHandler == null) {
        ensureNoOutParams(ms, boundSql);
        @SuppressWarnings("unchecked")
        List<E> list = (List<E>) tcm.getObject(cache, key);
        if (list == null) {
          list = delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
          tcm.putObject(cache, key, list); // issue #578 and #116
        }
        return list;
      }
    }
    return delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

看到没，其实缓存是从 tcm 这个成员变量里取，而这个是什么呢，事务性缓存(直译下)，因为这个其实是用 MappedStatement 里的 Cache 作为key 从 tcm 的 map 取出来的

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public class TransactionalCacheManager {

  private final Map<Cache, TransactionalCache> transactionalCaches = new HashMap<>();

MappedStatement是被全局使用的，所以其实二级缓存是跟着 mapper 的 namespace 走的，可以被多个 CachingExecutor 获取到，就会出现线程安全问题，线程安全问题可以用SynchronizedCache来解决，就是加锁，但是对于事务中的脏读，使用了TransactionalCache来解决这个问题，

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public class TransactionalCache implements Cache {

  private static final Log log = LogFactory.getLog(TransactionalCache.class);

  private final Cache delegate;
  private boolean clearOnCommit;
  private final Map<Object, Object> entriesToAddOnCommit;
  private final Set<Object> entriesMissedInCache;

在事务还没提交的时候，会把中间状态的数据放在 entriesToAddOnCommit 中，只有在提交后会放进共享缓存中，

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public void commit() {
    if (clearOnCommit) {
      delegate.clear();
    }
    flushPendingEntries();
    reset();
  }

发表于 分类于 ， ， ， 阅读次数： Disqus：

小工记四

第四周去的时候让我们去了现在在住的房子里，去三楼整理东西了，蛮多的东西需要收拾整理，有些需要丢一下，以前往往是把不太要用的东西就放三楼了，但是后面就不会再去收拾整理，LD 跟丈母娘负责收拾，我不太知道哪些还要的，哪些不要了，而且本来也不擅长这种收拾🤦‍♂️，然后就变成她们收拾出来废纸箱，我负责拆掉，压平，这时候终于觉得体重还算是有点作用，总体来说这个事情我其实也不擅长，不擅长的主要是捆起来，可能我总是小题大做，因为纸箱大小不一，如果不做一下分类，然后把大的折小一些的话，直接绑起来，容易拎起来就散掉了，而且一些鞋盒子这种小件的纸盒会比较薄，冰箱这种大件的比较厚，厚的比较不容易变形，需要大力踩踏，而且扎的时候需要用体重压住捆实了之后那样子才是真的捆实的，不然待会又是松松垮垮容易滑出来散架，因为压住了捆好后，下来了之后箱子就会弹开了把绳子崩紧实，感觉又是掌握到生活小技巧了😃，我这里其实比较单调无聊，然后 LD 那可以说非常厉害了，一共理出来 11 把旧电扇，还有好多没用过全新的不锈钢脸盆大大小小的，感觉比店里在卖的还多，还有是有比较多小时候的东西，特别多小时候的衣服，其实这种对我来说最难了，可能需要读一下断舍离，蛮多东西都舍不得扔，但是其实是没啥用了，然后还占地方，这天应该算是比较轻松的一天了，上午主要是把收拾出来要的和不要的搬下楼，然后下午要去把纸板给卖掉。中午还是去小快餐店吃的，在住的家里理东西还有个好处就是中午吃完饭可以小憩一下，因为我个人是非常依赖午休的，不然下午完全没精神，而且心态也会比较烦躁，一方面是客观的的确比较疲惫，另一方面应该主观心理作用也有点影响，就像上班的时候也是觉得不午睡就会很难受，心理作用也有一点，不过总之能睡还是睡一会，真的没办法就心态好点，吃完午饭之后我们就推着小平板车去收废品的地方卖掉了上午我收拾捆起来的纸板，好像卖了一百多，都是直接过地磅了，不用一捆一捆地称，不过有个小插曲，那里另外一个大爷在倒他的三轮车的时候撞了我一下，还好车速慢，屁股上肉垫后，接下来就比较麻烦了，是LD 她们两姐妹从小到大的书，也要去卖掉，小平板车就载不下了，而且着实也不太好推，轮子不太平，导致推着很累，书有好多箱，本来是想去亲戚家借电动三轮车，因为不会开摩托的那种，摩托的那种 LD 邻居家就有，可是到了那发现那个也是很大，而且刹车是用脚踩的那种，俺爹不太放心，就说第二天周日他有空会帮忙去载了卖掉的，然后比较搞笑的来了，丈母娘看错了时间，以为已经快五点了，就让我们顺便在车里带点东西去在修的房子，放到那边三楼去，到了那还跟老丈人说已经这么迟了要赶紧去菜场买菜了，结果我们回来以后才发现看错了一个小时🤦‍♂️。
前面可以没提，前三周去的我们一般就周六去一天，然后周日因为要早点回杭州，而且可能想让我们周日能休息下，但是这周就因为周日的时候我爸要去帮忙载书，然后 LD 姐姐也会过来收拾东西，我们周日就又去整理收拾了，周日由于俺爹去的很早，我过去的时候书已经木有了，主要是去收拾东西了，把一些有用没用的继续整理，基本上三楼的就处理完毕了，舒了一大口气，毕竟让丈母娘一个人收拾实在是太累了，但是要扔掉的衣服比较棘手，附近知道的青蛙回收桶被推倒了，其他地方也不知道哪里有，我们就先载了一些东西去在修的房子那，然后去找青蛙桶，结果一个小区可以进，但是已经满了，另一个不让进，后来只能让 LD 姐姐带去她们小区扔了，塞了满满一车。因为要赶回杭州的车就没有等我爸一起回来，他还在那帮忙搞卫生间的墙缝。
虽然这两天不太热，活也不算很吃力，不过我这个体重和易出汗的体质，还是让短袖不知道湿透了多少次，灌了好多水和冰红茶（下午能提提神），回来周一早上称体重也比较喜人，差一点就达到阶段目标，可以想想去哪里吃之前想好的烤肉跟火锅了（估计吃完立马回到解放前）。

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

又做了个题，看记录是以前用 C++写过的，现在捋一捋思路，用 Java 再写了一下，思路还是比较清晰的，但是边界细节处理得比较差

简要介绍

Given a string s, find the length of the longest substring without repeating characters.

样例

Example 1:

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Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.

Example 2:

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Input: s = "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.

Example 3:

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Input: s = "pwwkew"
Output: 3
Explanation: The answer is "wke", with the length of 3.
Notice that the answer must be a substring, "pwke" is a subsequence and not a substring.

Example 4:

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Input: s = ""
Output: 0

就是一个最长不重复的字符串长度，因为也是中等难度的题，不太需要特别复杂的思考，最基本的就是O(N*N)两重循环，不过显然不太好，万一超时间，还有一种就是线性复杂度的了，这个就是需要搞定一个思路，比如字符串时 abcdefgaqwrty，比如遍历到第二个a的时候其实不用再从头去遍历了，只要把前面那个a给排除掉，继续往下算就好了

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class Solution {
    Map<String, Integer> counter = new HashMap<>();
    public int lengthOfLongestSubstring(String s) {
        int length = s.length();
        // 当前的长度
        int subStringLength = 0;
        // 最长的长度
        int maxSubStringLength = 0;
        // 考虑到重复的位置已经被跳过的情况，即已经在当前长度的字符串范围之前的重复字符不需要回溯
        int lastDuplicatePos = -1;
        for (int i = 0; i < length; i++) {
            // 使用 map 存储字符和上一次出现的位置，如果存在并且大于上一次重复位置
            if (counter.get(String.valueOf(s.charAt(i))) != null && counter.get(String.valueOf(s.charAt(i))) > lastDuplicatePos) {
                // 记录重复位置
                lastDuplicatePos = counter.get(String.valueOf(s.charAt(i)));
                // 重置不重复子串的长度，减去重复起点
                subStringLength = i - counter.get(String.valueOf(s.charAt(i))) - 1;
                // 替换当前位置
                counter.replace(String.valueOf(s.charAt(i)), i);
            } else {
                // 如果不存在就直接 put
                counter.put(String.valueOf(s.charAt(i)), i);
            }
            // 长度累加
            subStringLength++;
            if (subStringLength > maxSubStringLength) {
                // 简单替换
                maxSubStringLength = subStringLength;
            }
        }
        return maxSubStringLength;
    }
}

注释应该写的比较清楚了。

发表于 分类于 ， ， ， 阅读次数： Disqus：

小工记三

前面这两周周末也都去老丈人家帮忙了，上上周周六先是去了那个在装修的旧房子那，把三楼收拾了下，因为要搬进来住，来不及等二楼装修好，就要把三楼里的东西都整理干净，这个活感觉是比较 easy，原来是就准备把三楼当放东西仓储的地方了，我们乡下大部分三层楼都是这么用的，这次也是没办法，之前搬进来的木头什么的都搬出去，主要是这上面灰尘太多，后面清理鼻孔的时候都是黑色的了，把东西都搬出去以后主要是地还是很脏，就扫了地拖了地，因为是水泥地，灰尘又太多了，拖起来都是会灰尘扬起来，整个脱完了的确干净很多，然而这会就出了个大乌龙，我们清理的是三楼的西边一间，结果老丈人上来说要住东边那间的🤦‍♂️，不过其实西边的也得清理，因为还是要放被子什么的，不算是白费功夫，接着清理东边那间，之前这个房子做过群租房，里面有个高低铺的床，当时觉得可以用在放被子什么的就没扔，只是拆掉了放旁边，我们就把它擦干净了又装好，发现螺丝🔩少了几个，亘古不变的真理，拆了以后装要不就多几个要不就少几个，不是很牢靠，不过用来放放被子省得放地上总还是可以的，对了前面还做了个事情就是铺地毯，其实也不是地毯，就是类似于墙布雨篷布那种，别人不用了送给我们的，三楼水泥地也不会铺瓷砖地板了就放一下，干净好看点，不过大小不合适要裁一下，那把剪刀是真的太难用了，我手都要抽筋了，它就是刀口只有一小个点是能剪下来的，其他都是钝的，后来还是用刀片直接裁，铺好以后，真的感觉也不太一样了，焕然一新的感觉
差不多中午了就去吃饭了，之前两次是去了一家小饭店，还是还比较干净，但是店里菜不好吃，还死贵，这次去了一家小快餐店，口味好，便宜，味道是真的不错，带鱼跟黄鱼都好吃，一点都不腥，我对这类比较腥的鱼真的是很挑剔的，基本上除了家里做的很少吃外面的，那天抱着试试的态度吃了下，真的还不错，后来丈母娘说好像这家老板是给别人结婚喜事酒席当厨师的，怪不得做的好吃，其实本来是有一点小抗拒，怕不干净什么的，后来发现菜很好吃，而且可能是老丈人跟干活的师傅去吃的比较多，老板很客气，我们吃完饭，还给我们买了葡萄吃，不过这家店有一个槽点，就是饭比较不好吃，有时候会夹生，不过后面聊起来其实是这种小菜馆饭点的通病，烧的太早太多容易多出来浪费，烧的迟了不够吃，而且大的电饭锅比较不容易烧好。
下午前面还是在处理三楼的，窗户上各种钉子，实在是太多了，我后面在走廊上排了一排🤦‍♂️，有些是直接断了，有些是就撬了出来，感觉我在杭州租房也没有这样子各种钉钉子，挂下衣服什么的也不用这么多吧，比较不能理解，搞得到处都是钉子。那天我爸也去帮忙了，主要是在卫生间里做白缝，其实也是个技术活，印象中好像我小时候自己家里也做过这个事情，但是比较模糊了，后面我们三楼搞完了就去帮我爸了，前面是我老婆二爹在那先刷上白缝，这里叫白缝，有些考究的也叫美缝，就是瓷砖铺完之后的缝，如果不去弄的话，里面水泥的颜色就露出来了，而且容易渗水，所以就要用白水泥加胶水搅拌之后糊在缝上，但是也不是直接糊，先要把缝抠一抠，因为铺瓷砖的还不会仔细到每个缝里的水泥都是一样满，而且也需要一些空间糊上去，不然就太表面的一层很容易被水直接冲掉了，然后这次其实也不是用的白水泥，而是直接现成买来就已经配好的用来填缝的，兑水搅拌均匀就好了，后面就主要是我跟我爸在搞，那个时候真的觉得我实在是太胖了，蹲下去真的没一会就受不了了，膝盖什么的太难受了，后面我跪着刷，然后膝盖又疼，也是比较不容易，不过我爸动作很快，我中间跪累了休息一会，我爸就能搞一大片，后面其实我也没做多少（谦虚一下），总体来讲这次不是很累，就是蹲着跪着腿有点受不了，是应该好好减肥了。

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

这个问题也是面试中常被问到的，就抽空来了解下这个，跳过一大段前面初始化的逻辑，
对于一条select * from t1 where id = #{id}这样的 sql，在初始化扫描 mapper 的xml文件的时候会根据是否是 dynamic 来判断生成 DynamicSqlSource 还是 RawSqlSource，这里它是一条 RawSqlSource，
在这里做了替换，将#{}替换成了?

前面说的是否 dynamic 就是在这里进行判断

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// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseScriptNode
public SqlSource parseScriptNode() {
    MixedSqlNode rootSqlNode = parseDynamicTags(context);
    SqlSource sqlSource;
    if (isDynamic) {
      sqlSource = new DynamicSqlSource(configuration, rootSqlNode);
    } else {
      sqlSource = new RawSqlSource(configuration, rootSqlNode, parameterType);
    }
    return sqlSource;
  }
// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseDynamicTags
protected MixedSqlNode parseDynamicTags(XNode node) {
    List<SqlNode> contents = new ArrayList<>();
    NodeList children = node.getNode().getChildNodes();
    for (int i = 0; i < children.getLength(); i++) {
      XNode child = node.newXNode(children.item(i));
      if (child.getNode().getNodeType() == Node.CDATA_SECTION_NODE || child.getNode().getNodeType() == Node.TEXT_NODE) {
        String data = child.getStringBody("");
        TextSqlNode textSqlNode = new TextSqlNode(data);
        if (textSqlNode.isDynamic()) {
          contents.add(textSqlNode);
          isDynamic = true;
        } else {
          contents.add(new StaticTextSqlNode(data));
        }
      } else if (child.getNode().getNodeType() == Node.ELEMENT_NODE) { // issue #628
        String nodeName = child.getNode().getNodeName();
        NodeHandler handler = nodeHandlerMap.get(nodeName);
        if (handler == null) {
          throw new BuilderException("Unknown element <" + nodeName + "> in SQL statement.");
        }
        handler.handleNode(child, contents);
        isDynamic = true;
      }
    }
    return new MixedSqlNode(contents);
  }
// org.apache.ibatis.scripting.xmltags.TextSqlNode#isDynamic
  public boolean isDynamic() {
    DynamicCheckerTokenParser checker = new DynamicCheckerTokenParser();
    GenericTokenParser parser = createParser(checker);
    parser.parse(text);
    return checker.isDynamic();
  }
    private GenericTokenParser createParser(TokenHandler handler) {
    return new GenericTokenParser("${", "}", handler);
  }

可以看到其中一个条件就是是否有${}这种占位符，假如说上面的 sql 换成 ${}，那么可以看到它会在这里创建一个 dynamicSqlSource,

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// org.apache.ibatis.scripting.xmltags.DynamicSqlSource
public class DynamicSqlSource implements SqlSource {

  private final Configuration configuration;
  private final SqlNode rootSqlNode;

  public DynamicSqlSource(Configuration configuration, SqlNode rootSqlNode) {
    this.configuration = configuration;
    this.rootSqlNode = rootSqlNode;
  }

  @Override
  public BoundSql getBoundSql(Object parameterObject) {
    DynamicContext context = new DynamicContext(configuration, parameterObject);
    rootSqlNode.apply(context);
    SqlSourceBuilder sqlSourceParser = new SqlSourceBuilder(configuration);
    Class<?> parameterType = parameterObject == null ? Object.class : parameterObject.getClass();
    SqlSource sqlSource = sqlSourceParser.parse(context.getSql(), parameterType, context.getBindings());
    BoundSql boundSql = sqlSource.getBoundSql(parameterObject);
    context.getBindings().forEach(boundSql::setAdditionalParameter);
    return boundSql;
  }

}

这里眼尖的同学可能就可以看出来了，RawSqlSource 在初始化的时候已经经过了 parse，把#{}替换成了?占位符，但是 DynamicSqlSource 并没有
再看这个图，我们发现在这的时候还没有进行替换
然后往里跟
好像是这里了

这里 rootSqlNode.apply 其实是一个对原来 sql 的解析结果的一个循环调用，不同类型的标签会构成不同的 node，像这里就是一个 textSqlNode

可以发现到这我们的 sql 已经被替换了，而且是直接作为 string 类型替换的，所以可以明白了这个问题所在，就是注入，不过细心的同学发现其实这里是有个

理论上还是可以做过滤的，不过好像现在没用起来。
我们前面可以发现对于#{}是在启动扫描 mapper的 xml 文件就替换成了 ?,然后是在什么时候变成实际的值的呢

发现到这的时候还是没有替换，其实说白了也就是 prepareStatement 那一套，

在这里进行替换，会拿到 org.apache.ibatis.mapping.ParameterMapping，然后进行替换，因为会带着类型信息，所以不用担心注入咯

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， 阅读次数： Disqus：

因为活实在比较多，也不太好叫大工(活比较杂散)，相比上一次我跟 LD 俩人晚起了一点，我真的是只要有事，早上就醒的很早，准备八点出发的，六点就醒了，然后想继续睡就一直做梦🤦‍♂️，差不多八点半多到的丈人家，他们应该已经干了有一会了，我们到了以后就分配给我撬地板的活，上次说的那个敲掉柜子的房间里，还铺着质地还不错的木地板，但是也不想要了，得撬掉重新铺。
拿着撬棍和榔头就上楼去干了，浙江这几天的天气，最高温度一般 38、9，楼上那个房间也没风扇，有了也不能用，都是灰尘，撬了两下，我感觉我体内的水就像真气爆发一样变成汗炸了出来，眼睛全被汗糊住了，可能大部分人不太了解地板是怎么铺的，一般是在地面先铺一层混凝土，混凝土中间嵌进去规则的长条木条，然后真正的地板一块块的都是钉在那个木条上，用那种气枪钉和普通的钉子，并且块跟块之前还有一个木头的槽结构相互耦合，然后边缘的一圈在用较薄的木板将整个木地板封边(这些词都是我现造的)，边缘的用的钉子会更多，所以那几下真的很用力，而且撬地板，得蹲下起来，如此反复，对于我这个体重快超过身高的中年人来说的确是非常大的挑战，接下来继续撬了几个，已经有种要虚脱晕倒的感觉了，及时去喝水擦了汗，又歇了一会，为啥一上来就这么拼呢，主要是因为那个房间丈人在干活的时候是直接看得到的🤦‍♂️，后来被 LD 一顿教育，本来就是去帮忙的，又不是专业做这个的，急啥。
喝了水之后，又稍稍歇了一会，就开始继续撬了，本来觉得这个地板撬着好像还行，房间不大，没多久就撬完了，撬完之后喝了点饮料(补充点糖分，早餐吃得少，有点低血糖)，然后看到 LD 在撬下面的木条了，这个动作开始了那天最大的经验值收集行动，前面说了这个木条一般是跟混凝土一块铺上去的，但是谁也没想到，这个混凝土铺上去的时候竟然处理的这么随意，根本没考虑跟下面的贴合，所以撬木条的时候直接把木条跟木条中间大块大块的混凝土一块撬起来了，想想那重量，于是我这靠蛮力干活的，就用力把木条带着混凝土一块撬了起来，还沾沾自喜，但是发现结果是撬起来一块之后，体力值瞬间归零，上一篇我也提到了，其实干这类活也是很有技巧性的，但是上次的是我没学会，可能需要花时间学的，但是这次是LD 用她的纤细胳膊教会我的，我在撬的时候，屏住一口气，双手用力，起，大概是吃好几口奶的力气都用出来了，但是 LD 在我休息的时候，慢慢悠悠的，先把撬棍挤到木条或者混凝土跟下层的缝里，然后往下垫一小块混凝土碎石，然后轻轻松松的扳两下，就撬开了，亏我高中的时候引以为傲的物理成绩，作为物理课代表，这么浅显易懂的杠杆原理都完全不会用到生活里，后面在用这个技巧撬的过程中，真的觉得自己蠢到家了，当然在明白了用点杠杆原理之后，撬地板的活就变得慢慢悠悠，悠哉悠哉的了(其实还是很热的，披着毛巾擦眼睛)。
上午的活差不多完了，后面就是把撬出来的混凝土和地板条丢下去，地上铺着不用了的被子，然后就是午饭和午休环节了，午饭换了一家快餐，味道非常可以，下午的活就比较单调了，帮忙清理了上去扔下来的混凝土碎块跟木条，然后稍微打扫了下，老丈人就让我们回家了，接着上次说的，还是觉得比跑步啥的消耗大太多了，那汗流的，一口就能喝完一瓶 500 毫升左右的矿泉水。

发表于 更新于 分类于 ， ， ， ， ， ， 阅读次数： Disqus：

本来是想取个像现在那些公众号转了又转的文章标题，”面试官再问你xxxxx，就把这篇文章甩给他看”这种标题，但是觉得实在太 low 了，还是用一部我比较喜欢的电影里的一句台词，《人在囧途》里王宝强对着那张老板给他的欠条，看不懂字时候说的那句，这些都是些啥（第四声）
当我刚开始面 Java 的时候，其实我真的没注意这方面的东西，实话说就是不知道这些是啥，开发中用过 Interceptor和 Aop，了解 aop 的实现原理，但是不知道 Java web 中的 Filter 是怎么回事，知道 dubbo 的 filter，就这样，所以被问到了的确是回答不出来，可能就觉得这个渣渣，这么简单的都不会，所以还是花点时间来看看这个是个啥，为了避免我口吐芬芳，还是耐下性子来简单说下这几个东西
首先是 servlet，怎么去解释这个呢，因为之前是 PHPer，所以比较喜欢用它来举例子，在普通的 PHP 的 web 应用中一般有几部分组成，接受 HTTP 请求的是前置的 nginx 或者 apache，但是这俩玩意都是只能处理静态的请求，远古时代 PHP 和 HTML 混编是通过 apache 的 php module，跟后来 nginx 使用 php-fpm 其实道理类似，就是把请求中需要 PHP 处理的转发给 PHP，在 Java 中呢，是有个比较牛叉的叫 Tomcat 的，它可以把请求转成 servlet，而 servlet 其实就是一种实现了特定接口的 Java 代码，

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package javax.servlet;

import java.io.IOException;

/**
 * Defines methods that all servlets must implement.
 *
 * <p>
 * A servlet is a small Java program that runs within a Web server. Servlets
 * receive and respond to requests from Web clients, usually across HTTP, the
 * HyperText Transfer Protocol.
 *
 * <p>
 * To implement this interface, you can write a generic servlet that extends
 * <code>javax.servlet.GenericServlet</code> or an HTTP servlet that extends
 * <code>javax.servlet.http.HttpServlet</code>.
 *
 * <p>
 * This interface defines methods to initialize a servlet, to service requests,
 * and to remove a servlet from the server. These are known as life-cycle
 * methods and are called in the following sequence:
 * <ol>
 * <li>The servlet is constructed, then initialized with the <code>init</code>
 * method.
 * <li>Any calls from clients to the <code>service</code> method are handled.
 * <li>The servlet is taken out of service, then destroyed with the
 * <code>destroy</code> method, then garbage collected and finalized.
 * </ol>
 *
 * <p>
 * In addition to the life-cycle methods, this interface provides the
 * <code>getServletConfig</code> method, which the servlet can use to get any
 * startup information, and the <code>getServletInfo</code> method, which allows
 * the servlet to return basic information about itself, such as author,
 * version, and copyright.
 *
 * @see GenericServlet
 * @see javax.servlet.http.HttpServlet
 */
public interface Servlet {

    /**
     * Called by the servlet container to indicate to a servlet that the servlet
     * is being placed into service.
     *
     * <p>
     * The servlet container calls the <code>init</code> method exactly once
     * after instantiating the servlet. The <code>init</code> method must
     * complete successfully before the servlet can receive any requests.
     *
     * <p>
     * The servlet container cannot place the servlet into service if the
     * <code>init</code> method
     * <ol>
     * <li>Throws a <code>ServletException</code>
     * <li>Does not return within a time period defined by the Web server
     * </ol>
     *
     *
     * @param config
     *            a <code>ServletConfig</code> object containing the servlet's
     *            configuration and initialization parameters
     *
     * @exception ServletException
     *                if an exception has occurred that interferes with the
     *                servlet's normal operation
     *
     * @see UnavailableException
     * @see #getServletConfig
     */
    public void init(ServletConfig config) throws ServletException;

    /**
     *
     * Returns a {@link ServletConfig} object, which contains initialization and
     * startup parameters for this servlet. The <code>ServletConfig</code>
     * object returned is the one passed to the <code>init</code> method.
     *
     * <p>
     * Implementations of this interface are responsible for storing the
     * <code>ServletConfig</code> object so that this method can return it. The
     * {@link GenericServlet} class, which implements this interface, already
     * does this.
     *
     * @return the <code>ServletConfig</code> object that initializes this
     *         servlet
     *
     * @see #init
     */
    public ServletConfig getServletConfig();

    /**
     * Called by the servlet container to allow the servlet to respond to a
     * request.
     *
     * <p>
     * This method is only called after the servlet's <code>init()</code> method
     * has completed successfully.
     *
     * <p>
     * The status code of the response always should be set for a servlet that
     * throws or sends an error.
     *
     *
     * <p>
     * Servlets typically run inside multithreaded servlet containers that can
     * handle multiple requests concurrently. Developers must be aware to
     * synchronize access to any shared resources such as files, network
     * connections, and as well as the servlet's class and instance variables.
     * More information on multithreaded programming in Java is available in <a
     * href
     * ="http://java.sun.com/Series/Tutorial/java/threads/multithreaded.html">
     * the Java tutorial on multi-threaded programming</a>.
     *
     *
     * @param req
     *            the <code>ServletRequest</code> object that contains the
     *            client's request
     *
     * @param res
     *            the <code>ServletResponse</code> object that contains the
     *            servlet's response
     *
     * @exception ServletException
     *                if an exception occurs that interferes with the servlet's
     *                normal operation
     *
     * @exception IOException
     *                if an input or output exception occurs
     */
    public void service(ServletRequest req, ServletResponse res)
            throws ServletException, IOException;

    /**
     * Returns information about the servlet, such as author, version, and
     * copyright.
     *
     * <p>
     * The string that this method returns should be plain text and not markup
     * of any kind (such as HTML, XML, etc.).
     *
     * @return a <code>String</code> containing servlet information
     */
    public String getServletInfo();

    /**
     * Called by the servlet container to indicate to a servlet that the servlet
     * is being taken out of service. This method is only called once all
     * threads within the servlet's <code>service</code> method have exited or
     * after a timeout period has passed. After the servlet container calls this
     * method, it will not call the <code>service</code> method again on this
     * servlet.
     *
     * <p>
     * This method gives the servlet an opportunity to clean up any resources
     * that are being held (for example, memory, file handles, threads) and make
     * sure that any persistent state is synchronized with the servlet's current
     * state in memory.
     */
    public void destroy();
}

重点看 servlet 的 service方法，就是接受请求，处理完了给响应，不说细节，不然光 Tomcat 的能说半年，所以呢再进一步去理解，其实就能知道，就是一个先后的问题，盗个图

filter 跟后两者最大的不一样其实是一个基于 servlet，在非常外层做的处理，然后是 interceptor 的 prehandle 跟 posthandle，接着才是我们常规的 aop，就这么点事情，做个小试验吧(还是先补段代码吧)

Filter

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// ---------------------------------------------------- FilterChain Methods

    /**
     * Invoke the next filter in this chain, passing the specified request
     * and response.  If there are no more filters in this chain, invoke
     * the <code>service()</code> method of the servlet itself.
     *
     * @param request The servlet request we are processing
     * @param response The servlet response we are creating
     *
     * @exception IOException if an input/output error occurs
     * @exception ServletException if a servlet exception occurs
     */
    @Override
    public void doFilter(ServletRequest request, ServletResponse response)
        throws IOException, ServletException {

        if( Globals.IS_SECURITY_ENABLED ) {
            final ServletRequest req = request;
            final ServletResponse res = response;
            try {
                java.security.AccessController.doPrivileged(
                    new java.security.PrivilegedExceptionAction<Void>() {
                        @Override
                        public Void run()
                            throws ServletException, IOException {
                            internalDoFilter(req,res);
                            return null;
                        }
                    }
                );
            } catch( PrivilegedActionException pe) {
                Exception e = pe.getException();
                if (e instanceof ServletException)
                    throw (ServletException) e;
                else if (e instanceof IOException)
                    throw (IOException) e;
                else if (e instanceof RuntimeException)
                    throw (RuntimeException) e;
                else
                    throw new ServletException(e.getMessage(), e);
            }
        } else {
            internalDoFilter(request,response);
        }
    }
    private void internalDoFilter(ServletRequest request,
                                  ServletResponse response)
        throws IOException, ServletException {

        // Call the next filter if there is one
        if (pos < n) {
            ApplicationFilterConfig filterConfig = filters[pos++];
            try {
                Filter filter = filterConfig.getFilter();

                if (request.isAsyncSupported() && "false".equalsIgnoreCase(
                        filterConfig.getFilterDef().getAsyncSupported())) {
                    request.setAttribute(Globals.ASYNC_SUPPORTED_ATTR, Boolean.FALSE);
                }
                if( Globals.IS_SECURITY_ENABLED ) {
                    final ServletRequest req = request;
                    final ServletResponse res = response;
                    Principal principal =
                        ((HttpServletRequest) req).getUserPrincipal();

                    Object[] args = new Object[]{req, res, this};
                    SecurityUtil.doAsPrivilege ("doFilter", filter, classType, args, principal);
                } else {
                    filter.doFilter(request, response, this);
                }
            } catch (IOException | ServletException | RuntimeException e) {
                throw e;
            } catch (Throwable e) {
                e = ExceptionUtils.unwrapInvocationTargetException(e);
                ExceptionUtils.handleThrowable(e);
                throw new ServletException(sm.getString("filterChain.filter"), e);
            }
            return;
        }

        // We fell off the end of the chain -- call the servlet instance
        try {
            if (ApplicationDispatcher.WRAP_SAME_OBJECT) {
                lastServicedRequest.set(request);
                lastServicedResponse.set(response);
            }

            if (request.isAsyncSupported() && !servletSupportsAsync) {
                request.setAttribute(Globals.ASYNC_SUPPORTED_ATTR,
                        Boolean.FALSE);
            }
            // Use potentially wrapped request from this point
            if ((request instanceof HttpServletRequest) &&
                    (response instanceof HttpServletResponse) &&
                    Globals.IS_SECURITY_ENABLED ) {
                final ServletRequest req = request;
                final ServletResponse res = response;
                Principal principal =
                    ((HttpServletRequest) req).getUserPrincipal();
                Object[] args = new Object[]{req, res};
                SecurityUtil.doAsPrivilege("service",
                                           servlet,
                                           classTypeUsedInService,
                                           args,
                                           principal);
            } else {
                servlet.service(request, response);
            }
        } catch (IOException | ServletException | RuntimeException e) {
            throw e;
        } catch (Throwable e) {
            e = ExceptionUtils.unwrapInvocationTargetException(e);
            ExceptionUtils.handleThrowable(e);
            throw new ServletException(sm.getString("filterChain.servlet"), e);
        } finally {
            if (ApplicationDispatcher.WRAP_SAME_OBJECT) {
                lastServicedRequest.set(null);
                lastServicedResponse.set(null);
            }
        }
    }

注意看这一行
filter.doFilter(request, response, this);
是不是看懂了，就是个 filter 链，但是这个代码在哪呢，org.apache.catalina.core.ApplicationFilterChain#doFilter
然后是interceptor，

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protected void doDispatch(HttpServletRequest request, HttpServletResponse response) throws Exception {
        HttpServletRequest processedRequest = request;
        HandlerExecutionChain mappedHandler = null;
        boolean multipartRequestParsed = false;
        WebAsyncManager asyncManager = WebAsyncUtils.getAsyncManager(request);

        try {
            try {
                ModelAndView mv = null;
                Object dispatchException = null;

                try {
                    processedRequest = this.checkMultipart(request);
                    multipartRequestParsed = processedRequest != request;
                    mappedHandler = this.getHandler(processedRequest);
                    if (mappedHandler == null) {
                        this.noHandlerFound(processedRequest, response);
                        return;
                    }

                    HandlerAdapter ha = this.getHandlerAdapter(mappedHandler.getHandler());
                    String method = request.getMethod();
                    boolean isGet = "GET".equals(method);
                    if (isGet || "HEAD".equals(method)) {
                        long lastModified = ha.getLastModified(request, mappedHandler.getHandler());
                        if ((new ServletWebRequest(request, response)).checkNotModified(lastModified) && isGet) {
                            return;
                        }
                    }

                    /** 
                     * 看这里看这里‼️
                     */
                    if (!mappedHandler.applyPreHandle(processedRequest, response)) {
                        return;
                    }

                    mv = ha.handle(processedRequest, response, mappedHandler.getHandler());
                    if (asyncManager.isConcurrentHandlingStarted()) {
                        return;
                    }

                    this.applyDefaultViewName(processedRequest, mv);
                    /** 
                     * 再看这里看这里‼️
                     */
                    mappedHandler.applyPostHandle(processedRequest, response, mv);
                } catch (Exception var20) {
                    dispatchException = var20;
                } catch (Throwable var21) {
                    dispatchException = new NestedServletException("Handler dispatch failed", var21);
                }

                this.processDispatchResult(processedRequest, response, mappedHandler, mv, (Exception)dispatchException);
            } catch (Exception var22) {
                this.triggerAfterCompletion(processedRequest, response, mappedHandler, var22);
            } catch (Throwable var23) {
                this.triggerAfterCompletion(processedRequest, response, mappedHandler, new NestedServletException("Handler processing failed", var23));
            }

        } finally {
            if (asyncManager.isConcurrentHandlingStarted()) {
                if (mappedHandler != null) {
                    mappedHandler.applyAfterConcurrentHandlingStarted(processedRequest, response);
                }
            } else if (multipartRequestParsed) {
                this.cleanupMultipart(processedRequest);
            }

        }
    }

代码在哪呢，org.springframework.web.servlet.DispatcherServlet#doDispatch，然后才是我们自己写的 aop，是不是差不多明白了，嗯，接下来是例子
写个 filter

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public class DemoFilter extends HttpServlet implements Filter {
    @Override
    public void init(FilterConfig filterConfig) throws ServletException {
        System.out.println("==>DemoFilter启动");
    }

    @Override
    public void doFilter(ServletRequest servletRequest, ServletResponse servletResponse, FilterChain filterChain) throws IOException, ServletException {
        // 将请求转换成HttpServletRequest 请求
        HttpServletRequest req = (HttpServletRequest) servletRequest;
        HttpServletResponse resp = (HttpServletResponse) servletResponse;
        System.out.println("before filter");
        filterChain.doFilter(req, resp);
        System.out.println("after filter");
    }

    @Override
    public void destroy() {

    }
}

因为用的springboot，所以就不写 web.xml 了，写个配置类

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@Configuration
public class FilterConfiguration {
    @Bean
    public FilterRegistrationBean filterDemo4Registration() {
        FilterRegistrationBean registration = new FilterRegistrationBean();
        //注入过滤器
        registration.setFilter(new DemoFilter());
        //拦截规则
        registration.addUrlPatterns("/*");
        //过滤器名称
        registration.setName("DemoFilter");
        //是否自动注册 false 取消Filter的自动注册
        registration.setEnabled(true);
        //过滤器顺序
        registration.setOrder(1);
        return registration;
    }

}

然后再来个 interceptor 和 aop,以及一个简单的请求处理

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public class DemoInterceptor extends HandlerInterceptorAdapter {
    @Override
    public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception {
        System.out.println("preHandle test");
        return true;
    }

    @Override
    public void postHandle(HttpServletRequest request, HttpServletResponse response, Object handler, ModelAndView modelAndView) throws Exception {
        System.out.println("postHandle test");
    }
}
@Aspect
@Component
public class DemoAspect {

    @Pointcut("execution( public * com.nicksxs.springbootdemo.demo.DemoController.*())")
    public void point() {

    }

    @Before("point()")
    public void doBefore(){
        System.out.println("==doBefore==");
    }

    @After("point()")
    public void doAfter(){
        System.out.println("==doAfter==");
    }
}
@RestController
public class DemoController {

    @RequestMapping("/hello")
    @ResponseBody
    public String hello() {
        return "hello world";
    }
}

好了，请求一下，看看 stdout，

搞定完事儿~

发表于 分类于 ， ， ， 阅读次数： Disqus：

这周回家提前约好了要去老丈人家帮下忙，因为在翻修下老房子，活不是特别整的那种，所以大部分都是自己干，或者找个大工临时干几天（我们这那种比较专业的泥工匠叫做大工），像我这样去帮忙的，就是干点小工（把给大工帮忙的，干些偏体力活的叫做小工）的活。从大学毕业以后真的蛮少帮家里干活了，以前上学的时候放假还是帮家里淘个米，简单的扫地拖地啥的，当然刚高考完的时候，还去我爸厂里帮忙干了几天的活，实在是比较累，不过现在想着是觉得自己那时候比较牛，而不是特别排斥这个活，相对于现在的工作来说，导致了一系列的职业病，颈椎腰背都很僵硬，眼镜也不好，还有反流，像我爸那种活反而是脑力加体力的比较好的结合。
这一天的活前半部分主要是在清理厨房，瓷砖上的油污和墙上天花板上即将脱落的石灰或者白色涂料层，这种活特别是瓷砖上的油污，之前在自己家里也干活，还是比较熟悉的，不过前面主要是LD 在干，我主要是先搞墙上和天花板上的，干活还是很需要技巧的，如果直接去铲，那基本我会变成一个灰人，而且吸一鼻子灰，老丈人比较专业，先接上软管用水冲，一冲效果特别好，有些石灰涂料层直接就冲掉了，冲完之后先用带加长杆的刀片铲铲了一圈墙面，说实话因为老房子之前租出去了，所以墙面什么的被糟蹋的比较难看，一层一层的，不过这还算还好，后面主要是天花板上的，这可难倒我了，从小我爸妈是比较把我当小孩管着，爬上爬下的基本都是我爸搞定，但是到了老丈人家也只得硬着头皮上了，爬到跳（一种建筑工地用的架子）上，还有点晃，小心脏扑通扑通跳，而且带加长杆的铲子还是比较重的，铲一会手也有点累，不过坚持着铲完了，上面还是比较平整的，不过下来的时候又把我难住了🤦‍♂️，往下爬的时候有根杆子要跨过去，由于裤子比较紧，强行一把跨过去怕抽筋，所以以一个非常尴尬的姿势停留休息了一会，再跨了过去，幸好事后问 LD，他们都没看到，哈哈哈，然后就是帮忙一起搞瓷砖上的油污，这个太有经验了，不过老丈人更有意思，一会试试啤酒，一会用用沙子，后面在午饭前基本就弄的比较干净了，就坐着等吃饭了，下午午休了会，就继续干活了。
下午是我这次体验的重点了，因为要清理以前贴的墙纸，真的是个很麻烦的活，只能说贴墙纸的师傅活干得太好了，基本不可能整个撕下来，想用铲子一点点铲下来也不行，太轻了就只铲掉表面一层，太重了就把墙纸跟墙面的石灰啥的整个铲下来了，而且手又累又酸，后来想着是不是继续用水冲一下，对着一小面墙试验了下，效果还不错，但是又发现了个问题，那一面墙又有一块是后面糊上去的，铲掉外层的石灰后不平，然后就是最最重头的，也是让我后遗症持续到第二天的，要把那一块糊上去的水泥敲下来，毛估下大概是敲了80%左右，剩下的我的手已经不会用力了，因为那一块应该是要糊上去的始作俑者，就一块里面凹进去的，我拿着榔头敲到我手已经没法使劲了，而且大下午，感觉没五分钟，我的汗已经糊满脸，眼睛也睁不开，不然就流到眼睛里了，此处获得成就一：用榔头敲墙壁，也是个技术加体力的活，而且需要非常好的技巧，否则手马上就废了，敲下去的反作用力，没一会就不行了，然后是看着老丈人兄弟帮忙拆一个柜子，在我看来是个几天都搞不定的活，他轻轻松松在我敲墙的那会就搞定了，以前总觉得我干的活非常有技术含量，可是这个事情真的也是很有技巧啊，它是个把一间房间分隔开的柜子，从底到顶上，还带着门，我还在旁边帮忙撬一下脚踢，一根木条撬半天，唉，成就二：专业的人就是不一样。
最后就是成就三了：我之前沾沾自喜的跑了多少步，做了什么锻炼，其实都是渣渣，像这样干一天活，没经历过的，基本大半天就废了，反过来说，如果能经常去这么干一天活，跑步啥的都是渣渣，消耗的能量远远超过跑个十公里啥的。

发表于 更新于 分类于 ， 阅读次数： Disqus：

Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

Example:

Given this linked list: 1->2->3->4->5

For k = 2, you should return: 2->1->4->3->5

For k = 3, you should return: 3->2->1->4->5

Note:

  • Only constant extra memory is allowed.
  • You may not alter the values in the list’s nodes, only nodes itself may be changed.

这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
简单来看下

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public ListNode reverseKGroup(ListNode head, int k) {
    if (k <= 1) {
        return head;
    }
    if (head == null) {
        return null;
    }
    ListNode tempEnd = head;
    ListNode tempHead = head;
    ListNode lastHead = null;
    boolean isHeadSet = false;
    while (true) {
        // k 个一组获取头尾
        for (int i = 0; i < k - 1; i++) {
            System.out.println(tempEnd.val);
            if (tempEnd.next != null) {
                tempEnd  = tempEnd.next;
            } else {
                tempEnd = null;
                break;
            }
        }
        if (tempEnd != null) {
            if (!isHeadSet) {
                // 只有在第一组返回的时候需要赋值给 head
                head = reverse(null, tempHead, tempEnd);
                isHeadSet = !isHeadSet;
            } else {
                // 后面的需要将前一组的尾巴传进去，作为后一组头
                reverse(lastHead, tempHead, tempEnd);
            }
            if (tempHead.next != null) {
                // 如果后续节点还有
                lastHead = tempHead;
                tempEnd = tempHead.next;
                tempHead = tempHead.next;
            } else {
                break;
            }
        } else {
            break;
        }
    }

    return head;
}
public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
    // k 个一组的头尾传进来
    ListNode tail = null;
    while (tail != tempEnd) {
        // tail 表示是将头往后换时，把后面那个先拿着
        tail = tempHead.next;
        // 然后就是交换
        tempHead.next = tempEnd.next;
        tempEnd.next = tempHead;
        tempHead = tail;
    }
    if (lastHead !=null) {
        // 如果不是第一组，则需要接上前一组的尾巴
        lastHead.next = tail;
    }
    return tail;
}

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

用了比较久的 grep 命令，其实都只是用了最最基本的功能来查日志，

譬如

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grep 'xxx' xxxx.log

然后有挺多情况比如想要找日志里带一些符号什么的，就需要用到一些特殊的

比如这样\"userId\":\"123456\"，因为比如用户 ID 有时候会跟其他的 id 一样，只用具体的值 123456 来查的话干扰信息太多了，如果直接这样

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grep '\"userId\":\"123456\"' xxxx.log

好像不行，盲猜就是符号的问题，特别是\和"这两个，

之前一直是想试一下，但是没成功，昨天在排查一个问题的时候发现了，只要把这些都转义了就行了

grep '\\\"userId\\\":\\\"123456\\\"' xxxx.log

0%
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table{width:auto}.gist table td{border:0}pre code{background:0 0;padding:0;text-shadow:none}.blockquote-center{border-left:0;margin:40px 0;padding:0;position:relative;text-align:center}.blockquote-center::after,.blockquote-center::before{left:0;line-height:1;opacity:.6;position:absolute;width:100%}.blockquote-center::before{border-top:1px solid #ccc;text-align:left;top:-20px;content:'\f10d';font-family:'Font Awesome 6 Free';font-weight:900}.blockquote-center::after{border-bottom:1px solid #ccc;bottom:-20px;text-align:right;content:'\f10e';font-family:'Font Awesome 6 Free';font-weight:900}.blockquote-center div,.blockquote-center p{text-align:center}.group-picture{margin-bottom:20px}.group-picture .group-picture-row{display:flex;gap:3px;margin-bottom:3px}.group-picture .group-picture-column{flex:1}.group-picture .group-picture-column img{height:100%;margin:0;object-fit:cover;width:100%}.post-body .label{color:#555;padding:0 2px}.post-body .label.default{background:#f0f0f0}.post-body .label.primary{background:#efe6f7}.post-body .label.info{background:#e5f2f8}.post-body .label.success{background:#e7f4e9}.post-body .label.warning{background:#fcf6e1}.post-body .label.danger{background:#fae8eb}.post-body .link-grid{display:grid;grid-gap:1.5rem;gap:1.5rem;grid-template-columns:1fr 1fr;margin-bottom:20px;padding:1rem}.post-body .link-grid .link-grid-container{border:solid #ddd;box-shadow:1rem 1rem .5rem rgba(0,0,0,.5);min-height:5rem;min-width:0;padding:.5rem;position:relative;transition:background .3s}.post-body .link-grid .link-grid-container:hover{animation:.5s next-shake;background:var(--card-bg-color)}.post-body .link-grid .link-grid-container:active{box-shadow:.5rem .5rem .25rem rgba(0,0,0,.5);transform:translate(.2rem,.2rem)}.post-body .link-grid .link-grid-container .link-grid-image{border:1px solid #ddd;border-radius:50%;box-sizing:border-box;height:5rem;padding:3px;position:absolute;width:5rem}.post-body .link-grid .link-grid-container p{margin:0 1rem 0 6rem}.post-body .link-grid .link-grid-container p:first-of-type{font-size:1.2em}.post-body .link-grid .link-grid-container p:last-of-type{font-size:.8em;line-height:1.3rem;opacity:.7}.post-body .link-grid .link-grid-container a{border:0;height:100%;left:0;position:absolute;top:0;width:100%}@keyframes next-shake{0%{transform:translate(1pt,1pt) rotate(0)}10%{transform:translate(-1pt,-2pt) rotate(-1deg)}20%{transform:translate(-3pt,0) rotate(1deg)}30%{transform:translate(3pt,2pt) rotate(0)}40%{transform:translate(1pt,-1pt) rotate(1deg)}50%{transform:translate(-1pt,2pt) rotate(-1deg)}60%{transform:translate(-3pt,1pt) rotate(0)}70%{transform:translate(3pt,1pt) rotate(-1deg)}80%{transform:translate(-1pt,-1pt) rotate(1deg)}90%{transform:translate(1pt,2pt) rotate(0)}100%{transform:translate(1pt,-2pt) rotate(-1deg)}}.post-body .note{border-radius:3px;margin-bottom:20px;padding:1em;position:relative;border:1px solid #eee;border-left-width:5px}.post-body .note 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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， 阅读次数： Disqus：

因为活实在比较多，也不太好叫大工(活比较杂散)，相比上一次我跟 LD 俩人晚起了一点，我真的是只要有事，早上就醒的很早，准备八点出发的，六点就醒了，然后想继续睡就一直做梦🤦‍♂️，差不多八点半多到的丈人家，他们应该已经干了有一会了，我们到了以后就分配给我撬地板的活，上次说的那个敲掉柜子的房间里，还铺着质地还不错的木地板，但是也不想要了，得撬掉重新铺。
拿着撬棍和榔头就上楼去干了，浙江这几天的天气，最高温度一般 38、9，楼上那个房间也没风扇，有了也不能用，都是灰尘，撬了两下，我感觉我体内的水就像真气爆发一样变成汗炸了出来，眼睛全被汗糊住了，可能大部分人不太了解地板是怎么铺的，一般是在地面先铺一层混凝土，混凝土中间嵌进去规则的长条木条，然后真正的地板一块块的都是钉在那个木条上，用那种气枪钉和普通的钉子，并且块跟块之前还有一个木头的槽结构相互耦合，然后边缘的一圈在用较薄的木板将整个木地板封边(这些词都是我现造的)，边缘的用的钉子会更多，所以那几下真的很用力，而且撬地板，得蹲下起来，如此反复，对于我这个体重快超过身高的中年人来说的确是非常大的挑战，接下来继续撬了几个，已经有种要虚脱晕倒的感觉了，及时去喝水擦了汗，又歇了一会，为啥一上来就这么拼呢，主要是因为那个房间丈人在干活的时候是直接看得到的🤦‍♂️，后来被 LD 一顿教育，本来就是去帮忙的，又不是专业做这个的，急啥。
喝了水之后，又稍稍歇了一会，就开始继续撬了，本来觉得这个地板撬着好像还行，房间不大，没多久就撬完了，撬完之后喝了点饮料(补充点糖分，早餐吃得少，有点低血糖)，然后看到 LD 在撬下面的木条了，这个动作开始了那天最大的经验值收集行动，前面说了这个木条一般是跟混凝土一块铺上去的，但是谁也没想到，这个混凝土铺上去的时候竟然处理的这么随意，根本没考虑跟下面的贴合，所以撬木条的时候直接把木条跟木条中间大块大块的混凝土一块撬起来了，想想那重量，于是我这靠蛮力干活的，就用力把木条带着混凝土一块撬了起来，还沾沾自喜，但是发现结果是撬起来一块之后，体力值瞬间归零，上一篇我也提到了，其实干这类活也是很有技巧性的，但是上次的是我没学会，可能需要花时间学的，但是这次是LD 用她的纤细胳膊教会我的，我在撬的时候，屏住一口气，双手用力，起，大概是吃好几口奶的力气都用出来了，但是 LD 在我休息的时候，慢慢悠悠的，先把撬棍挤到木条或者混凝土跟下层的缝里，然后往下垫一小块混凝土碎石，然后轻轻松松的扳两下，就撬开了，亏我高中的时候引以为傲的物理成绩，作为物理课代表，这么浅显易懂的杠杆原理都完全不会用到生活里，后面在用这个技巧撬的过程中，真的觉得自己蠢到家了，当然在明白了用点杠杆原理之后，撬地板的活就变得慢慢悠悠，悠哉悠哉的了(其实还是很热的，披着毛巾擦眼睛)。
上午的活差不多完了，后面就是把撬出来的混凝土和地板条丢下去，地上铺着不用了的被子，然后就是午饭和午休环节了，午饭换了一家快餐，味道非常可以，下午的活就比较单调了，帮忙清理了上去扔下来的混凝土碎块跟木条，然后稍微打扫了下，老丈人就让我们回家了，接着上次说的，还是觉得比跑步啥的消耗大太多了，那汗流的，一口就能喝完一瓶 500 毫升左右的矿泉水。

发表于 更新于 分类于 ， ， ， ， ， ， 阅读次数： Disqus：

本来是想取个像现在那些公众号转了又转的文章标题，”面试官再问你xxxxx，就把这篇文章甩给他看”这种标题，但是觉得实在太 low 了，还是用一部我比较喜欢的电影里的一句台词，《人在囧途》里王宝强对着那张老板给他的欠条，看不懂字时候说的那句，这些都是些啥（第四声）
当我刚开始面 Java 的时候，其实我真的没注意这方面的东西，实话说就是不知道这些是啥，开发中用过 Interceptor和 Aop，了解 aop 的实现原理，但是不知道 Java web 中的 Filter 是怎么回事，知道 dubbo 的 filter，就这样，所以被问到了的确是回答不出来，可能就觉得这个渣渣，这么简单的都不会，所以还是花点时间来看看这个是个啥，为了避免我口吐芬芳，还是耐下性子来简单说下这几个东西
首先是 servlet，怎么去解释这个呢，因为之前是 PHPer，所以比较喜欢用它来举例子，在普通的 PHP 的 web 应用中一般有几部分组成，接受 HTTP 请求的是前置的 nginx 或者 apache，但是这俩玩意都是只能处理静态的请求，远古时代 PHP 和 HTML 混编是通过 apache 的 php module，跟后来 nginx 使用 php-fpm 其实道理类似，就是把请求中需要 PHP 处理的转发给 PHP，在 Java 中呢，是有个比较牛叉的叫 Tomcat 的，它可以把请求转成 servlet，而 servlet 其实就是一种实现了特定接口的 Java 代码，

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package javax.servlet;

import java.io.IOException;

/**
 * Defines methods that all servlets must implement.
 *
 * <p>
 * A servlet is a small Java program that runs within a Web server. Servlets
 * receive and respond to requests from Web clients, usually across HTTP, the
 * HyperText Transfer Protocol.
 *
 * <p>
 * To implement this interface, you can write a generic servlet that extends
 * <code>javax.servlet.GenericServlet</code> or an HTTP servlet that extends
 * <code>javax.servlet.http.HttpServlet</code>.
 *
 * <p>
 * This interface defines methods to initialize a servlet, to service requests,
 * and to remove a servlet from the server. These are known as life-cycle
 * methods and are called in the following sequence:
 * <ol>
 * <li>The servlet is constructed, then initialized with the <code>init</code>
 * method.
 * <li>Any calls from clients to the <code>service</code> method are handled.
 * <li>The servlet is taken out of service, then destroyed with the
 * <code>destroy</code> method, then garbage collected and finalized.
 * </ol>
 *
 * <p>
 * In addition to the life-cycle methods, this interface provides the
 * <code>getServletConfig</code> method, which the servlet can use to get any
 * startup information, and the <code>getServletInfo</code> method, which allows
 * the servlet to return basic information about itself, such as author,
 * version, and copyright.
 *
 * @see GenericServlet
 * @see javax.servlet.http.HttpServlet
 */
public interface Servlet {

    /**
     * Called by the servlet container to indicate to a servlet that the servlet
     * is being placed into service.
     *
     * <p>
     * The servlet container calls the <code>init</code> method exactly once
     * after instantiating the servlet. The <code>init</code> method must
     * complete successfully before the servlet can receive any requests.
     *
     * <p>
     * The servlet container cannot place the servlet into service if the
     * <code>init</code> method
     * <ol>
     * <li>Throws a <code>ServletException</code>
     * <li>Does not return within a time period defined by the Web server
     * </ol>
     *
     *
     * @param config
     *            a <code>ServletConfig</code> object containing the servlet's
     *            configuration and initialization parameters
     *
     * @exception ServletException
     *                if an exception has occurred that interferes with the
     *                servlet's normal operation
     *
     * @see UnavailableException
     * @see #getServletConfig
     */
    public void init(ServletConfig config) throws ServletException;

    /**
     *
     * Returns a {@link ServletConfig} object, which contains initialization and
     * startup parameters for this servlet. The <code>ServletConfig</code>
     * object returned is the one passed to the <code>init</code> method.
     *
     * <p>
     * Implementations of this interface are responsible for storing the
     * <code>ServletConfig</code> object so that this method can return it. The
     * {@link GenericServlet} class, which implements this interface, already
     * does this.
     *
     * @return the <code>ServletConfig</code> object that initializes this
     *         servlet
     *
     * @see #init
     */
    public ServletConfig getServletConfig();

    /**
     * Called by the servlet container to allow the servlet to respond to a
     * request.
     *
     * <p>
     * This method is only called after the servlet's <code>init()</code> method
     * has completed successfully.
     *
     * <p>
     * The status code of the response always should be set for a servlet that
     * throws or sends an error.
     *
     *
     * <p>
     * Servlets typically run inside multithreaded servlet containers that can
     * handle multiple requests concurrently. Developers must be aware to
     * synchronize access to any shared resources such as files, network
     * connections, and as well as the servlet's class and instance variables.
     * More information on multithreaded programming in Java is available in <a
     * href
     * ="http://java.sun.com/Series/Tutorial/java/threads/multithreaded.html">
     * the Java tutorial on multi-threaded programming</a>.
     *
     *
     * @param req
     *            the <code>ServletRequest</code> object that contains the
     *            client's request
     *
     * @param res
     *            the <code>ServletResponse</code> object that contains the
     *            servlet's response
     *
     * @exception ServletException
     *                if an exception occurs that interferes with the servlet's
     *                normal operation
     *
     * @exception IOException
     *                if an input or output exception occurs
     */
    public void service(ServletRequest req, ServletResponse res)
            throws ServletException, IOException;

    /**
     * Returns information about the servlet, such as author, version, and
     * copyright.
     *
     * <p>
     * The string that this method returns should be plain text and not markup
     * of any kind (such as HTML, XML, etc.).
     *
     * @return a <code>String</code> containing servlet information
     */
    public String getServletInfo();

    /**
     * Called by the servlet container to indicate to a servlet that the servlet
     * is being taken out of service. This method is only called once all
     * threads within the servlet's <code>service</code> method have exited or
     * after a timeout period has passed. After the servlet container calls this
     * method, it will not call the <code>service</code> method again on this
     * servlet.
     *
     * <p>
     * This method gives the servlet an opportunity to clean up any resources
     * that are being held (for example, memory, file handles, threads) and make
     * sure that any persistent state is synchronized with the servlet's current
     * state in memory.
     */
    public void destroy();
}

重点看 servlet 的 service方法，就是接受请求，处理完了给响应，不说细节，不然光 Tomcat 的能说半年，所以呢再进一步去理解，其实就能知道，就是一个先后的问题，盗个图

filter 跟后两者最大的不一样其实是一个基于 servlet，在非常外层做的处理，然后是 interceptor 的 prehandle 跟 posthandle，接着才是我们常规的 aop，就这么点事情，做个小试验吧(还是先补段代码吧)

Filter

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// ---------------------------------------------------- FilterChain Methods

    /**
     * Invoke the next filter in this chain, passing the specified request
     * and response.  If there are no more filters in this chain, invoke
     * the <code>service()</code> method of the servlet itself.
     *
     * @param request The servlet request we are processing
     * @param response The servlet response we are creating
     *
     * @exception IOException if an input/output error occurs
     * @exception ServletException if a servlet exception occurs
     */
    @Override
    public void doFilter(ServletRequest request, ServletResponse response)
        throws IOException, ServletException {

        if( Globals.IS_SECURITY_ENABLED ) {
            final ServletRequest req = request;
            final ServletResponse res = response;
            try {
                java.security.AccessController.doPrivileged(
                    new java.security.PrivilegedExceptionAction<Void>() {
                        @Override
                        public Void run()
                            throws ServletException, IOException {
                            internalDoFilter(req,res);
                            return null;
                        }
                    }
                );
            } catch( PrivilegedActionException pe) {
                Exception e = pe.getException();
                if (e instanceof ServletException)
                    throw (ServletException) e;
                else if (e instanceof IOException)
                    throw (IOException) e;
                else if (e instanceof RuntimeException)
                    throw (RuntimeException) e;
                else
                    throw new ServletException(e.getMessage(), e);
            }
        } else {
            internalDoFilter(request,response);
        }
    }
    private void internalDoFilter(ServletRequest request,
                                  ServletResponse response)
        throws IOException, ServletException {

        // Call the next filter if there is one
        if (pos < n) {
            ApplicationFilterConfig filterConfig = filters[pos++];
            try {
                Filter filter = filterConfig.getFilter();

                if (request.isAsyncSupported() && "false".equalsIgnoreCase(
                        filterConfig.getFilterDef().getAsyncSupported())) {
                    request.setAttribute(Globals.ASYNC_SUPPORTED_ATTR, Boolean.FALSE);
                }
                if( Globals.IS_SECURITY_ENABLED ) {
                    final ServletRequest req = request;
                    final ServletResponse res = response;
                    Principal principal =
                        ((HttpServletRequest) req).getUserPrincipal();

                    Object[] args = new Object[]{req, res, this};
                    SecurityUtil.doAsPrivilege ("doFilter", filter, classType, args, principal);
                } else {
                    filter.doFilter(request, response, this);
                }
            } catch (IOException | ServletException | RuntimeException e) {
                throw e;
            } catch (Throwable e) {
                e = ExceptionUtils.unwrapInvocationTargetException(e);
                ExceptionUtils.handleThrowable(e);
                throw new ServletException(sm.getString("filterChain.filter"), e);
            }
            return;
        }

        // We fell off the end of the chain -- call the servlet instance
        try {
            if (ApplicationDispatcher.WRAP_SAME_OBJECT) {
                lastServicedRequest.set(request);
                lastServicedResponse.set(response);
            }

            if (request.isAsyncSupported() && !servletSupportsAsync) {
                request.setAttribute(Globals.ASYNC_SUPPORTED_ATTR,
                        Boolean.FALSE);
            }
            // Use potentially wrapped request from this point
            if ((request instanceof HttpServletRequest) &&
                    (response instanceof HttpServletResponse) &&
                    Globals.IS_SECURITY_ENABLED ) {
                final ServletRequest req = request;
                final ServletResponse res = response;
                Principal principal =
                    ((HttpServletRequest) req).getUserPrincipal();
                Object[] args = new Object[]{req, res};
                SecurityUtil.doAsPrivilege("service",
                                           servlet,
                                           classTypeUsedInService,
                                           args,
                                           principal);
            } else {
                servlet.service(request, response);
            }
        } catch (IOException | ServletException | RuntimeException e) {
            throw e;
        } catch (Throwable e) {
            e = ExceptionUtils.unwrapInvocationTargetException(e);
            ExceptionUtils.handleThrowable(e);
            throw new ServletException(sm.getString("filterChain.servlet"), e);
        } finally {
            if (ApplicationDispatcher.WRAP_SAME_OBJECT) {
                lastServicedRequest.set(null);
                lastServicedResponse.set(null);
            }
        }
    }

注意看这一行
filter.doFilter(request, response, this);
是不是看懂了，就是个 filter 链，但是这个代码在哪呢，org.apache.catalina.core.ApplicationFilterChain#doFilter
然后是interceptor，

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protected void doDispatch(HttpServletRequest request, HttpServletResponse response) throws Exception {
        HttpServletRequest processedRequest = request;
        HandlerExecutionChain mappedHandler = null;
        boolean multipartRequestParsed = false;
        WebAsyncManager asyncManager = WebAsyncUtils.getAsyncManager(request);

        try {
            try {
                ModelAndView mv = null;
                Object dispatchException = null;

                try {
                    processedRequest = this.checkMultipart(request);
                    multipartRequestParsed = processedRequest != request;
                    mappedHandler = this.getHandler(processedRequest);
                    if (mappedHandler == null) {
                        this.noHandlerFound(processedRequest, response);
                        return;
                    }

                    HandlerAdapter ha = this.getHandlerAdapter(mappedHandler.getHandler());
                    String method = request.getMethod();
                    boolean isGet = "GET".equals(method);
                    if (isGet || "HEAD".equals(method)) {
                        long lastModified = ha.getLastModified(request, mappedHandler.getHandler());
                        if ((new ServletWebRequest(request, response)).checkNotModified(lastModified) && isGet) {
                            return;
                        }
                    }

                    /** 
                     * 看这里看这里‼️
                     */
                    if (!mappedHandler.applyPreHandle(processedRequest, response)) {
                        return;
                    }

                    mv = ha.handle(processedRequest, response, mappedHandler.getHandler());
                    if (asyncManager.isConcurrentHandlingStarted()) {
                        return;
                    }

                    this.applyDefaultViewName(processedRequest, mv);
                    /** 
                     * 再看这里看这里‼️
                     */
                    mappedHandler.applyPostHandle(processedRequest, response, mv);
                } catch (Exception var20) {
                    dispatchException = var20;
                } catch (Throwable var21) {
                    dispatchException = new NestedServletException("Handler dispatch failed", var21);
                }

                this.processDispatchResult(processedRequest, response, mappedHandler, mv, (Exception)dispatchException);
            } catch (Exception var22) {
                this.triggerAfterCompletion(processedRequest, response, mappedHandler, var22);
            } catch (Throwable var23) {
                this.triggerAfterCompletion(processedRequest, response, mappedHandler, new NestedServletException("Handler processing failed", var23));
            }

        } finally {
            if (asyncManager.isConcurrentHandlingStarted()) {
                if (mappedHandler != null) {
                    mappedHandler.applyAfterConcurrentHandlingStarted(processedRequest, response);
                }
            } else if (multipartRequestParsed) {
                this.cleanupMultipart(processedRequest);
            }

        }
    }

代码在哪呢，org.springframework.web.servlet.DispatcherServlet#doDispatch，然后才是我们自己写的 aop，是不是差不多明白了，嗯，接下来是例子
写个 filter

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public class DemoFilter extends HttpServlet implements Filter {
    @Override
    public void init(FilterConfig filterConfig) throws ServletException {
        System.out.println("==>DemoFilter启动");
    }

    @Override
    public void doFilter(ServletRequest servletRequest, ServletResponse servletResponse, FilterChain filterChain) throws IOException, ServletException {
        // 将请求转换成HttpServletRequest 请求
        HttpServletRequest req = (HttpServletRequest) servletRequest;
        HttpServletResponse resp = (HttpServletResponse) servletResponse;
        System.out.println("before filter");
        filterChain.doFilter(req, resp);
        System.out.println("after filter");
    }

    @Override
    public void destroy() {

    }
}

因为用的springboot，所以就不写 web.xml 了，写个配置类

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@Configuration
public class FilterConfiguration {
    @Bean
    public FilterRegistrationBean filterDemo4Registration() {
        FilterRegistrationBean registration = new FilterRegistrationBean();
        //注入过滤器
        registration.setFilter(new DemoFilter());
        //拦截规则
        registration.addUrlPatterns("/*");
        //过滤器名称
        registration.setName("DemoFilter");
        //是否自动注册 false 取消Filter的自动注册
        registration.setEnabled(true);
        //过滤器顺序
        registration.setOrder(1);
        return registration;
    }

}

然后再来个 interceptor 和 aop,以及一个简单的请求处理

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public class DemoInterceptor extends HandlerInterceptorAdapter {
    @Override
    public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception {
        System.out.println("preHandle test");
        return true;
    }

    @Override
    public void postHandle(HttpServletRequest request, HttpServletResponse response, Object handler, ModelAndView modelAndView) throws Exception {
        System.out.println("postHandle test");
    }
}
@Aspect
@Component
public class DemoAspect {

    @Pointcut("execution( public * com.nicksxs.springbootdemo.demo.DemoController.*())")
    public void point() {

    }

    @Before("point()")
    public void doBefore(){
        System.out.println("==doBefore==");
    }

    @After("point()")
    public void doAfter(){
        System.out.println("==doAfter==");
    }
}
@RestController
public class DemoController {

    @RequestMapping("/hello")
    @ResponseBody
    public String hello() {
        return "hello world";
    }
}

好了，请求一下，看看 stdout，

搞定完事儿~

发表于 分类于 ， ， ， 阅读次数： Disqus：

这周回家提前约好了要去老丈人家帮下忙，因为在翻修下老房子，活不是特别整的那种，所以大部分都是自己干，或者找个大工临时干几天（我们这那种比较专业的泥工匠叫做大工），像我这样去帮忙的，就是干点小工（把给大工帮忙的，干些偏体力活的叫做小工）的活。从大学毕业以后真的蛮少帮家里干活了，以前上学的时候放假还是帮家里淘个米，简单的扫地拖地啥的，当然刚高考完的时候，还去我爸厂里帮忙干了几天的活，实在是比较累，不过现在想着是觉得自己那时候比较牛，而不是特别排斥这个活，相对于现在的工作来说，导致了一系列的职业病，颈椎腰背都很僵硬，眼镜也不好，还有反流，像我爸那种活反而是脑力加体力的比较好的结合。
这一天的活前半部分主要是在清理厨房，瓷砖上的油污和墙上天花板上即将脱落的石灰或者白色涂料层，这种活特别是瓷砖上的油污，之前在自己家里也干活，还是比较熟悉的，不过前面主要是LD 在干，我主要是先搞墙上和天花板上的，干活还是很需要技巧的，如果直接去铲，那基本我会变成一个灰人，而且吸一鼻子灰，老丈人比较专业，先接上软管用水冲，一冲效果特别好，有些石灰涂料层直接就冲掉了，冲完之后先用带加长杆的刀片铲铲了一圈墙面，说实话因为老房子之前租出去了，所以墙面什么的被糟蹋的比较难看，一层一层的，不过这还算还好，后面主要是天花板上的，这可难倒我了，从小我爸妈是比较把我当小孩管着，爬上爬下的基本都是我爸搞定，但是到了老丈人家也只得硬着头皮上了，爬到跳（一种建筑工地用的架子）上，还有点晃，小心脏扑通扑通跳，而且带加长杆的铲子还是比较重的，铲一会手也有点累，不过坚持着铲完了，上面还是比较平整的，不过下来的时候又把我难住了🤦‍♂️，往下爬的时候有根杆子要跨过去，由于裤子比较紧，强行一把跨过去怕抽筋，所以以一个非常尴尬的姿势停留休息了一会，再跨了过去，幸好事后问 LD，他们都没看到，哈哈哈，然后就是帮忙一起搞瓷砖上的油污，这个太有经验了，不过老丈人更有意思，一会试试啤酒，一会用用沙子，后面在午饭前基本就弄的比较干净了，就坐着等吃饭了，下午午休了会，就继续干活了。
下午是我这次体验的重点了，因为要清理以前贴的墙纸，真的是个很麻烦的活，只能说贴墙纸的师傅活干得太好了，基本不可能整个撕下来，想用铲子一点点铲下来也不行，太轻了就只铲掉表面一层，太重了就把墙纸跟墙面的石灰啥的整个铲下来了，而且手又累又酸，后来想着是不是继续用水冲一下，对着一小面墙试验了下，效果还不错，但是又发现了个问题，那一面墙又有一块是后面糊上去的，铲掉外层的石灰后不平，然后就是最最重头的，也是让我后遗症持续到第二天的，要把那一块糊上去的水泥敲下来，毛估下大概是敲了80%左右，剩下的我的手已经不会用力了，因为那一块应该是要糊上去的始作俑者，就一块里面凹进去的，我拿着榔头敲到我手已经没法使劲了，而且大下午，感觉没五分钟，我的汗已经糊满脸，眼睛也睁不开，不然就流到眼睛里了，此处获得成就一：用榔头敲墙壁，也是个技术加体力的活，而且需要非常好的技巧，否则手马上就废了，敲下去的反作用力，没一会就不行了，然后是看着老丈人兄弟帮忙拆一个柜子，在我看来是个几天都搞不定的活，他轻轻松松在我敲墙的那会就搞定了，以前总觉得我干的活非常有技术含量，可是这个事情真的也是很有技巧啊，它是个把一间房间分隔开的柜子，从底到顶上，还带着门，我还在旁边帮忙撬一下脚踢，一根木条撬半天，唉，成就二：专业的人就是不一样。
最后就是成就三了：我之前沾沾自喜的跑了多少步，做了什么锻炼，其实都是渣渣，像这样干一天活，没经历过的，基本大半天就废了，反过来说，如果能经常去这么干一天活，跑步啥的都是渣渣，消耗的能量远远超过跑个十公里啥的。

发表于 更新于 分类于 ， 阅读次数： Disqus：

Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

Example:

Given this linked list: 1->2->3->4->5

For k = 2, you should return: 2->1->4->3->5

For k = 3, you should return: 3->2->1->4->5

Note:

  • Only constant extra memory is allowed.
  • You may not alter the values in the list’s nodes, only nodes itself may be changed.

这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
简单来看下

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public ListNode reverseKGroup(ListNode head, int k) {
    if (k <= 1) {
        return head;
    }
    if (head == null) {
        return null;
    }
    ListNode tempEnd = head;
    ListNode tempHead = head;
    ListNode lastHead = null;
    boolean isHeadSet = false;
    while (true) {
        // k 个一组获取头尾
        for (int i = 0; i < k - 1; i++) {
            System.out.println(tempEnd.val);
            if (tempEnd.next != null) {
                tempEnd  = tempEnd.next;
            } else {
                tempEnd = null;
                break;
            }
        }
        if (tempEnd != null) {
            if (!isHeadSet) {
                // 只有在第一组返回的时候需要赋值给 head
                head = reverse(null, tempHead, tempEnd);
                isHeadSet = !isHeadSet;
            } else {
                // 后面的需要将前一组的尾巴传进去，作为后一组头
                reverse(lastHead, tempHead, tempEnd);
            }
            if (tempHead.next != null) {
                // 如果后续节点还有
                lastHead = tempHead;
                tempEnd = tempHead.next;
                tempHead = tempHead.next;
            } else {
                break;
            }
        } else {
            break;
        }
    }

    return head;
}
public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
    // k 个一组的头尾传进来
    ListNode tail = null;
    while (tail != tempEnd) {
        // tail 表示是将头往后换时，把后面那个先拿着
        tail = tempHead.next;
        // 然后就是交换
        tempHead.next = tempEnd.next;
        tempEnd.next = tempHead;
        tempHead = tail;
    }
    if (lastHead !=null) {
        // 如果不是第一组，则需要接上前一组的尾巴
        lastHead.next = tail;
    }
    return tail;
}

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

用了比较久的 grep 命令，其实都只是用了最最基本的功能来查日志，

譬如

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grep 'xxx' xxxx.log

然后有挺多情况比如想要找日志里带一些符号什么的，就需要用到一些特殊的

比如这样\"userId\":\"123456\"，因为比如用户 ID 有时候会跟其他的 id 一样，只用具体的值 123456 来查的话干扰信息太多了，如果直接这样

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grep '\"userId\":\"123456\"' xxxx.log

好像不行，盲猜就是符号的问题，特别是\和"这两个，

之前一直是想试一下，但是没成功，昨天在排查一个问题的时候发现了，只要把这些都转义了就行了

grep '\\\"userId\\\":\\\"123456\\\"' xxxx.log

0%
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}Nicksxs's Blog - What hurts more, the pain of hard work or the pain of regret?

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What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

redis中对于 set 其实有两种处理，对于元素均为整型，并且元素数目较少时，使用 intset 作为底层数据结构，否则使用 dict 作为底层数据结构，先看一下代码先

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typedef struct intset {
    // 编码方式
    uint32_t encoding;
    // 集合包含的元素数量
    uint32_t length;
    // 保存元素的数组
    int8_t contents[];
} intset;

/* Note that these encodings are ordered, so:
 * INTSET_ENC_INT16 < INTSET_ENC_INT32 < INTSET_ENC_INT64. */
#define INTSET_ENC_INT16 (sizeof(int16_t))
#define INTSET_ENC_INT32 (sizeof(int32_t))
#define INTSET_ENC_INT64 (sizeof(int64_t))

一眼看，为啥整型还需要编码，然后 int8_t 怎么能存下大整形呢，带着这些疑问，我们一步步分析下去，这里的编码其实指的是这个整型集合里存的究竟是多大的整型，16 位，还是 32 位，还是 64 位，结构体下面的宏定义就是表示了 encoding 的可能取值，INTSET_ENC_INT16 表示每个元素用2个字节存储，INTSET_ENC_INT32 表示每个元素用4个字节存储，INTSET_ENC_INT64 表示每个元素用8个字节存储。因此，intset中存储的整数最多只能占用64bit。length 就是正常的表示集合中元素的数量。最奇怪的应该就是这个 contents 了，是个 int8_t 的数组，那放毛线数据啊，最小的都有 16 位，这里我在看代码和《redis 设计与实现》的时候也有点懵逼，后来查了下发现这是个比较取巧的用法，这里我用自己的理解表述一下，先看看 8，16，32，64 的关系，一眼看就知道都是 2 的 N 次，并且呈两倍关系，而且 8 位刚好一个字节，所以呢其实这里的contents 不是个常规意义上的 int8_t 类型的数组，而是个柔性数组。看下 wiki 的定义

Flexible array members1 were introduced in the C99 standard of the C programming language (in particular, in section §6.7.2.1, item 16, page 103).2 It is a member of a struct, which is an array without a given dimension. It must be the last member of such a struct and it must be accompanied by at least one other member, as in the following example:

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struct vectord {
    size_t len;
    double arr[]; // the flexible array member must be last
};

在初始化这个 intset 的时候，这个contents数组是不占用空间的，后面的反正用到了申请，那么这里就有一个问题，给出了三种可能的 encoding 值，他们能随便换吗，显然不行，首先在 intset 中数据的存放是有序的，这个有部分原因是方便二分查找，然后存放数据其实随着数据的大小不同会有一个升级的过程，看下图

新创建的intset只有一个header，总共8个字节。其中encoding = 2, length = 0, 类型都是uint32_t，各占 4 字节，添加15, 5两个元素之后，因为它们是比较小的整数，都能使用2个字节表示，所以encoding不变，值还是2，也就是默认的 INTSET_ENC_INT16，当添加32768的时候，它不再能用2个字节来表示了（2个字节能表达的数据范围是-215~215-1，而32768等于215，超出范围了），因此encoding必须升级到INTSET_ENC_INT32（值为4），即用4个字节表示一个元素。在添加每个元素的过程中，intset始终保持从小到大有序。与ziplist类似，intset也是按小端（little endian）模式存储的（参见维基百科词条Endianness）。比如，在上图中intset添加完所有数据之后，表示encoding字段的4个字节应该解释成0x00000004，而第4个数据应该解释成0x00008000 = 32768

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跳表 skiplist

跳表是个在我们日常的代码中不太常用到的数据结构，相对来讲就没有像数组，链表，字典，散列，树等结构那么熟悉，所以就从头开始分析下，首先是链表，跳表跟链表都有个表字（太硬扯了我🤦‍♀️），注意这是个有序链表

如上图，在这个链表里如果我要找到 23，是不是我需要从3，5，9开始一直往后找直到找到 23，也就是说时间复杂度是 O(N),N 的一次幂复杂度，那么我们来看看第二个

这个结构跟原先有点不一样，它给链表中偶数位的节点又加了一个指针把它们链接起来，这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找，先找到 5，然后是 10，接着是 19，然后是 28，这时候发现 28 比 23 大了，那我在退回到 19，然后从下一层原来的链表往前找，

这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。
前面的其实是跳表的引子，真正的跳表其实不是这样，因为上面的其实有个比较大的问题，就是插入一个元素后需要调整每个元素的指针，在 redis 中的跳表其实是做了个随机层数的优化，因为沿着前面的例子，其实当数据量很大的时候，是不是层数越多，其查询效率越高，但是随着层数变多，要保持这种严格的层数规则其实也会增大处理复杂度，所以 redis 插入每个元素的时候都是使用随机的方式，看一眼代码

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/* ZSETs use a specialized version of Skiplists */
typedef struct zskiplistNode {
    sds ele;
    double score;
    struct zskiplistNode *backward;
    struct zskiplistLevel {
        struct zskiplistNode *forward;
        unsigned long span;
    } level[];
} zskiplistNode;

typedef struct zskiplist {
    struct zskiplistNode *header, *tail;
    unsigned long length;
    int level;
} zskiplist;

typedef struct zset {
    dict *dict;
    zskiplist *zsl;
} zset;

忘了说了，redis 是把 skiplist 跳表用在 zset 里，zset 是个有序的集合，可以看到 zskiplist 就是个跳表的结构，里面用 header 保存跳表的表头，tail 保存表尾，还有长度和最大层级，具体的跳表节点元素使用 zskiplistNode 表示，里面包含了 sds 类型的元素值，double 类型的分值，用来排序，一个 backward 后向指针和一个 zskiplistLevel 数组，每个 level 包含了一个前向指针，和一个 span，span 表示的是跳表前向指针的跨度，这里再补充一点，前面说了为了灵活这个跳表的新增修改，redis 使用了随机层高的方式插入新节点，但是如果所有节点都随机到很高的层级或者所有都很低的话，跳表的效率优势就会减小，所以 redis 使用了个小技巧，贴下代码

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#define ZSKIPLIST_P 0.25      /* Skiplist P = 1/4 */
int zslRandomLevel(void) {
    int level = 1;
    while ((random()&0xFFFF) < (ZSKIPLIST_P * 0xFFFF))
        level += 1;
    return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL;
}

当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值，因此保持了一个相对递减的概率
可以简单分析下，当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1，就意味着当有 1 - 1/4也就是3/4的概率是直接跳出，所以一层的概率是3/4,也就是 1-P，二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。

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redis是现在服务端很常用的缓存中间件，其实原来还有memcache之类的竞品，但是现在貌似 redis 快一统江湖，这里当然不是在吹，只是个人角度的一个感觉，不权威只是主观感觉。
redis 主要有五种数据结构，Strings，Lists，Sets，Hashes，Sorted Sets，这五种数据结构先简单介绍下，Strings类型的其实就是我们最常用的 key-value，实际开发中也会用的最多；Lists是列表，这个有些会用来做队列，因为 redis 目前常用的版本支持丰富的列表操作；还有是Sets集合，这个主要的特点就是集合中元素不重复，可以用在有这类需求的场景里；Hashes是叫散列，类似于 Python 中的字典结构；还有就是Sorted Sets这个是个有序集合；一眼看这些其实没啥特别的，除了最后这个有序集合，不过去了解背后的实现方式还是比较有意思的。

SDS 简单动态字符串

先从Strings开始说，了解过 C 语言的应该知道，C 语言中的字符串其实是个 char[] 字符数组，redis 也不例外，只是最开始的版本就对这个做了一丢丢的优化，而正是这一丢丢的优化，让这个 redis 的使用效率提升了数倍

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struct sdshdr {
    // 字符串长度
    int len;
    // 字符串空余字符数
    int free;
    // 字符串内容
    char buf[];
};

这里引用了 redis 在 github 上最早的 2.2 版本的代码，代码路径是https://github.com/antirez/redis/blob/2.2/src/sds.h,可以看到这个结构体里只有仨元素，两个 int 型和一个 char 型数组，两个 int 型其实就是我说的优化，因为 C 语言本身的字符串数组，有两个问题，一个是要知道它实际已被占用的长度，需要去遍历这个数组，第二个就是比较容易踩坑的是遍历的时候要注意它有个以\0作为结尾的特点；通过上面的两个 int 型参数，一个是知道字符串目前的长度，一个是知道字符串还剩余多少位空间，这样子坐着两个操作从 O(N)简化到了O(1)了，还有第二个 free 还有个比较重要的作用就是能防止 C 字符串的溢出问题，在存储之前可以先判断 free 长度，如果长度不够就先扩容了，先介绍到这，这个系列可以写蛮多的，慢慢介绍吧

链表

链表是比较常见的数据结构了，但是因为 redis 是用 C 写的，所以在不依赖第三方库的情况下只能自己写一个了，redis 的链表是个有头的链表，而且是无环的，具体的结构我也找了 github 上最早版本的代码

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typedef struct listNode {
    // 前置节点
    struct listNode *prev;
    // 后置节点
    struct listNode *next;
    // 值
    void *value;
} listNode;

typedef struct list {
    // 链表表头
    listNode *head;
    // 当前节点，也可以说是最后节点
    listNode *tail;
    // 节点复制函数
    void *(*dup)(void *ptr);
    // 节点值释放函数
    void (*free)(void *ptr);
    // 节点值比较函数
    int (*match)(void *ptr, void *key);
    // 链表包含的节点数量
    unsigned int len;
} list;

代码地址是这个https://github.com/antirez/redis/blob/2.2/src/adlist.h
可以看下节点是由listNode承载的，包括值和一个指向前节点跟一个指向后一节点的两个指针，然后值是 void 指针类型，所以可以承载不同类型的值
然后是 list结构用来承载一个链表，包含了表头，和表尾，复制函数，释放函数和比较函数，还有链表长度，因为包含了前两个节点，找到表尾节点跟表头都是 O(1)的时间复杂度，还有节点数量，其实这个跟 SDS 是同一个做法，就是空间换时间，这也是写代码里比较常见的做法，以此让一些高频的操作提速。

字典

字典也是个常用的数据结构，其实只是叫法不同，数据结构中叫 hash 散列，Java 中叫 Map，PHP 中是数组 array，Python 中也叫字典 dict，因为纯 C 语言本身不带这些数据结构，所以这也是个痛并快乐着的过程，享受 C 语言的高性能的同时也要接受它只提供了语言的基本功能的现实，各种轮子都需要自己造，redis 同样实现了自己的字典
下面来看看代码

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typedef struct dictEntry {
    void *key;
    void *val;
    struct dictEntry *next;
} dictEntry;

typedef struct dictType {
    unsigned int (*hashFunction)(const void *key);
    void *(*keyDup)(void *privdata, const void *key);
    void *(*valDup)(void *privdata, const void *obj);
    int (*keyCompare)(void *privdata, const void *key1, const void *key2);
    void (*keyDestructor)(void *privdata, void *key);
    void (*valDestructor)(void *privdata, void *obj);
} dictType;

/* This is our hash table structure. Every dictionary has two of this as we
 * implement incremental rehashing, for the old to the new table. */
typedef struct dictht {
    dictEntry **table;
    unsigned long size;
    unsigned long sizemask;
    unsigned long used;
} dictht;

typedef struct dict {
    dictType *type;
    void *privdata;
    dictht ht[2];
    int rehashidx; /* rehashing not in progress if rehashidx == -1 */
    int iterators; /* number of iterators currently running */
} dict;

看了下这个 2.2 版本的代码跟最新版的其实也差的不是很多，所以还是照旧用老代码，可以看到上面四个结构体中，其实只有三个是存储数据用的，dictType 是用来放操作函数的，那么三个存放数据的结构体分别是干嘛的，这时候感觉需要一个图来说明比较好，稍等，我去画个图~

这个图看着应该比较清楚这些都是用来干嘛的了，dict 是我们的主体结构，它有一个指向 dictType 的指针，这里面包含了字典的操作函数，然后是一个私有数据指针，接下来是一个 dictht 的数组，包含两个dictht，这个就是用来存数据的了，然后是 rehashidx 表示重哈希的状态，当是-1 的时候表示当前没有重哈希，iterators 表示正在遍历的迭代器的数量。
首先说说为啥需要有两个 dictht，这是因为字典 dict 这个数据结构随着数据量的增减，会需要在中途做扩容或者缩容操作，如果只有一个的话，对它进行扩容缩容时会影响正常的访问和修改操作，或者说保证正常查询，修改的正确性会比较复杂，并且因为需要高效利用空间，不能一下子申请一个非常大的空间来存很少的数据。当 dict 中 dictht 中的数据量超过 size 的时候负载就超过了 1，就需要进行扩容，这里的其实跟 Java 中的 HashMap 比较类似，超过一定的负载之后进行扩容。这里为啥 size 会超过 1 呢，可能有部分不了解这类结构的同学会比较奇怪，其实就是上图中画的，在数据结构中对于散列的冲突有几类解决方法，比如转换成链表，二次散列，找下个空槽等，这里就使用了链表法，或者说拉链法。当一个新元素通过 hashFunction 得出的 key 跟 sizemask 取模之后的值相同了，那就将其放在原来的节点之前，变成链表挂在数组 dictht.table下面，放在原有节点前是考虑到可能会优先访问。
忘了说明下 dictht 跟 dictEntry 的关系了，dictht 就是个哈希表，它里面是个dictEntry 的二维数组，而 dictEntry 是个包含了 key-value 结构之外还有一个 next 指针，因此可以将哈希冲突的以链表的形式保存下来。
在重点说下重哈希，可能同样写 Java 的同学对这个比较有感觉，跟 HashMap 一样，会以 2 的 N 次方进行扩容，那么扩容的方法就会比较简单，每个键重哈希要不就在原来这个槽，要不就在原来的槽加原 dictht.size 的位置；然后是重头戏，具体是怎么做扩容呢，其实这里就把第二个 ht 用上了，其实这两个hashtable 的具体作用有点类似于 jvm 中的两个 survival 区，但是又不全一样，因为 redis 在扩容的时候是采用的渐进式地重哈希，什么叫渐进式的呢，就是它不是像 jvm 那种标记复制的模式直接将一个 eden 区和原来的 survival 区存活的对象复制到另一个 survival 区，而是在每一次添加，删除，查找或者更新操作时，都会额外的帮忙搬运一部分的原 dictht 中的数据，这里会根据 rehashidx 的值来判断，如果是-1 表示并没有在重哈希中，如果是 0 表示开始重哈希了，然后rehashidx 还会随着每次的帮忙搬运往上加，但全部被搬运完成后 rehashidx 又变回了-1，又可以扯到Java 中的 Concurrent HashMap, 他在扩容的时候也使用了类似的操作。

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这是个 Java 面试的高频问题，我也遇到过，以往都是觉得这类题没意思，网上一搜一大堆，也不愿意记，其实说回来，主要还是没静下心来好好去理解，今天无意中看到一个课程，基本帮我把一些疑惑的点讲清楚了，首先单例是啥意思，这个其实是有范围一说，比如我起了个Spring Boot应用，在这个应用范围内，我的常规 bean 是单例的，意味着 getBean 的时候其实永远只会拿到那一个对象，那要怎么来写一个单例呢，首先就是传说中的饿汉模式，也是最简单的

饿汉模式

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public class Singleton1 {
    // 首先，将构造方法变成私有的
    private Singleton1() {};
    // 创建私有静态实例，这样第一次使用的时候就会进行创建
    private static Singleton instance = new Singleton1();

    // 使用这个对象都是通过这个 getInstance 来获取
    public static Singleton1 getInstance() {
        return instance;
    }
    // 瞎写一个静态方法。这里想说的是，如果我们只是要调用 Singleton.getDate(...)，
    // 本来是不想要生成 Singleton 实例的，不过没办法，已经生成了
    public static Date getDate(String mode) {return new Date();}
}

上面借鉴了一些代码，其实这是最基本，也不会错的方法，但是正如其中getDate方法里说的问题，有时候并没有想那这个对象，但是因为我调用了这个类的静态方法，导致对象已经生成了，可能这也是饿汉模式名字的来由，不管三七二十一给你生成个单例就完事了，不管有没有用，但是这种个人觉得也没啥大问题，如果是面试的话最好说出来它的缺点

饱汉模式

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public class Singleton2 {
    // 首先，也是先堵死 new Singleton() 这条路,将构造方法变成私有
    private Singleton2() {}
    // 和饿汉模式相比，这边不需要先实例化出来，注意这里的 volatile，它是必须的
    private static volatile Singleton2 instance = null;

    private int m = 9;

    public static Singleton getInstance() {
        if (instance == null) {
            // 加锁
            synchronized (Singleton2.class) {
                // 这一次判断也是必须的，不然会有并发问题
                if (instance == null) {
                    instance = new Singleton2();
                }
            }
        }
        return instance;
    }
}

这里容易错的有三点，理解了其实就比较好记了

第一点，为啥不在 getInstance 上整个代码块加 synchronized，这个其实比较容易理解，就是锁的力度太大，性能太差了，这点其实也要去理解，可以举个夸张的例子，比如我一个电商的服务，如果为了避免一个人的订单出现问题，是不是可以从请求入口就把他锁住，到请求结束释放，那么里面做的事情都有保障，然而这显然不可能，因为我们想要这种竞态条件抢占资源的时间尽量减少，防止其他线程等待。
第二点，为啥synchronized之已经检查了 instance == null,还要在里面再检查一次，这个有个术语，叫 double check lock,但是为啥要这么做呢，其实很简单，想象当有两个线程，都过了第一步为空判断，这个时候只有一个线程能拿到这个锁，另一个线程就等待了，如果不再判断一次，那么第一个线程新建完对象释放锁之后，第二个线程又能拿到锁，再去创建一个对象。
第三点，为啥要volatile关键字，原先对它的理解是它修饰的变量在 JMM 中能及时将变量值写到主存中，但是它还有个很重要的作用，就是防止指令重排序，instance = new Singleton();这行代码其实在底层是分成三条指令执行的，第一条是在堆上申请了一块内存放这个对象，但是对象的字段啥的都还是默认值，第二条是设置对象的值，比如上面的 m 是 9，然后第三条是将这个对象和虚拟机栈上的指针建立引用关联，那么如果我不用volatile关键字，这三条指令就有可能出现重排，比如变成了 1-3-2 这种顺序，当执行完第二步时，有个线程来访问这个对象了，先判断是不是空，发现不是空的，就拿去直接用了，是不是就出现问题了，所以这个volatile也是不可缺少的

嵌套类

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public class Singleton3 {

    private Singleton3() {}
    // 主要是使用了 嵌套类可以访问外部类的静态属性和静态方法 的特性
    private static class Holder {
        private static Singleton3 instance = new Singleton3();
    }
    public static Singleton3 getInstance() {
        return Holder.instance;
    }
}

这个我个人感觉是饿汉模式的升级版，可以在调用getInstance的时候去实例化对象，也是比较推荐的

枚举单例

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public enum Singleton {
    INSTANCE;
    
    public void doSomething(){
        //todo doSomething
    }
}

枚举很特殊，它在类加载的时候会初始化里面的所有的实例，而且 JVM 保证了它们不会再被实例化，所以它天生就是单例的。

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

redis中对于 set 其实有两种处理，对于元素均为整型，并且元素数目较少时，使用 intset 作为底层数据结构，否则使用 dict 作为底层数据结构，先看一下代码先

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typedef struct intset {
    // 编码方式
    uint32_t encoding;
    // 集合包含的元素数量
    uint32_t length;
    // 保存元素的数组
    int8_t contents[];
} intset;

/* Note that these encodings are ordered, so:
 * INTSET_ENC_INT16 < INTSET_ENC_INT32 < INTSET_ENC_INT64. */
#define INTSET_ENC_INT16 (sizeof(int16_t))
#define INTSET_ENC_INT32 (sizeof(int32_t))
#define INTSET_ENC_INT64 (sizeof(int64_t))

一眼看，为啥整型还需要编码，然后 int8_t 怎么能存下大整形呢，带着这些疑问，我们一步步分析下去，这里的编码其实指的是这个整型集合里存的究竟是多大的整型，16 位，还是 32 位，还是 64 位，结构体下面的宏定义就是表示了 encoding 的可能取值，INTSET_ENC_INT16 表示每个元素用2个字节存储，INTSET_ENC_INT32 表示每个元素用4个字节存储，INTSET_ENC_INT64 表示每个元素用8个字节存储。因此，intset中存储的整数最多只能占用64bit。length 就是正常的表示集合中元素的数量。最奇怪的应该就是这个 contents 了，是个 int8_t 的数组，那放毛线数据啊，最小的都有 16 位，这里我在看代码和《redis 设计与实现》的时候也有点懵逼，后来查了下发现这是个比较取巧的用法，这里我用自己的理解表述一下，先看看 8，16，32，64 的关系，一眼看就知道都是 2 的 N 次，并且呈两倍关系，而且 8 位刚好一个字节，所以呢其实这里的contents 不是个常规意义上的 int8_t 类型的数组，而是个柔性数组。看下 wiki 的定义

Flexible array members1 were introduced in the C99 standard of the C programming language (in particular, in section §6.7.2.1, item 16, page 103).2 It is a member of a struct, which is an array without a given dimension. It must be the last member of such a struct and it must be accompanied by at least one other member, as in the following example:

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struct vectord {
    size_t len;
    double arr[]; // the flexible array member must be last
};

在初始化这个 intset 的时候，这个contents数组是不占用空间的，后面的反正用到了申请，那么这里就有一个问题，给出了三种可能的 encoding 值，他们能随便换吗，显然不行，首先在 intset 中数据的存放是有序的，这个有部分原因是方便二分查找，然后存放数据其实随着数据的大小不同会有一个升级的过程，看下图

新创建的intset只有一个header，总共8个字节。其中encoding = 2, length = 0, 类型都是uint32_t，各占 4 字节，添加15, 5两个元素之后，因为它们是比较小的整数，都能使用2个字节表示，所以encoding不变，值还是2，也就是默认的 INTSET_ENC_INT16，当添加32768的时候，它不再能用2个字节来表示了（2个字节能表达的数据范围是-215~215-1，而32768等于215，超出范围了），因此encoding必须升级到INTSET_ENC_INT32（值为4），即用4个字节表示一个元素。在添加每个元素的过程中，intset始终保持从小到大有序。与ziplist类似，intset也是按小端（little endian）模式存储的（参见维基百科词条Endianness）。比如，在上图中intset添加完所有数据之后，表示encoding字段的4个字节应该解释成0x00000004，而第4个数据应该解释成0x00008000 = 32768

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

跳表 skiplist

跳表是个在我们日常的代码中不太常用到的数据结构，相对来讲就没有像数组，链表，字典，散列，树等结构那么熟悉，所以就从头开始分析下，首先是链表，跳表跟链表都有个表字（太硬扯了我🤦‍♀️），注意这是个有序链表

如上图，在这个链表里如果我要找到 23，是不是我需要从3，5，9开始一直往后找直到找到 23，也就是说时间复杂度是 O(N),N 的一次幂复杂度，那么我们来看看第二个

这个结构跟原先有点不一样，它给链表中偶数位的节点又加了一个指针把它们链接起来，这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找，先找到 5，然后是 10，接着是 19，然后是 28，这时候发现 28 比 23 大了，那我在退回到 19，然后从下一层原来的链表往前找，

这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。
前面的其实是跳表的引子，真正的跳表其实不是这样，因为上面的其实有个比较大的问题，就是插入一个元素后需要调整每个元素的指针，在 redis 中的跳表其实是做了个随机层数的优化，因为沿着前面的例子，其实当数据量很大的时候，是不是层数越多，其查询效率越高，但是随着层数变多，要保持这种严格的层数规则其实也会增大处理复杂度，所以 redis 插入每个元素的时候都是使用随机的方式，看一眼代码

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/* ZSETs use a specialized version of Skiplists */
typedef struct zskiplistNode {
    sds ele;
    double score;
    struct zskiplistNode *backward;
    struct zskiplistLevel {
        struct zskiplistNode *forward;
        unsigned long span;
    } level[];
} zskiplistNode;

typedef struct zskiplist {
    struct zskiplistNode *header, *tail;
    unsigned long length;
    int level;
} zskiplist;

typedef struct zset {
    dict *dict;
    zskiplist *zsl;
} zset;

忘了说了，redis 是把 skiplist 跳表用在 zset 里，zset 是个有序的集合，可以看到 zskiplist 就是个跳表的结构，里面用 header 保存跳表的表头，tail 保存表尾，还有长度和最大层级，具体的跳表节点元素使用 zskiplistNode 表示，里面包含了 sds 类型的元素值，double 类型的分值，用来排序，一个 backward 后向指针和一个 zskiplistLevel 数组，每个 level 包含了一个前向指针，和一个 span，span 表示的是跳表前向指针的跨度，这里再补充一点，前面说了为了灵活这个跳表的新增修改，redis 使用了随机层高的方式插入新节点，但是如果所有节点都随机到很高的层级或者所有都很低的话，跳表的效率优势就会减小，所以 redis 使用了个小技巧，贴下代码

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#define ZSKIPLIST_P 0.25      /* Skiplist P = 1/4 */
int zslRandomLevel(void) {
    int level = 1;
    while ((random()&0xFFFF) < (ZSKIPLIST_P * 0xFFFF))
        level += 1;
    return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL;
}

当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值，因此保持了一个相对递减的概率
可以简单分析下，当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1，就意味着当有 1 - 1/4也就是3/4的概率是直接跳出，所以一层的概率是3/4,也就是 1-P，二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

redis是现在服务端很常用的缓存中间件，其实原来还有memcache之类的竞品，但是现在貌似 redis 快一统江湖，这里当然不是在吹，只是个人角度的一个感觉，不权威只是主观感觉。
redis 主要有五种数据结构，Strings，Lists，Sets，Hashes，Sorted Sets，这五种数据结构先简单介绍下，Strings类型的其实就是我们最常用的 key-value，实际开发中也会用的最多；Lists是列表，这个有些会用来做队列，因为 redis 目前常用的版本支持丰富的列表操作；还有是Sets集合，这个主要的特点就是集合中元素不重复，可以用在有这类需求的场景里；Hashes是叫散列，类似于 Python 中的字典结构；还有就是Sorted Sets这个是个有序集合；一眼看这些其实没啥特别的，除了最后这个有序集合，不过去了解背后的实现方式还是比较有意思的。

SDS 简单动态字符串

先从Strings开始说，了解过 C 语言的应该知道，C 语言中的字符串其实是个 char[] 字符数组，redis 也不例外，只是最开始的版本就对这个做了一丢丢的优化，而正是这一丢丢的优化，让这个 redis 的使用效率提升了数倍

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struct sdshdr {
    // 字符串长度
    int len;
    // 字符串空余字符数
    int free;
    // 字符串内容
    char buf[];
};

这里引用了 redis 在 github 上最早的 2.2 版本的代码，代码路径是https://github.com/antirez/redis/blob/2.2/src/sds.h,可以看到这个结构体里只有仨元素，两个 int 型和一个 char 型数组，两个 int 型其实就是我说的优化，因为 C 语言本身的字符串数组，有两个问题，一个是要知道它实际已被占用的长度，需要去遍历这个数组，第二个就是比较容易踩坑的是遍历的时候要注意它有个以\0作为结尾的特点；通过上面的两个 int 型参数，一个是知道字符串目前的长度，一个是知道字符串还剩余多少位空间，这样子坐着两个操作从 O(N)简化到了O(1)了，还有第二个 free 还有个比较重要的作用就是能防止 C 字符串的溢出问题，在存储之前可以先判断 free 长度，如果长度不够就先扩容了，先介绍到这，这个系列可以写蛮多的，慢慢介绍吧

链表

链表是比较常见的数据结构了，但是因为 redis 是用 C 写的，所以在不依赖第三方库的情况下只能自己写一个了，redis 的链表是个有头的链表，而且是无环的，具体的结构我也找了 github 上最早版本的代码

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typedef struct listNode {
    // 前置节点
    struct listNode *prev;
    // 后置节点
    struct listNode *next;
    // 值
    void *value;
} listNode;

typedef struct list {
    // 链表表头
    listNode *head;
    // 当前节点，也可以说是最后节点
    listNode *tail;
    // 节点复制函数
    void *(*dup)(void *ptr);
    // 节点值释放函数
    void (*free)(void *ptr);
    // 节点值比较函数
    int (*match)(void *ptr, void *key);
    // 链表包含的节点数量
    unsigned int len;
} list;

代码地址是这个https://github.com/antirez/redis/blob/2.2/src/adlist.h
可以看下节点是由listNode承载的，包括值和一个指向前节点跟一个指向后一节点的两个指针，然后值是 void 指针类型，所以可以承载不同类型的值
然后是 list结构用来承载一个链表，包含了表头，和表尾，复制函数，释放函数和比较函数，还有链表长度，因为包含了前两个节点，找到表尾节点跟表头都是 O(1)的时间复杂度，还有节点数量，其实这个跟 SDS 是同一个做法，就是空间换时间，这也是写代码里比较常见的做法，以此让一些高频的操作提速。

字典

字典也是个常用的数据结构，其实只是叫法不同，数据结构中叫 hash 散列，Java 中叫 Map，PHP 中是数组 array，Python 中也叫字典 dict，因为纯 C 语言本身不带这些数据结构，所以这也是个痛并快乐着的过程，享受 C 语言的高性能的同时也要接受它只提供了语言的基本功能的现实，各种轮子都需要自己造，redis 同样实现了自己的字典
下面来看看代码

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typedef struct dictEntry {
    void *key;
    void *val;
    struct dictEntry *next;
} dictEntry;

typedef struct dictType {
    unsigned int (*hashFunction)(const void *key);
    void *(*keyDup)(void *privdata, const void *key);
    void *(*valDup)(void *privdata, const void *obj);
    int (*keyCompare)(void *privdata, const void *key1, const void *key2);
    void (*keyDestructor)(void *privdata, void *key);
    void (*valDestructor)(void *privdata, void *obj);
} dictType;

/* This is our hash table structure. Every dictionary has two of this as we
 * implement incremental rehashing, for the old to the new table. */
typedef struct dictht {
    dictEntry **table;
    unsigned long size;
    unsigned long sizemask;
    unsigned long used;
} dictht;

typedef struct dict {
    dictType *type;
    void *privdata;
    dictht ht[2];
    int rehashidx; /* rehashing not in progress if rehashidx == -1 */
    int iterators; /* number of iterators currently running */
} dict;

看了下这个 2.2 版本的代码跟最新版的其实也差的不是很多，所以还是照旧用老代码，可以看到上面四个结构体中，其实只有三个是存储数据用的，dictType 是用来放操作函数的，那么三个存放数据的结构体分别是干嘛的，这时候感觉需要一个图来说明比较好，稍等，我去画个图~

这个图看着应该比较清楚这些都是用来干嘛的了，dict 是我们的主体结构，它有一个指向 dictType 的指针，这里面包含了字典的操作函数，然后是一个私有数据指针，接下来是一个 dictht 的数组，包含两个dictht，这个就是用来存数据的了，然后是 rehashidx 表示重哈希的状态，当是-1 的时候表示当前没有重哈希，iterators 表示正在遍历的迭代器的数量。
首先说说为啥需要有两个 dictht，这是因为字典 dict 这个数据结构随着数据量的增减，会需要在中途做扩容或者缩容操作，如果只有一个的话，对它进行扩容缩容时会影响正常的访问和修改操作，或者说保证正常查询，修改的正确性会比较复杂，并且因为需要高效利用空间，不能一下子申请一个非常大的空间来存很少的数据。当 dict 中 dictht 中的数据量超过 size 的时候负载就超过了 1，就需要进行扩容，这里的其实跟 Java 中的 HashMap 比较类似，超过一定的负载之后进行扩容。这里为啥 size 会超过 1 呢，可能有部分不了解这类结构的同学会比较奇怪，其实就是上图中画的，在数据结构中对于散列的冲突有几类解决方法，比如转换成链表，二次散列，找下个空槽等，这里就使用了链表法，或者说拉链法。当一个新元素通过 hashFunction 得出的 key 跟 sizemask 取模之后的值相同了，那就将其放在原来的节点之前，变成链表挂在数组 dictht.table下面，放在原有节点前是考虑到可能会优先访问。
忘了说明下 dictht 跟 dictEntry 的关系了，dictht 就是个哈希表，它里面是个dictEntry 的二维数组，而 dictEntry 是个包含了 key-value 结构之外还有一个 next 指针，因此可以将哈希冲突的以链表的形式保存下来。
在重点说下重哈希，可能同样写 Java 的同学对这个比较有感觉，跟 HashMap 一样，会以 2 的 N 次方进行扩容，那么扩容的方法就会比较简单，每个键重哈希要不就在原来这个槽，要不就在原来的槽加原 dictht.size 的位置；然后是重头戏，具体是怎么做扩容呢，其实这里就把第二个 ht 用上了，其实这两个hashtable 的具体作用有点类似于 jvm 中的两个 survival 区，但是又不全一样，因为 redis 在扩容的时候是采用的渐进式地重哈希，什么叫渐进式的呢，就是它不是像 jvm 那种标记复制的模式直接将一个 eden 区和原来的 survival 区存活的对象复制到另一个 survival 区，而是在每一次添加，删除，查找或者更新操作时，都会额外的帮忙搬运一部分的原 dictht 中的数据，这里会根据 rehashidx 的值来判断，如果是-1 表示并没有在重哈希中，如果是 0 表示开始重哈希了，然后rehashidx 还会随着每次的帮忙搬运往上加，但全部被搬运完成后 rehashidx 又变回了-1，又可以扯到Java 中的 Concurrent HashMap, 他在扩容的时候也使用了类似的操作。

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

这是个 Java 面试的高频问题，我也遇到过，以往都是觉得这类题没意思，网上一搜一大堆，也不愿意记，其实说回来，主要还是没静下心来好好去理解，今天无意中看到一个课程，基本帮我把一些疑惑的点讲清楚了，首先单例是啥意思，这个其实是有范围一说，比如我起了个Spring Boot应用，在这个应用范围内，我的常规 bean 是单例的，意味着 getBean 的时候其实永远只会拿到那一个对象，那要怎么来写一个单例呢，首先就是传说中的饿汉模式，也是最简单的

饿汉模式

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public class Singleton1 {
    // 首先，将构造方法变成私有的
    private Singleton1() {};
    // 创建私有静态实例，这样第一次使用的时候就会进行创建
    private static Singleton instance = new Singleton1();

    // 使用这个对象都是通过这个 getInstance 来获取
    public static Singleton1 getInstance() {
        return instance;
    }
    // 瞎写一个静态方法。这里想说的是，如果我们只是要调用 Singleton.getDate(...)，
    // 本来是不想要生成 Singleton 实例的，不过没办法，已经生成了
    public static Date getDate(String mode) {return new Date();}
}

上面借鉴了一些代码，其实这是最基本，也不会错的方法，但是正如其中getDate方法里说的问题，有时候并没有想那这个对象，但是因为我调用了这个类的静态方法，导致对象已经生成了，可能这也是饿汉模式名字的来由，不管三七二十一给你生成个单例就完事了，不管有没有用，但是这种个人觉得也没啥大问题，如果是面试的话最好说出来它的缺点

饱汉模式

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public class Singleton2 {
    // 首先，也是先堵死 new Singleton() 这条路,将构造方法变成私有
    private Singleton2() {}
    // 和饿汉模式相比，这边不需要先实例化出来，注意这里的 volatile，它是必须的
    private static volatile Singleton2 instance = null;

    private int m = 9;

    public static Singleton getInstance() {
        if (instance == null) {
            // 加锁
            synchronized (Singleton2.class) {
                // 这一次判断也是必须的，不然会有并发问题
                if (instance == null) {
                    instance = new Singleton2();
                }
            }
        }
        return instance;
    }
}

这里容易错的有三点，理解了其实就比较好记了

第一点，为啥不在 getInstance 上整个代码块加 synchronized，这个其实比较容易理解，就是锁的力度太大，性能太差了，这点其实也要去理解，可以举个夸张的例子，比如我一个电商的服务，如果为了避免一个人的订单出现问题，是不是可以从请求入口就把他锁住，到请求结束释放，那么里面做的事情都有保障，然而这显然不可能，因为我们想要这种竞态条件抢占资源的时间尽量减少，防止其他线程等待。
第二点，为啥synchronized之已经检查了 instance == null,还要在里面再检查一次，这个有个术语，叫 double check lock,但是为啥要这么做呢，其实很简单，想象当有两个线程，都过了第一步为空判断，这个时候只有一个线程能拿到这个锁，另一个线程就等待了，如果不再判断一次，那么第一个线程新建完对象释放锁之后，第二个线程又能拿到锁，再去创建一个对象。
第三点，为啥要volatile关键字，原先对它的理解是它修饰的变量在 JMM 中能及时将变量值写到主存中，但是它还有个很重要的作用，就是防止指令重排序，instance = new Singleton();这行代码其实在底层是分成三条指令执行的，第一条是在堆上申请了一块内存放这个对象，但是对象的字段啥的都还是默认值，第二条是设置对象的值，比如上面的 m 是 9，然后第三条是将这个对象和虚拟机栈上的指针建立引用关联，那么如果我不用volatile关键字，这三条指令就有可能出现重排，比如变成了 1-3-2 这种顺序，当执行完第二步时，有个线程来访问这个对象了，先判断是不是空，发现不是空的，就拿去直接用了，是不是就出现问题了，所以这个volatile也是不可缺少的

嵌套类

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public class Singleton3 {

    private Singleton3() {}
    // 主要是使用了 嵌套类可以访问外部类的静态属性和静态方法 的特性
    private static class Holder {
        private static Singleton3 instance = new Singleton3();
    }
    public static Singleton3 getInstance() {
        return Holder.instance;
    }
}

这个我个人感觉是饿汉模式的升级版，可以在调用getInstance的时候去实例化对象，也是比较推荐的

枚举单例

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public enum Singleton {
    INSTANCE;
    
    public void doSomething(){
        //todo doSomething
    }
}

枚举很特殊，它在类加载的时候会初始化里面的所有的实例，而且 JVM 保证了它们不会再被实例化，所以它天生就是单例的。

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

0%
\ No newline at end of file diff --git a/search.xml b/search.xml index 629ef508d6..720f092319 100644 --- a/search.xml +++ b/search.xml @@ -1,5 +1,25 @@ + + 村上春树《1Q84》读后感 + /2019/12/18/1Q84%E8%AF%BB%E5%90%8E%E6%84%9F/ + 看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

+

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

+

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

+

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

+

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

+

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

+

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

+]]> + + 读后感 + 生活 + 村上春树 + + + 读后感 + + 2019年终总结 /2020/02/01/2019%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/ @@ -29,26 +49,6 @@ 2019 - - 村上春树《1Q84》读后感 - /2019/12/18/1Q84%E8%AF%BB%E5%90%8E%E6%84%9F/ - 看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

-

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

-

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

-

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

-

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

-

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

-

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

-]]> - - 生活 - 读后感 - 村上春树 - - - 读后感 - - 2020 年终总结 /2021/03/31/2020-%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/ @@ -133,6 +133,24 @@ 拖更 + + 34_Search_for_a_Range + /2016/08/14/34-Search-for-a-Range/ + question

34. Search for a Range

Original Page

+

Given a sorted array of integers, find the starting and ending position of a given target value.

+

Your algorithm’s runtime complexity must be in the order of O(log n).

+

If the target is not found in the array, return [-1, -1].

+

For example,
Given [5, 7, 7, 8, 8, 10] and target value 8,
return [3, 4].

+

analysis

一开始就想到了二分查找，但是原来做二分查找的时候一般都是找到确定的那个数就完成了，
这里的情况比较特殊，需要找到整个区间，所以需要两遍查找，并且一个是找到小于target
的最大索引，一个是找到大于target的最大索引，代码参考leetcode discuss,这位仁
兄也做了详细的分析解释。

+

code

class Solution {
public:
    vector<int> searchRange(vector<int>& nums, int target) {
        vector<int> ret(2, -1);
        int i = 0, j = nums.size() - 1;
        int mid;
        while(i < j){
            mid = (i + j) / 2;
            if(nums[mid] < target) i = mid + 1;
            else j = mid;
        }
        if(nums[i] != target) return ret;
        else {
            ret[0] = i;
            if((i+1) < (nums.size() - 1) && nums[i+1] > target){
                ret[1] = i;
                return ret;
            }
        }   //一点小优化
        j = nums.size() - 1;
        while(i < j){
            mid = (i + j) / 2 + 1;
            if(nums[mid] > target) j = mid - 1;
            else i = mid;
        }
        ret[1] = j;
        return ret;
    }
};
]]> + + leetcode + + + leetcode + c++ + + 2022 年终总结 /2023/01/15/2022-%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/ @@ -191,21 +209,55 @@ - 34_Search_for_a_Range - /2016/08/14/34-Search-for-a-Range/ - question

34. Search for a Range

Original Page

-

Given a sorted array of integers, find the starting and ending position of a given target value.

-

Your algorithm’s runtime complexity must be in the order of O(log n).

-

If the target is not found in the array, return [-1, -1].

-

For example,
Given [5, 7, 7, 8, 8, 10] and target value 8,
return [3, 4].

-

analysis

一开始就想到了二分查找，但是原来做二分查找的时候一般都是找到确定的那个数就完成了，
这里的情况比较特殊，需要找到整个区间，所以需要两遍查找，并且一个是找到小于target
的最大索引，一个是找到大于target的最大索引，代码参考leetcode discuss,这位仁
兄也做了详细的分析解释。

-

code

class Solution {
public:
    vector<int> searchRange(vector<int>& nums, int target) {
        vector<int> ret(2, -1);
        int i = 0, j = nums.size() - 1;
        int mid;
        while(i < j){
            mid = (i + j) / 2;
            if(nums[mid] < target) i = mid + 1;
            else j = mid;
        }
        if(nums[i] != target) return ret;
        else {
            ret[0] = i;
            if((i+1) < (nums.size() - 1) && nums[i+1] > target){
                ret[1] = i;
                return ret;
            }
        }   //一点小优化
        j = nums.size() - 1;
        while(i < j){
            mid = (i + j) / 2 + 1;
            if(nums[mid] > target) j = mid - 1;
            else i = mid;
        }
        ret[1] = j;
        return ret;
    }
};
]]> + AQS篇一 + /2021/02/14/AQS%E7%AF%87%E4%B8%80/ + 很多东西都是时看时新，而且时间长了也会忘，所以再来复习下，也会有一些新的角度看法这次来聊下AQS的内容，主要是这几个点，

+

第一个线程

第一个线程抢到锁了，此时state跟阻塞队列是怎么样的，其实这里是之前没理解对的地方

+
/**
         * Fair version of tryAcquire.  Don't grant access unless
         * recursive call or no waiters or is first.
         */
        protected final boolean tryAcquire(int acquires) {
            final Thread current = Thread.currentThread();
            int c = getState();
            // 这里如果state还是0说明锁还空着
            if (c == 0) {
                // 因为是公平锁版本的，先去看下是否阻塞队列里有排着队的
                if (!hasQueuedPredecessors() &&
                    compareAndSetState(0, acquires)) {
                    // 没有排队的，并且state使用cas设置成功的就标记当前占有锁的线程是我
                    setExclusiveOwnerThread(current);
                    // 然后其实就返回了，包括阻塞队列的head和tail节点和waitStatus都没有设置
                    return true;
                }
            }
            else if (current == getExclusiveOwnerThread()) {
                int nextc = c + acquires;
                if (nextc < 0)
                    throw new Error("Maximum lock count exceeded");
                setState(nextc);
                return true;
            }
            // 这里就是第二个线程会返回false
            return false;
        }
    }
+ +

第二个线程

当第二个线程进来的时候应该是怎么样，结合代码来看

+
/**
     * Acquires in exclusive mode, ignoring interrupts.  Implemented
     * by invoking at least once {@link #tryAcquire},
     * returning on success.  Otherwise the thread is queued, possibly
     * repeatedly blocking and unblocking, invoking {@link
     * #tryAcquire} until success.  This method can be used
     * to implement method {@link Lock#lock}.
     *
     * @param arg the acquire argument.  This value is conveyed to
     *        {@link #tryAcquire} but is otherwise uninterpreted and
     *        can represent anything you like.
     */
    public final void acquire(int arg) {
        // 前面第一种情况是tryAcquire直接成功了，这个if判断第一个条件就是false，就不往下执行了
        // 如果是第二个线程，第一个条件获取锁不成功，条件判断!tryAcquire(arg) == true，就会走
        // acquireQueued(addWaiter(Node.EXCLUSIVE), arg)
        if (!tryAcquire(arg) &&
            acquireQueued(addWaiter(Node.EXCLUSIVE), arg))
            selfInterrupt();
    }
+ +

然后来看下addWaiter的逻辑

+
/**
     * Creates and enqueues node for current thread and given mode.
     *
     * @param mode Node.EXCLUSIVE for exclusive, Node.SHARED for shared
     * @return the new node
     */
    private Node addWaiter(Node mode) {
        // 这里是包装成一个node
        Node node = new Node(Thread.currentThread(), mode);
        // Try the fast path of enq; backup to full enq on failure
        // 最快的方式就是把当前线程的节点放在阻塞队列的最后
        Node pred = tail;
        // 只有当tail，也就是pred不为空的时候可以直接接上
        if (pred != null) {
            node.prev = pred;
            // 如果这里cas成功了，就直接接上返回了
            if (compareAndSetTail(pred, node)) {
                pred.next = node;
                return node;
            }
        }
        // 不然就会继续走到这里
        enq(node);
        return node;
    }
+ +

然后就是enq的逻辑了

+
/**
     * Inserts node into queue, initializing if necessary. See picture above.
     * @param node the node to insert
     * @return node's predecessor
     */
    private Node enq(final Node node) {
        for (;;) {
            // 如果状态没变化的话，tail这时还是null的
            Node t = tail;
            if (t == null) { // Must initialize
                // 这里就会初始化头结点，就是个空节点
                if (compareAndSetHead(new Node()))
                    // tail也赋值成head
                    tail = head;
            } else {
                // 这里就设置tail了
                node.prev = t;
                if (compareAndSetTail(t, node)) {
                    t.next = node;
                    return t;
                }
            }
        }
    }
+ +

所以从这里可以看出来，其实head头结点不是个真实的带有线程的节点，并且不是在第一个线程进来的时候设置的

+

解锁

通过代码来看下

+
/**
     * Attempts to release this lock.
     *
     * <p>If the current thread is the holder of this lock then the hold
     * count is decremented.  If the hold count is now zero then the lock
     * is released.  If the current thread is not the holder of this
     * lock then {@link IllegalMonitorStateException} is thrown.
     *
     * @throws IllegalMonitorStateException if the current thread does not
     *         hold this lock
     */
    public void unlock() {
        // 释放锁
        sync.release(1);
    }
/**
     * Releases in exclusive mode.  Implemented by unblocking one or
     * more threads if {@link #tryRelease} returns true.
     * This method can be used to implement method {@link Lock#unlock}.
     *
     * @param arg the release argument.  This value is conveyed to
     *        {@link #tryRelease} but is otherwise uninterpreted and
     *        can represent anything you like.
     * @return the value returned from {@link #tryRelease}
     */
    public final boolean release(int arg) {
        // 尝试去释放
        if (tryRelease(arg)) {
            Node h = head;
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }
protected final boolean tryRelease(int releases) {
            int c = getState() - releases;
            if (Thread.currentThread() != getExclusiveOwnerThread())
                throw new IllegalMonitorStateException();
            boolean free = false;
    		// 判断是否完全释放锁，因为可重入
            if (c == 0) {
                free = true;
                setExclusiveOwnerThread(null);
            }
            setState(c);
            return free;
        }
// 这段代码和上面的一致，只是为了顺序性，又拷下来看下

public final boolean release(int arg) {
        // 尝试去释放，如果是完全释放，返回的就是true，否则是false
        if (tryRelease(arg)) {
            Node h = head;
            // 这里判断头结点是否为空以及waitStatus的状态，前面说了head节点其实是
            // 在第二个线程进来的时候初始化的，如果是空的话说明没后续节点，并且waitStatus
            // 也表示了后续的等待状态
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }

/**
     * Wakes up node's successor, if one exists.
     *
     * @param node the node
     */
// 唤醒后继节点
    private void unparkSuccessor(Node node) {
        /*
         * If status is negative (i.e., possibly needing signal) try
         * to clear in anticipation of signalling.  It is OK if this
         * fails or if status is changed by waiting thread.
         */
        int ws = node.waitStatus;
        if (ws < 0)
            compareAndSetWaitStatus(node, ws, 0);

        /*
         * Thread to unpark is held in successor, which is normally
         * just the next node.  But if cancelled or apparently null,
         * traverse backwards from tail to find the actual
         * non-cancelled successor.
         */
        Node s = node.next;
        // 如果后继节点是空或者当前节点取消等待了
        if (s == null || s.waitStatus > 0) {
            s = null;
            // 从后往前找，找到非取消的节点，注意这里不是找到就退出，而是一直找到头
            // 所以不必担心中间有取消的
            for (Node t = tail; t != null && t != node; t = t.prev)
                if (t.waitStatus <= 0)
                    s = t;
        }
        if (s != null)
            // 将其唤醒
            LockSupport.unpark(s.thread);
    }
+ + + + +]]> - leetcode + Java + 并发 - leetcode - c++ + java + 并发 + j.u.c + aqs + + + + AbstractQueuedSynchronizer + /2019/09/23/AbstractQueuedSynchronizer/ + 最近看了大神的 AQS 的文章，之前总是断断续续地看一点，每次都知难而退，下次看又从头开始，昨天总算硬着头皮看完了第一部分
首先 AQS 只要有这些属性

+
// 头结点，你直接把它当做 当前持有锁的线程 可能是最好理解的
private transient volatile Node head;

// 阻塞的尾节点，每个新的节点进来，都插入到最后，也就形成了一个链表
private transient volatile Node tail;

// 这个是最重要的，代表当前锁的状态，0代表没有被占用，大于 0 代表有线程持有当前锁
// 这个值可以大于 1，是因为锁可以重入，每次重入都加上 1
private volatile int state;

// 代表当前持有独占锁的线程，举个最重要的使用例子，因为锁可以重入
// reentrantLock.lock()可以嵌套调用多次，所以每次用这个来判断当前线程是否已经拥有了锁
// if (currentThread == getExclusiveOwnerThread()) {state++}
private transient Thread exclusiveOwnerThread; //继承自AbstractOwnableSynchronizer
+

大概了解了 aqs 底层的双向等待队列，
结构是这样的

每个 node 里面主要是的代码结构也比较简单

+
static final class Node {
    // 标识节点当前在共享模式下
    static final Node SHARED = new Node();
    // 标识节点当前在独占模式下
    static final Node EXCLUSIVE = null;

    // ======== 下面的几个int常量是给waitStatus用的 ===========
    /** waitStatus value to indicate thread has cancelled */
    // 代码此线程取消了争抢这个锁
    static final int CANCELLED =  1;
    /** waitStatus value to indicate successor's thread needs unparking */
    // 官方的描述是，其表示当前node的后继节点对应的线程需要被唤醒
    static final int SIGNAL    = -1;
    /** waitStatus value to indicate thread is waiting on condition */
    // 本文不分析condition，所以略过吧，下一篇文章会介绍这个
    static final int CONDITION = -2;
    /**
     * waitStatus value to indicate the next acquireShared should
     * unconditionally propagate
     */
    // 同样的不分析，略过吧
    static final int PROPAGATE = -3;
    // =====================================================


    // 取值为上面的1、-1、-2、-3，或者0(以后会讲到)
    // 这么理解，暂时只需要知道如果这个值 大于0 代表此线程取消了等待，
    //    ps: 半天抢不到锁，不抢了，ReentrantLock是可以指定timeouot的。。。
    volatile int waitStatus;
    // 前驱节点的引用
    volatile Node prev;
    // 后继节点的引用
    volatile Node next;
    // 这个就是线程本尊
    volatile Thread thread;

}
+

其实可以主要关注这个 waitStatus 因为这个是后面的节点给前面的节点设置的，等于-1 的时候代表后面有节点等待，需要去唤醒，
这里使用了一个变种的 CLH 队列实现，CLH 队列相关内容可以查看这篇 自旋锁、排队自旋锁、MCS锁、CLH锁

+]]> + + java + + + java + aqs @@ -248,6 +300,47 @@ environment + + Apollo 的 value 注解是怎么自动更新的 + /2020/11/01/Apollo-%E7%9A%84-value-%E6%B3%A8%E8%A7%A3%E6%98%AF%E6%80%8E%E4%B9%88%E8%87%AA%E5%8A%A8%E6%9B%B4%E6%96%B0%E7%9A%84/ + 在前司和目前公司，用的配置中心都是使用的 Apollo，经过了业界验证，比较强大的配置管理系统，特别是在0.10 后开始支持对使用 value 注解的配置值进行自动更新，今天刚好有个同学问到我，就顺便写篇文章记录下，其实也是借助于 spring 强大的 bean 生命周期管理，可以实现BeanPostProcessor接口，使用postProcessBeforeInitialization方法，来对bean 内部的属性和方法进行判断，是否有 value 注解，如果有就是将它注册到一个 map 中，可以看到这个方法com.ctrip.framework.apollo.spring.annotation.SpringValueProcessor#processField

+
@Override
  protected void processField(Object bean, String beanName, Field field) {
    // register @Value on field
    Value value = field.getAnnotation(Value.class);
    if (value == null) {
      return;
    }
    Set<String> keys = placeholderHelper.extractPlaceholderKeys(value.value());

    if (keys.isEmpty()) {
      return;
    }

    for (String key : keys) {
      SpringValue springValue = new SpringValue(key, value.value(), bean, beanName, field, false);
      springValueRegistry.register(beanFactory, key, springValue);
      logger.debug("Monitoring {}", springValue);
    }
  }
+

然后我们看下这个springValueRegistry是啥玩意

+
public class SpringValueRegistry {
  private static final long CLEAN_INTERVAL_IN_SECONDS = 5;
  private final Map<BeanFactory, Multimap<String, SpringValue>> registry = Maps.newConcurrentMap();
  private final AtomicBoolean initialized = new AtomicBoolean(false);
  private final Object LOCK = new Object();

  public void register(BeanFactory beanFactory, String key, SpringValue springValue) {
    if (!registry.containsKey(beanFactory)) {
      synchronized (LOCK) {
        if (!registry.containsKey(beanFactory)) {
          registry.put(beanFactory, LinkedListMultimap.<String, SpringValue>create());
        }
      }
    }

    registry.get(beanFactory).put(key, springValue);

    // lazy initialize
    if (initialized.compareAndSet(false, true)) {
      initialize();
    }
  }
+

这类其实就是个 map 来存放 springvalue，然后有com.ctrip.framework.apollo.spring.property.AutoUpdateConfigChangeListener来监听更新操作，当有变更时

+
@Override
 public void onChange(ConfigChangeEvent changeEvent) {
   Set<String> keys = changeEvent.changedKeys();
   if (CollectionUtils.isEmpty(keys)) {
     return;
   }
   for (String key : keys) {
     // 1. check whether the changed key is relevant
     Collection<SpringValue> targetValues = springValueRegistry.get(beanFactory, key);
     if (targetValues == null || targetValues.isEmpty()) {
       continue;
     }

     // 2. check whether the value is really changed or not (since spring property sources have hierarchies)
     // 这里其实有一点比较绕，是因为 Apollo 里的 namespace 划分，会出现 key 相同，但是 namespace 不同的情况，所以会有个优先级存在，所以需要去校验 environment 里面的是否已经更新，如果未更新则表示不需要更新
     if (!shouldTriggerAutoUpdate(changeEvent, key)) {
       continue;
     }

     // 3. update the value
     for (SpringValue val : targetValues) {
       updateSpringValue(val);
     }
   }
 }
+

其实原理很简单，就是得了解知道下

+]]> + + Java + Apollo + value + + + Java + Apollo + environment + value + 注解 + + + + Clone Graph Part I + /2014/12/30/Clone-Graph-Part-I/ + problem
Clone a graph. Input is a Node pointer. Return the Node pointer of the cloned graph.

A graph is defined below:
struct Node {
vector neighbors;
}
+ + +

code

typedef unordered_map<Node *, Node *> Map;
 
Node *clone(Node *graph) {
    if (!graph) return NULL;
 
    Map map;
    queue<Node *> q;
    q.push(graph);
 
    Node *graphCopy = new Node();
    map[graph] = graphCopy;
 
    while (!q.empty()) {
        Node *node = q.front();
        q.pop();
        int n = node->neighbors.size();
        for (int i = 0; i < n; i++) {
            Node *neighbor = node->neighbors[i];
            // no copy exists
            if (map.find(neighbor) == map.end()) {
                Node *p = new Node();
                map[node]->neighbors.push_back(p);
                map[neighbor] = p;
                q.push(neighbor);
            } else {     // a copy already exists
                map[node]->neighbors.push_back(map[neighbor]);
            }
        }
    }
 
    return graphCopy;
}
+

anlysis

using the Breadth-first traversal
and use a map to save the neighbors not to be duplicated.

+]]> + + leetcode + + + leetcode + C++ + + Apollo 客户端启动过程分析 /2022/09/18/Apollo-%E5%AE%A2%E6%88%B7%E7%AB%AF%E5%90%AF%E5%8A%A8%E8%BF%87%E7%A8%8B%E5%88%86%E6%9E%90/ @@ -298,79 +391,37 @@ - Apollo 的 value 注解是怎么自动更新的 - /2020/11/01/Apollo-%E7%9A%84-value-%E6%B3%A8%E8%A7%A3%E6%98%AF%E6%80%8E%E4%B9%88%E8%87%AA%E5%8A%A8%E6%9B%B4%E6%96%B0%E7%9A%84/ - 在前司和目前公司，用的配置中心都是使用的 Apollo，经过了业界验证，比较强大的配置管理系统，特别是在0.10 后开始支持对使用 value 注解的配置值进行自动更新，今天刚好有个同学问到我，就顺便写篇文章记录下，其实也是借助于 spring 强大的 bean 生命周期管理，可以实现BeanPostProcessor接口，使用postProcessBeforeInitialization方法，来对bean 内部的属性和方法进行判断，是否有 value 注解，如果有就是将它注册到一个 map 中，可以看到这个方法com.ctrip.framework.apollo.spring.annotation.SpringValueProcessor#processField

-
@Override
  protected void processField(Object bean, String beanName, Field field) {
    // register @Value on field
    Value value = field.getAnnotation(Value.class);
    if (value == null) {
      return;
    }
    Set<String> keys = placeholderHelper.extractPlaceholderKeys(value.value());

    if (keys.isEmpty()) {
      return;
    }

    for (String key : keys) {
      SpringValue springValue = new SpringValue(key, value.value(), bean, beanName, field, false);
      springValueRegistry.register(beanFactory, key, springValue);
      logger.debug("Monitoring {}", springValue);
    }
  }
-

然后我们看下这个springValueRegistry是啥玩意

-
public class SpringValueRegistry {
  private static final long CLEAN_INTERVAL_IN_SECONDS = 5;
  private final Map<BeanFactory, Multimap<String, SpringValue>> registry = Maps.newConcurrentMap();
  private final AtomicBoolean initialized = new AtomicBoolean(false);
  private final Object LOCK = new Object();

  public void register(BeanFactory beanFactory, String key, SpringValue springValue) {
    if (!registry.containsKey(beanFactory)) {
      synchronized (LOCK) {
        if (!registry.containsKey(beanFactory)) {
          registry.put(beanFactory, LinkedListMultimap.<String, SpringValue>create());
        }
      }
    }

    registry.get(beanFactory).put(key, springValue);

    // lazy initialize
    if (initialized.compareAndSet(false, true)) {
      initialize();
    }
  }
-

这类其实就是个 map 来存放 springvalue，然后有com.ctrip.framework.apollo.spring.property.AutoUpdateConfigChangeListener来监听更新操作，当有变更时

-
@Override
 public void onChange(ConfigChangeEvent changeEvent) {
   Set<String> keys = changeEvent.changedKeys();
   if (CollectionUtils.isEmpty(keys)) {
     return;
   }
   for (String key : keys) {
     // 1. check whether the changed key is relevant
     Collection<SpringValue> targetValues = springValueRegistry.get(beanFactory, key);
     if (targetValues == null || targetValues.isEmpty()) {
       continue;
     }

     // 2. check whether the value is really changed or not (since spring property sources have hierarchies)
     // 这里其实有一点比较绕，是因为 Apollo 里的 namespace 划分，会出现 key 相同，但是 namespace 不同的情况，所以会有个优先级存在，所以需要去校验 environment 里面的是否已经更新，如果未更新则表示不需要更新
     if (!shouldTriggerAutoUpdate(changeEvent, key)) {
       continue;
     }

     // 3. update the value
     for (SpringValue val : targetValues) {
       updateSpringValue(val);
     }
   }
 }
-

其实原理很简单，就是得了解知道下

-]]> - - Java - Apollo - value - - - Java - Apollo - environment - value - 注解 - - - - AbstractQueuedSynchronizer - /2019/09/23/AbstractQueuedSynchronizer/ - 最近看了大神的 AQS 的文章，之前总是断断续续地看一点，每次都知难而退，下次看又从头开始，昨天总算硬着头皮看完了第一部分
首先 AQS 只要有这些属性

-
// 头结点，你直接把它当做 当前持有锁的线程 可能是最好理解的
private transient volatile Node head;

// 阻塞的尾节点，每个新的节点进来，都插入到最后，也就形成了一个链表
private transient volatile Node tail;

// 这个是最重要的，代表当前锁的状态，0代表没有被占用，大于 0 代表有线程持有当前锁
// 这个值可以大于 1，是因为锁可以重入，每次重入都加上 1
private volatile int state;

// 代表当前持有独占锁的线程，举个最重要的使用例子，因为锁可以重入
// reentrantLock.lock()可以嵌套调用多次，所以每次用这个来判断当前线程是否已经拥有了锁
// if (currentThread == getExclusiveOwnerThread()) {state++}
private transient Thread exclusiveOwnerThread; //继承自AbstractOwnableSynchronizer
-

大概了解了 aqs 底层的双向等待队列，
结构是这样的

每个 node 里面主要是的代码结构也比较简单

-
static final class Node {
    // 标识节点当前在共享模式下
    static final Node SHARED = new Node();
    // 标识节点当前在独占模式下
    static final Node EXCLUSIVE = null;

    // ======== 下面的几个int常量是给waitStatus用的 ===========
    /** waitStatus value to indicate thread has cancelled */
    // 代码此线程取消了争抢这个锁
    static final int CANCELLED =  1;
    /** waitStatus value to indicate successor's thread needs unparking */
    // 官方的描述是，其表示当前node的后继节点对应的线程需要被唤醒
    static final int SIGNAL    = -1;
    /** waitStatus value to indicate thread is waiting on condition */
    // 本文不分析condition，所以略过吧，下一篇文章会介绍这个
    static final int CONDITION = -2;
    /**
     * waitStatus value to indicate the next acquireShared should
     * unconditionally propagate
     */
    // 同样的不分析，略过吧
    static final int PROPAGATE = -3;
    // =====================================================


    // 取值为上面的1、-1、-2、-3，或者0(以后会讲到)
    // 这么理解，暂时只需要知道如果这个值 大于0 代表此线程取消了等待，
    //    ps: 半天抢不到锁，不抢了，ReentrantLock是可以指定timeouot的。。。
    volatile int waitStatus;
    // 前驱节点的引用
    volatile Node prev;
    // 后继节点的引用
    volatile Node next;
    // 这个就是线程本尊
    volatile Thread thread;

}
-

其实可以主要关注这个 waitStatus 因为这个是后面的节点给前面的节点设置的，等于-1 的时候代表后面有节点等待，需要去唤醒，
这里使用了一个变种的 CLH 队列实现，CLH 队列相关内容可以查看这篇 自旋锁、排队自旋锁、MCS锁、CLH锁

-]]> - - java - - - java - aqs - - - - AQS篇一 - /2021/02/14/AQS%E7%AF%87%E4%B8%80/ - 很多东西都是时看时新，而且时间长了也会忘，所以再来复习下，也会有一些新的角度看法这次来聊下AQS的内容，主要是这几个点，

-

第一个线程

第一个线程抢到锁了，此时state跟阻塞队列是怎么样的，其实这里是之前没理解对的地方

-
/**
         * Fair version of tryAcquire.  Don't grant access unless
         * recursive call or no waiters or is first.
         */
        protected final boolean tryAcquire(int acquires) {
            final Thread current = Thread.currentThread();
            int c = getState();
            // 这里如果state还是0说明锁还空着
            if (c == 0) {
                // 因为是公平锁版本的，先去看下是否阻塞队列里有排着队的
                if (!hasQueuedPredecessors() &&
                    compareAndSetState(0, acquires)) {
                    // 没有排队的，并且state使用cas设置成功的就标记当前占有锁的线程是我
                    setExclusiveOwnerThread(current);
                    // 然后其实就返回了，包括阻塞队列的head和tail节点和waitStatus都没有设置
                    return true;
                }
            }
            else if (current == getExclusiveOwnerThread()) {
                int nextc = c + acquires;
                if (nextc < 0)
                    throw new Error("Maximum lock count exceeded");
                setState(nextc);
                return true;
            }
            // 这里就是第二个线程会返回false
            return false;
        }
    }
- -

第二个线程

当第二个线程进来的时候应该是怎么样，结合代码来看

-
/**
     * Acquires in exclusive mode, ignoring interrupts.  Implemented
     * by invoking at least once {@link #tryAcquire},
     * returning on success.  Otherwise the thread is queued, possibly
     * repeatedly blocking and unblocking, invoking {@link
     * #tryAcquire} until success.  This method can be used
     * to implement method {@link Lock#lock}.
     *
     * @param arg the acquire argument.  This value is conveyed to
     *        {@link #tryAcquire} but is otherwise uninterpreted and
     *        can represent anything you like.
     */
    public final void acquire(int arg) {
        // 前面第一种情况是tryAcquire直接成功了，这个if判断第一个条件就是false，就不往下执行了
        // 如果是第二个线程，第一个条件获取锁不成功，条件判断!tryAcquire(arg) == true，就会走
        // acquireQueued(addWaiter(Node.EXCLUSIVE), arg)
        if (!tryAcquire(arg) &&
            acquireQueued(addWaiter(Node.EXCLUSIVE), arg))
            selfInterrupt();
    }
- -

然后来看下addWaiter的逻辑

-
/**
     * Creates and enqueues node for current thread and given mode.
     *
     * @param mode Node.EXCLUSIVE for exclusive, Node.SHARED for shared
     * @return the new node
     */
    private Node addWaiter(Node mode) {
        // 这里是包装成一个node
        Node node = new Node(Thread.currentThread(), mode);
        // Try the fast path of enq; backup to full enq on failure
        // 最快的方式就是把当前线程的节点放在阻塞队列的最后
        Node pred = tail;
        // 只有当tail，也就是pred不为空的时候可以直接接上
        if (pred != null) {
            node.prev = pred;
            // 如果这里cas成功了，就直接接上返回了
            if (compareAndSetTail(pred, node)) {
                pred.next = node;
                return node;
            }
        }
        // 不然就会继续走到这里
        enq(node);
        return node;
    }
- -

然后就是enq的逻辑了

-
/**
     * Inserts node into queue, initializing if necessary. See picture above.
     * @param node the node to insert
     * @return node's predecessor
     */
    private Node enq(final Node node) {
        for (;;) {
            // 如果状态没变化的话，tail这时还是null的
            Node t = tail;
            if (t == null) { // Must initialize
                // 这里就会初始化头结点，就是个空节点
                if (compareAndSetHead(new Node()))
                    // tail也赋值成head
                    tail = head;
            } else {
                // 这里就设置tail了
                node.prev = t;
                if (compareAndSetTail(t, node)) {
                    t.next = node;
                    return t;
                }
            }
        }
    }
- -

所以从这里可以看出来，其实head头结点不是个真实的带有线程的节点，并且不是在第一个线程进来的时候设置的

-

解锁

通过代码来看下

-
/**
     * Attempts to release this lock.
     *
     * <p>If the current thread is the holder of this lock then the hold
     * count is decremented.  If the hold count is now zero then the lock
     * is released.  If the current thread is not the holder of this
     * lock then {@link IllegalMonitorStateException} is thrown.
     *
     * @throws IllegalMonitorStateException if the current thread does not
     *         hold this lock
     */
    public void unlock() {
        // 释放锁
        sync.release(1);
    }
/**
     * Releases in exclusive mode.  Implemented by unblocking one or
     * more threads if {@link #tryRelease} returns true.
     * This method can be used to implement method {@link Lock#unlock}.
     *
     * @param arg the release argument.  This value is conveyed to
     *        {@link #tryRelease} but is otherwise uninterpreted and
     *        can represent anything you like.
     * @return the value returned from {@link #tryRelease}
     */
    public final boolean release(int arg) {
        // 尝试去释放
        if (tryRelease(arg)) {
            Node h = head;
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }
protected final boolean tryRelease(int releases) {
            int c = getState() - releases;
            if (Thread.currentThread() != getExclusiveOwnerThread())
                throw new IllegalMonitorStateException();
            boolean free = false;
    		// 判断是否完全释放锁，因为可重入
            if (c == 0) {
                free = true;
                setExclusiveOwnerThread(null);
            }
            setState(c);
            return free;
        }
// 这段代码和上面的一致，只是为了顺序性，又拷下来看下

public final boolean release(int arg) {
        // 尝试去释放，如果是完全释放，返回的就是true，否则是false
        if (tryRelease(arg)) {
            Node h = head;
            // 这里判断头结点是否为空以及waitStatus的状态，前面说了head节点其实是
            // 在第二个线程进来的时候初始化的，如果是空的话说明没后续节点，并且waitStatus
            // 也表示了后续的等待状态
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }

/**
     * Wakes up node's successor, if one exists.
     *
     * @param node the node
     */
// 唤醒后继节点
    private void unparkSuccessor(Node node) {
        /*
         * If status is negative (i.e., possibly needing signal) try
         * to clear in anticipation of signalling.  It is OK if this
         * fails or if status is changed by waiting thread.
         */
        int ws = node.waitStatus;
        if (ws < 0)
            compareAndSetWaitStatus(node, ws, 0);

        /*
         * Thread to unpark is held in successor, which is normally
         * just the next node.  But if cancelled or apparently null,
         * traverse backwards from tail to find the actual
         * non-cancelled successor.
         */
        Node s = node.next;
        // 如果后继节点是空或者当前节点取消等待了
        if (s == null || s.waitStatus > 0) {
            s = null;
            // 从后往前找，找到非取消的节点，注意这里不是找到就退出，而是一直找到头
            // 所以不必担心中间有取消的
            for (Node t = tail; t != null && t != node; t = t.prev)
                if (t.waitStatus <= 0)
                    s = t;
        }
        if (s != null)
            // 将其唤醒
            LockSupport.unpark(s.thread);
    }
+ Comparator使用小记 + /2020/04/05/Comparator%E4%BD%BF%E7%94%A8%E5%B0%8F%E8%AE%B0/ + 在Java8的stream之前，将对象进行排序的时候，可能需要对象实现Comparable接口，或者自己实现一个Comparator，

+

比如这样子

+

我的对象是Entity

+
public class Entity {

    private Long id;

    private Long sortValue;

    public Long getId() {
        return id;
    }

    public void setId(Long id) {
        this.id = id;
    }

    public Long getSortValue() {
        return sortValue;
    }

    public void setSortValue(Long sortValue) {
        this.sortValue = sortValue;
    }
}
+

Comparator

+
public class MyComparator implements Comparator {
    @Override
    public int compare(Object o1, Object o2) {
        Entity e1 = (Entity) o1;
        Entity e2 = (Entity) o2;
        if (e1.getSortValue() < e2.getSortValue()) {
            return -1;
        } else if (e1.getSortValue().equals(e2.getSortValue())) {
            return 0;
        } else {
            return 1;
        }
    }
}
+

比较代码

+
private static MyComparator myComparator = new MyComparator();

    public static void main(String[] args) {
        List<Entity> list = new ArrayList<Entity>();
        Entity e1 = new Entity();
        e1.setId(1L);
        e1.setSortValue(1L);
        list.add(e1);
        Entity e2 = new Entity();
        e2.setId(2L);
        e2.setSortValue(null);
        list.add(e2);
        Collections.sort(list, myComparator);
+

看到这里的e2的排序值是null，在Comparator中如果要正常运行的话，就得判空之类的，这里有两点需要，一个是不想写这个MyComparator，然后也没那么好排除掉list里排序值，那么有什么办法能解决这种问题呢，应该说java的这方面真的是很强大

+

+

看一下nullsFirst的实现

+
final static class NullComparator<T> implements Comparator<T>, Serializable {
        private static final long serialVersionUID = -7569533591570686392L;
        private final boolean nullFirst;
        // if null, non-null Ts are considered equal
        private final Comparator<T> real;

        @SuppressWarnings("unchecked")
        NullComparator(boolean nullFirst, Comparator<? super T> real) {
            this.nullFirst = nullFirst;
            this.real = (Comparator<T>) real;
        }

        @Override
        public int compare(T a, T b) {
            if (a == null) {
                return (b == null) ? 0 : (nullFirst ? -1 : 1);
            } else if (b == null) {
                return nullFirst ? 1: -1;
            } else {
                return (real == null) ? 0 : real.compare(a, b);
            }
        }
+

核心代码就是下面这段，其实就是帮我们把前面要做的事情做掉了，是不是挺方便的，小记一下哈

]]> Java - 并发 + 集合 - java - 并发 - j.u.c - aqs + Java + Stream + Comparator + 排序 + sort + nullsfirst @@ -395,52 +446,44 @@ - Disruptor 系列二 - /2022/02/27/Disruptor-%E7%B3%BB%E5%88%97%E4%BA%8C/ - 这里开始慢慢深入的讲一下 disruptor，首先是 lock free , 相比于前面介绍的两个阻塞队列，
disruptor 本身是不直接使用锁的，因为本身的设计是单个线程去生产，通过 cas 来维护头指针，
不直接维护尾指针，这样就减少了锁的使用，提升了性能；第二个是这次介绍的重点，
减少 false sharing 的情况，也就是常说的 伪共享 问题，那么什么叫 伪共享 呢，
这里要扯到一些 cpu 缓存的知识，

譬如我在用的这个笔记本

这里就可能看到 L2 Cache 就是针对每个核的

这里可以看到现代 CPU 的结构里，分为三级缓存，越靠近 cpu 的速度越快，存储容量越小，
而 L1 跟 L2 是 CPU 核专属的每个核都有自己的 L1 和 L2 的，其中 L1 还分为数据和指令，
像我上面的图中显示的 L1 Cache 只有 64KB 大小，其中数据 32KB，指令 32KB，
而 L2 则有 256KB，L3 有 4MB，其中的 Line Size 是我们这里比较重要的一个值，
CPU 其实会就近地从 Cache 中读取数据，碰到 Cache Miss 就再往下一级 Cache 读取，
每次读取是按照缓存行 Cache Line 读取，并且也遵循了“就近原则”，
也就是相近的数据有可能也会马上被读取，所以以行的形式读取，然而这也造成了 false sharing，
因为类似于 ArrayBlockingQueue，需要有 takeIndex , putIndex , count , 因为在同一个类中，
很有可能存在于同一个 Cache Line 中，但是这几个值会被不同的线程修改，
导致从 Cache 取出来以后立马就会被失效，所谓的就近原则也就没用了，
因为需要反复地标记 dirty 脏位，然后把 Cache 刷掉，就造成了false sharing这种情况
而在 disruptor 中则使用了填充的方式，让我的头指针能够不产生false sharing

-
class LhsPadding
{
    protected long p1, p2, p3, p4, p5, p6, p7;
}

class Value extends LhsPadding
{
    protected volatile long value;
}

class RhsPadding extends Value
{
    protected long p9, p10, p11, p12, p13, p14, p15;
}

/**
 * <p>Concurrent sequence class used for tracking the progress of
 * the ring buffer and event processors.  Support a number
 * of concurrent operations including CAS and order writes.
 *
 * <p>Also attempts to be more efficient with regards to false
 * sharing by adding padding around the volatile field.
 */
public class Sequence extends RhsPadding
{
-

通过代码可以看到，sequence 中其实真正有意义的是 value 字段，因为需要在多线程环境下可见也
使用了volatile 关键字，而 LhsPadding 和 RhsPadding 分别在value 前后填充了各
7 个 long 型的变量，long 型的变量在 Java 中是占用 8 bytes，这样就相当于不管怎么样，
value 都会单独使用一个缓存行，使得其不会产生 false sharing 的问题。

-]]> - - Java - - - Java - Disruptor - - - - Comparator使用小记 - /2020/04/05/Comparator%E4%BD%BF%E7%94%A8%E5%B0%8F%E8%AE%B0/ - 在Java8的stream之前，将对象进行排序的时候，可能需要对象实现Comparable接口，或者自己实现一个Comparator，

-

比如这样子

-

我的对象是Entity

-
public class Entity {

    private Long id;

    private Long sortValue;

    public Long getId() {
        return id;
    }

    public void setId(Long id) {
        this.id = id;
    }

    public Long getSortValue() {
        return sortValue;
    }

    public void setSortValue(Long sortValue) {
        this.sortValue = sortValue;
    }
}
- -

Comparator

-
public class MyComparator implements Comparator {
    @Override
    public int compare(Object o1, Object o2) {
        Entity e1 = (Entity) o1;
        Entity e2 = (Entity) o2;
        if (e1.getSortValue() < e2.getSortValue()) {
            return -1;
        } else if (e1.getSortValue().equals(e2.getSortValue())) {
            return 0;
        } else {
            return 1;
        }
    }
}
- -

比较代码

-
private static MyComparator myComparator = new MyComparator();

    public static void main(String[] args) {
        List<Entity> list = new ArrayList<Entity>();
        Entity e1 = new Entity();
        e1.setId(1L);
        e1.setSortValue(1L);
        list.add(e1);
        Entity e2 = new Entity();
        e2.setId(2L);
        e2.setSortValue(null);
        list.add(e2);
        Collections.sort(list, myComparator);
+ Disruptor 系列三 + /2022/09/25/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%89/ + 原来一直有点被误导，
gatingSequences用来标识每个 processer 的操作位点，但是怎么记录更新有点搞不清楚
其实问题在于 gatingSequences 是个 Sequence 数组，首先要看下怎么加进去的，
可以看到是在 com.lmax.disruptor.RingBuffer#addGatingSequences 这个方法里添加
首先是 com.lmax.disruptor.dsl.Disruptor#handleEventsWith(com.lmax.disruptor.EventHandler<? super T>...)
然后执行 com.lmax.disruptor.dsl.Disruptor#createEventProcessors(com.lmax.disruptor.Sequence[], com.lmax.disruptor.EventHandler<? super T>[])

+
EventHandlerGroup<T> createEventProcessors(
        final Sequence[] barrierSequences,
        final EventHandler<? super T>[] eventHandlers)
    {
        checkNotStarted();

        final Sequence[] processorSequences = new Sequence[eventHandlers.length];
        final SequenceBarrier barrier = ringBuffer.newBarrier(barrierSequences);

        for (int i = 0, eventHandlersLength = eventHandlers.length; i < eventHandlersLength; i++)
        {
            final EventHandler<? super T> eventHandler = eventHandlers[i];

            // 这里将 handler 包装成一个 BatchEventProcessor
            final BatchEventProcessor<T> batchEventProcessor =
                new BatchEventProcessor<>(ringBuffer, barrier, eventHandler);

            if (exceptionHandler != null)
            {
                batchEventProcessor.setExceptionHandler(exceptionHandler);
            }

            consumerRepository.add(batchEventProcessor, eventHandler, barrier);
            processorSequences[i] = batchEventProcessor.getSequence();
        }

        updateGatingSequencesForNextInChain(barrierSequences, processorSequences);

        return new EventHandlerGroup<>(this, consumerRepository, processorSequences);
    }
-

看到这里的e2的排序值是null，在Comparator中如果要正常运行的话，就得判空之类的，这里有两点需要，一个是不想写这个MyComparator，然后也没那么好排除掉list里排序值，那么有什么办法能解决这种问题呢，应该说java的这方面真的是很强大

-

-

看一下nullsFirst的实现

-
final static class NullComparator<T> implements Comparator<T>, Serializable {
        private static final long serialVersionUID = -7569533591570686392L;
        private final boolean nullFirst;
        // if null, non-null Ts are considered equal
        private final Comparator<T> real;

        @SuppressWarnings("unchecked")
        NullComparator(boolean nullFirst, Comparator<? super T> real) {
            this.nullFirst = nullFirst;
            this.real = (Comparator<T>) real;
        }

        @Override
        public int compare(T a, T b) {
            if (a == null) {
                return (b == null) ? 0 : (nullFirst ? -1 : 1);
            } else if (b == null) {
                return nullFirst ? 1: -1;
            } else {
                return (real == null) ? 0 : real.compare(a, b);
            }
        }
+

在 BatchEventProcessor 在类内有个定义 sequence

+
private final Sequence sequence = new Sequence(Sequencer.INITIAL_CURSOR_VALUE);
+

然后在上面循环中的这一句取出来

+
processorSequences[i] = batchEventProcessor.getSequence();
+

调用com.lmax.disruptor.dsl.Disruptor#updateGatingSequencesForNextInChain 方法

+
private void updateGatingSequencesForNextInChain(final Sequence[] barrierSequences, final Sequence[] processorSequences)
    {
        if (processorSequences.length > 0)
        {
            // 然后在这里添加
            ringBuffer.addGatingSequences(processorSequences);
            for (final Sequence barrierSequence : barrierSequences)
            {
                ringBuffer.removeGatingSequence(barrierSequence);
            }
            consumerRepository.unMarkEventProcessorsAsEndOfChain(barrierSequences);
        }
    }
-

核心代码就是下面这段，其实就是帮我们把前面要做的事情做掉了，是不是挺方便的，小记一下哈

+

而如何更新则是在处理器 com.lmax.disruptor.BatchEventProcessor#run 中

+
public void run()
    {
        if (running.compareAndSet(IDLE, RUNNING))
        {
            sequenceBarrier.clearAlert();

            notifyStart();
            try
            {
                if (running.get() == RUNNING)
                {
                    processEvents();
                }
            }
            finally
            {
                notifyShutdown();
                running.set(IDLE);
            }
        }
        else
        {
            // This is a little bit of guess work.  The running state could of changed to HALTED by
            // this point.  However, Java does not have compareAndExchange which is the only way
            // to get it exactly correct.
            if (running.get() == RUNNING)
            {
                throw new IllegalStateException("Thread is already running");
            }
            else
            {
                earlyExit();
            }
        }
    }
+

然后是

+
private void processEvents()
    {
        T event = null;
        long nextSequence = sequence.get() + 1L;

        while (true)
        {
            try
            {
                final long availableSequence = sequenceBarrier.waitFor(nextSequence);
                if (batchStartAware != null)
                {
                    batchStartAware.onBatchStart(availableSequence - nextSequence + 1);
                }

                while (nextSequence <= availableSequence)
                {
                    event = dataProvider.get(nextSequence);
                    eventHandler.onEvent(event, nextSequence, nextSequence == availableSequence);
                    nextSequence++;
                }
                // 如果正常处理完，那就是会更新为 availableSequence，因为都处理好了
                sequence.set(availableSequence);
            }
            catch (final TimeoutException e)
            {
                notifyTimeout(sequence.get());
            }
            catch (final AlertException ex)
            {
                if (running.get() != RUNNING)
                {
                    break;
                }
            }
            catch (final Throwable ex)
            {
                handleEventException(ex, nextSequence, event);
                // 如果是异常就只是 nextSequence
                sequence.set(nextSequence);
                nextSequence++;
            }
        }
    }
]]> Java - 集合 Java - Stream - Comparator - 排序 - sort - nullsfirst + Disruptor + + + + Disruptor 系列二 + /2022/02/27/Disruptor-%E7%B3%BB%E5%88%97%E4%BA%8C/ + 这里开始慢慢深入的讲一下 disruptor，首先是 lock free , 相比于前面介绍的两个阻塞队列，
disruptor 本身是不直接使用锁的，因为本身的设计是单个线程去生产，通过 cas 来维护头指针，
不直接维护尾指针，这样就减少了锁的使用，提升了性能；第二个是这次介绍的重点，
减少 false sharing 的情况，也就是常说的 伪共享 问题，那么什么叫 伪共享 呢，
这里要扯到一些 cpu 缓存的知识，

譬如我在用的这个笔记本

这里就可能看到 L2 Cache 就是针对每个核的

这里可以看到现代 CPU 的结构里，分为三级缓存，越靠近 cpu 的速度越快，存储容量越小，
而 L1 跟 L2 是 CPU 核专属的每个核都有自己的 L1 和 L2 的，其中 L1 还分为数据和指令，
像我上面的图中显示的 L1 Cache 只有 64KB 大小，其中数据 32KB，指令 32KB，
而 L2 则有 256KB，L3 有 4MB，其中的 Line Size 是我们这里比较重要的一个值，
CPU 其实会就近地从 Cache 中读取数据，碰到 Cache Miss 就再往下一级 Cache 读取，
每次读取是按照缓存行 Cache Line 读取，并且也遵循了“就近原则”，
也就是相近的数据有可能也会马上被读取，所以以行的形式读取，然而这也造成了 false sharing，
因为类似于 ArrayBlockingQueue，需要有 takeIndex , putIndex , count , 因为在同一个类中，
很有可能存在于同一个 Cache Line 中，但是这几个值会被不同的线程修改，
导致从 Cache 取出来以后立马就会被失效，所谓的就近原则也就没用了，
因为需要反复地标记 dirty 脏位，然后把 Cache 刷掉，就造成了false sharing这种情况
而在 disruptor 中则使用了填充的方式，让我的头指针能够不产生false sharing

+
class LhsPadding
{
    protected long p1, p2, p3, p4, p5, p6, p7;
}

class Value extends LhsPadding
{
    protected volatile long value;
}

class RhsPadding extends Value
{
    protected long p9, p10, p11, p12, p13, p14, p15;
}

/**
 * <p>Concurrent sequence class used for tracking the progress of
 * the ring buffer and event processors.  Support a number
 * of concurrent operations including CAS and order writes.
 *
 * <p>Also attempts to be more efficient with regards to false
 * sharing by adding padding around the volatile field.
 */
public class Sequence extends RhsPadding
{
+

通过代码可以看到，sequence 中其实真正有意义的是 value 字段，因为需要在多线程环境下可见也
使用了volatile 关键字，而 LhsPadding 和 RhsPadding 分别在value 前后填充了各
7 个 long 型的变量，long 型的变量在 Java 中是占用 8 bytes，这样就相当于不管怎么样，
value 都会单独使用一个缓存行，使得其不会产生 false sharing 的问题。

+]]> + + Java + + + Java + Disruptor @@ -465,23 +508,6 @@ 负载均衡 - - Clone Graph Part I - /2014/12/30/Clone-Graph-Part-I/ - problem
Clone a graph. Input is a Node pointer. Return the Node pointer of the cloned graph.

A graph is defined below:
struct Node {
vector neighbors;
}
- - -

code

typedef unordered_map<Node *, Node *> Map;
 
Node *clone(Node *graph) {
    if (!graph) return NULL;
 
    Map map;
    queue<Node *> q;
    q.push(graph);
 
    Node *graphCopy = new Node();
    map[graph] = graphCopy;
 
    while (!q.empty()) {
        Node *node = q.front();
        q.pop();
        int n = node->neighbors.size();
        for (int i = 0; i < n; i++) {
            Node *neighbor = node->neighbors[i];
            // no copy exists
            if (map.find(neighbor) == map.end()) {
                Node *p = new Node();
                map[node]->neighbors.push_back(p);
                map[neighbor] = p;
                q.push(neighbor);
            } else {     // a copy already exists
                map[node]->neighbors.push_back(map[neighbor]);
            }
        }
    }
 
    return graphCopy;
}
-

anlysis

using the Breadth-first traversal
and use a map to save the neighbors not to be duplicated.

-]]> - - leetcode - - - leetcode - C++ - - Filter, Interceptor, Aop, 啥, 啥, 啥? 这些都是啥? /2020/08/22/Filter-Intercepter-Aop-%E5%95%A5-%E5%95%A5-%E5%95%A5-%E8%BF%99%E4%BA%9B%E9%83%BD%E6%98%AF%E5%95%A5/ @@ -549,29 +575,41 @@ - Disruptor 系列三 - /2022/09/25/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%89/ - 原来一直有点被误导，
gatingSequences用来标识每个 processer 的操作位点，但是怎么记录更新有点搞不清楚
其实问题在于 gatingSequences 是个 Sequence 数组，首先要看下怎么加进去的，
可以看到是在 com.lmax.disruptor.RingBuffer#addGatingSequences 这个方法里添加
首先是 com.lmax.disruptor.dsl.Disruptor#handleEventsWith(com.lmax.disruptor.EventHandler<? super T>...)
然后执行 com.lmax.disruptor.dsl.Disruptor#createEventProcessors(com.lmax.disruptor.Sequence[], com.lmax.disruptor.EventHandler<? super T>[])

-
EventHandlerGroup<T> createEventProcessors(
        final Sequence[] barrierSequences,
        final EventHandler<? super T>[] eventHandlers)
    {
        checkNotStarted();

        final Sequence[] processorSequences = new Sequence[eventHandlers.length];
        final SequenceBarrier barrier = ringBuffer.newBarrier(barrierSequences);

        for (int i = 0, eventHandlersLength = eventHandlers.length; i < eventHandlersLength; i++)
        {
            final EventHandler<? super T> eventHandler = eventHandlers[i];

            // 这里将 handler 包装成一个 BatchEventProcessor
            final BatchEventProcessor<T> batchEventProcessor =
                new BatchEventProcessor<>(ringBuffer, barrier, eventHandler);

            if (exceptionHandler != null)
            {
                batchEventProcessor.setExceptionHandler(exceptionHandler);
            }

            consumerRepository.add(batchEventProcessor, eventHandler, barrier);
            processorSequences[i] = batchEventProcessor.getSequence();
        }

        updateGatingSequencesForNextInChain(barrierSequences, processorSequences);

        return new EventHandlerGroup<>(this, consumerRepository, processorSequences);
    }
- -

在 BatchEventProcessor 在类内有个定义 sequence

-
private final Sequence sequence = new Sequence(Sequencer.INITIAL_CURSOR_VALUE);
-

然后在上面循环中的这一句取出来

-
processorSequences[i] = batchEventProcessor.getSequence();
-

调用com.lmax.disruptor.dsl.Disruptor#updateGatingSequencesForNextInChain 方法

-
private void updateGatingSequencesForNextInChain(final Sequence[] barrierSequences, final Sequence[] processorSequences)
    {
        if (processorSequences.length > 0)
        {
            // 然后在这里添加
            ringBuffer.addGatingSequences(processorSequences);
            for (final Sequence barrierSequence : barrierSequences)
            {
                ringBuffer.removeGatingSequence(barrierSequence);
            }
            consumerRepository.unMarkEventProcessorsAsEndOfChain(barrierSequences);
        }
    }
- -

而如何更新则是在处理器 com.lmax.disruptor.BatchEventProcessor#run 中

-
public void run()
    {
        if (running.compareAndSet(IDLE, RUNNING))
        {
            sequenceBarrier.clearAlert();

            notifyStart();
            try
            {
                if (running.get() == RUNNING)
                {
                    processEvents();
                }
            }
            finally
            {
                notifyShutdown();
                running.set(IDLE);
            }
        }
        else
        {
            // This is a little bit of guess work.  The running state could of changed to HALTED by
            // this point.  However, Java does not have compareAndExchange which is the only way
            // to get it exactly correct.
            if (running.get() == RUNNING)
            {
                throw new IllegalStateException("Thread is already running");
            }
            else
            {
                earlyExit();
            }
        }
    }
-

然后是

-
private void processEvents()
    {
        T event = null;
        long nextSequence = sequence.get() + 1L;

        while (true)
        {
            try
            {
                final long availableSequence = sequenceBarrier.waitFor(nextSequence);
                if (batchStartAware != null)
                {
                    batchStartAware.onBatchStart(availableSequence - nextSequence + 1);
                }

                while (nextSequence <= availableSequence)
                {
                    event = dataProvider.get(nextSequence);
                    eventHandler.onEvent(event, nextSequence, nextSequence == availableSequence);
                    nextSequence++;
                }
                // 如果正常处理完，那就是会更新为 availableSequence，因为都处理好了
                sequence.set(availableSequence);
            }
            catch (final TimeoutException e)
            {
                notifyTimeout(sequence.get());
            }
            catch (final AlertException ex)
            {
                if (running.get() != RUNNING)
                {
                    break;
                }
            }
            catch (final Throwable ex)
            {
                handleEventException(ex, nextSequence, event);
                // 如果是异常就只是 nextSequence
                sequence.set(nextSequence);
                nextSequence++;
            }
        }
    }
+ Headscale下篇-自定义中转derper + /2024/04/14/Headscale%E4%B8%8B%E7%AF%87-%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%AD%E8%BD%ACderper/ + derper是 tailscale 已经开源的 tailscale 中转服务，可以进行自己搭建，不然就是使用 tailscale 提供的 derper 节点，我的体验是在晚上高峰期会比较卡，所以有能力可以自建的话也是比较推荐的，不过相对比前面的设置会麻烦点

+

第一个就是默认这个是需要 https 的，所以需要给 derper 服务申请个域名，或者有了域名就加个解析，比如 dp.hello.com，申请完域名就需要申请证书了，这个刚好就学习下使用 acme.sh这个工具，还是非常不错的，我用的是腾讯云的域名解析服务，其实是收购的 dnspod 的服务，acme.sh 安装就很简单，一条命令

+

`

+
curl  https://get.acme.sh | sh
+

然后成功了之后就要把.zshrc 生效下，默认会把脚本路径加入到当前的 shell 的 rc 文件里，然后就是申请证书，但是有两种方式，一种就是在域名指定的 ip 机器对应的访问路径下放置验证的文件

+
acme.sh  --issue  -d domain.tld -d www.domain.tld  --webroot  /home/wwwroot/domain.tld/
+

比如这样指定 webroot，另一个中更方便的方式就是通过 dns 认证的方式，我这边以 dnspod 举例，这边要特别注意的是 dnspod 虽然被腾讯云收购，但是这个方式还是需要用 dnspod 原来自己的 api token 验证的

+

+

然后拿到生成的 id 跟 token 设置到环境变量里，需要保存好这个 token，后面是看不到了的

+
export DP_Id=123456
export DP_Key=xxxxxx
+

然后就可以用命令进行验证了

+
acme.sh --issue --dns dns_dp -d xxx.hello.com
+

这个是 dnspod 家的，其他的也可以在这里找到验证方式

+

申请完成后会把证书放在用户目录下的.acme.sh 目录中，记得把cer文件改成 crt，并且文件名只能是域名 xxx.hello.com.crt，我没试过其他的行不行，从别的文章里看到需要一致，然后就是启动 derper 了

+

我们通过 docker 的方式，

+
sudo docker run --restart always \
  --name derper -p 12345:12345 -p 3478:3478/udp \
  -v /$dir/.acme.sh/xxxxx.hello.com_ecc/:/app/certs \
  -v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock \
  -e DERP_CERT_MODE=manual \
  -e DERP_ADDR=:12345 \
  -e DERP_DOMAIN=xxxxx.hello.com \
  -e DERP_VERIFY_CLIENTS=true \
  -d ghcr.io/yangchuansheng/derper:latest
+

 前一个端口 12345可以自己随意定，后面$dir改成自己的.acme.sh目录所在的路径，然后

+

-v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock \ 这一行 和-e DERP_VERIFY_CLIENTS=true \ 是为了能够让 derper 能够安全一些，就是在本机开一个 tailscale 客户端，连接接到 headscale 服务，可以参考前面的添加节点，然后把这个启动客户端的进程映射进docker里面，因为本身 derper 就没有特别的认证逻辑，所以就让同一个headscale服务的客户端进程来验证是属于同一个headscale服务的节点才允许加入中转，实际这样就跑起来了一个 derper 服务这样可以用 docker 命令查看日志

+

sudo docker logs -f derper 查看是不是有问题，然后如果没有问题的话还不一定代表 derper 起来了，比较直接的方法就是直接打开域名加上端口的地址，比如 xxxx.hello.com:12345 这个,

+

image

+

显示这个就代表服务已经正常启动运行了，然后就是需要修改 headscale 的配置文件了，首先要设置 derper 的配置文件，类似这样

+
regions:
  100:
    regionid: 100
    regioncode: ahk 
    regionname: Aliyun Hongkong 
    nodes:
      - name: 100a
        regionid: 100
        hostname: xxxxx.hello.com
        ipv4:
        stunport: 3478
        stunonly: false
        derpport: 12345
+

regionid表示区域 id，一个区域下可以后多个 node，node的 name 是节点的唯一识别码，ipv4 可以不填，有 hostname 就可以，然后stunonly 是 false 表示不只使用 stun还可以使用 derp

+

然后就是修改 headscale 自身的配置文件，把共用的 derper 配置改成

+

image

+

上面的 urls 注释掉，后面的 paths 添加刚才的配置文件路径，这样就可以了，至于连接是不是走的这个

+

可以用 tailscale netcheck 查看最近的 derper 节点已经延迟，看到使用了自己的 derper 就对了

]]> - Java + headscale - Java - Disruptor + headscale @@ -591,6 +629,39 @@

这就是个文件，内存也完全没有被占满的迹象，原来也是文件路径的问题

# For production:
# db_path: /var/lib/headscale/db.sqlite
db_path: /var/lib/headscale/db.sqlite

都改成绝对路径就可以了，然后这里还有个就是要对/var/lib/headscale/和/etc/headscale/等路径赋予headscale用户权限，有时候对这类问题的排查真的蛮头疼，日志报错都不是真实的错误信息，开源项目对这些错误的提示真的也需要优化，后续的譬如mac也加入节点等后面再开篇讲

+]]> + + headscale + + + headscale + + + + Headscale渐入佳境-路由设置 + /2024/04/07/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83-%E8%B7%AF%E7%94%B1%E8%AE%BE%E7%BD%AE/ + headscale 在前面两篇初体验和添加节点 主要是介绍了如何搭建和使用，但是这里有个很大的缺点，比如我在想要两地的网络能够连通，但是两地的网络内都不止一个机器，那想让他们连通就需要把它们都加入这个 headscale 所组成的虚拟局域网，并且每个都是这个虚拟局域网的ip，跟原来的 ip 地址不一样会比较难记忆，所以有没有办法不用这么麻烦呢，当然是有的，那就是 headscale 的路由功能。
原先比如我们有两个设备，在两地，通过headscale我把这两个异地节点都添加进去了，节点 A 的 ip比如是 10.0.0.1，另一个节点 B 是 10.0.0.2，在之前的状态下，我的节点 A 只能通过 10.0.0.2 来连接节点 B，如果节点 A 想要连接节点 B 所在地的局域网内部其他节点，我们就可以通过节点 B 的客户端登录的时候开启路由转发，这样节点 B 就会充当一个路由转发的功能，并且不需要通过10.0.0这个网段来访问，直接可以通过节点 B所在的局域网地址来访问，比如192.168.2.10，具体操作就是把客户端启动方式改成命令行的，主要就是 --advertise-routes, 指明需要转发的网段

+
tailscale up --advertise-routes=192.168.xx.0/24 --accept-dns=false --login-server=http://serverIp:serverPort --unattended
+

前面的 192.168.xx.0 就是节点 B 所在局域网的网段，然后 login-server 是 headscale 的服务 ip 跟端口，这个本身客户端就会使用，另外这个还有一个前提的，需要开启服务器的端口转发，对于 Windows 其实默认就可以，Linux 的话就需要设置下

+
$ echo 'net.ipv4.ip_forward = 1' | tee /etc/sysctl.d/ipforwarding.conf
$ echo 'net.ipv6.conf.all.forwarding = 1' | tee -a /etc/sysctl.d/ipforwarding.conf
$ sysctl -p /etc/sysctl.d/ipforwarding.conf
+ +

到这里我们只完成了第一步，第二步需要在 headscale 服务端启用这条路由配置
首先可以用 sudo headscale routes 查看当前有的路由
sudo headscale routes enable -h 这里看下怎么启用，因为网上有些是用-i 指定哪一条路由的，但是我使用的这个版本是用-r 的
sudo headscale routes enable -r 1 然后用这个命令启用就行了


这样我们就能够很方便地在单个节点互连之后访问到对方局域网内的其他节点

+]]> + + headscale + + + headscale + + + + Headscale渐入佳境补充篇-自定义中转derper的证书问题 + /2024/05/26/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83%E8%A1%A5%E5%85%85%E7%AF%87-%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%AD%E8%BD%ACderper%E7%9A%84%E8%AF%81%E4%B9%A6%E9%97%AE%E9%A2%98/ + 之前自定义部署的derper在Mac端和Windows使用时没啥问题，但是在我想作为网关的Armbian小机器上一直会报 “x509: certificate signed by unknown authority”
错误，原因在于之前在通过acme生成证书以后我是直接把cer证书重命名成crt就映射到derper的docker目录里，对于Mac和Windows客户端应该是有兼容逻辑，而实际使用需要完整的证书去识别认证机构，否则就会被认为是自定义证书，在一些常规的证书认证逻辑里就会报上面的问题
这边需要先把生成的 fullchain.cer 完整证书转成crt格式的

+
openssl x509 -inform PEM -in fullchain.cer -out derper.demo.com.crt
+

这边是PEM格式的证书，如果是DER的话就改一下

+
openssl x509 -inform DER -in certificate.cer -out certificate.crt
+

转换完成后就重新启动下docker
这里的原因主要是fullchain.cer是包含了完整的证书链，而ca.cer只是包含用户证书，所以需要替换一下，这算是个小问题

]]> headscale @@ -629,41 +700,35 @@ - Headscale下篇-自定义中转derper - /2024/04/14/Headscale%E4%B8%8B%E7%AF%87-%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%AD%E8%BD%ACderper/ - derper是 tailscale 已经开源的 tailscale 中转服务，可以进行自己搭建，不然就是使用 tailscale 提供的 derper 节点，我的体验是在晚上高峰期会比较卡，所以有能力可以自建的话也是比较推荐的，不过相对比前面的设置会麻烦点

-

第一个就是默认这个是需要 https 的，所以需要给 derper 服务申请个域名，或者有了域名就加个解析，比如 dp.hello.com，申请完域名就需要申请证书了，这个刚好就学习下使用 acme.sh这个工具，还是非常不错的，我用的是腾讯云的域名解析服务，其实是收购的 dnspod 的服务，acme.sh 安装就很简单，一条命令

-

`

-
curl  https://get.acme.sh | sh
-

然后成功了之后就要把.zshrc 生效下，默认会把脚本路径加入到当前的 shell 的 rc 文件里，然后就是申请证书，但是有两种方式，一种就是在域名指定的 ip 机器对应的访问路径下放置验证的文件

-
acme.sh  --issue  -d domain.tld -d www.domain.tld  --webroot  /home/wwwroot/domain.tld/
-

比如这样指定 webroot，另一个中更方便的方式就是通过 dns 认证的方式，我这边以 dnspod 举例，这边要特别注意的是 dnspod 虽然被腾讯云收购，但是这个方式还是需要用 dnspod 原来自己的 api token 验证的

-

-

然后拿到生成的 id 跟 token 设置到环境变量里，需要保存好这个 token，后面是看不到了的

-
export DP_Id=123456
export DP_Key=xxxxxx
-

然后就可以用命令进行验证了

-
acme.sh --issue --dns dns_dp -d xxx.hello.com
-

这个是 dnspod 家的，其他的也可以在这里找到验证方式

-

申请完成后会把证书放在用户目录下的.acme.sh 目录中，记得把cer文件改成 crt，并且文件名只能是域名 xxx.hello.com.crt，我没试过其他的行不行，从别的文章里看到需要一致，然后就是启动 derper 了

-

我们通过 docker 的方式，

-
sudo docker run --restart always \
  --name derper -p 12345:12345 -p 3478:3478/udp \
  -v /$dir/.acme.sh/xxxxx.hello.com_ecc/:/app/certs \
  -v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock \
  -e DERP_CERT_MODE=manual \
  -e DERP_ADDR=:12345 \
  -e DERP_DOMAIN=xxxxx.hello.com \
  -e DERP_VERIFY_CLIENTS=true \
  -d ghcr.io/yangchuansheng/derper:latest
-

 前一个端口 12345可以自己随意定，后面$dir改成自己的.acme.sh目录所在的路径，然后

-

-v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock \ 这一行 和-e DERP_VERIFY_CLIENTS=true \ 是为了能够让 derper 能够安全一些，就是在本机开一个 tailscale 客户端，连接接到 headscale 服务，可以参考前面的添加节点，然后把这个启动客户端的进程映射进docker里面，因为本身 derper 就没有特别的认证逻辑，所以就让同一个headscale服务的客户端进程来验证是属于同一个headscale服务的节点才允许加入中转，实际这样就跑起来了一个 derper 服务这样可以用 docker 命令查看日志

-

sudo docker logs -f derper 查看是不是有问题，然后如果没有问题的话还不一定代表 derper 起来了，比较直接的方法就是直接打开域名加上端口的地址，比如 xxxx.hello.com:12345 这个,

-

image

-

显示这个就代表服务已经正常启动运行了，然后就是需要修改 headscale 的配置文件了，首先要设置 derper 的配置文件，类似这样

-
regions:
  100:
    regionid: 100
    regioncode: ahk 
    regionname: Aliyun Hongkong 
    nodes:
      - name: 100a
        regionid: 100
        hostname: xxxxx.hello.com
        ipv4:
        stunport: 3478
        stunonly: false
        derpport: 12345
-

regionid表示区域 id，一个区域下可以后多个 node，node的 name 是节点的唯一识别码，ipv4 可以不填，有 hostname 就可以，然后stunonly 是 false 表示不只使用 stun还可以使用 derp

-

然后就是修改 headscale 自身的配置文件，把共用的 derper 配置改成

-

image

-

上面的 urls 注释掉，后面的 paths 添加刚才的配置文件路径，这样就可以了，至于连接是不是走的这个

-

可以用 tailscale netcheck 查看最近的 derper 节点已经延迟，看到使用了自己的 derper 就对了

+ Java 线程池系列-准备篇 + /2024/02/04/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E5%87%86%E5%A4%87%E7%AF%87/ + Java 线程池是 Java 并发体系里的重要组成部分，不过说起线程池一般都是几个参数，以及参数的含义和逻辑，为了更适合刚开始学习的同学或者说有一些会对这么个习惯性的概括讲法有点疑惑的，我们就从零开始，从最基础的开始看起，先来看下里面用来判断线程池状态和线程数量的部分

+
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
private static final int COUNT_BITS = Integer.SIZE - 3;
private static final int CAPACITY   = (1 << COUNT_BITS) - 1;

// runState is stored in the high-order bits
private static final int RUNNING    = -1 << COUNT_BITS;
private static final int SHUTDOWN   =  0 << COUNT_BITS;
private static final int STOP       =  1 << COUNT_BITS;
private static final int TIDYING    =  2 << COUNT_BITS;
private static final int TERMINATED =  3 << COUNT_BITS;

// Packing and unpacking ctl
private static int runStateOf(int c)     { return c & ~CAPACITY; }
private static int workerCountOf(int c)  { return c & CAPACITY; }
private static int ctlOf(int rs, int wc) { return rs | wc; }
+

这里我们先看下 COUNT_BITS 它是 Integer.SIZE - 3，Integer.SIZE 这个是 32，也就是代表了 Java 用了四个 Byte 来存一个 Integer, 那么 COUNT_BITS 就是 29，
这个 29 是用了干嘛的呢，逐步往下看，为什么先介绍 COUNT_BITS，因为第一行的 ctl 的值是通过 ctlOf(RUNNING, 0) 算出来的，而 RUNNING 又是通过等于 -1 左移 COUNT_BITS 位来得到，移动 29 位后，还剩下三位，而这三位只需要表示后面五种状态，其中只有 RUNNING 是负值，这样就巧妙的只利用了 ctl 的前三位来存储线程池的状态而后面的 29 位就是用来存储线程数量的，具体怎么取呢，我们在来看下 CAPACITY 它是 (1 << COUNT_BITS) - 1 把 1 左移 29 位后减 1，也就是 536870911，这里再补充下，左移一位就等于是乘以 2，左移 29 位就是乘以 2 的 29 次，减了 1 就是 29 位能存的最大值，代表线程池最大的线程容量，用二进制来表示就是 32 位长度的int，前三位是 0，后 29 位是 1，代表了线程池的容量，那么 runStateOf 和 workerCountOf 的理解就很简单了，
runStateOf 是 c 跟 CAPACITY 取反之后的值做”与”操作，CAPACITY 取反就变成了前三位是 1，后面的二十九位是 0，而对于”与”操作，后面的二十九位无论是什么都会被忽略，也就是取了前三位的值
workerCountOf 正好相反，直接跟 CAPACITY 做 “与” 操作，也就是取后二十九位的值
ctlOf 就是直接对 rs 与 wc 做了 “或” 操作
而前面也提到了只有 RUNNING 是负数，那么就可以直接做大小比较来判断线程池状态
这个可能对很多同学来说是非常简单的，但是对于一部分同学来说就是个拦路虎，希望能对这部分同学有所帮助。

]]> - headscale + Java - headscale + Java + + + + Java 线程池系列-第一篇 + /2024/02/11/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%80%E7%AF%87/ + 这一篇我们继续聊线程池，一般线程池会介绍我们的参数，我先不一样一些
我们先来翻译一下

+
 An {@link ExecutorService} that executes each submitted task using
 one of possibly several pooled threads, normally configured
 using {@link Executors} factory methods.
 // executorService 使用一个线程池中的线程来执行每个提交的任务，经常使用 Executors 工厂方法

 <p>Thread pools address two different problems: they usually
 provide improved performance when executing large numbers of
 asynchronous tasks, due to reduced per-task invocation overhead,
 and they provide a means of bounding and managing the resources,
 including threads, consumed when executing a collection of tasks.
 Each {@code ThreadPoolExecutor} also maintains some basic
 statistics, such as the number of completed tasks.
 // 线程池解决两个不同的问题，通常是为了在提交大量异步任务时，通过减少每个任务的调度开销来提高性能
 // 并且提供了一种管理和约束资源，包括线程消耗的方法，每个 ThreadPoolExecutor还会管理一些基础的统计信息
 // 比如完成的任务数量
 

 <p>To be useful across a wide range of contexts, this class
 provides many adjustable parameters and extensibility
 hooks. However, programmers are urged to use the more convenient
 {@link Executors} factory methods {@link
 Executors#newCachedThreadPool} (unbounded thread pool, with
 automatic thread reclamation), {@link Executors#newFixedThreadPool}
 (fixed size thread pool) and {@link
 Executors#newSingleThreadExecutor} (single background thread), that
 preconfigure settings for the most common usage
 scenarios. Otherwise, use the following guide when manually
 configuring and tuning this class:
 // 为了在各种情况下都能派上用场，该类提供了许多可调整的参数和扩展钩子。不过，我们建议程序员使用更方便的 Executors 工厂方法 Executors.
 // newCachedThreadPool（无限制线程池，可自动回收线程）、Executors.newFixedThreadPool（固定大小线程池）和 Executors.
 // newSingleThreadExecutor（单后台线程），这些方法可为最常见的使用场景预先配置设置。否则，在手动配置和调整该类时，请使用以下指南：

 Core and maximum pool sizes
 // 核心和最大线程池大小
    A ThreadPoolExecutor will automatically adjust the pool size (see getPoolSize) according to the bounds set by corePoolSize (see getCorePoolSize) and maximumPoolSize (see getMaximumPoolSize). When a new task is submitted in method execute(Runnable), and fewer than corePoolSize threads are running, a new thread is created to handle the request, even if other worker threads are idle. If there are more than corePoolSize but less than maximumPoolSize threads running, a new thread will be created only if the queue is full. By setting corePoolSize and maximumPoolSize the same, you create a fixed-size thread pool. By setting maximumPoolSize to an essentially unbounded value such as Integer.MAX_VALUE, you allow the pool to accommodate an arbitrary number of concurrent tasks. Most typically, core and maximum pool sizes are set only upon construction, but they may also be changed dynamically using setCorePoolSize and setMaximumPoolSize.
    // ThreadPoolExecutor 会根据 corePoolSize（参见 getCorePoolSize）和 maximumPoolSize（参见 getMaximumPoolSize）设置的界限自动调整池大小（参见 getPoolSize）。在方法 execute(Runnable) 中提交新任务时，如果运行的线程少于 corePoolSize，即使其他工作线程处于空闲状态，也会创建一个新线程来处理请求。如果运行的线程多于 corePoolSize 但少于 maximumPoolSize，则只有在队列已满时才会创建新线程。如果将 corePoolSize 和 maximumPoolSize 设置为相同，就可以创建一个固定大小的线程池。如果将 maximumPoolSize 设置为 Integer.MAX_VALUE 等基本无限制的值，就可以让池容纳任意数量的并发任务。通常，核心和最大线程池大小只在构建时设置，但也可以使用 setCorePoolSize 和 setMaximumPoolSize 动态更改。

On-demand construction
// 按需构建
    By default, even core threads are initially created and started only when new tasks arrive, but this can be overridden dynamically using method prestartCoreThread or prestartAllCoreThreads. You probably want to prestart threads if you construct the pool with a non-empty queue.
    // 默认情况下，即使是核心线程也是只有当新任务到来时才会初始创建和启动，但这可以使用方法 prestartCoreThread 或 prestartAllCoreThreads 进行动态重载。如果使用非空队列构建线程池，则可能需要预启动线程。
Creating new threads
// 创建新线程
    /** New threads are created using a ThreadFactory. If not otherwise specified, a Executors.defaultThreadFactory is used, that creates threads to all be in the same ThreadGroup and with the same NORM_PRIORITY priority and non-daemon status. By supplying a different ThreadFactory, you can alter the thread's name, thread group, priority, daemon status, etc. If a ThreadFactory fails to create a thread when asked by returning null from newThread, the executor will continue, but might not be able to execute any tasks. Threads should possess the "modifyThread" RuntimePermission. If worker threads or other threads using the pool do not possess this permission, service may be degraded: configuration changes may not take effect in a timely manner, and a shutdown pool may remain in a state in which termination is possible but not completed. */
    // 新线程是使用线程工厂（ThreadFactory）创建的。如果未另行指定，将使用 Executors.defaultThreadFactory 创建线程，创建的线程将全部位于同一线程组，并具有相同的 NORM_PRIORITY 优先级和非守护进程状态。通过提供不同的 ThreadFactory，可以更改线程的名称、线程组、优先级、守护进程状态等。如果线程工厂无法按要求创建线程，从 newThread 返回空值，执行器将继续运行，但可能无法执行任何任务。线程应拥有 "modifyThread "运行时权限。如果工作线程或使用池的其他线程不具备此权限，服务可能会降级：配置更改可能无法及时生效，关闭的池可能会一直处于可以终止但无法完成的状态。
Keep-alive times
// 保活时间
    If the pool currently has more than corePoolSize threads, excess threads will be terminated if they have been idle for more than the keepAliveTime (see getKeepAliveTime(TimeUnit)). This provides a means of reducing resource consumption when the pool is not being actively used. If the pool becomes more active later, new threads will be constructed. This parameter can also be changed dynamically using method setKeepAliveTime(long, TimeUnit). Using a value of Long.MAX_VALUE TimeUnit.NANOSECONDS effectively disables idle threads from ever terminating prior to shut down. By default, the keep-alive policy applies only when there are more than corePoolSize threads. But method allowCoreThreadTimeOut(boolean) can be used to apply this time-out policy to core threads as well, so long as the keepAliveTime value is non-zero.
    // 如果当前池中的线程数超过 corePoolSize，多余的线程在闲置时间超过 keepAliveTime（请参阅 getKeepAliveTime(TimeUnit)）时将被终止。这提供了一种在线程池未被频繁使用时减少资源消耗的方法。如果以后池变得更加活跃，就会构建新线程。也可以使用方法 setKeepAliveTime(long, TimeUnit) 动态更改该参数。使用 Long.MAX_VALUE TimeUnit.NANOSECONDS 值可有效禁止闲置线程在关闭前终止。默认情况下，只有当线程数超过 corePoolSize 时，才会采用保持连接策略。但只要 keepAliveTime 值不为零，就可以使用方法 allowCoreThreadTimeOut(boolean) 将超时策略也应用到核心线程。
Queuing
// 队列
    Any BlockingQueue may be used to transfer and hold submitted tasks. The use of this queue interacts with pool sizing:
    // 任何阻塞队列（BlockingQueue）都可用于传输和保留已提交的任务。该队列的使用与线程池大小有关：
    If fewer than corePoolSize threads are running, the Executor always prefers adding a new thread rather than queuing.
    // 如果运行的线程少于 corePoolSize，执行器总是倾向于添加新线程而不是队列。
    If corePoolSize or more threads are running, the Executor always prefers queuing a request rather than adding a new thread.
    // 如果运行的线程数大于或等于 corePoolSize，执行器总是倾向于将请求排队，而不是添加新线程。
    If a request cannot be queued, a new thread is created unless this would exceed maximumPoolSize, in which case, the task will be rejected.
    // 如果请求无法进队列，执行器会创建一个新线程，除非这样做会超过最大线程池大小（maximumPoolSize），在这种情况下，任务会被拒绝。
    There are three general strategies for queuing:
    // 入队一般有三种策略：
    Direct handoffs. A good default choice for a work queue is a SynchronousQueue that hands off tasks to threads without otherwise holding them. Here, an attempt to queue a task will fail if no threads are immediately available to run it, so a new thread will be constructed. This policy avoids lockups when handling sets of requests that might have internal dependencies. Direct handoffs generally require unbounded maximumPoolSizes to avoid rejection of new submitted tasks. This in turn admits the possibility of unbounded thread growth when commands continue to arrive on average faster than they can be processed.
    // 直接交接。工作队列的一个很好的默认选择是同步队列（SynchronousQueue），它可以将任务移交给线程，而不会以其他方式保留任务。在这里，如果没有线程可立即运行任务，则队列任务的尝试将失败，因此会构建一个新的线程。在处理可能存在内部依赖关系的请求集时，这种策略可以避免死锁。直接交接通常需要无限制的最大线程池大小，以避免新提交的任务被拒绝。反过来，当命令的平均到达速度超过其处理速度时，就有可能导致线程无限制增长。
    /** Unbounded queues. Using an unbounded queue (for example a LinkedBlockingQueue without a predefined capacity) will cause new tasks to wait in the queue when all corePoolSize threads are busy. Thus, no more than corePoolSize threads will ever be created. (And the value of the maximumPoolSize therefore doesn't have any effect.) This may be appropriate when each task is completely independent of others, so tasks cannot affect each others execution; for example, in a web page server. While this style of queuing can be useful in smoothing out transient bursts of requests, it admits the possibility of unbounded work queue growth when commands continue to arrive on average faster than they can be processed. */
    // 无界队列。使用无界队列（例如没有预定义容量的 LinkedBlockingQueue）会导致新任务在所有 corePoolSize 线程都繁忙时在队列中等待。因此，创建的线程不会超过 corePoolSize。(当每个任务都完全独立于其他任务，因此任务之间不会相互影响执行时，例如在网页服务器中，这种方式可能比较合适。虽然这种队列方式在平滑瞬时突发请求方面很有用，但当命令的平均到达速度持续超过其处理速度时，就会导致工作队列无限制地增长。
    Bounded queues. A bounded queue (for example, an ArrayBlockingQueue) helps prevent resource exhaustion when used with finite maximumPoolSizes, but can be more difficult to tune and control. Queue sizes and maximum pool sizes may be traded off for each other: Using large queues and small pools minimizes CPU usage, OS resources, and context-switching overhead, but can lead to artificially low throughput. If tasks frequently block (for example if they are I/O bound), a system may be able to schedule time for more threads than you otherwise allow. Use of small queues generally requires larger pool sizes, which keeps CPUs busier but may encounter unacceptable scheduling overhead, which also decreases throughput.
    // 有界队列 有界队列（例如 ArrayBlockingQueue）与有限的最大线程池大小一起使用时，有助于防止资源耗尽，但可能更难调整和控制。队列大小和最大池大小可以相互权衡： 使用大队列和小池可以最大限度地减少 CPU 占用率、操作系统资源和上下文切换开销，但会导致人为的低吞吐量。如果任务经常出现阻塞（例如，如果任务受 I/O 约束），系统可能会为更多线程安排时间，而不考虑其他因素。使用小队列通常需要更大的池规模，这将使 CPU 更繁忙，但可能会遇到无法接受的调度开销，这也会降低吞吐量。
Rejected tasks
// 被拒绝的任务
    New tasks submitted in method execute(Runnable) will be rejected when the Executor has been shut down, and also when the Executor uses finite bounds for both maximum threads and work queue capacity, and is saturated. In either case, the execute method invokes the RejectedExecutionHandler.rejectedExecution(Runnable, ThreadPoolExecutor) method of its RejectedExecutionHandler. Four predefined handler policies are provided:
    // 如果执行器已关闭，或者执行器使用了最大线程数和工作队列容量的有限界限，并已达到饱和，那么在 execute(Runnable) 方法中提交的新任务将被拒绝。在这两种情况下，execute 方法都会调用其 RejectedExecutionHandler 的 RejectedExecutionHandler.rejectedExecution(Runnable, ThreadPoolExecutor) 方法。提供了四种预定义的处理程序策略：
    In the default ThreadPoolExecutor.AbortPolicy, the handler throws a runtime RejectedExecutionException upon rejection.
    // 在默认的 ThreadPoolExecutor.AbortPolicy 中，处理程序会在拒绝时抛出运行时 RejectedExecutionException。
    In ThreadPoolExecutor.CallerRunsPolicy, the thread that invokes execute itself runs the task. This provides a simple feedback control mechanism that will slow down the rate that new tasks are submitted.
    // 在 ThreadPoolExecutor.CallerRunsPolicy 中，调用执行本身的线程会运行任务。这提供了一个简单的反馈控制机制，可减慢提交新任务的速度。
    In ThreadPoolExecutor.DiscardPolicy, a task that cannot be executed is simply dropped.
    // 在 ThreadPoolExecutor.DiscardPolicy 中，无法执行的任务会被直接丢弃。
    In ThreadPoolExecutor.DiscardOldestPolicy, if the executor is not shut down, the task at the head of the work queue is dropped, and then execution is retried (which can fail again, causing this to be repeated.)
    // 在 ThreadPoolExecutor.DiscardOldestPolicy 中，如果执行器没有关闭，工作队列头部的任务就会被丢弃，然后重新执行（可能会再次失败，导致重复执行）。
    It is possible to define and use other kinds of RejectedExecutionHandler classes. Doing so requires some care especially when policies are designed to work only under particular capacity or queuing policies.
    // 我们还可以定义和使用其他类型的 RejectedExecutionHandler 类。这样做需要小心谨慎，尤其是当策略设计为仅在特定容量或队列策略下运行时。
Hook methods
// 钩子方法
    This class provides protected overridable beforeExecute(Thread, Runnable) and afterExecute(Runnable, Throwable) methods that are called before and after execution of each task. These can be used to manipulate the execution environment; for example, reinitializing ThreadLocals, gathering statistics, or adding log entries. Additionally, method terminated can be overridden to perform any special processing that needs to be done once the Executor has fully terminated.
    // 该类提供 protected 的可重载 beforeExecute(Thread, Runnable) 和 afterExecute(Runnable, Throwable) 方法，可在每个任务执行前后调用。这些方法可用于操纵执行环境，例如，重新初始化线程位置、收集统计数据或添加日志条目。此外，还可以重写终止方法，以便在执行器完全终止后执行任何需要进行的特殊处理。(一些动态线程池技术就用到了这两个方法)
    If hook or callback methods throw exceptions, internal worker threads may in turn fail and abruptly terminate.
    // 如果钩子或回调方法抛出异常，内部工作线程可能会失败并突然终止。
Queue maintenance
// 队列维护
    Method getQueue() allows access to the work queue for purposes of monitoring and debugging. Use of this method for any other purpose is strongly discouraged. Two supplied methods, remove(Runnable) and purge are available to assist in storage reclamation when large numbers of queued tasks become cancelled.
    // 方法 getQueue() 允许访问工作队列，以便进行监控和调试。强烈建议不要将此方法用于任何其他目的。当大量队列任务被取消时，可使用 remove(Runnable) 和 purge 这两个提供的方法来帮助回收存储空间。(不建议直接将获取到的队列进行修改操作)
Finalization
// 终止
    A pool that is no longer referenced in a program AND has no remaining threads will be shutdown automatically. If you would like to ensure that unreferenced pools are reclaimed even if users forget to call shutdown, then you must arrange that unused threads eventually die, by setting appropriate keep-alive times, using a lower bound of zero core threads and/or setting allowCoreThreadTimeOut(boolean).
    // 程序中不再引用且没有剩余线程的池将自动关闭。如果要确保即使用户忘记调用 shutdown 也能回收未引用的池，则必须通过设置适当的保持连接时间、使用零核心线程下限和/或设置 allowCoreThreadTimeOut(boolean) 来使得未使用的线程最终被停止。
+

通过前面的简单翻译我们就能大致略窥得线程池配置全貌，继续看一个构造方法

+
public ThreadPoolExecutor(int corePoolSize,
                          int maximumPoolSize,
                          long keepAliveTime,
                          TimeUnit unit,
                          BlockingQueue<Runnable> workQueue) {
    this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
         Executors.defaultThreadFactory(), defaultHandler);
}
private static final RejectedExecutionHandler defaultHandler =
    new AbortPolicy();
+

这个构造方法就主要配置了 corePoolSize 核心线程数，maximumPoolSize 最大线程数, keepAliveTime 线程保活时间，unit 时间单位，workQueue 工作队列，而传入后可以看到使用了默认的线程工厂和默认的拒绝处理器

+
public ThreadPoolExecutor(int corePoolSize,
                          int maximumPoolSize,
                          long keepAliveTime,
                          TimeUnit unit,
                          BlockingQueue<Runnable> workQueue,
                          ThreadFactory threadFactory,
                          RejectedExecutionHandler handler) {
    if (corePoolSize < 0 ||
        maximumPoolSize <= 0 ||
        maximumPoolSize < corePoolSize ||
        keepAliveTime < 0)
        throw new IllegalArgumentException();
    if (workQueue == null || threadFactory == null || handler == null)
        throw new NullPointerException();
    this.acc = System.getSecurityManager() == null ?
            null :
            AccessController.getContext();
    this.corePoolSize = corePoolSize;
    this.maximumPoolSize = maximumPoolSize;
    this.workQueue = workQueue;
    this.keepAliveTime = unit.toNanos(keepAliveTime);
    this.threadFactory = threadFactory;
    this.handler = handler;
}
+

最终会执行到这里做一些校验判断，包括几个参数校验和配置赋值

+]]> + + Java + + + Java @@ -692,11 +757,15 @@ - Java 线程池系列-准备篇 - /2024/02/04/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E5%87%86%E5%A4%87%E7%AF%87/ - Java 线程池是 Java 并发体系里的重要组成部分，不过说起线程池一般都是几个参数，以及参数的含义和逻辑，为了更适合刚开始学习的同学或者说有一些会对这么个习惯性的概括讲法有点疑惑的，我们就从零开始，从最基础的开始看起，先来看下里面用来判断线程池状态和线程数量的部分

-
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
private static final int COUNT_BITS = Integer.SIZE - 3;
private static final int CAPACITY   = (1 << COUNT_BITS) - 1;

// runState is stored in the high-order bits
private static final int RUNNING    = -1 << COUNT_BITS;
private static final int SHUTDOWN   =  0 << COUNT_BITS;
private static final int STOP       =  1 << COUNT_BITS;
private static final int TIDYING    =  2 << COUNT_BITS;
private static final int TERMINATED =  3 << COUNT_BITS;

// Packing and unpacking ctl
private static int runStateOf(int c)     { return c & ~CAPACITY; }
private static int workerCountOf(int c)  { return c & CAPACITY; }
private static int ctlOf(int rs, int wc) { return rs | wc; }
-

这里我们先看下 COUNT_BITS 它是 Integer.SIZE - 3，Integer.SIZE 这个是 32，也就是代表了 Java 用了四个 Byte 来存一个 Integer, 那么 COUNT_BITS 就是 29，
这个 29 是用了干嘛的呢，逐步往下看，为什么先介绍 COUNT_BITS，因为第一行的 ctl 的值是通过 ctlOf(RUNNING, 0) 算出来的，而 RUNNING 又是通过等于 -1 左移 COUNT_BITS 位来得到，移动 29 位后，还剩下三位，而这三位只需要表示后面五种状态，其中只有 RUNNING 是负值，这样就巧妙的只利用了 ctl 的前三位来存储线程池的状态而后面的 29 位就是用来存储线程数量的，具体怎么取呢，我们在来看下 CAPACITY 它是 (1 << COUNT_BITS) - 1 把 1 左移 29 位后减 1，也就是 536870911，这里再补充下，左移一位就等于是乘以 2，左移 29 位就是乘以 2 的 29 次，减了 1 就是 29 位能存的最大值，代表线程池最大的线程容量，用二进制来表示就是 32 位长度的int，前三位是 0，后 29 位是 1，代表了线程池的容量，那么 runStateOf 和 workerCountOf 的理解就很简单了，
runStateOf 是 c 跟 CAPACITY 取反之后的值做”与”操作，CAPACITY 取反就变成了前三位是 1，后面的二十九位是 0，而对于”与”操作，后面的二十九位无论是什么都会被忽略，也就是取了前三位的值
workerCountOf 正好相反，直接跟 CAPACITY 做 “与” 操作，也就是取后二十九位的值
ctlOf 就是直接对 rs 与 wc 做了 “或” 操作
而前面也提到了只有 RUNNING 是负数，那么就可以直接做大小比较来判断线程池状态
这个可能对很多同学来说是非常简单的，但是对于一部分同学来说就是个拦路虎，希望能对这部分同学有所帮助。

+ Java 线程池系列-第三篇 + /2024/02/24/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%89%E7%AF%87/ + 第三篇我们要来讲下 Worker 这个“打工人”，线程池里实际在干活的同学
Worker 实现了 Runnable 接口，所以在前面介绍的 addWorker 中
线程启动其实就调用了下面的 run 方法，而 run 方法则调用了内部的
runWorker 方法

+

private final class Worker
    extends AbstractQueuedSynchronizer
    implements Runnable
{
    /**
     * This class will never be serialized, but we provide a
     * serialVersionUID to suppress a javac warning.
     */
    private static final long serialVersionUID = 6138294804551838833L;

    /** Thread this worker is running in.  Null if factory fails. */
    final Thread thread;
    /** Initial task to run.  Possibly null. */
    Runnable firstTask;
    /** Per-thread task counter */
    volatile long completedTasks;

    /**
     * Creates with given first task and thread from ThreadFactory.
     * @param firstTask the first task (null if none)
     */
    Worker(Runnable firstTask) {
        setState(-1); // inhibit interrupts until runWorker
        this.firstTask = firstTask;
        this.thread = getThreadFactory().newThread(this);
    }

    /** Delegates main run loop to outer runWorker  */
    // 启动线程 Worker
    public void run() {
        runWorker(this);
    }
+

介绍下这个 runWorker 方法
这里就会先处理 firstTask，如果 firstTask 是 null 则会去队列里取
里面会有两个钩子，一个是执行前，一个是执行后，注意这个循环是没有常规
退出逻辑的，说明是一直在往队列里获取，除非获取队列里的任务超时失败了
这样就到最后会去执行 processWorkerExit

+
final void runWorker(Worker w) {
        Thread wt = Thread.currentThread();
        Runnable task = w.firstTask;
        w.firstTask = null;
        w.unlock(); // allow interrupts
        boolean completedAbruptly = true;
        try {
            // firstTask 不为空或者从队列里获取到任务
            while (task != null || (task = getTask()) != null) {
                w.lock();
                // If pool is stopping, ensure thread is interrupted;
                // if not, ensure thread is not interrupted.  This
                // requires a recheck in second case to deal with
                // shutdownNow race while clearing interrupt
                if ((runStateAtLeast(ctl.get(), STOP) ||
                     (Thread.interrupted() &&
                      runStateAtLeast(ctl.get(), STOP))) &&
                    !wt.isInterrupted())
                    wt.interrupt();
                try {
                    // 前置执行钩子
                    beforeExecute(wt, task);
                    Throwable thrown = null;
                    try {
                        // 任务执行
                        task.run();
                    } catch (RuntimeException x) {
                        thrown = x; throw x;
                    } catch (Error x) {
                        thrown = x; throw x;
                    } catch (Throwable x) {
                        thrown = x; throw new Error(x);
                    } finally {
                        // 执行完后的钩子
                        afterExecute(task, thrown);
                    }
                } finally {
                    // 最后会执行到这，清空任务
                    task = null;
                    w.completedTasks++;
                    w.unlock();
                }
            }
            completedAbruptly = false;
        } finally {
            // 循环的最后就会去退出Worker
            processWorkerExit(w, completedAbruptly);
        }
    }
+

而这个 processWorkerExit 也有个比较特殊的逻辑
照理我们会想到这里可能就是线程在处理完队列里的任务
以后，就会判断下是否要退出，如果是核心线程的话就不用
如果是非核心线程就会退出了，但是这里其实有个替换逻辑
就是最后有个 addWorker 调用

+
private void processWorkerExit(Worker w, boolean completedAbruptly) {
    if (completedAbruptly) // If abrupt, then workerCount wasn't adjusted
        decrementWorkerCount();

    final ReentrantLock mainLock = this.mainLock;
    mainLock.lock();
    try {
        completedTaskCount += w.completedTasks;
        workers.remove(w);
    } finally {
        mainLock.unlock();
    }

    tryTerminate();

    int c = ctl.get();
    // 判断状态
    if (runStateLessThan(c, STOP)) {
        if (!completedAbruptly) {
            // 核心线程是否也需要退出
            int min = allowCoreThreadTimeOut ? 0 : corePoolSize;
            if (min == 0 && ! workQueue.isEmpty())
                min = 1;
            // 这里代表非核心线程就可以不用替换，也就是回收线程了
            if (workerCountOf(c) >= min)
                return; // replacement not needed
        }
        // 否则是替换线程
        addWorker(null, false);
    }
}
+

这里主要讲了线程 Worker 运行的逻辑

]]> Java @@ -706,19 +775,19 @@ - Headscale渐入佳境补充篇-自定义中转derper的证书问题 - /2024/05/26/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83%E8%A1%A5%E5%85%85%E7%AF%87-%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%AD%E8%BD%ACderper%E7%9A%84%E8%AF%81%E4%B9%A6%E9%97%AE%E9%A2%98/ - 之前自定义部署的derper在Mac端和Windows使用时没啥问题，但是在我想作为网关的Armbian小机器上一直会报 “x509: certificate signed by unknown authority”
错误，原因在于之前在通过acme生成证书以后我是直接把cer证书重命名成crt就映射到derper的docker目录里，对于Mac和Windows客户端应该是有兼容逻辑，而实际使用需要完整的证书去识别认证机构，否则就会被认为是自定义证书，在一些常规的证书认证逻辑里就会报上面的问题
这边需要先把生成的 fullchain.cer 完整证书转成crt格式的

-
openssl x509 -inform PEM -in fullchain.cer -out derper.demo.com.crt
-

这边是PEM格式的证书，如果是DER的话就改一下

-
openssl x509 -inform DER -in certificate.cer -out certificate.crt
-

转换完成后就重新启动下docker
这里的原因主要是fullchain.cer是包含了完整的证书链，而ca.cer只是包含用户证书，所以需要替换一下，这算是个小问题

+ Java 线程池系列-第二篇 + /2024/02/18/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%BA%8C%E7%AF%87/ + 介绍了线程池的目的和实现概述，以及如何初始化的，我们就来开始看看线程池最重要的执行过程
老规矩，还是先把注释翻译下，这个对理解逻辑其实非常重要，后面可以循着注释的逻辑来看代码
第一步，如果是少于核心线程数的线程正在运行，那么尝试去开启一个新线程，并把提交的命令
command 最为 first task，这里调用了 addWorker，会自动检查运行状态和线程数，并通过
返回 false 来提示不应该增加线程，比如已经有其他任务先创建了线程导致已经超过了核心线程数
第二步，在前面不符合的情况下，也就是线程数已经大于等于核心线程数了或者在调用 addWorker
的时候校验发现线程数已经大于等于核心线程数或者线程池运行状态不是正在运行了就会进入下一个判断
首先还是判断线程池是否正在运行，然后将 command 放进队列，如果成功进队了，还需要再次进行校验
如果线程池状态不是正在运行了，则需要再出队，然后执行拒绝策略，如果状态正常，但是线程被回收完了
那需要创建线程
第三步，如果不能正常进队列，会尝试再启动一个新线程，这里表示核心线程数满了，并且队列也满了，
则需要再开启一个新线程，如果开启失败则执行拒绝策略
代码注释基本已经把代码讲得很详细了

+
public void execute(Runnable command) {
        if (command == null)
            throw new NullPointerException();
        /*
         * Proceed in 3 steps:
         *
         * 1. If fewer than corePoolSize threads are running, try to
         * start a new thread with the given command as its first
         * task.  The call to addWorker atomically checks runState and
         * workerCount, and so prevents false alarms that would add
         * threads when it shouldn't, by returning false.
         *
         * 2. If a task can be successfully queued, then we still need
         * to double-check whether we should have added a thread
         * (because existing ones died since last checking) or that
         * the pool shut down since entry into this method. So we
         * recheck state and if necessary roll back the enqueuing if
         * stopped, or start a new thread if there are none.
         *
         * 3. If we cannot queue task, then we try to add a new
         * thread.  If it fails, we know we are shut down or saturated
         * and so reject the task.
         */
        int c = ctl.get();
        // 这就是第一步
        if (workerCountOf(c) < corePoolSize) {
            if (addWorker(command, true))
                return;
            // 注意这里是第一步可能失败了，获取一下最新的状态
            c = ctl.get();
        }
        // 这里是第二步
        if (isRunning(c) && workQueue.offer(command)) {
            // 每次都获取最新的状态
            int recheck = ctl.get();
            if (! isRunning(recheck) && remove(command))
                reject(command);
            else if (workerCountOf(recheck) == 0)
                // 这里是判断如果一个线程都没了的话，注意添加的是非核心线程
                addWorker(null, false);
        }
        // 这里第三步是添加线程，并且是非核心线程
        else if (!addWorker(command, false))
            reject(command);
    }
+

这里面比较重要的就是 addWorker 方法，我们也来看一下
这里用到了 break retry; 就是在循环中跳出到这个 retry 的位置继续执行
然后还是获取运行状态，如果是非运行状态，
并且rs == SHUTDOWN 与 firstTask == null 和 ! workQueue.isEmpty()，至少一个为false
因为只有当 SHUTDOWN 状态才会出现后面两者为 false 的情况，如果是 SHUTDOWN ，队列不为空还是需要执行完队列里的任务
然后是判断线程数量与传入参数 core 还有核心线程数跟最大线程数的对比，如果是超过了就返回 false 也就是外层的第一步的内部
if 就会继续执行后面的获取线程池状态，然后是 cas 增加线程数量，如果失败则 break 跳到方法开头，如果成功则继续重新获取 c
判断线程池运行状态，如果不一致则从外层继续进入
后面的就是新建一个 worker，然后获取锁，继续判断线程池状态，如果在运行中或者队列不为空（隐含条件）则继续执行添加 worker
并判断是否需要更新历史最大线程数，更改线程添加状态，然后启动线程，标记线程启动状态

+
private boolean addWorker(Runnable firstTask, boolean core) {
        retry:
        for (;;) {
            int c = ctl.get();
            int rs = runStateOf(c);

            // Check if queue empty only if necessary.
            if (rs >= SHUTDOWN && ! (rs == SHUTDOWN && firstTask == null && ! workQueue.isEmpty()))
                return false;

            for (;;) {
                int wc = workerCountOf(c);
                if (wc >= CAPACITY ||
                    wc >= (core ? corePoolSize : maximumPoolSize))
                    return false;
                if (compareAndIncrementWorkerCount(c))
                    break retry;
                c = ctl.get();  // Re-read ctl
                if (runStateOf(c) != rs)
                    continue retry;
                // else CAS failed due to workerCount change; retry inner loop
            }
        }

        boolean workerStarted = false;
        boolean workerAdded = false;
        Worker w = null;
        try {
            w = new Worker(firstTask);
            final Thread t = w.thread;
            if (t != null) {
                final ReentrantLock mainLock = this.mainLock;
                mainLock.lock();
                try {
                    // Recheck while holding lock.
                    // Back out on ThreadFactory failure or if
                    // shut down before lock acquired.
                    int rs = runStateOf(ctl.get());

                    if (rs < SHUTDOWN ||
                        (rs == SHUTDOWN && firstTask == null)) {
                        if (t.isAlive()) // precheck that t is startable
                            throw new IllegalThreadStateException();
                        workers.add(w);
                        int s = workers.size();
                        if (s > largestPoolSize)
                            largestPoolSize = s;
                        workerAdded = true;
                    }
                } finally {
                    mainLock.unlock();
                }
                if (workerAdded) {
                    t.start();
                    workerStarted = true;
                }
            }
        } finally {
            if (! workerStarted)
                addWorkerFailed(w);
        }
        return workerStarted;
    }
+

后面会继续讲下线程如何执行队列里的任务

]]> - headscale + Java - headscale + Java @@ -730,24 +799,6 @@

阻塞队列的 poll 主要是通过锁，和notEmpty这个 condition 来等待制定的时间
指定时间后开始 dequeue 出队

public E poll(long timeout, TimeUnit unit) throws InterruptedException {
    long nanos = unit.toNanos(timeout);
    final ReentrantLock lock = this.lock;
    lock.lockInterruptibly();
    try {
        while (count == 0) {
            if (nanos <= 0)
                return null;
            nanos = notEmpty.awaitNanos(nanos);
        }
        return dequeue();
    } finally {
        lock.unlock();
    }
}

第二部分比较重要，有的同学的问题是为什么不一开始开到最大线程，而是达到核心线程数后就进队列了，
其实池化技术最简单的原因就是希望能够复用这些线程，因为创建销毁他们的成本太大了，如果直接最大线程数的话
其实都不用用到线程池技术了，直接有多少任务就开多少线程，用完就丢了，阐述下我认为比较重要的概念点

-]]> - - Java - - - Java - - - - Java 线程池系列-第一篇 - /2024/02/11/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%80%E7%AF%87/ - 这一篇我们继续聊线程池，一般线程池会介绍我们的参数，我先不一样一些
我们先来翻译一下

-
 An {@link ExecutorService} that executes each submitted task using
 one of possibly several pooled threads, normally configured
 using {@link Executors} factory methods.
 // executorService 使用一个线程池中的线程来执行每个提交的任务，经常使用 Executors 工厂方法

 <p>Thread pools address two different problems: they usually
 provide improved performance when executing large numbers of
 asynchronous tasks, due to reduced per-task invocation overhead,
 and they provide a means of bounding and managing the resources,
 including threads, consumed when executing a collection of tasks.
 Each {@code ThreadPoolExecutor} also maintains some basic
 statistics, such as the number of completed tasks.
 // 线程池解决两个不同的问题，通常是为了在提交大量异步任务时，通过减少每个任务的调度开销来提高性能
 // 并且提供了一种管理和约束资源，包括线程消耗的方法，每个 ThreadPoolExecutor还会管理一些基础的统计信息
 // 比如完成的任务数量
 

 <p>To be useful across a wide range of contexts, this class
 provides many adjustable parameters and extensibility
 hooks. However, programmers are urged to use the more convenient
 {@link Executors} factory methods {@link
 Executors#newCachedThreadPool} (unbounded thread pool, with
 automatic thread reclamation), {@link Executors#newFixedThreadPool}
 (fixed size thread pool) and {@link
 Executors#newSingleThreadExecutor} (single background thread), that
 preconfigure settings for the most common usage
 scenarios. Otherwise, use the following guide when manually
 configuring and tuning this class:
 // 为了在各种情况下都能派上用场，该类提供了许多可调整的参数和扩展钩子。不过，我们建议程序员使用更方便的 Executors 工厂方法 Executors.
 // newCachedThreadPool（无限制线程池，可自动回收线程）、Executors.newFixedThreadPool（固定大小线程池）和 Executors.
 // newSingleThreadExecutor（单后台线程），这些方法可为最常见的使用场景预先配置设置。否则，在手动配置和调整该类时，请使用以下指南：

 Core and maximum pool sizes
 // 核心和最大线程池大小
    A ThreadPoolExecutor will automatically adjust the pool size (see getPoolSize) according to the bounds set by corePoolSize (see getCorePoolSize) and maximumPoolSize (see getMaximumPoolSize). When a new task is submitted in method execute(Runnable), and fewer than corePoolSize threads are running, a new thread is created to handle the request, even if other worker threads are idle. If there are more than corePoolSize but less than maximumPoolSize threads running, a new thread will be created only if the queue is full. By setting corePoolSize and maximumPoolSize the same, you create a fixed-size thread pool. By setting maximumPoolSize to an essentially unbounded value such as Integer.MAX_VALUE, you allow the pool to accommodate an arbitrary number of concurrent tasks. Most typically, core and maximum pool sizes are set only upon construction, but they may also be changed dynamically using setCorePoolSize and setMaximumPoolSize.
    // ThreadPoolExecutor 会根据 corePoolSize（参见 getCorePoolSize）和 maximumPoolSize（参见 getMaximumPoolSize）设置的界限自动调整池大小（参见 getPoolSize）。在方法 execute(Runnable) 中提交新任务时，如果运行的线程少于 corePoolSize，即使其他工作线程处于空闲状态，也会创建一个新线程来处理请求。如果运行的线程多于 corePoolSize 但少于 maximumPoolSize，则只有在队列已满时才会创建新线程。如果将 corePoolSize 和 maximumPoolSize 设置为相同，就可以创建一个固定大小的线程池。如果将 maximumPoolSize 设置为 Integer.MAX_VALUE 等基本无限制的值，就可以让池容纳任意数量的并发任务。通常，核心和最大线程池大小只在构建时设置，但也可以使用 setCorePoolSize 和 setMaximumPoolSize 动态更改。

On-demand construction
// 按需构建
    By default, even core threads are initially created and started only when new tasks arrive, but this can be overridden dynamically using method prestartCoreThread or prestartAllCoreThreads. You probably want to prestart threads if you construct the pool with a non-empty queue.
    // 默认情况下，即使是核心线程也是只有当新任务到来时才会初始创建和启动，但这可以使用方法 prestartCoreThread 或 prestartAllCoreThreads 进行动态重载。如果使用非空队列构建线程池，则可能需要预启动线程。
Creating new threads
// 创建新线程
    /** New threads are created using a ThreadFactory. If not otherwise specified, a Executors.defaultThreadFactory is used, that creates threads to all be in the same ThreadGroup and with the same NORM_PRIORITY priority and non-daemon status. By supplying a different ThreadFactory, you can alter the thread's name, thread group, priority, daemon status, etc. If a ThreadFactory fails to create a thread when asked by returning null from newThread, the executor will continue, but might not be able to execute any tasks. Threads should possess the "modifyThread" RuntimePermission. If worker threads or other threads using the pool do not possess this permission, service may be degraded: configuration changes may not take effect in a timely manner, and a shutdown pool may remain in a state in which termination is possible but not completed. */
    // 新线程是使用线程工厂（ThreadFactory）创建的。如果未另行指定，将使用 Executors.defaultThreadFactory 创建线程，创建的线程将全部位于同一线程组，并具有相同的 NORM_PRIORITY 优先级和非守护进程状态。通过提供不同的 ThreadFactory，可以更改线程的名称、线程组、优先级、守护进程状态等。如果线程工厂无法按要求创建线程，从 newThread 返回空值，执行器将继续运行，但可能无法执行任何任务。线程应拥有 "modifyThread "运行时权限。如果工作线程或使用池的其他线程不具备此权限，服务可能会降级：配置更改可能无法及时生效，关闭的池可能会一直处于可以终止但无法完成的状态。
Keep-alive times
// 保活时间
    If the pool currently has more than corePoolSize threads, excess threads will be terminated if they have been idle for more than the keepAliveTime (see getKeepAliveTime(TimeUnit)). This provides a means of reducing resource consumption when the pool is not being actively used. If the pool becomes more active later, new threads will be constructed. This parameter can also be changed dynamically using method setKeepAliveTime(long, TimeUnit). Using a value of Long.MAX_VALUE TimeUnit.NANOSECONDS effectively disables idle threads from ever terminating prior to shut down. By default, the keep-alive policy applies only when there are more than corePoolSize threads. But method allowCoreThreadTimeOut(boolean) can be used to apply this time-out policy to core threads as well, so long as the keepAliveTime value is non-zero.
    // 如果当前池中的线程数超过 corePoolSize，多余的线程在闲置时间超过 keepAliveTime（请参阅 getKeepAliveTime(TimeUnit)）时将被终止。这提供了一种在线程池未被频繁使用时减少资源消耗的方法。如果以后池变得更加活跃，就会构建新线程。也可以使用方法 setKeepAliveTime(long, TimeUnit) 动态更改该参数。使用 Long.MAX_VALUE TimeUnit.NANOSECONDS 值可有效禁止闲置线程在关闭前终止。默认情况下，只有当线程数超过 corePoolSize 时，才会采用保持连接策略。但只要 keepAliveTime 值不为零，就可以使用方法 allowCoreThreadTimeOut(boolean) 将超时策略也应用到核心线程。
Queuing
// 队列
    Any BlockingQueue may be used to transfer and hold submitted tasks. The use of this queue interacts with pool sizing:
    // 任何阻塞队列（BlockingQueue）都可用于传输和保留已提交的任务。该队列的使用与线程池大小有关：
    If fewer than corePoolSize threads are running, the Executor always prefers adding a new thread rather than queuing.
    // 如果运行的线程少于 corePoolSize，执行器总是倾向于添加新线程而不是队列。
    If corePoolSize or more threads are running, the Executor always prefers queuing a request rather than adding a new thread.
    // 如果运行的线程数大于或等于 corePoolSize，执行器总是倾向于将请求排队，而不是添加新线程。
    If a request cannot be queued, a new thread is created unless this would exceed maximumPoolSize, in which case, the task will be rejected.
    // 如果请求无法进队列，执行器会创建一个新线程，除非这样做会超过最大线程池大小（maximumPoolSize），在这种情况下，任务会被拒绝。
    There are three general strategies for queuing:
    // 入队一般有三种策略：
    Direct handoffs. A good default choice for a work queue is a SynchronousQueue that hands off tasks to threads without otherwise holding them. Here, an attempt to queue a task will fail if no threads are immediately available to run it, so a new thread will be constructed. This policy avoids lockups when handling sets of requests that might have internal dependencies. Direct handoffs generally require unbounded maximumPoolSizes to avoid rejection of new submitted tasks. This in turn admits the possibility of unbounded thread growth when commands continue to arrive on average faster than they can be processed.
    // 直接交接。工作队列的一个很好的默认选择是同步队列（SynchronousQueue），它可以将任务移交给线程，而不会以其他方式保留任务。在这里，如果没有线程可立即运行任务，则队列任务的尝试将失败，因此会构建一个新的线程。在处理可能存在内部依赖关系的请求集时，这种策略可以避免死锁。直接交接通常需要无限制的最大线程池大小，以避免新提交的任务被拒绝。反过来，当命令的平均到达速度超过其处理速度时，就有可能导致线程无限制增长。
    /** Unbounded queues. Using an unbounded queue (for example a LinkedBlockingQueue without a predefined capacity) will cause new tasks to wait in the queue when all corePoolSize threads are busy. Thus, no more than corePoolSize threads will ever be created. (And the value of the maximumPoolSize therefore doesn't have any effect.) This may be appropriate when each task is completely independent of others, so tasks cannot affect each others execution; for example, in a web page server. While this style of queuing can be useful in smoothing out transient bursts of requests, it admits the possibility of unbounded work queue growth when commands continue to arrive on average faster than they can be processed. */
    // 无界队列。使用无界队列（例如没有预定义容量的 LinkedBlockingQueue）会导致新任务在所有 corePoolSize 线程都繁忙时在队列中等待。因此，创建的线程不会超过 corePoolSize。(当每个任务都完全独立于其他任务，因此任务之间不会相互影响执行时，例如在网页服务器中，这种方式可能比较合适。虽然这种队列方式在平滑瞬时突发请求方面很有用，但当命令的平均到达速度持续超过其处理速度时，就会导致工作队列无限制地增长。
    Bounded queues. A bounded queue (for example, an ArrayBlockingQueue) helps prevent resource exhaustion when used with finite maximumPoolSizes, but can be more difficult to tune and control. Queue sizes and maximum pool sizes may be traded off for each other: Using large queues and small pools minimizes CPU usage, OS resources, and context-switching overhead, but can lead to artificially low throughput. If tasks frequently block (for example if they are I/O bound), a system may be able to schedule time for more threads than you otherwise allow. Use of small queues generally requires larger pool sizes, which keeps CPUs busier but may encounter unacceptable scheduling overhead, which also decreases throughput.
    // 有界队列 有界队列（例如 ArrayBlockingQueue）与有限的最大线程池大小一起使用时，有助于防止资源耗尽，但可能更难调整和控制。队列大小和最大池大小可以相互权衡： 使用大队列和小池可以最大限度地减少 CPU 占用率、操作系统资源和上下文切换开销，但会导致人为的低吞吐量。如果任务经常出现阻塞（例如，如果任务受 I/O 约束），系统可能会为更多线程安排时间，而不考虑其他因素。使用小队列通常需要更大的池规模，这将使 CPU 更繁忙，但可能会遇到无法接受的调度开销，这也会降低吞吐量。
Rejected tasks
// 被拒绝的任务
    New tasks submitted in method execute(Runnable) will be rejected when the Executor has been shut down, and also when the Executor uses finite bounds for both maximum threads and work queue capacity, and is saturated. In either case, the execute method invokes the RejectedExecutionHandler.rejectedExecution(Runnable, ThreadPoolExecutor) method of its RejectedExecutionHandler. Four predefined handler policies are provided:
    // 如果执行器已关闭，或者执行器使用了最大线程数和工作队列容量的有限界限，并已达到饱和，那么在 execute(Runnable) 方法中提交的新任务将被拒绝。在这两种情况下，execute 方法都会调用其 RejectedExecutionHandler 的 RejectedExecutionHandler.rejectedExecution(Runnable, ThreadPoolExecutor) 方法。提供了四种预定义的处理程序策略：
    In the default ThreadPoolExecutor.AbortPolicy, the handler throws a runtime RejectedExecutionException upon rejection.
    // 在默认的 ThreadPoolExecutor.AbortPolicy 中，处理程序会在拒绝时抛出运行时 RejectedExecutionException。
    In ThreadPoolExecutor.CallerRunsPolicy, the thread that invokes execute itself runs the task. This provides a simple feedback control mechanism that will slow down the rate that new tasks are submitted.
    // 在 ThreadPoolExecutor.CallerRunsPolicy 中，调用执行本身的线程会运行任务。这提供了一个简单的反馈控制机制，可减慢提交新任务的速度。
    In ThreadPoolExecutor.DiscardPolicy, a task that cannot be executed is simply dropped.
    // 在 ThreadPoolExecutor.DiscardPolicy 中，无法执行的任务会被直接丢弃。
    In ThreadPoolExecutor.DiscardOldestPolicy, if the executor is not shut down, the task at the head of the work queue is dropped, and then execution is retried (which can fail again, causing this to be repeated.)
    // 在 ThreadPoolExecutor.DiscardOldestPolicy 中，如果执行器没有关闭，工作队列头部的任务就会被丢弃，然后重新执行（可能会再次失败，导致重复执行）。
    It is possible to define and use other kinds of RejectedExecutionHandler classes. Doing so requires some care especially when policies are designed to work only under particular capacity or queuing policies.
    // 我们还可以定义和使用其他类型的 RejectedExecutionHandler 类。这样做需要小心谨慎，尤其是当策略设计为仅在特定容量或队列策略下运行时。
Hook methods
// 钩子方法
    This class provides protected overridable beforeExecute(Thread, Runnable) and afterExecute(Runnable, Throwable) methods that are called before and after execution of each task. These can be used to manipulate the execution environment; for example, reinitializing ThreadLocals, gathering statistics, or adding log entries. Additionally, method terminated can be overridden to perform any special processing that needs to be done once the Executor has fully terminated.
    // 该类提供 protected 的可重载 beforeExecute(Thread, Runnable) 和 afterExecute(Runnable, Throwable) 方法，可在每个任务执行前后调用。这些方法可用于操纵执行环境，例如，重新初始化线程位置、收集统计数据或添加日志条目。此外，还可以重写终止方法，以便在执行器完全终止后执行任何需要进行的特殊处理。(一些动态线程池技术就用到了这两个方法)
    If hook or callback methods throw exceptions, internal worker threads may in turn fail and abruptly terminate.
    // 如果钩子或回调方法抛出异常，内部工作线程可能会失败并突然终止。
Queue maintenance
// 队列维护
    Method getQueue() allows access to the work queue for purposes of monitoring and debugging. Use of this method for any other purpose is strongly discouraged. Two supplied methods, remove(Runnable) and purge are available to assist in storage reclamation when large numbers of queued tasks become cancelled.
    // 方法 getQueue() 允许访问工作队列，以便进行监控和调试。强烈建议不要将此方法用于任何其他目的。当大量队列任务被取消时，可使用 remove(Runnable) 和 purge 这两个提供的方法来帮助回收存储空间。(不建议直接将获取到的队列进行修改操作)
Finalization
// 终止
    A pool that is no longer referenced in a program AND has no remaining threads will be shutdown automatically. If you would like to ensure that unreferenced pools are reclaimed even if users forget to call shutdown, then you must arrange that unused threads eventually die, by setting appropriate keep-alive times, using a lower bound of zero core threads and/or setting allowCoreThreadTimeOut(boolean).
    // 程序中不再引用且没有剩余线程的池将自动关闭。如果要确保即使用户忘记调用 shutdown 也能回收未引用的池，则必须通过设置适当的保持连接时间、使用零核心线程下限和/或设置 allowCoreThreadTimeOut(boolean) 来使得未使用的线程最终被停止。
-

通过前面的简单翻译我们就能大致略窥得线程池配置全貌，继续看一个构造方法

-
public ThreadPoolExecutor(int corePoolSize,
                          int maximumPoolSize,
                          long keepAliveTime,
                          TimeUnit unit,
                          BlockingQueue<Runnable> workQueue) {
    this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
         Executors.defaultThreadFactory(), defaultHandler);
}
private static final RejectedExecutionHandler defaultHandler =
    new AbortPolicy();
-

这个构造方法就主要配置了 corePoolSize 核心线程数，maximumPoolSize 最大线程数, keepAliveTime 线程保活时间，unit 时间单位，workQueue 工作队列，而传入后可以看到使用了默认的线程工厂和默认的拒绝处理器

-
public ThreadPoolExecutor(int corePoolSize,
                          int maximumPoolSize,
                          long keepAliveTime,
                          TimeUnit unit,
                          BlockingQueue<Runnable> workQueue,
                          ThreadFactory threadFactory,
                          RejectedExecutionHandler handler) {
    if (corePoolSize < 0 ||
        maximumPoolSize <= 0 ||
        maximumPoolSize < corePoolSize ||
        keepAliveTime < 0)
        throw new IllegalArgumentException();
    if (workQueue == null || threadFactory == null || handler == null)
        throw new NullPointerException();
    this.acc = System.getSecurityManager() == null ?
            null :
            AccessController.getContext();
    this.corePoolSize = corePoolSize;
    this.maximumPoolSize = maximumPoolSize;
    this.workQueue = workQueue;
    this.keepAliveTime = unit.toNanos(keepAliveTime);
    this.threadFactory = threadFactory;
    this.handler = handler;
}
-

最终会执行到这里做一些校验判断，包括几个参数校验和配置赋值

]]> Java @@ -781,8 +832,8 @@ leetcode - java leetcode + java 题解 @@ -807,8 +858,8 @@ leetcode - java leetcode + java 题解 @@ -836,8 +887,8 @@ leetcode - java leetcode + java 题解 @@ -854,16 +905,16 @@

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

]]> - Java leetcode + Java Binary Tree java Binary Tree DFS - java leetcode + java 题解 Binary Tree DFS @@ -871,21 +922,36 @@ - Java 线程池系列-第三篇 - /2024/02/24/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%89%E7%AF%87/ - 第三篇我们要来讲下 Worker 这个“打工人”，线程池里实际在干活的同学
Worker 实现了 Runnable 接口，所以在前面介绍的 addWorker 中
线程启动其实就调用了下面的 run 方法，而 run 方法则调用了内部的
runWorker 方法

-

private final class Worker
    extends AbstractQueuedSynchronizer
    implements Runnable
{
    /**
     * This class will never be serialized, but we provide a
     * serialVersionUID to suppress a javac warning.
     */
    private static final long serialVersionUID = 6138294804551838833L;

    /** Thread this worker is running in.  Null if factory fails. */
    final Thread thread;
    /** Initial task to run.  Possibly null. */
    Runnable firstTask;
    /** Per-thread task counter */
    volatile long completedTasks;

    /**
     * Creates with given first task and thread from ThreadFactory.
     * @param firstTask the first task (null if none)
     */
    Worker(Runnable firstTask) {
        setState(-1); // inhibit interrupts until runWorker
        this.firstTask = firstTask;
        this.thread = getThreadFactory().newThread(this);
    }

    /** Delegates main run loop to outer runWorker  */
    // 启动线程 Worker
    public void run() {
        runWorker(this);
    }
-

介绍下这个 runWorker 方法
这里就会先处理 firstTask，如果 firstTask 是 null 则会去队列里取
里面会有两个钩子，一个是执行前，一个是执行后，注意这个循环是没有常规
退出逻辑的，说明是一直在往队列里获取，除非获取队列里的任务超时失败了
这样就到最后会去执行 processWorkerExit

-
final void runWorker(Worker w) {
        Thread wt = Thread.currentThread();
        Runnable task = w.firstTask;
        w.firstTask = null;
        w.unlock(); // allow interrupts
        boolean completedAbruptly = true;
        try {
            // firstTask 不为空或者从队列里获取到任务
            while (task != null || (task = getTask()) != null) {
                w.lock();
                // If pool is stopping, ensure thread is interrupted;
                // if not, ensure thread is not interrupted.  This
                // requires a recheck in second case to deal with
                // shutdownNow race while clearing interrupt
                if ((runStateAtLeast(ctl.get(), STOP) ||
                     (Thread.interrupted() &&
                      runStateAtLeast(ctl.get(), STOP))) &&
                    !wt.isInterrupted())
                    wt.interrupt();
                try {
                    // 前置执行钩子
                    beforeExecute(wt, task);
                    Throwable thrown = null;
                    try {
                        // 任务执行
                        task.run();
                    } catch (RuntimeException x) {
                        thrown = x; throw x;
                    } catch (Error x) {
                        thrown = x; throw x;
                    } catch (Throwable x) {
                        thrown = x; throw new Error(x);
                    } finally {
                        // 执行完后的钩子
                        afterExecute(task, thrown);
                    }
                } finally {
                    // 最后会执行到这，清空任务
                    task = null;
                    w.completedTasks++;
                    w.unlock();
                }
            }
            completedAbruptly = false;
        } finally {
            // 循环的最后就会去退出Worker
            processWorkerExit(w, completedAbruptly);
        }
    }
-

而这个 processWorkerExit 也有个比较特殊的逻辑
照理我们会想到这里可能就是线程在处理完队列里的任务
以后，就会判断下是否要退出，如果是核心线程的话就不用
如果是非核心线程就会退出了，但是这里其实有个替换逻辑
就是最后有个 addWorker 调用

-
private void processWorkerExit(Worker w, boolean completedAbruptly) {
    if (completedAbruptly) // If abrupt, then workerCount wasn't adjusted
        decrementWorkerCount();

    final ReentrantLock mainLock = this.mainLock;
    mainLock.lock();
    try {
        completedTaskCount += w.completedTasks;
        workers.remove(w);
    } finally {
        mainLock.unlock();
    }

    tryTerminate();

    int c = ctl.get();
    // 判断状态
    if (runStateLessThan(c, STOP)) {
        if (!completedAbruptly) {
            // 核心线程是否也需要退出
            int min = allowCoreThreadTimeOut ? 0 : corePoolSize;
            if (min == 0 && ! workQueue.isEmpty())
                min = 1;
            // 这里代表非核心线程就可以不用替换，也就是回收线程了
            if (workerCountOf(c) >= min)
                return; // replacement not needed
        }
        // 否则是替换线程
        addWorker(null, false);
    }
}
-

这里主要讲了线程 Worker 运行的逻辑

-]]> + Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析 + /2020/12/13/Leetcode-105-%E4%BB%8E%E5%89%8D%E5%BA%8F%E4%B8%8E%E4%B8%AD%E5%BA%8F%E9%81%8D%E5%8E%86%E5%BA%8F%E5%88%97%E6%9E%84%E9%80%A0%E4%BA%8C%E5%8F%89%E6%A0%91-Construct-Binary-Tree-from-Preorder-and-Inorder-Traversal-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

+

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

+

例子:

preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]
+

返回的二叉树

+
  3
 / \
9  20
  /  \
 15   7
+ + +

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

+
class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}
]]> + leetcode Java + Binary Tree + java + Binary Tree + DFS - Java + leetcode + java + 题解 + Binary Tree + 二叉树 + 递归 + Preorder Traversal + Inorder Traversal + 前序 + 中序 @@ -912,43 +978,10 @@ leetcode - java leetcode - 题解 - Print FooBar Alternately - - - - Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析 - /2020/12/13/Leetcode-105-%E4%BB%8E%E5%89%8D%E5%BA%8F%E4%B8%8E%E4%B8%AD%E5%BA%8F%E9%81%8D%E5%8E%86%E5%BA%8F%E5%88%97%E6%9E%84%E9%80%A0%E4%BA%8C%E5%8F%89%E6%A0%91-Construct-Binary-Tree-from-Preorder-and-Inorder-Traversal-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

-

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

-

例子:

preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]
-

返回的二叉树

-
  3
 / \
9  20
  /  \
 15   7
- - -

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

-
class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}
]]> - - Java - leetcode - Binary Tree - java - Binary Tree - DFS - - java - leetcode 题解 - Binary Tree - 二叉树 - 递归 - Preorder Traversal - Inorder Traversal - 前序 - 中序 + Print FooBar Alternately @@ -967,20 +1000,48 @@

结果图

难得有个 100%，贴个图哈哈

]]> - Java leetcode + Java Binary Tree java Binary Tree - java leetcode + java 题解 Binary Tree 二叉树 + + Leetcode 121 买卖股票的最佳时机(Best Time to Buy and Sell Stock) 题解分析 + /2021/03/14/Leetcode-121-%E4%B9%B0%E5%8D%96%E8%82%A1%E7%A5%A8%E7%9A%84%E6%9C%80%E4%BD%B3%E6%97%B6%E6%9C%BA-Best-Time-to-Buy-and-Sell-Stock-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

You are given an array prices where prices[i] is the price of a given stock on the ith day.

+

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

+

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

+

给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。

+

你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。

+

返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。

+

简单分析

其实这个跟二叉树的最长路径和有点类似，需要找到整体的最大收益，但是在迭代过程中需要一个当前的值

+
int maxSofar = 0;
public int maxProfit(int[] prices) {
    if (prices.length <= 1) {
        return 0;
    }
    int maxIn = prices[0];
    int maxOut = prices[0];
    for (int i = 1; i < prices.length; i++) {
        if (maxIn > prices[i]) {
            // 当循环当前值小于之前的买入值时就当成买入值，同时卖出也要更新
            maxIn = prices[i];
            maxOut = prices[i];
        }
        if (prices[i] > maxOut) {
            // 表示一个可卖出点，即比买入值高时
            maxOut = prices[i];
            // 需要设置一个历史值
            maxSofar = Math.max(maxSofar, maxOut - maxIn);
        }
    }
    return maxSofar;
}
+ +

总结下

一开始看到 easy 就觉得是很简单，就没有 maxSofar ，但是一提交就出现问题了
对于[2, 4, 1]这种就会变成 0，所以还是需要一个历史值来存放历史最大值，这题有点动态规划的意思

+]]> + + leetcode + Java + DP + java + DP + + + leetcode + java + 题解 + DP + + Leetcode 1260 二维网格迁移 ( Shift 2D Grid *Easy* ) 题解分析 /2022/07/22/Leetcode-1260-%E4%BA%8C%E7%BB%B4%E7%BD%91%E6%A0%BC%E8%BF%81%E7%A7%BB-Shift-2D-Grid-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1016,38 +1077,10 @@ leetcode - java leetcode - 题解 - Shift 2D Grid - - - - Leetcode 121 买卖股票的最佳时机(Best Time to Buy and Sell Stock) 题解分析 - /2021/03/14/Leetcode-121-%E4%B9%B0%E5%8D%96%E8%82%A1%E7%A5%A8%E7%9A%84%E6%9C%80%E4%BD%B3%E6%97%B6%E6%9C%BA-Best-Time-to-Buy-and-Sell-Stock-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

You are given an array prices where prices[i] is the price of a given stock on the ith day.

-

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

-

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

-

给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。

-

你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。

-

返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。

-

简单分析

其实这个跟二叉树的最长路径和有点类似，需要找到整体的最大收益，但是在迭代过程中需要一个当前的值

-
int maxSofar = 0;
public int maxProfit(int[] prices) {
    if (prices.length <= 1) {
        return 0;
    }
    int maxIn = prices[0];
    int maxOut = prices[0];
    for (int i = 1; i < prices.length; i++) {
        if (maxIn > prices[i]) {
            // 当循环当前值小于之前的买入值时就当成买入值，同时卖出也要更新
            maxIn = prices[i];
            maxOut = prices[i];
        }
        if (prices[i] > maxOut) {
            // 表示一个可卖出点，即比买入值高时
            maxOut = prices[i];
            // 需要设置一个历史值
            maxSofar = Math.max(maxSofar, maxOut - maxIn);
        }
    }
    return maxSofar;
}
- -

总结下

一开始看到 easy 就觉得是很简单，就没有 maxSofar ，但是一提交就出现问题了
对于[2, 4, 1]这种就会变成 0，所以还是需要一个历史值来存放历史最大值，这题有点动态规划的意思

-]]> - - Java - leetcode - DP - java - DP - - java - leetcode 题解 - DP + Shift 2D Grid @@ -1068,15 +1101,15 @@ ]]> - Java leetcode + Java stack java stack - java leetcode + java 题解 stack min stack @@ -1112,8 +1145,8 @@ leetcode - java leetcode + java 题解 3Sum Closest @@ -1130,15 +1163,15 @@

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

]]> - Java leetcode + Java Linked List java Linked List - java leetcode + java 题解 Linked List @@ -1171,8 +1204,8 @@ leetcode - java leetcode + java 题解 @@ -1192,19 +1225,54 @@

这里唯二需要注意的就是两个点，一个是循环条件需要包含进位值还存在的情况，还有一个是最后一个节点，如果是空的了，就不要在 new 一个出来了，写的比较挫

]]> - Java leetcode + Java linked list java linked list - java leetcode + java 题解 linked list + + Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析 + /2022/07/02/Leetcode-20-%E6%9C%89%E6%95%88%E7%9A%84%E6%8B%AC%E5%8F%B7-Valid-Parentheses-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.

+

An input string is valid if:

+
    +
  1. Open brackets must be closed by the same type of brackets.
    2. +
  2. Open brackets must be closed in the correct order.
    3. +
+

示例

Example 1:

+

Input: s = “()”
Output: true

+
+

Example 2:

+

Input: s = “()[]{}”
Output: true

+
+

Example 3:

+

Input: s = “(]”
Output: false

+
+

Constraints:

    +
  • 1 <= s.length <= 10^4
    • +
  • s consists of parentheses only '()[]{}'.
    • +
+

解析

easy题，并且看起来也是比较简单的，三种括号按对匹配，直接用栈来做，栈里面存的是括号的类型，如果是左括号，就放入栈中，如果是右括号，就把栈顶的元素弹出，如果弹出的元素不是左括号，就返回false，如果弹出的元素是左括号，就继续往下走，如果遍历完了，如果栈里面还有元素，就返回false，如果遍历完了，如果栈里面没有元素，就返回true

+

代码

class Solution {
    public boolean isValid(String s) {

        if (s.length() % 2 != 0) {
            return false;
        }
        Stack<String> stk = new Stack<>();
        for (int i = 0; i < s.length(); i++) {
            if (s.charAt(i) == '{' || s.charAt(i) == '(' || s.charAt(i) == '[') {
                stk.push(String.valueOf(s.charAt(i)));
                continue;
            }
            if (s.charAt(i) == '}') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '{') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ']') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '[') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ')') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '(') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }

        }
        return stk.size() == 0;
    }
}
+ +]]> + + Java + leetcode + + + leetcode + java + + Leetcode 234 回文链表(Palindrome Linked List) 题解分析 /2020/11/15/Leetcode-234-%E5%9B%9E%E6%96%87%E8%81%94%E8%A1%A8-Palindrome-Linked-List-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1215,15 +1283,15 @@

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
]]> - Java leetcode + Java Linked List java Linked List - java leetcode + java 题解 Linked List @@ -1243,62 +1311,53 @@ Lowest Common Ancestor of a Binary Tree - java leetcode + java 题解 Lowest Common Ancestor of a Binary Tree - Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析 - /2022/07/02/Leetcode-20-%E6%9C%89%E6%95%88%E7%9A%84%E6%8B%AC%E5%8F%B7-Valid-Parentheses-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.

-

An input string is valid if:

-
    -
  1. Open brackets must be closed by the same type of brackets.
    2. -
  2. Open brackets must be closed in the correct order.
    3. -
-

示例

Example 1:

-

Input: s = “()”
Output: true

-
-

Example 2:

-

Input: s = “()[]{}”
Output: true

-
-

Example 3:

-

Input: s = “(]”
Output: false

-
-

Constraints:

    -
  • 1 <= s.length <= 10^4
    • -
  • s consists of parentheses only '()[]{}'.
    • + Leetcode 25 Reverse Nodes in k-Group 题解分析 + /2020/08/16/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90-1/ + Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

    +

    k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

    +

    Example:

    Given this linked list: 1->2->3->4->5

    +

    For k = 2, you should return: 2->1->4->3->5

    +

    For k = 3, you should return: 3->2->1->4->5

    +

    Note:

      +
    • Only constant extra memory is allowed.
      • +
    • You may not alter the values in the list’s nodes, only nodes itself may be changed.
    -

    解析

    easy题，并且看起来也是比较简单的，三种括号按对匹配，直接用栈来做，栈里面存的是括号的类型，如果是左括号，就放入栈中，如果是右括号，就把栈顶的元素弹出，如果弹出的元素不是左括号，就返回false，如果弹出的元素是左括号，就继续往下走，如果遍历完了，如果栈里面还有元素，就返回false，如果遍历完了，如果栈里面没有元素，就返回true

    -

    代码

    class Solution {
        public boolean isValid(String s) {

            if (s.length() % 2 != 0) {
                return false;
            }
            Stack<String> stk = new Stack<>();
            for (int i = 0; i < s.length(); i++) {
                if (s.charAt(i) == '{' || s.charAt(i) == '(' || s.charAt(i) == '[') {
                    stk.push(String.valueOf(s.charAt(i)));
                    continue;
                }
                if (s.charAt(i) == '}') {
                    if (stk.isEmpty()) {
                        return false;
                    }
                    String cur = stk.peek();
                    if (cur.charAt(0) != '{') {
                        return false;
                    } else {
                        stk.pop();
                    }
                    continue;
                }
                if (s.charAt(i) == ']') {
                    if (stk.isEmpty()) {
                        return false;
                    }
                    String cur = stk.peek();
                    if (cur.charAt(0) != '[') {
                        return false;
                    } else {
                        stk.pop();
                    }
                    continue;
                }
                if (s.charAt(i) == ')') {
                    if (stk.isEmpty()) {
                        return false;
                    }
                    String cur = stk.peek();
                    if (cur.charAt(0) != '(') {
                        return false;
                    } else {
                        stk.pop();
                    }
                    continue;
                }

            }
            return stk.size() == 0;
        }
    }
    - -]]>     +

    这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
    简单来看下

    +
    public ListNode reverseKGroup(ListNode head, int k) {
        if (k <= 1) {
            return head;
        }
        if (head == null) {
            return null;
        }
        ListNode tempEnd = head;
        ListNode tempHead = head;
        ListNode lastHead = null;
        boolean isHeadSet = false;
        while (true) {
            // k 个一组获取头尾
            for (int i = 0; i < k - 1; i++) {
                System.out.println(tempEnd.val);
                if (tempEnd.next != null) {
                    tempEnd  = tempEnd.next;
                } else {
                    tempEnd = null;
                    break;
                }
            }
            if (tempEnd != null) {
                if (!isHeadSet) {
                    // 只有在第一组返回的时候需要赋值给 head
                    head = reverse(null, tempHead, tempEnd);
                    isHeadSet = !isHeadSet;
                } else {
                    // 后面的需要将前一组的尾巴传进去，作为后一组头
                    reverse(lastHead, tempHead, tempEnd);
                }
                if (tempHead.next != null) {
                    // 如果后续节点还有
                    lastHead = tempHead;
                    tempEnd = tempHead.next;
                    tempHead = tempHead.next;
                } else {
                    break;
                }
            } else {
                break;
            }
        }

        return head;
    }
    public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
        // k 个一组的头尾传进来
        ListNode tail = null;
        while (tail != tempEnd) {
            // tail 表示是将头往后换时，把后面那个先拿着
            tail = tempHead.next;
            // 然后就是交换
            tempHead.next = tempEnd.next;
            tempEnd.next = tempHead;
            tempHead = tail;
        }
        if (lastHead !=null) {
            // 如果不是第一组，则需要接上前一组的尾巴
            lastHead.next = tail;
        }
        return tail;
    }
    ]]> Java leetcode - java leetcode + java + 题解 - Headscale渐入佳境-路由设置 - /2024/04/07/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83-%E8%B7%AF%E7%94%B1%E8%AE%BE%E7%BD%AE/ - headscale 在前面两篇初体验和添加节点 主要是介绍了如何搭建和使用，但是这里有个很大的缺点，比如我在想要两地的网络能够连通，但是两地的网络内都不止一个机器，那想让他们连通就需要把它们都加入这个 headscale 所组成的虚拟局域网，并且每个都是这个虚拟局域网的ip，跟原来的 ip 地址不一样会比较难记忆，所以有没有办法不用这么麻烦呢，当然是有的，那就是 headscale 的路由功能。
    原先比如我们有两个设备，在两地，通过headscale我把这两个异地节点都添加进去了，节点 A 的 ip比如是 10.0.0.1，另一个节点 B 是 10.0.0.2，在之前的状态下，我的节点 A 只能通过 10.0.0.2 来连接节点 B，如果节点 A 想要连接节点 B 所在地的局域网内部其他节点，我们就可以通过节点 B 的客户端登录的时候开启路由转发，这样节点 B 就会充当一个路由转发的功能，并且不需要通过10.0.0这个网段来访问，直接可以通过节点 B所在的局域网地址来访问，比如192.168.2.10，具体操作就是把客户端启动方式改成命令行的，主要就是 --advertise-routes, 指明需要转发的网段

    -
    tailscale up --advertise-routes=192.168.xx.0/24 --accept-dns=false --login-server=http://serverIp:serverPort --unattended
    -

    前面的 192.168.xx.0 就是节点 B 所在局域网的网段，然后 login-server 是 headscale 的服务 ip 跟端口，这个本身客户端就会使用，另外这个还有一个前提的，需要开启服务器的端口转发，对于 Windows 其实默认就可以，Linux 的话就需要设置下

    -
    $ echo 'net.ipv4.ip_forward = 1' | tee /etc/sysctl.d/ipforwarding.conf
    $ echo 'net.ipv6.conf.all.forwarding = 1' | tee -a /etc/sysctl.d/ipforwarding.conf
    $ sysctl -p /etc/sysctl.d/ipforwarding.conf
    + Leetcode 25 Reverse Nodes in k-Group 题解分析-再解分析 + /2024/01/21/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 上一次主要是给了一个解题方案，没有具体讲解，这次又做到了就来看下几种方案，链表转置一直是我比较疑惑的问题，特别是边界处理，而这个问题要把难度加大了
    我先讲一下我一开始的思路和解题方法，首先就是写一个转置方法，就处理 k 个一组的内部转置，然后外部循环处理分组以及前后连接等问题，但是这里就涉及到一个问题，就是前后连接的话对于整个链表头，也就是第一个 k 元素组来说，头是个空的，就需要额外处理，一开始就是用判空做额外处理，然后在前后连接的时候也有一些困扰，看了自己的代码库发现其实之前也做过，而且发现这个思路跟以前做的还是一样的，只不过在处理 k 个元素内部的转置的时候有了点差异

    一开始的思路是这样的，想了下其实还是比较有问题，从处理难度上来说，在 k 组内处理的时候一开始 A 节点会是悬空的，然后得处理到组内最后一个元素的时候再接上，逻辑会更复杂，而另一种思路就是直接把 A 先挪到 C 后面，这样每次只需要移动一个，可能思路上会更清晰一点

    也就是这样的，这种方案就是之前发过的题解代码，上一篇

    +

    而最后这种则代码会简单一些，但是需要一定的理解成本，比较重要的一点是我们加了个虚拟的头结点，第一步会先获取下链表的总长度，然后在内循环里处理转置，重点就是分四步，
    也就是图里的，第一步先把 cur 的下一个节点设置成 next 的 next 节点，这里就是图里 A 连接到 C，第二步是把 next 的 next 节点设置成虚拟头的 next节点，第三步是把 pre 的next 节点设置成 next，第四步是 next 往后移动

    +
    cur.next = next.next;
    next.next = pre.next;
    pre.next = next;
    next = cur.next;
    -

    到这里我们只完成了第一步，第二步需要在 headscale 服务端启用这条路由配置
    首先可以用 sudo headscale routes 查看当前有的路由
    sudo headscale routes enable -h 这里看下怎么启用，因为网上有些是用-i 指定哪一条路由的，但是我使用的这个版本是用-r 的
    sudo headscale routes enable -r 1 然后用这个命令启用就行了


    这样我们就能够很方便地在单个节点互连之后访问到对方局域网内的其他节点

    +


    在备注下代码

    ]]>     - headscale + Java + leetcode - headscale + leetcode + java + 题解     @@ -1324,8 +1383,8 @@ leetcode - java leetcode + java 题解 First Bad Version @@ -1346,59 +1405,43 @@

    注释应该写的比较清楚了。

    ]]> - Java leetcode + Java 字符串 - online java string - java leetcode - 题解 - string - -     - - Leetcode 25 Reverse Nodes in k-Group 题解分析 - /2020/08/16/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90-1/ - Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

    -

    k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

    -

    Example:

    Given this linked list: 1->2->3->4->5

    -

    For k = 2, you should return: 2->1->4->3->5

    -

    For k = 3, you should return: 3->2->1->4->5

    -

    Note:

      -
    • Only constant extra memory is allowed.
      • -
    • You may not alter the values in the list’s nodes, only nodes itself may be changed.
      • -
    -

    这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
    简单来看下

    -
    public ListNode reverseKGroup(ListNode head, int k) {
        if (k <= 1) {
            return head;
        }
        if (head == null) {
            return null;
        }
        ListNode tempEnd = head;
        ListNode tempHead = head;
        ListNode lastHead = null;
        boolean isHeadSet = false;
        while (true) {
            // k 个一组获取头尾
            for (int i = 0; i < k - 1; i++) {
                System.out.println(tempEnd.val);
                if (tempEnd.next != null) {
                    tempEnd  = tempEnd.next;
                } else {
                    tempEnd = null;
                    break;
                }
            }
            if (tempEnd != null) {
                if (!isHeadSet) {
                    // 只有在第一组返回的时候需要赋值给 head
                    head = reverse(null, tempHead, tempEnd);
                    isHeadSet = !isHeadSet;
                } else {
                    // 后面的需要将前一组的尾巴传进去，作为后一组头
                    reverse(lastHead, tempHead, tempEnd);
                }
                if (tempHead.next != null) {
                    // 如果后续节点还有
                    lastHead = tempHead;
                    tempEnd = tempHead.next;
                    tempHead = tempHead.next;
                } else {
                    break;
                }
            } else {
                break;
            }
        }

        return head;
    }
    public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
        // k 个一组的头尾传进来
        ListNode tail = null;
        while (tail != tempEnd) {
            // tail 表示是将头往后换时，把后面那个先拿着
            tail = tempHead.next;
            // 然后就是交换
            tempHead.next = tempEnd.next;
            tempEnd.next = tempHead;
            tempHead = tail;
        }
        if (lastHead !=null) {
            // 如果不是第一组，则需要接上前一组的尾巴
            lastHead.next = tail;
        }
        return tail;
    }
    ]]>     - - Java - leetcode - - java - leetcode 题解 + string     - Leetcode 25 Reverse Nodes in k-Group 题解分析-再解分析 - /2024/01/21/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 上一次主要是给了一个解题方案，没有具体讲解，这次又做到了就来看下几种方案，链表转置一直是我比较疑惑的问题，特别是边界处理，而这个问题要把难度加大了
    我先讲一下我一开始的思路和解题方法，首先就是写一个转置方法，就处理 k 个一组的内部转置，然后外部循环处理分组以及前后连接等问题，但是这里就涉及到一个问题，就是前后连接的话对于整个链表头，也就是第一个 k 元素组来说，头是个空的，就需要额外处理，一开始就是用判空做额外处理，然后在前后连接的时候也有一些困扰，看了自己的代码库发现其实之前也做过，而且发现这个思路跟以前做的还是一样的，只不过在处理 k 个元素内部的转置的时候有了点差异

    一开始的思路是这样的，想了下其实还是比较有问题，从处理难度上来说，在 k 组内处理的时候一开始 A 节点会是悬空的，然后得处理到组内最后一个元素的时候再接上，逻辑会更复杂，而另一种思路就是直接把 A 先挪到 C 后面，这样每次只需要移动一个，可能思路上会更清晰一点

    也就是这样的，这种方案就是之前发过的题解代码，上一篇

    -

    而最后这种则代码会简单一些，但是需要一定的理解成本，比较重要的一点是我们加了个虚拟的头结点，第一步会先获取下链表的总长度，然后在内循环里处理转置，重点就是分四步，
    也就是图里的，第一步先把 cur 的下一个节点设置成 next 的 next 节点，这里就是图里 A 连接到 C，第二步是把 next 的 next 节点设置成虚拟头的 next节点，第三步是把 pre 的next 节点设置成 next，第四步是 next 往后移动

    -
    cur.next = next.next;
    next.next = pre.next;
    pre.next = next;
    next = cur.next;
    + Leetcode 31 Next Permutation 题解分析 + /2024/05/06/Leetcode-31-Next-Permutation-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 这题的题目介绍有一定的误导性，没有把字典序说明白，虽然的确是常规意义的字典序，但是题目介绍给的序列顺序反而会让人觉得那样不对
    这里我们逐步深入地来讲这个题，也类似于找规律
    注意：这里的递减递增序列都是从前往后看的
    字典序可以就当查字典，或者默认的字符串排序方式，就是对于一个序列，逐个字符对比，同一位的相同再对比下一位的，例如 11在12前面，12在13前面，
    那对于同一组字符或者像题中的数字，以最简单的12来分析，因为这个就两种排列，12跟21，按字典序排列，12在前，21在后
    通过这个仔细点也能发现，或者再举个例子123，这个排列就会多一些了

    +
    123
    132
    213
    231
    312
    321
    +

    一共有这么几种，不过举这个例子是为了更具体的发现规律，对于第一个数字相同的情况，比如123跟132，是132在后，发现规律没，递增序列是最小的，递减序列是最大的，因为2个数字 只有两种排列，扩展到3个数字，也可以把它拆分开，同样是第一个数字是1，后面两个数字就又回到了前面12序列的情况，
    所以就可以把问题分成两部分来解决，首先从后往前找到递减序列
    通过例子来讲

    +
    1 4 6 5 3 2
    +

    为什么要找到递减序列，因为递减序列已经是最大的了，要做调整要在这个之前调整，并且要找的是下一个序列，所以要是最接近的那个，否则我找到的可能就是下N个了，这个有点类似于数学里进位的意思，比如十九，九已经是个位里最大的了，找下一个比它大的只能是前面一位加一，但是加一以后需要九变到这一位的最小，也就是零。
    对于上面的例子，递减序列就是6532，第一个非递减的是4，我要找下一个就是要比当前这个大，那我就去后面找比4大的，至于为什么能找到，因为这是递减序列边界之外的，说明肯定有比它大的，那要找哪个呢，要找比4大的数字里最小的，这样才是最近的下一个序列，并且因为后面是递减序列，所以可以直接从后往前找第一个
    也就是会找到 5
    找到 5 之后就做交换

    +
    1 5 6 4 3 2
    +

    这样是不是就行了呢，不行，因为这不是下一个，而是下N个，当4变成5之后，这个序列不管5后面的数字怎么变都是比 146532 大的，但是要找的是下一个，最接近的那个，则需要后面的序列变成最小，也就是变成递增序列，也就是

    +
    1 5 2 3 4 6
    +

    这里有两种例外情况，或者严格来说是一种
    第一种是本身整个就是递减序列了，这在题中说了，下一个就是从头再开始，那就也很简单整个排序成递增的就行了
    第二种是本身就是个完全递增的，那其实就交换末两位，但这种跟上面的可以糅合在一起，比如就是

    +
    1 2 3 4 5 6
    +

    单个6构不成递增或递减，找到的第一个非递减的数字就是 5 ，那就是在 5 后面找一个比它大的做交换，这时只有 6 ，所以就 5 跟 6 交换下，然后单个 5 做排序也等于不用做
    再结合代码来看

    +
    public void nextPermutation(int[] nums) {
            int n = nums.length, i = n - 2, j = n - 1;
            // 这个while循环是为了从后往前找到递减序列的分界点
            // 循环完成后i就是递减序列左边的第一个数字位置
            while (i >= 0 && nums[i] >= nums[i + 1]) --i;
            if (i >= 0) {
                // 表示有找到分界点，然后就从后往前找到第一个比nums[i]大的做交换
                while (nums[j] <= nums[i]) --j;
                // 这个while循环在找到第一个nums[j] > nums[i]时退出
                // 下面就是做交换
                int temp = nums[i];
                nums[i] = nums[j];
                nums[j] = temp;
            }
            // 因为前面已经保证了逆序，这里只要排成正序就是最小的了
            Arrays.sort(nums, i+1, nums.length);
        }
    -


    在备注下代码

    ]]>     Java leetcode - java leetcode + java 题解     @@ -1427,12 +1470,36 @@ leetcode - java leetcode + java 题解 Intersection of Two Arrays + + Leetcode 42 接雨水 (Trapping Rain Water) 题解分析 + /2021/07/04/Leetcode-42-%E6%8E%A5%E9%9B%A8%E6%B0%B4-Trapping-Rain-Water-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

    给定 n 个非负整数表示每个宽度为 1 的柱子的高度图，计算按此排列的柱子，下雨之后能接多少雨水。

    +

    示例


    输入：height = [0,1,0,2,1,0,1,3,2,1,2,1]
    输出：6
    解释：上面是由数组 [0,1,0,2,1,0,1,3,2,1,2,1] 表示的高度图，在这种情况下，可以接 6 个单位的雨水（蓝色部分表示雨水）。

    +

    简单分析

    其实最开始的想法是从左到右扫区间，就是示例中的第一个水槽跟第二个水槽都可以用这个办法解决

    前面这种是属于右侧比左侧高的情况，对于左侧高右侧低的就不行了，（写这篇的时候想起来可以再反着扫一遍可能可以）

    所以这个方案不好，贴一下这个方案的代码

    +
    public int trap(int[] height) {
        int lastLeft = -1;
        int sum = 0;
        int tempSum = 0;
        boolean startFlag = true;
        for (int j : height) {
            if (startFlag && j <= 0) {
                startFlag = false;
                continue;
            }
            if (j >= lastLeft) {
                sum += tempSum;
                tempSum = 0;
                lastLeft = j;
            } else {
                tempSum += lastLeft - j;
            }
        }
        return sum;
    }
    +

    后面结合网上的解法，其实可以反过来，对于每个格子找左右侧的最大值，取小的那个和当前格子的差值就是这一个的储水量了

    理解了这种想法，代码其实就不难了

    +

    代码

    int n = height.length;
    if (n <= 2) {
        return 0;
    }
    // 思路转变下，其实可以对于每一格算储水量，算法就是找到这一格左边的最高点跟这一格右边的最高点，
    // 比较两侧的最高点，取小的那个，然后再跟当前格子的高度对比，差值就是当前格的储水量
    int maxL[] = new int[n];
    int maxR[] = new int[n];
    int max = height[0];
    maxL[0] = 0;
    // 计算左侧的最高点
    for (int i = 1; i < n - 1; i++) {
        maxL[i] = max;
        if (max < height[i]) {
            max = height[i];
        }
    }
    max = height[n - 1];
    maxR[n - 1] = 0;
    int tempSum, sum = 0;
    // 计算右侧的最高点，并且同步算出来储水量，节省一个循环
    for (int i = n - 2; i > 0; i--) {
        maxR[i] = max;
        if (height[i] > max) {
            max = height[i];
        }
        tempSum = Math.min(maxL[i], maxR[i]) - height[i];
        if (tempSum > 0) {
            sum += tempSum;
        }
    }
    return sum;
    +]]>     + + Java + leetcode + + + leetcode + java + dp + 代码题解 + Trapping Rain Water + 接雨水 + Leetcode 42 + +     Leetcode 4 寻找两个正序数组的中位数 ( Median of Two Sorted Arrays *Hard* ) 题解分析 /2022/03/27/Leetcode-4-%E5%AF%BB%E6%89%BE%E4%B8%A4%E4%B8%AA%E6%AD%A3%E5%BA%8F%E6%95%B0%E7%BB%84%E7%9A%84%E4%B8%AD%E4%BD%8D%E6%95%B0-Median-of-Two-Sorted-Arrays-Hard-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1453,8 +1520,8 @@ leetcode - java leetcode + java 题解 Median of Two Sorted Arrays @@ -1474,35 +1541,37 @@ Rotate Image - java leetcode + java 题解 Rotate Image 矩阵 - Leetcode 42 接雨水 (Trapping Rain Water) 题解分析 - /2021/07/04/Leetcode-42-%E6%8E%A5%E9%9B%A8%E6%B0%B4-Trapping-Rain-Water-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

    给定 n 个非负整数表示每个宽度为 1 的柱子的高度图，计算按此排列的柱子，下雨之后能接多少雨水。

    -

    示例


    输入：height = [0,1,0,2,1,0,1,3,2,1,2,1]
    输出：6
    解释：上面是由数组 [0,1,0,2,1,0,1,3,2,1,2,1] 表示的高度图，在这种情况下，可以接 6 个单位的雨水（蓝色部分表示雨水）。

    -

    简单分析

    其实最开始的想法是从左到右扫区间，就是示例中的第一个水槽跟第二个水槽都可以用这个办法解决

    前面这种是属于右侧比左侧高的情况，对于左侧高右侧低的就不行了，（写这篇的时候想起来可以再反着扫一遍可能可以）

    所以这个方案不好，贴一下这个方案的代码

    -
    public int trap(int[] height) {
        int lastLeft = -1;
        int sum = 0;
        int tempSum = 0;
        boolean startFlag = true;
        for (int j : height) {
            if (startFlag && j <= 0) {
                startFlag = false;
                continue;
            }
            if (j >= lastLeft) {
                sum += tempSum;
                tempSum = 0;
                lastLeft = j;
            } else {
                tempSum += lastLeft - j;
            }
        }
        return sum;
    }
    -

    后面结合网上的解法，其实可以反过来，对于每个格子找左右侧的最大值，取小的那个和当前格子的差值就是这一个的储水量了

    理解了这种想法，代码其实就不难了

    -

    代码

    int n = height.length;
    if (n <= 2) {
        return 0;
    }
    // 思路转变下，其实可以对于每一格算储水量，算法就是找到这一格左边的最高点跟这一格右边的最高点，
    // 比较两侧的最高点，取小的那个，然后再跟当前格子的高度对比，差值就是当前格的储水量
    int maxL[] = new int[n];
    int maxR[] = new int[n];
    int max = height[0];
    maxL[0] = 0;
    // 计算左侧的最高点
    for (int i = 1; i < n - 1; i++) {
        maxL[i] = max;
        if (max < height[i]) {
            max = height[i];
        }
    }
    max = height[n - 1];
    maxR[n - 1] = 0;
    int tempSum, sum = 0;
    // 计算右侧的最高点，并且同步算出来储水量，节省一个循环
    for (int i = n - 2; i > 0; i--) {
        maxR[i] = max;
        if (height[i] > max) {
            max = height[i];
        }
        tempSum = Math.min(maxL[i], maxR[i]) - height[i];
        if (tempSum > 0) {
            sum += tempSum;
        }
    }
    return sum;
    + Leetcode 572 Subtree of Another Tree 题解分析 + /2024/01/28/Leetcode-572-Subtree-of-Another-Tree-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

    Given the roots of two binary trees root and subRoot, return true if there is a subtree of root with the same structure and node values of subRoot and false otherwise.

    +

    A subtree of a binary tree tree is a tree that consists of a node in tree and all of this node’s descendants. The tree tree could also be considered as a subtree of itself.

    +

    示例 1

    +
    +

    Input: root = [3,4,5,1,2], subRoot = [4,1,2]
    Output: true

    +
    +

    示例 2

    +
    +

    Input: root = [3,4,5,1,2,null,null,null,null,0], subRoot = [4,1,2]
    Output: false

    +
    +

    解析

    需要判断subRoot 为根节点的这棵树是不是 root 为根节点的这个树的子树，题应该不是特别难理解，只是需要注意，这里是子树，而不是一个子结构，也就是示例 2 的判断结果是 false，因为左边子树还有个 0，只能认为是左边的一部分，但不是子树，思路分析也不难，就是递归去判断，只不过是两种维度，第一种就是找到子树的根节点，也就是在 root 树找到 subRoot 的根节点相同的节点，这个就会用到递归，如果找到了一个，就开始递归的对比所有子节点，必须完全一样

    +

    代码

    public boolean isSubtree(TreeNode root, TreeNode subRoot) {
           if (subRoot == null || root == null) {
               return false;
           }
           if (root.val == subRoot.val) {
               // 如果 root 节点跟 subRoot 节点相同，就开始递归对比所有的子节点
               if (isSameTree(root.left, subRoot.left) && isSameTree(root.right, subRoot.right)) {
                   return true;
               }
           }
           // 如果 root 节点不同，就开始递归往下找与 subRoot 相同的起始节点，这里是或的关系，因为在左右子树任一找到一个就行
           return isSubtree(root.left, subRoot) || isSubtree(root.right, subRoot);
       }
       // 需要完全相同，也就是说不能像示例 2 那样多一个子节点这种，以及其他不同的情况，反正就是要完全一样
       public boolean isSameTree(TreeNode root, TreeNode subTree) {
           // 如果递归到这个节点都为空也认为是相同的
           if (root == null && subTree == null) {
               return true;
           }
           // 因为有前面的判断，这里就隐含了 subTree != null 这个条件
           if (root == null) {
               return false;
           }
           // 也因为有前面的判断，这里就隐含了  root != null 这个条件
           if (subTree == null) {
               return false;
           }
           // 两个都不为空，但是值不相同，也是 false
           if (root.val != subTree.val) {
               return false;
           }
           // 跟节点相同，就开始递归判断左右子树，必须两者都相同
           return isSameTree(root.left, subTree.left) && isSameTree(root.right, subTree.right);
       }
    ]]>     Java leetcode - java leetcode - dp - 代码题解 - Trapping Rain Water - 接雨水 - Leetcode 42 + java + 题解     @@ -1533,8 +1602,8 @@ leetcode - java leetcode + java @@ -1562,8 +1631,8 @@ leetcode - java leetcode + java 题解 @@ -1593,8 +1662,8 @@ leetcode - java leetcode + java 题解 Remove Duplicates from Sorted List @@ -1627,8 +1696,8 @@ leetcode - java leetcode + java 题解 @@ -1667,8 +1736,8 @@ ]]> Linux - 命令 小技巧 + 命令 grep grep 查日志 @@ -1733,30 +1802,15 @@ - Leetcode 31 Next Permutation 题解分析 - /2024/05/06/Leetcode-31-Next-Permutation-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 这题的题目介绍有一定的误导性，没有把字典序说明白，虽然的确是常规意义的字典序，但是题目介绍给的序列顺序反而会让人觉得那样不对
    这里我们逐步深入地来讲这个题，也类似于找规律
    注意：这里的递减递增序列都是从前往后看的
    字典序可以就当查字典，或者默认的字符串排序方式，就是对于一个序列，逐个字符对比，同一位的相同再对比下一位的，例如 11在12前面，12在13前面，
    那对于同一组字符或者像题中的数字，以最简单的12来分析，因为这个就两种排列，12跟21，按字典序排列，12在前，21在后
    通过这个仔细点也能发现，或者再举个例子123，这个排列就会多一些了

    -
    123
    132
    213
    231
    312
    321
    -

    一共有这么几种，不过举这个例子是为了更具体的发现规律，对于第一个数字相同的情况，比如123跟132，是132在后，发现规律没，递增序列是最小的，递减序列是最大的，因为2个数字 只有两种排列，扩展到3个数字，也可以把它拆分开，同样是第一个数字是1，后面两个数字就又回到了前面12序列的情况，
    所以就可以把问题分成两部分来解决，首先从后往前找到递减序列
    通过例子来讲

    -
    1 4 6 5 3 2
    -

    为什么要找到递减序列，因为递减序列已经是最大的了，要做调整要在这个之前调整，并且要找的是下一个序列，所以要是最接近的那个，否则我找到的可能就是下N个了，这个有点类似于数学里进位的意思，比如十九，九已经是个位里最大的了，找下一个比它大的只能是前面一位加一，但是加一以后需要九变到这一位的最小，也就是零。
    对于上面的例子，递减序列就是6532，第一个非递减的是4，我要找下一个就是要比当前这个大，那我就去后面找比4大的，至于为什么能找到，因为这是递减序列边界之外的，说明肯定有比它大的，那要找哪个呢，要找比4大的数字里最小的，这样才是最近的下一个序列，并且因为后面是递减序列，所以可以直接从后往前找第一个
    也就是会找到 5
    找到 5 之后就做交换

    -
    1 5 6 4 3 2
    -

    这样是不是就行了呢，不行，因为这不是下一个，而是下N个，当4变成5之后，这个序列不管5后面的数字怎么变都是比 146532 大的，但是要找的是下一个，最接近的那个，则需要后面的序列变成最小，也就是变成递增序列，也就是

    -
    1 5 2 3 4 6
    -

    这里有两种例外情况，或者严格来说是一种
    第一种是本身整个就是递减序列了，这在题中说了，下一个就是从头再开始，那就也很简单整个排序成递增的就行了
    第二种是本身就是个完全递增的，那其实就交换末两位，但这种跟上面的可以糅合在一起，比如就是

    -
    1 2 3 4 5 6
    -

    单个6构不成递增或递减，找到的第一个非递减的数字就是 5 ，那就是在 5 后面找一个比它大的做交换，这时只有 6 ，所以就 5 跟 6 交换下，然后单个 5 做排序也等于不用做
    再结合代码来看

    -
    public void nextPermutation(int[] nums) {
            int n = nums.length, i = n - 2, j = n - 1;
            // 这个while循环是为了从后往前找到递减序列的分界点
            // 循环完成后i就是递减序列左边的第一个数字位置
            while (i >= 0 && nums[i] >= nums[i + 1]) --i;
            if (i >= 0) {
                // 表示有找到分界点，然后就从后往前找到第一个比nums[i]大的做交换
                while (nums[j] <= nums[i]) --j;
                // 这个while循环在找到第一个nums[j] > nums[i]时退出
                // 下面就是做交换
                int temp = nums[i];
                nums[i] = nums[j];
                nums[j] = temp;
            }
            // 因为前面已经保证了逆序，这里只要排成正序就是最小的了
            Arrays.sort(nums, i+1, nums.length);
        }
    - + NX30Pro 刷成 Openwrt-ImmortalWrt 后作为有线中继的配置方法 + /2024/06/16/NX30Pro-%E5%88%B7%E6%88%90-Openwrt-ImmortalWrt-%E5%90%8E%E4%BD%9C%E4%B8%BA%E6%9C%89%E7%BA%BF%E4%B8%AD%E7%BB%A7%E7%9A%84%E9%85%8D%E7%BD%AE%E6%96%B9%E6%B3%95/ + 因为买了两个NX30Pro，主要是奔着可以刷Openwrt，然后把两个都刷成了ImmortalWrt，结果发现要作为有线中继的话设置还有点麻烦，所以记录下
    首先要设置个与主路由相同网段的静态地址，方便管理
    在网络-LAN-基本设置-协议设置成“静态地址”
    然后IPv4地址就是刚才说的静态地址，可以设个好记的
    子网掩码就默认的，255.255.255.0，
    IPv4网关就是主路由地址，DNS也设置成主路由地址
    基础设置改成忽略此接口-不提供DHCP服务，由主路由提供
    然后在高级设置中将IPv6的 路由通告服务和DHCPv6服务跟NDP代理禁用掉，
    物理设置中勾选桥接接口
    目前我是这样设置可以作为有线中继连通网络了，后续有变化继续更新

    ]]>     - Java - leetcode + openwrt - java - leetcode - 题解 + openwrt     @@ -1866,22 +1920,6 @@ c++ - - Java 线程池系列-第二篇 - /2024/02/18/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%BA%8C%E7%AF%87/ - 介绍了线程池的目的和实现概述，以及如何初始化的，我们就来开始看看线程池最重要的执行过程
    老规矩，还是先把注释翻译下，这个对理解逻辑其实非常重要，后面可以循着注释的逻辑来看代码
    第一步，如果是少于核心线程数的线程正在运行，那么尝试去开启一个新线程，并把提交的命令
    command 最为 first task，这里调用了 addWorker，会自动检查运行状态和线程数，并通过
    返回 false 来提示不应该增加线程，比如已经有其他任务先创建了线程导致已经超过了核心线程数
    第二步，在前面不符合的情况下，也就是线程数已经大于等于核心线程数了或者在调用 addWorker
    的时候校验发现线程数已经大于等于核心线程数或者线程池运行状态不是正在运行了就会进入下一个判断
    首先还是判断线程池是否正在运行，然后将 command 放进队列，如果成功进队了，还需要再次进行校验
    如果线程池状态不是正在运行了，则需要再出队，然后执行拒绝策略，如果状态正常，但是线程被回收完了
    那需要创建线程
    第三步，如果不能正常进队列，会尝试再启动一个新线程，这里表示核心线程数满了，并且队列也满了，
    则需要再开启一个新线程，如果开启失败则执行拒绝策略
    代码注释基本已经把代码讲得很详细了

    -
    public void execute(Runnable command) {
            if (command == null)
                throw new NullPointerException();
            /*
             * Proceed in 3 steps:
             *
             * 1. If fewer than corePoolSize threads are running, try to
             * start a new thread with the given command as its first
             * task.  The call to addWorker atomically checks runState and
             * workerCount, and so prevents false alarms that would add
             * threads when it shouldn't, by returning false.
             *
             * 2. If a task can be successfully queued, then we still need
             * to double-check whether we should have added a thread
             * (because existing ones died since last checking) or that
             * the pool shut down since entry into this method. So we
             * recheck state and if necessary roll back the enqueuing if
             * stopped, or start a new thread if there are none.
             *
             * 3. If we cannot queue task, then we try to add a new
             * thread.  If it fails, we know we are shut down or saturated
             * and so reject the task.
             */
            int c = ctl.get();
            // 这就是第一步
            if (workerCountOf(c) < corePoolSize) {
                if (addWorker(command, true))
                    return;
                // 注意这里是第一步可能失败了，获取一下最新的状态
                c = ctl.get();
            }
            // 这里是第二步
            if (isRunning(c) && workQueue.offer(command)) {
                // 每次都获取最新的状态
                int recheck = ctl.get();
                if (! isRunning(recheck) && remove(command))
                    reject(command);
                else if (workerCountOf(recheck) == 0)
                    // 这里是判断如果一个线程都没了的话，注意添加的是非核心线程
                    addWorker(null, false);
            }
            // 这里第三步是添加线程，并且是非核心线程
            else if (!addWorker(command, false))
                reject(command);
        }
    -

    这里面比较重要的就是 addWorker 方法，我们也来看一下
    这里用到了 break retry; 就是在循环中跳出到这个 retry 的位置继续执行
    然后还是获取运行状态，如果是非运行状态，
    并且rs == SHUTDOWN 与 firstTask == null 和 ! workQueue.isEmpty()，至少一个为false
    因为只有当 SHUTDOWN 状态才会出现后面两者为 false 的情况，如果是 SHUTDOWN ，队列不为空还是需要执行完队列里的任务
    然后是判断线程数量与传入参数 core 还有核心线程数跟最大线程数的对比，如果是超过了就返回 false 也就是外层的第一步的内部
    if 就会继续执行后面的获取线程池状态，然后是 cas 增加线程数量，如果失败则 break 跳到方法开头，如果成功则继续重新获取 c
    判断线程池运行状态，如果不一致则从外层继续进入
    后面的就是新建一个 worker，然后获取锁，继续判断线程池状态，如果在运行中或者队列不为空（隐含条件）则继续执行添加 worker
    并判断是否需要更新历史最大线程数，更改线程添加状态，然后启动线程，标记线程启动状态

    -
    private boolean addWorker(Runnable firstTask, boolean core) {
            retry:
            for (;;) {
                int c = ctl.get();
                int rs = runStateOf(c);

                // Check if queue empty only if necessary.
                if (rs >= SHUTDOWN && ! (rs == SHUTDOWN && firstTask == null && ! workQueue.isEmpty()))
                    return false;

                for (;;) {
                    int wc = workerCountOf(c);
                    if (wc >= CAPACITY ||
                        wc >= (core ? corePoolSize : maximumPoolSize))
                        return false;
                    if (compareAndIncrementWorkerCount(c))
                        break retry;
                    c = ctl.get();  // Re-read ctl
                    if (runStateOf(c) != rs)
                        continue retry;
                    // else CAS failed due to workerCount change; retry inner loop
                }
            }

            boolean workerStarted = false;
            boolean workerAdded = false;
            Worker w = null;
            try {
                w = new Worker(firstTask);
                final Thread t = w.thread;
                if (t != null) {
                    final ReentrantLock mainLock = this.mainLock;
                    mainLock.lock();
                    try {
                        // Recheck while holding lock.
                        // Back out on ThreadFactory failure or if
                        // shut down before lock acquired.
                        int rs = runStateOf(ctl.get());

                        if (rs < SHUTDOWN ||
                            (rs == SHUTDOWN && firstTask == null)) {
                            if (t.isAlive()) // precheck that t is startable
                                throw new IllegalThreadStateException();
                            workers.add(w);
                            int s = workers.size();
                            if (s > largestPoolSize)
                                largestPoolSize = s;
                            workerAdded = true;
                        }
                    } finally {
                        mainLock.unlock();
                    }
                    if (workerAdded) {
                        t.start();
                        workerStarted = true;
                    }
                }
            } finally {
                if (! workerStarted)
                    addWorkerFailed(w);
            }
            return workerStarted;
        }
    -

    后面会继续讲下线程如何执行队列里的任务

    -]]>     - - Java - - - Java - -     Tomcat 系列篇一-介绍下 connector /2023/09/10/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E4%B8%80-%E4%BB%8B%E7%BB%8D%E4%B8%8B-connector/ @@ -2029,38 +2067,6 @@

    这里包括了 cluster 的启动和 realm 的启动，然后将 child 子组件添加到线程池里启动，然后循环 get 结果，接下去就是 pipeline 的启动，设置开始中状态，后面是 ContainerBackgroundProcessor 的启动
    然后是停止方法

    @Override
        protected synchronized void stopInternal() throws LifecycleException {

            // Stop our thread
            if (monitorFuture != null) {
                monitorFuture.cancel(true);
                monitorFuture = null;
            }
            threadStop();

            setState(LifecycleState.STOPPING);

            // Stop the Valves in our pipeline (including the basic), if any
            if (pipeline instanceof Lifecycle &&
                    ((Lifecycle) pipeline).getState().isAvailable()) {
                ((Lifecycle) pipeline).stop();
            }

            // Stop our child containers, if any
            Container children[] = findChildren();
            List<Future<Void>> results = new ArrayList<>();
            for (Container child : children) {
                results.add(startStopExecutor.submit(new StopChild(child)));
            }

            boolean fail = false;
            for (Future<Void> result : results) {
                try {
                    result.get();
                } catch (Exception e) {
                    log.error(sm.getString("containerBase.threadedStopFailed"), e);
                    fail = true;
                }
            }
            if (fail) {
                throw new LifecycleException(
                        sm.getString("containerBase.threadedStopFailed"));
            }

            // Stop our subordinate components, if any
            Realm realm = getRealmInternal();
            if (realm instanceof Lifecycle) {
                ((Lifecycle) realm).stop();
            }
            Cluster cluster = getClusterInternal();
            if (cluster instanceof Lifecycle) {
                ((Lifecycle) cluster).stop();
            }
        }

    也是类似的，只不过停止顺序和刚才开始的顺序反了一下

    -]]> - - Java - SpringBoot - Tomcat - - - Java - Tomcat - SpringBoot - -     - - Tomcat 系列篇八-介绍下 Tomcat 里的线程池用处 - /2023/11/05/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%85%AB-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%94%A8%E5%A4%84/ - 线程池在 Tomcat 中也是非常重要的工具，这里我们简单介绍下 Tomcat 中的线程池，在 container 的启动过程中
    org.apache.catalina.core.ContainerBase#initInternal

    -
    @Override
        protected void initInternal() throws LifecycleException {
            reconfigureStartStopExecutor(getStartStopThreads());
            super.initInternal();
        }
    -

    这里先会获取线程数，

    -
    @Override
    public int getStartStopThreads() {
        return startStopThreads;
    }
    -

    默认的 ContainerBase 的设置线程数是 1

    -
    private int startStopThreads = 1;
    -

    那就会按照 org.apache.catalina.core.ContainerBase#reconfigureStartStopExecutor 来设置线程池类型

    -
    private void reconfigureStartStopExecutor(int threads) {
        if (threads == 1) {
            // Use a fake executor
            if (!(startStopExecutor instanceof InlineExecutorService)) {
                startStopExecutor = new InlineExecutorService();
            }
        } else {
            // Delegate utility execution to the Service
            Server server = Container.getService(this).getServer();
            server.setUtilityThreads(threads);
            startStopExecutor = server.getUtilityExecutor();
        }
    }
    -

    此时线程池类型是 null 也就会走到上一个分支中，new 一个 InlineExecutorService 出来
    前面的这些其实是在 StandardEngine 初始化过程中，也就是 initInternal 时候进行的
    然后具体的使用是在 startInternal 开始方法中，

    -
    // Start our child containers, if any
    Container children[] = findChildren();
    List<Future<Void>> results = new ArrayList<>();
    for (Container child : children) {
        results.add(startStopExecutor.submit(new StartChild(child)));
    }
    -

    会获取当前 StandardEngine 的子组件提交给线程池进行启动
    而这个 StartChild 也比较简单

    -
    private static class StartChild implements Callable<Void> {

        private Container child;

        public StartChild(Container child) {
            this.child = child;
        }

        @Override
        public Void call() throws LifecycleException {
            child.start();
            return null;
        }
    }
    -

    就是调用子组件的 start 方法，
    在 org.apache.tomcat.util.threads.InlineExecutorService 的父类，java.util.concurrent.AbstractExecutorService 中先会把 Callable 包装成 FutureTask

    -
    public <T> Future<T> submit(Callable<T> task) {
        if (task == null) throw new NullPointerException();
        RunnableFuture<T> ftask = newTaskFor(task);
        execute(ftask);
        return ftask;
    }
    protected <T> RunnableFuture<T> newTaskFor(Callable<T> callable) {
        return new FutureTask<T>(callable);
    }
    -

    然后进行执行，

    -
    @Override
    public void execute(Runnable command) {
        synchronized (lock) {
            if (shutdown) {
                throw new RejectedExecutionException();
            }
            taskRunning = true;
        }
        command.run();
        synchronized (lock) {
            taskRunning = false;
            if (shutdown) {
                terminated = true;
                lock.notifyAll();
            }
        }
    }
    -

    这个就是我们 startStopExecutor 的主要作用，帮我们启动子组件
    对于 Container 来说启动的就是 host，而其实 host 也是继承了 ContainerBase 的，后面可以再继续介绍下

    ]]>     Java @@ -2104,31 +2110,25 @@     - Tomcat 系列篇十-介绍下 Tomcat 里的 Mapper 作用 - /2023/11/26/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84-Mapper-%E4%BD%9C%E7%94%A8/ - Mapper 顾名思义是作一个映射作用，在 Tomcat 中会根据域名找到 host 组件，再根据 uri 可以找到对应的 context 和 wrapper 组件，但是对于当前这个环境 (Springboot) 会有一点小区别
    之前说到
    请求会经过 coyote 适配器进行进一步处理，
    org.apache.coyote.http11.Http11Processor#service

    -
    // Process the request in the adapter
    if (getErrorState().isIoAllowed()) {
        try {
            rp.setStage(org.apache.coyote.Constants.STAGE_SERVICE);
            getAdapter().service(request, response);
    -

    然后到 coyoteAdapter 的 service
    org.apache.catalina.connector.CoyoteAdapter#service

    -
    try {
        // Parse and set Catalina and configuration specific
        // request parameters
        postParseSuccess = postParseRequest(req, request, res, response);
        if (postParseSuccess) {
            //check valves if we support async
            request.setAsyncSupported(
                    connector.getService().getContainer().getPipeline().isAsyncSupported());
            // Calling the container
            // ----------> 到这就是调用 pipeline 去处理了，我们要关注上面的 postParseRequest
            connector.getService().getContainer().getPipeline().getFirst().invoke(
                    request, response);
        }
    -

    主要先看到 postParseRequest
    在 postParseRequest 的代码里会调用 Mapper 的 map 方法

    -
    while (mapRequired) {
        // This will map the the latest version by default
        connector.getService().getMapper().map(serverName, decodedURI,
                version, request.getMappingData());

        // If there is no context at this point, either this is a 404
        // because no ROOT context has been deployed or the URI was invalid
        // so no context could be mapped.
        if (request.getContext() == null) {
            // Allow processing to continue.
    -

    而后面的 context 就是在 map 方法里处理塞进去的
    往里看就是 org.apache.catalina.mapper.Mapper#internalMap
    第一步找的是 host

    -
    // Virtual host mapping
            MappedHost[] hosts = this.hosts;
            MappedHost mappedHost = exactFindIgnoreCase(hosts, host);
    -

    而在后续代码里继续设置 context

    -
    if (contextVersion == null) {
                // Return the latest version
                // The versions array is known to contain at least one element
                contextVersion = contextVersions[versionCount - 1];
            }
            mappingData.context = contextVersion.object;
    -

    然后是 wrapper

    -
    // Wrapper mapping
    if (!contextVersion.isPaused()) {
        internalMapWrapper(contextVersion, uri, mappingData);
    }
    -

    在这个方法 org.apache.catalina.mapper.Mapper#internalMapWrapper

    -
    // Rule 7 -- Default servlet
    if (mappingData.wrapper == null && !checkJspWelcomeFiles) {
        if (contextVersion.defaultWrapper != null) {
            mappingData.wrapper = contextVersion.defaultWrapper.object;
            mappingData.requestPath.setChars
                (path.getBuffer(), path.getStart(), path.getLength());
            mappingData.wrapperPath.setChars
                (path.getBuffer(), path.getStart(), path.getLength());
            mappingData.matchType = MappingMatch.DEFAULT;
    -

    这里就设置了 wrapper ，因为我们是在 Springboot 中，所以只有默认的 dispatchServlet
    上面主要是在请求处理过程中的查找映射过程，一开始的注册是从 MapperListener 开始的
    MapperListener 继承了 LifecycleMbeanBase，也就是有了 Lifecycle 状态变化那一套

    -
    @Override
    public void startInternal() throws LifecycleException {

        setState(LifecycleState.STARTING);

        Engine engine = service.getContainer();
        if (engine == null) {
            return;
        }

        findDefaultHost();

        addListeners(engine);

        Container[] conHosts = engine.findChildren();
        for (Container conHost : conHosts) {
            Host host = (Host) conHost;
            if (!LifecycleState.NEW.equals(host.getState())) {
                // Registering the host will register the context and wrappers
                registerHost(host);
            }
        }
    }
    -

    在启动过程中就会去把 engine 的子容器 host 找出来进行注册，就是调用 registerHost 方法

    -
    private void registerHost(Host host) {

        String[] aliases = host.findAliases();
        mapper.addHost(host.getName(), aliases, host);

        for (Container container : host.findChildren()) {
            if (container.getState().isAvailable()) {
                registerContext((Context) container);
            }
        }

        // Default host may have changed
        findDefaultHost();

        if(log.isDebugEnabled()) {
            log.debug(sm.getString("mapperListener.registerHost",
                    host.getName(), domain, service));
        }
    }
    -

    在这里面会添加 host 组件，注册 context 等，注册context 里还会处理 wrapper 的添加记录

    -
    private void registerContext(Context context) {

        String contextPath = context.getPath();
        if ("/".equals(contextPath)) {
            contextPath = "";
        }
        Host host = (Host)context.getParent();

        WebResourceRoot resources = context.getResources();
        String[] welcomeFiles = context.findWelcomeFiles();
        List<WrapperMappingInfo> wrappers = new ArrayList<>();

        for (Container container : context.findChildren()) {
            prepareWrapperMappingInfo(context, (Wrapper) container, wrappers);

            if(log.isDebugEnabled()) {
                log.debug(sm.getString("mapperListener.registerWrapper",
                        container.getName(), contextPath, service));
            }
        }

        mapper.addContextVersion(host.getName(), host, contextPath,
                context.getWebappVersion(), context, welcomeFiles, resources,
                wrappers);

        if(log.isDebugEnabled()) {
            log.debug(sm.getString("mapperListener.registerContext",
                    contextPath, service));
        }
    }
    -

    就是在 org.apache.catalina.mapper.Mapper#addContextVersion 方法中

    -
    public void addContextVersion(String hostName, Host host, String path,
            String version, Context context, String[] welcomeResources,
            WebResourceRoot resources, Collection<WrapperMappingInfo> wrappers) {

        hostName = renameWildcardHost(hostName);

        MappedHost mappedHost  = exactFind(hosts, hostName);
        if (mappedHost == null) {
            addHost(hostName, new String[0], host);
            mappedHost = exactFind(hosts, hostName);
            if (mappedHost == null) {
                log.error(sm.getString("mapper.addContext.noHost", hostName));
                return;
            }
        }
        if (mappedHost.isAlias()) {
            log.error(sm.getString("mapper.addContext.hostIsAlias", hostName));
            return;
        }
        int slashCount = slashCount(path);
        synchronized (mappedHost) {
            ContextVersion newContextVersion = new ContextVersion(version,
                    path, slashCount, context, resources, welcomeResources);
            if (wrappers != null) {
                addWrappers(newContextVersion, wrappers);
            }
    -

    大致介绍了下 Mapper 的逻辑

    + Tomcat 系列篇八-介绍下 Tomcat 里的线程池用处 + /2023/11/05/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%85%AB-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%94%A8%E5%A4%84/ + 线程池在 Tomcat 中也是非常重要的工具，这里我们简单介绍下 Tomcat 中的线程池，在 container 的启动过程中
    org.apache.catalina.core.ContainerBase#initInternal

    +
    @Override
        protected void initInternal() throws LifecycleException {
            reconfigureStartStopExecutor(getStartStopThreads());
            super.initInternal();
        }
    +

    这里先会获取线程数，

    +
    @Override
    public int getStartStopThreads() {
        return startStopThreads;
    }
    +

    默认的 ContainerBase 的设置线程数是 1

    +
    private int startStopThreads = 1;
    +

    那就会按照 org.apache.catalina.core.ContainerBase#reconfigureStartStopExecutor 来设置线程池类型

    +
    private void reconfigureStartStopExecutor(int threads) {
        if (threads == 1) {
            // Use a fake executor
            if (!(startStopExecutor instanceof InlineExecutorService)) {
                startStopExecutor = new InlineExecutorService();
            }
        } else {
            // Delegate utility execution to the Service
            Server server = Container.getService(this).getServer();
            server.setUtilityThreads(threads);
            startStopExecutor = server.getUtilityExecutor();
        }
    }
    +

    此时线程池类型是 null 也就会走到上一个分支中，new 一个 InlineExecutorService 出来
    前面的这些其实是在 StandardEngine 初始化过程中，也就是 initInternal 时候进行的
    然后具体的使用是在 startInternal 开始方法中，

    +
    // Start our child containers, if any
    Container children[] = findChildren();
    List<Future<Void>> results = new ArrayList<>();
    for (Container child : children) {
        results.add(startStopExecutor.submit(new StartChild(child)));
    }
    +

    会获取当前 StandardEngine 的子组件提交给线程池进行启动
    而这个 StartChild 也比较简单

    +
    private static class StartChild implements Callable<Void> {

        private Container child;

        public StartChild(Container child) {
            this.child = child;
        }

        @Override
        public Void call() throws LifecycleException {
            child.start();
            return null;
        }
    }
    +

    就是调用子组件的 start 方法，
    在 org.apache.tomcat.util.threads.InlineExecutorService 的父类，java.util.concurrent.AbstractExecutorService 中先会把 Callable 包装成 FutureTask

    +
    public <T> Future<T> submit(Callable<T> task) {
        if (task == null) throw new NullPointerException();
        RunnableFuture<T> ftask = newTaskFor(task);
        execute(ftask);
        return ftask;
    }
    protected <T> RunnableFuture<T> newTaskFor(Callable<T> callable) {
        return new FutureTask<T>(callable);
    }
    +

    然后进行执行，

    +
    @Override
    public void execute(Runnable command) {
        synchronized (lock) {
            if (shutdown) {
                throw new RejectedExecutionException();
            }
            taskRunning = true;
        }
        command.run();
        synchronized (lock) {
            taskRunning = false;
            if (shutdown) {
                terminated = true;
                lock.notifyAll();
            }
        }
    }
    +

    这个就是我们 startStopExecutor 的主要作用，帮我们启动子组件
    对于 Container 来说启动的就是 host，而其实 host 也是继承了 ContainerBase 的，后面可以再继续介绍下

    ]]>     Java @@ -2183,14 +2183,83 @@     - Tomcat 系列篇十二-番外介绍下 Tomcat 的上传文件限制 - /2023/12/17/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81%E4%BA%8C-%E7%95%AA%E5%A4%96%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E7%9A%84%E4%B8%8A%E4%BC%A0%E6%96%87%E4%BB%B6%E9%99%90%E5%88%B6/ - 最近同学在把 springboot 升级到 2.x 版本的过程中碰到了小问题，可能升级变更里能找到信息，不过我们以学习为目的，可以看看代码是怎么样的
    报错是在这段代码里的
    org.apache.tomcat.util.http.fileupload.util.LimitedInputStream#checkLimit

    -
    private void checkLimit() throws IOException {
        if (count > sizeMax) {
            raiseError(sizeMax, count);
        }
    }
    -

    其中的 raiseError 是个抽象方法

    -
    protected abstract void raiseError(long pSizeMax, long pCount)
                throws IOException;
    -

    具体的实现是在

    -
    public FileItemStreamImpl(FileItemIteratorImpl pFileItemIterator, String pName, String pFieldName, String pContentType, boolean pFormField, long pContentLength) throws FileUploadException, IOException {
            this.fileItemIteratorImpl = pFileItemIterator;
            this.name = pName;
            this.fieldName = pFieldName;
            this.contentType = pContentType;
            this.formField = pFormField;
            long fileSizeMax = this.fileItemIteratorImpl.getFileSizeMax();
            if (fileSizeMax != -1L && pContentLength != -1L && pContentLength > fileSizeMax) {
                FileSizeLimitExceededException e = new FileSizeLimitExceededException(String.format("The field %s exceeds its maximum permitted size of %s bytes.", this.fieldName, fileSizeMax), pContentLength, fileSizeMax);
                e.setFileName(pName);
                e.setFieldName(pFieldName);
                throw new FileUploadIOException(e);
            } else {
                final MultipartStream.ItemInputStream itemStream = this.fileItemIteratorImpl.getMultiPartStream().newInputStream();
                InputStream istream = itemStream;
                if (fileSizeMax != -1L) {
                    istream = new LimitedInputStream(itemStream, fileSizeMax) {
                        protected void raiseError(long pSizeMax, long pCount) throws IOException {
                            itemStream.close(true);
                            FileSizeLimitExceededException e = new FileSizeLimitExceededException(String.format("The field %s exceeds its maximum permitted size of %s bytes.", FileItemStreamImpl.this.fieldName, pSizeMax), pCount, pSizeMax);
                            e.setFieldName(FileItemStreamImpl.this.fieldName);
                            e.setFileName(FileItemStreamImpl.this.name);
                            throw new FileUploadIOException(e);
                        }
                    };
                }

                this.stream = (InputStream)istream;
            }
        }
    + Tomcat 系列篇十-介绍下 Tomcat 里的 Mapper 作用 + /2023/11/26/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84-Mapper-%E4%BD%9C%E7%94%A8/ + Mapper 顾名思义是作一个映射作用，在 Tomcat 中会根据域名找到 host 组件，再根据 uri 可以找到对应的 context 和 wrapper 组件，但是对于当前这个环境 (Springboot) 会有一点小区别
    之前说到
    请求会经过 coyote 适配器进行进一步处理，
    org.apache.coyote.http11.Http11Processor#service

    +
    // Process the request in the adapter
    if (getErrorState().isIoAllowed()) {
        try {
            rp.setStage(org.apache.coyote.Constants.STAGE_SERVICE);
            getAdapter().service(request, response);
    +

    然后到 coyoteAdapter 的 service
    org.apache.catalina.connector.CoyoteAdapter#service

    +
    try {
        // Parse and set Catalina and configuration specific
        // request parameters
        postParseSuccess = postParseRequest(req, request, res, response);
        if (postParseSuccess) {
            //check valves if we support async
            request.setAsyncSupported(
                    connector.getService().getContainer().getPipeline().isAsyncSupported());
            // Calling the container
            // ----------> 到这就是调用 pipeline 去处理了，我们要关注上面的 postParseRequest
            connector.getService().getContainer().getPipeline().getFirst().invoke(
                    request, response);
        }
    +

    主要先看到 postParseRequest
    在 postParseRequest 的代码里会调用 Mapper 的 map 方法

    +
    while (mapRequired) {
        // This will map the the latest version by default
        connector.getService().getMapper().map(serverName, decodedURI,
                version, request.getMappingData());

        // If there is no context at this point, either this is a 404
        // because no ROOT context has been deployed or the URI was invalid
        // so no context could be mapped.
        if (request.getContext() == null) {
            // Allow processing to continue.
    +

    而后面的 context 就是在 map 方法里处理塞进去的
    往里看就是 org.apache.catalina.mapper.Mapper#internalMap
    第一步找的是 host

    +
    // Virtual host mapping
            MappedHost[] hosts = this.hosts;
            MappedHost mappedHost = exactFindIgnoreCase(hosts, host);
    +

    而在后续代码里继续设置 context

    +
    if (contextVersion == null) {
                // Return the latest version
                // The versions array is known to contain at least one element
                contextVersion = contextVersions[versionCount - 1];
            }
            mappingData.context = contextVersion.object;
    +

    然后是 wrapper

    +
    // Wrapper mapping
    if (!contextVersion.isPaused()) {
        internalMapWrapper(contextVersion, uri, mappingData);
    }
    +

    在这个方法 org.apache.catalina.mapper.Mapper#internalMapWrapper

    +
    // Rule 7 -- Default servlet
    if (mappingData.wrapper == null && !checkJspWelcomeFiles) {
        if (contextVersion.defaultWrapper != null) {
            mappingData.wrapper = contextVersion.defaultWrapper.object;
            mappingData.requestPath.setChars
                (path.getBuffer(), path.getStart(), path.getLength());
            mappingData.wrapperPath.setChars
                (path.getBuffer(), path.getStart(), path.getLength());
            mappingData.matchType = MappingMatch.DEFAULT;
    +

    这里就设置了 wrapper ，因为我们是在 Springboot 中，所以只有默认的 dispatchServlet
    上面主要是在请求处理过程中的查找映射过程，一开始的注册是从 MapperListener 开始的
    MapperListener 继承了 LifecycleMbeanBase，也就是有了 Lifecycle 状态变化那一套

    +
    @Override
    public void startInternal() throws LifecycleException {

        setState(LifecycleState.STARTING);

        Engine engine = service.getContainer();
        if (engine == null) {
            return;
        }

        findDefaultHost();

        addListeners(engine);

        Container[] conHosts = engine.findChildren();
        for (Container conHost : conHosts) {
            Host host = (Host) conHost;
            if (!LifecycleState.NEW.equals(host.getState())) {
                // Registering the host will register the context and wrappers
                registerHost(host);
            }
        }
    }
    +

    在启动过程中就会去把 engine 的子容器 host 找出来进行注册，就是调用 registerHost 方法

    +
    private void registerHost(Host host) {

        String[] aliases = host.findAliases();
        mapper.addHost(host.getName(), aliases, host);

        for (Container container : host.findChildren()) {
            if (container.getState().isAvailable()) {
                registerContext((Context) container);
            }
        }

        // Default host may have changed
        findDefaultHost();

        if(log.isDebugEnabled()) {
            log.debug(sm.getString("mapperListener.registerHost",
                    host.getName(), domain, service));
        }
    }
    +

    在这里面会添加 host 组件，注册 context 等，注册context 里还会处理 wrapper 的添加记录

    +
    private void registerContext(Context context) {

        String contextPath = context.getPath();
        if ("/".equals(contextPath)) {
            contextPath = "";
        }
        Host host = (Host)context.getParent();

        WebResourceRoot resources = context.getResources();
        String[] welcomeFiles = context.findWelcomeFiles();
        List<WrapperMappingInfo> wrappers = new ArrayList<>();

        for (Container container : context.findChildren()) {
            prepareWrapperMappingInfo(context, (Wrapper) container, wrappers);

            if(log.isDebugEnabled()) {
                log.debug(sm.getString("mapperListener.registerWrapper",
                        container.getName(), contextPath, service));
            }
        }

        mapper.addContextVersion(host.getName(), host, contextPath,
                context.getWebappVersion(), context, welcomeFiles, resources,
                wrappers);

        if(log.isDebugEnabled()) {
            log.debug(sm.getString("mapperListener.registerContext",
                    contextPath, service));
        }
    }
    +

    就是在 org.apache.catalina.mapper.Mapper#addContextVersion 方法中

    +
    public void addContextVersion(String hostName, Host host, String path,
            String version, Context context, String[] welcomeResources,
            WebResourceRoot resources, Collection<WrapperMappingInfo> wrappers) {

        hostName = renameWildcardHost(hostName);

        MappedHost mappedHost  = exactFind(hosts, hostName);
        if (mappedHost == null) {
            addHost(hostName, new String[0], host);
            mappedHost = exactFind(hosts, hostName);
            if (mappedHost == null) {
                log.error(sm.getString("mapper.addContext.noHost", hostName));
                return;
            }
        }
        if (mappedHost.isAlias()) {
            log.error(sm.getString("mapper.addContext.hostIsAlias", hostName));
            return;
        }
        int slashCount = slashCount(path);
        synchronized (mappedHost) {
            ContextVersion newContextVersion = new ContextVersion(version,
                    path, slashCount, context, resources, welcomeResources);
            if (wrappers != null) {
                addWrappers(newContextVersion, wrappers);
            }
    +

    大致介绍了下 Mapper 的逻辑

    +]]>     + + Java + SpringBoot + Tomcat + + + Java + Tomcat + SpringBoot + +     + + Tomcat 系列篇十一-介绍下 Tomcat 里的后台处理和热加载 + /2023/12/10/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81%E4%B8%80-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84%E5%90%8E%E5%8F%B0%E5%A4%84%E7%90%86%E5%92%8C%E7%83%AD%E5%8A%A0%E8%BD%BD/ + 这部分其实之前在讲线程池的时候也有点带到了， 主要是在这个类里
    org.apache.catalina.core.ContainerBase.ContainerBackgroundProcessor

    +
    protected class ContainerBackgroundProcessor implements Runnable {

            @Override
            public void run() {
                processChildren(ContainerBase.this);
            }

            protected void processChildren(Container container) {
                ClassLoader originalClassLoader = null;

                try {
                    if (container instanceof Context) {
                        Loader loader = ((Context) container).getLoader();
                        // Loader will be null for FailedContext instances
                        if (loader == null) {
                            return;
                        }

                        // Ensure background processing for Contexts and Wrappers
                        // is performed under the web app's class loader
                        originalClassLoader = ((Context) container).bind(false, null);
                    }
                    // 调用 Container 的 backgroundProcess
                    container.backgroundProcess();
                    // 然后寻找 children
                    Container[] children = container.findChildren();
                    for (Container child : children) {
                        // 如果 backgroundProcessorDelay <= 0 就调用执行
                        // 否则代表这个 Container 有之前第八篇说的 StartChild 这种
                        if (child.getBackgroundProcessorDelay() <= 0) {
                            processChildren(child);
                        }
                    }
                } catch (Throwable t) {
                    ExceptionUtils.handleThrowable(t);
                    log.error(sm.getString("containerBase.backgroundProcess.error"), t);
                } finally {
                    if (container instanceof Context) {
                        ((Context) container).unbind(false, originalClassLoader);
                    }
                }
            }
        }
    +

    这个触发方式是在 ContainerBase 里的

    +
    protected class ContainerBackgroundProcessorMonitor implements Runnable {
            @Override
            public void run() {
                if (getState().isAvailable()) {
                    threadStart();
                }
            }
        }
    + +

    而在这个 threadStart 里

    +
    protected void threadStart() {
        if (backgroundProcessorDelay > 0
                && (getState().isAvailable() || LifecycleState.STARTING_PREP.equals(getState()))
                && (backgroundProcessorFuture == null || backgroundProcessorFuture.isDone())) {
            if (backgroundProcessorFuture != null && backgroundProcessorFuture.isDone()) {
                // There was an error executing the scheduled task, get it and log it
                try {
                    backgroundProcessorFuture.get();
                } catch (InterruptedException | ExecutionException e) {
                    log.error(sm.getString("containerBase.backgroundProcess.error"), e);
                }
            }
            backgroundProcessorFuture = Container.getService(this).getServer().getUtilityExecutor()
                    .scheduleWithFixedDelay(new ContainerBackgroundProcessor(),
                            backgroundProcessorDelay, backgroundProcessorDelay,
                            TimeUnit.SECONDS);
        }
    }
    +

    就调用了线程池的 scheduleWithFixedDelay 方法提交了这个 ContainerBackgroundProcessor，
    仔细看代码会发现，

    +
    public StandardEngine() {

        super();
        pipeline.setBasic(new StandardEngineValve());
        /* Set the jmvRoute using the system property jvmRoute */
        try {
            setJvmRoute(System.getProperty("jvmRoute"));
        } catch(Exception ex) {
            log.warn(sm.getString("standardEngine.jvmRouteFail"));
        }
        // By default, the engine will hold the reloading thread
        backgroundProcessorDelay = 10;

    }
    +

    这个就不用开启后台热加载，而主要的热加载同学应该是
    org.apache.catalina.core.StandardContext#backgroundProcess

    +
    public void backgroundProcess() {

            if (!getState().isAvailable()) {
                return;
            }

            Loader loader = getLoader();
            if (loader != null) {
                try {
                    // 这里就用了 loader 的 backgroundProcess
                    loader.backgroundProcess();
                } catch (Exception e) {
                    log.warn(sm.getString(
                            "standardContext.backgroundProcess.loader", loader), e);
                }
            }
            Manager manager = getManager();
            if (manager != null) {
                try {
                    manager.backgroundProcess();
                } catch (Exception e) {
                    log.warn(sm.getString(
                            "standardContext.backgroundProcess.manager", manager),
                            e);
                }
            }
            WebResourceRoot resources = getResources();
            if (resources != null) {
                try {
                    resources.backgroundProcess();
                } catch (Exception e) {
                    log.warn(sm.getString(
                            "standardContext.backgroundProcess.resources",
                            resources), e);
                }
            }
            InstanceManager instanceManager = getInstanceManager();
            if (instanceManager != null) {
                try {
                    instanceManager.backgroundProcess();
                } catch (Exception e) {
                    log.warn(sm.getString(
                            "standardContext.backgroundProcess.instanceManager",
                            resources), e);
                }
            }
            super.backgroundProcess();
        }
    +

    loader 的后台处理就是

    +
    @Override
        public void backgroundProcess() {
            if (reloadable && modified()) {
                try {
                    Thread.currentThread().setContextClassLoader
                        (WebappLoader.class.getClassLoader());
                    if (context != null) {
                        context.reload();
                    }
                } finally {
                    if (context != null && context.getLoader() != null) {
                        Thread.currentThread().setContextClassLoader
                            (context.getLoader().getClassLoader());
                    }
                }
            }
        }
    +

    然后又会回到 context 的 reload，也就是 StandardContext 的 reload

    +
    @Override
    public synchronized void reload() {

        // Validate our current component state
        if (!getState().isAvailable()) {
            throw new IllegalStateException
                (sm.getString("standardContext.notStarted", getName()));
        }

        if(log.isInfoEnabled()) {
            log.info(sm.getString("standardContext.reloadingStarted",
                    getName()));
        }

        // Stop accepting requests temporarily.
        setPaused(true);

        try {
            stop();
        } catch (LifecycleException e) {
            log.error(
                sm.getString("standardContext.stoppingContext", getName()), e);
        }

        try {
            start();
        } catch (LifecycleException e) {
            log.error(
                sm.getString("standardContext.startingContext", getName()), e);
        }

        setPaused(false);

        if(log.isInfoEnabled()) {
            log.info(sm.getString("standardContext.reloadingCompleted",
                    getName()));
        }

    }
    +

    这样就是线程池结合后台处理，还是有些复杂的。

    +]]>     + + Java + SpringBoot + Tomcat + + + Java + Tomcat + SpringBoot + +     + + Tomcat 系列篇十二-番外介绍下 Tomcat 的上传文件限制 + /2023/12/17/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81%E4%BA%8C-%E7%95%AA%E5%A4%96%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E7%9A%84%E4%B8%8A%E4%BC%A0%E6%96%87%E4%BB%B6%E9%99%90%E5%88%B6/ + 最近同学在把 springboot 升级到 2.x 版本的过程中碰到了小问题，可能升级变更里能找到信息，不过我们以学习为目的，可以看看代码是怎么样的
    报错是在这段代码里的
    org.apache.tomcat.util.http.fileupload.util.LimitedInputStream#checkLimit

    +
    private void checkLimit() throws IOException {
        if (count > sizeMax) {
            raiseError(sizeMax, count);
        }
    }
    +

    其中的 raiseError 是个抽象方法

    +
    protected abstract void raiseError(long pSizeMax, long pCount)
                throws IOException;
    +

    具体的实现是在

    +
    public FileItemStreamImpl(FileItemIteratorImpl pFileItemIterator, String pName, String pFieldName, String pContentType, boolean pFormField, long pContentLength) throws FileUploadException, IOException {
            this.fileItemIteratorImpl = pFileItemIterator;
            this.name = pName;
            this.fieldName = pFieldName;
            this.contentType = pContentType;
            this.formField = pFormField;
            long fileSizeMax = this.fileItemIteratorImpl.getFileSizeMax();
            if (fileSizeMax != -1L && pContentLength != -1L && pContentLength > fileSizeMax) {
                FileSizeLimitExceededException e = new FileSizeLimitExceededException(String.format("The field %s exceeds its maximum permitted size of %s bytes.", this.fieldName, fileSizeMax), pContentLength, fileSizeMax);
                e.setFileName(pName);
                e.setFieldName(pFieldName);
                throw new FileUploadIOException(e);
            } else {
                final MultipartStream.ItemInputStream itemStream = this.fileItemIteratorImpl.getMultiPartStream().newInputStream();
                InputStream istream = itemStream;
                if (fileSizeMax != -1L) {
                    istream = new LimitedInputStream(itemStream, fileSizeMax) {
                        protected void raiseError(long pSizeMax, long pCount) throws IOException {
                            itemStream.close(true);
                            FileSizeLimitExceededException e = new FileSizeLimitExceededException(String.format("The field %s exceeds its maximum permitted size of %s bytes.", FileItemStreamImpl.this.fieldName, pSizeMax), pCount, pSizeMax);
                            e.setFieldName(FileItemStreamImpl.this.fieldName);
                            e.setFileName(FileItemStreamImpl.this.name);
                            throw new FileUploadIOException(e);
                        }
                    };
                }

                this.stream = (InputStream)istream;
            }
        }

    后面也会介绍到，这里我们其实主要是要找到这个 pSizeMax 是哪里来的
    通过阅读代码会发现跟这个类 MultipartConfigElement 有关系
    而在升级后的 springboot 中这个类已经有了自动装配类，也就是
    org.springframework.boot.autoconfigure.web.servlet.MultipartAutoConfiguration

    有了这个自动装配

    @Configuration(
        proxyBeanMethods = false
    )
    @ConditionalOnClass({Servlet.class, StandardServletMultipartResolver.class, MultipartConfigElement.class})
    @ConditionalOnProperty(
        prefix = "spring.servlet.multipart",
        name = {"enabled"},
        matchIfMissing = true
    )
    @ConditionalOnWebApplication(
        type = Type.SERVLET
    )
    @EnableConfigurationProperties({MultipartProperties.class})
    public class MultipartAutoConfiguration {
        private final MultipartProperties multipartProperties;

        public MultipartAutoConfiguration(MultipartProperties multipartProperties) {
            this.multipartProperties = multipartProperties;
        }

        @Bean
        @ConditionalOnMissingBean({MultipartConfigElement.class, CommonsMultipartResolver.class})
        public MultipartConfigElement multipartConfigElement() {
            return this.multipartProperties.createMultipartConfig();
        }
    @@ -2240,29 +2309,37 @@     - Leetcode 572 Subtree of Another Tree 题解分析 - /2024/01/28/Leetcode-572-Subtree-of-Another-Tree-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

    Given the roots of two binary trees root and subRoot, return true if there is a subtree of root with the same structure and node values of subRoot and false otherwise.

    -

    A subtree of a binary tree tree is a tree that consists of a node in tree and all of this node’s descendants. The tree tree could also be considered as a subtree of itself.

    -

    示例 1

    -
    -

    Input: root = [3,4,5,1,2], subRoot = [4,1,2]
    Output: true

    -
    -

    示例 2

    -
    -

    Input: root = [3,4,5,1,2,null,null,null,null,0], subRoot = [4,1,2]
    Output: false

    -
    -

    解析

    需要判断subRoot 为根节点的这棵树是不是 root 为根节点的这个树的子树，题应该不是特别难理解，只是需要注意，这里是子树，而不是一个子结构，也就是示例 2 的判断结果是 false，因为左边子树还有个 0，只能认为是左边的一部分，但不是子树，思路分析也不难，就是递归去判断，只不过是两种维度，第一种就是找到子树的根节点，也就是在 root 树找到 subRoot 的根节点相同的节点，这个就会用到递归，如果找到了一个，就开始递归的对比所有子节点，必须完全一样

    -

    代码

    public boolean isSubtree(TreeNode root, TreeNode subRoot) {
           if (subRoot == null || root == null) {
               return false;
           }
           if (root.val == subRoot.val) {
               // 如果 root 节点跟 subRoot 节点相同，就开始递归对比所有的子节点
               if (isSameTree(root.left, subRoot.left) && isSameTree(root.right, subRoot.right)) {
                   return true;
               }
           }
           // 如果 root 节点不同，就开始递归往下找与 subRoot 相同的起始节点，这里是或的关系，因为在左右子树任一找到一个就行
           return isSubtree(root.left, subRoot) || isSubtree(root.right, subRoot);
       }
       // 需要完全相同，也就是说不能像示例 2 那样多一个子节点这种，以及其他不同的情况，反正就是要完全一样
       public boolean isSameTree(TreeNode root, TreeNode subTree) {
           // 如果递归到这个节点都为空也认为是相同的
           if (root == null && subTree == null) {
               return true;
           }
           // 因为有前面的判断，这里就隐含了 subTree != null 这个条件
           if (root == null) {
               return false;
           }
           // 也因为有前面的判断，这里就隐含了  root != null 这个条件
           if (subTree == null) {
               return false;
           }
           // 两个都不为空，但是值不相同，也是 false
           if (root.val != subTree.val) {
               return false;
           }
           // 跟节点相同，就开始递归判断左右子树，必须两者都相同
           return isSameTree(root.left, subTree.left) && isSameTree(root.right, subTree.right);
       }
    + Tomcat 系列篇四-介绍下 Valve 架构 + /2023/10/01/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%9B%9B-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Valve-%E6%9E%B6%E6%9E%84/ + valve 是 Tomcat 架构中比较重要的一个组成部分，
    之前说到

    +
    connector.getService().getContainer().getPipeline().getFirst().invoke(
                            request, response);
    +

    这段代码是通过 CoyoteAdapter 将请求处理往 container 传，这里就有个 pipeline 机制，这个 pipeline 可以看一下接口

    +
    public interface Pipeline extends Contained {

        public Valve getBasic();

        public void setBasic(Valve valve);

        public void addValve(Valve valve);

        public Valve[] getValves();

        public void removeValve(Valve valve);

        public Valve getFirst();

        public boolean isAsyncSupported();

        public void findNonAsyncValves(Set<String> result);
    }
    +

    这里可以往 pipeline 里添加 valve，然后看下 valve 的接口

    +
    public interface Valve {

        public Valve getNext();

        public void setNext(Valve valve);

        public void backgroundProcess();

        public void invoke(Request request, Response response)
            throws IOException, ServletException;


        public boolean isAsyncSupported();
    }
    +

    这里主要看的就是 getNext 跟 setNext，就变成了一个有序的 pipeline，然后就是 invoke 方法，其实 pipeline 是由两部分组成，valve 在其中起到了前后衔接的重要作用，而且可以再 invoke 中进一步串联调用

    图中我们可以看到，对于 container 这个 pipeline，是没设置 first 的，只有 basic，basic 就是个兜底的 valve，在 StandardPipeline 中的 getFirst 实现

    +
    @Override
    public Valve getFirst() {
        if (first != null) {
            return first;
        }

        return basic;
    }
    +

    取不到 first 就降级到 basic，也就是这里的 StandardEngineValve，
    它的 invoke 我们来看下

    +
    @Override
    public final void invoke(Request request, Response response)
        throws IOException, ServletException {

        // Select the Host to be used for this Request
        Host host = request.getHost();
        if (host == null) {
            // HTTP 0.9 or HTTP 1.0 request without a host when no default host
            // is defined.
            // Don't overwrite an existing error
            if (!response.isError()) {
                response.sendError(404);
            }
            return;
        }
        if (request.isAsyncSupported()) {
            request.setAsyncSupported(host.getPipeline().isAsyncSupported());
        }

        // Ask this Host to process this request
        host.getPipeline().getFirst().invoke(request, response);
    }
    +

    比较简单，就是调用 host 中的 pipeline 里的第一个 valve 来处理

    第一个是 org.apache.catalina.valves.ErrorReportValve，
    这里处理的其实是先调用了 next

    +
    @Override
        public void invoke(Request request, Response response) throws IOException, ServletException {

            // Perform the request
            getNext().invoke(request, response);

            if (response.isCommitted()) {
                if (response.setErrorReported()) {
                    // Error wasn't previously reported but we can't write an error
                    // page because the response has already been committed.

                    // See if IO is allowed
                    AtomicBoolean ioAllowed = new AtomicBoolean(true);
                    response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, ioAllowed);

                    if (ioAllowed.get()) {
                        // I/O is currently still allowed. Flush any data that is
                        // still to be written to the client.
                        try {
                            response.flushBuffer();
                        } catch (Throwable t) {
                            ExceptionUtils.handleThrowable(t);
                        }
                        // Now close immediately to signal to the client that
                        // something went wrong
                        response.getCoyoteResponse().action(ActionCode.CLOSE_NOW,
                                request.getAttribute(RequestDispatcher.ERROR_EXCEPTION));
                    }
                }
                return;
            }

            Throwable throwable = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION);

            // If an async request is in progress and is not going to end once this
            // container thread finishes, do not process any error page here.
            if (request.isAsync() && !request.isAsyncCompleting()) {
                return;
            }

            if (throwable != null && !response.isError()) {
                // Make sure that the necessary methods have been called on the
                // response. (It is possible a component may just have set the
                // Throwable. Tomcat won't do that but other components might.)
                // These are safe to call at this point as we know that the response
                // has not been committed.
                response.reset();
                response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR);
            }

            // One way or another, response.sendError() will have been called before
            // execution reaches this point and suspended the response. Need to
            // reverse that so this valve can write to the response.
            response.setSuspended(false);

            try {
                report(request, response, throwable);
            } catch (Throwable tt) {
                ExceptionUtils.handleThrowable(tt);
            }
        }
    +

    虽然是放在 first，实际是先调用 next 的 invoke，也就是 org.apache.catalina.core.StandardHostValve 的invoke 方法

    +
    @Override
        public final void invoke(Request request, Response response)
            throws IOException, ServletException {

            // Select the Context to be used for this Request
            Context context = request.getContext();
            if (context == null) {
                // Don't overwrite an existing error
                if (!response.isError()) {
                    response.sendError(404);
                }
                return;
            }

            if (request.isAsyncSupported()) {
                request.setAsyncSupported(context.getPipeline().isAsyncSupported());
            }

            boolean asyncAtStart = request.isAsync();

            try {
                context.bind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER);

                if (!asyncAtStart && !context.fireRequestInitEvent(request.getRequest())) {
                    // Don't fire listeners during async processing (the listener
                    // fired for the request that called startAsync()).
                    // If a request init listener throws an exception, the request
                    // is aborted.
                    return;
                }

                // Ask this Context to process this request. Requests that are
                // already in error must have been routed here to check for
                // application defined error pages so DO NOT forward them to the the
                // application for processing.
                try {
                    if (!response.isErrorReportRequired()) {
                        // 交给 context 去处理请求了
                        context.getPipeline().getFirst().invoke(request, response);
                    }
                } catch (Throwable t) {
                    ExceptionUtils.handleThrowable(t);
                    container.getLogger().error("Exception Processing " + request.getRequestURI(), t);
                    // If a new error occurred while trying to report a previous
                    // error allow the original error to be reported.
                    if (!response.isErrorReportRequired()) {
                        request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, t);
                        throwable(request, response, t);
                    }
                }

                // Now that the request/response pair is back under container
                // control lift the suspension so that the error handling can
                // complete and/or the container can flush any remaining data
                response.setSuspended(false);

                Throwable t = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION);

                // Protect against NPEs if the context was destroyed during a
                // long running request.
                if (!context.getState().isAvailable()) {
                    return;
                }

                // Look for (and render if found) an application level error page
                if (response.isErrorReportRequired()) {
                    // If an error has occurred that prevents further I/O, don't waste time
                    // producing an error report that will never be read
                    AtomicBoolean result = new AtomicBoolean(false);
                    response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, result);
                    if (result.get()) {
                        if (t != null) {
                            throwable(request, response, t);
                        } else {
                            status(request, response);
                        }
                    }
                }

                if (!request.isAsync() && !asyncAtStart) {
                    context.fireRequestDestroyEvent(request.getRequest());
                }
            } finally {
                // Access a session (if present) to update last accessed time, based
                // on a strict interpretation of the specification
                if (ACCESS_SESSION) {
                    request.getSession(false);
                }

                context.unbind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER);
            }
        }
    +

    这里的往下就是调用 context 的 pipeline 去处理请求了, StandardContext 的 pipeline 里的 first 是
    org.apache.catalina.authenticator.NonLoginAuthenticator，处理认证相关的，然后 basic 就是
    org.apache.catalina.core.StandardContextValve, 这里的来看下

    +
    @Override
        public final void invoke(Request request, Response response)
            throws IOException, ServletException {

            // Disallow any direct access to resources under WEB-INF or META-INF
            MessageBytes requestPathMB = request.getRequestPathMB();
            if ((requestPathMB.startsWithIgnoreCase("/META-INF/", 0))
                    || (requestPathMB.equalsIgnoreCase("/META-INF"))
                    || (requestPathMB.startsWithIgnoreCase("/WEB-INF/", 0))
                    || (requestPathMB.equalsIgnoreCase("/WEB-INF"))) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND);
                return;
            }

            // Select the Wrapper to be used for this Request
            Wrapper wrapper = request.getWrapper();
            if (wrapper == null || wrapper.isUnavailable()) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND);
                return;
            }

            // Acknowledge the request
            try {
                response.sendAcknowledgement(ContinueResponseTiming.IMMEDIATELY);
            } catch (IOException ioe) {
                container.getLogger().error(sm.getString(
                        "standardContextValve.acknowledgeException"), ioe);
                request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, ioe);
                response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR);
                return;
            }

            if (request.isAsyncSupported()) {
                request.setAsyncSupported(wrapper.getPipeline().isAsyncSupported());
            }
            wrapper.getPipeline().getFirst().invoke(request, response);
        }
    +

    会调用 wrapper 的 pipeline 去处理请求，这里也只有一个
    org.apache.catalina.core.StandardWrapperValve
    这部分的逻辑比较长，因为要串联后面的 filter 流程

    +
    @Override
        public final void invoke(Request request, Response response)
            throws IOException, ServletException {

            // Initialize local variables we may need
            boolean unavailable = false;
            Throwable throwable = null;
            // This should be a Request attribute...
            long t1=System.currentTimeMillis();
            requestCount.incrementAndGet();
            StandardWrapper wrapper = (StandardWrapper) getContainer();
            Servlet servlet = null;
            Context context = (Context) wrapper.getParent();

            // Check for the application being marked unavailable
            if (!context.getState().isAvailable()) {
                response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                               sm.getString("standardContext.isUnavailable"));
                unavailable = true;
            }

            // Check for the servlet being marked unavailable
            if (!unavailable && wrapper.isUnavailable()) {
                container.getLogger().info(sm.getString("standardWrapper.isUnavailable",
                        wrapper.getName()));
                long available = wrapper.getAvailable();
                if ((available > 0L) && (available < Long.MAX_VALUE)) {
                    response.setDateHeader("Retry-After", available);
                    response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                            sm.getString("standardWrapper.isUnavailable",
                                    wrapper.getName()));
                } else if (available == Long.MAX_VALUE) {
                    response.sendError(HttpServletResponse.SC_NOT_FOUND,
                            sm.getString("standardWrapper.notFound",
                                    wrapper.getName()));
                }
                unavailable = true;
            }

            // Allocate a servlet instance to process this request
            try {
                if (!unavailable) {
                    servlet = wrapper.allocate();
                }
            } catch (UnavailableException e) {
                container.getLogger().error(
                        sm.getString("standardWrapper.allocateException",
                                wrapper.getName()), e);
                long available = wrapper.getAvailable();
                if ((available > 0L) && (available < Long.MAX_VALUE)) {
                    response.setDateHeader("Retry-After", available);
                    response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                               sm.getString("standardWrapper.isUnavailable",
                                            wrapper.getName()));
                } else if (available == Long.MAX_VALUE) {
                    response.sendError(HttpServletResponse.SC_NOT_FOUND,
                               sm.getString("standardWrapper.notFound",
                                            wrapper.getName()));
                }
            } catch (ServletException e) {
                container.getLogger().error(sm.getString("standardWrapper.allocateException",
                                 wrapper.getName()), StandardWrapper.getRootCause(e));
                throwable = e;
                exception(request, response, e);
            } catch (Throwable e) {
                ExceptionUtils.handleThrowable(e);
                container.getLogger().error(sm.getString("standardWrapper.allocateException",
                                 wrapper.getName()), e);
                throwable = e;
                exception(request, response, e);
                servlet = null;
            }

            MessageBytes requestPathMB = request.getRequestPathMB();
            DispatcherType dispatcherType = DispatcherType.REQUEST;
            if (request.getDispatcherType()==DispatcherType.ASYNC) {
                dispatcherType = DispatcherType.ASYNC;
            }
            request.setAttribute(Globals.DISPATCHER_TYPE_ATTR,dispatcherType);
            request.setAttribute(Globals.DISPATCHER_REQUEST_PATH_ATTR,
                    requestPathMB);
            // Create the filter chain for this request
            ApplicationFilterChain filterChain =
                    ApplicationFilterFactory.createFilterChain(request, wrapper, servlet);

            // Call the filter chain for this request
            // NOTE: This also calls the servlet's service() method
            Container container = this.container;
            try {
                if ((servlet != null) && (filterChain != null)) {
                    // Swallow output if needed
                    if (context.getSwallowOutput()) {
                        try {
                            SystemLogHandler.startCapture();
                            if (request.isAsyncDispatching()) {
                                request.getAsyncContextInternal().doInternalDispatch();
                            } else {
                                filterChain.doFilter(request.getRequest(),
                                        response.getResponse());
                            }
                        } finally {
                            String log = SystemLogHandler.stopCapture();
                            if (log != null && log.length() > 0) {
                                context.getLogger().info(log);
                            }
                        }
                    } else {
                        if (request.isAsyncDispatching()) {
                            request.getAsyncContextInternal().doInternalDispatch();
                        } else {
                            filterChain.doFilter
                                (request.getRequest(), response.getResponse());
                        }
                    }

                }
            } catch (ClientAbortException | CloseNowException e) {
                if (container.getLogger().isDebugEnabled()) {
                    container.getLogger().debug(sm.getString(
                            "standardWrapper.serviceException", wrapper.getName(),
                            context.getName()), e);
                }
                throwable = e;
                exception(request, response, e);
            } catch (IOException e) {
                container.getLogger().error(sm.getString(
                        "standardWrapper.serviceException", wrapper.getName(),
                        context.getName()), e);
                throwable = e;
                exception(request, response, e);
            } catch (UnavailableException e) {
                container.getLogger().error(sm.getString(
                        "standardWrapper.serviceException", wrapper.getName(),
                        context.getName()), e);
                //            throwable = e;
                //            exception(request, response, e);
                wrapper.unavailable(e);
                long available = wrapper.getAvailable();
                if ((available > 0L) && (available < Long.MAX_VALUE)) {
                    response.setDateHeader("Retry-After", available);
                    response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                               sm.getString("standardWrapper.isUnavailable",
                                            wrapper.getName()));
                } else if (available == Long.MAX_VALUE) {
                    response.sendError(HttpServletResponse.SC_NOT_FOUND,
                                sm.getString("standardWrapper.notFound",
                                            wrapper.getName()));
                }
                // Do not save exception in 'throwable', because we
                // do not want to do exception(request, response, e) processing
            } catch (ServletException e) {
                Throwable rootCause = StandardWrapper.getRootCause(e);
                if (!(rootCause instanceof ClientAbortException)) {
                    container.getLogger().error(sm.getString(
                            "standardWrapper.serviceExceptionRoot",
                            wrapper.getName(), context.getName(), e.getMessage()),
                            rootCause);
                }
                throwable = e;
                exception(request, response, e);
            } catch (Throwable e) {
                ExceptionUtils.handleThrowable(e);
                container.getLogger().error(sm.getString(
                        "standardWrapper.serviceException", wrapper.getName(),
                        context.getName()), e);
                throwable = e;
                exception(request, response, e);
            } finally {
                // Release the filter chain (if any) for this request
                if (filterChain != null) {
                    filterChain.release();
                }

                // Deallocate the allocated servlet instance
                try {
                    if (servlet != null) {
                        wrapper.deallocate(servlet);
                    }
                } catch (Throwable e) {
                    ExceptionUtils.handleThrowable(e);
                    container.getLogger().error(sm.getString("standardWrapper.deallocateException",
                                     wrapper.getName()), e);
                    if (throwable == null) {
                        throwable = e;
                        exception(request, response, e);
                    }
                }

                // If this servlet has been marked permanently unavailable,
                // unload it and release this instance
                try {
                    if ((servlet != null) &&
                        (wrapper.getAvailable() == Long.MAX_VALUE)) {
                        wrapper.unload();
                    }
                } catch (Throwable e) {
                    ExceptionUtils.handleThrowable(e);
                    container.getLogger().error(sm.getString("standardWrapper.unloadException",
                                     wrapper.getName()), e);
                    if (throwable == null) {
                        exception(request, response, e);
                    }
                }
                long t2=System.currentTimeMillis();

                long time=t2-t1;
                processingTime += time;
                if( time > maxTime) {
                    maxTime=time;
                }
                if( time < minTime) {
                    minTime=time;
                }
            }
        }
    +

    这里就会创建 ApplicationFilterChain 然后进行
    filterChain.doFilter(request.getRequest(), response.getResponse());
    doFilter 处理

    ]]>     Java - leetcode + SpringBoot + Tomcat - java - leetcode - 题解 + Java + Tomcat + SpringBoot     @@ -2292,34 +2369,23 @@ - Tomcat 系列篇十一-介绍下 Tomcat 里的后台处理和热加载 - /2023/12/10/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81%E4%B8%80-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84%E5%90%8E%E5%8F%B0%E5%A4%84%E7%90%86%E5%92%8C%E7%83%AD%E5%8A%A0%E8%BD%BD/ - 这部分其实之前在讲线程池的时候也有点带到了， 主要是在这个类里
    org.apache.catalina.core.ContainerBase.ContainerBackgroundProcessor

    -
    protected class ContainerBackgroundProcessor implements Runnable {

            @Override
            public void run() {
                processChildren(ContainerBase.this);
            }

            protected void processChildren(Container container) {
                ClassLoader originalClassLoader = null;

                try {
                    if (container instanceof Context) {
                        Loader loader = ((Context) container).getLoader();
                        // Loader will be null for FailedContext instances
                        if (loader == null) {
                            return;
                        }

                        // Ensure background processing for Contexts and Wrappers
                        // is performed under the web app's class loader
                        originalClassLoader = ((Context) container).bind(false, null);
                    }
                    // 调用 Container 的 backgroundProcess
                    container.backgroundProcess();
                    // 然后寻找 children
                    Container[] children = container.findChildren();
                    for (Container child : children) {
                        // 如果 backgroundProcessorDelay <= 0 就调用执行
                        // 否则代表这个 Container 有之前第八篇说的 StartChild 这种
                        if (child.getBackgroundProcessorDelay() <= 0) {
                            processChildren(child);
                        }
                    }
                } catch (Throwable t) {
                    ExceptionUtils.handleThrowable(t);
                    log.error(sm.getString("containerBase.backgroundProcess.error"), t);
                } finally {
                    if (container instanceof Context) {
                        ((Context) container).unbind(false, originalClassLoader);
                    }
                }
            }
        }
    -

    这个触发方式是在 ContainerBase 里的

    -
    protected class ContainerBackgroundProcessorMonitor implements Runnable {
            @Override
            public void run() {
                if (getState().isAvailable()) {
                    threadStart();
                }
            }
        }
    + two sum + /2015/01/14/Two-Sum/ + problem

    Given an array of integers, find two numbers such that they add up to a specific target number.

    +

    The function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based.

    + +

    You may assume that each input would have exactly one solution.

    +

    Input: numbers={2, 7, 11, 15}, target=9
    Output: index1=1, index2=2

    +

    code

    struct Node
    {
        int num, pos;
    };
    bool cmp(Node a, Node b)
    {
        return a.num < b.num;
    }
    class Solution {
    public:
        vector<int> twoSum(vector<int> &numbers, int target) {
            // Start typing your C/C++ solution below
            // DO NOT write int main() function
            vector<int> result;
            vector<Node> array;
            for (int i = 0; i < numbers.size(); i++)
            {
                Node temp;
                temp.num = numbers[i];
                temp.pos = i;
                array.push_back(temp);
            }

            sort(array.begin(), array.end(), cmp);
            for (int i = 0, j = array.size() - 1; i != j;)
            {
                int sum = array[i].num + array[j].num;
                if (sum == target)
                {
                    if (array[i].pos < array[j].pos)
                    {
                        result.push_back(array[i].pos + 1);
                        result.push_back(array[j].pos + 1);
                    } else
                    {
                        result.push_back(array[j].pos + 1);
                        result.push_back(array[i].pos + 1);
                    }
                    break;
                } else if (sum < target)
                {
                    i++;
                } else if (sum > target)
                {
                    j--;
                }
            }
            return result;
        }
    };
    -

    而在这个 threadStart 里

    -
    protected void threadStart() {
        if (backgroundProcessorDelay > 0
                && (getState().isAvailable() || LifecycleState.STARTING_PREP.equals(getState()))
                && (backgroundProcessorFuture == null || backgroundProcessorFuture.isDone())) {
            if (backgroundProcessorFuture != null && backgroundProcessorFuture.isDone()) {
                // There was an error executing the scheduled task, get it and log it
                try {
                    backgroundProcessorFuture.get();
                } catch (InterruptedException | ExecutionException e) {
                    log.error(sm.getString("containerBase.backgroundProcess.error"), e);
                }
            }
            backgroundProcessorFuture = Container.getService(this).getServer().getUtilityExecutor()
                    .scheduleWithFixedDelay(new ContainerBackgroundProcessor(),
                            backgroundProcessorDelay, backgroundProcessorDelay,
                            TimeUnit.SECONDS);
        }
    }
    -

    就调用了线程池的 scheduleWithFixedDelay 方法提交了这个 ContainerBackgroundProcessor，
    仔细看代码会发现，

    -
    public StandardEngine() {

        super();
        pipeline.setBasic(new StandardEngineValve());
        /* Set the jmvRoute using the system property jvmRoute */
        try {
            setJvmRoute(System.getProperty("jvmRoute"));
        } catch(Exception ex) {
            log.warn(sm.getString("standardEngine.jvmRouteFail"));
        }
        // By default, the engine will hold the reloading thread
        backgroundProcessorDelay = 10;

    }
    -

    这个就不用开启后台热加载，而主要的热加载同学应该是
    org.apache.catalina.core.StandardContext#backgroundProcess

    -
    public void backgroundProcess() {

            if (!getState().isAvailable()) {
                return;
            }

            Loader loader = getLoader();
            if (loader != null) {
                try {
                    // 这里就用了 loader 的 backgroundProcess
                    loader.backgroundProcess();
                } catch (Exception e) {
                    log.warn(sm.getString(
                            "standardContext.backgroundProcess.loader", loader), e);
                }
            }
            Manager manager = getManager();
            if (manager != null) {
                try {
                    manager.backgroundProcess();
                } catch (Exception e) {
                    log.warn(sm.getString(
                            "standardContext.backgroundProcess.manager", manager),
                            e);
                }
            }
            WebResourceRoot resources = getResources();
            if (resources != null) {
                try {
                    resources.backgroundProcess();
                } catch (Exception e) {
                    log.warn(sm.getString(
                            "standardContext.backgroundProcess.resources",
                            resources), e);
                }
            }
            InstanceManager instanceManager = getInstanceManager();
            if (instanceManager != null) {
                try {
                    instanceManager.backgroundProcess();
                } catch (Exception e) {
                    log.warn(sm.getString(
                            "standardContext.backgroundProcess.instanceManager",
                            resources), e);
                }
            }
            super.backgroundProcess();
        }
    -

    loader 的后台处理就是

    -
    @Override
        public void backgroundProcess() {
            if (reloadable && modified()) {
                try {
                    Thread.currentThread().setContextClassLoader
                        (WebappLoader.class.getClassLoader());
                    if (context != null) {
                        context.reload();
                    }
                } finally {
                    if (context != null && context.getLoader() != null) {
                        Thread.currentThread().setContextClassLoader
                            (context.getLoader().getClassLoader());
                    }
                }
            }
        }
    -

    然后又会回到 context 的 reload，也就是 StandardContext 的 reload

    -
    @Override
    public synchronized void reload() {

        // Validate our current component state
        if (!getState().isAvailable()) {
            throw new IllegalStateException
                (sm.getString("standardContext.notStarted", getName()));
        }

        if(log.isInfoEnabled()) {
            log.info(sm.getString("standardContext.reloadingStarted",
                    getName()));
        }

        // Stop accepting requests temporarily.
        setPaused(true);

        try {
            stop();
        } catch (LifecycleException e) {
            log.error(
                sm.getString("standardContext.stoppingContext", getName()), e);
        }

        try {
            start();
        } catch (LifecycleException e) {
            log.error(
                sm.getString("standardContext.startingContext", getName()), e);
        }

        setPaused(false);

        if(log.isInfoEnabled()) {
            log.info(sm.getString("standardContext.reloadingCompleted",
                    getName()));
        }

    }
    -

    这样就是线程池结合后台处理，还是有些复杂的。

    +

    Analysis

    sort the array, then test from head and end, until catch the right answer

    ]]>     - Java - SpringBoot - Tomcat + leetcode - Java - Tomcat - SpringBoot + leetcode + c++     @@ -2335,6 +2401,24 @@ Windows + + ambari-summary + /2017/05/09/ambari-summary/ + 初识ambari

    ambari是一个大数据平台的管理工具，包含了hadoop, yarn, hive, hbase, spark等大数据的基础架构和工具，简化了数据平台的搭建，之前只是在同事搭建好平台后的一些使用，这次有机会从头开始用ambari来搭建一个测试的数据平台，过程中也踩到不少坑，简单记录下。

    +

    简单过程

      +
    • 第一个坑
      在刚开始是按照官网的指南，用maven构建，因为GFW的原因，导致反复失败等待，也就是这个guide，因为对maven不熟悉导致有些按图索骥，浪费了很多时间，之后才知道可以直接加repo用yum安装，然而用yum安装马上就出现了第二个坑。
      • +
    • 第二个坑
      因为在线的repo还是因为网络原因很慢很慢，用proxychains勉强把ambari-server本身安装好了，ambari.repo将这个放进/etc/yum.repos.d/路径下，然后yum update && yum install ambari-server安装即可，如果有条件就用proxychains走下代理。
      • +
    • 第三步
      安装好ambari-server后先执行ambari-server setup做一些初始化设置，其中包含了JDK路径的设置，数据库设置，设置好就OK了，然后执行ambari-server start启动服务，这里有个小插曲，因为ambari-server涉及到这么多服务，所以管理控制监控之类的模块是必不可少的，这部分可以在ambari-server的web ui界面安装，也可以命令行提前安装，这部分被称为HDF Management Pack，运行ambari-server install-mpack \ --mpack=http://public-repo-1.hortonworks.com/HDF/centos7/2.x/updates/2.1.4.0/tars/hdf_ambari_mp/hdf-ambari-mpack-2.1.4.0-5.tar.gz \ --purge \ --verbose
      安装，当然这个压缩包可以下载之后指到本地路径安装，然后就可以重启ambari-server
      • +
    +]]>     + + data analysis + + + hadoop + cluster + +     binary-watch /2016/09/29/binary-watch/ @@ -2359,60 +2443,6 @@ c++ - - two sum - /2015/01/14/Two-Sum/ - problem

    Given an array of integers, find two numbers such that they add up to a specific target number.

    -

    The function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based.

    - -

    You may assume that each input would have exactly one solution.

    -

    Input: numbers={2, 7, 11, 15}, target=9
    Output: index1=1, index2=2

    -

    code

    struct Node
    {
        int num, pos;
    };
    bool cmp(Node a, Node b)
    {
        return a.num < b.num;
    }
    class Solution {
    public:
        vector<int> twoSum(vector<int> &numbers, int target) {
            // Start typing your C/C++ solution below
            // DO NOT write int main() function
            vector<int> result;
            vector<Node> array;
            for (int i = 0; i < numbers.size(); i++)
            {
                Node temp;
                temp.num = numbers[i];
                temp.pos = i;
                array.push_back(temp);
            }

            sort(array.begin(), array.end(), cmp);
            for (int i = 0, j = array.size() - 1; i != j;)
            {
                int sum = array[i].num + array[j].num;
                if (sum == target)
                {
                    if (array[i].pos < array[j].pos)
                    {
                        result.push_back(array[i].pos + 1);
                        result.push_back(array[j].pos + 1);
                    } else
                    {
                        result.push_back(array[j].pos + 1);
                        result.push_back(array[i].pos + 1);
                    }
                    break;
                } else if (sum < target)
                {
                    i++;
                } else if (sum > target)
                {
                    j--;
                }
            }
            return result;
        }
    };
    - -

    Analysis

    sort the array, then test from head and end, until catch the right answer

    -]]>     - - leetcode - - - leetcode - c++ - -     - - Tomcat 系列篇四-介绍下 Valve 架构 - /2023/10/01/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%9B%9B-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Valve-%E6%9E%B6%E6%9E%84/ - valve 是 Tomcat 架构中比较重要的一个组成部分，
    之前说到

    -
    connector.getService().getContainer().getPipeline().getFirst().invoke(
                            request, response);
    -

    这段代码是通过 CoyoteAdapter 将请求处理往 container 传，这里就有个 pipeline 机制，这个 pipeline 可以看一下接口

    -
    public interface Pipeline extends Contained {

        public Valve getBasic();

        public void setBasic(Valve valve);

        public void addValve(Valve valve);

        public Valve[] getValves();

        public void removeValve(Valve valve);

        public Valve getFirst();

        public boolean isAsyncSupported();

        public void findNonAsyncValves(Set<String> result);
    }
    -

    这里可以往 pipeline 里添加 valve，然后看下 valve 的接口

    -
    public interface Valve {

        public Valve getNext();

        public void setNext(Valve valve);

        public void backgroundProcess();

        public void invoke(Request request, Response response)
            throws IOException, ServletException;


        public boolean isAsyncSupported();
    }
    -

    这里主要看的就是 getNext 跟 setNext，就变成了一个有序的 pipeline，然后就是 invoke 方法，其实 pipeline 是由两部分组成，valve 在其中起到了前后衔接的重要作用，而且可以再 invoke 中进一步串联调用

    图中我们可以看到，对于 container 这个 pipeline，是没设置 first 的，只有 basic，basic 就是个兜底的 valve，在 StandardPipeline 中的 getFirst 实现

    -
    @Override
    public Valve getFirst() {
        if (first != null) {
            return first;
        }

        return basic;
    }
    -

    取不到 first 就降级到 basic，也就是这里的 StandardEngineValve，
    它的 invoke 我们来看下

    -
    @Override
    public final void invoke(Request request, Response response)
        throws IOException, ServletException {

        // Select the Host to be used for this Request
        Host host = request.getHost();
        if (host == null) {
            // HTTP 0.9 or HTTP 1.0 request without a host when no default host
            // is defined.
            // Don't overwrite an existing error
            if (!response.isError()) {
                response.sendError(404);
            }
            return;
        }
        if (request.isAsyncSupported()) {
            request.setAsyncSupported(host.getPipeline().isAsyncSupported());
        }

        // Ask this Host to process this request
        host.getPipeline().getFirst().invoke(request, response);
    }
    -

    比较简单，就是调用 host 中的 pipeline 里的第一个 valve 来处理

    第一个是 org.apache.catalina.valves.ErrorReportValve，
    这里处理的其实是先调用了 next

    -
    @Override
        public void invoke(Request request, Response response) throws IOException, ServletException {

            // Perform the request
            getNext().invoke(request, response);

            if (response.isCommitted()) {
                if (response.setErrorReported()) {
                    // Error wasn't previously reported but we can't write an error
                    // page because the response has already been committed.

                    // See if IO is allowed
                    AtomicBoolean ioAllowed = new AtomicBoolean(true);
                    response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, ioAllowed);

                    if (ioAllowed.get()) {
                        // I/O is currently still allowed. Flush any data that is
                        // still to be written to the client.
                        try {
                            response.flushBuffer();
                        } catch (Throwable t) {
                            ExceptionUtils.handleThrowable(t);
                        }
                        // Now close immediately to signal to the client that
                        // something went wrong
                        response.getCoyoteResponse().action(ActionCode.CLOSE_NOW,
                                request.getAttribute(RequestDispatcher.ERROR_EXCEPTION));
                    }
                }
                return;
            }

            Throwable throwable = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION);

            // If an async request is in progress and is not going to end once this
            // container thread finishes, do not process any error page here.
            if (request.isAsync() && !request.isAsyncCompleting()) {
                return;
            }

            if (throwable != null && !response.isError()) {
                // Make sure that the necessary methods have been called on the
                // response. (It is possible a component may just have set the
                // Throwable. Tomcat won't do that but other components might.)
                // These are safe to call at this point as we know that the response
                // has not been committed.
                response.reset();
                response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR);
            }

            // One way or another, response.sendError() will have been called before
            // execution reaches this point and suspended the response. Need to
            // reverse that so this valve can write to the response.
            response.setSuspended(false);

            try {
                report(request, response, throwable);
            } catch (Throwable tt) {
                ExceptionUtils.handleThrowable(tt);
            }
        }
    -

    虽然是放在 first，实际是先调用 next 的 invoke，也就是 org.apache.catalina.core.StandardHostValve 的invoke 方法

    -
    @Override
        public final void invoke(Request request, Response response)
            throws IOException, ServletException {

            // Select the Context to be used for this Request
            Context context = request.getContext();
            if (context == null) {
                // Don't overwrite an existing error
                if (!response.isError()) {
                    response.sendError(404);
                }
                return;
            }

            if (request.isAsyncSupported()) {
                request.setAsyncSupported(context.getPipeline().isAsyncSupported());
            }

            boolean asyncAtStart = request.isAsync();

            try {
                context.bind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER);

                if (!asyncAtStart && !context.fireRequestInitEvent(request.getRequest())) {
                    // Don't fire listeners during async processing (the listener
                    // fired for the request that called startAsync()).
                    // If a request init listener throws an exception, the request
                    // is aborted.
                    return;
                }

                // Ask this Context to process this request. Requests that are
                // already in error must have been routed here to check for
                // application defined error pages so DO NOT forward them to the the
                // application for processing.
                try {
                    if (!response.isErrorReportRequired()) {
                        // 交给 context 去处理请求了
                        context.getPipeline().getFirst().invoke(request, response);
                    }
                } catch (Throwable t) {
                    ExceptionUtils.handleThrowable(t);
                    container.getLogger().error("Exception Processing " + request.getRequestURI(), t);
                    // If a new error occurred while trying to report a previous
                    // error allow the original error to be reported.
                    if (!response.isErrorReportRequired()) {
                        request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, t);
                        throwable(request, response, t);
                    }
                }

                // Now that the request/response pair is back under container
                // control lift the suspension so that the error handling can
                // complete and/or the container can flush any remaining data
                response.setSuspended(false);

                Throwable t = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION);

                // Protect against NPEs if the context was destroyed during a
                // long running request.
                if (!context.getState().isAvailable()) {
                    return;
                }

                // Look for (and render if found) an application level error page
                if (response.isErrorReportRequired()) {
                    // If an error has occurred that prevents further I/O, don't waste time
                    // producing an error report that will never be read
                    AtomicBoolean result = new AtomicBoolean(false);
                    response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, result);
                    if (result.get()) {
                        if (t != null) {
                            throwable(request, response, t);
                        } else {
                            status(request, response);
                        }
                    }
                }

                if (!request.isAsync() && !asyncAtStart) {
                    context.fireRequestDestroyEvent(request.getRequest());
                }
            } finally {
                // Access a session (if present) to update last accessed time, based
                // on a strict interpretation of the specification
                if (ACCESS_SESSION) {
                    request.getSession(false);
                }

                context.unbind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER);
            }
        }
    -

    这里的往下就是调用 context 的 pipeline 去处理请求了, StandardContext 的 pipeline 里的 first 是
    org.apache.catalina.authenticator.NonLoginAuthenticator，处理认证相关的，然后 basic 就是
    org.apache.catalina.core.StandardContextValve, 这里的来看下

    -
    @Override
        public final void invoke(Request request, Response response)
            throws IOException, ServletException {

            // Disallow any direct access to resources under WEB-INF or META-INF
            MessageBytes requestPathMB = request.getRequestPathMB();
            if ((requestPathMB.startsWithIgnoreCase("/META-INF/", 0))
                    || (requestPathMB.equalsIgnoreCase("/META-INF"))
                    || (requestPathMB.startsWithIgnoreCase("/WEB-INF/", 0))
                    || (requestPathMB.equalsIgnoreCase("/WEB-INF"))) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND);
                return;
            }

            // Select the Wrapper to be used for this Request
            Wrapper wrapper = request.getWrapper();
            if (wrapper == null || wrapper.isUnavailable()) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND);
                return;
            }

            // Acknowledge the request
            try {
                response.sendAcknowledgement(ContinueResponseTiming.IMMEDIATELY);
            } catch (IOException ioe) {
                container.getLogger().error(sm.getString(
                        "standardContextValve.acknowledgeException"), ioe);
                request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, ioe);
                response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR);
                return;
            }

            if (request.isAsyncSupported()) {
                request.setAsyncSupported(wrapper.getPipeline().isAsyncSupported());
            }
            wrapper.getPipeline().getFirst().invoke(request, response);
        }
    -

    会调用 wrapper 的 pipeline 去处理请求，这里也只有一个
    org.apache.catalina.core.StandardWrapperValve
    这部分的逻辑比较长，因为要串联后面的 filter 流程

    -
    @Override
        public final void invoke(Request request, Response response)
            throws IOException, ServletException {

            // Initialize local variables we may need
            boolean unavailable = false;
            Throwable throwable = null;
            // This should be a Request attribute...
            long t1=System.currentTimeMillis();
            requestCount.incrementAndGet();
            StandardWrapper wrapper = (StandardWrapper) getContainer();
            Servlet servlet = null;
            Context context = (Context) wrapper.getParent();

            // Check for the application being marked unavailable
            if (!context.getState().isAvailable()) {
                response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                               sm.getString("standardContext.isUnavailable"));
                unavailable = true;
            }

            // Check for the servlet being marked unavailable
            if (!unavailable && wrapper.isUnavailable()) {
                container.getLogger().info(sm.getString("standardWrapper.isUnavailable",
                        wrapper.getName()));
                long available = wrapper.getAvailable();
                if ((available > 0L) && (available < Long.MAX_VALUE)) {
                    response.setDateHeader("Retry-After", available);
                    response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                            sm.getString("standardWrapper.isUnavailable",
                                    wrapper.getName()));
                } else if (available == Long.MAX_VALUE) {
                    response.sendError(HttpServletResponse.SC_NOT_FOUND,
                            sm.getString("standardWrapper.notFound",
                                    wrapper.getName()));
                }
                unavailable = true;
            }

            // Allocate a servlet instance to process this request
            try {
                if (!unavailable) {
                    servlet = wrapper.allocate();
                }
            } catch (UnavailableException e) {
                container.getLogger().error(
                        sm.getString("standardWrapper.allocateException",
                                wrapper.getName()), e);
                long available = wrapper.getAvailable();
                if ((available > 0L) && (available < Long.MAX_VALUE)) {
                    response.setDateHeader("Retry-After", available);
                    response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                               sm.getString("standardWrapper.isUnavailable",
                                            wrapper.getName()));
                } else if (available == Long.MAX_VALUE) {
                    response.sendError(HttpServletResponse.SC_NOT_FOUND,
                               sm.getString("standardWrapper.notFound",
                                            wrapper.getName()));
                }
            } catch (ServletException e) {
                container.getLogger().error(sm.getString("standardWrapper.allocateException",
                                 wrapper.getName()), StandardWrapper.getRootCause(e));
                throwable = e;
                exception(request, response, e);
            } catch (Throwable e) {
                ExceptionUtils.handleThrowable(e);
                container.getLogger().error(sm.getString("standardWrapper.allocateException",
                                 wrapper.getName()), e);
                throwable = e;
                exception(request, response, e);
                servlet = null;
            }

            MessageBytes requestPathMB = request.getRequestPathMB();
            DispatcherType dispatcherType = DispatcherType.REQUEST;
            if (request.getDispatcherType()==DispatcherType.ASYNC) {
                dispatcherType = DispatcherType.ASYNC;
            }
            request.setAttribute(Globals.DISPATCHER_TYPE_ATTR,dispatcherType);
            request.setAttribute(Globals.DISPATCHER_REQUEST_PATH_ATTR,
                    requestPathMB);
            // Create the filter chain for this request
            ApplicationFilterChain filterChain =
                    ApplicationFilterFactory.createFilterChain(request, wrapper, servlet);

            // Call the filter chain for this request
            // NOTE: This also calls the servlet's service() method
            Container container = this.container;
            try {
                if ((servlet != null) && (filterChain != null)) {
                    // Swallow output if needed
                    if (context.getSwallowOutput()) {
                        try {
                            SystemLogHandler.startCapture();
                            if (request.isAsyncDispatching()) {
                                request.getAsyncContextInternal().doInternalDispatch();
                            } else {
                                filterChain.doFilter(request.getRequest(),
                                        response.getResponse());
                            }
                        } finally {
                            String log = SystemLogHandler.stopCapture();
                            if (log != null && log.length() > 0) {
                                context.getLogger().info(log);
                            }
                        }
                    } else {
                        if (request.isAsyncDispatching()) {
                            request.getAsyncContextInternal().doInternalDispatch();
                        } else {
                            filterChain.doFilter
                                (request.getRequest(), response.getResponse());
                        }
                    }

                }
            } catch (ClientAbortException | CloseNowException e) {
                if (container.getLogger().isDebugEnabled()) {
                    container.getLogger().debug(sm.getString(
                            "standardWrapper.serviceException", wrapper.getName(),
                            context.getName()), e);
                }
                throwable = e;
                exception(request, response, e);
            } catch (IOException e) {
                container.getLogger().error(sm.getString(
                        "standardWrapper.serviceException", wrapper.getName(),
                        context.getName()), e);
                throwable = e;
                exception(request, response, e);
            } catch (UnavailableException e) {
                container.getLogger().error(sm.getString(
                        "standardWrapper.serviceException", wrapper.getName(),
                        context.getName()), e);
                //            throwable = e;
                //            exception(request, response, e);
                wrapper.unavailable(e);
                long available = wrapper.getAvailable();
                if ((available > 0L) && (available < Long.MAX_VALUE)) {
                    response.setDateHeader("Retry-After", available);
                    response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                               sm.getString("standardWrapper.isUnavailable",
                                            wrapper.getName()));
                } else if (available == Long.MAX_VALUE) {
                    response.sendError(HttpServletResponse.SC_NOT_FOUND,
                                sm.getString("standardWrapper.notFound",
                                            wrapper.getName()));
                }
                // Do not save exception in 'throwable', because we
                // do not want to do exception(request, response, e) processing
            } catch (ServletException e) {
                Throwable rootCause = StandardWrapper.getRootCause(e);
                if (!(rootCause instanceof ClientAbortException)) {
                    container.getLogger().error(sm.getString(
                            "standardWrapper.serviceExceptionRoot",
                            wrapper.getName(), context.getName(), e.getMessage()),
                            rootCause);
                }
                throwable = e;
                exception(request, response, e);
            } catch (Throwable e) {
                ExceptionUtils.handleThrowable(e);
                container.getLogger().error(sm.getString(
                        "standardWrapper.serviceException", wrapper.getName(),
                        context.getName()), e);
                throwable = e;
                exception(request, response, e);
            } finally {
                // Release the filter chain (if any) for this request
                if (filterChain != null) {
                    filterChain.release();
                }

                // Deallocate the allocated servlet instance
                try {
                    if (servlet != null) {
                        wrapper.deallocate(servlet);
                    }
                } catch (Throwable e) {
                    ExceptionUtils.handleThrowable(e);
                    container.getLogger().error(sm.getString("standardWrapper.deallocateException",
                                     wrapper.getName()), e);
                    if (throwable == null) {
                        throwable = e;
                        exception(request, response, e);
                    }
                }

                // If this servlet has been marked permanently unavailable,
                // unload it and release this instance
                try {
                    if ((servlet != null) &&
                        (wrapper.getAvailable() == Long.MAX_VALUE)) {
                        wrapper.unload();
                    }
                } catch (Throwable e) {
                    ExceptionUtils.handleThrowable(e);
                    container.getLogger().error(sm.getString("standardWrapper.unloadException",
                                     wrapper.getName()), e);
                    if (throwable == null) {
                        exception(request, response, e);
                    }
                }
                long t2=System.currentTimeMillis();

                long time=t2-t1;
                processingTime += time;
                if( time > maxTime) {
                    maxTime=time;
                }
                if( time < minTime) {
                    minTime=time;
                }
            }
        }
    -

    这里就会创建 ApplicationFilterChain 然后进行
    filterChain.doFilter(request.getRequest(), response.getResponse());
    doFilter 处理

    -]]>     - - Java - SpringBoot - Tomcat - - - Java - Tomcat - SpringBoot - -     dnsmasq的一个使用注意点 /2023/04/16/dnsmasq%E7%9A%84%E4%B8%80%E4%B8%AA%E4%BD%BF%E7%94%A8%E6%B3%A8%E6%84%8F%E7%82%B9/ @@ -2448,38 +2478,6 @@ mysql - - docker比一般多一点的初学者介绍三 - /2020/03/21/docker%E6%AF%94%E4%B8%80%E8%88%AC%E5%A4%9A%E4%B8%80%E7%82%B9%E7%9A%84%E5%88%9D%E5%AD%A6%E8%80%85%E4%BB%8B%E7%BB%8D%E4%B8%89/ - 运行第一个 Dockerfile

    上一篇的 Dockerfile 我们停留在构建阶段，现在来把它跑起来

    -
    docker run -d -p 80 --name static_web nicksxs/static_web \
    nginx -g "daemon off;"
    -

    这里的-d表示以分离模型运行docker (detached),然后-p 是表示将容器的 80 端口开放给宿主机，然后容器名就叫 static_web,使用了我们上次构建的 static_web 镜像，后面的是让 nginx 在前台运行

    可以看到返回了个容器 id，但是具体情况没出现，也没连上去，那我们想看看怎么访问在 Dockerfile 里写的静态页面，我们来看下docker 进程

    发现为我们随机分配了一个宿主机的端口，32768，去服务器的防火墙把这个外网端口开一下，看看是不是符合我们的预期呢

    好像不太对额，应该是 ubuntu 安装的 nginx 的默认工作目录不对，我们来进容器看看，再熟悉下命令docker exec -it 4792455ca2ed /bin/bash
    记得容器 id 换成自己的，进入容器后得找找 nginx 的配置文件，通常在/etc/nginx,/usr/local/etc等目录下，然后找到我们的目录是在这

    所以把刚才的内容复制过去再试试

    目标达成，give me five✌️

    -

    第二个 Dockerfile

    然后就想来动态一点的，毕竟写过 PHP，就来试试 PHP
    再建一个目录叫 dynamic_web，里面创建 src 目录，放一个 index.php
    内容是

    -
    <?php
    echo "Hello World!";
    -

    然后在 dynamic_web 目录下创建 Dockerfile，

    -
    FROM trafex/alpine-nginx-php7:latest
    COPY src/ /var/www/html
    EXPOSE 80
    -

    Dockerfile 虽然只有三行，不过要着重说明下，这个底包其实不是docker 官方的，有两点考虑，一点是官方的基本都是 php apache 的镜像，还有就是 alpine这个，截取一段中文介绍

    -
    -

    Alpine 操作系统是一个面向安全的轻型 Linux 发行版。它不同于通常 Linux 发行版，Alpine 采用了 musl libc 和 busybox 以减小系统的体积和运行时资源消耗，但功能上比 busybox 又完善的多，因此得到开源社区越来越多的青睐。在保持瘦身的同时，Alpine 还提供了自己的包管理工具 apk，可以通过 https://pkgs.alpinelinux.org/packages 网站上查询包信息，也可以直接通过 apk 命令直接查询和安装各种软件。
    Alpine 由非商业组织维护的，支持广泛场景的 Linux发行版，它特别为资深/重度Linux用户而优化，关注安全，性能和资源效能。Alpine 镜像可以适用于更多常用场景，并且是一个优秀的可以适用于生产的基础系统/环境。

    -
    -
    -

    Alpine Docker 镜像也继承了 Alpine Linux 发行版的这些优势。相比于其他 Docker 镜像，它的容量非常小，仅仅只有 5 MB 左右（对比 Ubuntu 系列镜像接近 200 MB），且拥有非常友好的包管理机制。官方镜像来自 docker-alpine 项目。

    -
    -
    -

    目前 Docker 官方已开始推荐使用 Alpine 替代之前的 Ubuntu 做为基础镜像环境。这样会带来多个好处。包括镜像下载速度加快，镜像安全性提高，主机之间的切换更方便，占用更少磁盘空间等。

    -
    -

    一方面在没有镜像的情况下，拉取 docker 镜像还是比较费力的，第二个就是也能节省硬盘空间，所以目前有大部分的 docker 镜像都将 alpine 作为基础镜像了
    然后再来构建下

    这里还有个点，就是上面的那个镜像我们也是 EXPOSE 80端口，然后外部宿主机会随机映射一个端口，为了偷个懒，我们就直接指定外部端口了
    docker run -d -p 80:80 dynamic_web打开浏览器发现访问不了，咋回事呢
    因为我们没看trafex/alpine-nginx-php7:latest这个镜像说明,它内部的服务是 8080 端口的，所以我们映射的暴露端口应该是 8080，再用 docker run -d -p 80:8080 dynamic_web这个启动，

    -]]>     - - Docker - 介绍 - - - Docker - namespace - Dockerfile - -     docker比一般多一点的初学者介绍 /2020/03/08/docker%E6%AF%94%E4%B8%80%E8%88%AC%E5%A4%9A%E4%B8%80%E7%82%B9%E7%9A%84%E5%88%9D%E5%AD%A6%E8%80%85%E4%BB%8B%E7%BB%8D/ @@ -2535,67 +2533,35 @@ - ambari-summary - /2017/05/09/ambari-summary/ - 初识ambari

    ambari是一个大数据平台的管理工具，包含了hadoop, yarn, hive, hbase, spark等大数据的基础架构和工具，简化了数据平台的搭建，之前只是在同事搭建好平台后的一些使用，这次有机会从头开始用ambari来搭建一个测试的数据平台，过程中也踩到不少坑，简单记录下。

    -

    简单过程

      -
    • 第一个坑
      在刚开始是按照官网的指南，用maven构建，因为GFW的原因，导致反复失败等待，也就是这个guide，因为对maven不熟悉导致有些按图索骥，浪费了很多时间，之后才知道可以直接加repo用yum安装，然而用yum安装马上就出现了第二个坑。
      • -
    • 第二个坑
      因为在线的repo还是因为网络原因很慢很慢，用proxychains勉强把ambari-server本身安装好了，ambari.repo将这个放进/etc/yum.repos.d/路径下，然后yum update && yum install ambari-server安装即可，如果有条件就用proxychains走下代理。
      • -
    • 第三步
      安装好ambari-server后先执行ambari-server setup做一些初始化设置，其中包含了JDK路径的设置，数据库设置，设置好就OK了，然后执行ambari-server start启动服务，这里有个小插曲，因为ambari-server涉及到这么多服务，所以管理控制监控之类的模块是必不可少的，这部分可以在ambari-server的web ui界面安装，也可以命令行提前安装，这部分被称为HDF Management Pack，运行ambari-server install-mpack \ --mpack=http://public-repo-1.hortonworks.com/HDF/centos7/2.x/updates/2.1.4.0/tars/hdf_ambari_mp/hdf-ambari-mpack-2.1.4.0-5.tar.gz \ --purge \ --verbose
      安装，当然这个压缩包可以下载之后指到本地路径安装，然后就可以重启ambari-server
      • -
    -]]>     - - data analysis - - - hadoop - cluster - -     - - docker比一般多一点的初学者介绍四 - /2022/12/25/docker%E6%AF%94%E4%B8%80%E8%88%AC%E5%A4%9A%E4%B8%80%E7%82%B9%E7%9A%84%E5%88%9D%E5%AD%A6%E8%80%85%E4%BB%8B%E7%BB%8D%E5%9B%9B/ - 这次单独介绍下docker体系里非常重要的cgroup，docker对资源的限制也是基于cgroup构建的，
    简单尝试
    新建一个shell脚本

    -
    #!/bin/bash
    while true;do
        echo "1"
    done
    - -

    直接执行的话就是单核100%的cpu

    -

    首先在cgroup下面建个目录

    -
    mkdir -p /sys/fs/cgroup/cpu/sxs_test/
    -

    查看目录下的文件

    其中cpuacct开头的表示cpu相关的统计信息，
    我们要配置cpu的额度，是在cpu.cfs_quota_us中

    -
    echo 2000 > /sys/fs/cgroup/cpu/sxs_test/cpu.cfs_quota_us
    -

    这样表示可以使用2%的cpu，总的配额是在cpu.cfs_period_us中

    -

    然后将当前进程输入到cgroup.procs,

    -
    echo $$ > /sys/fs/cgroup/cpu/sxs_test/cgroup.procs
    -

    这样就会自动继承当前进程产生的新进程
    再次执行就可以看到cpu被限制了

    -]]>     - - Docker - - - Docker - -     - - docker使用中发现的echo命令的一个小技巧及其他 - /2020/03/29/echo%E5%91%BD%E4%BB%A4%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E6%8A%80%E5%B7%A7/ - echo 实操技巧

    最近做 docker 系列，会经常需要进到 docker 内部，如上一篇介绍的，这些镜像一般都有用 ubuntu 或者alpine 这样的 Linux 系统作为底包，如果构建镜像的时候没有替换源的话，因为特殊的网络原因，在内部想编辑下东西要安装个类似于 vim 这样的编辑器就会很慢很慢，像视频里 two thousand years later~ 而且如果在容器内部想改源配置的话也要编辑器，就陷入了一个鸡生蛋，跟蛋生鸡的死锁问题中，对于 linux 大神来说应该有一万种方法解决这个问题，对于我这个渣渣来说可能只想到了这个土方法，先 cp backup 一下 sources.list, 再 echo “xxx” > sources.list, 这里就碰到了一个问题，这个 sources.list 一般不止一行，直接 echo 的话就解析不了了，不过 echo 可以支持”\n”转义，就是加-e看一下解释和示例，我这里使用了 tldr ，可以用 npm install -g tldr 安装，也可以直接用man， 或者–help 来查看使用方式

    -

    查看镜像底包

    还有一点也是在这个时候要安装 vim 之类的，得知道是什么镜像底包，如果是用 uname 指令，其实看到的是宿主机的系统，得用cat /etc/issue

    -


    这里稍稍记一下

    -

    寻找系统镜像源

    目前国内系统源用得比较多的是阿里云源，不过这里也推荐清华源, 中科大源, 浙大源 这里不要脸的推荐下母校的源，不过还不是很完善，尽情期待下。

    + docker比一般多一点的初学者介绍三 + /2020/03/21/docker%E6%AF%94%E4%B8%80%E8%88%AC%E5%A4%9A%E4%B8%80%E7%82%B9%E7%9A%84%E5%88%9D%E5%AD%A6%E8%80%85%E4%BB%8B%E7%BB%8D%E4%B8%89/ + 运行第一个 Dockerfile

    上一篇的 Dockerfile 我们停留在构建阶段，现在来把它跑起来

    +
    docker run -d -p 80 --name static_web nicksxs/static_web \
    nginx -g "daemon off;"
    +

    这里的-d表示以分离模型运行docker (detached),然后-p 是表示将容器的 80 端口开放给宿主机，然后容器名就叫 static_web,使用了我们上次构建的 static_web 镜像，后面的是让 nginx 在前台运行

    可以看到返回了个容器 id，但是具体情况没出现，也没连上去，那我们想看看怎么访问在 Dockerfile 里写的静态页面，我们来看下docker 进程

    发现为我们随机分配了一个宿主机的端口，32768，去服务器的防火墙把这个外网端口开一下，看看是不是符合我们的预期呢

    好像不太对额，应该是 ubuntu 安装的 nginx 的默认工作目录不对，我们来进容器看看，再熟悉下命令docker exec -it 4792455ca2ed /bin/bash
    记得容器 id 换成自己的，进入容器后得找找 nginx 的配置文件，通常在/etc/nginx,/usr/local/etc等目录下，然后找到我们的目录是在这

    所以把刚才的内容复制过去再试试

    目标达成，give me five✌️

    +

    第二个 Dockerfile

    然后就想来动态一点的，毕竟写过 PHP，就来试试 PHP
    再建一个目录叫 dynamic_web，里面创建 src 目录，放一个 index.php
    内容是

    +
    <?php
    echo "Hello World!";
    +

    然后在 dynamic_web 目录下创建 Dockerfile，

    +
    FROM trafex/alpine-nginx-php7:latest
    COPY src/ /var/www/html
    EXPOSE 80
    +

    Dockerfile 虽然只有三行，不过要着重说明下，这个底包其实不是docker 官方的，有两点考虑，一点是官方的基本都是 php apache 的镜像，还有就是 alpine这个，截取一段中文介绍

    +
    +

    Alpine 操作系统是一个面向安全的轻型 Linux 发行版。它不同于通常 Linux 发行版，Alpine 采用了 musl libc 和 busybox 以减小系统的体积和运行时资源消耗，但功能上比 busybox 又完善的多，因此得到开源社区越来越多的青睐。在保持瘦身的同时，Alpine 还提供了自己的包管理工具 apk，可以通过 https://pkgs.alpinelinux.org/packages 网站上查询包信息，也可以直接通过 apk 命令直接查询和安装各种软件。
    Alpine 由非商业组织维护的，支持广泛场景的 Linux发行版，它特别为资深/重度Linux用户而优化，关注安全，性能和资源效能。Alpine 镜像可以适用于更多常用场景，并且是一个优秀的可以适用于生产的基础系统/环境。

    +
    +
    +

    Alpine Docker 镜像也继承了 Alpine Linux 发行版的这些优势。相比于其他 Docker 镜像，它的容量非常小，仅仅只有 5 MB 左右（对比 Ubuntu 系列镜像接近 200 MB），且拥有非常友好的包管理机制。官方镜像来自 docker-alpine 项目。

    +
    +
    +

    目前 Docker 官方已开始推荐使用 Alpine 替代之前的 Ubuntu 做为基础镜像环境。这样会带来多个好处。包括镜像下载速度加快，镜像安全性提高，主机之间的切换更方便，占用更少磁盘空间等。

    +
    +

    一方面在没有镜像的情况下，拉取 docker 镜像还是比较费力的，第二个就是也能节省硬盘空间，所以目前有大部分的 docker 镜像都将 alpine 作为基础镜像了
    然后再来构建下

    这里还有个点，就是上面的那个镜像我们也是 EXPOSE 80端口，然后外部宿主机会随机映射一个端口，为了偷个懒，我们就直接指定外部端口了
    docker run -d -p 80:80 dynamic_web打开浏览器发现访问不了，咋回事呢
    因为我们没看trafex/alpine-nginx-php7:latest这个镜像说明,它内部的服务是 8080 端口的，所以我们映射的暴露端口应该是 8080，再用 docker run -d -p 80:8080 dynamic_web这个启动，

    ]]>     - Linux Docker - 命令 - echo - 发行版本 + 介绍 - linux Docker - echo - uname - 发行版 + namespace + Dockerfile     @@ -2624,6 +2590,29 @@ cgroup + + docker比一般多一点的初学者介绍四 + /2022/12/25/docker%E6%AF%94%E4%B8%80%E8%88%AC%E5%A4%9A%E4%B8%80%E7%82%B9%E7%9A%84%E5%88%9D%E5%AD%A6%E8%80%85%E4%BB%8B%E7%BB%8D%E5%9B%9B/ + 这次单独介绍下docker体系里非常重要的cgroup，docker对资源的限制也是基于cgroup构建的，
    简单尝试
    新建一个shell脚本

    +
    #!/bin/bash
    while true;do
        echo "1"
    done
    + +

    直接执行的话就是单核100%的cpu

    +

    首先在cgroup下面建个目录

    +
    mkdir -p /sys/fs/cgroup/cpu/sxs_test/
    +

    查看目录下的文件

    其中cpuacct开头的表示cpu相关的统计信息，
    我们要配置cpu的额度，是在cpu.cfs_quota_us中

    +
    echo 2000 > /sys/fs/cgroup/cpu/sxs_test/cpu.cfs_quota_us
    +

    这样表示可以使用2%的cpu，总的配额是在cpu.cfs_period_us中

    +

    然后将当前进程输入到cgroup.procs,

    +
    echo $$ > /sys/fs/cgroup/cpu/sxs_test/cgroup.procs
    +

    这样就会自动继承当前进程产生的新进程
    再次执行就可以看到cpu被限制了

    +]]>     + + Docker + + + Docker + +     dubbo 客户端配置的一个重要知识点 /2022/06/11/dubbo-%E5%AE%A2%E6%88%B7%E7%AB%AF%E9%85%8D%E7%BD%AE%E7%9A%84%E4%B8%80%E4%B8%AA%E9%87%8D%E8%A6%81%E7%9F%A5%E8%AF%86%E7%82%B9/ @@ -2649,6 +2638,55 @@ Dubbo + + docker使用中发现的echo命令的一个小技巧及其他 + /2020/03/29/echo%E5%91%BD%E4%BB%A4%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E6%8A%80%E5%B7%A7/ + echo 实操技巧

    最近做 docker 系列，会经常需要进到 docker 内部，如上一篇介绍的，这些镜像一般都有用 ubuntu 或者alpine 这样的 Linux 系统作为底包，如果构建镜像的时候没有替换源的话，因为特殊的网络原因，在内部想编辑下东西要安装个类似于 vim 这样的编辑器就会很慢很慢，像视频里 two thousand years later~ 而且如果在容器内部想改源配置的话也要编辑器，就陷入了一个鸡生蛋，跟蛋生鸡的死锁问题中，对于 linux 大神来说应该有一万种方法解决这个问题，对于我这个渣渣来说可能只想到了这个土方法，先 cp backup 一下 sources.list, 再 echo “xxx” > sources.list, 这里就碰到了一个问题，这个 sources.list 一般不止一行，直接 echo 的话就解析不了了，不过 echo 可以支持”\n”转义，就是加-e看一下解释和示例，我这里使用了 tldr ，可以用 npm install -g tldr 安装，也可以直接用man， 或者–help 来查看使用方式

    +

    查看镜像底包

    还有一点也是在这个时候要安装 vim 之类的，得知道是什么镜像底包，如果是用 uname 指令，其实看到的是宿主机的系统，得用cat /etc/issue

    +


    这里稍稍记一下

    +

    寻找系统镜像源

    目前国内系统源用得比较多的是阿里云源，不过这里也推荐清华源, 中科大源, 浙大源 这里不要脸的推荐下母校的源，不过还不是很完善，尽情期待下。

    +]]>     + + Linux + Docker + 命令 + echo + 发行版本 + + + linux + Docker + echo + uname + 发行版 + +     + + github 小技巧-更新 github host key + /2023/03/28/github-%E5%B0%8F%E6%8A%80%E5%B7%A7-%E6%9B%B4%E6%96%B0-github-host-key/ + 最近一次推送博客，发现报了个错推不上去，

    +
    WARNING: REMOTE HOST IDENTIFICATION HAS CHANGED!

    IT IS POSSIBLE THAT SOMEONE IS DOING SOMETHING NASTY!
    Someone could be eavesdropping on you right now (man-in-the-middle attack)!
    It is also possible that a host key has just been changed.
    +

    错误信息是这样，有点奇怪也没干啥，网上一搜发现是We updated our RSA SSH host key
    简单翻一下就是

    +
    +

    在3月24日协调世界时大约05:00时，出于谨慎，我们更换了用于保护 GitHub.com 的 Git 操作的 RSA SSH 主机密钥。我们这样做是为了保护我们的用户免受任何对手模仿 GitHub 或通过 SSH 窃听他们的 Git 操作的机会。此密钥不授予对 GitHub 基础设施或客户数据的访问权限。此更改仅影响通过使用 RSA 的 SSH 进行的 Git 操作。GitHub.com 和 HTTPS Git 操作的网络流量不受影响。

    +
    +

    要解决也比较简单就是重置下 host key，

    +
    +

    Host Key是服务器用来证明自己身份的一个永久性的非对称密钥

    +
    +

    使用

    +
    ssh-keygen -R github.com
    +

    然后在首次建立连接的时候同意下就可以了

    +]]>     + + ssh + 技巧 + + + ssh + 端口转发 + +     gogs使用webhook部署react单页应用 /2020/02/22/gogs%E4%BD%BF%E7%94%A8webhook%E9%83%A8%E7%BD%B2react%E5%8D%95%E9%A1%B5%E5%BA%94%E7%94%A8/ @@ -2683,29 +2721,21 @@ - github 小技巧-更新 github host key - /2023/03/28/github-%E5%B0%8F%E6%8A%80%E5%B7%A7-%E6%9B%B4%E6%96%B0-github-host-key/ - 最近一次推送博客，发现报了个错推不上去，

    -
    WARNING: REMOTE HOST IDENTIFICATION HAS CHANGED!

    IT IS POSSIBLE THAT SOMEONE IS DOING SOMETHING NASTY!
    Someone could be eavesdropping on you right now (man-in-the-middle attack)!
    It is also possible that a host key has just been changed.
    -

    错误信息是这样，有点奇怪也没干啥，网上一搜发现是We updated our RSA SSH host key
    简单翻一下就是

    -
    -

    在3月24日协调世界时大约05:00时，出于谨慎，我们更换了用于保护 GitHub.com 的 Git 操作的 RSA SSH 主机密钥。我们这样做是为了保护我们的用户免受任何对手模仿 GitHub 或通过 SSH 窃听他们的 Git 操作的机会。此密钥不授予对 GitHub 基础设施或客户数据的访问权限。此更改仅影响通过使用 RSA 的 SSH 进行的 Git 操作。GitHub.com 和 HTTPS Git 操作的网络流量不受影响。

    -
    -

    要解决也比较简单就是重置下 host key，

    -
    -

    Host Key是服务器用来证明自己身份的一个永久性的非对称密钥

    -
    -

    使用

    -
    ssh-keygen -R github.com
    -

    然后在首次建立连接的时候同意下就可以了

    + headscale 添加节点 + /2023/07/09/headscale-%E6%B7%BB%E5%8A%A0%E8%8A%82%E7%82%B9/ + 添加节点

    添加节点非常简单，比如 app store 或者官网可以下载 mac 的安装包，

    +

    安装包直接下载可以在这里，下载安装完后还需要做一些处理，才能让 Tailscale 使用 Headscale 作为控制服务器。当然，Headscale 已经给我们提供了详细的操作步骤，你只需要在浏览器中打开 URL：http://<HEADSCALE_PUB_IP>:<HEADSCALE_PUB_PORT>/apple，记得端口替换成自己的，就会看到这样的说明页

    +

    image

    +

    然后对于像我这样自己下载的客户端安装包，也就是standalone client，就可以用下面的命令

    +

    defaults write io.tailscale.ipn.macsys ControlURL http://<HEADSCALE_PUB_IP>:<HEADSCALE_PUB_PORT> 类似于 Windows 客户端需要写入注册表，就是把控制端的地址改成了我们自己搭建的 headscale 的，设置完以后就打开 tailscale 客户端右键点击 login，就会弹出一个浏览器地址

    +

    image

    +

    按照这个里面的命令去 headscale 的机器上执行，注意要替换 namespace，对于最新的 headscale 已经把 namespace 废弃改成 user 了，这点要注意了，其他客户端也同理，现在还有个好消息，安卓和 iOS 客户端也已经都可以用了，后面可以在介绍下局域网怎么部分打通和自建 derper。

    ]]>     - ssh - 技巧 + headscale - ssh - 端口转发 + headscale     @@ -2729,24 +2759,6 @@ hexo - - headscale 添加节点 - /2023/07/09/headscale-%E6%B7%BB%E5%8A%A0%E8%8A%82%E7%82%B9/ - 添加节点

    添加节点非常简单，比如 app store 或者官网可以下载 mac 的安装包，

    -

    安装包直接下载可以在这里，下载安装完后还需要做一些处理，才能让 Tailscale 使用 Headscale 作为控制服务器。当然，Headscale 已经给我们提供了详细的操作步骤，你只需要在浏览器中打开 URL：http://<HEADSCALE_PUB_IP>:<HEADSCALE_PUB_PORT>/apple，记得端口替换成自己的，就会看到这样的说明页

    -

    image

    -

    然后对于像我这样自己下载的客户端安装包，也就是standalone client，就可以用下面的命令

    -

    defaults write io.tailscale.ipn.macsys ControlURL http://<HEADSCALE_PUB_IP>:<HEADSCALE_PUB_PORT> 类似于 Windows 客户端需要写入注册表，就是把控制端的地址改成了我们自己搭建的 headscale 的，设置完以后就打开 tailscale 客户端右键点击 login，就会弹出一个浏览器地址

    -

    image

    -

    按照这个里面的命令去 headscale 的机器上执行，注意要替换 namespace，对于最新的 headscale 已经把 namespace 废弃改成 user 了，这点要注意了，其他客户端也同理，现在还有个好消息，安卓和 iOS 客户端也已经都可以用了，后面可以在介绍下局域网怎么部分打通和自建 derper。

    -]]>     - - headscale - - - headscale - -     invert-binary-tree /2015/06/22/invert-binary-tree/ @@ -2843,6 +2855,30 @@ Mybatis + + mybatis 的 $ 和 # 是有啥区别 + /2020/09/06/mybatis-%E7%9A%84-%E5%92%8C-%E6%98%AF%E6%9C%89%E5%95%A5%E5%8C%BA%E5%88%AB/ + 这个问题也是面试中常被问到的，就抽空来了解下这个，跳过一大段前面初始化的逻辑，
    对于一条select * from t1 where id = #{id}这样的 sql，在初始化扫描 mapper 的xml文件的时候会根据是否是 dynamic 来判断生成 DynamicSqlSource 还是 RawSqlSource，这里它是一条 RawSqlSource，
    在这里做了替换，将#{}替换成了?

    前面说的是否 dynamic 就是在这里进行判断

    +
    // org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseScriptNode
    public SqlSource parseScriptNode() {
        MixedSqlNode rootSqlNode = parseDynamicTags(context);
        SqlSource sqlSource;
        if (isDynamic) {
          sqlSource = new DynamicSqlSource(configuration, rootSqlNode);
        } else {
          sqlSource = new RawSqlSource(configuration, rootSqlNode, parameterType);
        }
        return sqlSource;
      }
    // org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseDynamicTags
    protected MixedSqlNode parseDynamicTags(XNode node) {
        List<SqlNode> contents = new ArrayList<>();
        NodeList children = node.getNode().getChildNodes();
        for (int i = 0; i < children.getLength(); i++) {
          XNode child = node.newXNode(children.item(i));
          if (child.getNode().getNodeType() == Node.CDATA_SECTION_NODE || child.getNode().getNodeType() == Node.TEXT_NODE) {
            String data = child.getStringBody("");
            TextSqlNode textSqlNode = new TextSqlNode(data);
            if (textSqlNode.isDynamic()) {
              contents.add(textSqlNode);
              isDynamic = true;
            } else {
              contents.add(new StaticTextSqlNode(data));
            }
          } else if (child.getNode().getNodeType() == Node.ELEMENT_NODE) { // issue #628
            String nodeName = child.getNode().getNodeName();
            NodeHandler handler = nodeHandlerMap.get(nodeName);
            if (handler == null) {
              throw new BuilderException("Unknown element <" + nodeName + "> in SQL statement.");
            }
            handler.handleNode(child, contents);
            isDynamic = true;
          }
        }
        return new MixedSqlNode(contents);
      }
    // org.apache.ibatis.scripting.xmltags.TextSqlNode#isDynamic
      public boolean isDynamic() {
        DynamicCheckerTokenParser checker = new DynamicCheckerTokenParser();
        GenericTokenParser parser = createParser(checker);
        parser.parse(text);
        return checker.isDynamic();
      }
        private GenericTokenParser createParser(TokenHandler handler) {
        return new GenericTokenParser("${", "}", handler);
      }
    +

    可以看到其中一个条件就是是否有${}这种占位符，假如说上面的 sql 换成 ${}，那么可以看到它会在这里创建一个 dynamicSqlSource,

    +
    // org.apache.ibatis.scripting.xmltags.DynamicSqlSource
    public class DynamicSqlSource implements SqlSource {

      private final Configuration configuration;
      private final SqlNode rootSqlNode;

      public DynamicSqlSource(Configuration configuration, SqlNode rootSqlNode) {
        this.configuration = configuration;
        this.rootSqlNode = rootSqlNode;
      }

      @Override
      public BoundSql getBoundSql(Object parameterObject) {
        DynamicContext context = new DynamicContext(configuration, parameterObject);
        rootSqlNode.apply(context);
        SqlSourceBuilder sqlSourceParser = new SqlSourceBuilder(configuration);
        Class<?> parameterType = parameterObject == null ? Object.class : parameterObject.getClass();
        SqlSource sqlSource = sqlSourceParser.parse(context.getSql(), parameterType, context.getBindings());
        BoundSql boundSql = sqlSource.getBoundSql(parameterObject);
        context.getBindings().forEach(boundSql::setAdditionalParameter);
        return boundSql;
      }

    }

    +

    这里眼尖的同学可能就可以看出来了，RawSqlSource 在初始化的时候已经经过了 parse，把#{}替换成了?占位符，但是 DynamicSqlSource 并没有
    再看这个图，我们发现在这的时候还没有进行替换
    然后往里跟
    好像是这里了

    这里 rootSqlNode.apply 其实是一个对原来 sql 的解析结果的一个循环调用，不同类型的标签会构成不同的 node，像这里就是一个 textSqlNode

    可以发现到这我们的 sql 已经被替换了，而且是直接作为 string 类型替换的，所以可以明白了这个问题所在，就是注入，不过细心的同学发现其实这里是有个

    理论上还是可以做过滤的，不过好像现在没用起来。
    我们前面可以发现对于#{}是在启动扫描 mapper的 xml 文件就替换成了 ?,然后是在什么时候变成实际的值的呢

    发现到这的时候还是没有替换，其实说白了也就是 prepareStatement 那一套，

    在这里进行替换，会拿到 org.apache.ibatis.mapping.ParameterMapping，然后进行替换，因为会带着类型信息，所以不用担心注入咯

    +]]>     + + Java + Mybatis + Mysql + Spring + Sql注入 + Mybatis + + + Java + Mysql + Mybatis + Sql注入 + +     mybatis 的缓存是怎么回事 /2020/10/03/mybatis-%E7%9A%84%E7%BC%93%E5%AD%98%E6%98%AF%E6%80%8E%E4%B9%88%E5%9B%9E%E4%BA%8B/ @@ -2975,30 +3011,6 @@ Mybatis - - mybatis 的 $ 和 # 是有啥区别 - /2020/09/06/mybatis-%E7%9A%84-%E5%92%8C-%E6%98%AF%E6%9C%89%E5%95%A5%E5%8C%BA%E5%88%AB/ - 这个问题也是面试中常被问到的，就抽空来了解下这个，跳过一大段前面初始化的逻辑，
    对于一条select * from t1 where id = #{id}这样的 sql，在初始化扫描 mapper 的xml文件的时候会根据是否是 dynamic 来判断生成 DynamicSqlSource 还是 RawSqlSource，这里它是一条 RawSqlSource，
    在这里做了替换，将#{}替换成了?

    前面说的是否 dynamic 就是在这里进行判断

    -
    // org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseScriptNode
    public SqlSource parseScriptNode() {
        MixedSqlNode rootSqlNode = parseDynamicTags(context);
        SqlSource sqlSource;
        if (isDynamic) {
          sqlSource = new DynamicSqlSource(configuration, rootSqlNode);
        } else {
          sqlSource = new RawSqlSource(configuration, rootSqlNode, parameterType);
        }
        return sqlSource;
      }
    // org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseDynamicTags
    protected MixedSqlNode parseDynamicTags(XNode node) {
        List<SqlNode> contents = new ArrayList<>();
        NodeList children = node.getNode().getChildNodes();
        for (int i = 0; i < children.getLength(); i++) {
          XNode child = node.newXNode(children.item(i));
          if (child.getNode().getNodeType() == Node.CDATA_SECTION_NODE || child.getNode().getNodeType() == Node.TEXT_NODE) {
            String data = child.getStringBody("");
            TextSqlNode textSqlNode = new TextSqlNode(data);
            if (textSqlNode.isDynamic()) {
              contents.add(textSqlNode);
              isDynamic = true;
            } else {
              contents.add(new StaticTextSqlNode(data));
            }
          } else if (child.getNode().getNodeType() == Node.ELEMENT_NODE) { // issue #628
            String nodeName = child.getNode().getNodeName();
            NodeHandler handler = nodeHandlerMap.get(nodeName);
            if (handler == null) {
              throw new BuilderException("Unknown element <" + nodeName + "> in SQL statement.");
            }
            handler.handleNode(child, contents);
            isDynamic = true;
          }
        }
        return new MixedSqlNode(contents);
      }
    // org.apache.ibatis.scripting.xmltags.TextSqlNode#isDynamic
      public boolean isDynamic() {
        DynamicCheckerTokenParser checker = new DynamicCheckerTokenParser();
        GenericTokenParser parser = createParser(checker);
        parser.parse(text);
        return checker.isDynamic();
      }
        private GenericTokenParser createParser(TokenHandler handler) {
        return new GenericTokenParser("${", "}", handler);
      }
    -

    可以看到其中一个条件就是是否有${}这种占位符，假如说上面的 sql 换成 ${}，那么可以看到它会在这里创建一个 dynamicSqlSource,

    -
    // org.apache.ibatis.scripting.xmltags.DynamicSqlSource
    public class DynamicSqlSource implements SqlSource {

      private final Configuration configuration;
      private final SqlNode rootSqlNode;

      public DynamicSqlSource(Configuration configuration, SqlNode rootSqlNode) {
        this.configuration = configuration;
        this.rootSqlNode = rootSqlNode;
      }

      @Override
      public BoundSql getBoundSql(Object parameterObject) {
        DynamicContext context = new DynamicContext(configuration, parameterObject);
        rootSqlNode.apply(context);
        SqlSourceBuilder sqlSourceParser = new SqlSourceBuilder(configuration);
        Class<?> parameterType = parameterObject == null ? Object.class : parameterObject.getClass();
        SqlSource sqlSource = sqlSourceParser.parse(context.getSql(), parameterType, context.getBindings());
        BoundSql boundSql = sqlSource.getBoundSql(parameterObject);
        context.getBindings().forEach(boundSql::setAdditionalParameter);
        return boundSql;
      }

    }

    -

    这里眼尖的同学可能就可以看出来了，RawSqlSource 在初始化的时候已经经过了 parse，把#{}替换成了?占位符，但是 DynamicSqlSource 并没有
    再看这个图，我们发现在这的时候还没有进行替换
    然后往里跟
    好像是这里了

    这里 rootSqlNode.apply 其实是一个对原来 sql 的解析结果的一个循环调用，不同类型的标签会构成不同的 node，像这里就是一个 textSqlNode

    可以发现到这我们的 sql 已经被替换了，而且是直接作为 string 类型替换的，所以可以明白了这个问题所在，就是注入，不过细心的同学发现其实这里是有个

    理论上还是可以做过滤的，不过好像现在没用起来。
    我们前面可以发现对于#{}是在启动扫描 mapper的 xml 文件就替换成了 ?,然后是在什么时候变成实际的值的呢

    发现到这的时候还是没有替换，其实说白了也就是 prepareStatement 那一套，

    在这里进行替换，会拿到 org.apache.ibatis.mapping.ParameterMapping，然后进行替换，因为会带着类型信息，所以不用担心注入咯

    -]]>     - - Java - Mybatis - Mysql - Spring - Sql注入 - Mybatis - - - Java - Mysql - Mybatis - Sql注入 - -     mybatis系列-dataSource解析 /2023/01/08/mybatis%E7%B3%BB%E5%88%97-dataSource%E8%A7%A3%E6%9E%90/ @@ -3040,12 +3052,42 @@ - mybatis系列-foreach 解析 - /2023/06/11/mybatis%E7%B3%BB%E5%88%97-foreach-%E8%A7%A3%E6%9E%90/ - 在 org.apache.ibatis.builder.xml.XMLConfigBuilder#parseConfiguration 中进行配置解析，其中这一行就是解析 mappers

    -
    mapperElement(root.evalNode("mappers"));
    -

    具体的代码会执行到这

    -
    private void mapperElement(XNode parent) throws Exception {
      if (parent != null) {
        for (XNode child : parent.getChildren()) {
          if ("package".equals(child.getName())) {
            // 这里解析的不是 package
            String mapperPackage = child.getStringAttribute("name");
            configuration.addMappers(mapperPackage);
          } else {
            // 根据 resource 和 url 还有 mapperClass 判断
            String resource = child.getStringAttribute("resource");
            String url = child.getStringAttribute("url");
            String mapperClass = child.getStringAttribute("class");
            // resource 不为空其他为空的情况，就开始将 resource 读成输入流
            if (resource != null && url == null && mapperClass == null) {
              ErrorContext.instance().resource(resource);
              try(InputStream inputStream = Resources.getResourceAsStream(resource)) {
                // 初始化 XMLMapperBuilder 来解析 mapper
                XMLMapperBuilder mapperParser = new XMLMapperBuilder(inputStream, configuration, resource, configuration.getSqlFragments());
                mapperParser.parse();
              }
    + mybatis系列-sql 类的简单使用 + /2023/03/12/mybatis%E7%B3%BB%E5%88%97-sql-%E7%B1%BB%E7%9A%84%E7%AE%80%E5%8D%95%E4%BD%BF%E7%94%A8/ + mybatis 还有个比较有趣的功能，就是使用 SQL 类生成 sql，有点类似于 hibernate 或者像 php 的 laravel 框架等的，就是把sql 这种放在 xml 里或者代码里直接写 sql 用对象的形式

    +

    select语句

    比如这样

    +
    public static void main(String[] args) {
        String selectSql = new SQL() {{
            SELECT("id", "name");
            FROM("student");
            WHERE("id = #{id}");
        }}.toString();
        System.out.println(selectSql);
    }
    +

    打印出来就是

    +
    SELECT id, name
    FROM student
    WHERE (id = #{id})
    +

    应付简单的 sql 查询基本都可以这么解决，如果习惯这种模式，还是不错的，
    其实以面向对象的编程模式来说，这样是比较符合面向对象的，先不深入的解析这块的源码，先从使用角度讲一下

    +

    比如 update 语句

    String updateSql = new SQL() {{
                UPDATE("student");
                SET("name = #{name}");
                WHERE("id = #{id}");
            }}.toString();
    +

    打印输出就是

    +
    UPDATE student
    SET name = #{name}
    WHERE (id = #{id})
    + +

    insert 语句

    String insertSql = new SQL() {{
                INSERT_INTO("student");
                VALUES("name", "#{name}");
                VALUES("age", "#{age}");
            }}.toString();
            System.out.println(insertSql);
    +

    打印输出

    +
    INSERT INTO student
     (name, age)
    VALUES (#{name}, #{age})
    +

    delete语句

    String deleteSql = new SQL() {{
                DELETE_FROM("student");
                WHERE("id = #{id}");
            }}.toString();
            System.out.println(deleteSql);
    +

    打印输出

    +
    DELETE FROM student
    WHERE (id = #{id})
    +]]>     + + Java + Mybatis + + + Java + Mysql + Mybatis + +     + + mybatis系列-foreach 解析 + /2023/06/11/mybatis%E7%B3%BB%E5%88%97-foreach-%E8%A7%A3%E6%9E%90/ + 在 org.apache.ibatis.builder.xml.XMLConfigBuilder#parseConfiguration 中进行配置解析，其中这一行就是解析 mappers

    +
    mapperElement(root.evalNode("mappers"));
    +

    具体的代码会执行到这

    +
    private void mapperElement(XNode parent) throws Exception {
      if (parent != null) {
        for (XNode child : parent.getChildren()) {
          if ("package".equals(child.getName())) {
            // 这里解析的不是 package
            String mapperPackage = child.getStringAttribute("name");
            configuration.addMappers(mapperPackage);
          } else {
            // 根据 resource 和 url 还有 mapperClass 判断
            String resource = child.getStringAttribute("resource");
            String url = child.getStringAttribute("url");
            String mapperClass = child.getStringAttribute("class");
            // resource 不为空其他为空的情况，就开始将 resource 读成输入流
            if (resource != null && url == null && mapperClass == null) {
              ErrorContext.instance().resource(resource);
              try(InputStream inputStream = Resources.getResourceAsStream(resource)) {
                // 初始化 XMLMapperBuilder 来解析 mapper
                XMLMapperBuilder mapperParser = new XMLMapperBuilder(inputStream, configuration, resource, configuration.getSqlFragments());
                mapperParser.parse();
              }

    然后再是 parse 过程

    public void parse() {
      if (!configuration.isResourceLoaded(resource)) {
        // 解析 mapper 节点，也就是下图中的mapper
        configurationElement(parser.evalNode("/mapper"));
        configuration.addLoadedResource(resource);
        bindMapperForNamespace();
      }

      parsePendingResultMaps();
      parsePendingCacheRefs();
      parsePendingStatements();
    }

    image

    @@ -3123,24 +3165,52 @@     - mybatis系列-sql 类的简单使用 - /2023/03/12/mybatis%E7%B3%BB%E5%88%97-sql-%E7%B1%BB%E7%9A%84%E7%AE%80%E5%8D%95%E4%BD%BF%E7%94%A8/ - mybatis 还有个比较有趣的功能，就是使用 SQL 类生成 sql，有点类似于 hibernate 或者像 php 的 laravel 框架等的，就是把sql 这种放在 xml 里或者代码里直接写 sql 用对象的形式

    -

    select语句

    比如这样

    -
    public static void main(String[] args) {
        String selectSql = new SQL() {{
            SELECT("id", "name");
            FROM("student");
            WHERE("id = #{id}");
        }}.toString();
        System.out.println(selectSql);
    }
    -

    打印出来就是

    -
    SELECT id, name
    FROM student
    WHERE (id = #{id})
    -

    应付简单的 sql 查询基本都可以这么解决，如果习惯这种模式，还是不错的，
    其实以面向对象的编程模式来说，这样是比较符合面向对象的，先不深入的解析这块的源码，先从使用角度讲一下

    -

    比如 update 语句

    String updateSql = new SQL() {{
                UPDATE("student");
                SET("name = #{name}");
                WHERE("id = #{id}");
            }}.toString();
    -

    打印输出就是

    -
    UPDATE student
    SET name = #{name}
    WHERE (id = #{id})
    - -

    insert 语句

    String insertSql = new SQL() {{
                INSERT_INTO("student");
                VALUES("name", "#{name}");
                VALUES("age", "#{age}");
            }}.toString();
            System.out.println(insertSql);
    -

    打印输出

    -
    INSERT INTO student
     (name, age)
    VALUES (#{name}, #{age})
    -

    delete语句

    String deleteSql = new SQL() {{
                DELETE_FROM("student");
                WHERE("id = #{id}");
            }}.toString();
            System.out.println(deleteSql);
    -

    打印输出

    -
    DELETE FROM student
    WHERE (id = #{id})
    + mybatis系列-typeAliases系统 + /2023/01/01/mybatis%E7%B3%BB%E5%88%97-typeAliases%E7%B3%BB%E7%BB%9F/ + 其实前面已经聊到过这个概念，在mybatis的配置中，以及一些初始化逻辑都是用了typeAliases，

    +
    <typeAliases>
      <typeAlias alias="Author" type="domain.blog.Author"/>
      <typeAlias alias="Blog" type="domain.blog.Blog"/>
      <typeAlias alias="Comment" type="domain.blog.Comment"/>
      <typeAlias alias="Post" type="domain.blog.Post"/>
      <typeAlias alias="Section" type="domain.blog.Section"/>
      <typeAlias alias="Tag" type="domain.blog.Tag"/>
    </typeAliases>
    +

    可以在这里注册类型别名，然后在mybatis中配置使用时，可以简化这些类型的使用，其底层逻辑主要是一个map，

    +
    public class TypeAliasRegistry {

      private final Map<String, Class<?>> typeAliases = new HashMap<>();
    +

    以string作为key，class对象作为value，比如我们在一开始使用的配置文件

    +
    <dataSource type="POOLED">
        <property name="driver" value="${driver}"/>
        <property name="url" value="${url}"/>
        <property name="username" value="${username}"/>
        <property name="password" value="${password}"/>
    </dataSource>
    +

    这里使用的dataSource是POOLED，那它肯定是个别名或者需要对应处理
    而这个别名就是在Configuration的构造方法里初始化

    +
    public Configuration() {
        typeAliasRegistry.registerAlias("JDBC", JdbcTransactionFactory.class);
        typeAliasRegistry.registerAlias("MANAGED", ManagedTransactionFactory.class);

        typeAliasRegistry.registerAlias("JNDI", JndiDataSourceFactory.class);
        typeAliasRegistry.registerAlias("POOLED", PooledDataSourceFactory.class);
        typeAliasRegistry.registerAlias("UNPOOLED", UnpooledDataSourceFactory.class);

        typeAliasRegistry.registerAlias("PERPETUAL", PerpetualCache.class);
        typeAliasRegistry.registerAlias("FIFO", FifoCache.class);
        typeAliasRegistry.registerAlias("LRU", LruCache.class);
        typeAliasRegistry.registerAlias("SOFT", SoftCache.class);
        typeAliasRegistry.registerAlias("WEAK", WeakCache.class);

        typeAliasRegistry.registerAlias("DB_VENDOR", VendorDatabaseIdProvider.class);

        typeAliasRegistry.registerAlias("XML", XMLLanguageDriver.class);
        typeAliasRegistry.registerAlias("RAW", RawLanguageDriver.class);

        typeAliasRegistry.registerAlias("SLF4J", Slf4jImpl.class);
        typeAliasRegistry.registerAlias("COMMONS_LOGGING", JakartaCommonsLoggingImpl.class);
        typeAliasRegistry.registerAlias("LOG4J", Log4jImpl.class);
        typeAliasRegistry.registerAlias("LOG4J2", Log4j2Impl.class);
        typeAliasRegistry.registerAlias("JDK_LOGGING", Jdk14LoggingImpl.class);
        typeAliasRegistry.registerAlias("STDOUT_LOGGING", StdOutImpl.class);
        typeAliasRegistry.registerAlias("NO_LOGGING", NoLoggingImpl.class);

        typeAliasRegistry.registerAlias("CGLIB", CglibProxyFactory.class);
        typeAliasRegistry.registerAlias("JAVASSIST", JavassistProxyFactory.class);

        languageRegistry.setDefaultDriverClass(XMLLanguageDriver.class);
        languageRegistry.register(RawLanguageDriver.class);
      }
    +

    正是通过typeAliasRegistry.registerAlias("POOLED", PooledDataSourceFactory.class);这一行，注册了
    POOLED对应的别名类型是PooledDataSourceFactory.class
    具体的注册方法是在

    +
    public void registerAlias(String alias, Class<?> value) {
      if (alias == null) {
        throw new TypeException("The parameter alias cannot be null");
      }
      // issue #748
      // 转换成小写，
      String key = alias.toLowerCase(Locale.ENGLISH);
      // 判断是否已经注册过了
      if (typeAliases.containsKey(key) && typeAliases.get(key) != null && !typeAliases.get(key).equals(value)) {
        throw new TypeException("The alias '" + alias + "' is already mapped to the value '" + typeAliases.get(key).getName() + "'.");
      }
      // 放进map里
      typeAliases.put(key, value);
    }
    +

    而获取的逻辑在这

    +
    public <T> Class<T> resolveAlias(String string) {
        try {
          if (string == null) {
            return null;
          }
          // issue #748
          // 同样的转成小写
          String key = string.toLowerCase(Locale.ENGLISH);
          Class<T> value;
          if (typeAliases.containsKey(key)) {
            value = (Class<T>) typeAliases.get(key);
          } else {
            // 这里还有从路径下处理的逻辑
            value = (Class<T>) Resources.classForName(string);
          }
          return value;
        } catch (ClassNotFoundException e) {
          throw new TypeException("Could not resolve type alias '" + string + "'.  Cause: " + e, e);
        }
      }
    +

    逻辑比较简单，但是在mybatis中也是不可或缺的一块概念

    +]]>     + + Java + Mybatis + + + Java + Mysql + Mybatis + +     + + mybatis系列-入门篇 + /2022/11/27/mybatis%E7%B3%BB%E5%88%97-%E5%85%A5%E9%97%A8%E7%AF%87/ + mybatis是我们比较常用的orm框架，下面是官网的介绍

    +
    +

    MyBatis 是一款优秀的持久层框架，它支持自定义 SQL、存储过程以及高级映射。MyBatis 免除了几乎所有的 JDBC 代码以及设置参数和获取结果集的工作。MyBatis 可以通过简单的 XML 或注解来配置和映射原始类型、接口和 Java POJO（Plain Old Java Objects，普通老式 Java 对象）为数据库中的记录。

    +
    +

    mybatis一大特点，或者说比较为人熟知的应该就是比 hibernate 是更轻量化，为国人所爱好的orm框架，对于hibernate目前还没有深入的拆解过，后续可以也写一下，在使用体验上觉得是个比较精巧的框架，看代码也比较容易，所以就想写个系列，第一篇先是介绍下使用
    根据官网的文档上我们先来尝试一下简单使用
    首先我们有个简单的配置，这个文件是mybatis-config.xml

    +
    <?xml version="1.0" encoding="UTF-8" ?>
    <!DOCTYPE configuration
            PUBLIC "-//mybatis.org//DTD Config 3.0//EN"
            "https://mybatis.org/dtd/mybatis-3-config.dtd">
    <configuration>
        <!-- 需要加入的properties-->
        <properties resource="application-development.properties"/>
        <!-- 指出使用哪个环境，默认是development-->
        <environments default="development">
            <environment id="development">
            <!-- 指定事务管理器类型-->
                <transactionManager type="JDBC"/>
                <!-- 指定数据源类型-->
                <dataSource type="POOLED">
                    <!-- 下面就是具体的参数占位了-->
                    <property name="driver" value="${driver}"/>
                    <property name="url" value="${url}"/>
                    <property name="username" value="${username}"/>
                    <property name="password" value="${password}"/>
                </dataSource>
            </environment>
        </environments>
        <mappers>
            <!-- 指定mapper xml的位置或文件-->
            <mapper resource="mapper/StudentMapper.xml"/>
        </mappers>
    </configuration>
    +

    在代码里创建mybatis里重要入口

    +
    String resource = "mybatis-config.xml";
    InputStream inputStream = Resources.getResourceAsStream(resource);
    SqlSessionFactory sqlSessionFactory = new SqlSessionFactoryBuilder().build(inputStream);
    +

    然后我们上面的StudentMapper.xml

    +
    <?xml version="1.0" encoding="UTF-8" ?>
    <!DOCTYPE mapper
            PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN"
            "https://mybatis.org/dtd/mybatis-3-mapper.dtd">
    <mapper namespace="com.nicksxs.mybatisdemo.StudentMapper">
        <select id="selectStudent" resultType="com.nicksxs.mybatisdemo.StudentDO">
            select * from student where id = #{id}
        </select>
    </mapper>
    +

    那么我们就要使用这个mapper，

    +
    String resource = "mybatis-config.xml";
    InputStream inputStream = Resources.getResourceAsStream(resource);
    SqlSessionFactory sqlSessionFactory = new SqlSessionFactoryBuilder().build(inputStream);
    try (SqlSession session = sqlSessionFactory.openSession()) {
        StudentDO studentDO = session.selectOne("com.nicksxs.mybatisdemo.StudentMapper.selectStudent", 1);
        System.out.println("id is " + studentDO.getId() + " name is " +studentDO.getName());
    } catch (Exception e) {
        e.printStackTrace();
    }
    +

    sqlSessionFactory是sqlSession的工厂，我们可以通过sqlSessionFactory来创建sqlSession，而SqlSession 提供了在数据库执行 SQL 命令所需的所有方法。你可以通过 SqlSession 实例来直接执行已映射的 SQL 语句。可以看到mapper.xml中有定义mapper的namespace，就可以通过session.selectOne()传入namespace+id来调用这个方法
    但是这样调用比较不合理的点，或者说按后面mybatis优化之后我们可以指定mapper接口

    +
    public interface StudentMapper {

        public StudentDO selectStudent(Long id);
    }
    +

    就可以可以通过mapper接口获取方法，这样就不用涉及到未知的变量转换等异常

    +
    try (SqlSession session = sqlSessionFactory.openSession()) {
        StudentMapper mapper = session.getMapper(StudentMapper.class);
        StudentDO studentDO = mapper.selectStudent(1L);
        System.out.println("id is " + studentDO.getId() + " name is " +studentDO.getName());
    } catch (Exception e) {
        e.printStackTrace();
    }
    +

    这一篇咱们先介绍下简单的使用，后面可以先介绍下这些的原理。

    ]]>     Java @@ -3270,107 +3340,6 @@ Mybatis     - - mybatis系列-入门篇 - /2022/11/27/mybatis%E7%B3%BB%E5%88%97-%E5%85%A5%E9%97%A8%E7%AF%87/ - mybatis是我们比较常用的orm框架，下面是官网的介绍

    -
    -

    MyBatis 是一款优秀的持久层框架，它支持自定义 SQL、存储过程以及高级映射。MyBatis 免除了几乎所有的 JDBC 代码以及设置参数和获取结果集的工作。MyBatis 可以通过简单的 XML 或注解来配置和映射原始类型、接口和 Java POJO（Plain Old Java Objects，普通老式 Java 对象）为数据库中的记录。

    -
    -

    mybatis一大特点，或者说比较为人熟知的应该就是比 hibernate 是更轻量化，为国人所爱好的orm框架，对于hibernate目前还没有深入的拆解过，后续可以也写一下，在使用体验上觉得是个比较精巧的框架，看代码也比较容易，所以就想写个系列，第一篇先是介绍下使用
    根据官网的文档上我们先来尝试一下简单使用
    首先我们有个简单的配置，这个文件是mybatis-config.xml

    -
    <?xml version="1.0" encoding="UTF-8" ?>
    <!DOCTYPE configuration
            PUBLIC "-//mybatis.org//DTD Config 3.0//EN"
            "https://mybatis.org/dtd/mybatis-3-config.dtd">
    <configuration>
        <!-- 需要加入的properties-->
        <properties resource="application-development.properties"/>
        <!-- 指出使用哪个环境，默认是development-->
        <environments default="development">
            <environment id="development">
            <!-- 指定事务管理器类型-->
                <transactionManager type="JDBC"/>
                <!-- 指定数据源类型-->
                <dataSource type="POOLED">
                    <!-- 下面就是具体的参数占位了-->
                    <property name="driver" value="${driver}"/>
                    <property name="url" value="${url}"/>
                    <property name="username" value="${username}"/>
                    <property name="password" value="${password}"/>
                </dataSource>
            </environment>
        </environments>
        <mappers>
            <!-- 指定mapper xml的位置或文件-->
            <mapper resource="mapper/StudentMapper.xml"/>
        </mappers>
    </configuration>
    -

    在代码里创建mybatis里重要入口

    -
    String resource = "mybatis-config.xml";
    InputStream inputStream = Resources.getResourceAsStream(resource);
    SqlSessionFactory sqlSessionFactory = new SqlSessionFactoryBuilder().build(inputStream);
    -

    然后我们上面的StudentMapper.xml

    -
    <?xml version="1.0" encoding="UTF-8" ?>
    <!DOCTYPE mapper
            PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN"
            "https://mybatis.org/dtd/mybatis-3-mapper.dtd">
    <mapper namespace="com.nicksxs.mybatisdemo.StudentMapper">
        <select id="selectStudent" resultType="com.nicksxs.mybatisdemo.StudentDO">
            select * from student where id = #{id}
        </select>
    </mapper>
    -

    那么我们就要使用这个mapper，

    -
    String resource = "mybatis-config.xml";
    InputStream inputStream = Resources.getResourceAsStream(resource);
    SqlSessionFactory sqlSessionFactory = new SqlSessionFactoryBuilder().build(inputStream);
    try (SqlSession session = sqlSessionFactory.openSession()) {
        StudentDO studentDO = session.selectOne("com.nicksxs.mybatisdemo.StudentMapper.selectStudent", 1);
        System.out.println("id is " + studentDO.getId() + " name is " +studentDO.getName());
    } catch (Exception e) {
        e.printStackTrace();
    }
    -

    sqlSessionFactory是sqlSession的工厂，我们可以通过sqlSessionFactory来创建sqlSession，而SqlSession 提供了在数据库执行 SQL 命令所需的所有方法。你可以通过 SqlSession 实例来直接执行已映射的 SQL 语句。可以看到mapper.xml中有定义mapper的namespace，就可以通过session.selectOne()传入namespace+id来调用这个方法
    但是这样调用比较不合理的点，或者说按后面mybatis优化之后我们可以指定mapper接口

    -
    public interface StudentMapper {

        public StudentDO selectStudent(Long id);
    }
    -

    就可以可以通过mapper接口获取方法，这样就不用涉及到未知的变量转换等异常

    -
    try (SqlSession session = sqlSessionFactory.openSession()) {
        StudentMapper mapper = session.getMapper(StudentMapper.class);
        StudentDO studentDO = mapper.selectStudent(1L);
        System.out.println("id is " + studentDO.getId() + " name is " +studentDO.getName());
    } catch (Exception e) {
        e.printStackTrace();
    }
    -

    这一篇咱们先介绍下简单的使用，后面可以先介绍下这些的原理。

    -]]>     - - Java - Mybatis - - - Java - Mysql - Mybatis - -     - - nginx 日志小记 - /2022/04/17/nginx-%E6%97%A5%E5%BF%97%E5%B0%8F%E8%AE%B0/ - nginx 默认的日志有特定的格式，我们也可以自定义，

    -

    默认的格式是预定义的 combined

    -
    log_format combined '$remote_addr - $remote_user [$time_local] '
                        '"$request" $status $body_bytes_sent '
                        '"$http_referer" "$http_user_agent"';
    - -

    配置的日志可以使用这个默认的，如果满足需求的话

    -
    Syntax:	access_log path [format [buffer=size] [gzip[=level]] [flush=time] [if=condition]];
            access_log off;
    Default: access_log logs/access.log combined;
    Context: http, server, location, if in location, limit_except
    - -

    而如果需要额外的一些配置的话可以自己定义 log_format ，比如我想要给日志里加上请求时间，那就可以自己定义一个 log_format 比如添加下

    -
    $request_time
    request processing time in seconds with a milliseconds resolution;   
    time elapsed between the first bytes were read from the client and the log write after the last bytes were sent to the client
    - -
    log_format combined_extend '$remote_addr - $remote_user [$time_local] '
                        '"$request" $status $body_bytes_sent '
                        '"$http_referer" "$http_user_agent" "$request_time"';
    - -

    然后其他的比如还有 gzip 压缩，可以设置压缩级别，flush 刷盘时间还有根据条件控制

    -

    这里的条件控制简单看了下还比较厉害

    -

    比如我想对2xx 跟 3xx 的访问不记录日志

    -
    map $status $loggable {
        ~^[23]  0;
        default 1;
    }

    access_log /path/to/access.log combined if=$loggable;
    - -

    当 $loggable 是 0 或者空时表示 if 条件为否，上面的默认就是 1，只有当请求状态 status 是 2xx 或 3xx 时才是 0，代表不用记录，有了这个特性就可以更灵活地配置日志

    -

    文章主要参考了 nginx 的 log 模块的文档

    -]]>     - - nginx - - - nginx - 日志 - -     - - mybatis系列-typeAliases系统 - /2023/01/01/mybatis%E7%B3%BB%E5%88%97-typeAliases%E7%B3%BB%E7%BB%9F/ - 其实前面已经聊到过这个概念，在mybatis的配置中，以及一些初始化逻辑都是用了typeAliases，

    -
    <typeAliases>
      <typeAlias alias="Author" type="domain.blog.Author"/>
      <typeAlias alias="Blog" type="domain.blog.Blog"/>
      <typeAlias alias="Comment" type="domain.blog.Comment"/>
      <typeAlias alias="Post" type="domain.blog.Post"/>
      <typeAlias alias="Section" type="domain.blog.Section"/>
      <typeAlias alias="Tag" type="domain.blog.Tag"/>
    </typeAliases>
    -

    可以在这里注册类型别名，然后在mybatis中配置使用时，可以简化这些类型的使用，其底层逻辑主要是一个map，

    -
    public class TypeAliasRegistry {

      private final Map<String, Class<?>> typeAliases = new HashMap<>();
    -

    以string作为key，class对象作为value，比如我们在一开始使用的配置文件

    -
    <dataSource type="POOLED">
        <property name="driver" value="${driver}"/>
        <property name="url" value="${url}"/>
        <property name="username" value="${username}"/>
        <property name="password" value="${password}"/>
    </dataSource>
    -

    这里使用的dataSource是POOLED，那它肯定是个别名或者需要对应处理
    而这个别名就是在Configuration的构造方法里初始化

    -
    public Configuration() {
        typeAliasRegistry.registerAlias("JDBC", JdbcTransactionFactory.class);
        typeAliasRegistry.registerAlias("MANAGED", ManagedTransactionFactory.class);

        typeAliasRegistry.registerAlias("JNDI", JndiDataSourceFactory.class);
        typeAliasRegistry.registerAlias("POOLED", PooledDataSourceFactory.class);
        typeAliasRegistry.registerAlias("UNPOOLED", UnpooledDataSourceFactory.class);

        typeAliasRegistry.registerAlias("PERPETUAL", PerpetualCache.class);
        typeAliasRegistry.registerAlias("FIFO", FifoCache.class);
        typeAliasRegistry.registerAlias("LRU", LruCache.class);
        typeAliasRegistry.registerAlias("SOFT", SoftCache.class);
        typeAliasRegistry.registerAlias("WEAK", WeakCache.class);

        typeAliasRegistry.registerAlias("DB_VENDOR", VendorDatabaseIdProvider.class);

        typeAliasRegistry.registerAlias("XML", XMLLanguageDriver.class);
        typeAliasRegistry.registerAlias("RAW", RawLanguageDriver.class);

        typeAliasRegistry.registerAlias("SLF4J", Slf4jImpl.class);
        typeAliasRegistry.registerAlias("COMMONS_LOGGING", JakartaCommonsLoggingImpl.class);
        typeAliasRegistry.registerAlias("LOG4J", Log4jImpl.class);
        typeAliasRegistry.registerAlias("LOG4J2", Log4j2Impl.class);
        typeAliasRegistry.registerAlias("JDK_LOGGING", Jdk14LoggingImpl.class);
        typeAliasRegistry.registerAlias("STDOUT_LOGGING", StdOutImpl.class);
        typeAliasRegistry.registerAlias("NO_LOGGING", NoLoggingImpl.class);

        typeAliasRegistry.registerAlias("CGLIB", CglibProxyFactory.class);
        typeAliasRegistry.registerAlias("JAVASSIST", JavassistProxyFactory.class);

        languageRegistry.setDefaultDriverClass(XMLLanguageDriver.class);
        languageRegistry.register(RawLanguageDriver.class);
      }
    -

    正是通过typeAliasRegistry.registerAlias("POOLED", PooledDataSourceFactory.class);这一行，注册了
    POOLED对应的别名类型是PooledDataSourceFactory.class
    具体的注册方法是在

    -
    public void registerAlias(String alias, Class<?> value) {
      if (alias == null) {
        throw new TypeException("The parameter alias cannot be null");
      }
      // issue #748
      // 转换成小写，
      String key = alias.toLowerCase(Locale.ENGLISH);
      // 判断是否已经注册过了
      if (typeAliases.containsKey(key) && typeAliases.get(key) != null && !typeAliases.get(key).equals(value)) {
        throw new TypeException("The alias '" + alias + "' is already mapped to the value '" + typeAliases.get(key).getName() + "'.");
      }
      // 放进map里
      typeAliases.put(key, value);
    }
    -

    而获取的逻辑在这

    -
    public <T> Class<T> resolveAlias(String string) {
        try {
          if (string == null) {
            return null;
          }
          // issue #748
          // 同样的转成小写
          String key = string.toLowerCase(Locale.ENGLISH);
          Class<T> value;
          if (typeAliases.containsKey(key)) {
            value = (Class<T>) typeAliases.get(key);
          } else {
            // 这里还有从路径下处理的逻辑
            value = (Class<T>) Resources.classForName(string);
          }
          return value;
        } catch (ClassNotFoundException e) {
          throw new TypeException("Could not resolve type alias '" + string + "'.  Cause: " + e, e);
        }
      }
    -

    逻辑比较简单，但是在mybatis中也是不可或缺的一块概念

    -]]>     - - Java - Mybatis - - - Java - Mysql - Mybatis - -     - - NX30Pro 刷成 Openwrt-ImmortalWrt 后作为有线中继的配置方法 - /2024/06/16/NX30Pro-%E5%88%B7%E6%88%90-Openwrt-ImmortalWrt-%E5%90%8E%E4%BD%9C%E4%B8%BA%E6%9C%89%E7%BA%BF%E4%B8%AD%E7%BB%A7%E7%9A%84%E9%85%8D%E7%BD%AE%E6%96%B9%E6%B3%95/ - 因为买了两个NX30Pro，主要是奔着可以刷Openwrt，然后把两个都刷成了ImmortalWrt，结果发现要作为有线中继的话设置还有点麻烦，所以记录下
    首先要设置个与主路由相同网段的静态地址，方便管理
    在网络-LAN-基本设置-协议设置成“静态地址”
    然后IPv4地址就是刚才说的静态地址，可以设个好记的
    子网掩码就默认的，255.255.255.0，
    IPv4网关就是主路由地址，DNS也设置成主路由地址
    基础设置改成忽略此接口-不提供DHCP服务，由主路由提供
    然后在高级设置中将IPv6的 路由通告服务和DHCPv6服务跟NDP代理禁用掉，
    物理设置中勾选桥接接口
    目前我是这样设置可以作为有线中继连通网络了，后续有变化继续更新

    -]]>     - - openwrt - - - openwrt - -     mybatis系列-第一条sql的细节 /2022/12/11/mybatis%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%80%E6%9D%A1sql%E7%9A%84%E7%BB%86%E8%8A%82/ @@ -3432,55 +3401,109 @@ - pcre-intro-and-a-simple-package - /2015/01/16/pcre-intro-and-a-simple-package/ - Pcre
    -

    Perl Compatible Regular Expressions (PCRE) is a regular
    expression C library inspired by the regular expression
    capabilities in the Perl programming language, written
    by Philip Hazel, starting in summer 1997.

    -
    -

    因为最近工作内容的一部分需要做字符串的识别处理，所以就顺便用上了之前在PHP中用过的正则，在C/C++中本身不包含正则库，这里使用的pcre，对MFC开发，在这里提供了静态链接库，在引入lib跟.h文件后即可使用。

    - + nginx 日志小记 + /2022/04/17/nginx-%E6%97%A5%E5%BF%97%E5%B0%8F%E8%AE%B0/ + nginx 默认的日志有特定的格式，我们也可以自定义，

    +

    默认的格式是预定义的 combined

    +
    log_format combined '$remote_addr - $remote_user [$time_local] '
                        '"$request" $status $body_bytes_sent '
                        '"$http_referer" "$http_user_agent"';
    -

    Regular Expression Syntax

    然后是一些正则语法，官方的语法文档比较科学严谨，特别是对类似于贪婪匹配等细节的说明，当然一般的使用可以在网上找到很多匹配语法，例如这个。

    -

    PCRE函数介绍

    -

    pcre_compile
    原型：

    -
    -
    #include <pcre.h>
    pcre *pcre_compile(const char *pattern, int options, const char **errptr, int *erroffset, const unsigned char *tableptr);
    -

    功能：将一个正则表达式编译成一个内部表示，在匹配多个字符串时，可以加速匹配。其同pcre_compile2功能一样只是缺少一个参数errorcodeptr。
    参数：
    pattern 正则表达式
    options 为0，或者其他参数选项
    errptr 出错消息
    erroffset 出错位置
    tableptr 指向一个字符数组的指针，可以设置为空NULL

    -
    -

    pcre_exec
    原型：

    -
    -
    #include <pcre.h>
    int pcre_exec(const pcre *code, const pcre_extra *extra, const char *subject, int length, int startoffset, int options, int *ovector, int ovecsize)
    -

    功能：使用编译好的模式进行匹配，采用与Perl相似的算法，返回匹配串的偏移位置。
    参数：
    code 编译好的模式
    extra 指向一个pcre_extra结构体，可以为NULL
    subject 需要匹配的字符串
    length 匹配的字符串长度（Byte）
    startoffset 匹配的开始位置
    options 选项位
    ovector 指向一个结果的整型数组
    ovecsize 数组大小。

    -

    这里是两个最常用的函数的简单说明，pcre的静态库提供了一系列的函数以供使用，可以参考这个博客说明，另外对于以上函数的具体参数详细说明可以参考官网此处

    -

    一个丑陋的封装

    void COcxDemoDlg::pcre_exec_all(const pcre * re, PCRE_SPTR src, vector<pair<int, int>> &vc)
    {
    	int rc;
    	int ovector[30];
    	int i = 0;
    	pair<int, int> pr;
    	rc = pcre_exec(re, NULL, src, strlen(src), i, 0, ovector, 30);
    	for (; rc > 0;)
    	{
    		i = ovector[1];
    		pr.first = ovector[2];
    		pr.second = ovector[3];
    		vc.push_back(pr);
    		rc = pcre_exec(re, NULL, src, strlen(src), i, 0, ovector, 30);
    	}
    }
    -

    vector中是全文匹配后的索引对，只是简单地用下。

    +

    配置的日志可以使用这个默认的，如果满足需求的话

    +
    Syntax:	access_log path [format [buffer=size] [gzip[=level]] [flush=time] [if=condition]];
            access_log off;
    Default: access_log logs/access.log combined;
    Context: http, server, location, if in location, limit_except
    + +

    而如果需要额外的一些配置的话可以自己定义 log_format ，比如我想要给日志里加上请求时间，那就可以自己定义一个 log_format 比如添加下

    +
    $request_time
    request processing time in seconds with a milliseconds resolution;   
    time elapsed between the first bytes were read from the client and the log write after the last bytes were sent to the client
    + +
    log_format combined_extend '$remote_addr - $remote_user [$time_local] '
                        '"$request" $status $body_bytes_sent '
                        '"$http_referer" "$http_user_agent" "$request_time"';
    + +

    然后其他的比如还有 gzip 压缩，可以设置压缩级别，flush 刷盘时间还有根据条件控制

    +

    这里的条件控制简单看了下还比较厉害

    +

    比如我想对2xx 跟 3xx 的访问不记录日志

    +
    map $status $loggable {
        ~^[23]  0;
        default 1;
    }

    access_log /path/to/access.log combined if=$loggable;
    + +

    当 $loggable 是 0 或者空时表示 if 条件为否，上面的默认就是 1，只有当请求状态 status 是 2xx 或 3xx 时才是 0，代表不用记录，有了这个特性就可以更灵活地配置日志

    +

    文章主要参考了 nginx 的 log 模块的文档

    ]]>     - C++ + nginx - c++ - mfc + nginx + 日志     - powershell 初体验 - /2022/11/13/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C/ - powershell变量

    变量命名类似于php

    -
    PS C:\Users\Nicks> $a=1
    PS C:\Users\Nicks> $b=2
    PS C:\Users\Nicks> $a*$b
    2
    -

    有一个比较好用的是变量交换
    一般的语言做两个变量交换一般需要一个临时变量

    -
    $tmp=$a
    $a=$b
    $b=$tmp
    -

    而在powershell中可以这样

    -
    $a,$b=$b,$a
    PS C:\Users\Nicks> $a,$b=$b,$a
    PS C:\Users\Nicks> $a
    2
    PS C:\Users\Nicks> $b
    1
    -

    还可以通过这个

    -
    PS C:\Users\Nicks> ls variable:

    Name                           Value
    ----                           -----
    $                              $b
    ?                              True
    ^                              $b
    a                              2
    args                           {}
    b                              1
    -

    查看现存的变量
    当然一般脚本都是动态类型的，
    可以通过
    gettype方法

    + openresty + /2019/06/18/openresty/ + 目前公司要对一些新的产品功能做灰度测试，因为在后端业务代码层面添加判断比较麻烦，所以想在nginx上做点手脚，就想到了openresty
    前后也踩了不少坑，这边先写一点

    +

    首先是日志
    error_log logs/error.log debug;
    需要nginx开启日志的debug才能看到日志

    +

    使用 lua_code_cache off即可, 另外注意只有使用 content_by_lua_file 才会生效

    +
    http {
      lua_code_cache off;
    }

    location ~* /(\d+-.*)/api/orgunits/load_all(.*) {
       default_type 'application/json;charset=utf-8';
       content_by_lua_file /data/projects/xxx/current/lua/controller/load_data.lua;
    }
    + +

    使用lua给nginx请求response头添加内容可以用这个

    +
    ngx.header['response'] = 'header'
    + + +

    使用总结

    +

    后续：

    +
      +

    1. 一开始在本地环境的时候使用content_by_lua_file只关注了头，后来发到测试环境发现请求内容都没代理转发到后端服务上
      网上查了下发现content_by_lua_file是将请求的所有内容包括response都用这里面的lua脚本生成了，content这个词就表示是请求内容
      后来改成了access_by_lua_file就正常了，只是要去获取请求内容和修改响应头，并不是要完整的接管请求

      +
      2. +

    2. 后来又碰到了一个坑是nginx有个client_body_buffer_size的配置参数，nginx在32位和64位系统里有8K和16K两个默认值，当请求内容大于这两个值的时候，会把请求内容放到临时文件里，这个时候openresty里的ngx.req.get_post_args()就会报“failed to get post args: requesty body in temp file not supported”这个错误，将client_body_buffer_size这个参数配置调大一点就好了

      +
      3. +

    3. 还有就是lua的异常捕获，网上看一般是用pcall和xpcall来进行保护调用，因为问题主要出在cjson的decode，这里有两个解决方案，一个就是将cjson.decode使用pcall封装，

      + 
      local decode = require("cjson").decode

      function json_decode( str )
          local ok, t = pcall(decode, str)
          if not ok then
            return nil
          end

          return t
      end
      +

      这个是使用了pcall，称为保护调用，会在内部错误后返回两个参数，第一个是false，第二个是错误信息
      还有一种是使用cjson.safe包

      + 
      local json = require("cjson.safe")
      local str = [[ {"key:"value"} ]]

      local t = json.decode(str)
      if t then
          ngx.say(" --> ", type(t))
      end
      +

      cjson.safe包会在解析失败的时候返回nil

      +
      4. +

    4. 还有一个是redis链接时如果host使用的是域名的话会提示“failed to connect: no resolver defined to resolve “redis.xxxxxx.com””，这里需要使用nginx的resolver指令，
      resolver 8.8.8.8 valid=3600s;

      +
      5. +

    5. 还有一点补充下
      就是业务在使用redis的时候使用了db的特性，所以在lua访问redis的时候也需要执行db，这里lua的redis库也支持了这个特性，可以使用instance:select(config:get(‘db’))来切换db

      +
      6. +

    6. 性能优化tips
      建议是尽量少使用阶段钩子，例如content_by_lua_file，*_by_lua

      +
      7. +

    7. 发现一个不错的openresty站点
      地址

      +
      8. +
    ]]>     - 语言 + nginx - powershell + nginx + openresty + +     + + pcre-intro-and-a-simple-package + /2015/01/16/pcre-intro-and-a-simple-package/ + Pcre
    +

    Perl Compatible Regular Expressions (PCRE) is a regular
    expression C library inspired by the regular expression
    capabilities in the Perl programming language, written
    by Philip Hazel, starting in summer 1997.

    +
    +

    因为最近工作内容的一部分需要做字符串的识别处理，所以就顺便用上了之前在PHP中用过的正则，在C/C++中本身不包含正则库，这里使用的pcre，对MFC开发，在这里提供了静态链接库，在引入lib跟.h文件后即可使用。

    + + +

    Regular Expression Syntax

    然后是一些正则语法，官方的语法文档比较科学严谨，特别是对类似于贪婪匹配等细节的说明，当然一般的使用可以在网上找到很多匹配语法，例如这个。

    +

    PCRE函数介绍

    +

    pcre_compile
    原型：

    +
    +
    #include <pcre.h>
    pcre *pcre_compile(const char *pattern, int options, const char **errptr, int *erroffset, const unsigned char *tableptr);
    +

    功能：将一个正则表达式编译成一个内部表示，在匹配多个字符串时，可以加速匹配。其同pcre_compile2功能一样只是缺少一个参数errorcodeptr。
    参数：
    pattern 正则表达式
    options 为0，或者其他参数选项
    errptr 出错消息
    erroffset 出错位置
    tableptr 指向一个字符数组的指针，可以设置为空NULL

    +
    +

    pcre_exec
    原型：

    +
    +
    #include <pcre.h>
    int pcre_exec(const pcre *code, const pcre_extra *extra, const char *subject, int length, int startoffset, int options, int *ovector, int ovecsize)
    +

    功能：使用编译好的模式进行匹配，采用与Perl相似的算法，返回匹配串的偏移位置。
    参数：
    code 编译好的模式
    extra 指向一个pcre_extra结构体，可以为NULL
    subject 需要匹配的字符串
    length 匹配的字符串长度（Byte）
    startoffset 匹配的开始位置
    options 选项位
    ovector 指向一个结果的整型数组
    ovecsize 数组大小。

    +

    这里是两个最常用的函数的简单说明，pcre的静态库提供了一系列的函数以供使用，可以参考这个博客说明，另外对于以上函数的具体参数详细说明可以参考官网此处

    +

    一个丑陋的封装

    void COcxDemoDlg::pcre_exec_all(const pcre * re, PCRE_SPTR src, vector<pair<int, int>> &vc)
    {
    	int rc;
    	int ovector[30];
    	int i = 0;
    	pair<int, int> pr;
    	rc = pcre_exec(re, NULL, src, strlen(src), i, 0, ovector, 30);
    	for (; rc > 0;)
    	{
    		i = ovector[1];
    		pr.first = ovector[2];
    		pr.second = ovector[3];
    		vc.push_back(pr);
    		rc = pcre_exec(re, NULL, src, strlen(src), i, 0, ovector, 30);
    	}
    }
    +

    vector中是全文匹配后的索引对，只是简单地用下。

    +]]>     + + C++ + + + c++ + mfc     @@ -3575,6 +3598,26 @@ php + + powershell 初体验 + /2022/11/13/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C/ + powershell变量

    变量命名类似于php

    +
    PS C:\Users\Nicks> $a=1
    PS C:\Users\Nicks> $b=2
    PS C:\Users\Nicks> $a*$b
    2
    +

    有一个比较好用的是变量交换
    一般的语言做两个变量交换一般需要一个临时变量

    +
    $tmp=$a
    $a=$b
    $b=$tmp
    +

    而在powershell中可以这样

    +
    $a,$b=$b,$a
    PS C:\Users\Nicks> $a,$b=$b,$a
    PS C:\Users\Nicks> $a
    2
    PS C:\Users\Nicks> $b
    1
    +

    还可以通过这个

    +
    PS C:\Users\Nicks> ls variable:

    Name                           Value
    ----                           -----
    $                              $b
    ?                              True
    ^                              $b
    a                              2
    args                           {}
    b                              1
    +

    查看现存的变量
    当然一般脚本都是动态类型的，
    可以通过
    gettype方法

    +]]>     + + 语言 + + + powershell + +     powershell 初体验二 /2022/11/20/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C%E4%BA%8C/ @@ -3600,49 +3643,6 @@ powershell - - openresty - /2019/06/18/openresty/ - 目前公司要对一些新的产品功能做灰度测试，因为在后端业务代码层面添加判断比较麻烦，所以想在nginx上做点手脚，就想到了openresty
    前后也踩了不少坑，这边先写一点

    -

    首先是日志
    error_log logs/error.log debug;
    需要nginx开启日志的debug才能看到日志

    -

    使用 lua_code_cache off即可, 另外注意只有使用 content_by_lua_file 才会生效

    -
    http {
      lua_code_cache off;
    }

    location ~* /(\d+-.*)/api/orgunits/load_all(.*) {
       default_type 'application/json;charset=utf-8';
       content_by_lua_file /data/projects/xxx/current/lua/controller/load_data.lua;
    }
    - -

    使用lua给nginx请求response头添加内容可以用这个

    -
    ngx.header['response'] = 'header'
    - - -

    使用总结

    -

    后续：

    -
      -

    1. 一开始在本地环境的时候使用content_by_lua_file只关注了头，后来发到测试环境发现请求内容都没代理转发到后端服务上
      网上查了下发现content_by_lua_file是将请求的所有内容包括response都用这里面的lua脚本生成了，content这个词就表示是请求内容
      后来改成了access_by_lua_file就正常了，只是要去获取请求内容和修改响应头，并不是要完整的接管请求

      -
      2. -

    2. 后来又碰到了一个坑是nginx有个client_body_buffer_size的配置参数，nginx在32位和64位系统里有8K和16K两个默认值，当请求内容大于这两个值的时候，会把请求内容放到临时文件里，这个时候openresty里的ngx.req.get_post_args()就会报“failed to get post args: requesty body in temp file not supported”这个错误，将client_body_buffer_size这个参数配置调大一点就好了

      -
      3. -

    3. 还有就是lua的异常捕获，网上看一般是用pcall和xpcall来进行保护调用，因为问题主要出在cjson的decode，这里有两个解决方案，一个就是将cjson.decode使用pcall封装，

      - 
      local decode = require("cjson").decode

      function json_decode( str )
          local ok, t = pcall(decode, str)
          if not ok then
            return nil
          end

          return t
      end
      -

      这个是使用了pcall，称为保护调用，会在内部错误后返回两个参数，第一个是false，第二个是错误信息
      还有一种是使用cjson.safe包

      - 
      local json = require("cjson.safe")
      local str = [[ {"key:"value"} ]]

      local t = json.decode(str)
      if t then
          ngx.say(" --> ", type(t))
      end
      -

      cjson.safe包会在解析失败的时候返回nil

      -
      4. -

    4. 还有一个是redis链接时如果host使用的是域名的话会提示“failed to connect: no resolver defined to resolve “redis.xxxxxx.com””，这里需要使用nginx的resolver指令，
      resolver 8.8.8.8 valid=3600s;

      -
      5. -

    5. 还有一点补充下
      就是业务在使用redis的时候使用了db的特性，所以在lua访问redis的时候也需要执行db，这里lua的redis库也支持了这个特性，可以使用instance:select(config:get(‘db’))来切换db

      -
      6. -

    6. 性能优化tips
      建议是尽量少使用阶段钩子，例如content_by_lua_file，*_by_lua

      -
      7. -

    7. 发现一个不错的openresty站点
      地址

      -
      8. -
    -]]>     - - nginx - - - nginx - openresty - -     rabbitmq-tips /2017/04/25/rabbitmq-tips/ @@ -3665,6 +3665,21 @@ im + + redis 的 rdb 和 COW 介绍 + /2021/08/15/redis-%E7%9A%84-rdb-%E5%92%8C-COW-%E4%BB%8B%E7%BB%8D/ + redis 在使用 rdb 策略进行备份时，rdb 的意思是会在开启备份的时候将开启时间点的内存数据进行备份，并且可以设置时间，这样子就是这个策略其实还是不完全可靠的，如果是在这个间隔中宕机了，或者间隔过长，不过这个不在这次的要说的内容中，如果自己去写这个 rdb 的策略可能就有点类似于 mvcc 的 redolog，需要知道这个时间点之前的数据是怎么样的，防止后面更改的干扰，但是这样一方面需要有比较复杂的 mvcc 实现，另一方面是很占用存储空间，所以 redis 在这里面使用了 COW (Copy On Write) 技术，这个技术呢以前听过，也大致了解是怎么个意思，这次稍微具体地来看下，其实 redis 的 copy-on-write 就是来自于 linux 的 cow

    +

    Linux中的CopyOnWrite

    fork()之后，kernel把父进程中所有的内存页的权限都设为read-only，然后子进程的地址空间指向父进程。当父子进程都只读内存时，相安无事。当其中某个进程写内存时，CPU硬件检测到内存页是read-only的，于是触发页异常中断（page-fault），陷入kernel的一个中断例程。中断例程中，kernel就会把触发的异常的页复制一份，于是父子进程各自持有独立的一份。这个操作其实可以类比为写屏障，正常的读取是没问题的，当有写入时就会分裂。

    +

    CopyOnWrite的好处：

    1、减少分配和复制资源时带来的瞬时延迟；
    2、减少不必要的资源分配；
    CopyOnWrite的缺点：
    1、如果父子进程都需要进行大量的写操作，会产生大量的分页错误（页异常中断page-fault）;

    +

    Redis中的CopyOnWrite

    Redis在持久化时，如果是采用BGSAVE命令或者BGREWRITEAOF的方式，那Redis会fork出一个子进程来读取数据，从而写到磁盘中。
    总体来看，Redis还是读操作比较多。如果子进程存在期间，发生了大量的写操作，那可能就会出现很多的分页错误(页异常中断page-fault)，这样就得耗费不少性能在复制上。
    而在rehash阶段上，写操作是无法避免的。所以Redis在fork出子进程之后，将负载因子阈值提高，尽量减少写操作，避免不必要的内存写入操作，最大限度地节约内存。这里其实更巧妙了，在细节上去优化会产生大量页异常中断的情况。

    +]]>     + + redis + + + redis + +     redis数据结构介绍-第一部分 SDS，链表，字典 /2019/12/26/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D/ @@ -3693,13 +3708,16 @@ - redis数据结构介绍二-第二部分 跳表 - /2020/01/04/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%8C/ - 跳表 skiplist

    跳表是个在我们日常的代码中不太常用到的数据结构，相对来讲就没有像数组，链表，字典，散列，树等结构那么熟悉，所以就从头开始分析下，首先是链表，跳表跟链表都有个表字（太硬扯了我🤦‍♀️），注意这是个有序链表

    如上图，在这个链表里如果我要找到 23，是不是我需要从3，5，9开始一直往后找直到找到 23，也就是说时间复杂度是 O(N),N 的一次幂复杂度，那么我们来看看第二个

    这个结构跟原先有点不一样，它给链表中偶数位的节点又加了一个指针把它们链接起来，这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找，先找到 5，然后是 10，接着是 19，然后是 28，这时候发现 28 比 23 大了，那我在退回到 19，然后从下一层原来的链表往前找，

    这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。
    前面的其实是跳表的引子，真正的跳表其实不是这样，因为上面的其实有个比较大的问题，就是插入一个元素后需要调整每个元素的指针，在 redis 中的跳表其实是做了个随机层数的优化，因为沿着前面的例子，其实当数据量很大的时候，是不是层数越多，其查询效率越高，但是随着层数变多，要保持这种严格的层数规则其实也会增大处理复杂度，所以 redis 插入每个元素的时候都是使用随机的方式，看一眼代码

    -
    /* ZSETs use a specialized version of Skiplists */
    typedef struct zskiplistNode {
        sds ele;
        double score;
        struct zskiplistNode *backward;
        struct zskiplistLevel {
            struct zskiplistNode *forward;
            unsigned long span;
        } level[];
    } zskiplistNode;

    typedef struct zskiplist {
        struct zskiplistNode *header, *tail;
        unsigned long length;
        int level;
    } zskiplist;

    typedef struct zset {
        dict *dict;
        zskiplist *zsl;
    } zset;
    -

    忘了说了，redis 是把 skiplist 跳表用在 zset 里，zset 是个有序的集合，可以看到 zskiplist 就是个跳表的结构，里面用 header 保存跳表的表头，tail 保存表尾，还有长度和最大层级，具体的跳表节点元素使用 zskiplistNode 表示，里面包含了 sds 类型的元素值，double 类型的分值，用来排序，一个 backward 后向指针和一个 zskiplistLevel 数组，每个 level 包含了一个前向指针，和一个 span，span 表示的是跳表前向指针的跨度，这里再补充一点，前面说了为了灵活这个跳表的新增修改，redis 使用了随机层高的方式插入新节点，但是如果所有节点都随机到很高的层级或者所有都很低的话，跳表的效率优势就会减小，所以 redis 使用了个小技巧，贴下代码

    -
    #define ZSKIPLIST_P 0.25      /* Skiplist P = 1/4 */
    int zslRandomLevel(void) {
        int level = 1;
        while ((random()&0xFFFF) < (ZSKIPLIST_P * 0xFFFF))
            level += 1;
        return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL;
    }
    -

    当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值，因此保持了一个相对递减的概率
    可以简单分析下，当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1，就意味着当有 1 - 1/4也就是3/4的概率是直接跳出，所以一层的概率是3/4,也就是 1-P，二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。

    + redis数据结构介绍三-第三部分 整数集合 + /2020/01/10/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%B8%89/ + redis中对于 set 其实有两种处理，对于元素均为整型，并且元素数目较少时，使用 intset 作为底层数据结构，否则使用 dict 作为底层数据结构，先看一下代码先

    +
    typedef struct intset {
        // 编码方式
        uint32_t encoding;
        // 集合包含的元素数量
        uint32_t length;
        // 保存元素的数组
        int8_t contents[];
    } intset;

    /* Note that these encodings are ordered, so:
     * INTSET_ENC_INT16 < INTSET_ENC_INT32 < INTSET_ENC_INT64. */
    #define INTSET_ENC_INT16 (sizeof(int16_t))
    #define INTSET_ENC_INT32 (sizeof(int32_t))
    #define INTSET_ENC_INT64 (sizeof(int64_t))
    +

    一眼看，为啥整型还需要编码，然后 int8_t 怎么能存下大整形呢，带着这些疑问，我们一步步分析下去，这里的编码其实指的是这个整型集合里存的究竟是多大的整型，16 位，还是 32 位，还是 64 位，结构体下面的宏定义就是表示了 encoding 的可能取值，INTSET_ENC_INT16 表示每个元素用2个字节存储，INTSET_ENC_INT32 表示每个元素用4个字节存储，INTSET_ENC_INT64 表示每个元素用8个字节存储。因此，intset中存储的整数最多只能占用64bit。length 就是正常的表示集合中元素的数量。最奇怪的应该就是这个 contents 了，是个 int8_t 的数组，那放毛线数据啊，最小的都有 16 位，这里我在看代码和《redis 设计与实现》的时候也有点懵逼，后来查了下发现这是个比较取巧的用法，这里我用自己的理解表述一下，先看看 8，16，32，64 的关系，一眼看就知道都是 2 的 N 次，并且呈两倍关系，而且 8 位刚好一个字节，所以呢其实这里的contents 不是个常规意义上的 int8_t 类型的数组，而是个柔性数组。看下 wiki 的定义

    +
    +

    Flexible array members1 were introduced in the C99 standard of the C programming language (in particular, in section §6.7.2.1, item 16, page 103).2 It is a member of a struct, which is an array without a given dimension. It must be the last member of such a struct and it must be accompanied by at least one other member, as in the following example:

    +
    +
    struct vectord {
        size_t len;
        double arr[]; // the flexible array member must be last
    };
    +

    在初始化这个 intset 的时候，这个contents数组是不占用空间的，后面的反正用到了申请，那么这里就有一个问题，给出了三种可能的 encoding 值，他们能随便换吗，显然不行，首先在 intset 中数据的存放是有序的，这个有部分原因是方便二分查找，然后存放数据其实随着数据的大小不同会有一个升级的过程，看下图

    新创建的intset只有一个header，总共8个字节。其中encoding = 2, length = 0, 类型都是uint32_t，各占 4 字节，添加15, 5两个元素之后，因为它们是比较小的整数，都能使用2个字节表示，所以encoding不变，值还是2，也就是默认的 INTSET_ENC_INT16，当添加32768的时候，它不再能用2个字节来表示了（2个字节能表达的数据范围是-215~215-1，而32768等于215，超出范围了），因此encoding必须升级到INTSET_ENC_INT32（值为4），即用4个字节表示一个元素。在添加每个元素的过程中，intset始终保持从小到大有序。与ziplist类似，intset也是按小端（little endian）模式存储的（参见维基百科词条Endianness）。比如，在上图中intset添加完所有数据之后，表示encoding字段的4个字节应该解释成0x00000004，而第4个数据应该解释成0x00008000 = 32768

    ]]>     Redis @@ -3715,31 +3733,40 @@     - redis 的 rdb 和 COW 介绍 - /2021/08/15/redis-%E7%9A%84-rdb-%E5%92%8C-COW-%E4%BB%8B%E7%BB%8D/ - redis 在使用 rdb 策略进行备份时，rdb 的意思是会在开启备份的时候将开启时间点的内存数据进行备份，并且可以设置时间，这样子就是这个策略其实还是不完全可靠的，如果是在这个间隔中宕机了，或者间隔过长，不过这个不在这次的要说的内容中，如果自己去写这个 rdb 的策略可能就有点类似于 mvcc 的 redolog，需要知道这个时间点之前的数据是怎么样的，防止后面更改的干扰，但是这样一方面需要有比较复杂的 mvcc 实现，另一方面是很占用存储空间，所以 redis 在这里面使用了 COW (Copy On Write) 技术，这个技术呢以前听过，也大致了解是怎么个意思，这次稍微具体地来看下，其实 redis 的 copy-on-write 就是来自于 linux 的 cow

    -

    Linux中的CopyOnWrite

    fork()之后，kernel把父进程中所有的内存页的权限都设为read-only，然后子进程的地址空间指向父进程。当父子进程都只读内存时，相安无事。当其中某个进程写内存时，CPU硬件检测到内存页是read-only的，于是触发页异常中断（page-fault），陷入kernel的一个中断例程。中断例程中，kernel就会把触发的异常的页复制一份，于是父子进程各自持有独立的一份。这个操作其实可以类比为写屏障，正常的读取是没问题的，当有写入时就会分裂。

    -

    CopyOnWrite的好处：

    1、减少分配和复制资源时带来的瞬时延迟；
    2、减少不必要的资源分配；
    CopyOnWrite的缺点：
    1、如果父子进程都需要进行大量的写操作，会产生大量的分页错误（页异常中断page-fault）;

    -

    Redis中的CopyOnWrite

    Redis在持久化时，如果是采用BGSAVE命令或者BGREWRITEAOF的方式，那Redis会fork出一个子进程来读取数据，从而写到磁盘中。
    总体来看，Redis还是读操作比较多。如果子进程存在期间，发生了大量的写操作，那可能就会出现很多的分页错误(页异常中断page-fault)，这样就得耗费不少性能在复制上。
    而在rehash阶段上，写操作是无法避免的。所以Redis在fork出子进程之后，将负载因子阈值提高，尽量减少写操作，避免不必要的内存写入操作，最大限度地节约内存。这里其实更巧妙了，在细节上去优化会产生大量页异常中断的情况。

    + redis数据结构介绍二-第二部分 跳表 + /2020/01/04/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%8C/ + 跳表 skiplist

    跳表是个在我们日常的代码中不太常用到的数据结构，相对来讲就没有像数组，链表，字典，散列，树等结构那么熟悉，所以就从头开始分析下，首先是链表，跳表跟链表都有个表字（太硬扯了我🤦‍♀️），注意这是个有序链表

    如上图，在这个链表里如果我要找到 23，是不是我需要从3，5，9开始一直往后找直到找到 23，也就是说时间复杂度是 O(N),N 的一次幂复杂度，那么我们来看看第二个

    这个结构跟原先有点不一样，它给链表中偶数位的节点又加了一个指针把它们链接起来，这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找，先找到 5，然后是 10，接着是 19，然后是 28，这时候发现 28 比 23 大了，那我在退回到 19，然后从下一层原来的链表往前找，

    这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。
    前面的其实是跳表的引子，真正的跳表其实不是这样，因为上面的其实有个比较大的问题，就是插入一个元素后需要调整每个元素的指针，在 redis 中的跳表其实是做了个随机层数的优化，因为沿着前面的例子，其实当数据量很大的时候，是不是层数越多，其查询效率越高，但是随着层数变多，要保持这种严格的层数规则其实也会增大处理复杂度，所以 redis 插入每个元素的时候都是使用随机的方式，看一眼代码

    +
    /* ZSETs use a specialized version of Skiplists */
    typedef struct zskiplistNode {
        sds ele;
        double score;
        struct zskiplistNode *backward;
        struct zskiplistLevel {
            struct zskiplistNode *forward;
            unsigned long span;
        } level[];
    } zskiplistNode;

    typedef struct zskiplist {
        struct zskiplistNode *header, *tail;
        unsigned long length;
        int level;
    } zskiplist;

    typedef struct zset {
        dict *dict;
        zskiplist *zsl;
    } zset;
    +

    忘了说了，redis 是把 skiplist 跳表用在 zset 里，zset 是个有序的集合，可以看到 zskiplist 就是个跳表的结构，里面用 header 保存跳表的表头，tail 保存表尾，还有长度和最大层级，具体的跳表节点元素使用 zskiplistNode 表示，里面包含了 sds 类型的元素值，double 类型的分值，用来排序，一个 backward 后向指针和一个 zskiplistLevel 数组，每个 level 包含了一个前向指针，和一个 span，span 表示的是跳表前向指针的跨度，这里再补充一点，前面说了为了灵活这个跳表的新增修改，redis 使用了随机层高的方式插入新节点，但是如果所有节点都随机到很高的层级或者所有都很低的话，跳表的效率优势就会减小，所以 redis 使用了个小技巧，贴下代码

    +
    #define ZSKIPLIST_P 0.25      /* Skiplist P = 1/4 */
    int zslRandomLevel(void) {
        int level = 1;
        while ((random()&0xFFFF) < (ZSKIPLIST_P * 0xFFFF))
            level += 1;
        return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL;
    }
    +

    当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值，因此保持了一个相对递减的概率
    可以简单分析下，当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1，就意味着当有 1 - 1/4也就是3/4的概率是直接跳出，所以一层的概率是3/4,也就是 1-P，二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。

    ]]>     - redis + Redis + 数据结构 + C + 源码 + Redis redis + 数据结构 + 源码     - redis数据结构介绍三-第三部分 整数集合 - /2020/01/10/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%B8%89/ - redis中对于 set 其实有两种处理，对于元素均为整型，并且元素数目较少时，使用 intset 作为底层数据结构，否则使用 dict 作为底层数据结构，先看一下代码先

    -
    typedef struct intset {
        // 编码方式
        uint32_t encoding;
        // 集合包含的元素数量
        uint32_t length;
        // 保存元素的数组
        int8_t contents[];
    } intset;

    /* Note that these encodings are ordered, so:
     * INTSET_ENC_INT16 < INTSET_ENC_INT32 < INTSET_ENC_INT64. */
    #define INTSET_ENC_INT16 (sizeof(int16_t))
    #define INTSET_ENC_INT32 (sizeof(int32_t))
    #define INTSET_ENC_INT64 (sizeof(int64_t))
    -

    一眼看，为啥整型还需要编码，然后 int8_t 怎么能存下大整形呢，带着这些疑问，我们一步步分析下去，这里的编码其实指的是这个整型集合里存的究竟是多大的整型，16 位，还是 32 位，还是 64 位，结构体下面的宏定义就是表示了 encoding 的可能取值，INTSET_ENC_INT16 表示每个元素用2个字节存储，INTSET_ENC_INT32 表示每个元素用4个字节存储，INTSET_ENC_INT64 表示每个元素用8个字节存储。因此，intset中存储的整数最多只能占用64bit。length 就是正常的表示集合中元素的数量。最奇怪的应该就是这个 contents 了，是个 int8_t 的数组，那放毛线数据啊，最小的都有 16 位，这里我在看代码和《redis 设计与实现》的时候也有点懵逼，后来查了下发现这是个比较取巧的用法，这里我用自己的理解表述一下，先看看 8，16，32，64 的关系，一眼看就知道都是 2 的 N 次，并且呈两倍关系，而且 8 位刚好一个字节，所以呢其实这里的contents 不是个常规意义上的 int8_t 类型的数组，而是个柔性数组。看下 wiki 的定义

    -
    -

    Flexible array members1 were introduced in the C99 standard of the C programming language (in particular, in section §6.7.2.1, item 16, page 103).2 It is a member of a struct, which is an array without a given dimension. It must be the last member of such a struct and it must be accompanied by at least one other member, as in the following example:

    -
    -
    struct vectord {
        size_t len;
        double arr[]; // the flexible array member must be last
    };
    -

    在初始化这个 intset 的时候，这个contents数组是不占用空间的，后面的反正用到了申请，那么这里就有一个问题，给出了三种可能的 encoding 值，他们能随便换吗，显然不行，首先在 intset 中数据的存放是有序的，这个有部分原因是方便二分查找，然后存放数据其实随着数据的大小不同会有一个升级的过程，看下图

    新创建的intset只有一个header，总共8个字节。其中encoding = 2, length = 0, 类型都是uint32_t，各占 4 字节，添加15, 5两个元素之后，因为它们是比较小的整数，都能使用2个字节表示，所以encoding不变，值还是2，也就是默认的 INTSET_ENC_INT16，当添加32768的时候，它不再能用2个字节来表示了（2个字节能表达的数据范围是-215~215-1，而32768等于215，超出范围了），因此encoding必须升级到INTSET_ENC_INT32（值为4），即用4个字节表示一个元素。在添加每个元素的过程中，intset始终保持从小到大有序。与ziplist类似，intset也是按小端（little endian）模式存储的（参见维基百科词条Endianness）。比如，在上图中intset添加完所有数据之后，表示encoding字段的4个字节应该解释成0x00000004，而第4个数据应该解释成0x00008000 = 32768

    + redis数据结构介绍五-第五部分 对象 + /2020/01/20/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%94/ + 前面说了这么些数据结构，其实大家对于 redis 最初的印象应该就是个 key-value 的缓存，类似于 memcache，redis 其实也是个 key-value，key 还是一样的字符串，或者说就是用 redis 自己的动态字符串实现，但是 value 其实就是前面说的那些数据结构，差不多快说完了，还有个 quicklist 后面还有一篇，这里先介绍下 redis 对于这些不同类型的 value 是怎么实现的，首先看下 redisObject 的源码头文件

    +
    /* The actual Redis Object */
    #define OBJ_STRING 0    /* String object. */
    #define OBJ_LIST 1      /* List object. */
    #define OBJ_SET 2       /* Set object. */
    #define OBJ_ZSET 3      /* Sorted set object. */
    #define OBJ_HASH 4      /* Hash object. */
    /*
     * Objects encoding. Some kind of objects like Strings and Hashes can be
     * internally represented in multiple ways. The 'encoding' field of the object
     * is set to one of this fields for this object. */
    #define OBJ_ENCODING_RAW 0     /* Raw representation */
    #define OBJ_ENCODING_INT 1     /* Encoded as integer */
    #define OBJ_ENCODING_HT 2      /* Encoded as hash table */
    #define OBJ_ENCODING_ZIPMAP 3  /* Encoded as zipmap */
    #define OBJ_ENCODING_LINKEDLIST 4 /* No longer used: old list encoding. */
    #define OBJ_ENCODING_ZIPLIST 5 /* Encoded as ziplist */
    #define OBJ_ENCODING_INTSET 6  /* Encoded as intset */
    #define OBJ_ENCODING_SKIPLIST 7  /* Encoded as skiplist */
    #define OBJ_ENCODING_EMBSTR 8  /* Embedded sds string encoding */
    #define OBJ_ENCODING_QUICKLIST 9 /* Encoded as linked list of ziplists */
    #define OBJ_ENCODING_STREAM 10 /* Encoded as a radix tree of listpacks */

    #define LRU_BITS 24
    #define LRU_CLOCK_MAX ((1<<LRU_BITS)-1) /* Max value of obj->lru */
    #define LRU_CLOCK_RESOLUTION 1000 /* LRU clock resolution in ms */

    #define OBJ_SHARED_REFCOUNT INT_MAX
    typedef struct redisObject {
        unsigned type:4;
        unsigned encoding:4;
        unsigned lru:LRU_BITS; /* LRU time (relative to global lru_clock) or
                                * LFU data (least significant 8 bits frequency
                                * and most significant 16 bits access time). */
        int refcount;
        void *ptr;
    } robj;
    +

    主体结构就是这个 redisObject，

    +
      +
    • type： 字段表示对象的类型，它对应的就是 redis 的对外暴露的，或者说用户可以使用的五种类型，OBJ_STRING, OBJ_LIST, OBJ_SET, OBJ_ZSET, OBJ_HASH
      • +
    • encoding： 字段表示这个对象在 redis 内部的编码方式，由OBJ_ENCODING_开头的 11 种
      • +
    • lru: 做LRU替换算法用，占24个bit
      • +
    • refcount: 引用计数。它允许robj对象在某些情况下被共享。
      • +
    • ptr: 指向底层实现数据结构的指针
      当 type 是 OBJ_STRING 时，表示类型是个 string，它的编码方式 encoding 可能有 OBJ_ENCODING_RAW，OBJ_ENCODING_INT，OBJ_ENCODING_EMBSTR 三种
      当 type 是 OBJ_LIST 时，表示类型是 list，它的编码方式 encoding 是 OBJ_ENCODING_QUICKLIST，对于早一些的版本，2.2这种可能还会使用 OBJ_ENCODING_ZIPLIST，OBJ_ENCODING_LINKEDLIST
      当 type 是 OBJ_SET 时，是个集合，但是得看具体元素的类型，有可能使用整数集合，OBJ_ENCODING_INTSET, 如果元素不全是整型或者数量超过一定限制，那么编码就是 OBJ_ENCODING_HT hash table 了
      当 type 是 OBJ_ZSET 时，是个有序集合，它底层有可能使用的是 OBJ_ENCODING_ZIPLIST 或者 OBJ_ENCODING_SKIPLIST
      当 type 是 OBJ_HASH 时，一开始也是 OBJ_ENCODING_ZIPLIST，然后当数据量大于 hash_max_ziplist_entries 时会转成 OBJ_ENCODING_HT
      • +
    ]]>     Redis @@ -3796,16 +3823,20 @@     - redis数据结构介绍五-第五部分 对象 - /2020/01/20/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%94/ - ">}
    -

    这里有几点要注意的，第一是当惰性删除时会根据lazyfree_lazy_expire这个参数去判断是执行同步删除还是异步删除，另外一点是对于 slave，是不需要执行的，因为会在 master 过期时向 slave 发送 del 指令。
    光采用这个策略会有什么问题呢，假如一些key 一直未被访问，那这些 key 就不会过期了，导致一直被占用着内存，所以 redis 采取了懒汉式过期加定期过期策略，定期策略是怎么执行的呢

    -
    /* This function handles 'background' operations we are required to do
     * incrementally in Redis databases, such as active key expiring, resizing,
     * rehashing. */
    void databasesCron(void) {
        /* Expire keys by random sampling. Not required for slaves
         * as master will synthesize DELs for us. */
        if (server.active_expire_enabled) {
            if (server.masterhost == NULL) {
                activeExpireCycle(ACTIVE_EXPIRE_CYCLE_SLOW);
            } else {
                expireSlaveKeys();
            }
        }

        /* Defrag keys gradually. */
        activeDefragCycle();

        /* Perform hash tables rehashing if needed, but only if there are no
         * other processes saving the DB on disk. Otherwise rehashing is bad
         * as will cause a lot of copy-on-write of memory pages. */
        if (!hasActiveChildProcess()) {
            /* We use global counters so if we stop the computation at a given
             * DB we'll be able to start from the successive in the next
             * cron loop iteration. */
            static unsigned int resize_db = 0;
            static unsigned int rehash_db = 0;
            int dbs_per_call = CRON_DBS_PER_CALL;
            int j;

            /* Don't test more DBs than we have. */
            if (dbs_per_call > server.dbnum) dbs_per_call = server.dbnum;

            /* Resize */
            for (j = 0; j < dbs_per_call; j++) {
                tryResizeHashTables(resize_db % server.dbnum);
                resize_db++;
            }

            /* Rehash */
            if (server.activerehashing) {
                for (j = 0; j < dbs_per_call; j++) {
                    int work_done = incrementallyRehash(rehash_db);
                    if (work_done) {
                        /* If the function did some work, stop here, we'll do
                         * more at the next cron loop. */
                        break;
                    } else {
                        /* If this db didn't need rehash, we'll try the next one. */
                        rehash_db++;
                        rehash_db %= server.dbnum;
                    }
                }
            }
        }
    }
    /* Try to expire a few timed out keys. The algorithm used is adaptive and
     * will use few CPU cycles if there are few expiring keys, otherwise
     * it will get more aggressive to avoid that too much memory is used by
     * keys that can be removed from the keyspace.
     *
     * Every expire cycle tests multiple databases: the next call will start
     * again from the next db, with the exception of exists for time limit: in that
     * case we restart again from the last database we were processing. Anyway
     * no more than CRON_DBS_PER_CALL databases are tested at every iteration.
     *
     * The function can perform more or less work, depending on the "type"
     * argument. It can execute a "fast cycle" or a "slow cycle". The slow
     * cycle is the main way we collect expired cycles: this happens with
     * the "server.hz" frequency (usually 10 hertz).
     *
     * However the slow cycle can exit for timeout, since it used too much time.
     * For this reason the function is also invoked to perform a fast cycle
     * at every event loop cycle, in the beforeSleep() function. The fast cycle
     * will try to perform less work, but will do it much more often.
     *
     * The following are the details of the two expire cycles and their stop
     * conditions:
     *
     * If type is ACTIVE_EXPIRE_CYCLE_FAST the function will try to run a
     * "fast" expire cycle that takes no longer than EXPIRE_FAST_CYCLE_DURATION
     * microseconds, and is not repeated again before the same amount of time.
     * The cycle will also refuse to run at all if the latest slow cycle did not
     * terminate because of a time limit condition.
     *
     * If type is ACTIVE_EXPIRE_CYCLE_SLOW, that normal expire cycle is
     * executed, where the time limit is a percentage of the REDIS_HZ period
     * as specified by the ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC define. In the
     * fast cycle, the check of every database is interrupted once the number
     * of already expired keys in the database is estimated to be lower than
     * a given percentage, in order to avoid doing too much work to gain too
     * little memory.
     *
     * The configured expire "effort" will modify the baseline parameters in
     * order to do more work in both the fast and slow expire cycles.
     */

    #define ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP 20 /* Keys for each DB loop. */
    #define ACTIVE_EXPIRE_CYCLE_FAST_DURATION 1000 /* Microseconds. */
    #define ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC 25 /* Max % of CPU to use. */
    #define ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE 10 /* % of stale keys after which
                                                       we do extra efforts. */
    void activeExpireCycle(int type) {
        /* Adjust the running parameters according to the configured expire
         * effort. The default effort is 1, and the maximum configurable effort
         * is 10. */
        unsigned long
        effort = server.active_expire_effort-1, /* Rescale from 0 to 9. */
        config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
                               ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort,
        config_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
                                     ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort,
        config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
                                      2*effort,
        config_cycle_acceptable_stale = ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE-
                                        effort;

        /* This function has some global state in order to continue the work
         * incrementally across calls. */
        static unsigned int current_db = 0; /* Last DB tested. */
        static int timelimit_exit = 0;      /* Time limit hit in previous call? */
        static long long last_fast_cycle = 0; /* When last fast cycle ran. */

        int j, iteration = 0;
        int dbs_per_call = CRON_DBS_PER_CALL;
        long long start = ustime(), timelimit, elapsed;

        /* When clients are paused the dataset should be static not just from the
         * POV of clients not being able to write, but also from the POV of
         * expires and evictions of keys not being performed. */
        if (clientsArePaused()) return;

        if (type == ACTIVE_EXPIRE_CYCLE_FAST) {
            /* Don't start a fast cycle if the previous cycle did not exit
             * for time limit, unless the percentage of estimated stale keys is
             * too high. Also never repeat a fast cycle for the same period
             * as the fast cycle total duration itself. */
            if (!timelimit_exit &&
                server.stat_expired_stale_perc < config_cycle_acceptable_stale)
                return;

            if (start < last_fast_cycle + (long long)config_cycle_fast_duration*2)
                return;

            last_fast_cycle = start;
        }

        /* We usually should test CRON_DBS_PER_CALL per iteration, with
         * two exceptions:
         *
         * 1) Don't test more DBs than we have.
         * 2) If last time we hit the time limit, we want to scan all DBs
         * in this iteration, as there is work to do in some DB and we don't want
         * expired keys to use memory for too much time. */
        if (dbs_per_call > server.dbnum || timelimit_exit)
            dbs_per_call = server.dbnum;

        /* We can use at max 'config_cycle_slow_time_perc' percentage of CPU
         * time per iteration. Since this function gets called with a frequency of
         * server.hz times per second, the following is the max amount of
         * microseconds we can spend in this function. */
        timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
        timelimit_exit = 0;
        if (timelimit <= 0) timelimit = 1;

        if (type == ACTIVE_EXPIRE_CYCLE_FAST)
            timelimit = config_cycle_fast_duration; /* in microseconds. */

        /* Accumulate some global stats as we expire keys, to have some idea
         * about the number of keys that are already logically expired, but still
         * existing inside the database. */
        long total_sampled = 0;
        long total_expired = 0;

        for (j = 0; j < dbs_per_call && timelimit_exit == 0; j++) {
            /* Expired and checked in a single loop. */
            unsigned long expired, sampled;

            redisDb *db = server.db+(current_db % server.dbnum);

            /* Increment the DB now so we are sure if we run out of time
             * in the current DB we'll restart from the next. This allows to
             * distribute the time evenly across DBs. */
            current_db++;

            /* Continue to expire if at the end of the cycle more than 25%
             * of the keys were expired. */
            do {
                unsigned long num, slots;
                long long now, ttl_sum;
                int ttl_samples;
                iteration++;

                /* If there is nothing to expire try next DB ASAP. */
                if ((num = dictSize(db->expires)) == 0) {
                    db->avg_ttl = 0;
                    break;
                }
                slots = dictSlots(db->expires);
                now = mstime();

                /* When there are less than 1% filled slots, sampling the key
                 * space is expensive, so stop here waiting for better times...
                 * The dictionary will be resized asap. */
                if (num && slots > DICT_HT_INITIAL_SIZE &&
                    (num*100/slots < 1)) break;

                /* The main collection cycle. Sample random keys among keys
                 * with an expire set, checking for expired ones. */
                expired = 0;
                sampled = 0;
                ttl_sum = 0;
                ttl_samples = 0;

                if (num > config_keys_per_loop)
                    num = config_keys_per_loop;

                /* Here we access the low level representation of the hash table
                 * for speed concerns: this makes this code coupled with dict.c,
                 * but it hardly changed in ten years.
                 *
                 * Note that certain places of the hash table may be empty,
                 * so we want also a stop condition about the number of
                 * buckets that we scanned. However scanning for free buckets
                 * is very fast: we are in the cache line scanning a sequential
                 * array of NULL pointers, so we can scan a lot more buckets
                 * than keys in the same time. */
                long max_buckets = num*20;
                long checked_buckets = 0;

                while (sampled < num && checked_buckets < max_buckets) {
                    for (int table = 0; table < 2; table++) {
                        if (table == 1 && !dictIsRehashing(db->expires)) break;

                        unsigned long idx = db->expires_cursor;
                        idx &= db->expires->ht[table].sizemask;
                        dictEntry *de = db->expires->ht[table].table[idx];
                        long long ttl;

                        /* Scan the current bucket of the current table. */
                        checked_buckets++;
                        while(de) {
                            /* Get the next entry now since this entry may get
                             * deleted. */
                            dictEntry *e = de;
                            de = de->next;

                            ttl = dictGetSignedIntegerVal(e)-now;
                            if (activeExpireCycleTryExpire(db,e,now)) expired++;
                            if (ttl > 0) {
                                /* We want the average TTL of keys yet
                                 * not expired. */
                                ttl_sum += ttl;
                                ttl_samples++;
                            }
                            sampled++;
                        }
                    }
                    db->expires_cursor++;
                }
                total_expired += expired;
                total_sampled += sampled;

                /* Update the average TTL stats for this database. */
                if (ttl_samples) {
                    long long avg_ttl = ttl_sum/ttl_samples;

                    /* Do a simple running average with a few samples.
                     * We just use the current estimate with a weight of 2%
                     * and the previous estimate with a weight of 98%. */
                    if (db->avg_ttl == 0) db->avg_ttl = avg_ttl;
                    db->avg_ttl = (db->avg_ttl/50)*49 + (avg_ttl/50);
                }

                /* We can't block forever here even if there are many keys to
                 * expire. So after a given amount of milliseconds return to the
                 * caller waiting for the other active expire cycle. */
                if ((iteration & 0xf) == 0) { /* check once every 16 iterations. */
                    elapsed = ustime()-start;
                    if (elapsed > timelimit) {
                        timelimit_exit = 1;
                        server.stat_expired_time_cap_reached_count++;
                        break;
                    }
                }
                /* We don't repeat the cycle for the current database if there are
                 * an acceptable amount of stale keys (logically expired but yet
                 * not reclained). */
            } while ((expired*100/sampled) > config_cycle_acceptable_stale);
        }

        elapsed = ustime()-start;
        server.stat_expire_cycle_time_used += elapsed;
        latencyAddSampleIfNeeded("expire-cycle",elapsed/1000);

        /* Update our estimate of keys existing but yet to be expired.
         * Running average with this sample accounting for 5%. */
        double current_perc;
        if (total_sampled) {
            current_perc = (double)total_expired/total_sampled;
        } else
            current_perc = 0;
        server.stat_expired_stale_perc = (current_perc*0.05)+
                                         (server.stat_expired_stale_perc*0.95);
    }
    -

    执行定期清除分成两种类型，快和慢，分别由beforeSleep和databasesCron调用，快版有两个限制，一个是执行时长由ACTIVE_EXPIRE_CYCLE_FAST_DURATION限制，另一个是执行间隔是 2 倍的ACTIVE_EXPIRE_CYCLE_FAST_DURATION，另外这还可以由配置的server.active_expire_effort参数来控制，默认是 1，最大是 10

    -
    onfig_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
                                     ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort
    -

    然后会从一定数量的 db 中找出一定数量的带过期时间的 key（保存在 expires中），这里的数量是由

    -
    config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
                               ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort
    ```                                 
    控制，慢速的执行时长是
    ```C
    config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
                                      2*effort
    timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
    -

    这里还有一个额外的退出条件，如果当前数据库的抽样结果已经达到我们所允许的过期 key 百分比，则下次不再处理当前 db，继续处理下个 db

    + redis数据结构介绍四-第四部分 压缩表 + /2020/01/19/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E5%9B%9B/ + 在 redis 中还有一类表型数据结构叫压缩表，ziplist，它的目的是替代链表，链表是个很容易理解的数据结构，双向链表有前后指针，有带头结点的有的不带，但是链表有个比较大的问题是相对于普通的数组，它的内存不连续，碎片化的存储，内存利用效率不高，而且指针寻址相对于直接使用偏移量的话，也有一定的效率劣势，当然这不是主要的原因，ziplist 设计的主要目的是让链表的内存使用更高效

    +
    +

    The ziplist is a specially encoded dually linked list that is designed to be very memory efficient.
    这是摘自 redis 源码中ziplist.c 文件的注释，也说明了原因，它的大概结构是这样子

    +
    +
    <zlbytes> <zltail> <zllen> <entry> <entry> ... <entry> <zlend>
    +

    其中
    <zlbytes>表示 ziplist 占用的字节总数，类型是uint32_t，32 位的无符号整型，当然表示的字节数也包含自己本身占用的 4 个
    <zltail> 类型也是是uint32_t，表示ziplist表中最后一项（entry）在ziplist中的偏移字节数。<zltail>的存在，使得我们可以很方便地找到最后一项（不用遍历整个ziplist），从而可以在ziplist尾端快速地执行push或pop操作。
    <uint16_t zllen> 表示ziplist 中的数据项个数，因为是 16 位，所以当数量超过所能表示的最大的数量，它的 16 位全会置为 1，但是真实的数量需要遍历整个 ziplist 才能知道
    <entry>是具体的数据项，后面解释
    <zlend> ziplist 的最后一个字节，固定是255。
    再看一下<entry>中的具体结构，

    +
    <prevlen> <encoding> <entry-data>
    +

    首先这个<prevlen>有两种情况，一种是前面的元素的长度，如果是小于等于 253的时候就用一个uint8_t 来表示前一元素的长度，如果大于的话他将占用五个字节，第一个字节是 254，即表示这个字节已经表示不下了，需要后面的四个字节帮忙表示
    <encoding>这个就比较复杂，把源码的注释放下面先看下

    +
    * |00pppppp| - 1 byte
    *      String value with length less than or equal to 63 bytes (6 bits).
    *      "pppppp" represents the unsigned 6 bit length.
    * |01pppppp|qqqqqqqq| - 2 bytes
    *      String value with length less than or equal to 16383 bytes (14 bits).
    *      IMPORTANT: The 14 bit number is stored in big endian.
    * |10000000|qqqqqqqq|rrrrrrrr|ssssssss|tttttttt| - 5 bytes
    *      String value with length greater than or equal to 16384 bytes.
    *      Only the 4 bytes following the first byte represents the length
    *      up to 32^2-1. The 6 lower bits of the first byte are not used and
    *      are set to zero.
    *      IMPORTANT: The 32 bit number is stored in big endian.
    * |11000000| - 3 bytes
    *      Integer encoded as int16_t (2 bytes).
    * |11010000| - 5 bytes
    *      Integer encoded as int32_t (4 bytes).
    * |11100000| - 9 bytes
    *      Integer encoded as int64_t (8 bytes).
    * |11110000| - 4 bytes
    *      Integer encoded as 24 bit signed (3 bytes).
    * |11111110| - 2 bytes
    *      Integer encoded as 8 bit signed (1 byte).
    * |1111xxxx| - (with xxxx between 0000 and 1101) immediate 4 bit integer.
    *      Unsigned integer from 0 to 12. The encoded value is actually from
    *      1 to 13 because 0000 and 1111 can not be used, so 1 should be
    *      subtracted from the encoded 4 bit value to obtain the right value.
    * |11111111| - End of ziplist special entry.
    +

    首先如果 encoding 的前两位是 00 的话代表这个元素是个 6 位的字符串，即直接将数据保存在 encoding 中，不消耗额外的<entry-data>，如果前两位是 01 的话表示是个 14 位的字符串，如果是 10 的话表示encoding 块之后的四个字节是存放字符串类型的数据，encoding 的剩余 6 位置 0。
    如果 encoding 的前两位是 11 的话表示这是个整型，具体的如果后两位是00的话，表示后面是个2字节的 int16_t 类型，如果是01的话，后面是个4字节的int32_t,如果是10的话后面是8字节的int64_t,如果是 11 的话后面是 3 字节的有符号整型，这些都要最后 4 位都是 0 的情况噢
    剩下当是11111110时，则表示是一个1 字节的有符号数，如果是 1111xxxx，其中xxxx在0000 到 1101 表示实际的 1 到 13，为啥呢，因为 0000 前面已经用过了，而 1110 跟 1111 也都有用了。
    看个具体的例子(上下有点对不齐，将就看)

    +
    [0f 00 00 00] [0c 00 00 00] [02 00] [00 f3] [02 f6] [ff]
    |**zlbytes***|  |***zltail***|  |*zllen*|  |entry1 entry2|  |zlend|
    +

    第一部分代表整个 ziplist 有 15 个字节，zlbytes 自己占了 4 个 zltail 表示最后一个元素的偏移量，第 13 个字节起，zllen 表示有 2 个元素，第一个元素是00f3,00表示前一个元素长度是 0，本来前面就没元素(不过不知道这个能不能优化这一字节)，然后是 f3，换成二进制就是11110011,对照上面的注释，是落在|1111xxxx|这个类型里，注意这个其实是用 0001 到 1101 也就是 1到 13 来表示 0到 12，所以 f3 应该就是 2，第一个元素是 2，第二个元素呢，02 代表前一个元素也就是刚才说的这个，占用 2 字节，f6 展开也是刚才的类型，实际是 5，ff 表示 ziplist 的结尾，所以这个 ziplist 里面是两个元素，2 跟 5

    ]]>     Redis @@ -3821,53 +3852,86 @@     - redis数据结构介绍五-第五部分 对象 - /2020/01/20/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%94/ - 前面说了这么些数据结构，其实大家对于 redis 最初的印象应该就是个 key-value 的缓存，类似于 memcache，redis 其实也是个 key-value，key 还是一样的字符串，或者说就是用 redis 自己的动态字符串实现，但是 value 其实就是前面说的那些数据结构，差不多快说完了，还有个 quicklist 后面还有一篇，这里先介绍下 redis 对于这些不同类型的 value 是怎么实现的，首先看下 redisObject 的源码头文件

    -
    /* The actual Redis Object */
    #define OBJ_STRING 0    /* String object. */
    #define OBJ_LIST 1      /* List object. */
    #define OBJ_SET 2       /* Set object. */
    #define OBJ_ZSET 3      /* Sorted set object. */
    #define OBJ_HASH 4      /* Hash object. */
    /*
     * Objects encoding. Some kind of objects like Strings and Hashes can be
     * internally represented in multiple ways. The 'encoding' field of the object
     * is set to one of this fields for this object. */
    #define OBJ_ENCODING_RAW 0     /* Raw representation */
    #define OBJ_ENCODING_INT 1     /* Encoded as integer */
    #define OBJ_ENCODING_HT 2      /* Encoded as hash table */
    #define OBJ_ENCODING_ZIPMAP 3  /* Encoded as zipmap */
    #define OBJ_ENCODING_LINKEDLIST 4 /* No longer used: old list encoding. */
    #define OBJ_ENCODING_ZIPLIST 5 /* Encoded as ziplist */
    #define OBJ_ENCODING_INTSET 6  /* Encoded as intset */
    #define OBJ_ENCODING_SKIPLIST 7  /* Encoded as skiplist */
    #define OBJ_ENCODING_EMBSTR 8  /* Embedded sds string encoding */
    #define OBJ_ENCODING_QUICKLIST 9 /* Encoded as linked list of ziplists */
    #define OBJ_ENCODING_STREAM 10 /* Encoded as a radix tree of listpacks */

    #define LRU_BITS 24
    #define LRU_CLOCK_MAX ((1<<LRU_BITS)-1) /* Max value of obj->lru */
    #define LRU_CLOCK_RESOLUTION 1000 /* LRU clock resolution in ms */

    #define OBJ_SHARED_REFCOUNT INT_MAX
    typedef struct redisObject {
        unsigned type:4;
        unsigned encoding:4;
        unsigned lru:LRU_BITS; /* LRU time (relative to global lru_clock) or
                                * LFU data (least significant 8 bits frequency
                                * and most significant 16 bits access time). */
        int refcount;
        void *ptr;
    } robj;
    -

    主体结构就是这个 redisObject，

    -
      -
    • type： 字段表示对象的类型，它对应的就是 redis 的对外暴露的，或者说用户可以使用的五种类型，OBJ_STRING, OBJ_LIST, OBJ_SET, OBJ_ZSET, OBJ_HASH
      • -
    • encoding： 字段表示这个对象在 redis 内部的编码方式，由OBJ_ENCODING_开头的 11 种
      • -
    • lru: 做LRU替换算法用，占24个bit
      • -
    • refcount: 引用计数。它允许robj对象在某些情况下被共享。
      • -
    • ptr: 指向底层实现数据结构的指针
      当 type 是 OBJ_STRING 时，表示类型是个 string，它的编码方式 encoding 可能有 OBJ_ENCODING_RAW，OBJ_ENCODING_INT，OBJ_ENCODING_EMBSTR 三种
      当 type 是 OBJ_LIST 时，表示类型是 list，它的编码方式 encoding 是 OBJ_ENCODING_QUICKLIST，对于早一些的版本，2.2这种可能还会使用 OBJ_ENCODING_ZIPLIST，OBJ_ENCODING_LINKEDLIST
      当 type 是 OBJ_SET 时，是个集合，但是得看具体元素的类型，有可能使用整数集合，OBJ_ENCODING_INTSET, 如果元素不全是整型或者数量超过一定限制，那么编码就是 OBJ_ENCODING_HT hash table 了
      当 type 是 OBJ_ZSET 时，是个有序集合，它底层有可能使用的是 OBJ_ENCODING_ZIPLIST 或者 OBJ_ENCODING_SKIPLIST
      当 type 是 OBJ_HASH 时，一开始也是 OBJ_ENCODING_ZIPLIST，然后当数据量大于 hash_max_ziplist_entries 时会转成 OBJ_ENCODING_HT
      • -
    -]]>     - - Redis - 数据结构 - C - 源码 - Redis - - - redis - 数据结构 - 源码 + redis淘汰策略复习 + /2021/08/01/redis%E6%B7%98%E6%B1%B0%E7%AD%96%E7%95%A5%E5%A4%8D%E4%B9%A0/ + 前面复习了 redis 的过期策略，这里再复习下淘汰策略，淘汰跟过期的区别有时候会被混淆了，过期主要针对那些设置了过期时间的 key，应该说是一种逻辑策略，是主动的还是被动的加定时的，两种有各自的取舍，而淘汰也可以看成是一种保持系统稳定的策略，因为如果内存满了，不采取任何策略处理，那大概率会导致系统故障，之前其实主要从源码角度分析过redis 的 LRU 和 LFU，但这个是偏底层的实现，抠得比较细，那么具体的系统层面的配置是有哪些策略，来看下 redis labs 的介绍

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PolicyDescription
    noeviction 不逐出Returns an error if the memory limit has been reached when trying to insert more data，插入更多数据时，如果内存达到上限了，返回错误
    allkeys-lru 所有的 key 使用 lru 逐出Evicts the least recently used keys out of all keys 在所有 key 中逐出最近最少使用的
    allkeys-lfu 所有的 key 使用 lfu 逐出Evicts the least frequently used keys out of all keys 在所有 key 中逐出最近最不频繁使用的
    allkeys-random 所有的 key 中随机逐出Randomly evicts keys out of all keys 在所有 key 中随机逐出
    volatile-lruEvicts the least recently used keys out of all keys with an “expire” field set 在设置了过期时间的 key 空间 expire 中使用 lru 策略逐出
    volatile-lfuEvicts the least frequently used keys out of all keys with an “expire” field set 在设置了过期时间的 key 空间 expire 中使用 lfu 策略逐出
    volatile-randomRandomly evicts keys with an “expire” field set 在设置了过期时间的 key 空间 expire 中随机逐出
    volatile-ttlEvicts the shortest time-to-live keys out of all keys with an “expire” field set.在设置了过期时间的 key 空间 expire 中逐出更早过期的
    +

    而在这其中默认使用的策略是 volatile-lru，对 lru 跟 lfu 想有更多的了解可以看下我之前的文章redis系列介绍八-淘汰策略

    +]]>     + + redis + + + redis + 淘汰策略 + 应用 + Evict     - redis过期策略复习 - /2021/07/25/redis%E8%BF%87%E6%9C%9F%E7%AD%96%E7%95%A5%E5%A4%8D%E4%B9%A0/ - redis过期策略复习

    之前其实写过redis的过期的一些原理，这次主要是记录下，一些使用上的概念，主要是redis使用的过期策略是懒过期和定时清除，懒过期的其实比较简单，即是在key被访问的时候会顺带着判断下这个key是否已过期了，如果已经过期了，就不返回了，但是这种策略有个漏洞是如果有些key之后一直不会被访问了，就等于沉在池底了，所以需要有一个定时的清理机制，去从设置了过期的key池子（expires）里随机地捞key，具体的策略我们看下官网的解释

    -
      -
    1. Test 20 random keys from the set of keys with an associated expire.
      2. -
    2. Delete all the keys found expired.
      3. -
    3. If more than 25% of keys were expired, start again from step 1.
      4. -
    -

    从池子里随机获取20个key，将其中过期的key删掉，如果这其中有超过25%的key已经过期了，那就再来一次，以此保持过期的key不超过25%（左右），并且这个定时策略可以在redis的配置文件

    -
    # Redis calls an internal function to perform many background tasks, like
    # closing connections of clients in timeout, purging expired keys that are
    # never requested, and so forth.
    #
    # Not all tasks are performed with the same frequency, but Redis checks for
    # tasks to perform according to the specified "hz" value.
    #
    # By default "hz" is set to 10. Raising the value will use more CPU when
    # Redis is idle, but at the same time will make Redis more responsive when
    # there are many keys expiring at the same time, and timeouts may be
    # handled with more precision.
    #
    # The range is between 1 and 500, however a value over 100 is usually not
    # a good idea. Most users should use the default of 10 and raise this up to
    # 100 only in environments where very low latency is required.
    hz 10
    - -

    可以配置这个hz的值，代表的含义是每秒的执行次数，默认是10，其实也用了hz的普遍含义。有兴趣可以看看之前写的一篇文章redis系列介绍七-过期策略

    + redis系列介绍七-过期策略 + /2020/04/12/redis%E7%B3%BB%E5%88%97%E4%BB%8B%E7%BB%8D%E4%B8%83/ + 这一篇不再是数据结构介绍了，大致的数据结构基本都介绍了，这一篇主要是查漏补缺，或者说讲一些重要且基本的概念，也可能是经常被忽略的，很多讲 redis 的系列文章可能都会忽略，学习 redis 的时候也会，因为觉得源码学习就是讲主要的数据结构和“算法”学习了就好了。
    redis 的主要应用就是拿来作为高性能的缓存，那么缓存一般有些啥需要注意的，首先是访问速度，如果取得跟数据库一样快，那就没什么存在的意义，第二个是缓存的字面意思，我只是为了让数据读取快一些，通常大部分的场景这个是需要更新过期的，这里就把我要讲的第一点引出来了（真累，

    +

    redis过期策略

    redis 是如何过期缓存的，可以猜测下，最无脑的就是每个设置了过期时间的 key 都设个定时器，过期了就删除，这种显然消耗太大，清理地最及时，还有的就是 redis 正在采用的懒汉清理策略和定期清理
    懒汉策略就是在使用的时候去检查缓存是否过期，比如 get 操作时，先判断下这个 key 是否已经过期了，如果过期了就删掉，并且返回空，如果没过期则正常返回
    主要代码是

    +
    /* This function is called when we are going to perform some operation
     * in a given key, but such key may be already logically expired even if
     * it still exists in the database. The main way this function is called
     * is via lookupKey*() family of functions.
     *
     * The behavior of the function depends on the replication role of the
     * instance, because slave instances do not expire keys, they wait
     * for DELs from the master for consistency matters. However even
     * slaves will try to have a coherent return value for the function,
     * so that read commands executed in the slave side will be able to
     * behave like if the key is expired even if still present (because the
     * master has yet to propagate the DEL).
     *
     * In masters as a side effect of finding a key which is expired, such
     * key will be evicted from the database. Also this may trigger the
     * propagation of a DEL/UNLINK command in AOF / replication stream.
     *
     * The return value of the function is 0 if the key is still valid,
     * otherwise the function returns 1 if the key is expired. */
    int expireIfNeeded(redisDb *db, robj *key) {
        if (!keyIsExpired(db,key)) return 0;

        /* If we are running in the context of a slave, instead of
         * evicting the expired key from the database, we return ASAP:
         * the slave key expiration is controlled by the master that will
         * send us synthesized DEL operations for expired keys.
         *
         * Still we try to return the right information to the caller,
         * that is, 0 if we think the key should be still valid, 1 if
         * we think the key is expired at this time. */
        if (server.masterhost != NULL) return 1;

        /* Delete the key */
        server.stat_expiredkeys++;
        propagateExpire(db,key,server.lazyfree_lazy_expire);
        notifyKeyspaceEvent(NOTIFY_EXPIRED,
            "expired",key,db->id);
        return server.lazyfree_lazy_expire ? dbAsyncDelete(db,key) :
                                             dbSyncDelete(db,key);
    }

    /* Check if the key is expired. */
    int keyIsExpired(redisDb *db, robj *key) {
        mstime_t when = getExpire(db,key);
        mstime_t now;

        if (when < 0) return 0; /* No expire for this key */

        /* Don't expire anything while loading. It will be done later. */
        if (server.loading) return 0;

        /* If we are in the context of a Lua script, we pretend that time is
         * blocked to when the Lua script started. This way a key can expire
         * only the first time it is accessed and not in the middle of the
         * script execution, making propagation to slaves / AOF consistent.
         * See issue #1525 on Github for more information. */
        if (server.lua_caller) {
            now = server.lua_time_start;
        }
        /* If we are in the middle of a command execution, we still want to use
         * a reference time that does not change: in that case we just use the
         * cached time, that we update before each call in the call() function.
         * This way we avoid that commands such as RPOPLPUSH or similar, that
         * may re-open the same key multiple times, can invalidate an already
         * open object in a next call, if the next call will see the key expired,
         * while the first did not. */
        else if (server.fixed_time_expire > 0) {
            now = server.mstime;
        }
        /* For the other cases, we want to use the most fresh time we have. */
        else {
            now = mstime();
        }

        /* The key expired if the current (virtual or real) time is greater
         * than the expire time of the key. */
        return now > when;
    }
    /* Return the expire time of the specified key, or -1 if no expire
     * is associated with this key (i.e. the key is non volatile) */
    long long getExpire(redisDb *db, robj *key) {
        dictEntry *de;

        /* No expire? return ASAP */
        if (dictSize(db->expires) == 0 ||
           (de = dictFind(db->expires,key->ptr)) == NULL) return -1;

        /* The entry was found in the expire dict, this means it should also
         * be present in the main dict (safety check). */
        serverAssertWithInfo(NULL,key,dictFind(db->dict,key->ptr) != NULL);
        return dictGetSignedIntegerVal(de);
    }
    +

    这里有几点要注意的，第一是当惰性删除时会根据lazyfree_lazy_expire这个参数去判断是执行同步删除还是异步删除，另外一点是对于 slave，是不需要执行的，因为会在 master 过期时向 slave 发送 del 指令。
    光采用这个策略会有什么问题呢，假如一些key 一直未被访问，那这些 key 就不会过期了，导致一直被占用着内存，所以 redis 采取了懒汉式过期加定期过期策略，定期策略是怎么执行的呢

    +
    /* This function handles 'background' operations we are required to do
     * incrementally in Redis databases, such as active key expiring, resizing,
     * rehashing. */
    void databasesCron(void) {
        /* Expire keys by random sampling. Not required for slaves
         * as master will synthesize DELs for us. */
        if (server.active_expire_enabled) {
            if (server.masterhost == NULL) {
                activeExpireCycle(ACTIVE_EXPIRE_CYCLE_SLOW);
            } else {
                expireSlaveKeys();
            }
        }

        /* Defrag keys gradually. */
        activeDefragCycle();

        /* Perform hash tables rehashing if needed, but only if there are no
         * other processes saving the DB on disk. Otherwise rehashing is bad
         * as will cause a lot of copy-on-write of memory pages. */
        if (!hasActiveChildProcess()) {
            /* We use global counters so if we stop the computation at a given
             * DB we'll be able to start from the successive in the next
             * cron loop iteration. */
            static unsigned int resize_db = 0;
            static unsigned int rehash_db = 0;
            int dbs_per_call = CRON_DBS_PER_CALL;
            int j;

            /* Don't test more DBs than we have. */
            if (dbs_per_call > server.dbnum) dbs_per_call = server.dbnum;

            /* Resize */
            for (j = 0; j < dbs_per_call; j++) {
                tryResizeHashTables(resize_db % server.dbnum);
                resize_db++;
            }

            /* Rehash */
            if (server.activerehashing) {
                for (j = 0; j < dbs_per_call; j++) {
                    int work_done = incrementallyRehash(rehash_db);
                    if (work_done) {
                        /* If the function did some work, stop here, we'll do
                         * more at the next cron loop. */
                        break;
                    } else {
                        /* If this db didn't need rehash, we'll try the next one. */
                        rehash_db++;
                        rehash_db %= server.dbnum;
                    }
                }
            }
        }
    }
    /* Try to expire a few timed out keys. The algorithm used is adaptive and
     * will use few CPU cycles if there are few expiring keys, otherwise
     * it will get more aggressive to avoid that too much memory is used by
     * keys that can be removed from the keyspace.
     *
     * Every expire cycle tests multiple databases: the next call will start
     * again from the next db, with the exception of exists for time limit: in that
     * case we restart again from the last database we were processing. Anyway
     * no more than CRON_DBS_PER_CALL databases are tested at every iteration.
     *
     * The function can perform more or less work, depending on the "type"
     * argument. It can execute a "fast cycle" or a "slow cycle". The slow
     * cycle is the main way we collect expired cycles: this happens with
     * the "server.hz" frequency (usually 10 hertz).
     *
     * However the slow cycle can exit for timeout, since it used too much time.
     * For this reason the function is also invoked to perform a fast cycle
     * at every event loop cycle, in the beforeSleep() function. The fast cycle
     * will try to perform less work, but will do it much more often.
     *
     * The following are the details of the two expire cycles and their stop
     * conditions:
     *
     * If type is ACTIVE_EXPIRE_CYCLE_FAST the function will try to run a
     * "fast" expire cycle that takes no longer than EXPIRE_FAST_CYCLE_DURATION
     * microseconds, and is not repeated again before the same amount of time.
     * The cycle will also refuse to run at all if the latest slow cycle did not
     * terminate because of a time limit condition.
     *
     * If type is ACTIVE_EXPIRE_CYCLE_SLOW, that normal expire cycle is
     * executed, where the time limit is a percentage of the REDIS_HZ period
     * as specified by the ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC define. In the
     * fast cycle, the check of every database is interrupted once the number
     * of already expired keys in the database is estimated to be lower than
     * a given percentage, in order to avoid doing too much work to gain too
     * little memory.
     *
     * The configured expire "effort" will modify the baseline parameters in
     * order to do more work in both the fast and slow expire cycles.
     */

    #define ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP 20 /* Keys for each DB loop. */
    #define ACTIVE_EXPIRE_CYCLE_FAST_DURATION 1000 /* Microseconds. */
    #define ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC 25 /* Max % of CPU to use. */
    #define ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE 10 /* % of stale keys after which
                                                       we do extra efforts. */
    void activeExpireCycle(int type) {
        /* Adjust the running parameters according to the configured expire
         * effort. The default effort is 1, and the maximum configurable effort
         * is 10. */
        unsigned long
        effort = server.active_expire_effort-1, /* Rescale from 0 to 9. */
        config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
                               ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort,
        config_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
                                     ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort,
        config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
                                      2*effort,
        config_cycle_acceptable_stale = ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE-
                                        effort;

        /* This function has some global state in order to continue the work
         * incrementally across calls. */
        static unsigned int current_db = 0; /* Last DB tested. */
        static int timelimit_exit = 0;      /* Time limit hit in previous call? */
        static long long last_fast_cycle = 0; /* When last fast cycle ran. */

        int j, iteration = 0;
        int dbs_per_call = CRON_DBS_PER_CALL;
        long long start = ustime(), timelimit, elapsed;

        /* When clients are paused the dataset should be static not just from the
         * POV of clients not being able to write, but also from the POV of
         * expires and evictions of keys not being performed. */
        if (clientsArePaused()) return;

        if (type == ACTIVE_EXPIRE_CYCLE_FAST) {
            /* Don't start a fast cycle if the previous cycle did not exit
             * for time limit, unless the percentage of estimated stale keys is
             * too high. Also never repeat a fast cycle for the same period
             * as the fast cycle total duration itself. */
            if (!timelimit_exit &&
                server.stat_expired_stale_perc < config_cycle_acceptable_stale)
                return;

            if (start < last_fast_cycle + (long long)config_cycle_fast_duration*2)
                return;

            last_fast_cycle = start;
        }

        /* We usually should test CRON_DBS_PER_CALL per iteration, with
         * two exceptions:
         *
         * 1) Don't test more DBs than we have.
         * 2) If last time we hit the time limit, we want to scan all DBs
         * in this iteration, as there is work to do in some DB and we don't want
         * expired keys to use memory for too much time. */
        if (dbs_per_call > server.dbnum || timelimit_exit)
            dbs_per_call = server.dbnum;

        /* We can use at max 'config_cycle_slow_time_perc' percentage of CPU
         * time per iteration. Since this function gets called with a frequency of
         * server.hz times per second, the following is the max amount of
         * microseconds we can spend in this function. */
        timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
        timelimit_exit = 0;
        if (timelimit <= 0) timelimit = 1;

        if (type == ACTIVE_EXPIRE_CYCLE_FAST)
            timelimit = config_cycle_fast_duration; /* in microseconds. */

        /* Accumulate some global stats as we expire keys, to have some idea
         * about the number of keys that are already logically expired, but still
         * existing inside the database. */
        long total_sampled = 0;
        long total_expired = 0;

        for (j = 0; j < dbs_per_call && timelimit_exit == 0; j++) {
            /* Expired and checked in a single loop. */
            unsigned long expired, sampled;

            redisDb *db = server.db+(current_db % server.dbnum);

            /* Increment the DB now so we are sure if we run out of time
             * in the current DB we'll restart from the next. This allows to
             * distribute the time evenly across DBs. */
            current_db++;

            /* Continue to expire if at the end of the cycle more than 25%
             * of the keys were expired. */
            do {
                unsigned long num, slots;
                long long now, ttl_sum;
                int ttl_samples;
                iteration++;

                /* If there is nothing to expire try next DB ASAP. */
                if ((num = dictSize(db->expires)) == 0) {
                    db->avg_ttl = 0;
                    break;
                }
                slots = dictSlots(db->expires);
                now = mstime();

                /* When there are less than 1% filled slots, sampling the key
                 * space is expensive, so stop here waiting for better times...
                 * The dictionary will be resized asap. */
                if (num && slots > DICT_HT_INITIAL_SIZE &&
                    (num*100/slots < 1)) break;

                /* The main collection cycle. Sample random keys among keys
                 * with an expire set, checking for expired ones. */
                expired = 0;
                sampled = 0;
                ttl_sum = 0;
                ttl_samples = 0;

                if (num > config_keys_per_loop)
                    num = config_keys_per_loop;

                /* Here we access the low level representation of the hash table
                 * for speed concerns: this makes this code coupled with dict.c,
                 * but it hardly changed in ten years.
                 *
                 * Note that certain places of the hash table may be empty,
                 * so we want also a stop condition about the number of
                 * buckets that we scanned. However scanning for free buckets
                 * is very fast: we are in the cache line scanning a sequential
                 * array of NULL pointers, so we can scan a lot more buckets
                 * than keys in the same time. */
                long max_buckets = num*20;
                long checked_buckets = 0;

                while (sampled < num && checked_buckets < max_buckets) {
                    for (int table = 0; table < 2; table++) {
                        if (table == 1 && !dictIsRehashing(db->expires)) break;

                        unsigned long idx = db->expires_cursor;
                        idx &= db->expires->ht[table].sizemask;
                        dictEntry *de = db->expires->ht[table].table[idx];
                        long long ttl;

                        /* Scan the current bucket of the current table. */
                        checked_buckets++;
                        while(de) {
                            /* Get the next entry now since this entry may get
                             * deleted. */
                            dictEntry *e = de;
                            de = de->next;

                            ttl = dictGetSignedIntegerVal(e)-now;
                            if (activeExpireCycleTryExpire(db,e,now)) expired++;
                            if (ttl > 0) {
                                /* We want the average TTL of keys yet
                                 * not expired. */
                                ttl_sum += ttl;
                                ttl_samples++;
                            }
                            sampled++;
                        }
                    }
                    db->expires_cursor++;
                }
                total_expired += expired;
                total_sampled += sampled;

                /* Update the average TTL stats for this database. */
                if (ttl_samples) {
                    long long avg_ttl = ttl_sum/ttl_samples;

                    /* Do a simple running average with a few samples.
                     * We just use the current estimate with a weight of 2%
                     * and the previous estimate with a weight of 98%. */
                    if (db->avg_ttl == 0) db->avg_ttl = avg_ttl;
                    db->avg_ttl = (db->avg_ttl/50)*49 + (avg_ttl/50);
                }

                /* We can't block forever here even if there are many keys to
                 * expire. So after a given amount of milliseconds return to the
                 * caller waiting for the other active expire cycle. */
                if ((iteration & 0xf) == 0) { /* check once every 16 iterations. */
                    elapsed = ustime()-start;
                    if (elapsed > timelimit) {
                        timelimit_exit = 1;
                        server.stat_expired_time_cap_reached_count++;
                        break;
                    }
                }
                /* We don't repeat the cycle for the current database if there are
                 * an acceptable amount of stale keys (logically expired but yet
                 * not reclained). */
            } while ((expired*100/sampled) > config_cycle_acceptable_stale);
        }

        elapsed = ustime()-start;
        server.stat_expire_cycle_time_used += elapsed;
        latencyAddSampleIfNeeded("expire-cycle",elapsed/1000);

        /* Update our estimate of keys existing but yet to be expired.
         * Running average with this sample accounting for 5%. */
        double current_perc;
        if (total_sampled) {
            current_perc = (double)total_expired/total_sampled;
        } else
            current_perc = 0;
        server.stat_expired_stale_perc = (current_perc*0.05)+
                                         (server.stat_expired_stale_perc*0.95);
    }
    +

    执行定期清除分成两种类型，快和慢，分别由beforeSleep和databasesCron调用，快版有两个限制，一个是执行时长由ACTIVE_EXPIRE_CYCLE_FAST_DURATION限制，另一个是执行间隔是 2 倍的ACTIVE_EXPIRE_CYCLE_FAST_DURATION，另外这还可以由配置的server.active_expire_effort参数来控制，默认是 1，最大是 10

    +
    onfig_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
                                     ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort
    +

    然后会从一定数量的 db 中找出一定数量的带过期时间的 key（保存在 expires中），这里的数量是由

    +
    config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
                               ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort
    ```                                 
    控制，慢速的执行时长是
    ```C
    config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
                                      2*effort
    timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
    +

    这里还有一个额外的退出条件，如果当前数据库的抽样结果已经达到我们所允许的过期 key 百分比，则下次不再处理当前 db，继续处理下个 db

    ]]>     - redis + Redis + 数据结构 + C + 源码 + Redis redis - 应用 - 过期策略 + 数据结构 + 源码     @@ -3918,6 +3982,29 @@ 源码 + + redis过期策略复习 + /2021/07/25/redis%E8%BF%87%E6%9C%9F%E7%AD%96%E7%95%A5%E5%A4%8D%E4%B9%A0/ + redis过期策略复习

    之前其实写过redis的过期的一些原理，这次主要是记录下，一些使用上的概念，主要是redis使用的过期策略是懒过期和定时清除，懒过期的其实比较简单，即是在key被访问的时候会顺带着判断下这个key是否已过期了，如果已经过期了，就不返回了，但是这种策略有个漏洞是如果有些key之后一直不会被访问了，就等于沉在池底了，所以需要有一个定时的清理机制，去从设置了过期的key池子（expires）里随机地捞key，具体的策略我们看下官网的解释

    +
      +
    1. Test 20 random keys from the set of keys with an associated expire.
      2. +
    2. Delete all the keys found expired.
      3. +
    3. If more than 25% of keys were expired, start again from step 1.
      4. +
    +

    从池子里随机获取20个key，将其中过期的key删掉，如果这其中有超过25%的key已经过期了，那就再来一次，以此保持过期的key不超过25%（左右），并且这个定时策略可以在redis的配置文件

    +
    # Redis calls an internal function to perform many background tasks, like
    # closing connections of clients in timeout, purging expired keys that are
    # never requested, and so forth.
    #
    # Not all tasks are performed with the same frequency, but Redis checks for
    # tasks to perform according to the specified "hz" value.
    #
    # By default "hz" is set to 10. Raising the value will use more CPU when
    # Redis is idle, but at the same time will make Redis more responsive when
    # there are many keys expiring at the same time, and timeouts may be
    # handled with more precision.
    #
    # The range is between 1 and 500, however a value over 100 is usually not
    # a good idea. Most users should use the default of 10 and raise this up to
    # 100 only in environments where very low latency is required.
    hz 10
    + +

    可以配置这个hz的值，代表的含义是每秒的执行次数，默认是10，其实也用了hz的普遍含义。有兴趣可以看看之前写的一篇文章redis系列介绍七-过期策略

    +]]>     + + redis + + + redis + 应用 + 过期策略 + +     rust学习笔记-所有权三之切片 /2021/05/16/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0-%E6%89%80%E6%9C%89%E6%9D%83%E4%B8%89%E4%B9%8B%E5%88%87%E7%89%87/ @@ -3994,52 +4081,6 @@ 不可变引用 - - redis数据结构介绍四-第四部分 压缩表 - /2020/01/19/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E5%9B%9B/ - 在 redis 中还有一类表型数据结构叫压缩表，ziplist，它的目的是替代链表，链表是个很容易理解的数据结构，双向链表有前后指针，有带头结点的有的不带，但是链表有个比较大的问题是相对于普通的数组，它的内存不连续，碎片化的存储，内存利用效率不高，而且指针寻址相对于直接使用偏移量的话，也有一定的效率劣势，当然这不是主要的原因，ziplist 设计的主要目的是让链表的内存使用更高效

    -
    -

    The ziplist is a specially encoded dually linked list that is designed to be very memory efficient.
    这是摘自 redis 源码中ziplist.c 文件的注释，也说明了原因，它的大概结构是这样子

    -
    -
    <zlbytes> <zltail> <zllen> <entry> <entry> ... <entry> <zlend>
    -

    其中
    <zlbytes>表示 ziplist 占用的字节总数，类型是uint32_t，32 位的无符号整型，当然表示的字节数也包含自己本身占用的 4 个
    <zltail> 类型也是是uint32_t，表示ziplist表中最后一项（entry）在ziplist中的偏移字节数。<zltail>的存在，使得我们可以很方便地找到最后一项（不用遍历整个ziplist），从而可以在ziplist尾端快速地执行push或pop操作。
    <uint16_t zllen> 表示ziplist 中的数据项个数，因为是 16 位，所以当数量超过所能表示的最大的数量，它的 16 位全会置为 1，但是真实的数量需要遍历整个 ziplist 才能知道
    <entry>是具体的数据项，后面解释
    <zlend> ziplist 的最后一个字节，固定是255。
    再看一下<entry>中的具体结构，

    -
    <prevlen> <encoding> <entry-data>
    -

    首先这个<prevlen>有两种情况，一种是前面的元素的长度，如果是小于等于 253的时候就用一个uint8_t 来表示前一元素的长度，如果大于的话他将占用五个字节，第一个字节是 254，即表示这个字节已经表示不下了，需要后面的四个字节帮忙表示
    <encoding>这个就比较复杂，把源码的注释放下面先看下

    -
    * |00pppppp| - 1 byte
    *      String value with length less than or equal to 63 bytes (6 bits).
    *      "pppppp" represents the unsigned 6 bit length.
    * |01pppppp|qqqqqqqq| - 2 bytes
    *      String value with length less than or equal to 16383 bytes (14 bits).
    *      IMPORTANT: The 14 bit number is stored in big endian.
    * |10000000|qqqqqqqq|rrrrrrrr|ssssssss|tttttttt| - 5 bytes
    *      String value with length greater than or equal to 16384 bytes.
    *      Only the 4 bytes following the first byte represents the length
    *      up to 32^2-1. The 6 lower bits of the first byte are not used and
    *      are set to zero.
    *      IMPORTANT: The 32 bit number is stored in big endian.
    * |11000000| - 3 bytes
    *      Integer encoded as int16_t (2 bytes).
    * |11010000| - 5 bytes
    *      Integer encoded as int32_t (4 bytes).
    * |11100000| - 9 bytes
    *      Integer encoded as int64_t (8 bytes).
    * |11110000| - 4 bytes
    *      Integer encoded as 24 bit signed (3 bytes).
    * |11111110| - 2 bytes
    *      Integer encoded as 8 bit signed (1 byte).
    * |1111xxxx| - (with xxxx between 0000 and 1101) immediate 4 bit integer.
    *      Unsigned integer from 0 to 12. The encoded value is actually from
    *      1 to 13 because 0000 and 1111 can not be used, so 1 should be
    *      subtracted from the encoded 4 bit value to obtain the right value.
    * |11111111| - End of ziplist special entry.
    -

    首先如果 encoding 的前两位是 00 的话代表这个元素是个 6 位的字符串，即直接将数据保存在 encoding 中，不消耗额外的<entry-data>，如果前两位是 01 的话表示是个 14 位的字符串，如果是 10 的话表示encoding 块之后的四个字节是存放字符串类型的数据，encoding 的剩余 6 位置 0。
    如果 encoding 的前两位是 11 的话表示这是个整型，具体的如果后两位是00的话，表示后面是个2字节的 int16_t 类型，如果是01的话，后面是个4字节的int32_t,如果是10的话后面是8字节的int64_t,如果是 11 的话后面是 3 字节的有符号整型，这些都要最后 4 位都是 0 的情况噢
    剩下当是11111110时，则表示是一个1 字节的有符号数，如果是 1111xxxx，其中xxxx在0000 到 1101 表示实际的 1 到 13，为啥呢，因为 0000 前面已经用过了，而 1110 跟 1111 也都有用了。
    看个具体的例子(上下有点对不齐，将就看)

    -
    [0f 00 00 00] [0c 00 00 00] [02 00] [00 f3] [02 f6] [ff]
    |**zlbytes***|  |***zltail***|  |*zllen*|  |entry1 entry2|  |zlend|
    -

    第一部分代表整个 ziplist 有 15 个字节，zlbytes 自己占了 4 个 zltail 表示最后一个元素的偏移量，第 13 个字节起，zllen 表示有 2 个元素，第一个元素是00f3,00表示前一个元素长度是 0，本来前面就没元素(不过不知道这个能不能优化这一字节)，然后是 f3，换成二进制就是11110011,对照上面的注释，是落在|1111xxxx|这个类型里，注意这个其实是用 0001 到 1101 也就是 1到 13 来表示 0到 12，所以 f3 应该就是 2，第一个元素是 2，第二个元素呢，02 代表前一个元素也就是刚才说的这个，占用 2 字节，f6 展开也是刚才的类型，实际是 5，ff 表示 ziplist 的结尾，所以这个 ziplist 里面是两个元素，2 跟 5

    -]]>     - - Redis - 数据结构 - C - 源码 - Redis - - - redis - 数据结构 - 源码 - -     - - spark-little-tips - /2017/03/28/spark-little-tips/ - spark 的一些粗浅使用经验

    工作中学习使用了一下Spark做数据分析,主要是用spark的python接口，首先是pyspark.SparkContext(appName=xxx)，这是初始化一个Spark应用实例或者说会话，不能重复，
    返回的实例句柄就可以调用textFile(path)读取文本文件，这里的文本文件可以是HDFS上的文本文件，也可以普通文本文件，但是需要在Spark的所有集群上都存在，否则会
    读取失败，parallelize则可以将python生成的集合数据读取后转换成rdd(A Resilient Distributed Dataset (RDD),一种spark下的基本抽象数据集),基于这个RDD就可以做
    数据的流式计算，例如map reduce,在Spark中可以非常方便地实现

    -

    简单的mapreduce word count示例

    textFile = sc.parallelize([(1,1), (2,1), (3,1), (4,1), (5,1),(1,1), (2,1), (3,1), (4,1), (5,1)])
    data = textFile.reduceByKey(lambda x, y: x + y).collect()
    for _ in data:
        print(_)
    - - -

    结果

    (3, 2)
    (1, 2)
    (4, 2)
    (2, 2)
    (5, 2)
    -]]>     - - data analysis - - - spark - python - -     rust学习笔记-所有权一 /2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/ @@ -4070,6 +4111,23 @@ 新语言 + + spark-little-tips + /2017/03/28/spark-little-tips/ + spark 的一些粗浅使用经验

    工作中学习使用了一下Spark做数据分析,主要是用spark的python接口，首先是pyspark.SparkContext(appName=xxx)，这是初始化一个Spark应用实例或者说会话，不能重复，
    返回的实例句柄就可以调用textFile(path)读取文本文件，这里的文本文件可以是HDFS上的文本文件，也可以普通文本文件，但是需要在Spark的所有集群上都存在，否则会
    读取失败，parallelize则可以将python生成的集合数据读取后转换成rdd(A Resilient Distributed Dataset (RDD),一种spark下的基本抽象数据集),基于这个RDD就可以做
    数据的流式计算，例如map reduce,在Spark中可以非常方便地实现

    +

    简单的mapreduce word count示例

    textFile = sc.parallelize([(1,1), (2,1), (3,1), (4,1), (5,1),(1,1), (2,1), (3,1), (4,1), (5,1)])
    data = textFile.reduceByKey(lambda x, y: x + y).collect()
    for _ in data:
        print(_)
    + + +

    结果

    (3, 2)
    (1, 2)
    (4, 2)
    (2, 2)
    (5, 2)
    +]]>     + + data analysis + + + spark + python + +     spring boot中的 http 接口返回 json 形式的小注意点 /2023/06/25/spring-boot%E4%B8%AD%E7%9A%84-http-%E6%8E%A5%E5%8F%A3%E8%BF%94%E5%9B%9E-json-%E5%BD%A2%E5%BC%8F%E7%9A%84%E5%B0%8F%E6%B3%A8%E6%84%8F%E7%82%B9/ @@ -4288,49 +4346,6 @@ SpringBoot - - openresty - /2019/06/18/openresty/ - 目前公司要对一些新的产品功能做灰度测试，因为在后端业务代码层面添加判断比较麻烦，所以想在nginx上做点手脚，就想到了openresty
    前后也踩了不少坑，这边先写一点

    -

    首先是日志
    error_log logs/error.log debug;
    需要nginx开启日志的debug才能看到日志

    -

    使用 lua_code_cache off即可, 另外注意只有使用 content_by_lua_file 才会生效

    -
    http {
      lua_code_cache off;
    }

    location ~* /(\d+-.*)/api/orgunits/load_all(.*) {
       default_type 'application/json;charset=utf-8';
       content_by_lua_file /data/projects/xxx/current/lua/controller/load_data.lua;
    }
    - -

    使用lua给nginx请求response头添加内容可以用这个

    -
    ngx.header['response'] = 'header'
    - - -

    使用总结

    -

    后续：

    -
      -

    1. 一开始在本地环境的时候使用content_by_lua_file只关注了头，后来发到测试环境发现请求内容都没代理转发到后端服务上
      网上查了下发现content_by_lua_file是将请求的所有内容包括response都用这里面的lua脚本生成了，content这个词就表示是请求内容
      后来改成了access_by_lua_file就正常了，只是要去获取请求内容和修改响应头，并不是要完整的接管请求

      -
      2. -

    2. 后来又碰到了一个坑是nginx有个client_body_buffer_size的配置参数，nginx在32位和64位系统里有8K和16K两个默认值，当请求内容大于这两个值的时候，会把请求内容放到临时文件里，这个时候openresty里的ngx.req.get_post_args()就会报“failed to get post args: requesty body in temp file not supported”这个错误，将client_body_buffer_size这个参数配置调大一点就好了

      -
      3. -

    3. 还有就是lua的异常捕获，网上看一般是用pcall和xpcall来进行保护调用，因为问题主要出在cjson的decode，这里有两个解决方案，一个就是将cjson.decode使用pcall封装，

      - 
      local decode = require("cjson").decode

      function json_decode( str )
          local ok, t = pcall(decode, str)
          if not ok then
            return nil
          end

          return t
      end
      -

      这个是使用了pcall，称为保护调用，会在内部错误后返回两个参数，第一个是false，第二个是错误信息
      还有一种是使用cjson.safe包

      - 
      local json = require("cjson.safe")
      local str = [[ {"key:"value"} ]]

      local t = json.decode(str)
      if t then
          ngx.say(" --> ", type(t))
      end
      -

      cjson.safe包会在解析失败的时候返回nil

      -
      4. -

    4. 还有一个是redis链接时如果host使用的是域名的话会提示“failed to connect: no resolver defined to resolve “redis.xxxxxx.com””，这里需要使用nginx的resolver指令，
      resolver 8.8.8.8 valid=3600s;

      -
      5. -

    5. 还有一点补充下
      就是业务在使用redis的时候使用了db的特性，所以在lua访问redis的时候也需要执行db，这里lua的redis库也支持了这个特性，可以使用instance:select(config:get(‘db’))来切换db

      -
      6. -

    6. 性能优化tips
      建议是尽量少使用阶段钩子，例如content_by_lua_file，*_by_lua

      -
      7. -

    7. 发现一个不错的openresty站点
      地址

      -
      8. -
    -]]>     - - nginx - - - openresty - nginx - -     ssh 小技巧-端口转发 /2023/03/26/ssh-%E5%B0%8F%E6%8A%80%E5%B7%A7-%E7%AB%AF%E5%8F%A3%E8%BD%AC%E5%8F%91/ @@ -4410,72 +4425,15 @@ user3: - php 的调试方法-查看调用堆栈 - /2023/12/31/php-%E7%9A%84%E8%B0%83%E8%AF%95%E6%96%B9%E6%B3%95-%E6%9F%A5%E7%9C%8B%E8%B0%83%E7%94%A8%E5%A0%86%E6%A0%88/ - php 代码调试里的神器就是 echo 111;exit; 但是对于使用了接口和继承比较多的话，有时候比较难找，可能定位到了一段代码但是不知道怎么调用过来的，这时候就可以用这个方法

    -

    总结下来有三种，

    -

    第一种是最巧妙的

    function a() {
    echo 111;exit;
    }
    -

    比如本来是上面这样子，那么其实我们可以主动new 个异常

    -
    function a() {
    $e = new Exception();
    print_r($e->getTraceAsString());
    echo 111;exit;
    }
    -

    这样我的 trace 调用链路就出来了

    -

    第二种

    这个就是比较简单的，调用 php 自身提供的方法

    -
    debug_backtrace(int $options = DEBUG_BACKTRACE_PROVIDE_OBJECT, int $limit = 0): array
    -

    第一个参数是个掩码

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    debug_backtrace()Populates both indexes
    debug_backtrace(DEBUG_BACKTRACE_PROVIDE_OBJECT)
    debug_backtrace(1)
    debug_backtrace(0)Omits index "object" and populates index "args".
    debug_backtrace(DEBUG_BACKTRACE_IGNORE_ARGS)Omits index "object" and index "args".
    debug_backtrace(2)
    debug_backtrace(DEBUG_BACKTRACE_PROVIDE_OBJECTDEBUG_BACKTRACE_IGNORE_ARGS)
    debug_backtrace(3)
    -

    第二个参数是限制栈深度

    -

    第三种

    这个也是用自身的方法

    -
    debug_print_backtrace(int $options = 0, int $limit = 0): void
    -

    这里的第一个参数只有一个可以传的

    - - - - - - - -
    DEBUG_BACKTRACE_IGNORE_ARGSWhether or not to omit the “args” index, and thus all the function/method arguments, to save memory.
    -

    就是隐藏参数，不然如果对于一些框架代码，这个打印会非常大，需要注意下

    + win 下 vmware 虚拟机搭建黑裙 nas 的小思路 + /2023/06/04/win-%E4%B8%8B-vmware-%E8%99%9A%E6%8B%9F%E6%9C%BA%E6%90%AD%E5%BB%BA%E9%BB%91%E8%A3%99-nas-%E7%9A%84%E5%B0%8F%E6%80%9D%E8%B7%AF/ + 上次说 nas 的方案我是在 win10 下使用vmware workstation 搭建的黑裙虚拟机，采用 sata 物理磁盘直通的方式，算是跑通了黑裙的基础使用模式，但是后来发现的一个问题是之前没考虑到的，我买了不带 f 的处理器就是为了核显能做硬解，但是因为 cpu 是通过 vmware 虚拟的，目前看来是没法直通核显的，我是使用的 jellyfin 套件，一开始使用是默认的刮削方式，而且把电视剧当成了电影在刮削，所以基本不能看，后面使用了 tmm 作为刮削工具，可以手动填写 imdb 的id 来进行搜索，一般比较正式的剧都可以在豆瓣上找到的，然后让 jellyfin 只作为媒体管理器，但是前面的问题还是没解决，所以考虑了下可以在win10 下直接运行 jellyfin，媒体目录使用挂载在 nas 里的盘，这样 jellyfin 就能直接调用核显了，也算是把 win10 本身给利用起来了，并且文件的管理还是在黑裙中。
    现在想来其实我这个方案还是不太合理，cpu 性能有点过剩想通过虚拟机的形式进行隔离使用，但是购买带核显的 cpu 最大的目的却没有实现，如果是直接裸机部署黑裙的话，真的是觉得 cpu 有点太浪费了，毕竟 passmark评分有 1w3 的cpu，只用来跑黑裙，所以网上的很多建议也是合理的，不过我可能是 win10 用的比较多了，还是习惯有 win 的环境。

    ]]>     - php + nas - php + nas     @@ -4516,18 +4474,6 @@ user3: jvm - - win 下 vmware 虚拟机搭建黑裙 nas 的小思路 - /2023/06/04/win-%E4%B8%8B-vmware-%E8%99%9A%E6%8B%9F%E6%9C%BA%E6%90%AD%E5%BB%BA%E9%BB%91%E8%A3%99-nas-%E7%9A%84%E5%B0%8F%E6%80%9D%E8%B7%AF/ - 上次说 nas 的方案我是在 win10 下使用vmware workstation 搭建的黑裙虚拟机，采用 sata 物理磁盘直通的方式，算是跑通了黑裙的基础使用模式，但是后来发现的一个问题是之前没考虑到的，我买了不带 f 的处理器就是为了核显能做硬解，但是因为 cpu 是通过 vmware 虚拟的，目前看来是没法直通核显的，我是使用的 jellyfin 套件，一开始使用是默认的刮削方式，而且把电视剧当成了电影在刮削，所以基本不能看，后面使用了 tmm 作为刮削工具，可以手动填写 imdb 的id 来进行搜索，一般比较正式的剧都可以在豆瓣上找到的，然后让 jellyfin 只作为媒体管理器，但是前面的问题还是没解决，所以考虑了下可以在win10 下直接运行 jellyfin，媒体目录使用挂载在 nas 里的盘，这样 jellyfin 就能直接调用核显了，也算是把 win10 本身给利用起来了，并且文件的管理还是在黑裙中。
    现在想来其实我这个方案还是不太合理，cpu 性能有点过剩想通过虚拟机的形式进行隔离使用，但是购买带核显的 cpu 最大的目的却没有实现，如果是直接裸机部署黑裙的话，真的是觉得 cpu 有点太浪费了，毕竟 passmark评分有 1w3 的cpu，只用来跑黑裙，所以网上的很多建议也是合理的，不过我可能是 win10 用的比较多了，还是习惯有 win 的环境。

    -]]>     - - nas - - - nas - -     《寻羊历险记》读后感 /2023/07/23/%E3%80%8A%E5%AF%BB%E7%BE%8A%E5%8E%86%E9%99%A9%E8%AE%B0%E3%80%8B%E8%AF%BB%E5%90%8E%E6%84%9F/ @@ -4551,8 +4497,8 @@ user3: 生活     - 生活 读后感 + 生活 @@ -4573,47 +4519,38 @@ user3: - 上次的其他 外行聊国足 - /2022/03/06/%E4%B8%8A%E6%AC%A1%E7%9A%84%E5%85%B6%E4%BB%96-%E5%A4%96%E8%A1%8C%E8%81%8A%E5%9B%BD%E8%B6%B3/ - 上次本来想在换车牌后面聊下这个话题，为啥要聊这个话题呢，也很简单，在地铁上看到一对猜测是情侣或者比较关系好的男女同学在聊，因为是这位男同学是大学学的工科，然后自己爱好设计绘画相关的，可能还以此赚了点钱，在地铁上讨论男的要不要好好努力把大学课程完成好，大致的观点是没必要，本来就不适合，这一段我就不说了，恋爱人的嘴，信你个鬼。
    后面男的说在家里又跟他爹吵了关于男足的，估计是那次输了越南，实话说我不是个足球迷，对各方面技术相关也不熟，只是对包括这个人的解释和网上一些观点的看法，纯主观，这次地铁上这位说的大概意思是足球这个训练什么的很难的，要想赢越南也很难的，不是我们能嘴炮的；在网上看到一个赞同数很多的一个回答，说什么中国是个体育弱国，但是由于有一些乒乓球，跳水等小众项目比较厉害，让民众给误解了，首先我先来反驳下这个偷换概念的观点，第一所谓的体育弱国，跟我们觉得足球不应该这么差没半毛钱关系，因为体育弱国，我们的足球本来就不是顶尖的，也并不是去跟顶尖的球队去争，以足球为例，跟巴西，阿根廷，英国，德国，西班牙，意大利，法国这些足球强国，去比较，我相信没有一个足球迷会这么去做对比，因为我们足球历史最高排名是 1998 年的 37 名，最差是 100 名，把能数出来的强队都数完，估计都还不会到 37，所以根本没有跟强队去做对比，第二体育弱国，我们的体育投入是在逐年降低吗，我们是因战乱没法好好训练踢球？还是这帮傻逼就不争气，前面也说了我们足球世界排名最高 37，最低 100，那么前阵子我们输的越南是第几，目前我们的排名 77 名，越南 92 名，看明白了么，轮排名我们都不至于输越南，然后就是这个排名，这也是我想回应那位地铁上的兄弟，我觉得除了造核弹这种高精尖技术，绝大部分包含足球这类运动，遵循类二八原则，比如满分是 100 分，从 80 提到 90 分或者 90 分提到 100 分非常难，30 分提到 40 分，50 分提到 60 分我觉得都是可以凭后天努力达成的，基本不受天赋限制，这里可以以篮球来类比下，相对足球的确篮球没有那么火，或者行业市值没法比，但是也算是相对大众了，中国在篮球方面相对比较好一点，在 08 年奥运会冲进过八强，那也不是唯一的巅峰，但是我说这个其实是想说明两方面的事情，第一，像篮球一样，状态是有起起伏伏，排名也会变动，但是我觉得至少能维持一个相对稳定的总体排名和持平或者上升的趋势，这恰恰是我们这种所谓的“体育弱国”应该走的路线，第二就是去支持我的类二八原则的，可以看到我们的篮球这两年也很垃圾，排名跌到 29 了，那问题我觉得跟足球是一样的，就是不能脚踏实地，如斯科拉说的，中国篮球太缺少竞争，打得好不好都是这些人打，打输了还是照样拿钱，相对足球，篮球的技术我还是懂一些的，对比 08 年的中国男篮，的确像姚明跟王治郅这样的天赋型+努力型球员少了以后竞争力下降在所难免，但是去对比下基本功，传球，投篮，罚球稳定性，也完全不是一个水平的，这些就是我说的，可以通过努力训练拿 80 分的，只要拿到 80 分，甚至只要拿到 60 分，我觉得应该就还算对得起球迷了，就像 NBA 里球队也会有核心球员的更替，战绩起起伏伏，但是基本功这东西，防守积极性，我觉得不随核心球员的变化而变化，就像姚明这样的天赋，其实他应该还有一些先天缺陷，大脚趾较长等，但是他从 CBA 到 NBA，在 NBA 适应并且打成顶尖中锋，离不开刻苦训练，任何的成功都不是纯天赋的，必须要付出足够的努力。
    说回足球，如果像前面那么洗地（体育弱国），那能给我维持住一个稳定的排名我也能接受，问题是我们的经济物质资源比 2000 年前应该有了质的变化，身体素质也越来越好，即使是体育弱国，这么继续走下坡路，半死不活的，不觉得是打了自己的脸么。足球也需要基本功，基本的体能，力量这些，看看现在这些国足运动员的体型，对比下女足，说实话，如果男足这些运动员都练得不错的体脂率，耐力等，成绩即使不好，也不会比现在更差。
    纯主观吐槽，勿喷。

    + 一个经典的fastjson反序列化问题记录 + /2024/08/11/%E4%B8%80%E4%B8%AA%E7%BB%8F%E5%85%B8%E7%9A%84fastjson%E5%8F%8D%E5%BA%8F%E5%88%97%E5%8C%96%E9%97%AE%E9%A2%98%E8%AE%B0%E5%BD%95/ + 最近碰到一个问题，因为一些干扰因素导致排查的时候走了一段歧路，
    报错信息是

    +
    Exception in thread "main" com.alibaba.fastjson.JSONException: scan null error
    +

    用一个简单的demo来复现下

    +
    public enum DemoEnum {

        DEMO1("demo1", "desc");
        private String code;

        private String desc;

        DemoEnum(String code, String desc) {
            this.code = code;
            this.desc = desc;
        }
        public static DemoEnum getByCode(String code) {
            for (DemoEnum demoEnum : values()) {
                if (demoEnum.code.equals(code)) {
                    return demoEnum;
                }
            }
            return null;
        }

        public String getCode() {
            return code;
        }

        public void setCode(String code) {
            this.code = code;
        }

        public String getDesc() {
            return desc;
        }

        public void setDesc(String desc) {
            this.desc = desc;
        }
    }
    +

    先定义一个枚举类，然后有个demo类

    +
    public class FastJsonDemo implements Serializable {

        private static final long serialVersionUID = 1131767138182111892L;
        private String name;

        private Map<DemoEnum, String> map = new HashMap<>();

        public String getName() {
            return name;
        }

        public void setName(String name) {
            this.name = name;
        }

        public Map<DemoEnum, String> getMap() {
            return map;
        }

        public void setMap(Map<DemoEnum, String> map) {
            this.map = map;
        }
    }
    +

    其中map的key是上面定义的枚举类，然后在main方法中做一下序列化和反序列化

    +
    public class demo {

        public static void main(String[] args) {
            FastJsonDemo fastJsonDemo = new FastJsonDemo();
            fastJsonDemo.setName("nick");
            Map<DemoEnum, String> map = new HashMap<>();
            map.put(DemoEnum.getByCode("code"), "null key value");
            fastJsonDemo.setMap(map);
            FastJsonDemo decodeFastJsonDemo = JSONObject.parseObject(JSONObject.toJSONString(fastJsonDemo), FastJsonDemo.class);
        }
    }
    +

    这样就会出现这个异常

    +
    Exception in thread "main" com.alibaba.fastjson.JSONException: scan null error
    	at com.alibaba.fastjson.parser.JSONLexerBase.scanNullOrNew(JSONLexerBase.java:4531)
    	at com.alibaba.fastjson.parser.JSONLexerBase.nextToken(JSONLexerBase.java:154)
    	at com.alibaba.fastjson.parser.JSONLexerBase.nextToken(JSONLexerBase.java:358)
    	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.parseMap(MapDeserializer.java:227)
    	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.deserialze(MapDeserializer.java:61)
    	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.deserialze(MapDeserializer.java:41)
    	at com.alibaba.fastjson.parser.deserializer.FastjsonASMDeserializer_1_FastJsonDemo.deserialze(Unknown Source)
    	at com.alibaba.fastjson.parser.deserializer.JavaBeanDeserializer.deserialze(JavaBeanDeserializer.java:269)
    	at com.alibaba.fastjson.parser.DefaultJSONParser.parseObject(DefaultJSONParser.java:671)
    	at com.alibaba.fastjson.JSON.parseObject(JSON.java:365)
    	at com.alibaba.fastjson.JSON.parseObject(JSON.java:269)
    	at com.alibaba.fastjson.JSON.parseObject(JSON.java:488)
    	at fastjson.demo.main(demo.java:23)
    +

    这个异常有两个原因，第一当然是在map中出现了null作为key的数据，第二就是这个key是复杂对象
    在反序列化以后就会出现这个异常，因为最初出现这个异常是因为我改了另一个字符串字段，并且会反序列化成json就让我判断出现了误差
    这边做一个记录。

    ]]>     - 生活 - 运动 + java - 生活 + java     - nginx 日志小记 - /2022/04/17/nginx-%E6%97%A5%E5%BF%97%E5%B0%8F%E8%AE%B0/ - nginx 默认的日志有特定的格式，我们也可以自定义，

    -

    默认的格式是预定义的 combined

    -
    log_format combined '$remote_addr - $remote_user [$time_local] '
                        '"$request" $status $body_bytes_sent '
                        '"$http_referer" "$http_user_agent"';
    - -

    配置的日志可以使用这个默认的，如果满足需求的话

    -
    Syntax:	access_log path [format [buffer=size] [gzip[=level]] [flush=time] [if=condition]];
            access_log off;
    Default: access_log logs/access.log combined;
    Context: http, server, location, if in location, limit_except
    - -

    而如果需要额外的一些配置的话可以自己定义 log_format ，比如我想要给日志里加上请求时间，那就可以自己定义一个 log_format 比如添加下

    -
    $request_time
    request processing time in seconds with a milliseconds resolution;   
    time elapsed between the first bytes were read from the client and the log write after the last bytes were sent to the client
    - -
    log_format combined_extend '$remote_addr - $remote_user [$time_local] '
                        '"$request" $status $body_bytes_sent '
                        '"$http_referer" "$http_user_agent" "$request_time"';
    - -

    然后其他的比如还有 gzip 压缩，可以设置压缩级别，flush 刷盘时间还有根据条件控制

    -

    这里的条件控制简单看了下还比较厉害

    -

    比如我想对2xx 跟 3xx 的访问不记录日志

    -
    map $status $loggable {
        ~^[23]  0;
        default 1;
    }

    access_log /path/to/access.log combined if=$loggable;
    - -

    当 $loggable 是 0 或者空时表示 if 条件为否，上面的默认就是 1，只有当请求状态 status 是 2xx 或 3xx 时才是 0，代表不用记录，有了这个特性就可以更灵活地配置日志

    -

    文章主要参考了 nginx 的 log 模块的文档

    + 上次的其他 外行聊国足 + /2022/03/06/%E4%B8%8A%E6%AC%A1%E7%9A%84%E5%85%B6%E4%BB%96-%E5%A4%96%E8%A1%8C%E8%81%8A%E5%9B%BD%E8%B6%B3/ + 上次本来想在换车牌后面聊下这个话题，为啥要聊这个话题呢，也很简单，在地铁上看到一对猜测是情侣或者比较关系好的男女同学在聊，因为是这位男同学是大学学的工科，然后自己爱好设计绘画相关的，可能还以此赚了点钱，在地铁上讨论男的要不要好好努力把大学课程完成好，大致的观点是没必要，本来就不适合，这一段我就不说了，恋爱人的嘴，信你个鬼。
    后面男的说在家里又跟他爹吵了关于男足的，估计是那次输了越南，实话说我不是个足球迷，对各方面技术相关也不熟，只是对包括这个人的解释和网上一些观点的看法，纯主观，这次地铁上这位说的大概意思是足球这个训练什么的很难的，要想赢越南也很难的，不是我们能嘴炮的；在网上看到一个赞同数很多的一个回答，说什么中国是个体育弱国，但是由于有一些乒乓球，跳水等小众项目比较厉害，让民众给误解了，首先我先来反驳下这个偷换概念的观点，第一所谓的体育弱国，跟我们觉得足球不应该这么差没半毛钱关系，因为体育弱国，我们的足球本来就不是顶尖的，也并不是去跟顶尖的球队去争，以足球为例，跟巴西，阿根廷，英国，德国，西班牙，意大利，法国这些足球强国，去比较，我相信没有一个足球迷会这么去做对比，因为我们足球历史最高排名是 1998 年的 37 名，最差是 100 名，把能数出来的强队都数完，估计都还不会到 37，所以根本没有跟强队去做对比，第二体育弱国，我们的体育投入是在逐年降低吗，我们是因战乱没法好好训练踢球？还是这帮傻逼就不争气，前面也说了我们足球世界排名最高 37，最低 100，那么前阵子我们输的越南是第几，目前我们的排名 77 名，越南 92 名，看明白了么，轮排名我们都不至于输越南，然后就是这个排名，这也是我想回应那位地铁上的兄弟，我觉得除了造核弹这种高精尖技术，绝大部分包含足球这类运动，遵循类二八原则，比如满分是 100 分，从 80 提到 90 分或者 90 分提到 100 分非常难，30 分提到 40 分，50 分提到 60 分我觉得都是可以凭后天努力达成的，基本不受天赋限制，这里可以以篮球来类比下，相对足球的确篮球没有那么火，或者行业市值没法比，但是也算是相对大众了，中国在篮球方面相对比较好一点，在 08 年奥运会冲进过八强，那也不是唯一的巅峰，但是我说这个其实是想说明两方面的事情，第一，像篮球一样，状态是有起起伏伏，排名也会变动，但是我觉得至少能维持一个相对稳定的总体排名和持平或者上升的趋势，这恰恰是我们这种所谓的“体育弱国”应该走的路线，第二就是去支持我的类二八原则的，可以看到我们的篮球这两年也很垃圾，排名跌到 29 了，那问题我觉得跟足球是一样的，就是不能脚踏实地，如斯科拉说的，中国篮球太缺少竞争，打得好不好都是这些人打，打输了还是照样拿钱，相对足球，篮球的技术我还是懂一些的，对比 08 年的中国男篮，的确像姚明跟王治郅这样的天赋型+努力型球员少了以后竞争力下降在所难免，但是去对比下基本功，传球，投篮，罚球稳定性，也完全不是一个水平的，这些就是我说的，可以通过努力训练拿 80 分的，只要拿到 80 分，甚至只要拿到 60 分，我觉得应该就还算对得起球迷了，就像 NBA 里球队也会有核心球员的更替，战绩起起伏伏，但是基本功这东西，防守积极性，我觉得不随核心球员的变化而变化，就像姚明这样的天赋，其实他应该还有一些先天缺陷，大脚趾较长等，但是他从 CBA 到 NBA，在 NBA 适应并且打成顶尖中锋，离不开刻苦训练，任何的成功都不是纯天赋的，必须要付出足够的努力。
    说回足球，如果像前面那么洗地（体育弱国），那能给我维持住一个稳定的排名我也能接受，问题是我们的经济物质资源比 2000 年前应该有了质的变化，身体素质也越来越好，即使是体育弱国，这么继续走下坡路，半死不活的，不觉得是打了自己的脸么。足球也需要基本功，基本的体能，力量这些，看看现在这些国足运动员的体型，对比下女足，说实话，如果男足这些运动员都练得不错的体脂率，耐力等，成绩即使不好，也不会比现在更差。
    纯主观吐槽，勿喷。

    ]]>     - nginx + 生活 + 运动 - nginx - 日志 + 生活     @@ -4685,6 +4622,126 @@ user3: 中间件 + + 向量数据库学习-介绍一下HNSW算法 + /2024/06/23/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BHNSW%E7%AE%97%E6%B3%95/ + 结合AI写的一篇HNSW的介绍文章，算是向量数据库中比较核心的算法

    +

    1. 引言

    向量数据库在当今人工智能和机器学习领域扮演着越来越重要的角色。随着数据规模的不断扩大和应用场景的日益复杂,传统的数据库系统已经难以满足高维向量数据的快速检索需求。在这个背景下,近似最近邻(Approximate Nearest Neighbor, ANN)搜索问题成为了一个热点研究方向。

    +

    HNSW(Hierarchical Navigable Small World)算法是解决ANN问题的一种高效方法。它通过构建一个多层次的图结构,结合小世界网络的特性,实现了对高维向量数据的快速、精确检索。本文将深入浅出地介绍HNSW算法的核心思想、工作原理以及实际应用。

    +

    2. HNSW算法的核心思想

    HNSW算法的核心思想可以概括为三个关键点：分层结构、小世界图和近似搜索。

    +

    分层结构

    HNSW采用了一种多层次的图结构。想象一下,如果我们要在一个大城市中找到一个特定的地址,我们通常会先看城市地图,然后逐步缩小范围到区、街道,最后定位到具体门牌号。HNSW的分层结构就类似于这种由粗到细的搜索过程。这个就是之前介绍跳表的目的，就是现在稀疏的结构里可以快速定位，然后再往下层进行搜索，提高了搜索效率

    +

    小世界图

    小世界网络是一种特殊的图结构,它具有较短的平均路径长度和较高的聚类系数。在HNSW中,每一层都是一个小世界图。这种结构使得在图中的任意两点之间都存在一条相对较短的路径,大大提高了搜索效率。

    +

    近似搜索

    与传统的KNN(K-Nearest Neighbors)算法不同,HNSW通过牺牲一小部分精度来换取显著的速度提升。它不保证一定能找到真正的最近邻,但可以以极高的概率找到足够接近的点,而且搜索速度要快得多。

    +

    3. HNSW的工作原理

    数据结构:多层图的构建过程

      +
    1. HNSW从底层开始构建,每个数据点都会出现在底层。
      2. +
    2. 对于每个新插入的点,算法会随机决定它是否应该出现在上一层。这个过程一直持续到某一层,该点不再被选中。
      3. +
    3. 在每一层,新点会与该层的其他点建立连接,形成小世界图结构。
      4. +
    +

    搜索过程:自顶向下的贪婪搜索

      +
    1. 搜索从最顶层开始,选择一个随机起点。
      2. +
    2. 在当前层进行贪婪搜索,不断移动到离目标更近的邻居。
      3. +
    3. 当在当前层无法找到更近的点时,下降到下一层继续搜索。
      4. +
    4. 重复这个过程直到达到底层,得到最终结果。
      5. +
    +

    插入新向量的过程

      +
    1. 确定新向量应该出现在哪些层。
      2. +
    2. 从顶层开始,在每一层执行类似于搜索的过程,找到适合连接的邻居点。
      3. +
    3. 建立连接,同时可能需要删除一些现有连接以维持图的结构特性。

      论文中的算法是这样的
      4. +
    +
      +

    • 红色节点表示新插入的数据点。在这个例子中,新节点被插入到了层级0和层级1。

      +
      • +

    • 红色实线表示新建立的同层连接。新节点与其邻近节点建立了连接。

      +
      • +

    • 红色虚线表示新建立的跨层连接。新节点在不同层级之间建立了连接。

      +
      • +

    • 绿色虚线箭头表示插入过程的路径。这条路径展示了算法如何从顶层开始,逐层下降,最终确定新节点的插入位置。

      +
      • +

    • 其他颜色的节点和灰色的连接线表示原有的HNSW结构。

      +
      • +

    • 插入过程从顶层开始,沿着绿色虚线所示的路径向下搜索。

      +
      • +

    • 在每一层,算法都会寻找最近的邻居节点,并决定是否在该层插入新节点。

      +
      • +

    • 在本例中,新节点被插入到了层级0(基础层)和层级1。这是因为HNSW使用概率方法来决定节点应该出现在哪些层级。

      +
      • +

    • 插入后,新节点与其邻近节点建立连接(红色实线),以维持图的小世界特性。

      +
      • +

    • 同时,新节点也与其在不同层级的对应节点建立跨层连接(红色虚线),以确保层级之间的快速访问。

      +
      • +
    +

    4. HNSW的性能分析

    时间复杂度分析

      +
    • 搜索时间复杂度: O(log N),其中N是数据点的总数。
      • +
    • 插入时间复杂度: 平均情况下为O(log N)。
      • +
    +

    空间复杂度分析

      +
    • 空间复杂度: O(N log N),因为上层节点数量呈指数衰减。
      • +
    +

    与其他ANN算法的比较

    相比LSH(Locality-Sensitive Hashing)和Annoy等算法,HNSW在大多数场景下都能提供更好的查询性能和更高的准确率。然而,HNSW的内存消耗相对较高,这是它的一个潜在缺点。

    +

    5. HNSW的实际应用

    在推荐系统中的应用

    HNSW可以用于快速检索相似用户或物品,提高推荐系统的响应速度和准确性。例如,在音乐推荐中,可以用HNSW快速找到与用户喜好相似的歌曲。

    +

    在图像检索中的应用

    在大规模图像数据库中,HNSW可以实现快速的相似图像搜索。这在图像搜索引擎、视觉商品搜索等场景中非常有用。

    +

    在自然语言处理中的应用

    HNSW可以用于词嵌入或句子嵌入的快速检索,支持语义相似度计算、文本分类等任务。

    +

    6. HNSW的优化和变种

    参数调优技巧

      +
    • 层数选择:影响搜索速度和内存使用
      • +
    • 每层连接数:影响搜索准确度和构建时间
      • +
    • 候选集大小:影响搜索质量和速度的平衡
      • +
    +

    常见的HNSW变种算法介绍

      +
    • NSW-G:改进了图的构建方法,提高了搜索效率
      • +
    • HCNNG:结合了层次聚类,提高了在某些数据集上的性能
      • +
    +

    7. 实现HNSW的挑战与解决方案

    大规模数据集的处理

      +
    • 使用外部存储和分批处理
      • +
    • 采用压缩技术减少内存占用
      • +
    +

    并行化和分布式实现

      +
    • 搜索过程的并行化
      • +
    • 分布式索引构建和查询
      • +
    +

    动态更新和删除操作的处理

      +
    • 软删除技术
      • +
    • 定期重建索引
      • +
    +

    8. 总结与展望

    HNSW算法通过其独特的分层小世界图结构,在保证高准确率的同时实现了对高维向量数据的快速检索。它在推荐系统、图像检索、自然语言处理等多个领域都有广泛应用。

    +

    未来,向量数据库技术可能会朝着以下方向发展:

    +
      +
    1. 更高效的压缩和索引技术
      2. +
    2. 更好的动态更新支持
      3. +
    3. 与深度学习模型的更紧密集成
      4. +
    4. 针对特定领域的优化变体
      5. +
    +

    随着人工智能和大数据技术的不断发展,HNSW等高效的向量检索算法将在更广阔的应用场景中发挥重要作用。

    +]]>     + + 算法 + + + 算法 + +     + + 介绍一下Java的BiFunction + /2024/06/30/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BJava%E7%9A%84BiFunction/ + java的函数式接口是java8引入的很重要的特性，也让日常代码有了比较大的风格转变
    这里介绍下BiFunction，
    BiFunction的代码很短

    +
    @FunctionalInterface
    public interface BiFunction<T, U, R> {

        /**
         * Applies this function to the given arguments.
         *
         * @param t the first function argument
         * @param u the second function argument
         * @return the function result
         */
        R apply(T t, U u);

        /**
         * Returns a composed function that first applies this function to
         * its input, and then applies the {@code after} function to the result.
         * If evaluation of either function throws an exception, it is relayed to
         * the caller of the composed function.
         *
         * @param <V> the type of output of the {@code after} function, and of the
         *           composed function
         * @param after the function to apply after this function is applied
         * @return a composed function that first applies this function and then
         * applies the {@code after} function
         * @throws NullPointerException if after is null
         */
        default <V> BiFunction<T, U, V> andThen(Function<? super R, ? extends V> after) {
            Objects.requireNonNull(after);
            return (T t, U u) -> after.apply(apply(t, u));
        }
    }
    +

    默认的方法就跟普通的FunctionalInterface一样，就有一个apply方法，只是这里的特殊点有两个

    +

    返回值作为参数

    这个BiFunction有三个泛型参数，T，U跟R，而这个R其实是具体方法的返回值
    比如我们简单实现一个字符串拼接

    +
    BiFunction<String, String, String> biDemo = (s1, s2) -> s1 + s2;
    System.out.println(biDemo.apply("hello ", "world"));
    +

    apply返回的就是参数R

    +

    andThen

    这个方法是可以让结果再调用传入的方法，并返回结果

    +
    Function<String, Integer> funcDemo = String::length;
    System.out.println(biDemo.andThen(funcDemo).apply("hello ", "world"));
    +

    定义了个函数接口，并且给了实现，就是字符串获取长度
    然后传给biDemo，这样就可以输出拼接后的字符串长度

    +

    组合使用

    public <T1, T2, R1, R2> R2 combination(T1 t1, T2 t2, BiFunction<T1, T2, R1> biFunction, Function<R1, R2> function) {
        return biFunction.andThen(function).apply(t1, t2);
    }
    +

    这样就把前面的两个方法串起来了

    +]]>     + + java + + + java + stream + +     介绍下最近比较实用的端口转发 /2021/11/14/%E4%BB%8B%E7%BB%8D%E4%B8%8B%E6%9C%80%E8%BF%91%E6%AF%94%E8%BE%83%E5%AE%9E%E7%94%A8%E7%9A%84%E7%AB%AF%E5%8F%A3%E8%BD%AC%E5%8F%91/ @@ -4746,19 +4803,23 @@ user3: - 一个经典的fastjson反序列化问题记录 - /2024/08/11/%E4%B8%80%E4%B8%AA%E7%BB%8F%E5%85%B8%E7%9A%84fastjson%E5%8F%8D%E5%BA%8F%E5%88%97%E5%8C%96%E9%97%AE%E9%A2%98%E8%AE%B0%E5%BD%95/ - 最近碰到一个问题，因为一些干扰因素导致排查的时候走了一段歧路，
    报错信息是

    -
    Exception in thread "main" com.alibaba.fastjson.JSONException: scan null error
    -

    用一个简单的demo来复现下

    -
    public enum DemoEnum {

        DEMO1("demo1", "desc");
        private String code;

        private String desc;

        DemoEnum(String code, String desc) {
            this.code = code;
            this.desc = desc;
        }
        public static DemoEnum getByCode(String code) {
            for (DemoEnum demoEnum : values()) {
                if (demoEnum.code.equals(code)) {
                    return demoEnum;
                }
            }
            return null;
        }

        public String getCode() {
            return code;
        }

        public void setCode(String code) {
            this.code = code;
        }

        public String getDesc() {
            return desc;
        }

        public void setDesc(String desc) {
            this.desc = desc;
        }
    }
    -

    先定义一个枚举类，然后有个demo类

    -
    public class FastJsonDemo implements Serializable {

        private static final long serialVersionUID = 1131767138182111892L;
        private String name;

        private Map<DemoEnum, String> map = new HashMap<>();

        public String getName() {
            return name;
        }

        public void setName(String name) {
            this.name = name;
        }

        public Map<DemoEnum, String> getMap() {
            return map;
        }

        public void setMap(Map<DemoEnum, String> map) {
            this.map = map;
        }
    }
    -

    其中map的key是上面定义的枚举类，然后在main方法中做一下序列化和反序列化

    -
    public class demo {

        public static void main(String[] args) {
            FastJsonDemo fastJsonDemo = new FastJsonDemo();
            fastJsonDemo.setName("nick");
            Map<DemoEnum, String> map = new HashMap<>();
            map.put(DemoEnum.getByCode("code"), "null key value");
            fastJsonDemo.setMap(map);
            FastJsonDemo decodeFastJsonDemo = JSONObject.parseObject(JSONObject.toJSONString(fastJsonDemo), FastJsonDemo.class);
        }
    }
    -

    这样就会出现这个异常

    -
    Exception in thread "main" com.alibaba.fastjson.JSONException: scan null error
    	at com.alibaba.fastjson.parser.JSONLexerBase.scanNullOrNew(JSONLexerBase.java:4531)
    	at com.alibaba.fastjson.parser.JSONLexerBase.nextToken(JSONLexerBase.java:154)
    	at com.alibaba.fastjson.parser.JSONLexerBase.nextToken(JSONLexerBase.java:358)
    	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.parseMap(MapDeserializer.java:227)
    	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.deserialze(MapDeserializer.java:61)
    	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.deserialze(MapDeserializer.java:41)
    	at com.alibaba.fastjson.parser.deserializer.FastjsonASMDeserializer_1_FastJsonDemo.deserialze(Unknown Source)
    	at com.alibaba.fastjson.parser.deserializer.JavaBeanDeserializer.deserialze(JavaBeanDeserializer.java:269)
    	at com.alibaba.fastjson.parser.DefaultJSONParser.parseObject(DefaultJSONParser.java:671)
    	at com.alibaba.fastjson.JSON.parseObject(JSON.java:365)
    	at com.alibaba.fastjson.JSON.parseObject(JSON.java:269)
    	at com.alibaba.fastjson.JSON.parseObject(JSON.java:488)
    	at fastjson.demo.main(demo.java:23)
    -

    这个异常有两个原因，第一当然是在map中出现了null作为key的数据，第二就是这个key是复杂对象
    在反序列化以后就会出现这个异常，因为最初出现这个异常是因为我改了另一个字符串字段，并且会反序列化成json就让我判断出现了误差
    这边做一个记录。

    + 体验下Java 21的虚拟线程-协程 + /2024/08/18/%E4%BD%93%E9%AA%8C%E4%B8%8BJava-21%E7%9A%84%E8%99%9A%E6%8B%9F%E7%BA%BF%E7%A8%8B-%E5%8D%8F%E7%A8%8B/ + Java在后续版本中添加了虚拟线程，也是类似于php跟go的协程，对应操作系统的线程是在线程基础上模拟了一层子线程的逻辑，因为减少了操作系统的线程上下文切换开销，能够在常规业务场景带了比较大的性能提升，但也并非银弹，不能包治百病
    首先安装下jdk 21 版本，需要用 /usr/libexec/java_home 切换下JAVA_HOME
    然后在PATH中设置好

    +
    export JAVA_HOME=$(/usr/libexec/java_home -v 21)
    +

    首先是试一下线程版本

    +
    long start = System.currentTimeMillis();
    ExecutorService executor = Executors.newFixedThreadPool(200);
    for (int i = 0; i < 100000; i++) {
        executor.submit(() -> {
            try {
                // 线程睡眠 10ms，可以等同于模拟业务耗时10ms
                    TimeUnit.MILLISECONDS.sleep(10);
                } catch (InterruptedException e) {

            }
        });
    }
    executor.close();
    System.out.printf("totalMillis：%dms\n", System.currentTimeMillis() - start);
    +


    耗时5897ms

    +
    long start = System.currentTimeMillis();
    ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();
    for (int i = 0; i < 10000; i++) {
        executor.submit(() -> {
            // 模拟业务处理
            try {
                TimeUnit.MILLISECONDS.sleep(10);
            } catch (InterruptedException e) {

            }
        });
    }
    executor.close();
    System.out.printf("totalMillis：%dms\n", System.currentTimeMillis() - start);
    +


    耗时154ms
    相对来说还是能快很多的
    而核心的虚拟线程实现主要来自于调度

    +
    static Thread newVirtualThread(Executor scheduler,
                                   String name,
                                   int characteristics,
                                   Runnable task) {
        if (ContinuationSupport.isSupported()) {
            return new VirtualThread(scheduler, name, characteristics, task);
        } else {
            if (scheduler != null)
                throw new UnsupportedOperationException();
            return new BoundVirtualThread(name, characteristics, task);
        }
    }
    +

    在创建虚拟线程过程中，我们需要去处理调度器，初始时调度器为空

    +
    VirtualThread(Executor scheduler, String name, int characteristics, Runnable task) {
        super(name, characteristics, /*bound*/ false);
        Objects.requireNonNull(task);

        // choose scheduler if not specified
        if (scheduler == null) {
            Thread parent = Thread.currentThread();
            if (parent instanceof VirtualThread vparent) {
                scheduler = vparent.scheduler;
            } else {
                scheduler = DEFAULT_SCHEDULER;
            }
        }

        this.scheduler = scheduler;
        this.cont = new VThreadContinuation(this, task);
        this.runContinuation = this::runContinuation;
    }
    +

    而这个默认调度器就是

    +
    private static final ForkJoinPool DEFAULT_SCHEDULER = createDefaultScheduler();
    +

    对应创建的就是个ForkJoinPool

    +
    private static ForkJoinPool createDefaultScheduler() {
            ForkJoinWorkerThreadFactory factory = pool -> {
                PrivilegedAction<ForkJoinWorkerThread> pa = () -> new CarrierThread(pool);
                return AccessController.doPrivileged(pa);
            };
            PrivilegedAction<ForkJoinPool> pa = () -> {
                int parallelism, maxPoolSize, minRunnable;
                String parallelismValue = System.getProperty("jdk.virtualThreadScheduler.parallelism");
                String maxPoolSizeValue = System.getProperty("jdk.virtualThreadScheduler.maxPoolSize");
                String minRunnableValue = System.getProperty("jdk.virtualThreadScheduler.minRunnable");
                if (parallelismValue != null) {
                    parallelism = Integer.parseInt(parallelismValue);
                } else {
                    parallelism = Runtime.getRuntime().availableProcessors();
                }
                if (maxPoolSizeValue != null) {
                    maxPoolSize = Integer.parseInt(maxPoolSizeValue);
                    parallelism = Integer.min(parallelism, maxPoolSize);
                } else {
                    maxPoolSize = Integer.max(parallelism, 256);
                }
                if (minRunnableValue != null) {
                    minRunnable = Integer.parseInt(minRunnableValue);
                } else {
                    minRunnable = Integer.max(parallelism / 2, 1);
                }
                Thread.UncaughtExceptionHandler handler = (t, e) -> { };
                boolean asyncMode = true; // FIFO
                return new ForkJoinPool(parallelism, factory, handler, asyncMode,
                             0, maxPoolSize, minRunnable, pool -> true, 30, SECONDS);
            };
            return AccessController.doPrivileged(pa);
        }
    +

    可以看到参数都是通过系统参数获取，或者用系统的cpu数量来决定并行度，主体的逻辑就是既然系统线程开销大，那我就在系统线程内部模拟一个更小颗粒度的，在线程内部进行调度的模型，以此来减少系统切换开销，只不过细节还有很多需要研究，有兴趣的可以留言探讨

    ]]>     java @@ -4779,6 +4840,40 @@ user3: nas     + + 使用milvus和towhee实现简陋版的以图搜图 + /2024/08/04/%E4%BD%BF%E7%94%A8milvus%E5%92%8Ctowhee%E5%AE%9E%E7%8E%B0%E7%AE%80%E9%99%8B%E7%89%88%E7%9A%84%E4%BB%A5%E5%9B%BE%E6%90%9C%E5%9B%BE/ + 上次我们尝试用 towhee 跟 milvus 实现了图片的向量化，那么顺势我们就能在这个基础上实现以图搜图
    首先我们找一些图片，

    然后我们先把他们都向量化，存储在milvus里

    +
    from towhee import AutoPipes, AutoConfig, ops
    import towhee
    import os
    from pymilvus import MilvusClient
    import json
     
    # 1. 设置一个Milvus客户端
    client = MilvusClient(
        uri="http://localhost:19530"
    )
    insert_conf = AutoConfig.load_config('insert_milvus')
    insert_conf.collection_name = 'text_image_search'


    insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

    # 创建图像嵌入管道
    image_embedding_pipe = AutoPipes.pipeline('image-embedding')
    files = os.listdir("./images")
    for file in files: 
        file_path = os.path.join("./images", file)
        if os.path.isfile(file_path):
            embedding = image_embedding_pipe(file_path).get()[0]
            insert_pipe([file_path, embedding])
    +

    然后我们找一张神仙姐姐的其他图片，先把它 embedding 后在 milvus 里进行向量检索

    +
    image_embedding_pipe = AutoPipes.pipeline('image-embedding')

    # 生成嵌入
    embedding = image_embedding_pipe('./to_search.jpg').get()[0]
    print(embedding)
    res = client.search(
        collection_name="text_image_search",
        data=[embedding],
        limit=5,
        search_params={"metric_type": "IP", "params": {}}
    )
    result = json.dumps(res, indent=4)
    print(result)
    +

    我们检索出来5个结果，

    可以通过distance找到距离最近的这个是id=451291280409722600
    可以发现也是神仙姐姐的,只是作为参考
    to_search 目标图片是

    搜索的最短距离就是id对应的图片

    这张图片就是神仙姐姐

    +]]>     + + 算法 + + + 算法 + +     + + 关于 npe 的一个小记忆点 + /2023/07/16/%E5%85%B3%E4%BA%8E-npe-%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E8%AE%B0%E5%BF%86%E7%82%B9/ + Java 中最常见的一类问题或者说异常就是 NullPointerException，而往往这种异常我们在查看日志的时候都是根据打印出来的异常堆栈找到对应的代码以确定问题，但是有一种情况会出现一个比较奇怪的情况，虽然我们在日志中打印了异常

    +
    log.error("something error", e);
    +

    这样是可以打印出来日志，譬如 slf4j 就可以把异常作为最后一个参数传入

    +
    void error(String var1, Throwable var2);
    +

    有的时候如果不小心把异常通过占位符传入就可能出现异常被吞的情况，不过这点不是这次的重点
    即使我们正常作为最后一个参数传入了，也会出现只打印出来

    +
    something error, java.lang.NullPointerException: null
    +

    这样就导致了如果我们这个日志所代表的异常包含的代码范围比较大的话，就不确定具体是哪一行出现的异常，除非对这些代码逻辑非常清楚，这个是为什么呢，其实这也是 jvm 的一种优化机制，像我们目前主要还在用的 jdk8中使用的 jvm 虚拟机 hotspot 的一个热点异常机制，对于这样一直出现的相同异常，就认为是热点异常，后面只会打印异常名，不再打印异常堆栈，这样对于 io 性能能够提供一定的优化保证，这个可以通过 jvm 启动参数
    -XX:-OmitStackTraceInFastThrow 来关闭优化，这个问题其实在一开始造成了一定困扰，找不准具体是哪一行代码，不过在知道这个之后简单的重启也能暂时解决问题。

    +]]>     + + java + + + java + +     关于公共交通再吐个槽 /2021/03/21/%E5%85%B3%E4%BA%8E%E5%85%AC%E5%85%B1%E4%BA%A4%E9%80%9A%E5%86%8D%E5%90%90%E4%B8%AA%E6%A7%BD/ @@ -4807,8 +4902,8 @@ user3: 最近群里大佬发起了一个读书打卡活动，需要每天读一会书，在群里打卡分享感悟，争取一个月能读完一本书，说实话一天十分钟的读书时间倒是问题不大，不过每天都要打卡，而且一个月要读完一本书，其实难度还是有点大的，不过也想试试看。
    之前某某老大给自己立了个 flag，说要读一百本书，这对我来说挺难实现的，一则我也不喜欢书只读一小半，二则感觉对于喜欢看的内容范围还是比较有限制，可能也算是比较矫情，不爱追热门的各类东西，因为往往会有一些跟大众不一致的观点看法，显得格格不入。所以还是这个打卡活动可能会比较适合我，书是人类进步的阶梯。
    到现在是打卡了三天了，读的主要是白岩松的《幸福了吗》，对于白岩松，我们这一代人是比较熟悉，并且整体印象比较不错的一个央视主持人，从《焦点访谈》开始，到疫情期间的各类一线节目，可能对我来说是个三观比较正，敢于说一些真话的主持人，这中间其实是有个空档期，没怎么看电视，也不太关注了，只是在疫情期间的节目，还是一如既往地给人一种可靠的感觉，正好有一次偶然微信读书推荐了白岩松的这本书，就看了一部分，正好这次继续往下看，因为相对来讲不会很晦涩，并且从这位知名央视主持人的角度分享他的过往和想法看法，还是比较有意思的。
    从对汶川地震，08 年奥运等往事的回忆和一些细节的呈现，也让我了解比较多当时所不知道的，特别是汶川地震，那时的我还在读高中，真的是看着电视，作为“猛男”都忍不住泪目了，共和国之殇，多难兴邦，但是这对于当事人来说，都是一场醒不过来的噩梦。
    然后是对于足球的热爱，其实光这个就能掰扯很多，因为我不爱足球，只爱篮球，其中原因有的没的也挺多可以说的，但是看了他的书，才能比较深入的了解一个足球迷，对足球，对中国足球，对世界杯，对阿根廷的感情。
    接下去还是想能继续坚持下去，加油！

    ]]>     - 生活 读后感 + 生活 白岩松 幸福了吗 @@ -4820,32 +4915,6 @@ user3: 足球     - - 体验下Java 21的虚拟线程-协程 - /2024/08/18/%E4%BD%93%E9%AA%8C%E4%B8%8BJava-21%E7%9A%84%E8%99%9A%E6%8B%9F%E7%BA%BF%E7%A8%8B-%E5%8D%8F%E7%A8%8B/ - Java在后续版本中添加了虚拟线程，也是类似于php跟go的协程，对应操作系统的线程是在线程基础上模拟了一层子线程的逻辑，因为减少了操作系统的线程上下文切换开销，能够在常规业务场景带了比较大的性能提升，但也并非银弹，不能包治百病
    首先安装下jdk 21 版本，需要用 /usr/libexec/java_home 切换下JAVA_HOME
    然后在PATH中设置好

    -
    export JAVA_HOME=$(/usr/libexec/java_home -v 21)
    -

    首先是试一下线程版本

    -
    long start = System.currentTimeMillis();
    ExecutorService executor = Executors.newFixedThreadPool(200);
    for (int i = 0; i < 100000; i++) {
        executor.submit(() -> {
            try {
                // 线程睡眠 10ms，可以等同于模拟业务耗时10ms
                    TimeUnit.MILLISECONDS.sleep(10);
                } catch (InterruptedException e) {

            }
        });
    }
    executor.close();
    System.out.printf("totalMillis：%dms\n", System.currentTimeMillis() - start);
    -


    耗时5897ms

    -
    long start = System.currentTimeMillis();
    ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();
    for (int i = 0; i < 10000; i++) {
        executor.submit(() -> {
            // 模拟业务处理
            try {
                TimeUnit.MILLISECONDS.sleep(10);
            } catch (InterruptedException e) {

            }
        });
    }
    executor.close();
    System.out.printf("totalMillis：%dms\n", System.currentTimeMillis() - start);
    -


    耗时154ms
    相对来说还是能快很多的
    而核心的虚拟线程实现主要来自于调度

    -
    static Thread newVirtualThread(Executor scheduler,
                                   String name,
                                   int characteristics,
                                   Runnable task) {
        if (ContinuationSupport.isSupported()) {
            return new VirtualThread(scheduler, name, characteristics, task);
        } else {
            if (scheduler != null)
                throw new UnsupportedOperationException();
            return new BoundVirtualThread(name, characteristics, task);
        }
    }
    -

    在创建虚拟线程过程中，我们需要去处理调度器，初始时调度器为空

    -
    VirtualThread(Executor scheduler, String name, int characteristics, Runnable task) {
        super(name, characteristics, /*bound*/ false);
        Objects.requireNonNull(task);

        // choose scheduler if not specified
        if (scheduler == null) {
            Thread parent = Thread.currentThread();
            if (parent instanceof VirtualThread vparent) {
                scheduler = vparent.scheduler;
            } else {
                scheduler = DEFAULT_SCHEDULER;
            }
        }

        this.scheduler = scheduler;
        this.cont = new VThreadContinuation(this, task);
        this.runContinuation = this::runContinuation;
    }
    -

    而这个默认调度器就是

    -
    private static final ForkJoinPool DEFAULT_SCHEDULER = createDefaultScheduler();
    -

    对应创建的就是个ForkJoinPool

    -
    private static ForkJoinPool createDefaultScheduler() {
            ForkJoinWorkerThreadFactory factory = pool -> {
                PrivilegedAction<ForkJoinWorkerThread> pa = () -> new CarrierThread(pool);
                return AccessController.doPrivileged(pa);
            };
            PrivilegedAction<ForkJoinPool> pa = () -> {
                int parallelism, maxPoolSize, minRunnable;
                String parallelismValue = System.getProperty("jdk.virtualThreadScheduler.parallelism");
                String maxPoolSizeValue = System.getProperty("jdk.virtualThreadScheduler.maxPoolSize");
                String minRunnableValue = System.getProperty("jdk.virtualThreadScheduler.minRunnable");
                if (parallelismValue != null) {
                    parallelism = Integer.parseInt(parallelismValue);
                } else {
                    parallelism = Runtime.getRuntime().availableProcessors();
                }
                if (maxPoolSizeValue != null) {
                    maxPoolSize = Integer.parseInt(maxPoolSizeValue);
                    parallelism = Integer.min(parallelism, maxPoolSize);
                } else {
                    maxPoolSize = Integer.max(parallelism, 256);
                }
                if (minRunnableValue != null) {
                    minRunnable = Integer.parseInt(minRunnableValue);
                } else {
                    minRunnable = Integer.max(parallelism / 2, 1);
                }
                Thread.UncaughtExceptionHandler handler = (t, e) -> { };
                boolean asyncMode = true; // FIFO
                return new ForkJoinPool(parallelism, factory, handler, asyncMode,
                             0, maxPoolSize, minRunnable, pool -> true, 30, SECONDS);
            };
            return AccessController.doPrivileged(pa);
        }
    -

    可以看到参数都是通过系统参数获取，或者用系统的cpu数量来决定并行度，主体的逻辑就是既然系统线程开销大，那我就在系统线程内部模拟一个更小颗粒度的，在线程内部进行调度的模型，以此来减少系统切换开销，只不过细节还有很多需要研究，有兴趣的可以留言探讨

    -]]>     - - java - - - java - -     分享一次折腾老旧笔记本的体验-续篇 /2023/02/12/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%80%81%E6%97%A7%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%9A%84%E4%BD%93%E9%AA%8C-%E7%BB%AD%E7%AF%87/ @@ -4877,265 +4946,80 @@ user3: /2023/02/05/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%80%81%E6%97%A7%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%9A%84%E4%BD%93%E9%AA%8C/ 上大学的时候买了第一个笔记本，是联想的小y，y460，配置应该是 i5+2g 内存，500g 硬盘，ati 5650 的显卡，那时候还是比较时髦的带有集显和独显切换的，一些人也觉得它算是一代“神机”，陪我度过了大学四年，还有毕业后的第一年，记得中间还用的比较曲折，差不多第一学期末的时候硬盘感觉有时候会文件操作卡住，去售后看了下，果然硬盘出问题了，还在当场把硬盘的东西都拷贝到新买的移动硬盘里，不过联想的售后还是比较不错的，在保内可以直接免费换，如果出保了估计就要修不起了，后面出了个很大的问题，就是屏幕花屏了，而且它的花屏是横着的会有那种上升的变化，一开始感觉跟湿度还有温度有点关系，天气冷了就很容易出现，天气热了就好一点，出现的概率小一点，而且是合盖后再开会好，过一会又会上升，只是这么度过了保修期，去售后问了下，修一下要两千多，后来是在玉泉学校里面的维修店，好像是花了六百多，省是省了很多，但是后面使用的时候发现有点漏光，而且两三个亮点，总归还是给我那不富裕的经济条件带来了一个正常屏幕，不过是挺影响使用，严格来说都没法用了，后面基本是大半个屏幕花掉，接近整个屏幕花掉，至此大学就是这么用过去了。
    噢对了，中间在大二的时候加了一条 2g 的内存，因为像操作系统课需要用虚拟机，2g 内存不太够，不过那会用的都是 32 位的 win7 系统，实际使用用不到 4g 内存，得使用破解补丁才能识别到，后来在大学毕业后由于收入也低，想换其他电脑，特别是 mac，买不起，电脑在那会还会玩玩 DNF，但是风扇声很大，也想优化下，就买了个 msata 的固态硬盘，因为刚好有个口子留着，在那之前一直搜到的是把光驱位拆掉换上 sata 固态硬盘，这对于那会的我来说操作难度实在有点大，换上了msata 固态之后还是有比较大的提升的，把操作系统装到固态硬盘后其他盘就只放数据了，后面还装过黑苹果，只是不小心被室友强制关机了，之后就起不来了，也不知道啥原因，后续想继续按原来的操作装，也没成功，再往后就配了一台台式机，这个笔记本就放在那没啥用了，后面偶尔会拿出来用一下，特别是疫情那会充当了 LD 的临时使用机器。
    最近一次就是想把行车记录仪里的视频导出来，结果上传的时候我点了下 win10 的更新，重启后这个机器就起不来了，一开始以为顶多是个重装系统，结果重装也不行，就进不去，插着 U 盘也起不来，一开始还能进 BIOS，后面就直接进不去了，像内存条脏了，擦下金手指什么的，都没搞定，想着也只可能是主板上的部件坏了，所以就开始了这趟半作死之旅，买了块主板来换，拆这个笔记本是真的够难的，开机键那一条是真的拆不开，后面还把两个扣子扳坏了，里面的排线插口还几个也拔坏了，幸好用买来的主板装上还能用，但是后面就很奇怪了，因为这个电脑装了 Ubuntu 跟 win10 的双系统，Ubuntu 能正常起来，但是 win10 就是起不来，插上老毛桃或者 win10 的启动盘都是起不来，老毛桃是能起来，但是进不了 pe，插启动盘又报 0xc00000e9 错误码，现在想着是不是这个固态硬盘有点问题或者还是内存问题，只能后续再看了，拆机的时候也找不到视频，机器实在是太老了，后面再试试看。

    ]]>     - - Windows - 小技巧 - - - Windows - -     - - 分享一次比较诡异的 Windows 下 U盘无法退出的经历 - /2023/01/29/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%AF%94%E8%BE%83%E8%AF%A1%E5%BC%82%E7%9A%84-Windows-%E4%B8%8B-U%E7%9B%98%E6%97%A0%E6%B3%95%E9%80%80%E5%87%BA%E7%9A%84%E7%BB%8F%E5%8E%86/ - 作为一个 Windows 的老用户，并且也算是 Windows 系统的半个粉丝，但是秉承一贯的优缺点都应该说的原则，Windows 系统有一点缺点是真的挺难受，相信 Windows 用过比较久的都会经历过，就是 U盘无法退出的问题，在比较远古时代，这个问题似乎能采取的措施不多，关机再拔 U盘的方式是一种比较保险的方式，其他貌似有 360这种可以解除占用的，但是需要安装 360 软件，对于目前的使用环境来说有点得不偿失，也是比较流氓的一类软件了，目前在 Windows 环境我主要就安装了个火绒，或者就用 Windows 自带的 defender。

    -

    第一种

    最近这次经历也是有火绒的一定责任，在我尝试推出 U盘的时候提示了我被另一个大流氓软件，XlibabaProtect.exe 占用了，这个流氓软件真的是充分展示了某里的技术实力，试过 N 多种办法都关不掉也删不掉，尝试了很多种办法也没办法删除，但是后面换了种思路，一般这种情况肯定是有进程在占用 U盘里的内容，最新版本的 Powertoys 会在文件的右键菜单里添加一个叫 File Locksmith 的功能，可以用于检查正在使用哪些文件以及由哪些进程使用，但是可能是我的使用姿势不对，没有仔细看文档，它里面有个”以管理员身份重启”,可能会有用。
    这算是第一种方式，

    -

    第二种

    第二种方式是 Windows 任务管理器中性能 tab 下的”打开资源监视器”，
    ,假如我的 U 盘的盘符是F:
    就可以搜索到占用这个盘符下文件的进程，这里千万小心‼️‼️，不可轻易杀掉这些进程，有些系统进程如果轻易杀掉会导致蓝屏等问题，不可轻易尝试，除非能确认这些进程的作用。
    对于前两种方式对我来说都无效，

    -

    第三种

    所以尝试了第三种，
    就是磁盘脱机的方式，在”计算机”右键管理，点击”磁盘管理”,可以找到 U 盘盘符右键，点击”脱机”，然后再”推出”，这个对我来说也不行

    -

    第四种

    这种是唯一对我有效的，在开始菜单搜索”event”，可以搜到”事件查看器”,
    ，这个可以看到当前最近 Windows 发生的事件，打开这个后就点击U盘推出，因为推不出来也是一种错误事件，点击下刷新就能在这看到具体是因为什么推出不了，具体的进程信息
    最后发现是英特尔的驱动管理程序的一个进程，关掉就退出了，虽然前面说的某里的进程是流氓，但这边是真的冤枉它了

    -]]>     - - Windows - 小技巧 - - - Windows - -     - - 分享记录一下一个 git 操作方法 - /2022/02/06/%E5%88%86%E4%BA%AB%E8%AE%B0%E5%BD%95%E4%B8%80%E4%B8%8B%E4%B8%80%E4%B8%AA-git-%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95/ - 前阵子一个同事因为发现某个分支上的代码好像有缺失导致无法正常运行，然后就对比了下把缺失的代码从另一个分支上拷了过来，可能有所欠考虑，不过主要是说下操作过程和最后的处理方法，这位同学的操作是改一些代码commit 一下，这样的 commit 了大概五六次，并且已经 push 到了远端，然后就在想要怎么去处理，在本地可以 reset，已经到远端了，一个很不优雅的操作就是本地 reset 了用 force push，这个当然是不可取的，然后就是 revert 了，但是又已经 commit 了好几次了，网上看了下，好像处理方法还挺成熟的，git revert 命令本质上就是一个逆向的 git cherry-pick 操作。 它将你提交中的变更的以完全相反的方式的应用到一个新创建的提交中，本质上就是撤销或者倒转。可以理解为就是提交一个反向的操作，这里其实我们可以用range revert来进行 git revert, 用法就是

    -
    git revert OLDER_COMMIT^..NEWER_COMMIT
    -

    这样就可以解决上面的问题了，但是还有个问题是这样会根据前面的 commit 数量提交对应数量个 revert commit 会显得比较乱，如果要比较干净的 commit 历史，
    可以看下 git revert 命令说明

    然后就可以用 -n 参数，表示不自动提交

    -
    git revert -n OLDER_COMMIT^..NEWER_COMMIT
    git commit -m "revert OLDER_COMMIT to NEWER_COMMIT"
    - - -]]>     - - git - 小技巧 - - - git - -     - - 分享记录一下一个 scp 操作方法 - /2022/02/06/%E5%88%86%E4%BA%AB%E8%AE%B0%E5%BD%95%E4%B8%80%E4%B8%8B%E4%B8%80%E4%B8%AA-scp-%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95/ - scp 是个在服务器之间拷贝文件的一个常用命令，有时候有个场景是比如我们需要拷贝一些带有共同前缀的文件，但是有一个问题是比如我们有使用 zsh 的话，会出现一个报错，

    -
    zsh: no matches found: root@100.100.100.100://root/prefix*
    -

    这里就比较奇怪了，这个前缀的文件肯定是有的，这里其实是由于 zsh 会对 * 进行展开，这个可以在例如 ls 命令在使用中就可以发现 zsh 有这个特性
    需要使用双引号或单引号将路径包起来或者在*之前加反斜杠\来阻止对*展开和转义

    -
    scp root@100.100.100.100://root/prefix* .
    -

    通过使用双引号"进行转义

    -
    scp root@100.100.100.100:"//root/prefix*" .
    -

    或者可以将 shell 从 zsh 切换成 bash

    -]]>     - - shell - 小技巧 - - - scp - -     - - 向量数据库 Milvus 安装初体验 - /2024/07/21/%E5%90%91%E9%87%8F%E6%95%B0%E6%8D%AE%E5%BA%93-Milvus-%E5%AE%89%E8%A3%85%E5%88%9D%E4%BD%93%E9%AA%8C/ - 之前做了个简单的铺垫，作为大模型应用技术领域非常重要的一环，向量数据库我们在前面有做一些引导性的介绍，其中的索引技术，
    而在众多向量数据库比较有代表性的 Milvus，这边我们来尝试安装 Milvus 初体验一下
    因为只是初体验，不作为生产环境使用，所以就用最简单的方式，Docker单机部署的方式
    首先需要需要有Docker环境
    确认下 docker 命令可执行
    然后拉取启动脚本

    -
    wget https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh
    bash standalone_embed.sh start
    -

    启动过程中需要注意一些问题，这也是有点想吐槽的，这个官方的启动脚本竟然都不是稳定成功的，最开始就是失败的，后面重新删除拉下来的镜像再启动就好了
    并且官方提供的demo python版本的示例也是有问题的
    因为主要是java的缘故，就用了java的sdk来尝试
    只是我们可以先运行一个ui工具

    -
    docker run -p 8000:3000 -e MILVUS_URL={milvus server IP}:19530 zilliz/attu:v2.4
    -


    这里就能看到运行情况
    然后我们运行个简单的代码

    -
    package MilvusDemo;

    import com.google.gson.Gson;
    import com.google.gson.JsonObject;
    import com.google.gson.JsonParser;
    import io.milvus.v2.client.ConnectConfig;
    import io.milvus.v2.client.MilvusClientV2;
    import io.milvus.v2.common.DataType;
    import io.milvus.v2.common.IndexParam;
    import io.milvus.v2.service.collection.request.AddFieldReq;
    import io.milvus.v2.service.collection.request.CreateCollectionReq;
    import io.milvus.v2.service.index.request.CreateIndexReq;
    import io.milvus.v2.service.vector.request.InsertReq;

    import java.util.ArrayList;
    import java.util.List;

    /**
     * @author shixuesen
     * @date 2024/7/21
     */
    public class Demo {

        public static void main(String[] args) {
            ConnectConfig connectConfig = ConnectConfig.builder()
                            .uri("http://127.0.0.1:19530")
                                    .build();
            // 连接配置
            MilvusClientV2 clientV2 = new MilvusClientV2(connectConfig);
            // define a Collection Schema
            CreateCollectionReq.CollectionSchema collectionSchema = clientV2.createSchema();
    // add two fileds, id and vector
            Integer dim = 5;
            // 这里的 CollectionSchema 就类似于mysql的数据库表结构
            collectionSchema.addField(AddFieldReq.builder().fieldName("my_id").dataType(DataType.Int64).isPrimaryKey(Boolean.TRUE).autoID(Boolean.FALSE).description("id").build());
            collectionSchema.addField(AddFieldReq.builder().fieldName("my_vector").dataType(DataType.FloatVector).dimension(dim).build());

            CreateCollectionReq req = CreateCollectionReq
                    .builder()
                    .collectionSchema(collectionSchema)
                    .collectionName("quick_setup")
                    .dimension(dim).build();
            //  通过schema 生成 Collection，就类似于mysql中的表
            clientV2.createCollection(req);

            // 然后可以对字段创建索引
            IndexParam indexParam = IndexParam.builder()
                    .fieldName("my_id")
                    .build();
            IndexParam vertorIndexParam = IndexParam.builder()
                            .fieldName("my_vector")
                                    .indexType(IndexParam.IndexType.AUTOINDEX)
                                            .metricType(IndexParam.MetricType.IP)
                                                    .build();
            List<IndexParam> indexParamList = new ArrayList<>();
            indexParamList.add(indexParam);
            indexParamList.add(vertorIndexParam);


            // 接着创建索引
            clientV2.createIndex(CreateIndexReq.builder()
                    .collectionName("quick_setup")
                    .indexParams(indexParamList)
                    .build());
            JsonObject jsonObject  = JsonParser.parseString("{\"my_id\": 0, \"my_vector\": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], \"color\": \"pink_8682\"}").getAsJsonObject();
            List<JsonObject> jsonData = new ArrayList<>();
            jsonData.add(jsonObject);
            // 然后插入数据
            clientV2.insert(InsertReq.builder().collectionName("quick_setup")
                    .data(jsonData).build());

            // 插入之后我们想进行查询，就用向量进行搜索，不过要先加载Collection
            clientV2.loadCollection(LoadCollectionReq.builder().collectionName("quick_setup").build());
            SearchResp searchResp = clientV2.search(SearchReq.builder()
                    .collectionName("quick_setup")
                    .data(Collections.singletonList(new FloatVec(new float[]{0.3580376395471989F, -0.6023495712049978F, 0.18414012509913835F, -0.26286205330961354F, 0.9029438446296592F})))
                    .topK(10)
                    .outputFields(Collections.singletonList("*"))
                    .build()
            );
            for (List<SearchResp.SearchResult> searchResult : searchResp.getSearchResults()) {
                System.out.println(searchResult);
            }


        }
    }
    -

    我们用同样的向量进行搜索

    -
    [SearchResp.SearchResult(entity={my_id=0, $meta={"color":"pink_8682"}, my_vector=[0.35803765, -0.6023496, 0.18414013, -0.26286206, 0.90294385]}, score=1.4093276, id=0)]
    -

    得出的score就是很高的，只是做了下尝试，官方的示例代码是少得可怜，也不全，很难想象是目前比较热门的向量数据库

    -]]>     - - 算法 - - - 算法 - -     - - 周末我在老丈人家打了天小工 - /2020/08/16/%E5%91%A8%E6%9C%AB%E6%88%91%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E6%89%93%E4%BA%86%E5%A4%A9%E5%B0%8F%E5%B7%A5/ - 这周回家提前约好了要去老丈人家帮下忙，因为在翻修下老房子，活不是特别整的那种，所以大部分都是自己干，或者找个大工临时干几天（我们这那种比较专业的泥工匠叫做大工），像我这样去帮忙的，就是干点小工（把给大工帮忙的，干些偏体力活的叫做小工）的活。从大学毕业以后真的蛮少帮家里干活了，以前上学的时候放假还是帮家里淘个米，简单的扫地拖地啥的，当然刚高考完的时候，还去我爸厂里帮忙干了几天的活，实在是比较累，不过现在想着是觉得自己那时候比较牛，而不是特别排斥这个活，相对于现在的工作来说，导致了一系列的职业病，颈椎腰背都很僵硬，眼镜也不好，还有反流，像我爸那种活反而是脑力加体力的比较好的结合。
    这一天的活前半部分主要是在清理厨房，瓷砖上的油污和墙上天花板上即将脱落的石灰或者白色涂料层，这种活特别是瓷砖上的油污，之前在自己家里也干活，还是比较熟悉的，不过前面主要是LD 在干，我主要是先搞墙上和天花板上的，干活还是很需要技巧的，如果直接去铲，那基本我会变成一个灰人，而且吸一鼻子灰，老丈人比较专业，先接上软管用水冲，一冲效果特别好，有些石灰涂料层直接就冲掉了，冲完之后先用带加长杆的刀片铲铲了一圈墙面，说实话因为老房子之前租出去了，所以墙面什么的被糟蹋的比较难看，一层一层的，不过这还算还好，后面主要是天花板上的，这可难倒我了，从小我爸妈是比较把我当小孩管着，爬上爬下的基本都是我爸搞定，但是到了老丈人家也只得硬着头皮上了，爬到跳（一种建筑工地用的架子）上，还有点晃，小心脏扑通扑通跳，而且带加长杆的铲子还是比较重的，铲一会手也有点累，不过坚持着铲完了，上面还是比较平整的，不过下来的时候又把我难住了🤦‍♂️，往下爬的时候有根杆子要跨过去，由于裤子比较紧，强行一把跨过去怕抽筋，所以以一个非常尴尬的姿势停留休息了一会，再跨了过去，幸好事后问 LD，他们都没看到，哈哈哈，然后就是帮忙一起搞瓷砖上的油污，这个太有经验了，不过老丈人更有意思，一会试试啤酒，一会用用沙子，后面在午饭前基本就弄的比较干净了，就坐着等吃饭了，下午午休了会，就继续干活了。
    下午是我这次体验的重点了，因为要清理以前贴的墙纸，真的是个很麻烦的活，只能说贴墙纸的师傅活干得太好了，基本不可能整个撕下来，想用铲子一点点铲下来也不行，太轻了就只铲掉表面一层，太重了就把墙纸跟墙面的石灰啥的整个铲下来了，而且手又累又酸，后来想着是不是继续用水冲一下，对着一小面墙试验了下，效果还不错，但是又发现了个问题，那一面墙又有一块是后面糊上去的，铲掉外层的石灰后不平，然后就是最最重头的，也是让我后遗症持续到第二天的，要把那一块糊上去的水泥敲下来，毛估下大概是敲了80%左右，剩下的我的手已经不会用力了，因为那一块应该是要糊上去的始作俑者，就一块里面凹进去的，我拿着榔头敲到我手已经没法使劲了，而且大下午，感觉没五分钟，我的汗已经糊满脸，眼睛也睁不开，不然就流到眼睛里了，此处获得成就一：用榔头敲墙壁，也是个技术加体力的活，而且需要非常好的技巧，否则手马上就废了，敲下去的反作用力，没一会就不行了，然后是看着老丈人兄弟帮忙拆一个柜子，在我看来是个几天都搞不定的活，他轻轻松松在我敲墙的那会就搞定了，以前总觉得我干的活非常有技术含量，可是这个事情真的也是很有技巧啊，它是个把一间房间分隔开的柜子，从底到顶上，还带着门，我还在旁边帮忙撬一下脚踢，一根木条撬半天，唉，成就二：专业的人就是不一样。
    最后就是成就三了：我之前沾沾自喜的跑了多少步，做了什么锻炼，其实都是渣渣，像这样干一天活，没经历过的，基本大半天就废了，反过来说，如果能经常去这么干一天活，跑步啥的都是渣渣，消耗的能量远远超过跑个十公里啥的。

    -]]>     - - 生活 - 运动 - 跑步 - 干活 - - - 生活 - 运动 - 减肥 - 跑步 - 干活 - -     - - 介绍一下Java的BiFunction - /2024/06/30/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BJava%E7%9A%84BiFunction/ - java的函数式接口是java8引入的很重要的特性，也让日常代码有了比较大的风格转变
    这里介绍下BiFunction，
    BiFunction的代码很短

    -
    @FunctionalInterface
    public interface BiFunction<T, U, R> {

        /**
         * Applies this function to the given arguments.
         *
         * @param t the first function argument
         * @param u the second function argument
         * @return the function result
         */
        R apply(T t, U u);

        /**
         * Returns a composed function that first applies this function to
         * its input, and then applies the {@code after} function to the result.
         * If evaluation of either function throws an exception, it is relayed to
         * the caller of the composed function.
         *
         * @param <V> the type of output of the {@code after} function, and of the
         *           composed function
         * @param after the function to apply after this function is applied
         * @return a composed function that first applies this function and then
         * applies the {@code after} function
         * @throws NullPointerException if after is null
         */
        default <V> BiFunction<T, U, V> andThen(Function<? super R, ? extends V> after) {
            Objects.requireNonNull(after);
            return (T t, U u) -> after.apply(apply(t, u));
        }
    }
    -

    默认的方法就跟普通的FunctionalInterface一样，就有一个apply方法，只是这里的特殊点有两个

    -

    返回值作为参数

    这个BiFunction有三个泛型参数，T，U跟R，而这个R其实是具体方法的返回值
    比如我们简单实现一个字符串拼接

    -
    BiFunction<String, String, String> biDemo = (s1, s2) -> s1 + s2;
    System.out.println(biDemo.apply("hello ", "world"));
    -

    apply返回的就是参数R

    -

    andThen

    这个方法是可以让结果再调用传入的方法，并返回结果

    -
    Function<String, Integer> funcDemo = String::length;
    System.out.println(biDemo.andThen(funcDemo).apply("hello ", "world"));
    -

    定义了个函数接口，并且给了实现，就是字符串获取长度
    然后传给biDemo，这样就可以输出拼接后的字符串长度

    -

    组合使用

    public <T1, T2, R1, R2> R2 combination(T1 t1, T2 t2, BiFunction<T1, T2, R1> biFunction, Function<R1, R2> function) {
        return biFunction.andThen(function).apply(t1, t2);
    }
    -

    这样就把前面的两个方法串起来了

    -]]>     - - java - - - java - stream - -     - - 关于 npe 的一个小记忆点 - /2023/07/16/%E5%85%B3%E4%BA%8E-npe-%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E8%AE%B0%E5%BF%86%E7%82%B9/ - Java 中最常见的一类问题或者说异常就是 NullPointerException，而往往这种异常我们在查看日志的时候都是根据打印出来的异常堆栈找到对应的代码以确定问题，但是有一种情况会出现一个比较奇怪的情况，虽然我们在日志中打印了异常

    -
    log.error("something error", e);
    -

    这样是可以打印出来日志，譬如 slf4j 就可以把异常作为最后一个参数传入

    -
    void error(String var1, Throwable var2);
    -

    有的时候如果不小心把异常通过占位符传入就可能出现异常被吞的情况，不过这点不是这次的重点
    即使我们正常作为最后一个参数传入了，也会出现只打印出来

    -
    something error, java.lang.NullPointerException: null
    -

    这样就导致了如果我们这个日志所代表的异常包含的代码范围比较大的话，就不确定具体是哪一行出现的异常，除非对这些代码逻辑非常清楚，这个是为什么呢，其实这也是 jvm 的一种优化机制，像我们目前主要还在用的 jdk8中使用的 jvm 虚拟机 hotspot 的一个热点异常机制，对于这样一直出现的相同异常，就认为是热点异常，后面只会打印异常名，不再打印异常堆栈，这样对于 io 性能能够提供一定的优化保证，这个可以通过 jvm 启动参数
    -XX:-OmitStackTraceInFastThrow 来关闭优化，这个问题其实在一开始造成了一定困扰，找不准具体是哪一行代码，不过在知道这个之后简单的重启也能暂时解决问题。

    -]]>     - - java - - - java - -     - - 在 wsl 2 中开启 ssh 连接 - /2023/04/23/%E5%9C%A8-wsl-2-%E4%B8%AD%E5%BC%80%E5%90%AF-ssh-%E8%BF%9E%E6%8E%A5/ - 之前在 wsl 1 中开启 ssh 其实很方便，只要把 sshd 服务起来就好了，但是在 wsl 2 中就不太一样了，
    我这边使用的是 wsl 2 中的 Ubuntu 20.04，直接启动 sshd 服务是没法让其他机器连接的，而且都没有 ifconfig 命令可以查看 ip
    不过可以用直接用 ip a来查看，可以看到这个 ip 是172网段的，而在 wsl 1 中可以看到 ip 就是 win 的 ip，
    所以需要做一些操作，首先要安装 openssl-server

    -
    sudo apt update
    sudo apt install openssh-server
    -

    另外如果需要提高安全性，可以wsl 中配置 hosts.allow

    -
    sshd:192.168.xx.
    -

    先定一个子网段，然后对于ssh的配置，可以做以下修改

    -
    Port 22
    PasswordAuthentication yes
    -

    在进行重启

    -
    sudo service ssh --full-restart
    -

    配置以后发现上面的问题，没法远程登录，因为 wsl 2 是基于 hyper-v 虚拟机实现的，并且 ip 使用的是一个虚拟出来的子网 ip，所以需要在 Windows 这一层配置端口的转发，可以通过命令netsh interface portproxy show v4tov4看到

    截图是我已经添加好了的，先把原来的删除，再进行添加

    -
    netsh interface portproxy delete v4tov4 listenaddress=0.0.0.0 listenport=22
    netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=22 connectaddress=172.19.129.207 connectport=22
    -

    也可以全量删除

    -
    netsh int portproxy reset all
    -

    但是这样也不能直接访问了，还需要开启防火墙

    -
    netsh advfirewall firewall add rule name="WSL SSH" dir=in action=allow protocol=TCP localport=22
    - - - -]]>     - - wsl - - - wsl - -     - - 向量数据库学习-介绍一下HNSW算法 - /2024/06/23/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BHNSW%E7%AE%97%E6%B3%95/ - 结合AI写的一篇HNSW的介绍文章，算是向量数据库中比较核心的算法

    -

    1. 引言

    向量数据库在当今人工智能和机器学习领域扮演着越来越重要的角色。随着数据规模的不断扩大和应用场景的日益复杂,传统的数据库系统已经难以满足高维向量数据的快速检索需求。在这个背景下,近似最近邻(Approximate Nearest Neighbor, ANN)搜索问题成为了一个热点研究方向。

    -

    HNSW(Hierarchical Navigable Small World)算法是解决ANN问题的一种高效方法。它通过构建一个多层次的图结构,结合小世界网络的特性,实现了对高维向量数据的快速、精确检索。本文将深入浅出地介绍HNSW算法的核心思想、工作原理以及实际应用。

    -

    2. HNSW算法的核心思想

    HNSW算法的核心思想可以概括为三个关键点：分层结构、小世界图和近似搜索。

    -

    分层结构

    HNSW采用了一种多层次的图结构。想象一下,如果我们要在一个大城市中找到一个特定的地址,我们通常会先看城市地图,然后逐步缩小范围到区、街道,最后定位到具体门牌号。HNSW的分层结构就类似于这种由粗到细的搜索过程。这个就是之前介绍跳表的目的，就是现在稀疏的结构里可以快速定位，然后再往下层进行搜索，提高了搜索效率

    -

    小世界图

    小世界网络是一种特殊的图结构,它具有较短的平均路径长度和较高的聚类系数。在HNSW中,每一层都是一个小世界图。这种结构使得在图中的任意两点之间都存在一条相对较短的路径,大大提高了搜索效率。

    -

    近似搜索

    与传统的KNN(K-Nearest Neighbors)算法不同,HNSW通过牺牲一小部分精度来换取显著的速度提升。它不保证一定能找到真正的最近邻,但可以以极高的概率找到足够接近的点,而且搜索速度要快得多。

    -

    3. HNSW的工作原理

    数据结构:多层图的构建过程

      -
    1. HNSW从底层开始构建,每个数据点都会出现在底层。
      2. -
    2. 对于每个新插入的点,算法会随机决定它是否应该出现在上一层。这个过程一直持续到某一层,该点不再被选中。
      3. -
    3. 在每一层,新点会与该层的其他点建立连接,形成小世界图结构。
      4. -
    -

    搜索过程:自顶向下的贪婪搜索

      -
    1. 搜索从最顶层开始,选择一个随机起点。
      2. -
    2. 在当前层进行贪婪搜索,不断移动到离目标更近的邻居。
      3. -
    3. 当在当前层无法找到更近的点时,下降到下一层继续搜索。
      4. -
    4. 重复这个过程直到达到底层,得到最终结果。
      5. -
    -

    插入新向量的过程

      -
    1. 确定新向量应该出现在哪些层。
      2. -
    2. 从顶层开始,在每一层执行类似于搜索的过程,找到适合连接的邻居点。
      3. -
    3. 建立连接,同时可能需要删除一些现有连接以维持图的结构特性。

      论文中的算法是这样的
      4. -
    -
      -

    • 红色节点表示新插入的数据点。在这个例子中,新节点被插入到了层级0和层级1。

      -
      • -

    • 红色实线表示新建立的同层连接。新节点与其邻近节点建立了连接。

      -
      • -

    • 红色虚线表示新建立的跨层连接。新节点在不同层级之间建立了连接。

      -
      • -

    • 绿色虚线箭头表示插入过程的路径。这条路径展示了算法如何从顶层开始,逐层下降,最终确定新节点的插入位置。

      -
      • -

    • 其他颜色的节点和灰色的连接线表示原有的HNSW结构。

      -
      • -

    • 插入过程从顶层开始,沿着绿色虚线所示的路径向下搜索。

      -
      • -

    • 在每一层,算法都会寻找最近的邻居节点,并决定是否在该层插入新节点。

      -
      • -

    • 在本例中,新节点被插入到了层级0(基础层)和层级1。这是因为HNSW使用概率方法来决定节点应该出现在哪些层级。

      -
      • -

    • 插入后,新节点与其邻近节点建立连接(红色实线),以维持图的小世界特性。

      -
      • -

    • 同时,新节点也与其在不同层级的对应节点建立跨层连接(红色虚线),以确保层级之间的快速访问。

      -
      • -
    -

    4. HNSW的性能分析

    时间复杂度分析

      -
    • 搜索时间复杂度: O(log N),其中N是数据点的总数。
      • -
    • 插入时间复杂度: 平均情况下为O(log N)。
      • -
    -

    空间复杂度分析

      -
    • 空间复杂度: O(N log N),因为上层节点数量呈指数衰减。
      • -
    -

    与其他ANN算法的比较

    相比LSH(Locality-Sensitive Hashing)和Annoy等算法,HNSW在大多数场景下都能提供更好的查询性能和更高的准确率。然而,HNSW的内存消耗相对较高,这是它的一个潜在缺点。

    -

    5. HNSW的实际应用

    在推荐系统中的应用

    HNSW可以用于快速检索相似用户或物品,提高推荐系统的响应速度和准确性。例如,在音乐推荐中,可以用HNSW快速找到与用户喜好相似的歌曲。

    -

    在图像检索中的应用

    在大规模图像数据库中,HNSW可以实现快速的相似图像搜索。这在图像搜索引擎、视觉商品搜索等场景中非常有用。

    -

    在自然语言处理中的应用

    HNSW可以用于词嵌入或句子嵌入的快速检索,支持语义相似度计算、文本分类等任务。

    -

    6. HNSW的优化和变种

    参数调优技巧

      -
    • 层数选择:影响搜索速度和内存使用
      • -
    • 每层连接数:影响搜索准确度和构建时间
      • -
    • 候选集大小:影响搜索质量和速度的平衡
      • -
    -

    常见的HNSW变种算法介绍

      -
    • NSW-G:改进了图的构建方法,提高了搜索效率
      • -
    • HCNNG:结合了层次聚类,提高了在某些数据集上的性能
      • -
    -

    7. 实现HNSW的挑战与解决方案

    大规模数据集的处理

      -
    • 使用外部存储和分批处理
      • -
    • 采用压缩技术减少内存占用
      • -
    -

    并行化和分布式实现

      -
    • 搜索过程的并行化
      • -
    • 分布式索引构建和查询
      • -
    -

    动态更新和删除操作的处理

      -
    • 软删除技术
      • -
    • 定期重建索引
      • -
    -

    8. 总结与展望

    HNSW算法通过其独特的分层小世界图结构,在保证高准确率的同时实现了对高维向量数据的快速检索。它在推荐系统、图像检索、自然语言处理等多个领域都有广泛应用。

    -

    未来,向量数据库技术可能会朝着以下方向发展:

    -
      -
    1. 更高效的压缩和索引技术
      2. -
    2. 更好的动态更新支持
      3. -
    3. 与深度学习模型的更紧密集成
      4. -
    4. 针对特定领域的优化变体
      5. -
    -

    随着人工智能和大数据技术的不断发展,HNSW等高效的向量检索算法将在更广阔的应用场景中发挥重要作用。

    + + Windows + 小技巧 + + + Windows + +     + + 分享一次比较诡异的 Windows 下 U盘无法退出的经历 + /2023/01/29/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%AF%94%E8%BE%83%E8%AF%A1%E5%BC%82%E7%9A%84-Windows-%E4%B8%8B-U%E7%9B%98%E6%97%A0%E6%B3%95%E9%80%80%E5%87%BA%E7%9A%84%E7%BB%8F%E5%8E%86/ + 作为一个 Windows 的老用户，并且也算是 Windows 系统的半个粉丝，但是秉承一贯的优缺点都应该说的原则，Windows 系统有一点缺点是真的挺难受，相信 Windows 用过比较久的都会经历过，就是 U盘无法退出的问题，在比较远古时代，这个问题似乎能采取的措施不多，关机再拔 U盘的方式是一种比较保险的方式，其他貌似有 360这种可以解除占用的，但是需要安装 360 软件，对于目前的使用环境来说有点得不偿失，也是比较流氓的一类软件了，目前在 Windows 环境我主要就安装了个火绒，或者就用 Windows 自带的 defender。

    +

    第一种

    最近这次经历也是有火绒的一定责任，在我尝试推出 U盘的时候提示了我被另一个大流氓软件，XlibabaProtect.exe 占用了，这个流氓软件真的是充分展示了某里的技术实力，试过 N 多种办法都关不掉也删不掉，尝试了很多种办法也没办法删除，但是后面换了种思路，一般这种情况肯定是有进程在占用 U盘里的内容，最新版本的 Powertoys 会在文件的右键菜单里添加一个叫 File Locksmith 的功能，可以用于检查正在使用哪些文件以及由哪些进程使用，但是可能是我的使用姿势不对，没有仔细看文档，它里面有个”以管理员身份重启”,可能会有用。
    这算是第一种方式，

    +

    第二种

    第二种方式是 Windows 任务管理器中性能 tab 下的”打开资源监视器”，
    ,假如我的 U 盘的盘符是F:
    就可以搜索到占用这个盘符下文件的进程，这里千万小心‼️‼️，不可轻易杀掉这些进程，有些系统进程如果轻易杀掉会导致蓝屏等问题，不可轻易尝试，除非能确认这些进程的作用。
    对于前两种方式对我来说都无效，

    +

    第三种

    所以尝试了第三种，
    就是磁盘脱机的方式，在”计算机”右键管理，点击”磁盘管理”,可以找到 U 盘盘符右键，点击”脱机”，然后再”推出”，这个对我来说也不行

    +

    第四种

    这种是唯一对我有效的，在开始菜单搜索”event”，可以搜到”事件查看器”,
    ，这个可以看到当前最近 Windows 发生的事件，打开这个后就点击U盘推出，因为推不出来也是一种错误事件，点击下刷新就能在这看到具体是因为什么推出不了，具体的进程信息
    最后发现是英特尔的驱动管理程序的一个进程，关掉就退出了，虽然前面说的某里的进程是流氓，但这边是真的冤枉它了

    +]]>     + + Windows + 小技巧 + + + Windows + +     + + 分享记录一下一个 git 操作方法 + /2022/02/06/%E5%88%86%E4%BA%AB%E8%AE%B0%E5%BD%95%E4%B8%80%E4%B8%8B%E4%B8%80%E4%B8%AA-git-%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95/ + 前阵子一个同事因为发现某个分支上的代码好像有缺失导致无法正常运行，然后就对比了下把缺失的代码从另一个分支上拷了过来，可能有所欠考虑，不过主要是说下操作过程和最后的处理方法，这位同学的操作是改一些代码commit 一下，这样的 commit 了大概五六次，并且已经 push 到了远端，然后就在想要怎么去处理，在本地可以 reset，已经到远端了，一个很不优雅的操作就是本地 reset 了用 force push，这个当然是不可取的，然后就是 revert 了，但是又已经 commit 了好几次了，网上看了下，好像处理方法还挺成熟的，git revert 命令本质上就是一个逆向的 git cherry-pick 操作。 它将你提交中的变更的以完全相反的方式的应用到一个新创建的提交中，本质上就是撤销或者倒转。可以理解为就是提交一个反向的操作，这里其实我们可以用range revert来进行 git revert, 用法就是

    +
    git revert OLDER_COMMIT^..NEWER_COMMIT
    +

    这样就可以解决上面的问题了，但是还有个问题是这样会根据前面的 commit 数量提交对应数量个 revert commit 会显得比较乱，如果要比较干净的 commit 历史，
    可以看下 git revert 命令说明

    然后就可以用 -n 参数，表示不自动提交

    +
    git revert -n OLDER_COMMIT^..NEWER_COMMIT
    git commit -m "revert OLDER_COMMIT to NEWER_COMMIT"
    + + +]]>     + + git + 小技巧 + + + git + +     + + 分享记录一下一个 scp 操作方法 + /2022/02/06/%E5%88%86%E4%BA%AB%E8%AE%B0%E5%BD%95%E4%B8%80%E4%B8%8B%E4%B8%80%E4%B8%AA-scp-%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95/ + scp 是个在服务器之间拷贝文件的一个常用命令，有时候有个场景是比如我们需要拷贝一些带有共同前缀的文件，但是有一个问题是比如我们有使用 zsh 的话，会出现一个报错，

    +
    zsh: no matches found: root@100.100.100.100://root/prefix*
    +

    这里就比较奇怪了，这个前缀的文件肯定是有的，这里其实是由于 zsh 会对 * 进行展开，这个可以在例如 ls 命令在使用中就可以发现 zsh 有这个特性
    需要使用双引号或单引号将路径包起来或者在*之前加反斜杠\来阻止对*展开和转义

    +
    scp root@100.100.100.100://root/prefix* .
    +

    通过使用双引号"进行转义

    +
    scp root@100.100.100.100:"//root/prefix*" .
    +

    或者可以将 shell 从 zsh 切换成 bash

    +]]>     + + shell + 小技巧 + + + scp + +     + + 向量数据库 Milvus 安装初体验 + /2024/07/21/%E5%90%91%E9%87%8F%E6%95%B0%E6%8D%AE%E5%BA%93-Milvus-%E5%AE%89%E8%A3%85%E5%88%9D%E4%BD%93%E9%AA%8C/ + 之前做了个简单的铺垫，作为大模型应用技术领域非常重要的一环，向量数据库我们在前面有做一些引导性的介绍，其中的索引技术，
    而在众多向量数据库比较有代表性的 Milvus，这边我们来尝试安装 Milvus 初体验一下
    因为只是初体验，不作为生产环境使用，所以就用最简单的方式，Docker单机部署的方式
    首先需要需要有Docker环境
    确认下 docker 命令可执行
    然后拉取启动脚本

    +
    wget https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh
    bash standalone_embed.sh start
    +

    启动过程中需要注意一些问题，这也是有点想吐槽的，这个官方的启动脚本竟然都不是稳定成功的，最开始就是失败的，后面重新删除拉下来的镜像再启动就好了
    并且官方提供的demo python版本的示例也是有问题的
    因为主要是java的缘故，就用了java的sdk来尝试
    只是我们可以先运行一个ui工具

    +
    docker run -p 8000:3000 -e MILVUS_URL={milvus server IP}:19530 zilliz/attu:v2.4
    +


    这里就能看到运行情况
    然后我们运行个简单的代码

    +
    package MilvusDemo;

    import com.google.gson.Gson;
    import com.google.gson.JsonObject;
    import com.google.gson.JsonParser;
    import io.milvus.v2.client.ConnectConfig;
    import io.milvus.v2.client.MilvusClientV2;
    import io.milvus.v2.common.DataType;
    import io.milvus.v2.common.IndexParam;
    import io.milvus.v2.service.collection.request.AddFieldReq;
    import io.milvus.v2.service.collection.request.CreateCollectionReq;
    import io.milvus.v2.service.index.request.CreateIndexReq;
    import io.milvus.v2.service.vector.request.InsertReq;

    import java.util.ArrayList;
    import java.util.List;

    /**
     * @author shixuesen
     * @date 2024/7/21
     */
    public class Demo {

        public static void main(String[] args) {
            ConnectConfig connectConfig = ConnectConfig.builder()
                            .uri("http://127.0.0.1:19530")
                                    .build();
            // 连接配置
            MilvusClientV2 clientV2 = new MilvusClientV2(connectConfig);
            // define a Collection Schema
            CreateCollectionReq.CollectionSchema collectionSchema = clientV2.createSchema();
    // add two fileds, id and vector
            Integer dim = 5;
            // 这里的 CollectionSchema 就类似于mysql的数据库表结构
            collectionSchema.addField(AddFieldReq.builder().fieldName("my_id").dataType(DataType.Int64).isPrimaryKey(Boolean.TRUE).autoID(Boolean.FALSE).description("id").build());
            collectionSchema.addField(AddFieldReq.builder().fieldName("my_vector").dataType(DataType.FloatVector).dimension(dim).build());

            CreateCollectionReq req = CreateCollectionReq
                    .builder()
                    .collectionSchema(collectionSchema)
                    .collectionName("quick_setup")
                    .dimension(dim).build();
            //  通过schema 生成 Collection，就类似于mysql中的表
            clientV2.createCollection(req);

            // 然后可以对字段创建索引
            IndexParam indexParam = IndexParam.builder()
                    .fieldName("my_id")
                    .build();
            IndexParam vertorIndexParam = IndexParam.builder()
                            .fieldName("my_vector")
                                    .indexType(IndexParam.IndexType.AUTOINDEX)
                                            .metricType(IndexParam.MetricType.IP)
                                                    .build();
            List<IndexParam> indexParamList = new ArrayList<>();
            indexParamList.add(indexParam);
            indexParamList.add(vertorIndexParam);


            // 接着创建索引
            clientV2.createIndex(CreateIndexReq.builder()
                    .collectionName("quick_setup")
                    .indexParams(indexParamList)
                    .build());
            JsonObject jsonObject  = JsonParser.parseString("{\"my_id\": 0, \"my_vector\": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], \"color\": \"pink_8682\"}").getAsJsonObject();
            List<JsonObject> jsonData = new ArrayList<>();
            jsonData.add(jsonObject);
            // 然后插入数据
            clientV2.insert(InsertReq.builder().collectionName("quick_setup")
                    .data(jsonData).build());

            // 插入之后我们想进行查询，就用向量进行搜索，不过要先加载Collection
            clientV2.loadCollection(LoadCollectionReq.builder().collectionName("quick_setup").build());
            SearchResp searchResp = clientV2.search(SearchReq.builder()
                    .collectionName("quick_setup")
                    .data(Collections.singletonList(new FloatVec(new float[]{0.3580376395471989F, -0.6023495712049978F, 0.18414012509913835F, -0.26286205330961354F, 0.9029438446296592F})))
                    .topK(10)
                    .outputFields(Collections.singletonList("*"))
                    .build()
            );
            for (List<SearchResp.SearchResult> searchResult : searchResp.getSearchResults()) {
                System.out.println(searchResult);
            }


        }
    }
    +

    我们用同样的向量进行搜索

    +
    [SearchResp.SearchResult(entity={my_id=0, $meta={"color":"pink_8682"}, my_vector=[0.35803765, -0.6023496, 0.18414013, -0.26286206, 0.90294385]}, score=1.4093276, id=0)]
    +

    得出的score就是很高的，只是做了下尝试，官方的示例代码是少得可怜，也不全，很难想象是目前比较热门的向量数据库

    ]]>     算法 @@ -5172,6 +5056,53 @@ user3: 算法     + + 周末我在老丈人家打了天小工 + /2020/08/16/%E5%91%A8%E6%9C%AB%E6%88%91%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E6%89%93%E4%BA%86%E5%A4%A9%E5%B0%8F%E5%B7%A5/ + 这周回家提前约好了要去老丈人家帮下忙，因为在翻修下老房子，活不是特别整的那种，所以大部分都是自己干，或者找个大工临时干几天（我们这那种比较专业的泥工匠叫做大工），像我这样去帮忙的，就是干点小工（把给大工帮忙的，干些偏体力活的叫做小工）的活。从大学毕业以后真的蛮少帮家里干活了，以前上学的时候放假还是帮家里淘个米，简单的扫地拖地啥的，当然刚高考完的时候，还去我爸厂里帮忙干了几天的活，实在是比较累，不过现在想着是觉得自己那时候比较牛，而不是特别排斥这个活，相对于现在的工作来说，导致了一系列的职业病，颈椎腰背都很僵硬，眼镜也不好，还有反流，像我爸那种活反而是脑力加体力的比较好的结合。
    这一天的活前半部分主要是在清理厨房，瓷砖上的油污和墙上天花板上即将脱落的石灰或者白色涂料层，这种活特别是瓷砖上的油污，之前在自己家里也干活，还是比较熟悉的，不过前面主要是LD 在干，我主要是先搞墙上和天花板上的，干活还是很需要技巧的，如果直接去铲，那基本我会变成一个灰人，而且吸一鼻子灰，老丈人比较专业，先接上软管用水冲，一冲效果特别好，有些石灰涂料层直接就冲掉了，冲完之后先用带加长杆的刀片铲铲了一圈墙面，说实话因为老房子之前租出去了，所以墙面什么的被糟蹋的比较难看，一层一层的，不过这还算还好，后面主要是天花板上的，这可难倒我了，从小我爸妈是比较把我当小孩管着，爬上爬下的基本都是我爸搞定，但是到了老丈人家也只得硬着头皮上了，爬到跳（一种建筑工地用的架子）上，还有点晃，小心脏扑通扑通跳，而且带加长杆的铲子还是比较重的，铲一会手也有点累，不过坚持着铲完了，上面还是比较平整的，不过下来的时候又把我难住了🤦‍♂️，往下爬的时候有根杆子要跨过去，由于裤子比较紧，强行一把跨过去怕抽筋，所以以一个非常尴尬的姿势停留休息了一会，再跨了过去，幸好事后问 LD，他们都没看到，哈哈哈，然后就是帮忙一起搞瓷砖上的油污，这个太有经验了，不过老丈人更有意思，一会试试啤酒，一会用用沙子，后面在午饭前基本就弄的比较干净了，就坐着等吃饭了，下午午休了会，就继续干活了。
    下午是我这次体验的重点了，因为要清理以前贴的墙纸，真的是个很麻烦的活，只能说贴墙纸的师傅活干得太好了，基本不可能整个撕下来，想用铲子一点点铲下来也不行，太轻了就只铲掉表面一层，太重了就把墙纸跟墙面的石灰啥的整个铲下来了，而且手又累又酸，后来想着是不是继续用水冲一下，对着一小面墙试验了下，效果还不错，但是又发现了个问题，那一面墙又有一块是后面糊上去的，铲掉外层的石灰后不平，然后就是最最重头的，也是让我后遗症持续到第二天的，要把那一块糊上去的水泥敲下来，毛估下大概是敲了80%左右，剩下的我的手已经不会用力了，因为那一块应该是要糊上去的始作俑者，就一块里面凹进去的，我拿着榔头敲到我手已经没法使劲了，而且大下午，感觉没五分钟，我的汗已经糊满脸，眼睛也睁不开，不然就流到眼睛里了，此处获得成就一：用榔头敲墙壁，也是个技术加体力的活，而且需要非常好的技巧，否则手马上就废了，敲下去的反作用力，没一会就不行了，然后是看着老丈人兄弟帮忙拆一个柜子，在我看来是个几天都搞不定的活，他轻轻松松在我敲墙的那会就搞定了，以前总觉得我干的活非常有技术含量，可是这个事情真的也是很有技巧啊，它是个把一间房间分隔开的柜子，从底到顶上，还带着门，我还在旁边帮忙撬一下脚踢，一根木条撬半天，唉，成就二：专业的人就是不一样。
    最后就是成就三了：我之前沾沾自喜的跑了多少步，做了什么锻炼，其实都是渣渣，像这样干一天活，没经历过的，基本大半天就废了，反过来说，如果能经常去这么干一天活，跑步啥的都是渣渣，消耗的能量远远超过跑个十公里啥的。

    +]]>     + + 生活 + 运动 + 跑步 + 干活 + + + 生活 + 运动 + 减肥 + 跑步 + 干活 + +     + + 在 wsl 2 中开启 ssh 连接 + /2023/04/23/%E5%9C%A8-wsl-2-%E4%B8%AD%E5%BC%80%E5%90%AF-ssh-%E8%BF%9E%E6%8E%A5/ + 之前在 wsl 1 中开启 ssh 其实很方便，只要把 sshd 服务起来就好了，但是在 wsl 2 中就不太一样了，
    我这边使用的是 wsl 2 中的 Ubuntu 20.04，直接启动 sshd 服务是没法让其他机器连接的，而且都没有 ifconfig 命令可以查看 ip
    不过可以用直接用 ip a来查看，可以看到这个 ip 是172网段的，而在 wsl 1 中可以看到 ip 就是 win 的 ip，
    所以需要做一些操作，首先要安装 openssl-server

    +
    sudo apt update
    sudo apt install openssh-server
    +

    另外如果需要提高安全性，可以wsl 中配置 hosts.allow

    +
    sshd:192.168.xx.
    +

    先定一个子网段，然后对于ssh的配置，可以做以下修改

    +
    Port 22
    PasswordAuthentication yes
    +

    在进行重启

    +
    sudo service ssh --full-restart
    +

    配置以后发现上面的问题，没法远程登录，因为 wsl 2 是基于 hyper-v 虚拟机实现的，并且 ip 使用的是一个虚拟出来的子网 ip，所以需要在 Windows 这一层配置端口的转发，可以通过命令netsh interface portproxy show v4tov4看到

    截图是我已经添加好了的，先把原来的删除，再进行添加

    +
    netsh interface portproxy delete v4tov4 listenaddress=0.0.0.0 listenport=22
    netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=22 connectaddress=172.19.129.207 connectport=22
    +

    也可以全量删除

    +
    netsh int portproxy reset all
    +

    但是这样也不能直接访问了，还需要开启防火墙

    +
    netsh advfirewall firewall add rule name="WSL SSH" dir=in action=allow protocol=TCP localport=22
    + + + +]]>     + + wsl + + + wsl + +     在老丈人家的小工记三 /2020/09/13/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E4%B8%89/ @@ -5233,20 +5164,31 @@ user3: - 小工周记一 - /2023/03/05/%E5%B0%8F%E5%B7%A5%E5%91%A8%E8%AE%B0%E4%B8%80/ - 开始修老房子又可以更新这个系列了，比较无聊，就是帮着干点零活的记录，这次过去起的比较早，前几天是在翻新瓦片，到这次周六是收尾了，到了的时候先是继续筛了沙子，上周也筛了，就是只筛了一点点，筛沙子的那个像纱窗一样的还是用一扇中空的中间有一根竖档的门钉上铁丝网做的，就是沙子一直放在外面，原来是有袋子装好的，后来是风吹雨打在上面的都已经破掉，还夹杂了很多树叶什么的，需要过下筛，并且前面都是下雨天，沙子都是湿的，不太像我以前看村里有人造房子筛沙子那样，用铲子铲上去就自己都下去的，湿的就是会在一坨，所以需要铲得比较少，然后撒的比较开，这个需要一点经验，然后如果有人一起的话就可以用扫把按住扫一下，这样就会筛得比较有效，不至于都滑下去，沙子本来大部分是可以筛出来的，还有一点就是这种情况网筛需要放得坡度小一点，不然就更容易直接往下调，袋子没破的就不用过筛了，只是湿掉了的是真的重，筛完了那些破掉了的袋子里的沙子，就没有特别的事情要做了，看到大工在那打墙，有一些敲下来的好的砖头就留着，需要削一下上面的混凝土，据大工说现在砖头要七八毛一块了，后面能够重新利用还是挺值钱的，我跟 LD 就用泥刀和铁锹的在那慢慢削，砖头上的泥灰有的比较牢固有的就像直接是沙子，泥刀刮一下就下来了，有的就结合得比较牢固，不过据说以前的砖头工艺上还比较落后，这个房子差不多是三十年前了的，砖头表面都是有点不平，甚至变形，那时候可能砖头是手工烧制的，现在的砖头比较工艺可好多了，不过可能也贵了很多，后来老丈人也过来了，指导了我们拌泥灰，就是水泥和黄沙混合，以前小时候可喜欢玩这个了，可是就也搞不清楚这个是怎么搅拌的，只看见是水泥跟黄沙围城一圈，中间放水，然后一点点搬进去，首先需要先把干的水泥跟黄沙进行混合，具体的比例是老丈人说的，拌的方式有点像堆沙堆，把水泥黄沙铲起来堆起来，就一直要往这个混合堆的尖尖上堆，这样子它自己滑下来能更好地混合，来回两趟就基本混合均匀了，然后就是跟以前看过的，中间扒拉出一个空间放水，然后慢慢把周围的混合好的泥沙推进去，需要注意不要太着急，旁边的推进去太多太快就会漏水出来，一个是会把旁边的地给弄脏，另一个也铲不回水，然后就是推完所有的都混合水了，就铲起来倒一下，再将铲子翻过来捣几下，后面我们就去吃饭了，去了一家叫金记的，又贵又不太好吃的店，就是离得比较近，六七个人只有六七个菜，吃完要四百多，也是有点离谱了。
    下午是重头戏，其实本来倒没啥事，就说帮忙搞下靠背（就是踢脚线上面到窗台附近的用木头还有其他材料的装饰性的），都撬撬掉，但是真的是有点离谱了，首先是撬棒真的很重，20 斤的重量（网上查的，没有真的查过），抡起来还要用力地铲进去，因为就是破坏性的要把整个都撬掉，对于我这种又没技巧又没力气的非专业选手，抡起撬棍铲两下手就开始痛了，只是也比较犟，不想刚开始弄就说太重了要休息，后面都完全靠的一点倔强劲撑着，看着里面的工艺感觉也是不容易的，直着横着的木条有好多，竖的一整条，每隔三五十公分，横着的就是三五十公分，每根都要用钉子钉起来，然后外层好像是贴上去，在同一个面的开了头之后就能靠着蛮力往下撬，但是到了转角就又要重新开头，而且最上面一根横条跟紧邻的那一块，大概十几公分，是横着的三条钉在一起，真的是大力都出不了奇迹了，用撬棍的一头用力地敲打都很震手，要从下面往上铲进去撬开一点，然后再从上面往下敲打，这里比较重要的是要小心钉子，我这次运气比较好，踩下去已经扎到了，不过正好在脚趾缝里，没有扎到脚，还是要小心的，做完这个我的手真的是差不多废了，上臂的疼痛已经动一下就受不了了，后面有撬下了最下面当踢脚线的小瓷砖，这个房子估计中间修过一次，两批水泥糊的，新的那批粘的特别牢，敲敲打打了半天才下来一点点，锤子敲上去跟一整块石头一样，震手又没有进展，整个搞完，楼上又在敲墙了，下面的灰尘也是从没见过，我一直在那洒水都完全没有缓解，就上去跟 LD 一起拣砖头，手痛到只能抬两块砖头都会痛了。
    回到家里开始越来越痛，两个手就完全没法动了，应该也是肌肉拉伤了，我这样是没足够的力气也不会什么技巧，像大工说的，他们也累也难，只是为了赚钱，不过他们有了经验跟技巧，会注意怎么使力不容易受伤，怎么样比较省力，还有一点就是即使这么累，他们一般也下午五点半就下班了，真的很累了，至少还有不少时间可以回家休息，而我们的职业呢，就像 LD 说的回家就像住酒店，就只是来洗澡睡个觉，希望能改善吧

    + 学习下LlamaIndex-初始篇 + /2024/04/21/%E5%AD%A6%E4%B9%A0%E4%B8%8BLlamaIndex-%E5%88%9D%E5%A7%8B%E7%AF%87/ + LlamaIndex 是目前比较新的大模型RAG框架，RAG是指 检索增强生成 (Retrieval-Augmented Generation, RAG)是指在利用大语言模型回答问题之前，先从外部知识库检索相关信息。RAG 被证明能显著提升答案的准确性，并特别是在知识密集型任务上减少模型的错误输出。通过引用信息来源，用户可以核实答案的准确性，从而增强对模型输出的信任。
    此外，RAG 有助于快速更新知识并引入特定领域的专业知识。
    RAG 有效结合了大语言模型的参数化知识和非参数化的外部知识库，成为实施大语言模型的关键方法之一。本文概述了 RAG 在大语言模型时代的发展模式，总结了三种模式：初级 RAG、高级 RAG 和模块化 RAG。
    首先我们可以了解下LlamaIndex的几大模块，
    使用大型语言模型(LLMs):无论是OpenAI还是任何托管的LLM,亦或是您自己本地运行的模型,LLM都被用于从索引和存储到查询和解析数据的每一个步骤。LlamaIndex提供了大量可靠经过测试的提示,我们也将向您展示如何自定义您自己的提示。
    LlamaIndex 主要有这五大块能力

    +
      +
    • Data connectors
      • +
    • Data indexes
      • +
    • Engines
      • +
    • Data agents
      • +
    • Application integrations
      • +
    +

    其中加载部分可以认为是初始的部分，我们可以从各个数据源去获取信息，比如各种文档，或者confluence，甚至api接口，只需要合适的数据加载器，我们就能将其转化为我们要的数据
    比如我想要将我博客里的Markdown文件作为知识库加载

    +
    reader = SimpleDirectoryReader(
        input_dir="./somedir/source/_posts/",
        required_exts=[".md"],
        recursive=True
    )
    docs = reader.load_data()
    +

    然后由文档来就构建我们的索引

    +
    index = VectorStoreIndex.from_documents(docs)
    +

    但是这里我们需要提前设置好对应的 embedding 模型，这个很重要，如果有条件推荐使用OpenAI的，如果想用本地的可以考虑使用BAAI智源出品的，只是要注意是否支持多语言，如果要使用本地embedding的话需要安装

    +
    pip install llama-index-embeddings-huggingface
    +

    这里我选了BAAI也就是智源研究所的bge-m3模型，因为这个是支持多语言的，如果是纯英文的可以用他们 BAAI/bge-small-en-v1.5

    +
    from llama_index.core import Settings
    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
    Settings.llm = Ollama(model="gemma:7b", request_timeout=60.0)
    Settings.embed_model = HuggingFaceEmbedding(
        model_name="BAAI/bge-m3"
    )
    index = VectorStoreIndex.from_documents(docs)
    query_engine = index.as_query_engine(llm=gemma_7b)
    response = query_engine.query("数据库主从延迟")
    print(response)
    +

    另外LLM 我使用了本地部署的 gemma:7b，因为想尝试下本地的链路，需要注意的是一些文档还是用的原来的service_context，在新版本中这个已经被改掉了，都是通过 Settings 来设置，此外还有诸如 SimpleDirectoryReader 这些包也被挪到了 llama_index.core 中，需要注意下
    然后我们就能使用这种方式来增强LLM，把我们的本地文档作为知识库来搜索，结合了大模型的能力。

    ]]>     - 生活 + LLM - 生活 - 小技巧 - 运动 - 减肥 - 跑步 - 干活 + LLM     @@ -5271,34 +5213,40 @@ user3: - 我是如何走上跑步这条不归路的 - /2020/07/26/%E6%88%91%E6%98%AF%E5%A6%82%E4%BD%95%E8%B5%B0%E4%B8%8A%E8%B7%91%E6%AD%A5%E8%BF%99%E6%9D%A1%E4%B8%8D%E5%BD%92%E8%B7%AF%E7%9A%84/ - 这周因为没有准备技术方面的内容加之之前有想分享下我和跑步的一些事情，我从小学开始就是个体育渣，因为体重大非常胖，小学的时候要做仰卧起坐，基本是一个都起不来，然后那时候跑步也是要我命那种，跟另外一个比较胖的同学在跑步队尾苟延残喘，只有立定跳远还行。

    -

    时光飞逝，我在初中高中的时候因为爱打篮球，以为自己体质已经有了质的变化，所以在体育课跑步的时候妄图跟一位体育非常好的同学一起跑，结果跟的快断气了，最终还是确认了自己是个体育渣，特别是到了大学的第一次体测跑一千米，跑完直接吐了，一则是大学太宅不运动，二则的确是底子不好。那么怎么会去跑步了呢，其实也没什么特殊的原因，就是工作以后因为运动得更少，体质差，而且越来越胖，所以就想运动下，加之跑步也是我觉得成本最低的运动了，刚好那时候17 年租的地方附近小区周围的路车不太多，一圈刚好一公里多，就觉得开始跑跑看，其实想想以前觉得一千米是非常远的，学校塑胶跑道才 400 米，一千米要两圈半，太难了，但是后来在这个小区周围跑的时候好像跑了一圈以后还能再跑一点，最后跑了两圈，可把自己牛坏了，我都能跑两千米了，哈哈，这是个什么概念呢，大学里最让我绝望的两项体育相关的内容就是一千米和十二分钟跑，一千米把我跑吐了，十二分钟跑及格五圈半也能让我跑完花一周时间恢复以及提前一周心理压力爆炸，虽然我那时候跑的不快，但是已经能跑两千米了，瞬间让自己自信心爆炸，并且跑完步出完汗的感觉是非常棒的，毕竟吃奶茶鸡排都能心安理得了，谁叫我跑步了呢😄，其实现在回去看，那时候跑得还算快的，因为还比较瘦，现在要跑得那么快心跳就太快了，关于心跳什么的后面说，开始建立起自信心之后，对跑步这件事就开始不那么排斥跟害怕了，毕竟我能跑两千米了，然后试试三公里，哇，也可以了呢，三公里是什么概念呢，我大学里跑过最多的一次是十二分钟跑五圈半还是六圈，也就是两公里多，不到三公里，几乎是生涯最长了，一时间产生了一些我可能是个被埋没的运动天才的错觉，其实细想下也能明白，只是速度足够慢了就能跑多一点，毕竟提测一千米是要跑进四分钟才及格，自己跑的时候一千米跑六分多钟已经算不慢了（对我自己来说），但是即使是这样还是对把跑步坚持下去这件事有了很大的正面激励作用，并且由于那时候上下班骑车，整个体重控制的比较理想，导致一时间误会跑步就能非常快的减肥（其实这是我跑步历程中比较大的误区之一），因为会在跑步前后称下体重，如果跑个五公里（后面可以跑五公里了），可能体重就能降 0.5 千克，但实际上这只是这五公里跑步身体流失的水分，喝杯水就回来了，那时候能控制体重主要是骑车跟跑步一起的作用，并且工作压力相对来讲比较小，没有过劳肥。

    -

    后面其实跑步慢慢变得一个比较习惯的运动了，从三公里，到五公里，到七公里，再到十公里，十公里差不多对我来说是个坎，一直还不能比较轻松的跑十公里，可能近一两年好了一些（原谅我只是跟自己比较，跟那些大神比差得不知道多远），其实对我来说每次都是个突破，因为其实与他人比较没有特别大意义，比较顶尖的差得太远，比较普通的也不行，都会打击自信心，比较比我差的就更没意义了，所以其实能挑战自己，能把自己的上限提高才是最有意义的，这也是我看着朋友圈里的一些大神的速度除了佩服赞叹之外没什么其他的惭愧或者说嫌弃自己的感觉（阿 Q 精神😄）。

    -

    一直感性地觉得，跑步能解压，跑完浑身汗，有种把身体的负能量都排出去的感觉，也把吃太多的罪恶感排解掉了🤦‍♂️，之前朋友有看一本书，书名差不多叫越跑越接近自己，这个也是我觉得挺准确的一句话，当跑到接近极限了，还想再继续再跑一点，再跑一点就能突破自己上一次的最远记录了，再跑一点就能又一次突破自己了，成人以后，毕业以后，进入社会以后，世事总是难以件件顺遂，磕磕绊绊的往前走，总觉得要崩溃了，但是还是得坚持，再熬一下，再拼一下，可能还是失败，但人生呢，运气好的人和事总是小概率的，唯有面对挫折，还是日拱一卒，来日方长，我们再坚持下，没准下一次，没准再跑一会，就能突破自己，达到新的境界。

    -

    另外个人后期对跑步的一些知识和理解也变得深入一些，比如伤膝盖，其实跑步的确伤膝盖，需要做一些准备和防护，最好的是适合自己的跑鞋和比较好的路（最好的是塑胶跑道了），也要注意热身跟跑后的拉伸（虽然我做的很差），还有就是注意心率，每个人有自己的适宜心率，我这就不冒充科普达人了，可以自行搜索关键字，先说到这吧~

    + 小工周记一 + /2023/03/05/%E5%B0%8F%E5%B7%A5%E5%91%A8%E8%AE%B0%E4%B8%80/ + 开始修老房子又可以更新这个系列了，比较无聊，就是帮着干点零活的记录，这次过去起的比较早，前几天是在翻新瓦片，到这次周六是收尾了，到了的时候先是继续筛了沙子，上周也筛了，就是只筛了一点点，筛沙子的那个像纱窗一样的还是用一扇中空的中间有一根竖档的门钉上铁丝网做的，就是沙子一直放在外面，原来是有袋子装好的，后来是风吹雨打在上面的都已经破掉，还夹杂了很多树叶什么的，需要过下筛，并且前面都是下雨天，沙子都是湿的，不太像我以前看村里有人造房子筛沙子那样，用铲子铲上去就自己都下去的，湿的就是会在一坨，所以需要铲得比较少，然后撒的比较开，这个需要一点经验，然后如果有人一起的话就可以用扫把按住扫一下，这样就会筛得比较有效，不至于都滑下去，沙子本来大部分是可以筛出来的，还有一点就是这种情况网筛需要放得坡度小一点，不然就更容易直接往下调，袋子没破的就不用过筛了，只是湿掉了的是真的重，筛完了那些破掉了的袋子里的沙子，就没有特别的事情要做了，看到大工在那打墙，有一些敲下来的好的砖头就留着，需要削一下上面的混凝土，据大工说现在砖头要七八毛一块了，后面能够重新利用还是挺值钱的，我跟 LD 就用泥刀和铁锹的在那慢慢削，砖头上的泥灰有的比较牢固有的就像直接是沙子，泥刀刮一下就下来了，有的就结合得比较牢固，不过据说以前的砖头工艺上还比较落后，这个房子差不多是三十年前了的，砖头表面都是有点不平，甚至变形，那时候可能砖头是手工烧制的，现在的砖头比较工艺可好多了，不过可能也贵了很多，后来老丈人也过来了，指导了我们拌泥灰，就是水泥和黄沙混合，以前小时候可喜欢玩这个了，可是就也搞不清楚这个是怎么搅拌的，只看见是水泥跟黄沙围城一圈，中间放水，然后一点点搬进去，首先需要先把干的水泥跟黄沙进行混合，具体的比例是老丈人说的，拌的方式有点像堆沙堆，把水泥黄沙铲起来堆起来，就一直要往这个混合堆的尖尖上堆，这样子它自己滑下来能更好地混合，来回两趟就基本混合均匀了，然后就是跟以前看过的，中间扒拉出一个空间放水，然后慢慢把周围的混合好的泥沙推进去，需要注意不要太着急，旁边的推进去太多太快就会漏水出来，一个是会把旁边的地给弄脏，另一个也铲不回水，然后就是推完所有的都混合水了，就铲起来倒一下，再将铲子翻过来捣几下，后面我们就去吃饭了，去了一家叫金记的，又贵又不太好吃的店，就是离得比较近，六七个人只有六七个菜，吃完要四百多，也是有点离谱了。
    下午是重头戏，其实本来倒没啥事，就说帮忙搞下靠背（就是踢脚线上面到窗台附近的用木头还有其他材料的装饰性的），都撬撬掉，但是真的是有点离谱了，首先是撬棒真的很重，20 斤的重量（网上查的，没有真的查过），抡起来还要用力地铲进去，因为就是破坏性的要把整个都撬掉，对于我这种又没技巧又没力气的非专业选手，抡起撬棍铲两下手就开始痛了，只是也比较犟，不想刚开始弄就说太重了要休息，后面都完全靠的一点倔强劲撑着，看着里面的工艺感觉也是不容易的，直着横着的木条有好多，竖的一整条，每隔三五十公分，横着的就是三五十公分，每根都要用钉子钉起来，然后外层好像是贴上去，在同一个面的开了头之后就能靠着蛮力往下撬，但是到了转角就又要重新开头，而且最上面一根横条跟紧邻的那一块，大概十几公分，是横着的三条钉在一起，真的是大力都出不了奇迹了，用撬棍的一头用力地敲打都很震手，要从下面往上铲进去撬开一点，然后再从上面往下敲打，这里比较重要的是要小心钉子，我这次运气比较好，踩下去已经扎到了，不过正好在脚趾缝里，没有扎到脚，还是要小心的，做完这个我的手真的是差不多废了，上臂的疼痛已经动一下就受不了了，后面有撬下了最下面当踢脚线的小瓷砖，这个房子估计中间修过一次，两批水泥糊的，新的那批粘的特别牢，敲敲打打了半天才下来一点点，锤子敲上去跟一整块石头一样，震手又没有进展，整个搞完，楼上又在敲墙了，下面的灰尘也是从没见过，我一直在那洒水都完全没有缓解，就上去跟 LD 一起拣砖头，手痛到只能抬两块砖头都会痛了。
    回到家里开始越来越痛，两个手就完全没法动了，应该也是肌肉拉伤了，我这样是没足够的力气也不会什么技巧，像大工说的，他们也累也难，只是为了赚钱，不过他们有了经验跟技巧，会注意怎么使力不容易受伤，怎么样比较省力，还有一点就是即使这么累，他们一般也下午五点半就下班了，真的很累了，至少还有不少时间可以回家休息，而我们的职业呢，就像 LD 说的回家就像住酒店，就只是来洗澡睡个觉，希望能改善吧

    ]]>     生活 - 运动 - 跑步 生活 + 小技巧 运动 减肥 跑步 + 干活     - 使用milvus和towhee实现简陋版的以图搜图 - /2024/08/04/%E4%BD%BF%E7%94%A8milvus%E5%92%8Ctowhee%E5%AE%9E%E7%8E%B0%E7%AE%80%E9%99%8B%E7%89%88%E7%9A%84%E4%BB%A5%E5%9B%BE%E6%90%9C%E5%9B%BE/ - 上次我们尝试用 towhee 跟 milvus 实现了图片的向量化，那么顺势我们就能在这个基础上实现以图搜图
    首先我们找一些图片，

    然后我们先把他们都向量化，存储在milvus里

    -
    from towhee import AutoPipes, AutoConfig, ops
    import towhee
    import os
    from pymilvus import MilvusClient
    import json
     
    # 1. 设置一个Milvus客户端
    client = MilvusClient(
        uri="http://localhost:19530"
    )
    insert_conf = AutoConfig.load_config('insert_milvus')
    insert_conf.collection_name = 'text_image_search'


    insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

    # 创建图像嵌入管道
    image_embedding_pipe = AutoPipes.pipeline('image-embedding')
    files = os.listdir("./images")
    for file in files: 
        file_path = os.path.join("./images", file)
        if os.path.isfile(file_path):
            embedding = image_embedding_pipe(file_path).get()[0]
            insert_pipe([file_path, embedding])
    -

    然后我们找一张神仙姐姐的其他图片，先把它 embedding 后在 milvus 里进行向量检索

    -
    image_embedding_pipe = AutoPipes.pipeline('image-embedding')

    # 生成嵌入
    embedding = image_embedding_pipe('./to_search.jpg').get()[0]
    print(embedding)
    res = client.search(
        collection_name="text_image_search",
        data=[embedding],
        limit=5,
        search_params={"metric_type": "IP", "params": {}}
    )
    result = json.dumps(res, indent=4)
    print(result)
    -

    我们检索出来5个结果，

    可以通过distance找到距离最近的这个是id=451291280409722600
    可以发现也是神仙姐姐的,只是作为参考
    to_search 目标图片是

    搜索的最短距离就是id对应的图片

    这张图片就是神仙姐姐

    + 尝试下图片向量化 + /2024/07/28/%E5%B0%9D%E8%AF%95%E4%B8%8B%E5%9B%BE%E7%89%87%E5%90%91%E9%87%8F%E5%8C%96/ + 之前觉得谷歌的以图搜图很厉害，现在似乎这个路径还毕竟清晰了，首先要有图片库，把它们向量化以后存储起来，然后对于目标图片也做向量化，再做检索
    那么我们先来做重要的这一步，图片的向量化，因为向量化以后就跟图片没关系了，直接用前面讲到的向量的近似搜索就可以做到以图搜图了
    这边我们用到了towhee工具，towhee是个机器学习的pipeline工具，可以做数据源（文件，图片，媒体，文本）–> 模型 –> 向量。
    首先我们安装towhee

    +
    pip install towhee towhee.models
    +

    这里有第一个坑，因为有torch依赖，但是目前torch支持的python版本是最高3.9，再往上可能就有问题了
    所以我们要先创建一个3.9的环境

    +
    conda create -n py9 python=3.9
    +

    然后我们会遇到第二个坑
    就是milvus的客户端

    +
    pip install pymilvus==2.3.0
    +

    再早的客户端会有依赖不支持，更新的客户端会连接不上
    然后再在milvus创建一个Collection

    然后这个是不对的，因为我使用attu界面创建的，默认第二个字段一定得是向量字段，所以又用了上次的Java代码来创建

    +
    CreateCollectionReq.CollectionSchema collectionSchema = clientV2.createSchema();
    // add two fileds, id and vector
            Integer dim = 2048;
            collectionSchema.addField(AddFieldReq.builder().fieldName("url").dataType(DataType.VarChar).build());
            collectionSchema.addField(AddFieldReq.builder().fieldName("embedding").dataType(DataType.FloatVector).dimension(dim).build());
            collectionSchema.addField(AddFieldReq.builder().fieldName("id").dataType(DataType.Int64).isPrimaryKey(Boolean.TRUE).autoID(Boolean.TRUE).description("id").build());

            CreateCollectionReq req = CreateCollectionReq
                    .builder()
                    .collectionSchema(collectionSchema)
                    .collectionName("text_image_search")
                    .dimension(dim).build();
            clientV2.createCollection(req);
    +

    并且注意字段顺序不能错，这个顺序在towhee的demo中也没地方直接修改
    看一下towhee的demo
    地址在这
    https://towhee.io/tasks/detail/pipeline/text-image-search

    +
    from towhee import AutoPipes, AutoConfig

    # set MilvusInsertConfig for the built-in insert_milvus pipeline
    insert_conf = AutoConfig.load_config('insert_milvus')
    insert_conf.collection_name = 'text_image_search'

    insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

    # generate embedding
    embedding = image_embedding('./test1.png').get()[0]

    # insert text and embedding into Milvus
    insert_pipe(['./test1.png', embedding])
    +

    第三个坑，这并不能跑起来

    +
     embedding = insert_pipe.image_embedding('./test.jpg').get()[0]
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
    AttributeError: 'RuntimePipeline' object has no attribute 'image_embedding'
    +

    因为找不到这个 image_embedding
    估计是这个也是包路径变更了，但是并没有什么文档可以找到，之前LlamaIndex还有点更新指南，后来是问了Claude才知道了

    +
    from towhee import AutoPipes, AutoConfig, ops


    # set MilvusInsertConfig for the built-in insert_milvus pipeline
    insert_conf = AutoConfig.load_config('insert_milvus')
    insert_conf.collection_name = 'text_image_search'


    insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

    # 创建图像嵌入管道
    image_embedding_pipe = AutoPipes.pipeline('image-embedding')

    # 生成嵌入
    embedding = image_embedding_pipe('./test.jpg').get()[0]


    # insert text and embedding into Milvus
    insert_pipe(['./test.jpg', embedding])
    +

    需要从 AutoPipes 中把这个 image-embedding 找出来
    然后就是上面说到的字段映射问题，默认是先url，再embedding字段，并且主键字段必须是autoId，不然也会缺少默认值，还是感叹下，工程化的东西还是要工程化的质量保证，否则变更都无从知晓，现在人工智能大热，大家都在追风，只是基础的软件还是要稳扎稳打，这样我们工程人员才能把它们更好的用起来，PS：娃真可爱，但真的好累

    ]]>     算法 @@ -5320,6 +5268,43 @@ user3: 囤物资     + + 我是如何走上跑步这条不归路的 + /2020/07/26/%E6%88%91%E6%98%AF%E5%A6%82%E4%BD%95%E8%B5%B0%E4%B8%8A%E8%B7%91%E6%AD%A5%E8%BF%99%E6%9D%A1%E4%B8%8D%E5%BD%92%E8%B7%AF%E7%9A%84/ + 这周因为没有准备技术方面的内容加之之前有想分享下我和跑步的一些事情，我从小学开始就是个体育渣，因为体重大非常胖，小学的时候要做仰卧起坐，基本是一个都起不来，然后那时候跑步也是要我命那种，跟另外一个比较胖的同学在跑步队尾苟延残喘，只有立定跳远还行。

    +

    时光飞逝，我在初中高中的时候因为爱打篮球，以为自己体质已经有了质的变化，所以在体育课跑步的时候妄图跟一位体育非常好的同学一起跑，结果跟的快断气了，最终还是确认了自己是个体育渣，特别是到了大学的第一次体测跑一千米，跑完直接吐了，一则是大学太宅不运动，二则的确是底子不好。那么怎么会去跑步了呢，其实也没什么特殊的原因，就是工作以后因为运动得更少，体质差，而且越来越胖，所以就想运动下，加之跑步也是我觉得成本最低的运动了，刚好那时候17 年租的地方附近小区周围的路车不太多，一圈刚好一公里多，就觉得开始跑跑看，其实想想以前觉得一千米是非常远的，学校塑胶跑道才 400 米，一千米要两圈半，太难了，但是后来在这个小区周围跑的时候好像跑了一圈以后还能再跑一点，最后跑了两圈，可把自己牛坏了，我都能跑两千米了，哈哈，这是个什么概念呢，大学里最让我绝望的两项体育相关的内容就是一千米和十二分钟跑，一千米把我跑吐了，十二分钟跑及格五圈半也能让我跑完花一周时间恢复以及提前一周心理压力爆炸，虽然我那时候跑的不快，但是已经能跑两千米了，瞬间让自己自信心爆炸，并且跑完步出完汗的感觉是非常棒的，毕竟吃奶茶鸡排都能心安理得了，谁叫我跑步了呢😄，其实现在回去看，那时候跑得还算快的，因为还比较瘦，现在要跑得那么快心跳就太快了，关于心跳什么的后面说，开始建立起自信心之后，对跑步这件事就开始不那么排斥跟害怕了，毕竟我能跑两千米了，然后试试三公里，哇，也可以了呢，三公里是什么概念呢，我大学里跑过最多的一次是十二分钟跑五圈半还是六圈，也就是两公里多，不到三公里，几乎是生涯最长了，一时间产生了一些我可能是个被埋没的运动天才的错觉，其实细想下也能明白，只是速度足够慢了就能跑多一点，毕竟提测一千米是要跑进四分钟才及格，自己跑的时候一千米跑六分多钟已经算不慢了（对我自己来说），但是即使是这样还是对把跑步坚持下去这件事有了很大的正面激励作用，并且由于那时候上下班骑车，整个体重控制的比较理想，导致一时间误会跑步就能非常快的减肥（其实这是我跑步历程中比较大的误区之一），因为会在跑步前后称下体重，如果跑个五公里（后面可以跑五公里了），可能体重就能降 0.5 千克，但实际上这只是这五公里跑步身体流失的水分，喝杯水就回来了，那时候能控制体重主要是骑车跟跑步一起的作用，并且工作压力相对来讲比较小，没有过劳肥。

    +

    后面其实跑步慢慢变得一个比较习惯的运动了，从三公里，到五公里，到七公里，再到十公里，十公里差不多对我来说是个坎，一直还不能比较轻松的跑十公里，可能近一两年好了一些（原谅我只是跟自己比较，跟那些大神比差得不知道多远），其实对我来说每次都是个突破，因为其实与他人比较没有特别大意义，比较顶尖的差得太远，比较普通的也不行，都会打击自信心，比较比我差的就更没意义了，所以其实能挑战自己，能把自己的上限提高才是最有意义的，这也是我看着朋友圈里的一些大神的速度除了佩服赞叹之外没什么其他的惭愧或者说嫌弃自己的感觉（阿 Q 精神😄）。

    +

    一直感性地觉得，跑步能解压，跑完浑身汗，有种把身体的负能量都排出去的感觉，也把吃太多的罪恶感排解掉了🤦‍♂️，之前朋友有看一本书，书名差不多叫越跑越接近自己，这个也是我觉得挺准确的一句话，当跑到接近极限了，还想再继续再跑一点，再跑一点就能突破自己上一次的最远记录了，再跑一点就能又一次突破自己了，成人以后，毕业以后，进入社会以后，世事总是难以件件顺遂，磕磕绊绊的往前走，总觉得要崩溃了，但是还是得坚持，再熬一下，再拼一下，可能还是失败，但人生呢，运气好的人和事总是小概率的，唯有面对挫折，还是日拱一卒，来日方长，我们再坚持下，没准下一次，没准再跑一会，就能突破自己，达到新的境界。

    +

    另外个人后期对跑步的一些知识和理解也变得深入一些，比如伤膝盖，其实跑步的确伤膝盖，需要做一些准备和防护，最好的是适合自己的跑鞋和比较好的路（最好的是塑胶跑道了），也要注意热身跟跑后的拉伸（虽然我做的很差），还有就是注意心率，每个人有自己的适宜心率，我这就不冒充科普达人了，可以自行搜索关键字，先说到这吧~

    +]]>     + + 生活 + 运动 + 跑步 + + + 生活 + 运动 + 减肥 + 跑步 + +     + + 折腾记-给玩客云刷上Armbian + /2024/03/31/%E6%8A%98%E8%85%BE%E8%AE%B0-%E7%BB%99%E7%8E%A9%E5%AE%A2%E4%BA%91%E5%88%B7%E4%B8%8AArmbian/ + 之前买来玩客云作为轻nas使用，但是由于只能外接硬盘，并且是usb2.0的接口，使用体验不是特别好，并且前期作为老母鸡还能兑换视频会员这种，后面取消了就不怎么使用了，前阵子玩客云停止提供服务了，所以就想可以折腾下，比如Armbian这样的，可以作为轻量服务器使用，功耗也比较低，跑一些简单的docker还是可以的，

    配置是这样的，存储小了点，其他就是一个普通小服务器的配置了，usb2.0有点弱，但是现在有一些是正常退役，还有很多是PCDN被禁了宽带以后咸鱼出售的，带上电源跟网线才30左右，还是比较有性价比的
    首先机器有几个版本，主要是1.0跟1.3，要注意1.3版本的主板上有个混淆点，会有一个1.2的版本，指的是具体存储或者网卡等组件的版本，玩客云版本是较大的 V1.3，而1.0则是比较小的字体，而且据网上的资料，1.0版本是有个MAC地址的贴条

    可以看上图的标示，箭头指向的是短接点，这是1.0版本的，1.3版本是找不到这个短接点，而是在反面（有S805芯片和存储的一面）
    新版手边没有，借用一张恩山的图

    这个在最开始我刷第一个的时候比较困扰我，因为不确定版本，网上又有很多都是只截一小块区域的图，也不知道相对位置
    首先如果是从闲鱼买来的已经刷成PCDN，某心云的需要通过短接刷底包之后再刷入系统，可以先用吹风机热风吹一会后盖，然后用一字螺丝刀从SD卡槽把外层的一片壳撬开，然后通过十字螺丝刀拧开之后就可以把主板拿出来
    然后需要镊子或者其他铁丝，能够短接的就行，外加一个双公头USB线
    刷机工具包可以用我这个分享的

    +
    https://www.alipan.com/s/sd4Wi6TDzeP
    提取码: 84or
    +

    第一步打开USB Burning Tool, 要短接刷入update.img

    通过文件导入网盘里的 update.img，然后先用双公头USB线连接电脑的USB口和玩客云靠近HDMI口的USB口，如果方便最好接到电脑后置的USB口，防止不稳定刷机终端，接下来就需要短接了，这里的姿势要先短接好，保持稳定，然后插上电源线，这里如果连接成功就会在软件左上会显示一栏连接成功，如果电脑连接音响也会有叮咚这样的声音，接下去就可以点开始进行刷入底包了，这里最大的困难点就是保持短接稳定后插电源，并且继续保持稳定刷入底包，底包刷入会比较快，等到刷机进度条走完刷成功后就点停止，然后关闭软件，拔掉玩客云电源，拔掉USB
    第二步是刷入Armbian系统，根据一些教程有些说需要先刷入5.8，我这边是直接刷入了5.9的系统，也就多一步，准备一个U盘，16G或者8G都行，先用Etcher把5.8系统刷入U盘，刷入完成后再关闭软件，推出U盘，将U盘插入玩客云上靠近网口的USB口，然后再通电，指示灯会蓝色-> 绿色 -> 蓝紫闪烁 -> 蓝色，这样就是刷机成功了，会需要一点时间，刷机完成后再把5.9的的刷入U盘，再继续刚才的步骤，此时需要把网线插入玩客云，等待正常亮灯显示后，在路由器上可以找到一个aml-s812的设备，在通过ssh连接，账号是root，密码是1234，进入系统后通过以下命令就可以刷入emmc了

    +
    cd /boot/install  sudo ./install.sh
    +

    完成后会自动断开，拔掉U盘后再用同样方法连接就可以了，后续怎么使用就是Linux相关的了，后面也可以讲下docker的操作，但要注意架构，这个是arm架构32位的

    +]]>     + + 折腾 + + + 折腾 + +     折腾记-给玩客云装上casaos /2024/04/28/%E6%8A%98%E8%85%BE%E8%AE%B0-%E7%BB%99%E7%8E%A9%E5%AE%A2%E4%BA%91%E8%A3%85%E4%B8%8Acasaos/ @@ -5354,13 +5339,9 @@ user3: - 折腾记-给玩客云刷上Armbian - /2024/03/31/%E6%8A%98%E8%85%BE%E8%AE%B0-%E7%BB%99%E7%8E%A9%E5%AE%A2%E4%BA%91%E5%88%B7%E4%B8%8AArmbian/ - 之前买来玩客云作为轻nas使用，但是由于只能外接硬盘，并且是usb2.0的接口，使用体验不是特别好，并且前期作为老母鸡还能兑换视频会员这种，后面取消了就不怎么使用了，前阵子玩客云停止提供服务了，所以就想可以折腾下，比如Armbian这样的，可以作为轻量服务器使用，功耗也比较低，跑一些简单的docker还是可以的，

    配置是这样的，存储小了点，其他就是一个普通小服务器的配置了，usb2.0有点弱，但是现在有一些是正常退役，还有很多是PCDN被禁了宽带以后咸鱼出售的，带上电源跟网线才30左右，还是比较有性价比的
    首先机器有几个版本，主要是1.0跟1.3，要注意1.3版本的主板上有个混淆点，会有一个1.2的版本，指的是具体存储或者网卡等组件的版本，玩客云版本是较大的 V1.3，而1.0则是比较小的字体，而且据网上的资料，1.0版本是有个MAC地址的贴条

    可以看上图的标示，箭头指向的是短接点，这是1.0版本的，1.3版本是找不到这个短接点，而是在反面（有S805芯片和存储的一面）
    新版手边没有，借用一张恩山的图

    这个在最开始我刷第一个的时候比较困扰我，因为不确定版本，网上又有很多都是只截一小块区域的图，也不知道相对位置
    首先如果是从闲鱼买来的已经刷成PCDN，某心云的需要通过短接刷底包之后再刷入系统，可以先用吹风机热风吹一会后盖，然后用一字螺丝刀从SD卡槽把外层的一片壳撬开，然后通过十字螺丝刀拧开之后就可以把主板拿出来
    然后需要镊子或者其他铁丝，能够短接的就行，外加一个双公头USB线
    刷机工具包可以用我这个分享的

    -
    https://www.alipan.com/s/sd4Wi6TDzeP
    提取码: 84or
    -

    第一步打开USB Burning Tool, 要短接刷入update.img

    通过文件导入网盘里的 update.img，然后先用双公头USB线连接电脑的USB口和玩客云靠近HDMI口的USB口，如果方便最好接到电脑后置的USB口，防止不稳定刷机终端，接下来就需要短接了，这里的姿势要先短接好，保持稳定，然后插上电源线，这里如果连接成功就会在软件左上会显示一栏连接成功，如果电脑连接音响也会有叮咚这样的声音，接下去就可以点开始进行刷入底包了，这里最大的困难点就是保持短接稳定后插电源，并且继续保持稳定刷入底包，底包刷入会比较快，等到刷机进度条走完刷成功后就点停止，然后关闭软件，拔掉玩客云电源，拔掉USB
    第二步是刷入Armbian系统，根据一些教程有些说需要先刷入5.8，我这边是直接刷入了5.9的系统，也就多一步，准备一个U盘，16G或者8G都行，先用Etcher把5.8系统刷入U盘，刷入完成后再关闭软件，推出U盘，将U盘插入玩客云上靠近网口的USB口，然后再通电，指示灯会蓝色-> 绿色 -> 蓝紫闪烁 -> 蓝色，这样就是刷机成功了，会需要一点时间，刷机完成后再把5.9的的刷入U盘，再继续刚才的步骤，此时需要把网线插入玩客云，等待正常亮灯显示后，在路由器上可以找到一个aml-s812的设备，在通过ssh连接，账号是root，密码是1234，进入系统后通过以下命令就可以刷入emmc了

    -
    cd /boot/install  sudo ./install.sh
    -

    完成后会自动断开，拔掉U盘后再用同样方法连接就可以了，后续怎么使用就是Linux相关的了，后面也可以讲下docker的操作，但要注意架构，这个是arm架构32位的

    + 折腾记-讲一下iptables的一个小问题 + /2024/05/05/%E6%8A%98%E8%85%BE%E8%AE%B0-%E8%AE%B2%E4%B8%80%E4%B8%8Biptables%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E9%97%AE%E9%A2%98/ + 很早之前就有用过iptables，那时候是早期版本的Ubuntu系统，配置防火墙的时候，是挺复杂的，就是在reject之前添加accept规则，并且当时很多网上资料都是在后续添加就行，这个其实在当时是有问题的，所以比较印象深刻。
    这次碰到的问题是对iptables的概念不熟悉有关，因为iptables的逻辑其实也有一定问题我觉得，比如最基本的命令，iptables -L
    这里help显示的是会展示所有chain的rules

    但是这样完全把table概念给忽略了，如果不注明理论上应该把所有table的展示出来，或者把包含这个参数并且是默认给出filter这个table的写清楚
    所以这次要补一下这部分概念
    iptables里首先就是有四种table，分别是 Filter, NAT, Mangle, Raw四种内建表
    并且层次结构是 iptables -> Tables -> Chains -> Rules.
    filter:
    This is the default table (if no -t option is passed). It contains the built-in chains INPUT (for packets destined to local sockets), FORWARD (for packets being routed through the box), and OUTPUT (for locally-generated packets).
    包含 INPUT，FORWORD和OUPUT三个chain，默认就是展示这个，所以就会导致用iptables -L 显示不了mangle table的规则
    nat:
    This table is consulted when a packet that creates a new connection is encountered. It consists of three built-ins: PREROUTING (for altering packets as soon as they come in), OUTPUT (for altering locally-generated packets before routing), and POSTROUTING (for altering packets as they are about to go out).
    nat包含 PREROUTING，OUTPUT，POSTROUTING三个chain
    mangle:
    This table is used for specialized packet alteration. Until kernel 2.4.17 it had two built-in chains: PREROUTING (for altering incoming packets before routing) and OUTPUT (for altering locally-generated packets before routing). Since kernel 2.4.18, three other built-in chains are also supported: INPUT (for packets coming into the box itself), FORWARD (for altering packets being routed through the box), and POSTROUTING (for altering packets as they are about to go out).
    mangle主要用于专门的数据包更改，包含 PREROUTING，INPUT，OUTPUT，FORWARD，POSTROUTING 五个chain，注意有内核版本区别，只是现在基本没那么老的内核了
    raw:
    This table is used mainly for configuring exemptions from connection tracking in combination with the NOTRACK target. It registers at the netfilter hooks with higher priority and is thus called before ip_conntrack, or any other IP tables. It provides the following built-in chains: PREROUTING (for packets arriving via any network interface) OUTPUT (for packets generated by local processes)
    包含 PREROUTING和OUTPUT，主要用来配置例外
    chain内部会有实际的rule规则，这个具体后面可以介绍

    ]]>     折腾 @@ -5458,15 +5439,15 @@ user3:     - 折腾记-讲一下iptables的一个小问题 - /2024/05/05/%E6%8A%98%E8%85%BE%E8%AE%B0-%E8%AE%B2%E4%B8%80%E4%B8%8Biptables%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E9%97%AE%E9%A2%98/ - 很早之前就有用过iptables，那时候是早期版本的Ubuntu系统，配置防火墙的时候，是挺复杂的，就是在reject之前添加accept规则，并且当时很多网上资料都是在后续添加就行，这个其实在当时是有问题的，所以比较印象深刻。
    这次碰到的问题是对iptables的概念不熟悉有关，因为iptables的逻辑其实也有一定问题我觉得，比如最基本的命令，iptables -L
    这里help显示的是会展示所有chain的rules

    但是这样完全把table概念给忽略了，如果不注明理论上应该把所有table的展示出来，或者把包含这个参数并且是默认给出filter这个table的写清楚
    所以这次要补一下这部分概念
    iptables里首先就是有四种table，分别是 Filter, NAT, Mangle, Raw四种内建表
    并且层次结构是 iptables -> Tables -> Chains -> Rules.
    filter:
    This is the default table (if no -t option is passed). It contains the built-in chains INPUT (for packets destined to local sockets), FORWARD (for packets being routed through the box), and OUTPUT (for locally-generated packets).
    包含 INPUT，FORWORD和OUPUT三个chain，默认就是展示这个，所以就会导致用iptables -L 显示不了mangle table的规则
    nat:
    This table is consulted when a packet that creates a new connection is encountered. It consists of three built-ins: PREROUTING (for altering packets as soon as they come in), OUTPUT (for altering locally-generated packets before routing), and POSTROUTING (for altering packets as they are about to go out).
    nat包含 PREROUTING，OUTPUT，POSTROUTING三个chain
    mangle:
    This table is used for specialized packet alteration. Until kernel 2.4.17 it had two built-in chains: PREROUTING (for altering incoming packets before routing) and OUTPUT (for altering locally-generated packets before routing). Since kernel 2.4.18, three other built-in chains are also supported: INPUT (for packets coming into the box itself), FORWARD (for altering packets being routed through the box), and POSTROUTING (for altering packets as they are about to go out).
    mangle主要用于专门的数据包更改，包含 PREROUTING，INPUT，OUTPUT，FORWARD，POSTROUTING 五个chain，注意有内核版本区别，只是现在基本没那么老的内核了
    raw:
    This table is used mainly for configuring exemptions from connection tracking in combination with the NOTRACK target. It registers at the netfilter hooks with higher priority and is thus called before ip_conntrack, or any other IP tables. It provides the following built-in chains: PREROUTING (for packets arriving via any network interface) OUTPUT (for packets generated by local processes)
    包含 PREROUTING和OUTPUT，主要用来配置例外
    chain内部会有实际的rule规则，这个具体后面可以介绍

    + 是何原因竟让两人深夜奔袭十公里 + /2022/06/05/%E6%98%AF%E4%BD%95%E5%8E%9F%E5%9B%A0%E7%AB%9F%E8%AE%A9%E4%B8%A4%E4%BA%BA%E6%B7%B1%E5%A4%9C%E5%A5%94%E8%A2%AD%E5%8D%81%E5%85%AC%E9%87%8C/ + 偶尔来个标题党，不过也是一次比较神奇的经历
    上周五下班后跟 LD 约好去吃牛蛙，某个朋友好像对这类都不太能接受，我以前小时候也不常吃，但是这类其实都是口味比较重，没有那种肉本身的腥味，而且肉质比较特殊，吃过几次以后就有点爱上了，这次刚好是 LD 买的新店开业券，比较优惠（我们俩都是有点勤俭持家的，想着小电驴还有三格电，这家店又有点远，骑车单趟大概要 10 公里左右，有点担心，LD 说应该可以的，就一起骑了过去（跟她轮换着骑电驴和共享单车），结果大概离吃牛蛙的店还有一辆公里的时候，电量就报警了，只有最后一个红色的了，一共是五格，最后一格是红色的，提示我们该充电了，这样子是真的有点慌了，之前开了几个月都是还有一两格电的时候就充电了，没有试验过究竟这最后一格电能开多远，总之先到了再说。
    这家牛蛙没想到还挺热闹的，我们到那已经快八点了，还有十几个排队的，有个人还想插队（向来是不惯着这种，一边去），旁边刚好是有些商店就逛了下，就跟常规的商业中心差不多，开业的比较早也算是这一边比较核心的商业综合体了，各种品牌都有，而且还有彩票售卖点的，只是不太理解现在的彩票都是兑图案的，而且要 10 块钱一张，我的概念里还是以前 2 块钱一张的双色球，偶尔能中个五块十块的。排队还剩四五个的时候我们就去门口坐着等了，又等了大概二十分钟才排到我们，靠近我们等的里面的位置，好像好几个小女生在那还叫了外卖奶茶，然后各种拍照，小朋友的生活还是丰富多彩的，我们到了就点了蒜蓉的，没有点传说中紫苏的，菜单上画了 N 个🌶，LD 还是想体验下说下次人多点可以试试，我们俩吃怕太辣了吃不消，口味还是不错的，这家貌似是 LD 闺蜜推荐的，口碑有保证。两个人光吃一个蛙锅就差不多了，本来还想再点个其他的，后面实在吃不下了就没点，吃完还是惯例点了个奶茶，不过是真的不好找，太大了。
    本来是就回个家的事了，结果就因为前面铺垫的小电驴已经只有一格电了，标题的深夜奔袭十公里就出现了，这个电驴估计续航也虚标挺严重的，电量也是这样，骑的时候显示只有一格电，关掉再开起来又有三格，然后我们回去骑了没一公里就没电了，这下是真的完球了，觉得车子也比较新，直接停外面也不放心，就开始了深夜的十公里推电驴奔袭，LD 看我太累还帮我中间推了一段，虽然是跑过十公里的，但是推着个没电的电驴，还是着实不容易的，LD 也是陪我推着车走，中间好几次说我们把电驴停着打车回去，把电池带回去充满了明天再过来骑车，可能是心态已经转变了，这应该算是一次很特殊的体验，从我们吃完出来大概十点，到最后我们推到小区，大概是过了两个小时的样子，说句深夜也不太过分，把这次这么推车看成了一种意志力的考验，很多事情也都是怕坚持，或者说怕不能坚持，想走得远，没有持续的努力坚持肯定是不行的，所以还是坚持着把车推回来（好吧，我其实主要是怕车被偷，毕竟刚来杭州上学没多久就被偷了自行车留下了阴影），中间感谢 LD，跟我轮着推了一段路，有些下坡的时候还在那坐着用脚蹬一下，离家里大概还有一公里的时候，有个骑电瓶车的大叔还停下来问我们是车破了还是没电了，应该是出于好意吧，最后快到的时候真的非常渴，买了2.5 升的水被我一口气喝了大半瓶，奶茶已经不能起到解渴的作用了，本来以为这样能消耗很多，结果第二天一称还重了，（我的称一定有问题 233

    ]]>     - 折腾 + 生活 - 折腾 + 生活     @@ -5475,16 +5456,37 @@ user3: 对于深度学习只能说我是个门外汉，开始学习，不过很多还搞不懂，做个记录和分享，基于《深度学习入门：基于 Python 的理论与实现》，
    第一章 Python 入门就不介绍了，不是重点，不过完全没有 Python 基础的可以看下，我之前算是学过一点点，
    第二章我觉得入门的方式比较不错，从感知机入手，有一些顾名思义，感知一些状态（输入），来做出反应，简单的就是比如通电了，我的灯就亮起来了，并且往后就是延伸到了计算机的最基础组成，与或门，与门与或门是最基础的，配合非门进行组成，可以作为计算机基础单元ALU 的组成基础，而更复杂的也可以由此进行搭建，这是个比较通俗的解释，根据书中的定义，感知机是具有输入和输出的算法。给定一个输入后，将输出一个既定的值，单层的感知机无法实现更复杂的异或门，但是可以通过 2 层感知机来实现，也就是一个与非门，一个或门，作为第一层的感知机，他们的输出作为与门的输入，就可以成为一个异或门。理论上多层感知机可以表示计算机。
    而后第三章引出了激活函数，也就是在前面的与或门和基础的感知机的基础上加上了输出的条件，前面与门或门都是最基础的 0,1 游戏，现在可以加上更复杂的判断条件，在输入的基础上配以权重，再加上偏置参数，表示被激活的容易程度，这种激活函数可以被称为阶跃函数，如果超过了一定的值就代表被激活，没有则不激活，但是实际在神经元中被使用的主要是用 sigmoid 函数，相比阶跃函数，sigmoid 函数是一个平滑的曲线，随着输入变化而连续变化，因为相对感知机，神经元需要的信号是连续的实数值信号，再往后则是对输出层的介绍，如果是回归问题，也就是根据输入预测一个（连续的）数值的问题，属于回归问题，可以用恒等函数，而对于分类问题，则使用 softmax 函数，这个函数的一个重要的点在于也是区分于简单的二元分类器，softmax 是将多个结果概率进行数值处理（归一化），也叫做归一化指数函数，对于不同的结果概率是将概率最大的进行放大，凸显其中最大的值并抑制远低于最大值的其他分量。使得其他概率值也能够被使用，但是减弱其份额权重。

    ]]>     - 深度学习 + 深度学习 + + + 深度学习 + +     + + 用 ollama 本地运行谷歌开源大模型 Gemma + /2024/03/24/%E7%94%A8-ollama-%E6%9C%AC%E5%9C%B0%E8%BF%90%E8%A1%8C%E5%A4%A7%E6%A8%A1%E5%9E%8B/ + 原先在 23 年初的时候调研过一些国产的大模型，包括复旦开源的 MOSS 和清华的 ChatGLM，那时候还是早期版本，需要在 Linux 上，并且有比较好的显卡，而且一般来讲都得是 N 卡，过程中需要安装 pytorch和比较多依赖，并且当时的效果也还比较差，所以后面就没有长期使用。
    最近看到谷歌在 2 月份开源了大模型 Gemma ，gemma 的博客在这里，想要在本地运行这个模型在现在这个阶段也变得简单很多，因为我们有了 ollama 工具

    可以通过这个工具来运行大模型，并且已经支持了谷歌开源的 Gemma

    我这边本地是 MacBook Pro 14 寸的，m3 pro 的处理器，18g 内存，刚好可以用 7b 量化的模型

    这里有推荐的模型和内存推荐匹配规则，16g 可以运行 13B 及以下模型
    下载安装完后我们可以用以下命令

    +
    ollama run gemma:7b
    +

    这里需要拉取模型，约5.2g 大小，考虑网络原因可能会比较慢

    我们可以简单来试试问个问题


    看出来回答的还是比较丰富的，谷歌出品还是比较有水平的，不至于像 ChatGLM 最初版本的在不做调优的情况下甚至有点前言不搭后语
    对于想使用 chatgpt 但是没条件，这也算是个低配平替了, 并且已经是个比较可用的了，同时也方便进行学习调优等
    如果想要类似于 chatgpt 那样的网页版，可以安装 open-webui
    可以通过 webui 访问 ollama 运行的大模型，
    用 docker 启动的命令也贴一下

    +
    docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
    +

    不过有个小问题就是 docker 镜像拉取会有点慢，可以添加下国内镜像加速

    +
    {
        "registry-mirrors": [
            "https://dockerproxy.com",
            "https://docker.mirrors.ustc.edu.cn",
            "https://docker.nju.edu.cn"
        ]
    }
    +


    这里有一个小区别，Gemma 在多轮会话的时候会在前面的答案基础上再完善。

    +

    补充一个在 windows 环境下，cpu 跑模型的也是可行的

    现在是大模型可以深入千家万户了，大家都可以尝试下，如果对日常的工作学习有一些效率上的提升也是好的

    +]]>     + + LLM - 深度学习 + LLM     - 是何原因竟让两人深夜奔袭十公里 - /2022/06/05/%E6%98%AF%E4%BD%95%E5%8E%9F%E5%9B%A0%E7%AB%9F%E8%AE%A9%E4%B8%A4%E4%BA%BA%E6%B7%B1%E5%A4%9C%E5%A5%94%E8%A2%AD%E5%8D%81%E5%85%AC%E9%87%8C/ - 偶尔来个标题党，不过也是一次比较神奇的经历
    上周五下班后跟 LD 约好去吃牛蛙，某个朋友好像对这类都不太能接受，我以前小时候也不常吃，但是这类其实都是口味比较重，没有那种肉本身的腥味，而且肉质比较特殊，吃过几次以后就有点爱上了，这次刚好是 LD 买的新店开业券，比较优惠（我们俩都是有点勤俭持家的，想着小电驴还有三格电，这家店又有点远，骑车单趟大概要 10 公里左右，有点担心，LD 说应该可以的，就一起骑了过去（跟她轮换着骑电驴和共享单车），结果大概离吃牛蛙的店还有一辆公里的时候，电量就报警了，只有最后一个红色的了，一共是五格，最后一格是红色的，提示我们该充电了，这样子是真的有点慌了，之前开了几个月都是还有一两格电的时候就充电了，没有试验过究竟这最后一格电能开多远，总之先到了再说。
    这家牛蛙没想到还挺热闹的，我们到那已经快八点了，还有十几个排队的，有个人还想插队（向来是不惯着这种，一边去），旁边刚好是有些商店就逛了下，就跟常规的商业中心差不多，开业的比较早也算是这一边比较核心的商业综合体了，各种品牌都有，而且还有彩票售卖点的，只是不太理解现在的彩票都是兑图案的，而且要 10 块钱一张，我的概念里还是以前 2 块钱一张的双色球，偶尔能中个五块十块的。排队还剩四五个的时候我们就去门口坐着等了，又等了大概二十分钟才排到我们，靠近我们等的里面的位置，好像好几个小女生在那还叫了外卖奶茶，然后各种拍照，小朋友的生活还是丰富多彩的，我们到了就点了蒜蓉的，没有点传说中紫苏的，菜单上画了 N 个🌶，LD 还是想体验下说下次人多点可以试试，我们俩吃怕太辣了吃不消，口味还是不错的，这家貌似是 LD 闺蜜推荐的，口碑有保证。两个人光吃一个蛙锅就差不多了，本来还想再点个其他的，后面实在吃不下了就没点，吃完还是惯例点了个奶茶，不过是真的不好找，太大了。
    本来是就回个家的事了，结果就因为前面铺垫的小电驴已经只有一格电了，标题的深夜奔袭十公里就出现了，这个电驴估计续航也虚标挺严重的，电量也是这样，骑的时候显示只有一格电，关掉再开起来又有三格，然后我们回去骑了没一公里就没电了，这下是真的完球了，觉得车子也比较新，直接停外面也不放心，就开始了深夜的十公里推电驴奔袭，LD 看我太累还帮我中间推了一段，虽然是跑过十公里的，但是推着个没电的电驴，还是着实不容易的，LD 也是陪我推着车走，中间好几次说我们把电驴停着打车回去，把电池带回去充满了明天再过来骑车，可能是心态已经转变了，这应该算是一次很特殊的体验，从我们吃完出来大概十点，到最后我们推到小区，大概是过了两个小时的样子，说句深夜也不太过分，把这次这么推车看成了一种意志力的考验，很多事情也都是怕坚持，或者说怕不能坚持，想走得远，没有持续的努力坚持肯定是不行的，所以还是坚持着把车推回来（好吧，我其实主要是怕车被偷，毕竟刚来杭州上学没多久就被偷了自行车留下了阴影），中间感谢 LD，跟我轮着推了一段路，有些下坡的时候还在那坐着用脚蹬一下，离家里大概还有一公里的时候，有个骑电瓶车的大叔还停下来问我们是车破了还是没电了，应该是出于好意吧，最后快到的时候真的非常渴，买了2.5 升的水被我一口气喝了大半瓶，奶茶已经不能起到解渴的作用了，本来以为这样能消耗很多，结果第二天一称还重了，（我的称一定有问题 233

    + 给小电驴上牌 + /2022/03/20/%E7%BB%99%E5%B0%8F%E7%94%B5%E9%A9%B4%E4%B8%8A%E7%89%8C/ + 三八节活动的时候下决心买了个小电驴，主要是上下班路上现在通勤条件越来越恶劣了，之前都是觉得坐公交就行了，实际路程就比较短，但是现在或者说大概是年前那两个月差不多就开始了，基本是堵一路，个人感觉是天目山路那边在修地铁，而且蚂蚁的几个空间都在那，上班的时间点都差不多，前一个修地铁感觉挺久了，机动车保有量也越来越多，总体是古墩路就越来越堵，还有个原因就是早上上班的点共享单车都被骑走了，有时候整整走一路都没一辆，有时候孤零零地有一辆基本都是破的；走路其实也是一种选择，但是因为要赶着上班，走得太慢就要很久，可能要 45 分钟这样，走得比较快就一身汗挺难受的。所以考虑自行车和电动车，这里还有一点就是不管是乘公交还是骑共享单车，其实都要从楼下走出去蛮远，公司回来也是，也就是这种通勤方式在准备阶段就花了比较多时间，比如总的从下班到到家的时间是半小时，可能在骑共享单车和公交车上的时间都不到十分钟，就比较难受。觉得这种比例太浪费时间，如果能有这种比较点对点的方式，估计能省时省力不少，前面说的骑共享单车的方式其实在之前是比较可行的，但是后来越来越少车，基本都是每周的前几天，周一到周三都是没有车，走路到公司再冷的天都是走出一身的汗，下雨天就更难受，本来下雨天应该是优先选择坐公交，但是一般下雨天堵车会更严重，而且车子到我上车的那个站，下雨天就挤得不行，总体说下来感觉事情都不打，但是几年下来，还是会挺不爽的。

    +

    电驴看的比较草率，主要是考虑续航，然后锂电池外加 48v 和 24AH，这样一般来讲还是价格比较高的，只是原来没预料到这个限速，以为现在的车子都比较快，但是现在的新国标车子都是 25km/h 的限速，然后 15km/h 都是会要提醒，虽然说有一些特殊的解除限速的方法，但是解了也就 35km/h ，差距不是特别大，而且现在的车子都是比较小，也不太能载东西，特别是上下班路程也不远的情况下，其实不是那么需要速度，就像我朋友说的，可能骑车的时间还不如等红绿灯多，所以就还好，也不打算解除限速，只是品牌上也仔细看，后来选了绿源，目前大部分还是雅迪，爱玛，台羚，绿源，小牛等，路上看的话还是雅迪比较多，不过价格也比较贵一点，还有就是小牛了，是比较新兴的品牌，手机 App 什么的做得比较好，而且也比较贵，最后以相对比较便宜的价格买了个锂电 48V24AH 的小车子，后来发现还是有点不方便的点就是没有比较大的筐，也不好装，这样就是下雨天雨衣什么的比较不方便放。

    +

    聊回来主题上牌这个事情，这个事情也是颇费心力，提车的时候店里的让我跟他早上一起去，但是因为不确定时间，也比较远就没跟着去，因为我是线上买的，线下自提，线下的店可能没啥利润可以拿，就不肯帮忙代上牌，朋友说在线下店里买是可以代上的，自己上牌过程也比较曲折，一开始是头盔没到，然后是等开发票，主要的东西就是需要骑着车子去车管所，不能只自己去，然后需要预约，附近比较近的都是提前一周就预约完了号了，要提前在支付宝上进行预约，比较空的就是店里推荐的景区大队，但是随之而来就是比较蛋疼的，这个景区大队太远了，看下骑车距离有十几公里，所以就有点拖延症，但是总归要上的，不然一直不能开是白买了，上牌的材料主要是车辆合格证，发票，然后车子上的浙品码，在车架上和电池上，然后车架号什么的都要跟合格证上完全对应，整体车子要跟合格证上一毛一样，如果有额外的反光镜，后面副座都需要拆掉，脚踏板要装上，到了那其实还比较顺利，就是十几公里外加那天比较冷，吹得头疼。

    ]]>     生活 @@ -5510,45 +5512,81 @@ user3:     - 给小电驴上牌 - /2022/03/20/%E7%BB%99%E5%B0%8F%E7%94%B5%E9%A9%B4%E4%B8%8A%E7%89%8C/ - 三八节活动的时候下决心买了个小电驴，主要是上下班路上现在通勤条件越来越恶劣了，之前都是觉得坐公交就行了，实际路程就比较短，但是现在或者说大概是年前那两个月差不多就开始了，基本是堵一路，个人感觉是天目山路那边在修地铁，而且蚂蚁的几个空间都在那，上班的时间点都差不多，前一个修地铁感觉挺久了，机动车保有量也越来越多，总体是古墩路就越来越堵，还有个原因就是早上上班的点共享单车都被骑走了，有时候整整走一路都没一辆，有时候孤零零地有一辆基本都是破的；走路其实也是一种选择，但是因为要赶着上班，走得太慢就要很久，可能要 45 分钟这样，走得比较快就一身汗挺难受的。所以考虑自行车和电动车，这里还有一点就是不管是乘公交还是骑共享单车，其实都要从楼下走出去蛮远，公司回来也是，也就是这种通勤方式在准备阶段就花了比较多时间，比如总的从下班到到家的时间是半小时，可能在骑共享单车和公交车上的时间都不到十分钟，就比较难受。觉得这种比例太浪费时间，如果能有这种比较点对点的方式，估计能省时省力不少，前面说的骑共享单车的方式其实在之前是比较可行的，但是后来越来越少车，基本都是每周的前几天，周一到周三都是没有车，走路到公司再冷的天都是走出一身的汗，下雨天就更难受，本来下雨天应该是优先选择坐公交，但是一般下雨天堵车会更严重，而且车子到我上车的那个站，下雨天就挤得不行，总体说下来感觉事情都不打，但是几年下来，还是会挺不爽的。

    -

    电驴看的比较草率，主要是考虑续航，然后锂电池外加 48v 和 24AH，这样一般来讲还是价格比较高的，只是原来没预料到这个限速，以为现在的车子都比较快，但是现在的新国标车子都是 25km/h 的限速，然后 15km/h 都是会要提醒，虽然说有一些特殊的解除限速的方法，但是解了也就 35km/h ，差距不是特别大，而且现在的车子都是比较小，也不太能载东西，特别是上下班路程也不远的情况下，其实不是那么需要速度，就像我朋友说的，可能骑车的时间还不如等红绿灯多，所以就还好，也不打算解除限速，只是品牌上也仔细看，后来选了绿源，目前大部分还是雅迪，爱玛，台羚，绿源，小牛等，路上看的话还是雅迪比较多，不过价格也比较贵一点，还有就是小牛了，是比较新兴的品牌，手机 App 什么的做得比较好，而且也比较贵，最后以相对比较便宜的价格买了个锂电 48V24AH 的小车子，后来发现还是有点不方便的点就是没有比较大的筐，也不好装，这样就是下雨天雨衣什么的比较不方便放。

    -

    聊回来主题上牌这个事情，这个事情也是颇费心力，提车的时候店里的让我跟他早上一起去，但是因为不确定时间，也比较远就没跟着去，因为我是线上买的，线下自提，线下的店可能没啥利润可以拿，就不肯帮忙代上牌，朋友说在线下店里买是可以代上的，自己上牌过程也比较曲折，一开始是头盔没到，然后是等开发票，主要的东西就是需要骑着车子去车管所，不能只自己去，然后需要预约，附近比较近的都是提前一周就预约完了号了，要提前在支付宝上进行预约，比较空的就是店里推荐的景区大队，但是随之而来就是比较蛋疼的，这个景区大队太远了，看下骑车距离有十几公里，所以就有点拖延症，但是总归要上的，不然一直不能开是白买了，上牌的材料主要是车辆合格证，发票，然后车子上的浙品码，在车架上和电池上，然后车架号什么的都要跟合格证上完全对应，整体车子要跟合格证上一毛一样，如果有额外的反光镜，后面副座都需要拆掉，脚踏板要装上，到了那其实还比较顺利，就是十几公里外加那天比较冷，吹得头疼。

    + 给局域网中的Ubuntu固定下ip + /2024/06/02/%E7%BB%99%E5%B1%80%E5%9F%9F%E7%BD%91%E4%B8%AD%E7%9A%84Ubuntu%E5%9B%BA%E5%AE%9A%E4%B8%8Bip/ + 将一个小点，在局域网中包括类似于homelab的或者仅仅只是搭一个用来当实验环境的，在局域网内机器比较少的时候大部分DHCP会给分配相同的ip，但是这不是一定的，当机器比较多，并且上下线比较频繁时就会出现ip地址变动，这对于一些使用场景比较讨厌，所以我想把ip固定下来，先简单引用下DHCP的原理，他分为下面几个阶段

    +

    DHCP发现（DISCOVER）

    client在物理子网上发送广播来寻找可用的服务器。网络管理员可以配置一个本地路由来转发DHCP包给另一个子网上的DHCP服务器。该client实现生成一个目的地址为255.255.255.255或者一个子网广播地址的UDP包。

    +

    客户也可以申请它使用的最后一个IP地址（在下面的例子里为192.168.1.100）。如果该客户所在的网络中此IP仍然可用，服务器就可以准许该申请。否则，就要看该服务器是授权的还是非授权的。授权服务器会拒绝请求，使得客户立刻申请一个新的IP。非授权服务器仅仅忽略掉请求，导致一个客户端请求的超时，于是客户端就会放弃此请求而去申请一个新的IP地址。

    +

    DHCP提供（OFFER）

    当DHCP服务器收到一个来自客户的IP租约请求时，它会提供一个IP租约。DHCP为客户保留一个IP地址，然后通过网络单播一个DHCPOFFER消息给客户。该消息包含客户的MAC地址、服务器提供的IP地址、子网掩码、租期以及提供IP的DHCP服务器的IP。

    +

    服务器基于在CHADDR字段指定的客户硬件地址来检查配置。这里的服务器，192.168.1.1，将IP地址指定于YIADDR字段。

    +

    DHCP请求（REQUEST）

    当客户PC收到一个IP租约提供时，它必须告诉所有其他的DHCP服务器它已经接受了一个租约提供。因此，该客户会发送一个DHCPREQUEST消息，其中包含提供租约的服务器的IP。当其他DHCP服务器收到了该消息后，它们会收回所有可能已提供给该客户的租约。然后它们把曾经给该客户保留的那个地址重新放回到可用地址池中，这样，它们就可以为其他计算机分配这个地址。任意数量的DHCP服务器都可以响应同一个IP租约请求，但是每一个客户网卡只能接受一个租约提供。

    +

    DHCP确认（Acknowledge，ACK）

    当DHCP服务器收到来自客户的REQUEST消息后，它就开始了配置过程的最后阶段。这个响应阶段包括发送一个DHCPACK包给客户。这个包包含租期和客户可能请求的其他所有配置信息。这时候，TCP/IP配置过程就完成了。

    +

    该服务器响应请求并发送响应给客户。整个系统期望客户来根据选项来配置其网卡。

    +

    DHCP释放（RELEASE）

    客户端向DHCP服务器发送一个请求以释放DHCP资源，并注销其IP地址。鉴于客户端更多的时候并不清楚何时用户会将其从网络中移除，此协议不会托管“DHCP释放的发送”。

    +

    DHCP NAK

    服务器回复客户，客户要求的IP不能被分配。

    +

    Ubuntu

    而对于Ubuntu，现在较新版本的设置方式也有点变化，比如我用的是22.04

    +
    cd /etc/netplan/
    +

    先去这个目录下查看配置文件
    我们要找的是这个 00-installer-config.yaml 文件

    原本的是长这样

    默认是通过dhcp分配ip
    我们要改成静态ip

    +
    network:
      renderer: networkd
      ethernets:
        ens33:
          addresses:
            - 192.168.1.xx/24
          nameservers:
            addresses: [114.114.114.114, 233.5.5.5]
          routes:
            - to: default
              via: 192.168.1.1
      version: 2
    +

    解释一下 ethernets中的ens33表示我们的网卡接口的标识
    可以通过 ip addr 查看

    第一个是本地回环，第二个可以看下是否跟内网ip对应上，如果是的话就这个ens33
    然后就是addresses填的地址，就是我们想要固定的ip地址，记得得是自己局域网网段的
    上面只是个示例，然后是nameservers里的是dns服务器，也是按需填写
    这里的114.114.114.114跟233.5.5.5分别是电信跟阿里云的，仅供参考
    然后是routes就是网关地址，按自己的环境填写，网络没有任何特殊处理的一般就是路由器
    接下去就是应用这个配置

    +
    sudo netplan apply
    +

    这样ip配置就生效了
    可以通过
    ip addr show ens33查看

    ]]>     - 生活 + linux - 生活 + linux     - 学习下LlamaIndex-初始篇 - /2024/04/21/%E5%AD%A6%E4%B9%A0%E4%B8%8BLlamaIndex-%E5%88%9D%E5%A7%8B%E7%AF%87/ - LlamaIndex 是目前比较新的大模型RAG框架，RAG是指 检索增强生成 (Retrieval-Augmented Generation, RAG)是指在利用大语言模型回答问题之前，先从外部知识库检索相关信息。RAG 被证明能显著提升答案的准确性，并特别是在知识密集型任务上减少模型的错误输出。通过引用信息来源，用户可以核实答案的准确性，从而增强对模型输出的信任。
    此外，RAG 有助于快速更新知识并引入特定领域的专业知识。
    RAG 有效结合了大语言模型的参数化知识和非参数化的外部知识库，成为实施大语言模型的关键方法之一。本文概述了 RAG 在大语言模型时代的发展模式，总结了三种模式：初级 RAG、高级 RAG 和模块化 RAG。
    首先我们可以了解下LlamaIndex的几大模块，
    使用大型语言模型(LLMs):无论是OpenAI还是任何托管的LLM,亦或是您自己本地运行的模型,LLM都被用于从索引和存储到查询和解析数据的每一个步骤。LlamaIndex提供了大量可靠经过测试的提示,我们也将向您展示如何自定义您自己的提示。
    LlamaIndex 主要有这五大块能力

    -
      -
    • Data connectors
      • -
    • Data indexes
      • -
    • Engines
      • -
    • Data agents
      • -
    • Application integrations
      • -
    -

    其中加载部分可以认为是初始的部分，我们可以从各个数据源去获取信息，比如各种文档，或者confluence，甚至api接口，只需要合适的数据加载器，我们就能将其转化为我们要的数据
    比如我想要将我博客里的Markdown文件作为知识库加载

    -
    reader = SimpleDirectoryReader(
        input_dir="./somedir/source/_posts/",
        required_exts=[".md"],
        recursive=True
    )
    docs = reader.load_data()
    -

    然后由文档来就构建我们的索引

    -
    index = VectorStoreIndex.from_documents(docs)
    -

    但是这里我们需要提前设置好对应的 embedding 模型，这个很重要，如果有条件推荐使用OpenAI的，如果想用本地的可以考虑使用BAAI智源出品的，只是要注意是否支持多语言，如果要使用本地embedding的话需要安装

    -
    pip install llama-index-embeddings-huggingface
    -

    这里我选了BAAI也就是智源研究所的bge-m3模型，因为这个是支持多语言的，如果是纯英文的可以用他们 BAAI/bge-small-en-v1.5

    -
    from llama_index.core import Settings
    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
    Settings.llm = Ollama(model="gemma:7b", request_timeout=60.0)
    Settings.embed_model = HuggingFaceEmbedding(
        model_name="BAAI/bge-m3"
    )
    index = VectorStoreIndex.from_documents(docs)
    query_engine = index.as_query_engine(llm=gemma_7b)
    response = query_engine.query("数据库主从延迟")
    print(response)
    -

    另外LLM 我使用了本地部署的 gemma:7b，因为想尝试下本地的链路，需要注意的是一些文档还是用的原来的service_context，在新版本中这个已经被改掉了，都是通过 Settings 来设置，此外还有诸如 SimpleDirectoryReader 这些包也被挪到了 llama_index.core 中，需要注意下
    然后我们就能使用这种方式来增强LLM，把我们的本地文档作为知识库来搜索，结合了大模型的能力。

    + 聊一下 Linux 中 dmesg 跟 journal 的差别 + /2024/07/14/%E8%81%8A%E4%B8%80%E4%B8%8B-Linux-%E4%B8%AD-dmesg-%E8%B7%9F-journal-%E7%9A%84%E5%B7%AE%E5%88%AB/ + 最近在迁移一个自己用的mysql实例，发现用 portainer 安装 mysql 一直失败，还以为是配置了自定义端口映射被系统防火墙限制，但后面不映射端口也是不行，一开始查看 journal日志也没什么发现，因为之前在腾讯云的小机器也是正常的部署，所以没觉得是可能会oom，一时间就有点没头绪，结果也是好奇就看了下dmesg，发现里面都是oom，给了1g的内存的ubuntu虚拟机，安装了一两个docker就不行了，不过这里比较重要的就是记录下dmesg 跟 journal日志的这点区别
    dmesg 打印了内核的日志缓冲，所以这个跟journal差别的第一点就是一个是个会被刷掉的缓冲区，另一个是记在文件，但更重要的是dmesg是打印的内核日志，而journal是会通过systemd收集日志，journal可以收集内核日志，但是可以进行配置，并且可能会晚于dmesg

    而对于这个oom，如果是系统的oom killer，那的确是有可能dmesg会能查看到，而journal看不到
    简单记录下，PS：奶娃是有点累，一天都没啥时间了

    ]]>     - LLM + linux - LLM + linux + +     + + 聊一下 Java 的日志系列三 + /2024/01/21/%E8%81%8A%E4%B8%80%E4%B8%8B-Java-%E7%9A%84%E6%97%A5%E5%BF%97%E7%B3%BB%E5%88%97%E4%B8%89/ + 上周因为一些事情没有更新在这里，是因为新电脑还没到，手头没有把移动硬盘里的 time machine 恢复出来的机器，所以单独更了一篇在新建的一个 cloudflare page 服务上，总体体验还可以，就是有个小点后面可以讲一下，继续完善下 Java 的这个日志，或者说主要讲的是 logback
    前面讲了logback 的初始化逻辑，后面就是我最开始来看这个的逻辑，日志的滚动策略的触发逻辑
    我们常规的记录日志的方式比如

    +
    logger.info("log some thing and biz info: {}",biz);
    +

    这里就调用了
    ch.qos.logback.classic.Logger#info(java.lang.String, java.lang.Object)

    +
    public void info(String format, Object arg) {
        filterAndLog_1(FQCN, null, Level.INFO, format, arg, null);
    }
    +

    这里的 FQCN 就是这个类的全限定名

    +
    public static final String FQCN = ch.qos.logback.classic.Logger.class.getName();
    +

    这个具体的 filterAndLog_1 方法还是在同一个 Logger 类里的
    ch.qos.logback.classic.Logger#filterAndLog_1

    +
    private void filterAndLog_1(final String localFQCN, final Marker marker, final Level level, final String msg, final Object param, final Throwable t) {

        final FilterReply decision = loggerContext.getTurboFilterChainDecision_1(marker, this, level, msg, param, t);

        if (decision == FilterReply.NEUTRAL) {
            if (effectiveLevelInt > level.levelInt) {
                return;
            }
        } else if (decision == FilterReply.DENY) {
            return;
        }

        buildLoggingEventAndAppend(localFQCN, marker, level, msg, new Object[] { param }, t);
    }
    +

    这里面先是走过滤器逻辑，如果是拒绝的，那就不往下执行，也就不执行下面的写日志逻辑了，如果是中性的，那就判断日志级别是否符合，如果直接是拒绝就 return 了，如果是 ACCEPT 就往下执行了

    +
    public enum FilterReply {
        DENY, NEUTRAL, ACCEPT;
    }
    +

    然后继续调用同样的类的方法
    ch.qos.logback.classic.Logger#buildLoggingEventAndAppend

    +
    private void buildLoggingEventAndAppend(final String localFQCN, final Marker marker, final Level level, final String msg, final Object[] params,
                    final Throwable t) {
        LoggingEvent le = new LoggingEvent(localFQCN, this, level, msg, t, params);
        le.setMarker(marker);
        callAppenders(le);
    }
    +

    先把日志包装成 LoggingEvent ，然后调用
    ch.qos.logback.classic.Logger#callAppenders

    +
    public void callAppenders(ILoggingEvent event) {
        int writes = 0;
        for (Logger l = this; l != null; l = l.parent) {
            writes += l.appendLoopOnAppenders(event);
            if (!l.additive) {
                break;
            }
        }
        // No appenders in hierarchy
        if (writes == 0) {
            loggerContext.noAppenderDefinedWarning(this);
        }
    }
    +

    这里会按 Logger 调用 appendLoopOnAppenders

    +
    private int appendLoopOnAppenders(ILoggingEvent event) {
        if (aai != null) {
            return aai.appendLoopOnAppenders(event);
        } else {
            return 0;
        }
    +

    继续调用事件的
    ch.qos.logback.core.spi.AppenderAttachableImpl#appendLoopOnAppenders

    +
    public int appendLoopOnAppenders(E e) {
        int size = 0;
        final Appender<E>[] appenderArray = appenderList.asTypedArray();
        final int len = appenderArray.length;
        for (int i = 0; i < len; i++) {
            appenderArray[i].doAppend(e);
            size++;
        }
        return size;
    }
    +

    因为 Logger 里有关联 appender，就具体调用关联的 appender 来添加日志
    然后就接着调用
    ch.qos.logback.core.UnsynchronizedAppenderBase#doAppend 方法

    +
    public void doAppend(E eventObject) {
            // WARNING: The guard check MUST be the first statement in the
            // doAppend() method.

            // prevent re-entry.
            if (Boolean.TRUE.equals(guard.get())) {
                return;
            }

            try {
                guard.set(Boolean.TRUE);

                if (!this.started) {
                    if (statusRepeatCount++ < ALLOWED_REPEATS) {
                        addStatus(new WarnStatus("Attempted to append to non started appender [" + name + "].", this));
                    }
                    return;
                }

                if (getFilterChainDecision(eventObject) == FilterReply.DENY) {
                    return;
                }

                // ok, we now invoke derived class' implementation of append
                this.append(eventObject);

            } catch (Exception e) {
                if (exceptionCount++ < ALLOWED_REPEATS) {
                    addError("Appender [" + name + "] failed to append.", e);
                }
            } finally {
                guard.set(Boolean.FALSE);
            }
        }
    +

    这里先判断了 guard 的状态，guard 是个 ThreadLocal，是为了拦截异常的无限循环调用，这里有些疑惑的，看了一圈可能相关的是前面的 Logger 会往父级查找，然后如果不同层级的 Logger 关联了相同的 appender 的话，或者这个 doAppend 又被其他 appender 调用了，就可能会出现循环调用，然后会在开始实际的 append的之前先设置 guard 状态，
    然后再执行 append，这里会走到
    ch.qos.logback.core.OutputStreamAppender#append

    +
    @Override
    protected void append(E eventObject) {
        if (!isStarted()) {
            return;
        }

        subAppend(eventObject);
    }
    +

    忘了补充下，我们这里使用的是
    ch.qos.logback.core.rolling.RollingFileAppender
    接下去就是调用的 RollingFileAppender 的 subAppend 方法

    +
    @Override
    protected void subAppend(E event) {
        // The roll-over check must precede actual writing. This is the
        // only correct behavior for time driven triggers.

        // We need to synchronize on triggeringPolicy so that only one rollover
        // occurs at a time
        synchronized (triggeringPolicy) {
            if (triggeringPolicy.isTriggeringEvent(currentlyActiveFile, event)) {
                rollover();
            }
        }

        super.subAppend(event);
    }
    +

    这里就会先判断是否触发事件，
    而这里就是主要的逻辑，看是否需要走 rollover，也就是具体的滚动逻辑

    +
    @Override
        public boolean isTriggeringEvent(File activeFile, final E event) {

            long time = getCurrentTime();

            // first check for roll-over based on time
            if (time >= nextCheck) {
                Date dateInElapsedPeriod = dateInCurrentPeriod;
                elapsedPeriodsFileName = tbrp.fileNamePatternWithoutCompSuffix.convertMultipleArguments(dateInElapsedPeriod, currentPeriodsCounter);
                currentPeriodsCounter = 0;
                setDateInCurrentPeriod(time);
                computeNextCheck();
                return true;
            }

            // next check for roll-over based on size
            if (invocationGate.isTooSoon(time)) {
                return false;
            }

            if (activeFile == null) {
                addWarn("activeFile == null");
                return false;
            }
            if (maxFileSize == null) {
                addWarn("maxFileSize = null");
                return false;
            }
            if (activeFile.length() >= maxFileSize.getSize()) {

                elapsedPeriodsFileName = tbrp.fileNamePatternWithoutCompSuffix.convertMultipleArguments(dateInCurrentPeriod, currentPeriodsCounter);
                currentPeriodsCounter++;
                return true;
            }

            return false;
        }
    +

    这里主要就用到了 nextCheck 判断，如果通过判断就可以执行滚动了，然后就是后面的判断，是否太快了，也就是 isTooSoon 然后再看当前的活跃日志以及最大文件大小是否设置，然后如果当前的活跃日志的大小超过了配置也会触发滚动

    +]]>     + + Java + + + Java     @@ -5590,34 +5628,6 @@ user3: Java - - 给局域网中的Ubuntu固定下ip - /2024/06/02/%E7%BB%99%E5%B1%80%E5%9F%9F%E7%BD%91%E4%B8%AD%E7%9A%84Ubuntu%E5%9B%BA%E5%AE%9A%E4%B8%8Bip/ - 将一个小点，在局域网中包括类似于homelab的或者仅仅只是搭一个用来当实验环境的，在局域网内机器比较少的时候大部分DHCP会给分配相同的ip，但是这不是一定的，当机器比较多，并且上下线比较频繁时就会出现ip地址变动，这对于一些使用场景比较讨厌，所以我想把ip固定下来，先简单引用下DHCP的原理，他分为下面几个阶段

    -

    DHCP发现（DISCOVER）

    client在物理子网上发送广播来寻找可用的服务器。网络管理员可以配置一个本地路由来转发DHCP包给另一个子网上的DHCP服务器。该client实现生成一个目的地址为255.255.255.255或者一个子网广播地址的UDP包。

    -

    客户也可以申请它使用的最后一个IP地址（在下面的例子里为192.168.1.100）。如果该客户所在的网络中此IP仍然可用，服务器就可以准许该申请。否则，就要看该服务器是授权的还是非授权的。授权服务器会拒绝请求，使得客户立刻申请一个新的IP。非授权服务器仅仅忽略掉请求，导致一个客户端请求的超时，于是客户端就会放弃此请求而去申请一个新的IP地址。

    -

    DHCP提供（OFFER）

    当DHCP服务器收到一个来自客户的IP租约请求时，它会提供一个IP租约。DHCP为客户保留一个IP地址，然后通过网络单播一个DHCPOFFER消息给客户。该消息包含客户的MAC地址、服务器提供的IP地址、子网掩码、租期以及提供IP的DHCP服务器的IP。

    -

    服务器基于在CHADDR字段指定的客户硬件地址来检查配置。这里的服务器，192.168.1.1，将IP地址指定于YIADDR字段。

    -

    DHCP请求（REQUEST）

    当客户PC收到一个IP租约提供时，它必须告诉所有其他的DHCP服务器它已经接受了一个租约提供。因此，该客户会发送一个DHCPREQUEST消息，其中包含提供租约的服务器的IP。当其他DHCP服务器收到了该消息后，它们会收回所有可能已提供给该客户的租约。然后它们把曾经给该客户保留的那个地址重新放回到可用地址池中，这样，它们就可以为其他计算机分配这个地址。任意数量的DHCP服务器都可以响应同一个IP租约请求，但是每一个客户网卡只能接受一个租约提供。

    -

    DHCP确认（Acknowledge，ACK）

    当DHCP服务器收到来自客户的REQUEST消息后，它就开始了配置过程的最后阶段。这个响应阶段包括发送一个DHCPACK包给客户。这个包包含租期和客户可能请求的其他所有配置信息。这时候，TCP/IP配置过程就完成了。

    -

    该服务器响应请求并发送响应给客户。整个系统期望客户来根据选项来配置其网卡。

    -

    DHCP释放（RELEASE）

    客户端向DHCP服务器发送一个请求以释放DHCP资源，并注销其IP地址。鉴于客户端更多的时候并不清楚何时用户会将其从网络中移除，此协议不会托管“DHCP释放的发送”。

    -

    DHCP NAK

    服务器回复客户，客户要求的IP不能被分配。

    -

    Ubuntu

    而对于Ubuntu，现在较新版本的设置方式也有点变化，比如我用的是22.04

    -
    cd /etc/netplan/
    -

    先去这个目录下查看配置文件
    我们要找的是这个 00-installer-config.yaml 文件

    原本的是长这样

    默认是通过dhcp分配ip
    我们要改成静态ip

    -
    network:
      renderer: networkd
      ethernets:
        ens33:
          addresses:
            - 192.168.1.xx/24
          nameservers:
            addresses: [114.114.114.114, 233.5.5.5]
          routes:
            - to: default
              via: 192.168.1.1
      version: 2
    -

    解释一下 ethernets中的ens33表示我们的网卡接口的标识
    可以通过 ip addr 查看

    第一个是本地回环，第二个可以看下是否跟内网ip对应上，如果是的话就这个ens33
    然后就是addresses填的地址，就是我们想要固定的ip地址，记得得是自己局域网网段的
    上面只是个示例，然后是nameservers里的是dns服务器，也是按需填写
    这里的114.114.114.114跟233.5.5.5分别是电信跟阿里云的，仅供参考
    然后是routes就是网关地址，按自己的环境填写，网络没有任何特殊处理的一般就是路由器
    接下去就是应用这个配置

    -
    sudo netplan apply
    -

    这样ip配置就生效了
    可以通过
    ip addr show ens33查看

    -]]>     - - linux - - - linux - -     聊一下 Java 的日志系列二 /2024/01/14/%E8%81%8A%E4%B8%80%E4%B8%8B-Java-%E7%9A%84%E6%97%A5%E5%BF%97%E7%B3%BB%E5%88%97%E4%BA%8C/ @@ -5685,32 +5695,6 @@ user3: Java - - 尝试下图片向量化 - /2024/07/28/%E5%B0%9D%E8%AF%95%E4%B8%8B%E5%9B%BE%E7%89%87%E5%90%91%E9%87%8F%E5%8C%96/ - 之前觉得谷歌的以图搜图很厉害，现在似乎这个路径还毕竟清晰了，首先要有图片库，把它们向量化以后存储起来，然后对于目标图片也做向量化，再做检索
    那么我们先来做重要的这一步，图片的向量化，因为向量化以后就跟图片没关系了，直接用前面讲到的向量的近似搜索就可以做到以图搜图了
    这边我们用到了towhee工具，towhee是个机器学习的pipeline工具，可以做数据源（文件，图片，媒体，文本）–> 模型 –> 向量。
    首先我们安装towhee

    -
    pip install towhee towhee.models
    -

    这里有第一个坑，因为有torch依赖，但是目前torch支持的python版本是最高3.9，再往上可能就有问题了
    所以我们要先创建一个3.9的环境

    -
    conda create -n py9 python=3.9
    -

    然后我们会遇到第二个坑
    就是milvus的客户端

    -
    pip install pymilvus==2.3.0
    -

    再早的客户端会有依赖不支持，更新的客户端会连接不上
    然后再在milvus创建一个Collection

    然后这个是不对的，因为我使用attu界面创建的，默认第二个字段一定得是向量字段，所以又用了上次的Java代码来创建

    -
    CreateCollectionReq.CollectionSchema collectionSchema = clientV2.createSchema();
    // add two fileds, id and vector
            Integer dim = 2048;
            collectionSchema.addField(AddFieldReq.builder().fieldName("url").dataType(DataType.VarChar).build());
            collectionSchema.addField(AddFieldReq.builder().fieldName("embedding").dataType(DataType.FloatVector).dimension(dim).build());
            collectionSchema.addField(AddFieldReq.builder().fieldName("id").dataType(DataType.Int64).isPrimaryKey(Boolean.TRUE).autoID(Boolean.TRUE).description("id").build());

            CreateCollectionReq req = CreateCollectionReq
                    .builder()
                    .collectionSchema(collectionSchema)
                    .collectionName("text_image_search")
                    .dimension(dim).build();
            clientV2.createCollection(req);
    -

    并且注意字段顺序不能错，这个顺序在towhee的demo中也没地方直接修改
    看一下towhee的demo
    地址在这
    https://towhee.io/tasks/detail/pipeline/text-image-search

    -
    from towhee import AutoPipes, AutoConfig

    # set MilvusInsertConfig for the built-in insert_milvus pipeline
    insert_conf = AutoConfig.load_config('insert_milvus')
    insert_conf.collection_name = 'text_image_search'

    insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

    # generate embedding
    embedding = image_embedding('./test1.png').get()[0]

    # insert text and embedding into Milvus
    insert_pipe(['./test1.png', embedding])
    -

    第三个坑，这并不能跑起来

    -
     embedding = insert_pipe.image_embedding('./test.jpg').get()[0]
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
    AttributeError: 'RuntimePipeline' object has no attribute 'image_embedding'
    -

    因为找不到这个 image_embedding
    估计是这个也是包路径变更了，但是并没有什么文档可以找到，之前LlamaIndex还有点更新指南，后来是问了Claude才知道了

    -
    from towhee import AutoPipes, AutoConfig, ops


    # set MilvusInsertConfig for the built-in insert_milvus pipeline
    insert_conf = AutoConfig.load_config('insert_milvus')
    insert_conf.collection_name = 'text_image_search'


    insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

    # 创建图像嵌入管道
    image_embedding_pipe = AutoPipes.pipeline('image-embedding')

    # 生成嵌入
    embedding = image_embedding_pipe('./test.jpg').get()[0]


    # insert text and embedding into Milvus
    insert_pipe(['./test.jpg', embedding])
    -

    需要从 AutoPipes 中把这个 image-embedding 找出来
    然后就是上面说到的字段映射问题，默认是先url，再embedding字段，并且主键字段必须是autoId，不然也会缺少默认值，还是感叹下，工程化的东西还是要工程化的质量保证，否则变更都无从知晓，现在人工智能大热，大家都在追风，只是基础的软件还是要稳扎稳打，这样我们工程人员才能把它们更好的用起来，PS：娃真可爱，但真的好累

    -]]>     - - 算法 - - - 算法 - -     聊一下 RocketMQ 的 DefaultMQPushConsumer 源码 /2020/06/26/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84-Consumer/ @@ -5790,41 +5774,114 @@ user3:

    第二个是每10分钟打印一次NameServer的配置参数。即KVConfigManager.configTable变量的内容。

    this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

                @Override
                public void run() {
                    NamesrvController.this.kvConfigManager.printAllPeriodically();
                }
            }, 1, 10, TimeUnit.MINUTES);
    -

    然后这个初始化就差不多完成了，后面只需要把remotingServer start一下就好了

    -

    处理请求

    直接上代码，其实主体是swtich case去判断

    -
    @Override
        public RemotingCommand processRequest(ChannelHandlerContext ctx,
            RemotingCommand request) throws RemotingCommandException {

            if (ctx != null) {
                log.debug("receive request, {} {} {}",
                    request.getCode(),
                    RemotingHelper.parseChannelRemoteAddr(ctx.channel()),
                    request);
            }


            switch (request.getCode()) {
                case RequestCode.PUT_KV_CONFIG:
                    return this.putKVConfig(ctx, request);
                case RequestCode.GET_KV_CONFIG:
                    return this.getKVConfig(ctx, request);
                case RequestCode.DELETE_KV_CONFIG:
                    return this.deleteKVConfig(ctx, request);
                case RequestCode.QUERY_DATA_VERSION:
                    return queryBrokerTopicConfig(ctx, request);
                case RequestCode.REGISTER_BROKER:
                    Version brokerVersion = MQVersion.value2Version(request.getVersion());
                    if (brokerVersion.ordinal() >= MQVersion.Version.V3_0_11.ordinal()) {
                        return this.registerBrokerWithFilterServer(ctx, request);
                    } else {
                        return this.registerBroker(ctx, request);
                    }
                case RequestCode.UNREGISTER_BROKER:
                    return this.unregisterBroker(ctx, request);
                case RequestCode.GET_ROUTEINTO_BY_TOPIC:
                    return this.getRouteInfoByTopic(ctx, request);
                case RequestCode.GET_BROKER_CLUSTER_INFO:
                    return this.getBrokerClusterInfo(ctx, request);
                case RequestCode.WIPE_WRITE_PERM_OF_BROKER:
                    return this.wipeWritePermOfBroker(ctx, request);
                case RequestCode.GET_ALL_TOPIC_LIST_FROM_NAMESERVER:
                    return getAllTopicListFromNameserver(ctx, request);
                case RequestCode.DELETE_TOPIC_IN_NAMESRV:
                    return deleteTopicInNamesrv(ctx, request);
                case RequestCode.GET_KVLIST_BY_NAMESPACE:
                    return this.getKVListByNamespace(ctx, request);
                case RequestCode.GET_TOPICS_BY_CLUSTER:
                    return this.getTopicsByCluster(ctx, request);
                case RequestCode.GET_SYSTEM_TOPIC_LIST_FROM_NS:
                    return this.getSystemTopicListFromNs(ctx, request);
                case RequestCode.GET_UNIT_TOPIC_LIST:
                    return this.getUnitTopicList(ctx, request);
                case RequestCode.GET_HAS_UNIT_SUB_TOPIC_LIST:
                    return this.getHasUnitSubTopicList(ctx, request);
                case RequestCode.GET_HAS_UNIT_SUB_UNUNIT_TOPIC_LIST:
                    return this.getHasUnitSubUnUnitTopicList(ctx, request);
                case RequestCode.UPDATE_NAMESRV_CONFIG:
                    return this.updateConfig(ctx, request);
                case RequestCode.GET_NAMESRV_CONFIG:
                    return this.getConfig(ctx, request);
                default:
                    break;
            }
            return null;
        }
    +

    然后这个初始化就差不多完成了，后面只需要把remotingServer start一下就好了

    +

    处理请求

    直接上代码，其实主体是swtich case去判断

    +
    @Override
        public RemotingCommand processRequest(ChannelHandlerContext ctx,
            RemotingCommand request) throws RemotingCommandException {

            if (ctx != null) {
                log.debug("receive request, {} {} {}",
                    request.getCode(),
                    RemotingHelper.parseChannelRemoteAddr(ctx.channel()),
                    request);
            }


            switch (request.getCode()) {
                case RequestCode.PUT_KV_CONFIG:
                    return this.putKVConfig(ctx, request);
                case RequestCode.GET_KV_CONFIG:
                    return this.getKVConfig(ctx, request);
                case RequestCode.DELETE_KV_CONFIG:
                    return this.deleteKVConfig(ctx, request);
                case RequestCode.QUERY_DATA_VERSION:
                    return queryBrokerTopicConfig(ctx, request);
                case RequestCode.REGISTER_BROKER:
                    Version brokerVersion = MQVersion.value2Version(request.getVersion());
                    if (brokerVersion.ordinal() >= MQVersion.Version.V3_0_11.ordinal()) {
                        return this.registerBrokerWithFilterServer(ctx, request);
                    } else {
                        return this.registerBroker(ctx, request);
                    }
                case RequestCode.UNREGISTER_BROKER:
                    return this.unregisterBroker(ctx, request);
                case RequestCode.GET_ROUTEINTO_BY_TOPIC:
                    return this.getRouteInfoByTopic(ctx, request);
                case RequestCode.GET_BROKER_CLUSTER_INFO:
                    return this.getBrokerClusterInfo(ctx, request);
                case RequestCode.WIPE_WRITE_PERM_OF_BROKER:
                    return this.wipeWritePermOfBroker(ctx, request);
                case RequestCode.GET_ALL_TOPIC_LIST_FROM_NAMESERVER:
                    return getAllTopicListFromNameserver(ctx, request);
                case RequestCode.DELETE_TOPIC_IN_NAMESRV:
                    return deleteTopicInNamesrv(ctx, request);
                case RequestCode.GET_KVLIST_BY_NAMESPACE:
                    return this.getKVListByNamespace(ctx, request);
                case RequestCode.GET_TOPICS_BY_CLUSTER:
                    return this.getTopicsByCluster(ctx, request);
                case RequestCode.GET_SYSTEM_TOPIC_LIST_FROM_NS:
                    return this.getSystemTopicListFromNs(ctx, request);
                case RequestCode.GET_UNIT_TOPIC_LIST:
                    return this.getUnitTopicList(ctx, request);
                case RequestCode.GET_HAS_UNIT_SUB_TOPIC_LIST:
                    return this.getHasUnitSubTopicList(ctx, request);
                case RequestCode.GET_HAS_UNIT_SUB_UNUNIT_TOPIC_LIST:
                    return this.getHasUnitSubUnUnitTopicList(ctx, request);
                case RequestCode.UPDATE_NAMESRV_CONFIG:
                    return this.updateConfig(ctx, request);
                case RequestCode.GET_NAMESRV_CONFIG:
                    return this.getConfig(ctx, request);
                default:
                    break;
            }
            return null;
        }
    + +

    以broker注册为例，

    +
    case RequestCode.REGISTER_BROKER:
                    Version brokerVersion = MQVersion.value2Version(request.getVersion());
                    if (brokerVersion.ordinal() >= MQVersion.Version.V3_0_11.ordinal()) {
                        return this.registerBrokerWithFilterServer(ctx, request);
                    } else {
                        return this.registerBroker(ctx, request);
                    }
    + +

    做了个简单的版本管理，我们看下前面一个的代码

    +
    public RemotingCommand registerBrokerWithFilterServer(ChannelHandlerContext ctx, RemotingCommand request)
        throws RemotingCommandException {
        final RemotingCommand response = RemotingCommand.createResponseCommand(RegisterBrokerResponseHeader.class);
        final RegisterBrokerResponseHeader responseHeader = (RegisterBrokerResponseHeader) response.readCustomHeader();
        final RegisterBrokerRequestHeader requestHeader =
            (RegisterBrokerRequestHeader) request.decodeCommandCustomHeader(RegisterBrokerRequestHeader.class);

        if (!checksum(ctx, request, requestHeader)) {
            response.setCode(ResponseCode.SYSTEM_ERROR);
            response.setRemark("crc32 not match");
            return response;
        }

        RegisterBrokerBody registerBrokerBody = new RegisterBrokerBody();

        if (request.getBody() != null) {
            try {
                registerBrokerBody = RegisterBrokerBody.decode(request.getBody(), requestHeader.isCompressed());
            } catch (Exception e) {
                throw new RemotingCommandException("Failed to decode RegisterBrokerBody", e);
            }
        } else {
            registerBrokerBody.getTopicConfigSerializeWrapper().getDataVersion().setCounter(new AtomicLong(0));
            registerBrokerBody.getTopicConfigSerializeWrapper().getDataVersion().setTimestamp(0);
        }

        RegisterBrokerResult result = this.namesrvController.getRouteInfoManager().registerBroker(
            requestHeader.getClusterName(),
            requestHeader.getBrokerAddr(),
            requestHeader.getBrokerName(),
            requestHeader.getBrokerId(),
            requestHeader.getHaServerAddr(),
            registerBrokerBody.getTopicConfigSerializeWrapper(),
            registerBrokerBody.getFilterServerList(),
            ctx.channel());

        responseHeader.setHaServerAddr(result.getHaServerAddr());
        responseHeader.setMasterAddr(result.getMasterAddr());

        byte[] jsonValue = this.namesrvController.getKvConfigManager().getKVListByNamespace(NamesrvUtil.NAMESPACE_ORDER_TOPIC_CONFIG);
        response.setBody(jsonValue);

        response.setCode(ResponseCode.SUCCESS);
        response.setRemark(null);
        return response;
    }
    + +

    可以看到主要的逻辑还是在org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#registerBroker这个方法里

    +
    public RegisterBrokerResult registerBroker(
            final String clusterName,
            final String brokerAddr,
            final String brokerName,
            final long brokerId,
            final String haServerAddr,
            final TopicConfigSerializeWrapper topicConfigWrapper,
            final List<String> filterServerList,
            final Channel channel) {
            RegisterBrokerResult result = new RegisterBrokerResult();
            try {
                try {
                    this.lock.writeLock().lockInterruptibly();

                  // 更新这个clusterAddrTable
                    Set<String> brokerNames = this.clusterAddrTable.get(clusterName);
                    if (null == brokerNames) {
                        brokerNames = new HashSet<String>();
                        this.clusterAddrTable.put(clusterName, brokerNames);
                    }
                    brokerNames.add(brokerName);

                    boolean registerFirst = false;

                  // 更新brokerAddrTable
                    BrokerData brokerData = this.brokerAddrTable.get(brokerName);
                    if (null == brokerData) {
                        registerFirst = true;
                        brokerData = new BrokerData(clusterName, brokerName, new HashMap<Long, String>());
                        this.brokerAddrTable.put(brokerName, brokerData);
                    }
                    Map<Long, String> brokerAddrsMap = brokerData.getBrokerAddrs();
                    //Switch slave to master: first remove <1, IP:PORT> in namesrv, then add <0, IP:PORT>
                    //The same IP:PORT must only have one record in brokerAddrTable
                    Iterator<Entry<Long, String>> it = brokerAddrsMap.entrySet().iterator();
                    while (it.hasNext()) {
                        Entry<Long, String> item = it.next();
                        if (null != brokerAddr && brokerAddr.equals(item.getValue()) && brokerId != item.getKey()) {
                            it.remove();
                        }
                    }

                    String oldAddr = brokerData.getBrokerAddrs().put(brokerId, brokerAddr);
                    registerFirst = registerFirst || (null == oldAddr);

                  // 更新了org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#topicQueueTable中的数据
                    if (null != topicConfigWrapper
                        && MixAll.MASTER_ID == brokerId) {
                        if (this.isBrokerTopicConfigChanged(brokerAddr, topicConfigWrapper.getDataVersion())
                            || registerFirst) {
                            ConcurrentMap<String, TopicConfig> tcTable =
                                topicConfigWrapper.getTopicConfigTable();
                            if (tcTable != null) {
                                for (Map.Entry<String, TopicConfig> entry : tcTable.entrySet()) {
                                    this.createAndUpdateQueueData(brokerName, entry.getValue());
                                }
                            }
                        }
                    }

                  // 更新活跃broker信息
                    BrokerLiveInfo prevBrokerLiveInfo = this.brokerLiveTable.put(brokerAddr,
                        new BrokerLiveInfo(
                            System.currentTimeMillis(),
                            topicConfigWrapper.getDataVersion(),
                            channel,
                            haServerAddr));
                    if (null == prevBrokerLiveInfo) {
                        log.info("new broker registered, {} HAServer: {}", brokerAddr, haServerAddr);
                    }

                  // 处理filter
                    if (filterServerList != null) {
                        if (filterServerList.isEmpty()) {
                            this.filterServerTable.remove(brokerAddr);
                        } else {
                            this.filterServerTable.put(brokerAddr, filterServerList);
                        }
                    }

                  // 当当前broker非master时返回master信息
                    if (MixAll.MASTER_ID != brokerId) {
                        String masterAddr = brokerData.getBrokerAddrs().get(MixAll.MASTER_ID);
                        if (masterAddr != null) {
                            BrokerLiveInfo brokerLiveInfo = this.brokerLiveTable.get(masterAddr);
                            if (brokerLiveInfo != null) {
                                result.setHaServerAddr(brokerLiveInfo.getHaServerAddr());
                                result.setMasterAddr(masterAddr);
                            }
                        }
                    }
                } finally {
                    this.lock.writeLock().unlock();
                }
            } catch (Exception e) {
                log.error("registerBroker Exception", e);
            }

            return result;
        }
    + +

    这个是注册 broker 的逻辑，再看下根据 topic 获取 broker 信息和 topic 信息，org.apache.rocketmq.namesrv.processor.DefaultRequestProcessor#getRouteInfoByTopic 主要是这个方法的逻辑

    +
    public RemotingCommand getRouteInfoByTopic(ChannelHandlerContext ctx,
            RemotingCommand request) throws RemotingCommandException {
            final RemotingCommand response = RemotingCommand.createResponseCommand(null);
            final GetRouteInfoRequestHeader requestHeader =
                (GetRouteInfoRequestHeader) request.decodeCommandCustomHeader(GetRouteInfoRequestHeader.class);

            TopicRouteData topicRouteData = this.namesrvController.getRouteInfoManager().pickupTopicRouteData(requestHeader.getTopic());

            if (topicRouteData != null) {
                if (this.namesrvController.getNamesrvConfig().isOrderMessageEnable()) {
                    String orderTopicConf =
                        this.namesrvController.getKvConfigManager().getKVConfig(NamesrvUtil.NAMESPACE_ORDER_TOPIC_CONFIG,
                            requestHeader.getTopic());
                    topicRouteData.setOrderTopicConf(orderTopicConf);
                }

                byte[] content = topicRouteData.encode();
                response.setBody(content);
                response.setCode(ResponseCode.SUCCESS);
                response.setRemark(null);
                return response;
            }

            response.setCode(ResponseCode.TOPIC_NOT_EXIST);
            response.setRemark("No topic route info in name server for the topic: " + requestHeader.getTopic()
                + FAQUrl.suggestTodo(FAQUrl.APPLY_TOPIC_URL));
            return response;
        }
    + +

    首先调用org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#pickupTopicRouteData从org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#topicQueueTable获取到org.apache.rocketmq.common.protocol.route.QueueData这里面存了 brokerName，再通过org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#brokerAddrTable里获取到 broker 的地址信息等，然后再获取 orderMessage 的配置。

    +

    简要分析了下 RocketMQ 的 NameServer 的代码，比较粗粒度。

    +]]> + + MQ + RocketMQ + 消息队列 + RocketMQ + 中间件 + RocketMQ + + + MQ + 消息队列 + RocketMQ + 削峰填谷 + 中间件 + 源码解析 + NameServer + +     + + 聊一下 RocketMQ 的消息存储三 + /2021/10/03/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%B8%89/ + ConsumeQueue 其实是定位到一个 topic 下的消息在 CommitLog 下的偏移量，它也是固定大小的

    +
    // ConsumeQueue file size,default is 30W
    private int mapedFileSizeConsumeQueue = 300000 * ConsumeQueue.CQ_STORE_UNIT_SIZE;

    public static final int CQ_STORE_UNIT_SIZE = 20;
    + +

    所以文件大小是5.7M 左右

    +

    5udpag

    +

    ConsumeQueue 的构建是通过org.apache.rocketmq.store.DefaultMessageStore.ReputMessageService运行后的 doReput 方法，而启动是的 reputFromOffset 则是通过org.apache.rocketmq.store.DefaultMessageStore#start中下面代码设置并启动

    +
    log.info("[SetReputOffset] maxPhysicalPosInLogicQueue={} clMinOffset={} clMaxOffset={} clConfirmedOffset={}",
                    maxPhysicalPosInLogicQueue, this.commitLog.getMinOffset(), this.commitLog.getMaxOffset(), this.commitLog.getConfirmOffset());
                this.reputMessageService.setReputFromOffset(maxPhysicalPosInLogicQueue);
                this.reputMessageService.start();
    + +

    看一下 doReput 的逻辑

    +
    private void doReput() {
                if (this.reputFromOffset < DefaultMessageStore.this.commitLog.getMinOffset()) {
                    log.warn("The reputFromOffset={} is smaller than minPyOffset={}, this usually indicate that the dispatch behind too much and the commitlog has expired.",
                        this.reputFromOffset, DefaultMessageStore.this.commitLog.getMinOffset());
                    this.reputFromOffset = DefaultMessageStore.this.commitLog.getMinOffset();
                }
                for (boolean doNext = true; this.isCommitLogAvailable() && doNext; ) {

                    if (DefaultMessageStore.this.getMessageStoreConfig().isDuplicationEnable()
                        && this.reputFromOffset >= DefaultMessageStore.this.getConfirmOffset()) {
                        break;
                    }

                  // 根据偏移量获取消息
                    SelectMappedBufferResult result = DefaultMessageStore.this.commitLog.getData(reputFromOffset);
                    if (result != null) {
                        try {
                            this.reputFromOffset = result.getStartOffset();

                            for (int readSize = 0; readSize < result.getSize() && doNext; ) {
                              // 消息校验和转换
                                DispatchRequest dispatchRequest =
                                    DefaultMessageStore.this.commitLog.checkMessageAndReturnSize(result.getByteBuffer(), false, false);
                                int size = dispatchRequest.getBufferSize() == -1 ? dispatchRequest.getMsgSize() : dispatchRequest.getBufferSize();

                                if (dispatchRequest.isSuccess()) {
                                    if (size > 0) {
                                      // 进行分发处理，包括 ConsumeQueue 和 IndexFile
                                        DefaultMessageStore.this.doDispatch(dispatchRequest);

                                        if (BrokerRole.SLAVE != DefaultMessageStore.this.getMessageStoreConfig().getBrokerRole()
                                            && DefaultMessageStore.this.brokerConfig.isLongPollingEnable()) {
                                            DefaultMessageStore.this.messageArrivingListener.arriving(dispatchRequest.getTopic(),
                                                dispatchRequest.getQueueId(), dispatchRequest.getConsumeQueueOffset() + 1,
                                                dispatchRequest.getTagsCode(), dispatchRequest.getStoreTimestamp(),
                                                dispatchRequest.getBitMap(), dispatchRequest.getPropertiesMap());
                                        }

                                        this.reputFromOffset += size;
                                        readSize += size;
                                        if (DefaultMessageStore.this.getMessageStoreConfig().getBrokerRole() == BrokerRole.SLAVE) {
                                            DefaultMessageStore.this.storeStatsService
                                                .getSinglePutMessageTopicTimesTotal(dispatchRequest.getTopic()).incrementAndGet();
                                            DefaultMessageStore.this.storeStatsService
                                                .getSinglePutMessageTopicSizeTotal(dispatchRequest.getTopic())
                                                .addAndGet(dispatchRequest.getMsgSize());
                                        }
                                    } else if (size == 0) {
                                        this.reputFromOffset = DefaultMessageStore.this.commitLog.rollNextFile(this.reputFromOffset);
                                        readSize = result.getSize();
                                    }
                                } else if (!dispatchRequest.isSuccess()) {

                                    if (size > 0) {
                                        log.error("[BUG]read total count not equals msg total size. reputFromOffset={}", reputFromOffset);
                                        this.reputFromOffset += size;
                                    } else {
                                        doNext = false;
                                        // If user open the dledger pattern or the broker is master node,
                                        // it will not ignore the exception and fix the reputFromOffset variable
                                        if (DefaultMessageStore.this.getMessageStoreConfig().isEnableDLegerCommitLog() ||
                                            DefaultMessageStore.this.brokerConfig.getBrokerId() == MixAll.MASTER_ID) {
                                            log.error("[BUG]dispatch message to consume queue error, COMMITLOG OFFSET: {}",
                                                this.reputFromOffset);
                                            this.reputFromOffset += result.getSize() - readSize;
                                        }
                                    }
                                }
                            }
                        } finally {
                            result.release();
                        }
                    } else {
                        doNext = false;
                    }
                }
            }
    + +

    分发的逻辑看到这

    +
        class CommitLogDispatcherBuildConsumeQueue implements CommitLogDispatcher {

            @Override
            public void dispatch(DispatchRequest request) {
                final int tranType = MessageSysFlag.getTransactionValue(request.getSysFlag());
                switch (tranType) {
                    case MessageSysFlag.TRANSACTION_NOT_TYPE:
                    case MessageSysFlag.TRANSACTION_COMMIT_TYPE:
                        DefaultMessageStore.this.putMessagePositionInfo(request);
                        break;
                    case MessageSysFlag.TRANSACTION_PREPARED_TYPE:
                    case MessageSysFlag.TRANSACTION_ROLLBACK_TYPE:
                        break;
                }
            }
        }
    public void putMessagePositionInfo(DispatchRequest dispatchRequest) {
            ConsumeQueue cq = this.findConsumeQueue(dispatchRequest.getTopic(), dispatchRequest.getQueueId());
            cq.putMessagePositionInfoWrapper(dispatchRequest);
        }
    + +

    真正存储的是在这

    +
    private boolean putMessagePositionInfo(final long offset, final int size, final long tagsCode,
        final long cqOffset) {

        if (offset + size <= this.maxPhysicOffset) {
            log.warn("Maybe try to build consume queue repeatedly maxPhysicOffset={} phyOffset={}", maxPhysicOffset, offset);
            return true;
        }

        this.byteBufferIndex.flip();
        this.byteBufferIndex.limit(CQ_STORE_UNIT_SIZE);
        this.byteBufferIndex.putLong(offset);
        this.byteBufferIndex.putInt(size);
        this.byteBufferIndex.putLong(tagsCode);
    + +

    这里也可以看到 ConsumeQueue 的存储格式，

    +

    AA6Tve

    +

    偏移量，消息大小，跟 tag 的 hashCode

    +]]>     + + MQ + RocketMQ + 消息队列 + + + MQ + 消息队列 + RocketMQ + +     + + 聊一下 RocketMQ 的消息存储二 + /2021/09/12/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%BA%8C/ + CommitLog 结构

    CommitLog 是 rocketmq 的服务端，也就是 broker 存储消息的的文件，跟 kafka 一样，也是顺序写入，当然消息是变长的，生成的规则是每个文件的默认1G =1024 * 1024 * 1024，commitlog的文件名fileName，名字长度为20位，左边补零，剩余为起始偏移量；比如00000000000000000000代表了第一个文件，起始偏移量为0，文件大小为1G=1 073 741 824Byte；当这个文件满了，第二个文件名字为00000000001073741824，起始偏移量为1073741824, 消息存储的时候会顺序写入文件，当文件满了则写入下一个文件，代码中的定义

    +
    // CommitLog file size,default is 1G
    private int mapedFileSizeCommitLog = 1024 * 1024 * 1024;
    + +

    kLahwW

    +

    本地跑个 demo 验证下，也是这样，这里奇妙有几个比较巧妙的点（个人观点），首先文件就刚好是 1G，并且按照大小偏移量去生成下一个文件，这样获取消息的时候按大小算一下就知道在哪个文件里了，

    +

    代码中写入 CommitLog 的逻辑可以从这开始看

    +
    public PutMessageResult putMessage(final MessageExtBrokerInner msg) {
            // Set the storage time
            msg.setStoreTimestamp(System.currentTimeMillis());
            // Set the message body BODY CRC (consider the most appropriate setting
            // on the client)
            msg.setBodyCRC(UtilAll.crc32(msg.getBody()));
            // Back to Results
            AppendMessageResult result = null;

            StoreStatsService storeStatsService = this.defaultMessageStore.getStoreStatsService();

            String topic = msg.getTopic();
            int queueId = msg.getQueueId();

            final int tranType = MessageSysFlag.getTransactionValue(msg.getSysFlag());
            if (tranType == MessageSysFlag.TRANSACTION_NOT_TYPE
                || tranType == MessageSysFlag.TRANSACTION_COMMIT_TYPE) {
                // Delay Delivery
                if (msg.getDelayTimeLevel() > 0) {
                    if (msg.getDelayTimeLevel() > this.defaultMessageStore.getScheduleMessageService().getMaxDelayLevel()) {
                        msg.setDelayTimeLevel(this.defaultMessageStore.getScheduleMessageService().getMaxDelayLevel());
                    }

                    topic = ScheduleMessageService.SCHEDULE_TOPIC;
                    queueId = ScheduleMessageService.delayLevel2QueueId(msg.getDelayTimeLevel());

                    // Backup real topic, queueId
                    MessageAccessor.putProperty(msg, MessageConst.PROPERTY_REAL_TOPIC, msg.getTopic());
                    MessageAccessor.putProperty(msg, MessageConst.PROPERTY_REAL_QUEUE_ID, String.valueOf(msg.getQueueId()));
                    msg.setPropertiesString(MessageDecoder.messageProperties2String(msg.getProperties()));

                    msg.setTopic(topic);
                    msg.setQueueId(queueId);
                }
            }

            long eclipseTimeInLock = 0;
            MappedFile unlockMappedFile = null;
            MappedFile mappedFile = this.mappedFileQueue.getLastMappedFile();

            putMessageLock.lock(); //spin or ReentrantLock ,depending on store config
            try {
                long beginLockTimestamp = this.defaultMessageStore.getSystemClock().now();
                this.beginTimeInLock = beginLockTimestamp;

                // Here settings are stored timestamp, in order to ensure an orderly
                // global
                msg.setStoreTimestamp(beginLockTimestamp);

                if (null == mappedFile || mappedFile.isFull()) {
                    mappedFile = this.mappedFileQueue.getLastMappedFile(0); // Mark: NewFile may be cause noise
                }
                if (null == mappedFile) {
                    log.error("create mapped file1 error, topic: " + msg.getTopic() + " clientAddr: " + msg.getBornHostString());
                    beginTimeInLock = 0;
                    return new PutMessageResult(PutMessageStatus.CREATE_MAPEDFILE_FAILED, null);
                }

                result = mappedFile.appendMessage(msg, this.appendMessageCallback);
                switch (result.getStatus()) {
                    case PUT_OK:
                        break;
                    case END_OF_FILE:
                        unlockMappedFile = mappedFile;
                        // Create a new file, re-write the message
                        mappedFile = this.mappedFileQueue.getLastMappedFile(0);
                        if (null == mappedFile) {
                            // XXX: warn and notify me
                            log.error("create mapped file2 error, topic: " + msg.getTopic() + " clientAddr: " + msg.getBornHostString());
                            beginTimeInLock = 0;
                            return new PutMessageResult(PutMessageStatus.CREATE_MAPEDFILE_FAILED, result);
                        }
                        result = mappedFile.appendMessage(msg, this.appendMessageCallback);
                        break;
                    case MESSAGE_SIZE_EXCEEDED:
                    case PROPERTIES_SIZE_EXCEEDED:
                        beginTimeInLock = 0;
                        return new PutMessageResult(PutMessageStatus.MESSAGE_ILLEGAL, result);
                    case UNKNOWN_ERROR:
                        beginTimeInLock = 0;
                        return new PutMessageResult(PutMessageStatus.UNKNOWN_ERROR, result);
                    default:
                        beginTimeInLock = 0;
                        return new PutMessageResult(PutMessageStatus.UNKNOWN_ERROR, result);
                }

                eclipseTimeInLock = this.defaultMessageStore.getSystemClock().now() - beginLockTimestamp;
                beginTimeInLock = 0;
            } finally {
                putMessageLock.unlock();
            }

            if (eclipseTimeInLock > 500) {
                log.warn("[NOTIFYME]putMessage in lock cost time(ms)={}, bodyLength={} AppendMessageResult={}", eclipseTimeInLock, msg.getBody().length, result);
            }

            if (null != unlockMappedFile && this.defaultMessageStore.getMessageStoreConfig().isWarmMapedFileEnable()) {
                this.defaultMessageStore.unlockMappedFile(unlockMappedFile);
            }

            PutMessageResult putMessageResult = new PutMessageResult(PutMessageStatus.PUT_OK, result);

            // Statistics
            storeStatsService.getSinglePutMessageTopicTimesTotal(msg.getTopic()).incrementAndGet();
            storeStatsService.getSinglePutMessageTopicSizeTotal(topic).addAndGet(result.getWroteBytes());

            handleDiskFlush(result, putMessageResult, msg);
            handleHA(result, putMessageResult, msg);

            return putMessageResult;
        }
    + +

    前面也看到在CommitLog 目录下是有大小为 1G 的文件组成，在实现逻辑中，其实是通过 org.apache.rocketmq.store.MappedFileQueue ，内部是存的一个MappedFile的队列，对于写入的场景每次都是通过org.apache.rocketmq.store.MappedFileQueue#getLastMappedFile() 获取最后一个文件，如果还没有创建，或者最后这个文件已经满了，那就调用 org.apache.rocketmq.store.MappedFileQueue#getLastMappedFile(long)

    +
    public MappedFile getLastMappedFile(final long startOffset, boolean needCreate) {
            long createOffset = -1;
      			// 调用前面的方法，只是从 mappedFileQueue 获取最后一个
            MappedFile mappedFileLast = getLastMappedFile();

            // 如果为空，计算下创建的偏移量
            if (mappedFileLast == null) {
                createOffset = startOffset - (startOffset % this.mappedFileSize);
            }
      
    				// 如果不为空，但是当前的文件写满了
            if (mappedFileLast != null && mappedFileLast.isFull()) {
                // 前一个的偏移量加上单个文件的偏移量，也就是 1G
                createOffset = mappedFileLast.getFileFromOffset() + this.mappedFileSize;
            }

            if (createOffset != -1 && needCreate) {
                // 根据 createOffset 转换成文件名进行创建
                String nextFilePath = this.storePath + File.separator + UtilAll.offset2FileName(createOffset);
                String nextNextFilePath = this.storePath + File.separator
                    + UtilAll.offset2FileName(createOffset + this.mappedFileSize);
                MappedFile mappedFile = null;

              	// 这里如果allocateMappedFileService 存在，就提交请求
                if (this.allocateMappedFileService != null) {
                    mappedFile = this.allocateMappedFileService.putRequestAndReturnMappedFile(nextFilePath,
                        nextNextFilePath, this.mappedFileSize);
                } else {
                    try {
                      // 否则就直接创建
                        mappedFile = new MappedFile(nextFilePath, this.mappedFileSize);
                    } catch (IOException e) {
                        log.error("create mappedFile exception", e);
                    }
                }

                if (mappedFile != null) {
                    if (this.mappedFiles.isEmpty()) {
                        mappedFile.setFirstCreateInQueue(true);
                    }
                    this.mappedFiles.add(mappedFile);
                }

                return mappedFile;
            }

            return mappedFileLast;
        }
    + +

    首先看下直接创建的，

    +
    public MappedFile(final String fileName, final int fileSize) throws IOException {
            init(fileName, fileSize);
        }
    private void init(final String fileName, final int fileSize) throws IOException {
            this.fileName = fileName;
            this.fileSize = fileSize;
            this.file = new File(fileName);
            this.fileFromOffset = Long.parseLong(this.file.getName());
            boolean ok = false;

            ensureDirOK(this.file.getParent());

            try {
              // 通过 RandomAccessFile 创建 fileChannel
                this.fileChannel = new RandomAccessFile(this.file, "rw").getChannel();
              // 做 mmap 映射
                this.mappedByteBuffer = this.fileChannel.map(MapMode.READ_WRITE, 0, fileSize);
                TOTAL_MAPPED_VIRTUAL_MEMORY.addAndGet(fileSize);
                TOTAL_MAPPED_FILES.incrementAndGet();
                ok = true;
            } catch (FileNotFoundException e) {
                log.error("create file channel " + this.fileName + " Failed. ", e);
                throw e;
            } catch (IOException e) {
                log.error("map file " + this.fileName + " Failed. ", e);
                throw e;
            } finally {
                if (!ok && this.fileChannel != null) {
                    this.fileChannel.close();
                }
            }
        }
    + +

    如果是提交给AllocateMappedFileService的话就用到了一些异步操作

    +
    public MappedFile putRequestAndReturnMappedFile(String nextFilePath, String nextNextFilePath, int fileSize) {
            int canSubmitRequests = 2;
            if (this.messageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
                if (this.messageStore.getMessageStoreConfig().isFastFailIfNoBufferInStorePool()
                    && BrokerRole.SLAVE != this.messageStore.getMessageStoreConfig().getBrokerRole()) { //if broker is slave, don't fast fail even no buffer in pool
                    canSubmitRequests = this.messageStore.getTransientStorePool().remainBufferNumbs() - this.requestQueue.size();
                }
            }
    				// 将请求放在 requestTable 中
            AllocateRequest nextReq = new AllocateRequest(nextFilePath, fileSize);
            boolean nextPutOK = this.requestTable.putIfAbsent(nextFilePath, nextReq) == null;
            // requestTable 使用了 concurrentHashMap，用文件名作为 key，防止并发
            if (nextPutOK) {
                // 这里判断了是否可以提交到 TransientStorePool，涉及读写分离，后面再细聊
                if (canSubmitRequests <= 0) {
                    log.warn("[NOTIFYME]TransientStorePool is not enough, so create mapped file error, " +
                        "RequestQueueSize : {}, StorePoolSize: {}", this.requestQueue.size(), this.messageStore.getTransientStorePool().remainBufferNumbs());
                    this.requestTable.remove(nextFilePath);
                    return null;
                }
              // 塞到阻塞队列中
                boolean offerOK = this.requestQueue.offer(nextReq);
                if (!offerOK) {
                    log.warn("never expected here, add a request to preallocate queue failed");
                }
                canSubmitRequests--;
            }

            // 这里的两个提交我猜测是为了多生成一个 CommitLog，
            AllocateRequest nextNextReq = new AllocateRequest(nextNextFilePath, fileSize);
            boolean nextNextPutOK = this.requestTable.putIfAbsent(nextNextFilePath, nextNextReq) == null;
            if (nextNextPutOK) {
                if (canSubmitRequests <= 0) {
                    log.warn("[NOTIFYME]TransientStorePool is not enough, so skip preallocate mapped file, " +
                        "RequestQueueSize : {}, StorePoolSize: {}", this.requestQueue.size(), this.messageStore.getTransientStorePool().remainBufferNumbs());
                    this.requestTable.remove(nextNextFilePath);
                } else {
                    boolean offerOK = this.requestQueue.offer(nextNextReq);
                    if (!offerOK) {
                        log.warn("never expected here, add a request to preallocate queue failed");
                    }
                }
            }

            if (hasException) {
                log.warn(this.getServiceName() + " service has exception. so return null");
                return null;
            }

            AllocateRequest result = this.requestTable.get(nextFilePath);
            try {
              // 这里就异步等着
                if (result != null) {
                    boolean waitOK = result.getCountDownLatch().await(waitTimeOut, TimeUnit.MILLISECONDS);
                    if (!waitOK) {
                        log.warn("create mmap timeout " + result.getFilePath() + " " + result.getFileSize());
                        return null;
                    } else {
                        this.requestTable.remove(nextFilePath);
                        return result.getMappedFile();
                    }
                } else {
                    log.error("find preallocate mmap failed, this never happen");
                }
            } catch (InterruptedException e) {
                log.warn(this.getServiceName() + " service has exception. ", e);
            }

            return null;
        }
    -

    以broker注册为例，

    -
    case RequestCode.REGISTER_BROKER:
                    Version brokerVersion = MQVersion.value2Version(request.getVersion());
                    if (brokerVersion.ordinal() >= MQVersion.Version.V3_0_11.ordinal()) {
                        return this.registerBrokerWithFilterServer(ctx, request);
                    } else {
                        return this.registerBroker(ctx, request);
                    }
    +

    而真正去执行文件操作的就是 AllocateMappedFileService的 run 方法

    +
    public void run() {
            log.info(this.getServiceName() + " service started");

            while (!this.isStopped() && this.mmapOperation()) {

            }
            log.info(this.getServiceName() + " service end");
        }
    private boolean mmapOperation() {
            boolean isSuccess = false;
            AllocateRequest req = null;
            try {
              // 从阻塞队列里获取请求
                req = this.requestQueue.take();
                AllocateRequest expectedRequest = this.requestTable.get(req.getFilePath());
                if (null == expectedRequest) {
                    log.warn("this mmap request expired, maybe cause timeout " + req.getFilePath() + " "
                        + req.getFileSize());
                    return true;
                }
                if (expectedRequest != req) {
                    log.warn("never expected here,  maybe cause timeout " + req.getFilePath() + " "
                        + req.getFileSize() + ", req:" + req + ", expectedRequest:" + expectedRequest);
                    return true;
                }

                if (req.getMappedFile() == null) {
                    long beginTime = System.currentTimeMillis();

                    MappedFile mappedFile;
                    if (messageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
                        try {
                          // 通过 transientStorePool 创建
                            mappedFile = ServiceLoader.load(MappedFile.class).iterator().next();
                            mappedFile.init(req.getFilePath(), req.getFileSize(), messageStore.getTransientStorePool());
                        } catch (RuntimeException e) {
                            log.warn("Use default implementation.");
                          // 默认创建
                            mappedFile = new MappedFile(req.getFilePath(), req.getFileSize(), messageStore.getTransientStorePool());
                        }
                    } else {
                      // 默认创建
                        mappedFile = new MappedFile(req.getFilePath(), req.getFileSize());
                    }

                    long eclipseTime = UtilAll.computeEclipseTimeMilliseconds(beginTime);
                    if (eclipseTime > 10) {
                        int queueSize = this.requestQueue.size();
                        log.warn("create mappedFile spent time(ms) " + eclipseTime + " queue size " + queueSize
                            + " " + req.getFilePath() + " " + req.getFileSize());
                    }

                    // pre write mappedFile
                    if (mappedFile.getFileSize() >= this.messageStore.getMessageStoreConfig()
                        .getMapedFileSizeCommitLog()
                        &&
                        this.messageStore.getMessageStoreConfig().isWarmMapedFileEnable()) {
                        mappedFile.warmMappedFile(this.messageStore.getMessageStoreConfig().getFlushDiskType(),
                            this.messageStore.getMessageStoreConfig().getFlushLeastPagesWhenWarmMapedFile());
                    }

                    req.setMappedFile(mappedFile);
                    this.hasException = false;
                    isSuccess = true;
                }
            } catch (InterruptedException e) {
                log.warn(this.getServiceName() + " interrupted, possibly by shutdown.");
                this.hasException = true;
                return false;
            } catch (IOException e) {
                log.warn(this.getServiceName() + " service has exception. ", e);
                this.hasException = true;
                if (null != req) {
                    requestQueue.offer(req);
                    try {
                        Thread.sleep(1);
                    } catch (InterruptedException ignored) {
                    }
                }
            } finally {
                if (req != null && isSuccess)
                  // 通知前面等待的
                    req.getCountDownLatch().countDown();
            }
            return true;
        }
    -

    做了个简单的版本管理，我们看下前面一个的代码

    -
    public RemotingCommand registerBrokerWithFilterServer(ChannelHandlerContext ctx, RemotingCommand request)
        throws RemotingCommandException {
        final RemotingCommand response = RemotingCommand.createResponseCommand(RegisterBrokerResponseHeader.class);
        final RegisterBrokerResponseHeader responseHeader = (RegisterBrokerResponseHeader) response.readCustomHeader();
        final RegisterBrokerRequestHeader requestHeader =
            (RegisterBrokerRequestHeader) request.decodeCommandCustomHeader(RegisterBrokerRequestHeader.class);

        if (!checksum(ctx, request, requestHeader)) {
            response.setCode(ResponseCode.SYSTEM_ERROR);
            response.setRemark("crc32 not match");
            return response;
        }

        RegisterBrokerBody registerBrokerBody = new RegisterBrokerBody();

        if (request.getBody() != null) {
            try {
                registerBrokerBody = RegisterBrokerBody.decode(request.getBody(), requestHeader.isCompressed());
            } catch (Exception e) {
                throw new RemotingCommandException("Failed to decode RegisterBrokerBody", e);
            }
        } else {
            registerBrokerBody.getTopicConfigSerializeWrapper().getDataVersion().setCounter(new AtomicLong(0));
            registerBrokerBody.getTopicConfigSerializeWrapper().getDataVersion().setTimestamp(0);
        }

        RegisterBrokerResult result = this.namesrvController.getRouteInfoManager().registerBroker(
            requestHeader.getClusterName(),
            requestHeader.getBrokerAddr(),
            requestHeader.getBrokerName(),
            requestHeader.getBrokerId(),
            requestHeader.getHaServerAddr(),
            registerBrokerBody.getTopicConfigSerializeWrapper(),
            registerBrokerBody.getFilterServerList(),
            ctx.channel());

        responseHeader.setHaServerAddr(result.getHaServerAddr());
        responseHeader.setMasterAddr(result.getMasterAddr());

        byte[] jsonValue = this.namesrvController.getKvConfigManager().getKVListByNamespace(NamesrvUtil.NAMESPACE_ORDER_TOPIC_CONFIG);
        response.setBody(jsonValue);

        response.setCode(ResponseCode.SUCCESS);
        response.setRemark(null);
        return response;
    }
    -

    可以看到主要的逻辑还是在org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#registerBroker这个方法里

    -
    public RegisterBrokerResult registerBroker(
            final String clusterName,
            final String brokerAddr,
            final String brokerName,
            final long brokerId,
            final String haServerAddr,
            final TopicConfigSerializeWrapper topicConfigWrapper,
            final List<String> filterServerList,
            final Channel channel) {
            RegisterBrokerResult result = new RegisterBrokerResult();
            try {
                try {
                    this.lock.writeLock().lockInterruptibly();

                  // 更新这个clusterAddrTable
                    Set<String> brokerNames = this.clusterAddrTable.get(clusterName);
                    if (null == brokerNames) {
                        brokerNames = new HashSet<String>();
                        this.clusterAddrTable.put(clusterName, brokerNames);
                    }
                    brokerNames.add(brokerName);

                    boolean registerFirst = false;

                  // 更新brokerAddrTable
                    BrokerData brokerData = this.brokerAddrTable.get(brokerName);
                    if (null == brokerData) {
                        registerFirst = true;
                        brokerData = new BrokerData(clusterName, brokerName, new HashMap<Long, String>());
                        this.brokerAddrTable.put(brokerName, brokerData);
                    }
                    Map<Long, String> brokerAddrsMap = brokerData.getBrokerAddrs();
                    //Switch slave to master: first remove <1, IP:PORT> in namesrv, then add <0, IP:PORT>
                    //The same IP:PORT must only have one record in brokerAddrTable
                    Iterator<Entry<Long, String>> it = brokerAddrsMap.entrySet().iterator();
                    while (it.hasNext()) {
                        Entry<Long, String> item = it.next();
                        if (null != brokerAddr && brokerAddr.equals(item.getValue()) && brokerId != item.getKey()) {
                            it.remove();
                        }
                    }

                    String oldAddr = brokerData.getBrokerAddrs().put(brokerId, brokerAddr);
                    registerFirst = registerFirst || (null == oldAddr);

                  // 更新了org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#topicQueueTable中的数据
                    if (null != topicConfigWrapper
                        && MixAll.MASTER_ID == brokerId) {
                        if (this.isBrokerTopicConfigChanged(brokerAddr, topicConfigWrapper.getDataVersion())
                            || registerFirst) {
                            ConcurrentMap<String, TopicConfig> tcTable =
                                topicConfigWrapper.getTopicConfigTable();
                            if (tcTable != null) {
                                for (Map.Entry<String, TopicConfig> entry : tcTable.entrySet()) {
                                    this.createAndUpdateQueueData(brokerName, entry.getValue());
                                }
                            }
                        }
                    }

                  // 更新活跃broker信息
                    BrokerLiveInfo prevBrokerLiveInfo = this.brokerLiveTable.put(brokerAddr,
                        new BrokerLiveInfo(
                            System.currentTimeMillis(),
                            topicConfigWrapper.getDataVersion(),
                            channel,
                            haServerAddr));
                    if (null == prevBrokerLiveInfo) {
                        log.info("new broker registered, {} HAServer: {}", brokerAddr, haServerAddr);
                    }

                  // 处理filter
                    if (filterServerList != null) {
                        if (filterServerList.isEmpty()) {
                            this.filterServerTable.remove(brokerAddr);
                        } else {
                            this.filterServerTable.put(brokerAddr, filterServerList);
                        }
                    }

                  // 当当前broker非master时返回master信息
                    if (MixAll.MASTER_ID != brokerId) {
                        String masterAddr = brokerData.getBrokerAddrs().get(MixAll.MASTER_ID);
                        if (masterAddr != null) {
                            BrokerLiveInfo brokerLiveInfo = this.brokerLiveTable.get(masterAddr);
                            if (brokerLiveInfo != null) {
                                result.setHaServerAddr(brokerLiveInfo.getHaServerAddr());
                                result.setMasterAddr(masterAddr);
                            }
                        }
                    }
                } finally {
                    this.lock.writeLock().unlock();
                }
            } catch (Exception e) {
                log.error("registerBroker Exception", e);
            }

            return result;
        }
    -

    这个是注册 broker 的逻辑，再看下根据 topic 获取 broker 信息和 topic 信息，org.apache.rocketmq.namesrv.processor.DefaultRequestProcessor#getRouteInfoByTopic 主要是这个方法的逻辑

    -
    public RemotingCommand getRouteInfoByTopic(ChannelHandlerContext ctx,
            RemotingCommand request) throws RemotingCommandException {
            final RemotingCommand response = RemotingCommand.createResponseCommand(null);
            final GetRouteInfoRequestHeader requestHeader =
                (GetRouteInfoRequestHeader) request.decodeCommandCustomHeader(GetRouteInfoRequestHeader.class);

            TopicRouteData topicRouteData = this.namesrvController.getRouteInfoManager().pickupTopicRouteData(requestHeader.getTopic());

            if (topicRouteData != null) {
                if (this.namesrvController.getNamesrvConfig().isOrderMessageEnable()) {
                    String orderTopicConf =
                        this.namesrvController.getKvConfigManager().getKVConfig(NamesrvUtil.NAMESPACE_ORDER_TOPIC_CONFIG,
                            requestHeader.getTopic());
                    topicRouteData.setOrderTopicConf(orderTopicConf);
                }

                byte[] content = topicRouteData.encode();
                response.setBody(content);
                response.setCode(ResponseCode.SUCCESS);
                response.setRemark(null);
                return response;
            }

            response.setCode(ResponseCode.TOPIC_NOT_EXIST);
            response.setRemark("No topic route info in name server for the topic: " + requestHeader.getTopic()
                + FAQUrl.suggestTodo(FAQUrl.APPLY_TOPIC_URL));
            return response;
        }
    -

    首先调用org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#pickupTopicRouteData从org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#topicQueueTable获取到org.apache.rocketmq.common.protocol.route.QueueData这里面存了 brokerName，再通过org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#brokerAddrTable里获取到 broker 的地址信息等，然后再获取 orderMessage 的配置。

    -

    简要分析了下 RocketMQ 的 NameServer 的代码，比较粗粒度。

    ]]>     MQ RocketMQ 消息队列 - RocketMQ - 中间件 - RocketMQ MQ 消息队列 RocketMQ - 削峰填谷 - 中间件 - 源码解析 - NameServer     @@ -5875,66 +5932,19 @@ user3: - 聊一下 Java 的日志系列三 - /2024/01/21/%E8%81%8A%E4%B8%80%E4%B8%8B-Java-%E7%9A%84%E6%97%A5%E5%BF%97%E7%B3%BB%E5%88%97%E4%B8%89/ - 上周因为一些事情没有更新在这里，是因为新电脑还没到，手头没有把移动硬盘里的 time machine 恢复出来的机器，所以单独更了一篇在新建的一个 cloudflare page 服务上，总体体验还可以，就是有个小点后面可以讲一下，继续完善下 Java 的这个日志，或者说主要讲的是 logback
    前面讲了logback 的初始化逻辑，后面就是我最开始来看这个的逻辑，日志的滚动策略的触发逻辑
    我们常规的记录日志的方式比如

    -
    logger.info("log some thing and biz info: {}",biz);
    -

    这里就调用了
    ch.qos.logback.classic.Logger#info(java.lang.String, java.lang.Object)

    -
    public void info(String format, Object arg) {
        filterAndLog_1(FQCN, null, Level.INFO, format, arg, null);
    }
    -

    这里的 FQCN 就是这个类的全限定名

    -
    public static final String FQCN = ch.qos.logback.classic.Logger.class.getName();
    -

    这个具体的 filterAndLog_1 方法还是在同一个 Logger 类里的
    ch.qos.logback.classic.Logger#filterAndLog_1

    -
    private void filterAndLog_1(final String localFQCN, final Marker marker, final Level level, final String msg, final Object param, final Throwable t) {

        final FilterReply decision = loggerContext.getTurboFilterChainDecision_1(marker, this, level, msg, param, t);

        if (decision == FilterReply.NEUTRAL) {
            if (effectiveLevelInt > level.levelInt) {
                return;
            }
        } else if (decision == FilterReply.DENY) {
            return;
        }

        buildLoggingEventAndAppend(localFQCN, marker, level, msg, new Object[] { param }, t);
    }
    -

    这里面先是走过滤器逻辑，如果是拒绝的，那就不往下执行，也就不执行下面的写日志逻辑了，如果是中性的，那就判断日志级别是否符合，如果直接是拒绝就 return 了，如果是 ACCEPT 就往下执行了

    -
    public enum FilterReply {
        DENY, NEUTRAL, ACCEPT;
    }
    -

    然后继续调用同样的类的方法
    ch.qos.logback.classic.Logger#buildLoggingEventAndAppend

    -
    private void buildLoggingEventAndAppend(final String localFQCN, final Marker marker, final Level level, final String msg, final Object[] params,
                    final Throwable t) {
        LoggingEvent le = new LoggingEvent(localFQCN, this, level, msg, t, params);
        le.setMarker(marker);
        callAppenders(le);
    }
    -

    先把日志包装成 LoggingEvent ，然后调用
    ch.qos.logback.classic.Logger#callAppenders

    -
    public void callAppenders(ILoggingEvent event) {
        int writes = 0;
        for (Logger l = this; l != null; l = l.parent) {
            writes += l.appendLoopOnAppenders(event);
            if (!l.additive) {
                break;
            }
        }
        // No appenders in hierarchy
        if (writes == 0) {
            loggerContext.noAppenderDefinedWarning(this);
        }
    }
    -

    这里会按 Logger 调用 appendLoopOnAppenders

    -
    private int appendLoopOnAppenders(ILoggingEvent event) {
        if (aai != null) {
            return aai.appendLoopOnAppenders(event);
        } else {
            return 0;
        }
    -

    继续调用事件的
    ch.qos.logback.core.spi.AppenderAttachableImpl#appendLoopOnAppenders

    -
    public int appendLoopOnAppenders(E e) {
        int size = 0;
        final Appender<E>[] appenderArray = appenderList.asTypedArray();
        final int len = appenderArray.length;
        for (int i = 0; i < len; i++) {
            appenderArray[i].doAppend(e);
            size++;
        }
        return size;
    }
    -

    因为 Logger 里有关联 appender，就具体调用关联的 appender 来添加日志
    然后就接着调用
    ch.qos.logback.core.UnsynchronizedAppenderBase#doAppend 方法

    -
    public void doAppend(E eventObject) {
            // WARNING: The guard check MUST be the first statement in the
            // doAppend() method.

            // prevent re-entry.
            if (Boolean.TRUE.equals(guard.get())) {
                return;
            }

            try {
                guard.set(Boolean.TRUE);

                if (!this.started) {
                    if (statusRepeatCount++ < ALLOWED_REPEATS) {
                        addStatus(new WarnStatus("Attempted to append to non started appender [" + name + "].", this));
                    }
                    return;
                }

                if (getFilterChainDecision(eventObject) == FilterReply.DENY) {
                    return;
                }

                // ok, we now invoke derived class' implementation of append
                this.append(eventObject);

            } catch (Exception e) {
                if (exceptionCount++ < ALLOWED_REPEATS) {
                    addError("Appender [" + name + "] failed to append.", e);
                }
            } finally {
                guard.set(Boolean.FALSE);
            }
        }
    -

    这里先判断了 guard 的状态，guard 是个 ThreadLocal，是为了拦截异常的无限循环调用，这里有些疑惑的，看了一圈可能相关的是前面的 Logger 会往父级查找，然后如果不同层级的 Logger 关联了相同的 appender 的话，或者这个 doAppend 又被其他 appender 调用了，就可能会出现循环调用，然后会在开始实际的 append的之前先设置 guard 状态，
    然后再执行 append，这里会走到
    ch.qos.logback.core.OutputStreamAppender#append

    -
    @Override
    protected void append(E eventObject) {
        if (!isStarted()) {
            return;
        }

        subAppend(eventObject);
    }
    -

    忘了补充下，我们这里使用的是
    ch.qos.logback.core.rolling.RollingFileAppender
    接下去就是调用的 RollingFileAppender 的 subAppend 方法

    -
    @Override
    protected void subAppend(E event) {
        // The roll-over check must precede actual writing. This is the
        // only correct behavior for time driven triggers.

        // We need to synchronize on triggeringPolicy so that only one rollover
        // occurs at a time
        synchronized (triggeringPolicy) {
            if (triggeringPolicy.isTriggeringEvent(currentlyActiveFile, event)) {
                rollover();
            }
        }

        super.subAppend(event);
    }
    -

    这里就会先判断是否触发事件，
    而这里就是主要的逻辑，看是否需要走 rollover，也就是具体的滚动逻辑

    -
    @Override
        public boolean isTriggeringEvent(File activeFile, final E event) {

            long time = getCurrentTime();

            // first check for roll-over based on time
            if (time >= nextCheck) {
                Date dateInElapsedPeriod = dateInCurrentPeriod;
                elapsedPeriodsFileName = tbrp.fileNamePatternWithoutCompSuffix.convertMultipleArguments(dateInElapsedPeriod, currentPeriodsCounter);
                currentPeriodsCounter = 0;
                setDateInCurrentPeriod(time);
                computeNextCheck();
                return true;
            }

            // next check for roll-over based on size
            if (invocationGate.isTooSoon(time)) {
                return false;
            }

            if (activeFile == null) {
                addWarn("activeFile == null");
                return false;
            }
            if (maxFileSize == null) {
                addWarn("maxFileSize = null");
                return false;
            }
            if (activeFile.length() >= maxFileSize.getSize()) {

                elapsedPeriodsFileName = tbrp.fileNamePatternWithoutCompSuffix.convertMultipleArguments(dateInCurrentPeriod, currentPeriodsCounter);
                currentPeriodsCounter++;
                return true;
            }

            return false;
        }
    -

    这里主要就用到了 nextCheck 判断，如果通过判断就可以执行滚动了，然后就是后面的判断，是否太快了，也就是 isTooSoon 然后再看当前的活跃日志以及最大文件大小是否设置，然后如果当前的活跃日志的大小超过了配置也会触发滚动

    -]]>     - - Java - - - Java - -     - - 聊一下 RocketMQ 的消息存储三 - /2021/10/03/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%B8%89/ - ConsumeQueue 其实是定位到一个 topic 下的消息在 CommitLog 下的偏移量，它也是固定大小的

    -
    // ConsumeQueue file size,default is 30W
    private int mapedFileSizeConsumeQueue = 300000 * ConsumeQueue.CQ_STORE_UNIT_SIZE;

    public static final int CQ_STORE_UNIT_SIZE = 20;
    - -

    所以文件大小是5.7M 左右

    -

    5udpag

    -

    ConsumeQueue 的构建是通过org.apache.rocketmq.store.DefaultMessageStore.ReputMessageService运行后的 doReput 方法，而启动是的 reputFromOffset 则是通过org.apache.rocketmq.store.DefaultMessageStore#start中下面代码设置并启动

    -
    log.info("[SetReputOffset] maxPhysicalPosInLogicQueue={} clMinOffset={} clMaxOffset={} clConfirmedOffset={}",
                    maxPhysicalPosInLogicQueue, this.commitLog.getMinOffset(), this.commitLog.getMaxOffset(), this.commitLog.getConfirmOffset());
                this.reputMessageService.setReputFromOffset(maxPhysicalPosInLogicQueue);
                this.reputMessageService.start();
    - -

    看一下 doReput 的逻辑

    -
    private void doReput() {
                if (this.reputFromOffset < DefaultMessageStore.this.commitLog.getMinOffset()) {
                    log.warn("The reputFromOffset={} is smaller than minPyOffset={}, this usually indicate that the dispatch behind too much and the commitlog has expired.",
                        this.reputFromOffset, DefaultMessageStore.this.commitLog.getMinOffset());
                    this.reputFromOffset = DefaultMessageStore.this.commitLog.getMinOffset();
                }
                for (boolean doNext = true; this.isCommitLogAvailable() && doNext; ) {

                    if (DefaultMessageStore.this.getMessageStoreConfig().isDuplicationEnable()
                        && this.reputFromOffset >= DefaultMessageStore.this.getConfirmOffset()) {
                        break;
                    }

                  // 根据偏移量获取消息
                    SelectMappedBufferResult result = DefaultMessageStore.this.commitLog.getData(reputFromOffset);
                    if (result != null) {
                        try {
                            this.reputFromOffset = result.getStartOffset();

                            for (int readSize = 0; readSize < result.getSize() && doNext; ) {
                              // 消息校验和转换
                                DispatchRequest dispatchRequest =
                                    DefaultMessageStore.this.commitLog.checkMessageAndReturnSize(result.getByteBuffer(), false, false);
                                int size = dispatchRequest.getBufferSize() == -1 ? dispatchRequest.getMsgSize() : dispatchRequest.getBufferSize();

                                if (dispatchRequest.isSuccess()) {
                                    if (size > 0) {
                                      // 进行分发处理，包括 ConsumeQueue 和 IndexFile
                                        DefaultMessageStore.this.doDispatch(dispatchRequest);

                                        if (BrokerRole.SLAVE != DefaultMessageStore.this.getMessageStoreConfig().getBrokerRole()
                                            && DefaultMessageStore.this.brokerConfig.isLongPollingEnable()) {
                                            DefaultMessageStore.this.messageArrivingListener.arriving(dispatchRequest.getTopic(),
                                                dispatchRequest.getQueueId(), dispatchRequest.getConsumeQueueOffset() + 1,
                                                dispatchRequest.getTagsCode(), dispatchRequest.getStoreTimestamp(),
                                                dispatchRequest.getBitMap(), dispatchRequest.getPropertiesMap());
                                        }

                                        this.reputFromOffset += size;
                                        readSize += size;
                                        if (DefaultMessageStore.this.getMessageStoreConfig().getBrokerRole() == BrokerRole.SLAVE) {
                                            DefaultMessageStore.this.storeStatsService
                                                .getSinglePutMessageTopicTimesTotal(dispatchRequest.getTopic()).incrementAndGet();
                                            DefaultMessageStore.this.storeStatsService
                                                .getSinglePutMessageTopicSizeTotal(dispatchRequest.getTopic())
                                                .addAndGet(dispatchRequest.getMsgSize());
                                        }
                                    } else if (size == 0) {
                                        this.reputFromOffset = DefaultMessageStore.this.commitLog.rollNextFile(this.reputFromOffset);
                                        readSize = result.getSize();
                                    }
                                } else if (!dispatchRequest.isSuccess()) {

                                    if (size > 0) {
                                        log.error("[BUG]read total count not equals msg total size. reputFromOffset={}", reputFromOffset);
                                        this.reputFromOffset += size;
                                    } else {
                                        doNext = false;
                                        // If user open the dledger pattern or the broker is master node,
                                        // it will not ignore the exception and fix the reputFromOffset variable
                                        if (DefaultMessageStore.this.getMessageStoreConfig().isEnableDLegerCommitLog() ||
                                            DefaultMessageStore.this.brokerConfig.getBrokerId() == MixAll.MASTER_ID) {
                                            log.error("[BUG]dispatch message to consume queue error, COMMITLOG OFFSET: {}",
                                                this.reputFromOffset);
                                            this.reputFromOffset += result.getSize() - readSize;
                                        }
                                    }
                                }
                            }
                        } finally {
                            result.release();
                        }
                    } else {
                        doNext = false;
                    }
                }
            }
    + 聊一下 RocketMQ 的消息存储四 + /2021/10/17/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E5%9B%9B/ + IndexFile 结构 hash 结构能够通过 key 寻找到对应在 CommitLog 中的位置

    +

    IndexFile 的构建则是分发给这个进行处理

    +
    class CommitLogDispatcherBuildIndex implements CommitLogDispatcher {

        @Override
        public void dispatch(DispatchRequest request) {
            if (DefaultMessageStore.this.messageStoreConfig.isMessageIndexEnable()) {
                DefaultMessageStore.this.indexService.buildIndex(request);
            }
        }
    }
    public void buildIndex(DispatchRequest req) {
            IndexFile indexFile = retryGetAndCreateIndexFile();
            if (indexFile != null) {
                long endPhyOffset = indexFile.getEndPhyOffset();
                DispatchRequest msg = req;
                String topic = msg.getTopic();
                String keys = msg.getKeys();
                if (msg.getCommitLogOffset() < endPhyOffset) {
                    return;
                }

                final int tranType = MessageSysFlag.getTransactionValue(msg.getSysFlag());
                switch (tranType) {
                    case MessageSysFlag.TRANSACTION_NOT_TYPE:
                    case MessageSysFlag.TRANSACTION_PREPARED_TYPE:
                    case MessageSysFlag.TRANSACTION_COMMIT_TYPE:
                        break;
                    case MessageSysFlag.TRANSACTION_ROLLBACK_TYPE:
                        return;
                }

                if (req.getUniqKey() != null) {
                    indexFile = putKey(indexFile, msg, buildKey(topic, req.getUniqKey()));
                    if (indexFile == null) {
                        log.error("putKey error commitlog {} uniqkey {}", req.getCommitLogOffset(), req.getUniqKey());
                        return;
                    }
                }

                if (keys != null && keys.length() > 0) {
                    String[] keyset = keys.split(MessageConst.KEY_SEPARATOR);
                    for (int i = 0; i < keyset.length; i++) {
                        String key = keyset[i];
                        if (key.length() > 0) {
                            indexFile = putKey(indexFile, msg, buildKey(topic, key));
                            if (indexFile == null) {
                                log.error("putKey error commitlog {} uniqkey {}", req.getCommitLogOffset(), req.getUniqKey());
                                return;
                            }
                        }
                    }
                }
            } else {
                log.error("build index error, stop building index");
            }
        }
    -

    分发的逻辑看到这

    -
        class CommitLogDispatcherBuildConsumeQueue implements CommitLogDispatcher {

            @Override
            public void dispatch(DispatchRequest request) {
                final int tranType = MessageSysFlag.getTransactionValue(request.getSysFlag());
                switch (tranType) {
                    case MessageSysFlag.TRANSACTION_NOT_TYPE:
                    case MessageSysFlag.TRANSACTION_COMMIT_TYPE:
                        DefaultMessageStore.this.putMessagePositionInfo(request);
                        break;
                    case MessageSysFlag.TRANSACTION_PREPARED_TYPE:
                    case MessageSysFlag.TRANSACTION_ROLLBACK_TYPE:
                        break;
                }
            }
        }
    public void putMessagePositionInfo(DispatchRequest dispatchRequest) {
            ConsumeQueue cq = this.findConsumeQueue(dispatchRequest.getTopic(), dispatchRequest.getQueueId());
            cq.putMessagePositionInfoWrapper(dispatchRequest);
        }
    +

    配置的数量

    +
    private boolean messageIndexEnable = true;
    private int maxHashSlotNum = 5000000;
    private int maxIndexNum = 5000000 * 4;
    -

    真正存储的是在这

    -
    private boolean putMessagePositionInfo(final long offset, final int size, final long tagsCode,
        final long cqOffset) {

        if (offset + size <= this.maxPhysicOffset) {
            log.warn("Maybe try to build consume queue repeatedly maxPhysicOffset={} phyOffset={}", maxPhysicOffset, offset);
            return true;
        }

        this.byteBufferIndex.flip();
        this.byteBufferIndex.limit(CQ_STORE_UNIT_SIZE);
        this.byteBufferIndex.putLong(offset);
        this.byteBufferIndex.putInt(size);
        this.byteBufferIndex.putLong(tagsCode);
    +

    最核心的其实是 IndexFile 的结构和如何写入

    +
    public boolean putKey(final String key, final long phyOffset, final long storeTimestamp) {
            if (this.indexHeader.getIndexCount() < this.indexNum) {
              // 获取 key 的 hash
                int keyHash = indexKeyHashMethod(key);
              // 计算属于哪个 slot
                int slotPos = keyHash % this.hashSlotNum;
              // 计算 slot 位置 因为结构是有个 indexHead，主要是分为三段 header，slot 和 index
                int absSlotPos = IndexHeader.INDEX_HEADER_SIZE + slotPos * hashSlotSize;

                FileLock fileLock = null;

                try {

                    // fileLock = this.fileChannel.lock(absSlotPos, hashSlotSize,
                    // false);
                    int slotValue = this.mappedByteBuffer.getInt(absSlotPos);
                    if (slotValue <= invalidIndex || slotValue > this.indexHeader.getIndexCount()) {
                        slotValue = invalidIndex;
                    }

                    long timeDiff = storeTimestamp - this.indexHeader.getBeginTimestamp();

                    timeDiff = timeDiff / 1000;

                    if (this.indexHeader.getBeginTimestamp() <= 0) {
                        timeDiff = 0;
                    } else if (timeDiff > Integer.MAX_VALUE) {
                        timeDiff = Integer.MAX_VALUE;
                    } else if (timeDiff < 0) {
                        timeDiff = 0;
                    }

                  // 计算索引存放位置，头部 + slot 数量 * slot 大小 + 已有的 index 数量 + index 大小
                    int absIndexPos =
                        IndexHeader.INDEX_HEADER_SIZE + this.hashSlotNum * hashSlotSize
                            + this.indexHeader.getIndexCount() * indexSize;
    							
                    this.mappedByteBuffer.putInt(absIndexPos, keyHash);
                    this.mappedByteBuffer.putLong(absIndexPos + 4, phyOffset);
                    this.mappedByteBuffer.putInt(absIndexPos + 4 + 8, (int) timeDiff);
                    this.mappedByteBuffer.putInt(absIndexPos + 4 + 8 + 4, slotValue);

                  // 存放的是数量位移，不是绝对位置
                    this.mappedByteBuffer.putInt(absSlotPos, this.indexHeader.getIndexCount());

                    if (this.indexHeader.getIndexCount() <= 1) {
                        this.indexHeader.setBeginPhyOffset(phyOffset);
                        this.indexHeader.setBeginTimestamp(storeTimestamp);
                    }

                    this.indexHeader.incHashSlotCount();
                    this.indexHeader.incIndexCount();
                    this.indexHeader.setEndPhyOffset(phyOffset);
                    this.indexHeader.setEndTimestamp(storeTimestamp);

                    return true;
                } catch (Exception e) {
                    log.error("putKey exception, Key: " + key + " KeyHashCode: " + key.hashCode(), e);
                } finally {
                    if (fileLock != null) {
                        try {
                            fileLock.release();
                        } catch (IOException e) {
                            log.error("Failed to release the lock", e);
                        }
                    }
                }
            } else {
                log.warn("Over index file capacity: index count = " + this.indexHeader.getIndexCount()
                    + "; index max num = " + this.indexNum);
            }

            return false;
        }
    -

    这里也可以看到 ConsumeQueue 的存储格式，

    -

    AA6Tve

    -

    偏移量，消息大小，跟 tag 的 hashCode

    +

    具体可以看一下这个简略的示意图

    ]]>     MQ @@ -5948,19 +5958,31 @@ user3:     - 聊一下 RocketMQ 的消息存储四 - /2021/10/17/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E5%9B%9B/ - IndexFile 结构 hash 结构能够通过 key 寻找到对应在 CommitLog 中的位置

    -

    IndexFile 的构建则是分发给这个进行处理

    -
    class CommitLogDispatcherBuildIndex implements CommitLogDispatcher {

        @Override
        public void dispatch(DispatchRequest request) {
            if (DefaultMessageStore.this.messageStoreConfig.isMessageIndexEnable()) {
                DefaultMessageStore.this.indexService.buildIndex(request);
            }
        }
    }
    public void buildIndex(DispatchRequest req) {
            IndexFile indexFile = retryGetAndCreateIndexFile();
            if (indexFile != null) {
                long endPhyOffset = indexFile.getEndPhyOffset();
                DispatchRequest msg = req;
                String topic = msg.getTopic();
                String keys = msg.getKeys();
                if (msg.getCommitLogOffset() < endPhyOffset) {
                    return;
                }

                final int tranType = MessageSysFlag.getTransactionValue(msg.getSysFlag());
                switch (tranType) {
                    case MessageSysFlag.TRANSACTION_NOT_TYPE:
                    case MessageSysFlag.TRANSACTION_PREPARED_TYPE:
                    case MessageSysFlag.TRANSACTION_COMMIT_TYPE:
                        break;
                    case MessageSysFlag.TRANSACTION_ROLLBACK_TYPE:
                        return;
                }

                if (req.getUniqKey() != null) {
                    indexFile = putKey(indexFile, msg, buildKey(topic, req.getUniqKey()));
                    if (indexFile == null) {
                        log.error("putKey error commitlog {} uniqkey {}", req.getCommitLogOffset(), req.getUniqKey());
                        return;
                    }
                }

                if (keys != null && keys.length() > 0) {
                    String[] keyset = keys.split(MessageConst.KEY_SEPARATOR);
                    for (int i = 0; i < keyset.length; i++) {
                        String key = keyset[i];
                        if (key.length() > 0) {
                            indexFile = putKey(indexFile, msg, buildKey(topic, key));
                            if (indexFile == null) {
                                log.error("putKey error commitlog {} uniqkey {}", req.getCommitLogOffset(), req.getUniqKey());
                                return;
                            }
                        }
                    }
                }
            } else {
                log.error("build index error, stop building index");
            }
        }
    + 聊一下 RocketMQ 的顺序消息 + /2021/08/29/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E9%A1%BA%E5%BA%8F%E6%B6%88%E6%81%AF/ + rocketmq 里有一种比较特殊的用法，就是顺序消息，比如订单的生命周期里，在创建，支付，签收等状态轮转中，会发出来对应的消息，这里面就比较需要去保证他们的顺序，当然在处理的业务代码也可以做对应的处理，结合消息重投，但是如果这里消息就能保证顺序性了，那么业务代码就能更好的关注业务代码的处理。

    +

    首先有一种情况是全局的有序，比如对于一个 topic 里就得发送线程保证只有一个，内部的 queue 也只有一个，消费线程也只有一个，这样就能比较容易的保证全局顺序性了，但是这里的问题就是完全限制了性能，不是很现实，在真实场景里很多都是比如对于同一个订单，需要去保证状态的轮转是按照预期的顺序来，而不必要全局的有序性。

    +

    对于这类的有序性，需要在发送和接收方都有对应的处理，在发送消息中，需要去指定 selector，即MessageQueueSelector，能够以固定的方式是分配到对应的 MessageQueue

    +

    比如像 RocketMQ 中的示例

    +
    SendResult sendResult = producer.send(msg, new MessageQueueSelector() {
                    @Override
                    public MessageQueue select(List<MessageQueue> mqs, Message msg, Object arg) {
                        Long id = (Long) arg;  //message queue is selected by #salesOrderID
                        long index = id % mqs.size();
                        return mqs.get((int) index);
                    }
                }, orderList.get(i).getOrderId());
    -

    配置的数量

    -
    private boolean messageIndexEnable = true;
    private int maxHashSlotNum = 5000000;
    private int maxIndexNum = 5000000 * 4;
    +

    而在消费侧有几个点比较重要，首先我们要保证一个 MessageQueue只被一个消费者消费，消费队列存在broker端，要保证 MessageQueue 只被一个消费者消费，那么消费者在进行消息拉取消费时就必须向mq服务器申请队列锁，消费者申请队列锁的代码存在于RebalanceService消息队列负载的实现代码中。

    +
    List<PullRequest> pullRequestList = new ArrayList<PullRequest>();
            for (MessageQueue mq : mqSet) {
                if (!this.processQueueTable.containsKey(mq)) {
                  // 判断是否顺序，如果是顺序消费的，则需要加锁
                    if (isOrder && !this.lock(mq)) {
                        log.warn("doRebalance, {}, add a new mq failed, {}, because lock failed", consumerGroup, mq);
                        continue;
                    }

                    this.removeDirtyOffset(mq);
                    ProcessQueue pq = new ProcessQueue();
                    long nextOffset = this.computePullFromWhere(mq);
                    if (nextOffset >= 0) {
                        ProcessQueue pre = this.processQueueTable.putIfAbsent(mq, pq);
                        if (pre != null) {
                            log.info("doRebalance, {}, mq already exists, {}", consumerGroup, mq);
                        } else {
                            log.info("doRebalance, {}, add a new mq, {}", consumerGroup, mq);
                            PullRequest pullRequest = new PullRequest();
                            pullRequest.setConsumerGroup(consumerGroup);
                            pullRequest.setNextOffset(nextOffset);
                            pullRequest.setMessageQueue(mq);
                            pullRequest.setProcessQueue(pq);
                            pullRequestList.add(pullRequest);
                            changed = true;
                        }
                    } else {
                        log.warn("doRebalance, {}, add new mq failed, {}", consumerGroup, mq);
                    }
                }
            }
    -

    最核心的其实是 IndexFile 的结构和如何写入

    -
    public boolean putKey(final String key, final long phyOffset, final long storeTimestamp) {
            if (this.indexHeader.getIndexCount() < this.indexNum) {
              // 获取 key 的 hash
                int keyHash = indexKeyHashMethod(key);
              // 计算属于哪个 slot
                int slotPos = keyHash % this.hashSlotNum;
              // 计算 slot 位置 因为结构是有个 indexHead，主要是分为三段 header，slot 和 index
                int absSlotPos = IndexHeader.INDEX_HEADER_SIZE + slotPos * hashSlotSize;

                FileLock fileLock = null;

                try {

                    // fileLock = this.fileChannel.lock(absSlotPos, hashSlotSize,
                    // false);
                    int slotValue = this.mappedByteBuffer.getInt(absSlotPos);
                    if (slotValue <= invalidIndex || slotValue > this.indexHeader.getIndexCount()) {
                        slotValue = invalidIndex;
                    }

                    long timeDiff = storeTimestamp - this.indexHeader.getBeginTimestamp();

                    timeDiff = timeDiff / 1000;

                    if (this.indexHeader.getBeginTimestamp() <= 0) {
                        timeDiff = 0;
                    } else if (timeDiff > Integer.MAX_VALUE) {
                        timeDiff = Integer.MAX_VALUE;
                    } else if (timeDiff < 0) {
                        timeDiff = 0;
                    }

                  // 计算索引存放位置，头部 + slot 数量 * slot 大小 + 已有的 index 数量 + index 大小
                    int absIndexPos =
                        IndexHeader.INDEX_HEADER_SIZE + this.hashSlotNum * hashSlotSize
                            + this.indexHeader.getIndexCount() * indexSize;
    							
                    this.mappedByteBuffer.putInt(absIndexPos, keyHash);
                    this.mappedByteBuffer.putLong(absIndexPos + 4, phyOffset);
                    this.mappedByteBuffer.putInt(absIndexPos + 4 + 8, (int) timeDiff);
                    this.mappedByteBuffer.putInt(absIndexPos + 4 + 8 + 4, slotValue);

                  // 存放的是数量位移，不是绝对位置
                    this.mappedByteBuffer.putInt(absSlotPos, this.indexHeader.getIndexCount());

                    if (this.indexHeader.getIndexCount() <= 1) {
                        this.indexHeader.setBeginPhyOffset(phyOffset);
                        this.indexHeader.setBeginTimestamp(storeTimestamp);
                    }

                    this.indexHeader.incHashSlotCount();
                    this.indexHeader.incIndexCount();
                    this.indexHeader.setEndPhyOffset(phyOffset);
                    this.indexHeader.setEndTimestamp(storeTimestamp);

                    return true;
                } catch (Exception e) {
                    log.error("putKey exception, Key: " + key + " KeyHashCode: " + key.hashCode(), e);
                } finally {
                    if (fileLock != null) {
                        try {
                            fileLock.release();
                        } catch (IOException e) {
                            log.error("Failed to release the lock", e);
                        }
                    }
                }
            } else {
                log.warn("Over index file capacity: index count = " + this.indexHeader.getIndexCount()
                    + "; index max num = " + this.indexNum);
            }

            return false;
        }
    +

    在申请到锁之后会创建 pullRequest 进行消息拉取，消息拉取部分的代码实现在PullMessageService中，

    +
    @Override
        public void run() {
            log.info(this.getServiceName() + " service started");

            while (!this.isStopped()) {
                try {
                    PullRequest pullRequest = this.pullRequestQueue.take();
                    this.pullMessage(pullRequest);
                } catch (InterruptedException ignored) {
                } catch (Exception e) {
                    log.error("Pull Message Service Run Method exception", e);
                }
            }

            log.info(this.getServiceName() + " service end");
        }
    -

    具体可以看一下这个简略的示意图

    +

    消息拉取完后，需要提交到ConsumeMessageService中进行消费，顺序消费的实现为ConsumeMessageOrderlyService，提交消息进行消费的方法为ConsumeMessageOrderlyService#submitConsumeRequest，具体实现如下：

    +
    @Override
    public void submitConsumeRequest(
        final List<MessageExt> msgs,
        final ProcessQueue processQueue,
        final MessageQueue messageQueue,
        final boolean dispathToConsume) {
        if (dispathToConsume) {
            ConsumeRequest consumeRequest = new ConsumeRequest(processQueue, messageQueue);
            this.consumeExecutor.submit(consumeRequest);
        }
    }
    + +

    构建了一个ConsumeRequest对象，它是个实现了 runnable 接口的类，并提交给了线程池来并行消费，看下顺序消费的ConsumeRequest的run方法实现：

    +
    @Override
            public void run() {
                if (this.processQueue.isDropped()) {
                    log.warn("run, the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                    return;
                }
    						// 获得 Consumer 消息队列锁，即单个线程独占
                final Object objLock = messageQueueLock.fetchLockObject(this.messageQueue);
                synchronized (objLock) {
                  // (广播模式) 或者 (集群模式 && Broker消息队列锁有效)
                    if (MessageModel.BROADCASTING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                        || (this.processQueue.isLocked() && !this.processQueue.isLockExpired())) {
                        final long beginTime = System.currentTimeMillis();
                      // 循环
                        for (boolean continueConsume = true; continueConsume; ) {
                            if (this.processQueue.isDropped()) {
                                log.warn("the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                                break;
                            }

                          // 消息队列分布式锁未锁定，提交延迟获得锁并消费请求
                            if (MessageModel.CLUSTERING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                                && !this.processQueue.isLocked()) {
                                log.warn("the message queue not locked, so consume later, {}", this.messageQueue);
                                ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 10);
                                break;
                            }

                          // 消息队列分布式锁已经过期，提交延迟获得锁并消费请求
                            if (MessageModel.CLUSTERING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                                && this.processQueue.isLockExpired()) {
                                log.warn("the message queue lock expired, so consume later, {}", this.messageQueue);
                                ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 10);
                                break;
                            }
    												// 当前周期消费时间超过连续时长，默认：60s，提交延迟消费请求。默认情况下，每消费1分钟休息10ms。
                            long interval = System.currentTimeMillis() - beginTime;
                            if (interval > MAX_TIME_CONSUME_CONTINUOUSLY) {
                                ConsumeMessageOrderlyService.this.submitConsumeRequestLater(processQueue, messageQueue, 10);
                                break;
                            }
    												// 获取消费消息。此处和并发消息请求不同，并发消息请求已经带了消费哪些消息。
                            final int consumeBatchSize =
                                ConsumeMessageOrderlyService.this.defaultMQPushConsumer.getConsumeMessageBatchMaxSize();

                            List<MessageExt> msgs = this.processQueue.takeMessags(consumeBatchSize);
                            defaultMQPushConsumerImpl.resetRetryAndNamespace(msgs, defaultMQPushConsumer.getConsumerGroup());
                            if (!msgs.isEmpty()) {
                                final ConsumeOrderlyContext context = new ConsumeOrderlyContext(this.messageQueue);

                                ConsumeOrderlyStatus status = null;

                                ConsumeMessageContext consumeMessageContext = null;
                                if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                    consumeMessageContext = new ConsumeMessageContext();
                                    consumeMessageContext
                                        .setConsumerGroup(ConsumeMessageOrderlyService.this.defaultMQPushConsumer.getConsumerGroup());
                                    consumeMessageContext.setNamespace(defaultMQPushConsumer.getNamespace());
                                    consumeMessageContext.setMq(messageQueue);
                                    consumeMessageContext.setMsgList(msgs);
                                    consumeMessageContext.setSuccess(false);
                                    // init the consume context type
                                    consumeMessageContext.setProps(new HashMap<String, String>());
                                    ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.executeHookBefore(consumeMessageContext);
                                }
    														// 执行消费
                                long beginTimestamp = System.currentTimeMillis();
                                ConsumeReturnType returnType = ConsumeReturnType.SUCCESS;
                                boolean hasException = false;
                                try {
                                    this.processQueue.getLockConsume().lock(); // 锁定处理队列
                                    if (this.processQueue.isDropped()) {
                                        log.warn("consumeMessage, the message queue not be able to consume, because it's dropped. {}",
                                            this.messageQueue);
                                        break;
                                    }

                                    status = messageListener.consumeMessage(Collections.unmodifiableList(msgs), context);
                                } catch (Throwable e) {
                                    log.warn("consumeMessage exception: {} Group: {} Msgs: {} MQ: {}",
                                        RemotingHelper.exceptionSimpleDesc(e),
                                        ConsumeMessageOrderlyService.this.consumerGroup,
                                        msgs,
                                        messageQueue);
                                    hasException = true;
                                } finally {
                                    this.processQueue.getLockConsume().unlock();  // 解锁
                                }

                                if (null == status
                                    || ConsumeOrderlyStatus.ROLLBACK == status
                                    || ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT == status) {
                                    log.warn("consumeMessage Orderly return not OK, Group: {} Msgs: {} MQ: {}",
                                        ConsumeMessageOrderlyService.this.consumerGroup,
                                        msgs,
                                        messageQueue);
                                }

                                long consumeRT = System.currentTimeMillis() - beginTimestamp;
                                if (null == status) {
                                    if (hasException) {
                                        returnType = ConsumeReturnType.EXCEPTION;
                                    } else {
                                        returnType = ConsumeReturnType.RETURNNULL;
                                    }
                                } else if (consumeRT >= defaultMQPushConsumer.getConsumeTimeout() * 60 * 1000) {
                                    returnType = ConsumeReturnType.TIME_OUT;
                                } else if (ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT == status) {
                                    returnType = ConsumeReturnType.FAILED;
                                } else if (ConsumeOrderlyStatus.SUCCESS == status) {
                                    returnType = ConsumeReturnType.SUCCESS;
                                }

                                if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                    consumeMessageContext.getProps().put(MixAll.CONSUME_CONTEXT_TYPE, returnType.name());
                                }

                                if (null == status) {
                                    status = ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT;
                                }

                                if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                    consumeMessageContext.setStatus(status.toString());
                                    consumeMessageContext
                                        .setSuccess(ConsumeOrderlyStatus.SUCCESS == status || ConsumeOrderlyStatus.COMMIT == status);
                                    ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.executeHookAfter(consumeMessageContext);
                                }

                                ConsumeMessageOrderlyService.this.getConsumerStatsManager()
                                    .incConsumeRT(ConsumeMessageOrderlyService.this.consumerGroup, messageQueue.getTopic(), consumeRT);

                                continueConsume = ConsumeMessageOrderlyService.this.processConsumeResult(msgs, status, context, this);
                            } else {
                                continueConsume = false;
                            }
                        }
                    } else {
                        if (this.processQueue.isDropped()) {
                            log.warn("the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                            return;
                        }

                        ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 100);
                    }
                }
            }
    + +

    获取到锁对象后，使用synchronized尝试申请线程级独占锁。

    +

    如果加锁成功，同一时刻只有一个线程进行消息消费。

    +

    如果加锁失败，会延迟100ms重新尝试向broker端申请锁定messageQueue，锁定成功后重新提交消费请求

    +

    创建消息拉取任务时，消息客户端向broker端申请锁定MessageQueue，使得一个MessageQueue同一个时刻只能被一个消费客户端消费。

    +

    消息消费时，多线程针对同一个消息队列的消费先尝试使用synchronized申请独占锁，加锁成功才能进行消费，使得一个MessageQueue同一个时刻只能被一个消费客户端中一个线程消费。
    这里其实还有很重要的一点是对processQueue的加锁，这里其实是保证了在 rebalance的过程中如果 processQueue 被分配给了另一个 consumer，但是当前已经被我这个 consumer 再消费，但是没提交，就有可能出现被两个消费者消费，所以得进行加锁保证不受 rebalance 影响。

    ]]>     MQ @@ -5973,6 +5995,30 @@ user3: RocketMQ     + + 聊一下 Spring 的 SmartLifecycle 使用 + /2024/07/07/%E8%81%8A%E4%B8%80%E4%B8%8B-Spring-%E7%9A%84-SmarLifecycle-%E4%BD%BF%E7%94%A8/ + 最近在学习过程中碰到一个比较有意思的Spring特性，就是 SmartLifecycle ，这个可以很轻松的融合进 Spring 生命周期

    +
    public interface SmartLifecycle extends Lifecycle, Phased {
        int DEFAULT_PHASE = Integer.MAX_VALUE;

        default boolean isAutoStartup() {
            return true;
        }

        default void stop(Runnable callback) {
            this.stop();
            callback.run();
        }

        default int getPhase() {
            return Integer.MAX_VALUE;
        }
    }
    +

    接口继承了 org.springframework.context.Lifecycle 跟 org.springframework.context.Phased
    其中默认实现了 isAutoStartup , 并且默认是返回 true，所以相对于 Lifecycle 能更智能些自动启动
    然后 Lifecycle 这个接口就比较简单，只有几个接口

    +
    public interface Lifecycle {
        void start();

        void stop();

        boolean isRunning();
    }
    +

    我们可以是实现下这个接口

    +
    @Component
    public class MySmartLifecycle implements SmartLifecycle {

        private volatile boolean isRunning;

        @Override
        public void start() {
            System.out.println("my smart life cycle start");
            isRunning = true;
        }

        @Override
        public void stop() {
            System.out.println("my smart life cycle end");
            isRunning = false;
        }

        @Override
        public boolean isRunning() {
            return isRunning;
        }
    }
    +

    这个 bean 会在 Spring 启动后被自动调用 start 方法

    这个就是在不深入 bean 的生命周期，并且是在 bean 已经都初始化以后调用
    同样会在 spring 容器关闭时调用 stop 方法

    如果有此类的需求就可以扩展此接口实现
    而这个其实是在 spring 生命周期中的 finishRefresh 方法中进行调用

    +
    protected void finishRefresh() {
        this.clearResourceCaches();
        this.initLifecycleProcessor();
        this.getLifecycleProcessor().onRefresh();
        this.publishEvent((ApplicationEvent)(new ContextRefreshedEvent(this)));
        if (!NativeDetector.inNativeImage()) {
            LiveBeansView.registerApplicationContext(this);
        }

    }
    +

    调用了 LifecycleProcessor 的 onRefresh 方法

    +
    public void onRefresh() {
        this.startBeans(true);
        this.running = true;
    }
    +

    这其中就会调用 start 方法

    +
    private void startBeans(boolean autoStartupOnly) {
        // 获取这些bean
        Map<String, Lifecycle> lifecycleBeans = this.getLifecycleBeans();
        Map<Integer, LifecycleGroup> phases = new TreeMap();
        lifecycleBeans.forEach((beanName, bean) -> {
            if (!autoStartupOnly || bean instanceof SmartLifecycle && ((SmartLifecycle)bean).isAutoStartup()) {
                int phase = this.getPhase(bean);
                ((LifecycleGroup)phases.computeIfAbsent(phase, (p) -> {
                    return new LifecycleGroup(phase, this.timeoutPerShutdownPhase, lifecycleBeans, autoStartupOnly);
                })).add(beanName, bean);
            }

        });
        if (!phases.isEmpty()) {
            phases.values().forEach(LifecycleGroup::start);
        }

    }
    +

    会先判断 isAutoStartup，然后将不同周期加入分组，然后再循环调用start

    +]]>     + + java + + + java + +     聊一下 SpringBoot 中使用的 cglib 作为动态代理中的一个注意点 /2021/09/19/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E4%B8%AD%E4%BD%BF%E7%94%A8%E7%9A%84-cglib-%E4%BD%9C%E4%B8%BA%E5%8A%A8%E6%80%81%E4%BB%A3%E7%90%86%E4%B8%AD%E7%9A%84%E4%B8%80%E4%B8%AA%E6%B3%A8%E6%84%8F%E7%82%B9/ @@ -6015,44 +6061,6 @@ user3: 事务 - - 聊一下 RocketMQ 的消息存储二 - /2021/09/12/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%BA%8C/ - CommitLog 结构

    CommitLog 是 rocketmq 的服务端，也就是 broker 存储消息的的文件，跟 kafka 一样，也是顺序写入，当然消息是变长的，生成的规则是每个文件的默认1G =1024 * 1024 * 1024，commitlog的文件名fileName，名字长度为20位，左边补零，剩余为起始偏移量；比如00000000000000000000代表了第一个文件，起始偏移量为0，文件大小为1G=1 073 741 824Byte；当这个文件满了，第二个文件名字为00000000001073741824，起始偏移量为1073741824, 消息存储的时候会顺序写入文件，当文件满了则写入下一个文件，代码中的定义

    -
    // CommitLog file size,default is 1G
    private int mapedFileSizeCommitLog = 1024 * 1024 * 1024;
    - -

    kLahwW

    -

    本地跑个 demo 验证下，也是这样，这里奇妙有几个比较巧妙的点（个人观点），首先文件就刚好是 1G，并且按照大小偏移量去生成下一个文件，这样获取消息的时候按大小算一下就知道在哪个文件里了，

    -

    代码中写入 CommitLog 的逻辑可以从这开始看

    -
    public PutMessageResult putMessage(final MessageExtBrokerInner msg) {
            // Set the storage time
            msg.setStoreTimestamp(System.currentTimeMillis());
            // Set the message body BODY CRC (consider the most appropriate setting
            // on the client)
            msg.setBodyCRC(UtilAll.crc32(msg.getBody()));
            // Back to Results
            AppendMessageResult result = null;

            StoreStatsService storeStatsService = this.defaultMessageStore.getStoreStatsService();

            String topic = msg.getTopic();
            int queueId = msg.getQueueId();

            final int tranType = MessageSysFlag.getTransactionValue(msg.getSysFlag());
            if (tranType == MessageSysFlag.TRANSACTION_NOT_TYPE
                || tranType == MessageSysFlag.TRANSACTION_COMMIT_TYPE) {
                // Delay Delivery
                if (msg.getDelayTimeLevel() > 0) {
                    if (msg.getDelayTimeLevel() > this.defaultMessageStore.getScheduleMessageService().getMaxDelayLevel()) {
                        msg.setDelayTimeLevel(this.defaultMessageStore.getScheduleMessageService().getMaxDelayLevel());
                    }

                    topic = ScheduleMessageService.SCHEDULE_TOPIC;
                    queueId = ScheduleMessageService.delayLevel2QueueId(msg.getDelayTimeLevel());

                    // Backup real topic, queueId
                    MessageAccessor.putProperty(msg, MessageConst.PROPERTY_REAL_TOPIC, msg.getTopic());
                    MessageAccessor.putProperty(msg, MessageConst.PROPERTY_REAL_QUEUE_ID, String.valueOf(msg.getQueueId()));
                    msg.setPropertiesString(MessageDecoder.messageProperties2String(msg.getProperties()));

                    msg.setTopic(topic);
                    msg.setQueueId(queueId);
                }
            }

            long eclipseTimeInLock = 0;
            MappedFile unlockMappedFile = null;
            MappedFile mappedFile = this.mappedFileQueue.getLastMappedFile();

            putMessageLock.lock(); //spin or ReentrantLock ,depending on store config
            try {
                long beginLockTimestamp = this.defaultMessageStore.getSystemClock().now();
                this.beginTimeInLock = beginLockTimestamp;

                // Here settings are stored timestamp, in order to ensure an orderly
                // global
                msg.setStoreTimestamp(beginLockTimestamp);

                if (null == mappedFile || mappedFile.isFull()) {
                    mappedFile = this.mappedFileQueue.getLastMappedFile(0); // Mark: NewFile may be cause noise
                }
                if (null == mappedFile) {
                    log.error("create mapped file1 error, topic: " + msg.getTopic() + " clientAddr: " + msg.getBornHostString());
                    beginTimeInLock = 0;
                    return new PutMessageResult(PutMessageStatus.CREATE_MAPEDFILE_FAILED, null);
                }

                result = mappedFile.appendMessage(msg, this.appendMessageCallback);
                switch (result.getStatus()) {
                    case PUT_OK:
                        break;
                    case END_OF_FILE:
                        unlockMappedFile = mappedFile;
                        // Create a new file, re-write the message
                        mappedFile = this.mappedFileQueue.getLastMappedFile(0);
                        if (null == mappedFile) {
                            // XXX: warn and notify me
                            log.error("create mapped file2 error, topic: " + msg.getTopic() + " clientAddr: " + msg.getBornHostString());
                            beginTimeInLock = 0;
                            return new PutMessageResult(PutMessageStatus.CREATE_MAPEDFILE_FAILED, result);
                        }
                        result = mappedFile.appendMessage(msg, this.appendMessageCallback);
                        break;
                    case MESSAGE_SIZE_EXCEEDED:
                    case PROPERTIES_SIZE_EXCEEDED:
                        beginTimeInLock = 0;
                        return new PutMessageResult(PutMessageStatus.MESSAGE_ILLEGAL, result);
                    case UNKNOWN_ERROR:
                        beginTimeInLock = 0;
                        return new PutMessageResult(PutMessageStatus.UNKNOWN_ERROR, result);
                    default:
                        beginTimeInLock = 0;
                        return new PutMessageResult(PutMessageStatus.UNKNOWN_ERROR, result);
                }

                eclipseTimeInLock = this.defaultMessageStore.getSystemClock().now() - beginLockTimestamp;
                beginTimeInLock = 0;
            } finally {
                putMessageLock.unlock();
            }

            if (eclipseTimeInLock > 500) {
                log.warn("[NOTIFYME]putMessage in lock cost time(ms)={}, bodyLength={} AppendMessageResult={}", eclipseTimeInLock, msg.getBody().length, result);
            }

            if (null != unlockMappedFile && this.defaultMessageStore.getMessageStoreConfig().isWarmMapedFileEnable()) {
                this.defaultMessageStore.unlockMappedFile(unlockMappedFile);
            }

            PutMessageResult putMessageResult = new PutMessageResult(PutMessageStatus.PUT_OK, result);

            // Statistics
            storeStatsService.getSinglePutMessageTopicTimesTotal(msg.getTopic()).incrementAndGet();
            storeStatsService.getSinglePutMessageTopicSizeTotal(topic).addAndGet(result.getWroteBytes());

            handleDiskFlush(result, putMessageResult, msg);
            handleHA(result, putMessageResult, msg);

            return putMessageResult;
        }
    - -

    前面也看到在CommitLog 目录下是有大小为 1G 的文件组成，在实现逻辑中，其实是通过 org.apache.rocketmq.store.MappedFileQueue ，内部是存的一个MappedFile的队列，对于写入的场景每次都是通过org.apache.rocketmq.store.MappedFileQueue#getLastMappedFile() 获取最后一个文件，如果还没有创建，或者最后这个文件已经满了，那就调用 org.apache.rocketmq.store.MappedFileQueue#getLastMappedFile(long)

    -
    public MappedFile getLastMappedFile(final long startOffset, boolean needCreate) {
            long createOffset = -1;
      			// 调用前面的方法，只是从 mappedFileQueue 获取最后一个
            MappedFile mappedFileLast = getLastMappedFile();

            // 如果为空，计算下创建的偏移量
            if (mappedFileLast == null) {
                createOffset = startOffset - (startOffset % this.mappedFileSize);
            }
      
    				// 如果不为空，但是当前的文件写满了
            if (mappedFileLast != null && mappedFileLast.isFull()) {
                // 前一个的偏移量加上单个文件的偏移量，也就是 1G
                createOffset = mappedFileLast.getFileFromOffset() + this.mappedFileSize;
            }

            if (createOffset != -1 && needCreate) {
                // 根据 createOffset 转换成文件名进行创建
                String nextFilePath = this.storePath + File.separator + UtilAll.offset2FileName(createOffset);
                String nextNextFilePath = this.storePath + File.separator
                    + UtilAll.offset2FileName(createOffset + this.mappedFileSize);
                MappedFile mappedFile = null;

              	// 这里如果allocateMappedFileService 存在，就提交请求
                if (this.allocateMappedFileService != null) {
                    mappedFile = this.allocateMappedFileService.putRequestAndReturnMappedFile(nextFilePath,
                        nextNextFilePath, this.mappedFileSize);
                } else {
                    try {
                      // 否则就直接创建
                        mappedFile = new MappedFile(nextFilePath, this.mappedFileSize);
                    } catch (IOException e) {
                        log.error("create mappedFile exception", e);
                    }
                }

                if (mappedFile != null) {
                    if (this.mappedFiles.isEmpty()) {
                        mappedFile.setFirstCreateInQueue(true);
                    }
                    this.mappedFiles.add(mappedFile);
                }

                return mappedFile;
            }

            return mappedFileLast;
        }
    - -

    首先看下直接创建的，

    -
    public MappedFile(final String fileName, final int fileSize) throws IOException {
            init(fileName, fileSize);
        }
    private void init(final String fileName, final int fileSize) throws IOException {
            this.fileName = fileName;
            this.fileSize = fileSize;
            this.file = new File(fileName);
            this.fileFromOffset = Long.parseLong(this.file.getName());
            boolean ok = false;

            ensureDirOK(this.file.getParent());

            try {
              // 通过 RandomAccessFile 创建 fileChannel
                this.fileChannel = new RandomAccessFile(this.file, "rw").getChannel();
              // 做 mmap 映射
                this.mappedByteBuffer = this.fileChannel.map(MapMode.READ_WRITE, 0, fileSize);
                TOTAL_MAPPED_VIRTUAL_MEMORY.addAndGet(fileSize);
                TOTAL_MAPPED_FILES.incrementAndGet();
                ok = true;
            } catch (FileNotFoundException e) {
                log.error("create file channel " + this.fileName + " Failed. ", e);
                throw e;
            } catch (IOException e) {
                log.error("map file " + this.fileName + " Failed. ", e);
                throw e;
            } finally {
                if (!ok && this.fileChannel != null) {
                    this.fileChannel.close();
                }
            }
        }
    - -

    如果是提交给AllocateMappedFileService的话就用到了一些异步操作

    -
    public MappedFile putRequestAndReturnMappedFile(String nextFilePath, String nextNextFilePath, int fileSize) {
            int canSubmitRequests = 2;
            if (this.messageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
                if (this.messageStore.getMessageStoreConfig().isFastFailIfNoBufferInStorePool()
                    && BrokerRole.SLAVE != this.messageStore.getMessageStoreConfig().getBrokerRole()) { //if broker is slave, don't fast fail even no buffer in pool
                    canSubmitRequests = this.messageStore.getTransientStorePool().remainBufferNumbs() - this.requestQueue.size();
                }
            }
    				// 将请求放在 requestTable 中
            AllocateRequest nextReq = new AllocateRequest(nextFilePath, fileSize);
            boolean nextPutOK = this.requestTable.putIfAbsent(nextFilePath, nextReq) == null;
            // requestTable 使用了 concurrentHashMap，用文件名作为 key，防止并发
            if (nextPutOK) {
                // 这里判断了是否可以提交到 TransientStorePool，涉及读写分离，后面再细聊
                if (canSubmitRequests <= 0) {
                    log.warn("[NOTIFYME]TransientStorePool is not enough, so create mapped file error, " +
                        "RequestQueueSize : {}, StorePoolSize: {}", this.requestQueue.size(), this.messageStore.getTransientStorePool().remainBufferNumbs());
                    this.requestTable.remove(nextFilePath);
                    return null;
                }
              // 塞到阻塞队列中
                boolean offerOK = this.requestQueue.offer(nextReq);
                if (!offerOK) {
                    log.warn("never expected here, add a request to preallocate queue failed");
                }
                canSubmitRequests--;
            }

            // 这里的两个提交我猜测是为了多生成一个 CommitLog，
            AllocateRequest nextNextReq = new AllocateRequest(nextNextFilePath, fileSize);
            boolean nextNextPutOK = this.requestTable.putIfAbsent(nextNextFilePath, nextNextReq) == null;
            if (nextNextPutOK) {
                if (canSubmitRequests <= 0) {
                    log.warn("[NOTIFYME]TransientStorePool is not enough, so skip preallocate mapped file, " +
                        "RequestQueueSize : {}, StorePoolSize: {}", this.requestQueue.size(), this.messageStore.getTransientStorePool().remainBufferNumbs());
                    this.requestTable.remove(nextNextFilePath);
                } else {
                    boolean offerOK = this.requestQueue.offer(nextNextReq);
                    if (!offerOK) {
                        log.warn("never expected here, add a request to preallocate queue failed");
                    }
                }
            }

            if (hasException) {
                log.warn(this.getServiceName() + " service has exception. so return null");
                return null;
            }

            AllocateRequest result = this.requestTable.get(nextFilePath);
            try {
              // 这里就异步等着
                if (result != null) {
                    boolean waitOK = result.getCountDownLatch().await(waitTimeOut, TimeUnit.MILLISECONDS);
                    if (!waitOK) {
                        log.warn("create mmap timeout " + result.getFilePath() + " " + result.getFileSize());
                        return null;
                    } else {
                        this.requestTable.remove(nextFilePath);
                        return result.getMappedFile();
                    }
                } else {
                    log.error("find preallocate mmap failed, this never happen");
                }
            } catch (InterruptedException e) {
                log.warn(this.getServiceName() + " service has exception. ", e);
            }

            return null;
        }
    - -

    而真正去执行文件操作的就是 AllocateMappedFileService的 run 方法

    -
    public void run() {
            log.info(this.getServiceName() + " service started");

            while (!this.isStopped() && this.mmapOperation()) {

            }
            log.info(this.getServiceName() + " service end");
        }
    private boolean mmapOperation() {
            boolean isSuccess = false;
            AllocateRequest req = null;
            try {
              // 从阻塞队列里获取请求
                req = this.requestQueue.take();
                AllocateRequest expectedRequest = this.requestTable.get(req.getFilePath());
                if (null == expectedRequest) {
                    log.warn("this mmap request expired, maybe cause timeout " + req.getFilePath() + " "
                        + req.getFileSize());
                    return true;
                }
                if (expectedRequest != req) {
                    log.warn("never expected here,  maybe cause timeout " + req.getFilePath() + " "
                        + req.getFileSize() + ", req:" + req + ", expectedRequest:" + expectedRequest);
                    return true;
                }

                if (req.getMappedFile() == null) {
                    long beginTime = System.currentTimeMillis();

                    MappedFile mappedFile;
                    if (messageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
                        try {
                          // 通过 transientStorePool 创建
                            mappedFile = ServiceLoader.load(MappedFile.class).iterator().next();
                            mappedFile.init(req.getFilePath(), req.getFileSize(), messageStore.getTransientStorePool());
                        } catch (RuntimeException e) {
                            log.warn("Use default implementation.");
                          // 默认创建
                            mappedFile = new MappedFile(req.getFilePath(), req.getFileSize(), messageStore.getTransientStorePool());
                        }
                    } else {
                      // 默认创建
                        mappedFile = new MappedFile(req.getFilePath(), req.getFileSize());
                    }

                    long eclipseTime = UtilAll.computeEclipseTimeMilliseconds(beginTime);
                    if (eclipseTime > 10) {
                        int queueSize = this.requestQueue.size();
                        log.warn("create mappedFile spent time(ms) " + eclipseTime + " queue size " + queueSize
                            + " " + req.getFilePath() + " " + req.getFileSize());
                    }

                    // pre write mappedFile
                    if (mappedFile.getFileSize() >= this.messageStore.getMessageStoreConfig()
                        .getMapedFileSizeCommitLog()
                        &&
                        this.messageStore.getMessageStoreConfig().isWarmMapedFileEnable()) {
                        mappedFile.warmMappedFile(this.messageStore.getMessageStoreConfig().getFlushDiskType(),
                            this.messageStore.getMessageStoreConfig().getFlushLeastPagesWhenWarmMapedFile());
                    }

                    req.setMappedFile(mappedFile);
                    this.hasException = false;
                    isSuccess = true;
                }
            } catch (InterruptedException e) {
                log.warn(this.getServiceName() + " interrupted, possibly by shutdown.");
                this.hasException = true;
                return false;
            } catch (IOException e) {
                log.warn(this.getServiceName() + " service has exception. ", e);
                this.hasException = true;
                if (null != req) {
                    requestQueue.offer(req);
                    try {
                        Thread.sleep(1);
                    } catch (InterruptedException ignored) {
                    }
                }
            } finally {
                if (req != null && isSuccess)
                  // 通知前面等待的
                    req.getCountDownLatch().countDown();
            }
            return true;
        }
    - - - - -]]>     - - MQ - RocketMQ - 消息队列 - - - MQ - 消息队列 - RocketMQ - -     聊一下 SpringBoot 中动态切换数据源的方法 /2021/09/26/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E4%B8%AD%E5%8A%A8%E6%80%81%E5%88%87%E6%8D%A2%E6%95%B0%E6%8D%AE%E6%BA%90%E7%9A%84%E6%96%B9%E6%B3%95/ @@ -6102,44 +6110,6 @@ user3: 数据源动态切换 - - 聊一下 RocketMQ 的顺序消息 - /2021/08/29/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E9%A1%BA%E5%BA%8F%E6%B6%88%E6%81%AF/ - rocketmq 里有一种比较特殊的用法，就是顺序消息，比如订单的生命周期里，在创建，支付，签收等状态轮转中，会发出来对应的消息，这里面就比较需要去保证他们的顺序，当然在处理的业务代码也可以做对应的处理，结合消息重投，但是如果这里消息就能保证顺序性了，那么业务代码就能更好的关注业务代码的处理。

    -

    首先有一种情况是全局的有序，比如对于一个 topic 里就得发送线程保证只有一个，内部的 queue 也只有一个，消费线程也只有一个，这样就能比较容易的保证全局顺序性了，但是这里的问题就是完全限制了性能，不是很现实，在真实场景里很多都是比如对于同一个订单，需要去保证状态的轮转是按照预期的顺序来，而不必要全局的有序性。

    -

    对于这类的有序性，需要在发送和接收方都有对应的处理，在发送消息中，需要去指定 selector，即MessageQueueSelector，能够以固定的方式是分配到对应的 MessageQueue

    -

    比如像 RocketMQ 中的示例

    -
    SendResult sendResult = producer.send(msg, new MessageQueueSelector() {
                    @Override
                    public MessageQueue select(List<MessageQueue> mqs, Message msg, Object arg) {
                        Long id = (Long) arg;  //message queue is selected by #salesOrderID
                        long index = id % mqs.size();
                        return mqs.get((int) index);
                    }
                }, orderList.get(i).getOrderId());
    - -

    而在消费侧有几个点比较重要，首先我们要保证一个 MessageQueue只被一个消费者消费，消费队列存在broker端，要保证 MessageQueue 只被一个消费者消费，那么消费者在进行消息拉取消费时就必须向mq服务器申请队列锁，消费者申请队列锁的代码存在于RebalanceService消息队列负载的实现代码中。

    -
    List<PullRequest> pullRequestList = new ArrayList<PullRequest>();
            for (MessageQueue mq : mqSet) {
                if (!this.processQueueTable.containsKey(mq)) {
                  // 判断是否顺序，如果是顺序消费的，则需要加锁
                    if (isOrder && !this.lock(mq)) {
                        log.warn("doRebalance, {}, add a new mq failed, {}, because lock failed", consumerGroup, mq);
                        continue;
                    }

                    this.removeDirtyOffset(mq);
                    ProcessQueue pq = new ProcessQueue();
                    long nextOffset = this.computePullFromWhere(mq);
                    if (nextOffset >= 0) {
                        ProcessQueue pre = this.processQueueTable.putIfAbsent(mq, pq);
                        if (pre != null) {
                            log.info("doRebalance, {}, mq already exists, {}", consumerGroup, mq);
                        } else {
                            log.info("doRebalance, {}, add a new mq, {}", consumerGroup, mq);
                            PullRequest pullRequest = new PullRequest();
                            pullRequest.setConsumerGroup(consumerGroup);
                            pullRequest.setNextOffset(nextOffset);
                            pullRequest.setMessageQueue(mq);
                            pullRequest.setProcessQueue(pq);
                            pullRequestList.add(pullRequest);
                            changed = true;
                        }
                    } else {
                        log.warn("doRebalance, {}, add new mq failed, {}", consumerGroup, mq);
                    }
                }
            }
    - -

    在申请到锁之后会创建 pullRequest 进行消息拉取，消息拉取部分的代码实现在PullMessageService中，

    -
    @Override
        public void run() {
            log.info(this.getServiceName() + " service started");

            while (!this.isStopped()) {
                try {
                    PullRequest pullRequest = this.pullRequestQueue.take();
                    this.pullMessage(pullRequest);
                } catch (InterruptedException ignored) {
                } catch (Exception e) {
                    log.error("Pull Message Service Run Method exception", e);
                }
            }

            log.info(this.getServiceName() + " service end");
        }
    - -

    消息拉取完后，需要提交到ConsumeMessageService中进行消费，顺序消费的实现为ConsumeMessageOrderlyService，提交消息进行消费的方法为ConsumeMessageOrderlyService#submitConsumeRequest，具体实现如下：

    -
    @Override
    public void submitConsumeRequest(
        final List<MessageExt> msgs,
        final ProcessQueue processQueue,
        final MessageQueue messageQueue,
        final boolean dispathToConsume) {
        if (dispathToConsume) {
            ConsumeRequest consumeRequest = new ConsumeRequest(processQueue, messageQueue);
            this.consumeExecutor.submit(consumeRequest);
        }
    }
    - -

    构建了一个ConsumeRequest对象，它是个实现了 runnable 接口的类，并提交给了线程池来并行消费，看下顺序消费的ConsumeRequest的run方法实现：

    -
    @Override
            public void run() {
                if (this.processQueue.isDropped()) {
                    log.warn("run, the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                    return;
                }
    						// 获得 Consumer 消息队列锁，即单个线程独占
                final Object objLock = messageQueueLock.fetchLockObject(this.messageQueue);
                synchronized (objLock) {
                  // (广播模式) 或者 (集群模式 && Broker消息队列锁有效)
                    if (MessageModel.BROADCASTING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                        || (this.processQueue.isLocked() && !this.processQueue.isLockExpired())) {
                        final long beginTime = System.currentTimeMillis();
                      // 循环
                        for (boolean continueConsume = true; continueConsume; ) {
                            if (this.processQueue.isDropped()) {
                                log.warn("the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                                break;
                            }

                          // 消息队列分布式锁未锁定，提交延迟获得锁并消费请求
                            if (MessageModel.CLUSTERING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                                && !this.processQueue.isLocked()) {
                                log.warn("the message queue not locked, so consume later, {}", this.messageQueue);
                                ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 10);
                                break;
                            }

                          // 消息队列分布式锁已经过期，提交延迟获得锁并消费请求
                            if (MessageModel.CLUSTERING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                                && this.processQueue.isLockExpired()) {
                                log.warn("the message queue lock expired, so consume later, {}", this.messageQueue);
                                ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 10);
                                break;
                            }
    												// 当前周期消费时间超过连续时长，默认：60s，提交延迟消费请求。默认情况下，每消费1分钟休息10ms。
                            long interval = System.currentTimeMillis() - beginTime;
                            if (interval > MAX_TIME_CONSUME_CONTINUOUSLY) {
                                ConsumeMessageOrderlyService.this.submitConsumeRequestLater(processQueue, messageQueue, 10);
                                break;
                            }
    												// 获取消费消息。此处和并发消息请求不同，并发消息请求已经带了消费哪些消息。
                            final int consumeBatchSize =
                                ConsumeMessageOrderlyService.this.defaultMQPushConsumer.getConsumeMessageBatchMaxSize();

                            List<MessageExt> msgs = this.processQueue.takeMessags(consumeBatchSize);
                            defaultMQPushConsumerImpl.resetRetryAndNamespace(msgs, defaultMQPushConsumer.getConsumerGroup());
                            if (!msgs.isEmpty()) {
                                final ConsumeOrderlyContext context = new ConsumeOrderlyContext(this.messageQueue);

                                ConsumeOrderlyStatus status = null;

                                ConsumeMessageContext consumeMessageContext = null;
                                if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                    consumeMessageContext = new ConsumeMessageContext();
                                    consumeMessageContext
                                        .setConsumerGroup(ConsumeMessageOrderlyService.this.defaultMQPushConsumer.getConsumerGroup());
                                    consumeMessageContext.setNamespace(defaultMQPushConsumer.getNamespace());
                                    consumeMessageContext.setMq(messageQueue);
                                    consumeMessageContext.setMsgList(msgs);
                                    consumeMessageContext.setSuccess(false);
                                    // init the consume context type
                                    consumeMessageContext.setProps(new HashMap<String, String>());
                                    ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.executeHookBefore(consumeMessageContext);
                                }
    														// 执行消费
                                long beginTimestamp = System.currentTimeMillis();
                                ConsumeReturnType returnType = ConsumeReturnType.SUCCESS;
                                boolean hasException = false;
                                try {
                                    this.processQueue.getLockConsume().lock(); // 锁定处理队列
                                    if (this.processQueue.isDropped()) {
                                        log.warn("consumeMessage, the message queue not be able to consume, because it's dropped. {}",
                                            this.messageQueue);
                                        break;
                                    }

                                    status = messageListener.consumeMessage(Collections.unmodifiableList(msgs), context);
                                } catch (Throwable e) {
                                    log.warn("consumeMessage exception: {} Group: {} Msgs: {} MQ: {}",
                                        RemotingHelper.exceptionSimpleDesc(e),
                                        ConsumeMessageOrderlyService.this.consumerGroup,
                                        msgs,
                                        messageQueue);
                                    hasException = true;
                                } finally {
                                    this.processQueue.getLockConsume().unlock();  // 解锁
                                }

                                if (null == status
                                    || ConsumeOrderlyStatus.ROLLBACK == status
                                    || ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT == status) {
                                    log.warn("consumeMessage Orderly return not OK, Group: {} Msgs: {} MQ: {}",
                                        ConsumeMessageOrderlyService.this.consumerGroup,
                                        msgs,
                                        messageQueue);
                                }

                                long consumeRT = System.currentTimeMillis() - beginTimestamp;
                                if (null == status) {
                                    if (hasException) {
                                        returnType = ConsumeReturnType.EXCEPTION;
                                    } else {
                                        returnType = ConsumeReturnType.RETURNNULL;
                                    }
                                } else if (consumeRT >= defaultMQPushConsumer.getConsumeTimeout() * 60 * 1000) {
                                    returnType = ConsumeReturnType.TIME_OUT;
                                } else if (ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT == status) {
                                    returnType = ConsumeReturnType.FAILED;
                                } else if (ConsumeOrderlyStatus.SUCCESS == status) {
                                    returnType = ConsumeReturnType.SUCCESS;
                                }

                                if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                    consumeMessageContext.getProps().put(MixAll.CONSUME_CONTEXT_TYPE, returnType.name());
                                }

                                if (null == status) {
                                    status = ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT;
                                }

                                if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                    consumeMessageContext.setStatus(status.toString());
                                    consumeMessageContext
                                        .setSuccess(ConsumeOrderlyStatus.SUCCESS == status || ConsumeOrderlyStatus.COMMIT == status);
                                    ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.executeHookAfter(consumeMessageContext);
                                }

                                ConsumeMessageOrderlyService.this.getConsumerStatsManager()
                                    .incConsumeRT(ConsumeMessageOrderlyService.this.consumerGroup, messageQueue.getTopic(), consumeRT);

                                continueConsume = ConsumeMessageOrderlyService.this.processConsumeResult(msgs, status, context, this);
                            } else {
                                continueConsume = false;
                            }
                        }
                    } else {
                        if (this.processQueue.isDropped()) {
                            log.warn("the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                            return;
                        }

                        ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 100);
                    }
                }
            }
    - -

    获取到锁对象后，使用synchronized尝试申请线程级独占锁。

    -

    如果加锁成功，同一时刻只有一个线程进行消息消费。

    -

    如果加锁失败，会延迟100ms重新尝试向broker端申请锁定messageQueue，锁定成功后重新提交消费请求

    -

    创建消息拉取任务时，消息客户端向broker端申请锁定MessageQueue，使得一个MessageQueue同一个时刻只能被一个消费客户端消费。

    -

    消息消费时，多线程针对同一个消息队列的消费先尝试使用synchronized申请独占锁，加锁成功才能进行消费，使得一个MessageQueue同一个时刻只能被一个消费客户端中一个线程消费。
    这里其实还有很重要的一点是对processQueue的加锁，这里其实是保证了在 rebalance的过程中如果 processQueue 被分配给了另一个 consumer，但是当前已经被我这个 consumer 再消费，但是没提交，就有可能出现被两个消费者消费，所以得进行加锁保证不受 rebalance 影响。

    -]]>     - - MQ - RocketMQ - 消息队列 - - - MQ - 消息队列 - RocketMQ - -     聊一下 SpringBoot 设置非 web 应用的方法 /2022/07/31/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E8%AE%BE%E7%BD%AE%E9%9D%9E-web-%E5%BA%94%E7%94%A8%E7%9A%84%E6%96%B9%E6%B3%95/ @@ -6155,15 +6125,115 @@ user3:

    相当于是把none 的类型和包括 servlet 和 reactive 放进了枚举类进行控制。

    ]]> - Java - SpringBoot + Java + SpringBoot + + + Java + Spring + SpringBoot + 自动装配 + AutoConfiguration + +     + + 聊一聊 https 的逻辑 + /2024/03/10/%E8%81%8A%E4%B8%80%E8%81%8A-https-%E7%9A%84%E9%80%BB%E8%BE%91/ + https 和 tls

    https 我原本的理解来自于几个概念

    +

    第一个是非对称加密

    +

    也就是公钥可以公开在网上，然后用公钥加密，由私有的私钥进行解密，然后就对 https 有了初步的偏颇的概念，就是浏览器用公钥进行加密，服务器端用私钥进行解密，这里再仔细想想会延伸出来几个问题，也是逐步完善我的后续理解

    +

    第二个就是非对称加密以后的对称加密

    +

    因为对加密的理解是超大的质数（这个还来源于大学的数据结构老师）相乘后会很难做质因数分解，比如最大的质数2^82,589,933 − 1，用十进制表示有24,862,048位，用现在计算机来算也是非常难的，那如果一直采用非对称加密，这个效率也会比较低，所以在网上查了一番之后知道了是先采用非对称加密，然后再用对称加密，因为非对称加密先有了安全保证，后续的消息就可以用对称加密的方式来进行传输安全了

    +

    第三个是最近一次看到的

    +

    也是一直缺乏思考的问题，因为 https 我们在使用的时候都会购买证书或者使用免费的证书，那这个证书和前面说的公私钥是什么关系，好像一直是悬空的，没想过这中间的相关性，证书究竟是怎么起作用的

    +

    下面是摘自 cloudflare 的 tls 握手流程，把证书的使用方式说的比较明白了

    +

    TLS 握手是由客户端和服务器交换的一系列数据报或消息。TLS 握手涉及多个步骤，因为客户端和服务器要交换完成握手和进行进一步对话所需的信息。

    +

    TLS 握手中的确切步骤将根据所使用的密钥交换算法的种类和双方支持的密码套件而有所不同。RSA 密钥交换算法虽然现在被认为不安全，但曾在 1.3 之前的 TLS 版本中使用。大致如下：

    +
      +
    1. “客户端问候（client hello）” 消息： 客户端通过向服务器发送“问候”消息来开始握手。该消息将包含客户端支持的 TLS 版本，支持的密码套件，以及称为一串称为“客户端随机数（client random）”的随机字节。
      2. +
    2. “服务器问候（server hello）”消息： 作为对 client hello 消息的回复，服务器发送一条消息，内含服务器的 SSL 证书、服务器选择的密码套件，以及“服务器随机数（server random）”，即由服务器生成的另一串随机字节。
      3. +
    3. 身份验证： 客户端使用颁发该证书的证书颁发机构验证服务器的 SSL 证书。此举确认服务器是其声称的身份，且客户端正在与该域的实际所有者进行交互。
      4. +
    4. 预主密钥： 客户端再发送一串随机字节，即“预主密钥（premaster secret）”。预主密钥是使用公钥加密的，只能使用服务器的私钥解密。（客户端从服务器的 SSL 证书中获得公钥。）
      5. +
    5. 私钥被使用：服务器对预主密钥进行解密。
      6. +
    6. 生成会话密钥：客户端和服务器均使用客户端随机数、服务器随机数和预主密钥生成会话密钥。双方应得到相同的结果。
      7. +
    7. 客户端就绪：客户端发送一条“已完成”消息，该消息用会话密钥加密。
      8. +
    8. 服务器就绪：服务器发送一条“已完成”消息，该消息用会话密钥加密。
      9. +
    9. 实现安全对称加密：已完成握手，并使用会话密钥继续进行通信。
      10. +
    +

    为了学习这个过程，我们尝试用 Chrome 的自带抓包工具，以往这是可以通过在 Chrome 地址栏中输入 chrome://net-internals/#events 来打开，现在变成了两部分，chrome://net-export/ 这里可以开始抓包，然后会记录下一个抓包日志文件里，

    +

    +

    然后再打开

    +

    https://netlog-viewer.appspot.com/#events 来查看具体的日志

    +

    这里我们以打开百度为例，即在打开 chrome://net-export/ 并启动抓包后，再在一个新 tab 打开 baidu.com，然后关闭

    +

    将日志文件在 https://netlog-viewer.appspot.com/#events 打开

    +

    +

    这里可以看到

    +

    +

    --> type 1 表示现在 tls 握手进行到哪一步了, 对应的值表示不同的阶段

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    代码(type)释义
    0HelloRequest
    1ClientHello
    2ServerHello
    11Certificate
    12ServerKeyExchange
    13CertificateRequest
    14ServerHelloDone
    15CertificateVerify
    16ClientKeyExchange
    20Finished
    +

    具体的对应关系是在这边

    +

    比如 11 之后表示我们从服务端收到了证书

    +

    +

    然后就要去验证证书的可靠性

    +

    +

    后面是验证后的结果

    +

    +

    +

    最后就是交换对称会话秘钥然后完成握手

    +

    +]]>     + + 网络 - Java - Spring - SpringBoot - 自动装配 - AutoConfiguration + http     @@ -6178,25 +6248,6 @@ user3: 生活 - - 用 ollama 本地运行谷歌开源大模型 Gemma - /2024/03/24/%E7%94%A8-ollama-%E6%9C%AC%E5%9C%B0%E8%BF%90%E8%A1%8C%E5%A4%A7%E6%A8%A1%E5%9E%8B/ - 原先在 23 年初的时候调研过一些国产的大模型，包括复旦开源的 MOSS 和清华的 ChatGLM，那时候还是早期版本，需要在 Linux 上，并且有比较好的显卡，而且一般来讲都得是 N 卡，过程中需要安装 pytorch和比较多依赖，并且当时的效果也还比较差，所以后面就没有长期使用。
    最近看到谷歌在 2 月份开源了大模型 Gemma ，gemma 的博客在这里，想要在本地运行这个模型在现在这个阶段也变得简单很多，因为我们有了 ollama 工具

    可以通过这个工具来运行大模型，并且已经支持了谷歌开源的 Gemma

    我这边本地是 MacBook Pro 14 寸的，m3 pro 的处理器，18g 内存，刚好可以用 7b 量化的模型

    这里有推荐的模型和内存推荐匹配规则，16g 可以运行 13B 及以下模型
    下载安装完后我们可以用以下命令

    -
    ollama run gemma:7b
    -

    这里需要拉取模型，约5.2g 大小，考虑网络原因可能会比较慢

    我们可以简单来试试问个问题


    看出来回答的还是比较丰富的，谷歌出品还是比较有水平的，不至于像 ChatGLM 最初版本的在不做调优的情况下甚至有点前言不搭后语
    对于想使用 chatgpt 但是没条件，这也算是个低配平替了, 并且已经是个比较可用的了，同时也方便进行学习调优等
    如果想要类似于 chatgpt 那样的网页版，可以安装 open-webui
    可以通过 webui 访问 ollama 运行的大模型，
    用 docker 启动的命令也贴一下

    -
    docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
    -

    不过有个小问题就是 docker 镜像拉取会有点慢，可以添加下国内镜像加速

    -
    {
        "registry-mirrors": [
            "https://dockerproxy.com",
            "https://docker.mirrors.ustc.edu.cn",
            "https://docker.nju.edu.cn"
        ]
    }
    -


    这里有一个小区别，Gemma 在多轮会话的时候会在前面的答案基础上再完善。

    -

    补充一个在 windows 环境下，cpu 跑模型的也是可行的

    现在是大模型可以深入千家万户了，大家都可以尝试下，如果对日常的工作学习有一些效率上的提升也是好的

    -]]>     - - LLM - - - LLM - -     聊在东京奥运会闭幕式这天-二 /2021/08/19/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9-%E4%BA%8C/ @@ -6399,9 +6450,6 @@ user3: Synchronized 关键字在 Java 的并发体系里也是非常重要的一个内容，首先比较常规的是知道它使用的方式，可以锁对象，可以锁代码块，也可以锁方法，看一个简单的 demo

    public class SynchronizedDemo {

        public static void main(String[] args) {
            SynchronizedDemo synchronizedDemo = new SynchronizedDemo();
            synchronizedDemo.lockMethod();
        }

        public synchronized void lockMethod() {
            System.out.println("here i'm locked");
        }

        public void lockSynchronizedDemo() {
            synchronized (this) {
                System.out.println("here lock class");
            }
        }
    }
    -

    然后来查看反编译结果，其实代码（日光）之下并无新事，即使是完全不懂的也可以通过一些词义看出一些意义

    -
      Last modified 2021年6月20日; size 729 bytes
      MD5 checksum dd9c529863bd7ff839a95481db578ad9
      Compiled from "SynchronizedDemo.java"
    public class SynchronizedDemo
      minor version: 0
      major version: 53
      flags: (0x0021) ACC_PUBLIC, ACC_SUPER
      this_class: #2                          // SynchronizedDemo
      super_class: #9                         // java/lang/Object
      interfaces: 0, fields: 0, methods: 4, attributes: 1
    Constant pool:
       #1 = Methodref          #9.#22         // java/lang/Object."<init>":()V
       #2 = Class              #23            // SynchronizedDemo
       #3 = Methodref          #2.#22         // SynchronizedDemo."<init>":()V
       #4 = Methodref          #2.#24         // SynchronizedDemo.lockMethod:()V
       #5 = Fieldref           #25.#26        // java/lang/System.out:Ljava/io/PrintStream;
       #6 = String             #27            // here i\'m locked
       #7 = Methodref          #28.#29        // java/io/PrintStream.println:(Ljava/lang/String;)V
       #8 = String             #30            // here lock class
       #9 = Class              #31            // java/lang/Object
      #10 = Utf8               <init>
      #11 = Utf8               ()V
      #12 = Utf8               Code
      #13 = Utf8onized void lockMethod() {
            System.out.println("here i'm locked");
        }

        public void lockSynchronizedDemo() {
            synchronized (this) {
                System.out.println("here lock class");
            }
        }
    }
    -

    然后来查看反编译结果，其实代码（日光）之下并无新事，即使是完全不懂的也可以通过一些词义看出一些意义

      Last modified 2021年6月20日; size 729 bytes
      MD5 checksum dd9c529863bd7ff839a95481db578ad9
      Compiled from "SynchronizedDemo.java"
    public class SynchronizedDemo
      minor version: 0
      major version: 53
      flags: (0x0021) ACC_PUBLIC, ACC_SUPER
      this_class: #2                          // SynchronizedDemo
      super_class: #9                         // java/lang/Object
      interfaces: 0, fields: 0, methods: 4, attributes: 1
    Constant pool:
       #1 = Methodref          #9.#22         // java/lang/Object."<init>":()V
       #2 = Class              #23            // SynchronizedDemo
       #3 = Methodref          #2.#22         // SynchronizedDemo."<init>":()V
       #4 = Methodref          #2.#24         // SynchronizedDemo.lockMethod:()V
       #5 = Fieldref           #25.#26        // java/lang/System.out:Ljava/io/PrintStream;
       #6 = String             #27            // here i\'m locked
       #7 = Methodref          #28.#29        // java/io/PrintStream.println:(Ljava/lang/String;)V
       #8 = String             #30            // here lock class
       #9 = Class              #31            // java/lang/Object
      #10 = Utf8               <init>
      #11 = Utf8               ()V
      #12 = Utf8               Code
      #13 = Utf8               LineNumberTable
      #14 = Utf8               main
      #15 = Utf8               ([Ljava/lang/String;)V
      #16 = Utf8               lockMethod
      #17 = Utf8               lockSynchronizedDemo
      #18 = Utf8               StackMapTable
      #19 = Class              #32            // java/lang/Throwable
      #20 = Utf8               SourceFile
      #21 = Utf8               SynchronizedDemo.java
      #22 = NameAndType        #10:#11        // "<init>":()V
      #23 = Utf8               SynchronizedDemo
      #24 = NameAndType        #16:#11        // lockMethod:()V
      #25 = Class              #33            // java/lang/System
      #26 = NameAndType        #34:#35        // out:Ljava/io/PrintStream;
      #27 = Utf8               here i\'m locked
      #28 = Class              #36            // java/io/PrintStream
      #29 = NameAndType        #37:#38        // println:(Ljava/lang/String;)V
      #30 = Utf8               here lock class
      #31 = Utf8               java/lang/Object
      #32 = Utf8               java/lang/Throwable
      #33 = Utf8               java/lang/System
      #34 = Utf8               out
      #35 = Utf8               Ljava/io/PrintStream;
      #36 = Utf8               java/io/PrintStream
      #37 = Utf8               println
      #38 = Utf8               (Ljava/lang/String;)V
    {
      public SynchronizedDemo();
        descriptor: ()V
        flags: (0x0001) ACC_PUBLIC
        Code:
          stack=1, locals=1, args_size=1
             0: aload_0
             1: invokespecial #1                  // Method java/lang/Object."<init>":()V
             4: return
          LineNumberTable:
            line 5: 0

      public static void main(java.lang.String[]);
        descriptor: ([Ljava/lang/String;)V
        flags: (0x0009) ACC_PUBLIC, ACC_STATIC
        Code:
          stack=2, locals=2, args_size=1
             0: new           #2                  // class SynchronizedDemo
             3: dup
             4: invokespecial #3                  // Method "<init>":()V
             7: astore_1
             8: aload_1
             9: invokevirtual #4                  // Method lockMethod:()V
            12: return
          LineNumberTable:
            line 8: 0
            line 9: 8
            line 10: 12

      public synchronized void lockMethod();
        descriptor: ()V
        flags: (0x0021) ACC_PUBLIC, ACC_SYNCHRONIZED
        Code:
          stack=2, locals=1, args_size=1
             0: getstatic     #5                  // Field java/lang/System.out:Ljava/io/PrintStream;
             3: ldc           #6                  // String here i\'m locked
             5: invokevirtual #7                  // Method java/io/PrintStream.println:(Ljava/lang/String;)V
             8: return
          LineNumberTable:
            line 13: 0
            line 14: 8

      public void lockSynchronizedDemo();
        descriptor: ()V
        flags: (0x0001) ACC_PUBLIC
        Code:
          stack=2, locals=3, args_size=1
             0: aload_0
             1: dup
             2: astore_1
             3: monitorenter
             4: getstatic     #5                  // Field java/lang/System.out:Ljava/io/PrintStream;
             7: ldc           #8                  // String here lock class
             9: invokevirtual #7                  // Method java/io/PrintStream.println:(Ljava/lang/String;)V
            12: aload_1
            13: monitorexit
            14: goto          22
            17: astore_2
            18: aload_1
            19: monitorexit
            20: aload_2
            21: athrow
            22: return
          Exception table:
             from    to  target type
                 4    14    17   any
                17    20    17   any
          LineNumberTable:
            line 17: 0
            line 18: 4
            line 19: 12
            line 20: 22
          StackMapTable: number_of_entries = 2
            frame_type = 255 /* full_frame */
              offset_delta = 17
              locals = [ class SynchronizedDemo, class java/lang/Object ]
              stack = [ class java/lang/Throwable ]
            frame_type = 250 /* chop */
              offset_delta = 4
    }
    SourceFile: "SynchronizedDemo.java"
    @@ -6420,6 +6468,54 @@ user3: 自旋     + + 聊聊 Java 的 equals 和 hashCode 方法 + /2021/01/03/%E8%81%8A%E8%81%8A-Java-%E7%9A%84-equals-%E5%92%8C-hashCode-%E6%96%B9%E6%B3%95/ + Java 中的这个话题也是比较常遇到的，关于这块原先也是比较忽略的，但是仔细想想又有点遗忘了就在这里记一下
    简单看下代码
    java.lang.Object#equals

    +
    public boolean equals(Object obj) {
            return (this == obj);
        }
    +

    对于所有对象的父类，equals 方法其实对比的就是对象的地址，也就是是否是同一个对象，试想如果像 Integer 或者 String 这种，我们没有重写 equals，那其实就等于是在用==，可能就没法达到我们的目的，所以像 String 这种常用的 jdk 类都是默认重写了
    java.lang.String#equals

    +
    public boolean equals(Object anObject) {
            if (this == anObject) {
                return true;
            }
            if (anObject instanceof String) {
                String anotherString = (String)anObject;
                int n = value.length;
                if (n == anotherString.value.length) {
                    char v1[] = value;
                    char v2[] = anotherString.value;
                    int i = 0;
                    while (n-- != 0) {
                        if (v1[i] != v2[i])
                            return false;
                        i++;
                    }
                    return true;
                }
            }
            return false;
        }
    +

    然后呢就是为啥一些书或者 effective java 中写了 equals 跟 hashCode 要一起重写，这里涉及到当对象作为 HashMap 的 key 的时候
    首先 HashMap 会使用 hashCode 去判断是否在同一个槽里，然后在通过 equals 去判断是否是同一个 key，是的话就替换，不是的话就链表接下去，如果不重写 hashCode 的话，默认的 object 的hashCode 是 native 方法，根据对象的地址生成的，这样其实对象的值相同的话，因为地址不同，HashMap 也会出现异常，所以需要重写，同时也需要重写 equals 方法，才能确认是同一个 key，而不是落在同一个槽的不同 key.

    +]]>     + + java + + + java + +     + + 聊聊 Java 的类加载机制二 + /2021/06/13/%E8%81%8A%E8%81%8A-Java-%E7%9A%84%E7%B1%BB%E5%8A%A0%E8%BD%BD%E6%9C%BA%E5%88%B6%E4%BA%8C/ + 类加载器

    类加载机制中说来说去其实也逃不开类加载器这个话题，我们就来说下类加载器这个话题，Java 在 jdk1.2 以后开始有了
    Java 虚拟机设计团队有意把加载阶段中的“通过一个类的全限定名来获取描述该类的二进制字节流”这个动作放到 Java 虚拟机外部去实现，以便让应用程序自己去决定如何去获取所需的类。实现这个动作的代码被称为“类加载器”(Class Loader).
    其实在 Java 中类加载器有一个很常用的作用，比如一个类的唯一性，其实是由加载它的类加载器和这个类一起来确定这个类在虚拟机的唯一性，这里也参考下周志明书里的例子

    +
    public class ClassLoaderTest {

        public static void main(String[] args) throws Exception {
            ClassLoader myLoader = new ClassLoader() {
                @Override
                public Class<?> loadClass(String name) throws ClassNotFoundException {
                    try {
                        String fileName = name.substring(name.lastIndexOf(".") + 1) + ".class";
                        InputStream is = getClass().getResourceAsStream(fileName);
                        if (is == null) {
                            return super.loadClass(name);
                        }
                        byte[] b = new byte[is.available()];
                        is.read(b);
                        return defineClass(name, b, 0, b.length);
                    } catch (IOException e) {
                        throw new ClassNotFoundException(name);
                    }
                }
            };
            Object object = myLoader.loadClass("com.nicksxs.demo.ClassLoaderTest").newInstance();
            System.out.println(object.getClass());
            System.out.println(object instanceof ClassLoaderTest);
        }
    }
    +

    可以看下结果

    这里说明了当一个是由虚拟机的应用程序类加载器所加载的和另一个由自己写的自定义类加载器加载的，虽然是同一个类，但是 instanceof 的结果就是 false 的

    +

    双亲委派

    自 JDK1.2 以来，Java 一直有些三层类加载器、双亲委派的类加载架构

    +

    启动类加载器

    首先是启动类加载器，Bootstrap Class Loader，这个类加载器负责加载放在\lib目录，或者被-Xbootclasspath参数所指定的路径中存放的，而且是Java 虚拟机能够识别的（按照文件名识别，如 rt.jar、tools.jar，名字不符合的类库即使放在 lib 目录中，也不会被加载）类库加载到虚拟机的内存中，启动类加载器无法被 Java 程序直接引用，用户在编写自定义类加载器时，如果需要把家在请求为派给引导类加载器去处理，那直接使用 null 代替即可，可以看下 java.lang.ClassLoader.getClassLoader()方法的代码片段

    +
    /**
         * Returns the class loader for the class.  Some implementations may use
         * null to represent the bootstrap class loader. This method will return
         * null in such implementations if this class was loaded by the bootstrap
         * class loader.
         *
         * <p> If a security manager is present, and the caller's class loader is
         * not null and the caller's class loader is not the same as or an ancestor of
         * the class loader for the class whose class loader is requested, then
         * this method calls the security manager's {@code checkPermission}
         * method with a {@code RuntimePermission("getClassLoader")}
         * permission to ensure it's ok to access the class loader for the class.
         *
         * <p>If this object
         * represents a primitive type or void, null is returned.
         *
         * @return  the class loader that loaded the class or interface
         *          represented by this object.
         * @throws SecurityException
         *    if a security manager exists and its
         *    {@code checkPermission} method denies
         *    access to the class loader for the class.
         * @see java.lang.ClassLoader
         * @see SecurityManager#checkPermission
         * @see java.lang.RuntimePermission
         */
        @CallerSensitive
        public ClassLoader getClassLoader() {
            ClassLoader cl = getClassLoader0();
            if (cl == null)
                return null;
            SecurityManager sm = System.getSecurityManager();
            if (sm != null) {
                ClassLoader.checkClassLoaderPermission(cl, Reflection.getCallerClass());
            }
            return cl;
        }
    +

    扩展类加载器

    这个类加载器是在类sun.misc.Launcher.ExtClassLoader中以 Java 代码的形式实现的，它负责在家\lib\ext 目录中，或者被 java.ext.dirs系统变量中所指定的路径中的所有类库，它其实目的是为了实现 Java 系统类库的扩展机制

    +

    应用程序类加载器

    这个类加载器是由sun.misc.Launcher.AppClassLoader实现，通过 java 代码，并且是 ClassLoader 类中的 getSystemClassLoader()方法的返回值，可以看一下代码

    +
    /**
         * Returns the system class loader for delegation.  This is the default
         * delegation parent for new <tt>ClassLoader</tt> instances, and is
         * typically the class loader used to start the application.
         *
         * <p> This method is first invoked early in the runtime's startup
         * sequence, at which point it creates the system class loader and sets it
         * as the context class loader of the invoking <tt>Thread</tt>.
         *
         * <p> The default system class loader is an implementation-dependent
         * instance of this class.
         *
         * <p> If the system property "<tt>java.system.class.loader</tt>" is defined
         * when this method is first invoked then the value of that property is
         * taken to be the name of a class that will be returned as the system
         * class loader.  The class is loaded using the default system class loader
         * and must define a public constructor that takes a single parameter of
         * type <tt>ClassLoader</tt> which is used as the delegation parent.  An
         * instance is then created using this constructor with the default system
         * class loader as the parameter.  The resulting class loader is defined
         * to be the system class loader.
         *
         * <p> If a security manager is present, and the invoker's class loader is
         * not <tt>null</tt> and the invoker's class loader is not the same as or
         * an ancestor of the system class loader, then this method invokes the
         * security manager's {@link
         * SecurityManager#checkPermission(java.security.Permission)
         * <tt>checkPermission</tt>} method with a {@link
         * RuntimePermission#RuntimePermission(String)
         * <tt>RuntimePermission("getClassLoader")</tt>} permission to verify
         * access to the system class loader.  If not, a
         * <tt>SecurityException</tt> will be thrown.  </p>
         *
         * @return  The system <tt>ClassLoader</tt> for delegation, or
         *          <tt>null</tt> if none
         *
         * @throws  SecurityException
         *          If a security manager exists and its <tt>checkPermission</tt>
         *          method doesn't allow access to the system class loader.
         *
         * @throws  IllegalStateException
         *          If invoked recursively during the construction of the class
         *          loader specified by the "<tt>java.system.class.loader</tt>"
         *          property.
         *
         * @throws  Error
         *          If the system property "<tt>java.system.class.loader</tt>"
         *          is defined but the named class could not be loaded, the
         *          provider class does not define the required constructor, or an
         *          exception is thrown by that constructor when it is invoked. The
         *          underlying cause of the error can be retrieved via the
         *          {@link Throwable#getCause()} method.
         *
         * @revised  1.4
         */
        @CallerSensitive
        public static ClassLoader getSystemClassLoader() {
            initSystemClassLoader();
            if (scl == null) {
                return null;
            }
            SecurityManager sm = System.getSecurityManager();
            if (sm != null) {
                checkClassLoaderPermission(scl, Reflection.getCallerClass());
            }
            return scl;
        }
        private static synchronized void initSystemClassLoader() {
            if (!sclSet) {
                if (scl != null)
                    throw new IllegalStateException("recursive invocation");
                // 主要的第一步是这
                sun.misc.Launcher l = sun.misc.Launcher.getLauncher();
                if (l != null) {
                    Throwable oops = null;
                    // 然后是这
                    scl = l.getClassLoader();
                    try {
                        scl = AccessController.doPrivileged(
                            new SystemClassLoaderAction(scl));
                    } catch (PrivilegedActionException pae) {
                        oops = pae.getCause();
                        if (oops instanceof InvocationTargetException) {
                            oops = oops.getCause();
                        }
                    }
                    if (oops != null) {
                        if (oops instanceof Error) {
                            throw (Error) oops;
                        } else {
                            // wrap the exception
                            throw new Error(oops);
                        }
                    }
                }
                sclSet = true;
            }
        }
    // 接着跟到sun.misc.Launcher#getClassLoader
    public ClassLoader getClassLoader() {
            return this.loader;
        }
    // 然后看到这 sun.misc.Launcher#Launcher
    public Launcher() {
            Launcher.ExtClassLoader var1;
            try {
                var1 = Launcher.ExtClassLoader.getExtClassLoader();
            } catch (IOException var10) {
                throw new InternalError("Could not create extension class loader", var10);
            }

            try {
                // 可以看到 就是 AppClassLoader
                this.loader = Launcher.AppClassLoader.getAppClassLoader(var1);
            } catch (IOException var9) {
                throw new InternalError("Could not create application class loader", var9);
            }

            Thread.currentThread().setContextClassLoader(this.loader);
            String var2 = System.getProperty("java.security.manager");
            if (var2 != null) {
                SecurityManager var3 = null;
                if (!"".equals(var2) && !"default".equals(var2)) {
                    try {
                        var3 = (SecurityManager)this.loader.loadClass(var2).newInstance();
                    } catch (IllegalAccessException var5) {
                    } catch (InstantiationException var6) {
                    } catch (ClassNotFoundException var7) {
                    } catch (ClassCastException var8) {
                    }
                } else {
                    var3 = new SecurityManager();
                }

                if (var3 == null) {
                    throw new InternalError("Could not create SecurityManager: " + var2);
                }

                System.setSecurityManager(var3);
            }

        }
    +

    它负责加载用户类路径(ClassPath)上所有的类库，我们可以直接在代码中使用这个类加载器，如果我们的代码中没有自定义的类在加载器，一般情况下这个就是程序中默认的类加载器

    +

    双亲委派模型


    双亲委派模型的工作过程是：如果一个类加载器收到了类加载的请求，它首先不会自己去尝试家在这个类，而是把这个请求为派给父类加载器去完成，每一个层次的类加载器都是如此，因此所有的家在请求最终都应该传送到最顶层的启动类加载器中，只有当父类加载器反馈自己无法完成加载请求（它的搜索范围中没有找到所需要的类）时，子加载器才会尝试自己去完成加载。
    使用双亲委派模型来组织类加载器之间的关系，一个显而易见的好处就是 Java 中的类随着它的类加载器一起举杯了一种带有优先级的层次关系。例如类 java.lang.Object，它存放在 rt.jar 之中，无论哪一个类加载器要家在这个类，最终都是委派给处于模型最顶层的启动类加载器进行加载，因此 Object 类在程序的各种类加载器环境中都能够保证是同一个类。反之，如果没有使用双薪委派模型，都由各个类加载器自行去加载的话，如果用户自己也编写了一个名为 java.lang.Object 的类，并放在程序的 ClassPath 中，那系统中就会出现多个不同的 Object 类，Java 类型体系中最基础的行为也就无从保证，应用程序将会变得一片混乱。
    可以来看下双亲委派模型的代码实现

    +
    /**
         * Loads the class with the specified <a href="#name">binary name</a>.  The
         * default implementation of this method searches for classes in the
         * following order:
         *
         * <ol>
         *
         *   <li><p> Invoke {@link #findLoadedClass(String)} to check if the class
         *   has already been loaded.  </p></li>
         *
         *   <li><p> Invoke the {@link #loadClass(String) <tt>loadClass</tt>} method
         *   on the parent class loader.  If the parent is <tt>null</tt> the class
         *   loader built-in to the virtual machine is used, instead.  </p></li>
         *
         *   <li><p> Invoke the {@link #findClass(String)} method to find the
         *   class.  </p></li>
         *
         * </ol>
         *
         * <p> If the class was found using the above steps, and the
         * <tt>resolve</tt> flag is true, this method will then invoke the {@link
         * #resolveClass(Class)} method on the resulting <tt>Class</tt> object.
         *
         * <p> Subclasses of <tt>ClassLoader</tt> are encouraged to override {@link
         * #findClass(String)}, rather than this method.  </p>
         *
         * <p> Unless overridden, this method synchronizes on the result of
         * {@link #getClassLoadingLock <tt>getClassLoadingLock</tt>} method
         * during the entire class loading process.
         *
         * @param  name
         *         The <a href="#name">binary name</a> of the class
         *
         * @param  resolve
         *         If <tt>true</tt> then resolve the class
         *
         * @return  The resulting <tt>Class</tt> object
         *
         * @throws  ClassNotFoundException
         *          If the class could not be found
         */
        protected Class<?> loadClass(String name, boolean resolve)
            throws ClassNotFoundException
        {
            synchronized (getClassLoadingLock(name)) {
                // First, check if the class has already been loaded
                Class<?> c = findLoadedClass(name);
                if (c == null) {
                    long t0 = System.nanoTime();
                    try {
                        if (parent != null) {
                            // 委托父类加载
                            c = parent.loadClass(name, false);
                        } else {
                            // 使用启动类加载器
                            c = findBootstrapClassOrNull(name);
                        }
                    } catch (ClassNotFoundException e) {
                        // ClassNotFoundException thrown if class not found
                        // from the non-null parent class loader
                    }

                    if (c == null) {
                        // If still not found, then invoke findClass in order
                        // to find the class.
                        long t1 = System.nanoTime();
                        // 调用自己的 findClass() 方法尝试进行加载
                        c = findClass(name);

                        // this is the defining class loader; record the stats
                        sun.misc.PerfCounter.getParentDelegationTime().addTime(t1 - t0);
                        sun.misc.PerfCounter.getFindClassTime().addElapsedTimeFrom(t1);
                        sun.misc.PerfCounter.getFindClasses().increment();
                    }
                }
                if (resolve) {
                    resolveClass(c);
                }
                return c;
            }
        }
    +

    破坏双亲委派

    关于破坏双亲委派模型，第一次是在 JDK1.2 之后引入了双亲委派模型之前，那么在那之前已经有了类加载器，所以java.lang.ClassLoader 中添加了一个 protected 方法 findClass()，并引导用户编写的类加载逻辑时尽可能去重写这个方法，而不是在 loadClass()中编写代码。这个跟上面的逻辑其实类似，当父类加载失败，会调用 findClass()来完成加载；第二次是因为这个模型本身还有一些不足之处，比如 SPI 这种，所以有设计了线程下上下文类加载器(Thread Context ClassLoader)。这个类加载器可以通过 java.lang.Thread 类的 java.lang.Thread#setContextClassLoader() 进行设置，然后第三种是为了追求程序动态性，这里有涉及到了 osgi 等概念，就不展开了

    +]]>     + + Java + + + Java + 类加载 + 加载 + 验证 + 准备 + 解析 + 初始化 + 链接 + 双亲委派 + +     聊聊 Java 的类加载机制一 /2020/11/08/%E8%81%8A%E8%81%8A-Java-%E7%9A%84%E7%B1%BB%E5%8A%A0%E8%BD%BD%E6%9C%BA%E5%88%B6/ @@ -6462,35 +6558,28 @@ user3: - 聊聊 Java 的类加载机制二 - /2021/06/13/%E8%81%8A%E8%81%8A-Java-%E7%9A%84%E7%B1%BB%E5%8A%A0%E8%BD%BD%E6%9C%BA%E5%88%B6%E4%BA%8C/ - 类加载器

    类加载机制中说来说去其实也逃不开类加载器这个话题，我们就来说下类加载器这个话题，Java 在 jdk1.2 以后开始有了
    Java 虚拟机设计团队有意把加载阶段中的“通过一个类的全限定名来获取描述该类的二进制字节流”这个动作放到 Java 虚拟机外部去实现，以便让应用程序自己去决定如何去获取所需的类。实现这个动作的代码被称为“类加载器”(Class Loader).
    其实在 Java 中类加载器有一个很常用的作用，比如一个类的唯一性，其实是由加载它的类加载器和这个类一起来确定这个类在虚拟机的唯一性，这里也参考下周志明书里的例子

    -
    public class ClassLoaderTest {

        public static void main(String[] args) throws Exception {
            ClassLoader myLoader = new ClassLoader() {
                @Override
                public Class<?> loadClass(String name) throws ClassNotFoundException {
                    try {
                        String fileName = name.substring(name.lastIndexOf(".") + 1) + ".class";
                        InputStream is = getClass().getResourceAsStream(fileName);
                        if (is == null) {
                            return super.loadClass(name);
                        }
                        byte[] b = new byte[is.available()];
                        is.read(b);
                        return defineClass(name, b, 0, b.length);
                    } catch (IOException e) {
                        throw new ClassNotFoundException(name);
                    }
                }
            };
            Object object = myLoader.loadClass("com.nicksxs.demo.ClassLoaderTest").newInstance();
            System.out.println(object.getClass());
            System.out.println(object instanceof ClassLoaderTest);
        }
    }
    -

    可以看下结果

    这里说明了当一个是由虚拟机的应用程序类加载器所加载的和另一个由自己写的自定义类加载器加载的，虽然是同一个类，但是 instanceof 的结果就是 false 的

    -

    双亲委派

    自 JDK1.2 以来，Java 一直有些三层类加载器、双亲委派的类加载架构

    -

    启动类加载器

    首先是启动类加载器，Bootstrap Class Loader，这个类加载器负责加载放在\lib目录，或者被-Xbootclasspath参数所指定的路径中存放的，而且是Java 虚拟机能够识别的（按照文件名识别，如 rt.jar、tools.jar，名字不符合的类库即使放在 lib 目录中，也不会被加载）类库加载到虚拟机的内存中，启动类加载器无法被 Java 程序直接引用，用户在编写自定义类加载器时，如果需要把家在请求为派给引导类加载器去处理，那直接使用 null 代替即可，可以看下 java.lang.ClassLoader.getClassLoader()方法的代码片段

    -
    /**
         * Returns the class loader for the class.  Some implementations may use
         * null to represent the bootstrap class loader. This method will return
         * null in such implementations if this class was loaded by the bootstrap
         * class loader.
         *
         * <p> If a security manager is present, and the caller's class loader is
         * not null and the caller's class loader is not the same as or an ancestor of
         * the class loader for the class whose class loader is requested, then
         * this method calls the security manager's {@code checkPermission}
         * method with a {@code RuntimePermission("getClassLoader")}
         * permission to ensure it's ok to access the class loader for the class.
         *
         * <p>If this object
         * represents a primitive type or void, null is returned.
         *
         * @return  the class loader that loaded the class or interface
         *          represented by this object.
         * @throws SecurityException
         *    if a security manager exists and its
         *    {@code checkPermission} method denies
         *    access to the class loader for the class.
         * @see java.lang.ClassLoader
         * @see SecurityManager#checkPermission
         * @see java.lang.RuntimePermission
         */
        @CallerSensitive
        public ClassLoader getClassLoader() {
            ClassLoader cl = getClassLoader0();
            if (cl == null)
                return null;
            SecurityManager sm = System.getSecurityManager();
            if (sm != null) {
                ClassLoader.checkClassLoaderPermission(cl, Reflection.getCallerClass());
            }
            return cl;
        }
    -

    扩展类加载器

    这个类加载器是在类sun.misc.Launcher.ExtClassLoader中以 Java 代码的形式实现的，它负责在家\lib\ext 目录中，或者被 java.ext.dirs系统变量中所指定的路径中的所有类库，它其实目的是为了实现 Java 系统类库的扩展机制

    -

    应用程序类加载器

    这个类加载器是由sun.misc.Launcher.AppClassLoader实现，通过 java 代码，并且是 ClassLoader 类中的 getSystemClassLoader()方法的返回值，可以看一下代码

    -
    /**
         * Returns the system class loader for delegation.  This is the default
         * delegation parent for new <tt>ClassLoader</tt> instances, and is
         * typically the class loader used to start the application.
         *
         * <p> This method is first invoked early in the runtime's startup
         * sequence, at which point it creates the system class loader and sets it
         * as the context class loader of the invoking <tt>Thread</tt>.
         *
         * <p> The default system class loader is an implementation-dependent
         * instance of this class.
         *
         * <p> If the system property "<tt>java.system.class.loader</tt>" is defined
         * when this method is first invoked then the value of that property is
         * taken to be the name of a class that will be returned as the system
         * class loader.  The class is loaded using the default system class loader
         * and must define a public constructor that takes a single parameter of
         * type <tt>ClassLoader</tt> which is used as the delegation parent.  An
         * instance is then created using this constructor with the default system
         * class loader as the parameter.  The resulting class loader is defined
         * to be the system class loader.
         *
         * <p> If a security manager is present, and the invoker's class loader is
         * not <tt>null</tt> and the invoker's class loader is not the same as or
         * an ancestor of the system class loader, then this method invokes the
         * security manager's {@link
         * SecurityManager#checkPermission(java.security.Permission)
         * <tt>checkPermission</tt>} method with a {@link
         * RuntimePermission#RuntimePermission(String)
         * <tt>RuntimePermission("getClassLoader")</tt>} permission to verify
         * access to the system class loader.  If not, a
         * <tt>SecurityException</tt> will be thrown.  </p>
         *
         * @return  The system <tt>ClassLoader</tt> for delegation, or
         *          <tt>null</tt> if none
         *
         * @throws  SecurityException
         *          If a security manager exists and its <tt>checkPermission</tt>
         *          method doesn't allow access to the system class loader.
         *
         * @throws  IllegalStateException
         *          If invoked recursively during the construction of the class
         *          loader specified by the "<tt>java.system.class.loader</tt>"
         *          property.
         *
         * @throws  Error
         *          If the system property "<tt>java.system.class.loader</tt>"
         *          is defined but the named class could not be loaded, the
         *          provider class does not define the required constructor, or an
         *          exception is thrown by that constructor when it is invoked. The
         *          underlying cause of the error can be retrieved via the
         *          {@link Throwable#getCause()} method.
         *
         * @revised  1.4
         */
        @CallerSensitive
        public static ClassLoader getSystemClassLoader() {
            initSystemClassLoader();
            if (scl == null) {
                return null;
            }
            SecurityManager sm = System.getSecurityManager();
            if (sm != null) {
                checkClassLoaderPermission(scl, Reflection.getCallerClass());
            }
            return scl;
        }
        private static synchronized void initSystemClassLoader() {
            if (!sclSet) {
                if (scl != null)
                    throw new IllegalStateException("recursive invocation");
                // 主要的第一步是这
                sun.misc.Launcher l = sun.misc.Launcher.getLauncher();
                if (l != null) {
                    Throwable oops = null;
                    // 然后是这
                    scl = l.getClassLoader();
                    try {
                        scl = AccessController.doPrivileged(
                            new SystemClassLoaderAction(scl));
                    } catch (PrivilegedActionException pae) {
                        oops = pae.getCause();
                        if (oops instanceof InvocationTargetException) {
                            oops = oops.getCause();
                        }
                    }
                    if (oops != null) {
                        if (oops instanceof Error) {
                            throw (Error) oops;
                        } else {
                            // wrap the exception
                            throw new Error(oops);
                        }
                    }
                }
                sclSet = true;
            }
        }
    // 接着跟到sun.misc.Launcher#getClassLoader
    public ClassLoader getClassLoader() {
            return this.loader;
        }
    // 然后看到这 sun.misc.Launcher#Launcher
    public Launcher() {
            Launcher.ExtClassLoader var1;
            try {
                var1 = Launcher.ExtClassLoader.getExtClassLoader();
            } catch (IOException var10) {
                throw new InternalError("Could not create extension class loader", var10);
            }

            try {
                // 可以看到 就是 AppClassLoader
                this.loader = Launcher.AppClassLoader.getAppClassLoader(var1);
            } catch (IOException var9) {
                throw new InternalError("Could not create application class loader", var9);
            }

            Thread.currentThread().setContextClassLoader(this.loader);
            String var2 = System.getProperty("java.security.manager");
            if (var2 != null) {
                SecurityManager var3 = null;
                if (!"".equals(var2) && !"default".equals(var2)) {
                    try {
                        var3 = (SecurityManager)this.loader.loadClass(var2).newInstance();
                    } catch (IllegalAccessException var5) {
                    } catch (InstantiationException var6) {
                    } catch (ClassNotFoundException var7) {
                    } catch (ClassCastException var8) {
                    }
                } else {
                    var3 = new SecurityManager();
                }

                if (var3 == null) {
                    throw new InternalError("Could not create SecurityManager: " + var2);
                }

                System.setSecurityManager(var3);
            }

        }
    -

    它负责加载用户类路径(ClassPath)上所有的类库，我们可以直接在代码中使用这个类加载器，如果我们的代码中没有自定义的类在加载器，一般情况下这个就是程序中默认的类加载器

    -

    双亲委派模型


    双亲委派模型的工作过程是：如果一个类加载器收到了类加载的请求，它首先不会自己去尝试家在这个类，而是把这个请求为派给父类加载器去完成，每一个层次的类加载器都是如此，因此所有的家在请求最终都应该传送到最顶层的启动类加载器中，只有当父类加载器反馈自己无法完成加载请求（它的搜索范围中没有找到所需要的类）时，子加载器才会尝试自己去完成加载。
    使用双亲委派模型来组织类加载器之间的关系，一个显而易见的好处就是 Java 中的类随着它的类加载器一起举杯了一种带有优先级的层次关系。例如类 java.lang.Object，它存放在 rt.jar 之中，无论哪一个类加载器要家在这个类，最终都是委派给处于模型最顶层的启动类加载器进行加载，因此 Object 类在程序的各种类加载器环境中都能够保证是同一个类。反之，如果没有使用双薪委派模型，都由各个类加载器自行去加载的话，如果用户自己也编写了一个名为 java.lang.Object 的类，并放在程序的 ClassPath 中，那系统中就会出现多个不同的 Object 类，Java 类型体系中最基础的行为也就无从保证，应用程序将会变得一片混乱。
    可以来看下双亲委派模型的代码实现

    -
    /**
         * Loads the class with the specified <a href="#name">binary name</a>.  The
         * default implementation of this method searches for classes in the
         * following order:
         *
         * <ol>
         *
         *   <li><p> Invoke {@link #findLoadedClass(String)} to check if the class
         *   has already been loaded.  </p></li>
         *
         *   <li><p> Invoke the {@link #loadClass(String) <tt>loadClass</tt>} method
         *   on the parent class loader.  If the parent is <tt>null</tt> the class
         *   loader built-in to the virtual machine is used, instead.  </p></li>
         *
         *   <li><p> Invoke the {@link #findClass(String)} method to find the
         *   class.  </p></li>
         *
         * </ol>
         *
         * <p> If the class was found using the above steps, and the
         * <tt>resolve</tt> flag is true, this method will then invoke the {@link
         * #resolveClass(Class)} method on the resulting <tt>Class</tt> object.
         *
         * <p> Subclasses of <tt>ClassLoader</tt> are encouraged to override {@link
         * #findClass(String)}, rather than this method.  </p>
         *
         * <p> Unless overridden, this method synchronizes on the result of
         * {@link #getClassLoadingLock <tt>getClassLoadingLock</tt>} method
         * during the entire class loading process.
         *
         * @param  name
         *         The <a href="#name">binary name</a> of the class
         *
         * @param  resolve
         *         If <tt>true</tt> then resolve the class
         *
         * @return  The resulting <tt>Class</tt> object
         *
         * @throws  ClassNotFoundException
         *          If the class could not be found
         */
        protected Class<?> loadClass(String name, boolean resolve)
            throws ClassNotFoundException
        {
            synchronized (getClassLoadingLock(name)) {
                // First, check if the class has already been loaded
                Class<?> c = findLoadedClass(name);
                if (c == null) {
                    long t0 = System.nanoTime();
                    try {
                        if (parent != null) {
                            // 委托父类加载
                            c = parent.loadClass(name, false);
                        } else {
                            // 使用启动类加载器
                            c = findBootstrapClassOrNull(name);
                        }
                    } catch (ClassNotFoundException e) {
                        // ClassNotFoundException thrown if class not found
                        // from the non-null parent class loader
                    }

                    if (c == null) {
                        // If still not found, then invoke findClass in order
                        // to find the class.
                        long t1 = System.nanoTime();
                        // 调用自己的 findClass() 方法尝试进行加载
                        c = findClass(name);

                        // this is the defining class loader; record the stats
                        sun.misc.PerfCounter.getParentDelegationTime().addTime(t1 - t0);
                        sun.misc.PerfCounter.getFindClassTime().addElapsedTimeFrom(t1);
                        sun.misc.PerfCounter.getFindClasses().increment();
                    }
                }
                if (resolve) {
                    resolveClass(c);
                }
                return c;
            }
        }
    -

    破坏双亲委派

    关于破坏双亲委派模型，第一次是在 JDK1.2 之后引入了双亲委派模型之前，那么在那之前已经有了类加载器，所以java.lang.ClassLoader 中添加了一个 protected 方法 findClass()，并引导用户编写的类加载逻辑时尽可能去重写这个方法，而不是在 loadClass()中编写代码。这个跟上面的逻辑其实类似，当父类加载失败，会调用 findClass()来完成加载；第二次是因为这个模型本身还有一些不足之处，比如 SPI 这种，所以有设计了线程下上下文类加载器(Thread Context ClassLoader)。这个类加载器可以通过 java.lang.Thread 类的 java.lang.Thread#setContextClassLoader() 进行设置，然后第三种是为了追求程序动态性，这里有涉及到了 osgi 等概念，就不展开了

    + 聊聊 Java 自带的那些*逆天*工具 + /2020/08/02/%E8%81%8A%E8%81%8A-Java-%E8%87%AA%E5%B8%A6%E7%9A%84%E9%82%A3%E4%BA%9B%E9%80%86%E5%A4%A9%E5%B7%A5%E5%85%B7/ + 原谅我的标题党，其实这些工具的确很厉害，之前其实介绍过一点相关的，是从我一次问题排查的过程中用到的，但是最近又有碰到一次排查问题，发现其实用 idea 直接 dump thread 是不现实的，毕竟服务器环境的没法这么操作，那就得用 Java 的那些工具了

    +

    jstack & jps

    譬如 jstack，这个命令其实不能更简单了
    看看 help 信息

    用-l参数可以打出锁的额外信息，然后后面的 pid 就是进程 id 咯，机智的小伙伴会问了（就你这个小白才问这么蠢的问题🤦‍♂️），怎么看 Java 应用的进程呢
    那就是 jps 了，命令也很简单，一般直接用 jps命令就好了，不过也可以 help 看一下

    稍微解释下，-q是只显示进程 id，-m是输出给main 方法的参数，比如我在配置中加给参数

    然后用 jps -m查看

    -v加上小 v 的话就是打印 jvm 参数

    还是有点东西，然后就继续介绍 jstack 了，然后我们看看 jstack 出来是啥，为了加点内容我加了个死锁

    +
    public static void main(String[] args) throws InterruptedException {
            SpringApplication.run(ThreadDumpDemoApplication.class, args);
            ReentrantLock lock1 = new ReentrantLock();
            ReentrantLock lock2 = new ReentrantLock();
            Thread t1 = new Thread() {
                @Override
                public void run() {
                    try {
                        lock1.lock();
                        TimeUnit.SECONDS.sleep(1);
                        lock2.lock();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
            };
            Thread t2 = new Thread() {
                @Override
                public void run() {
                    try {
                        lock2.lock();
                        TimeUnit.SECONDS.sleep(1);
                        lock1.lock();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
            };
            t1.setName("mythread1");
            t2.setName("mythread2");
            t1.start();
            t2.start();
            Thread.sleep(10000);
        }
    +

    然后看看出来时怎么样的

    +
    2020-08-02 21:50:32
    Full thread dump Java HotSpot(TM) 64-Bit Server VM (25.201-b09 mixed mode):

    "DestroyJavaVM" #147 prio=5 os_prio=31 tid=0x00007fc9dd807000 nid=0x2603 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "mythread2" #140 prio=5 os_prio=31 tid=0x00007fc9dd877000 nid=0x9903 waiting on condition [0x0000700006fb9000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$2.run(ThreadDumpDemoApplication.java:34)

       Locked ownable synchronizers:
            - <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)

    "mythread1" #139 prio=5 os_prio=31 tid=0x00007fc9de873800 nid=0x9a03 waiting on condition [0x0000700006eb6000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$1.run(ThreadDumpDemoApplication.java:22)

       Locked ownable synchronizers:
            - <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)

    "http-nio-8080-Acceptor" #137 daemon prio=5 os_prio=31 tid=0x00007fc9de1ac000 nid=0x9b03 runnable [0x0000700006db3000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.ServerSocketChannelImpl.accept0(Native Method)
            at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:422)
            at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:250)
            - locked <0x000000076f1e4820> (a java.lang.Object)
            at org.apache.tomcat.util.net.NioEndpoint.serverSocketAccept(NioEndpoint.java:469)
            at org.apache.tomcat.util.net.NioEndpoint.serverSocketAccept(NioEndpoint.java:71)
            at org.apache.tomcat.util.net.Acceptor.run(Acceptor.java:95)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-ClientPoller" #136 daemon prio=5 os_prio=31 tid=0x00007fc9dd876800 nid=0x6503 runnable [0x0000700006cb0000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)
            at sun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)
            at sun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)
            at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
            - locked <0x000000076f2978c8> (a sun.nio.ch.Util$3)
            - locked <0x000000076f2978b8> (a java.util.Collections$UnmodifiableSet)
            - locked <0x000000076f297798> (a sun.nio.ch.KQueueSelectorImpl)
            at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
            at org.apache.tomcat.util.net.NioEndpoint$Poller.run(NioEndpoint.java:709)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-10" #135 daemon prio=5 os_prio=31 tid=0x00007fc9de1af000 nid=0x9d03 waiting on condition [0x0000700006bad000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-9" #134 daemon prio=5 os_prio=31 tid=0x00007fc9de1ab800 nid=0x6403 waiting on condition [0x0000700006aaa000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-8" #133 daemon prio=5 os_prio=31 tid=0x00007fc9de873000 nid=0x9f03 waiting on condition [0x00007000069a7000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-7" #132 daemon prio=5 os_prio=31 tid=0x00007fc9df0a1800 nid=0xa103 waiting on condition [0x00007000068a4000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-6" #131 daemon prio=5 os_prio=31 tid=0x00007fc9df242800 nid=0x6103 waiting on condition [0x00007000067a1000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-5" #130 daemon prio=5 os_prio=31 tid=0x00007fc9de872000 nid=0x5f03 waiting on condition [0x000070000669e000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-4" #129 daemon prio=5 os_prio=31 tid=0x00007fc9de1a6000 nid=0x5e03 waiting on condition [0x000070000659b000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-3" #128 daemon prio=5 os_prio=31 tid=0x00007fc9de871800 nid=0x5c03 waiting on condition [0x0000700006498000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-2" #127 daemon prio=5 os_prio=31 tid=0x00007fc9dead9000 nid=0x5b03 waiting on condition [0x0000700006395000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-1" #126 daemon prio=5 os_prio=31 tid=0x00007fc9ddb00000 nid=0x5a03 waiting on condition [0x0000700006292000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-BlockPoller" #125 daemon prio=5 os_prio=31 tid=0x00007fc9df242000 nid=0xa503 runnable [0x000070000618f000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)
            at sun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)
            at sun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)
            at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
            - locked <0x000000076f1eea30> (a sun.nio.ch.Util$3)
            - locked <0x000000076f1ee198> (a java.util.Collections$UnmodifiableSet)
            - locked <0x000000076f1ee010> (a sun.nio.ch.KQueueSelectorImpl)
            at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
            at org.apache.tomcat.util.net.NioBlockingSelector$BlockPoller.run(NioBlockingSelector.java:313)

       Locked ownable synchronizers:
            - None

    "container-0" #124 prio=5 os_prio=31 tid=0x00007fc9df06a000 nid=0x5803 waiting on condition [0x000070000608c000]
       java.lang.Thread.State: TIMED_WAITING (sleeping)
            at java.lang.Thread.sleep(Native Method)
            at org.apache.catalina.core.StandardServer.await(StandardServer.java:570)
            at org.springframework.boot.web.embedded.tomcat.TomcatWebServer$1.run(TomcatWebServer.java:197)

       Locked ownable synchronizers:
            - None

    "Catalina-utility-2" #123 prio=1 os_prio=31 tid=0x00007fc9de886000 nid=0xa80f waiting on condition [0x0000700005f89000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076c88ab58> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1088)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Catalina-utility-1" #122 prio=1 os_prio=31 tid=0x00007fc9de884000 nid=0x5667 waiting on condition [0x0000700005e86000]
       java.lang.Thread.State: TIMED_WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076c88ab58> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "RMI Scheduler(0)" #15 daemon prio=5 os_prio=31 tid=0x00007fc9de9ee000 nid=0x5503 waiting on condition [0x0000700005d83000]
       java.lang.Thread.State: TIMED_WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x00000006c0015410> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Attach Listener" #13 daemon prio=9 os_prio=31 tid=0x00007fc9df149800 nid=0x3c07 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "RMI TCP Accept-0" #11 daemon prio=5 os_prio=31 tid=0x00007fc9df100000 nid=0x4003 runnable [0x0000700005977000]
       java.lang.Thread.State: RUNNABLE
            at java.net.PlainSocketImpl.socketAccept(Native Method)
            at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)
            at java.net.ServerSocket.implAccept(ServerSocket.java:545)
            at java.net.ServerSocket.accept(ServerSocket.java:513)
            at sun.management.jmxremote.LocalRMIServerSocketFactory$1.accept(LocalRMIServerSocketFactory.java:52)
            at sun.rmi.transport.tcp.TCPTransport$AcceptLoop.executeAcceptLoop(TCPTransport.java:405)
            at sun.rmi.transport.tcp.TCPTransport$AcceptLoop.run(TCPTransport.java:377)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Service Thread" #9 daemon prio=9 os_prio=31 tid=0x00007fc9df0ce800 nid=0x4103 runnable [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C1 CompilerThread2" #8 daemon prio=9 os_prio=31 tid=0x00007fc9df0ce000 nid=0x4203 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C2 CompilerThread1" #7 daemon prio=9 os_prio=31 tid=0x00007fc9de0a3800 nid=0x3503 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C2 CompilerThread0" #6 daemon prio=9 os_prio=31 tid=0x00007fc9de89b000 nid=0x3403 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "Monitor Ctrl-Break" #5 daemon prio=5 os_prio=31 tid=0x00007fc9df0ca000 nid=0x3303 runnable [0x0000700005468000]
       java.lang.Thread.State: RUNNABLE
            at java.net.SocketInputStream.socketRead0(Native Method)
            at java.net.SocketInputStream.socketRead(SocketInputStream.java:116)
            at java.net.SocketInputStream.read(SocketInputStream.java:171)
            at java.net.SocketInputStream.read(SocketInputStream.java:141)
            at sun.nio.cs.StreamDecoder.readBytes(StreamDecoder.java:284)
            at sun.nio.cs.StreamDecoder.implRead(StreamDecoder.java:326)
            at sun.nio.cs.StreamDecoder.read(StreamDecoder.java:178)
            - locked <0x00000006c001b760> (a java.io.InputStreamReader)
            at java.io.InputStreamReader.read(InputStreamReader.java:184)
            at java.io.BufferedReader.fill(BufferedReader.java:161)
            at java.io.BufferedReader.readLine(BufferedReader.java:324)
            - locked <0x00000006c001b760> (a java.io.InputStreamReader)
            at java.io.BufferedReader.readLine(BufferedReader.java:389)
            at com.intellij.rt.execution.application.AppMainV2$1.run(AppMainV2.java:64)

       Locked ownable synchronizers:
            - None

    "Signal Dispatcher" #4 daemon prio=9 os_prio=31 tid=0x00007fc9de824000 nid=0x4503 runnable [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "Finalizer" #3 daemon prio=8 os_prio=31 tid=0x00007fc9dd811800 nid=0x4f03 in Object.wait() [0x0000700005262000]
       java.lang.Thread.State: WAITING (on object monitor)
            at java.lang.Object.wait(Native Method)
            - waiting on <0x00000006c0008348> (a java.lang.ref.ReferenceQueue$Lock)
            at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:144)
            - locked <0x00000006c0008348> (a java.lang.ref.ReferenceQueue$Lock)
            at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:165)
            at java.lang.ref.Finalizer$FinalizerThread.run(Finalizer.java:216)

       Locked ownable synchronizers:
            - None

    "Reference Handler" #2 daemon prio=10 os_prio=31 tid=0x00007fc9de02a000 nid=0x5003 in Object.wait() [0x000070000515f000]
       java.lang.Thread.State: WAITING (on object monitor)
            at java.lang.Object.wait(Native Method)
            - waiting on <0x00000006c001b940> (a java.lang.ref.Reference$Lock)
            at java.lang.Object.wait(Object.java:502)
            at java.lang.ref.Reference.tryHandlePending(Reference.java:191)
            - locked <0x00000006c001b940> (a java.lang.ref.Reference$Lock)
            at java.lang.ref.Reference$ReferenceHandler.run(Reference.java:153)

       Locked ownable synchronizers:
            - None

    "VM Thread" os_prio=31 tid=0x00007fc9df00b800 nid=0x2c03 runnable 

    "GC task thread#0 (ParallelGC)" os_prio=31 tid=0x00007fc9de805000 nid=0x1e07 runnable 

    "GC task thread#1 (ParallelGC)" os_prio=31 tid=0x00007fc9de003800 nid=0x2a03 runnable 

    "GC task thread#2 (ParallelGC)" os_prio=31 tid=0x00007fc9df002000 nid=0x5403 runnable 

    "GC task thread#3 (ParallelGC)" os_prio=31 tid=0x00007fc9df002800 nid=0x5203 runnable 

    "VM Periodic Task Thread" os_prio=31 tid=0x00007fc9df11a800 nid=0x3a03 waiting on condition 

    JNI global references: 1087


    Found one Java-level deadlock:
    =============================
    "mythread2":
      waiting for ownable synchronizer 0x000000076f5d4330, (a java.util.concurrent.locks.ReentrantLock$NonfairSync),
      which is held by "mythread1"
    "mythread1":
      waiting for ownable synchronizer 0x000000076f5d4360, (a java.util.concurrent.locks.ReentrantLock$NonfairSync),
      which is held by "mythread2"

    Java stack information for the threads listed above:
    ===================================================
    "mythread2":
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$2.run(ThreadDumpDemoApplication.java:34)
    "mythread1":
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$1.run(ThreadDumpDemoApplication.java:22)

    Found 1 deadlock.
    +

    前面的信息其实上次就看过了，后面就可以发现有个死锁了，

    上面比较长，把主要的截出来，就是这边的，这点就很强大了。

    +

    jmap

    惯例还是看一下帮助信息

    这个相对命令比较多，不过因为现在 dump 下来我们可能会用文件模式，然后将文件下载下来使用 mat 进行分析，所以可以使用
    jmap -dump:live,format=b,file=heap.bin <pid>
    命令照着上面看的就是打印活着的对象，然后以二进制格式，文件名叫 heap.bin 然后最后就是进程 id，打印出来以后可以用 mat 打开

    这样就可以很清晰的看到应用里的各种信息，jmap 直接在命令中还可以看很多信息，比如使用jmap -histo <pid>打印对象的实例数和对象占用的内存

    jmap -finalizerinfo <pid> 打印正在等候回收的对象

    +

    小tips

    对于一些应用内存已经占满了，jstack 和 jmap 可能会连不上的情况，可以使用-F参数强制打印线程或者 dump 文件，但是要注意这两者使用的用户必须与 java 进程启动用户一致，并且使用的 jdk 也要一致

    ]]>     Java + Thread dump + 工具 + 问题排查 Java - 类加载 - 加载 - 验证 - 准备 - 解析 - 初始化 - 链接 - 双亲委派 + JPS + JStack + JMap     @@ -6504,8 +6593,8 @@ user3: ]]> Linux - 命令 小技巧 + 命令 top top 排序 @@ -6517,31 +6606,6 @@ user3: top - - 聊聊 Java 自带的那些*逆天*工具 - /2020/08/02/%E8%81%8A%E8%81%8A-Java-%E8%87%AA%E5%B8%A6%E7%9A%84%E9%82%A3%E4%BA%9B%E9%80%86%E5%A4%A9%E5%B7%A5%E5%85%B7/ - 原谅我的标题党，其实这些工具的确很厉害，之前其实介绍过一点相关的，是从我一次问题排查的过程中用到的，但是最近又有碰到一次排查问题，发现其实用 idea 直接 dump thread 是不现实的，毕竟服务器环境的没法这么操作，那就得用 Java 的那些工具了

    -

    jstack & jps

    譬如 jstack，这个命令其实不能更简单了
    看看 help 信息

    用-l参数可以打出锁的额外信息，然后后面的 pid 就是进程 id 咯，机智的小伙伴会问了（就你这个小白才问这么蠢的问题🤦‍♂️），怎么看 Java 应用的进程呢
    那就是 jps 了，命令也很简单，一般直接用 jps命令就好了，不过也可以 help 看一下

    稍微解释下，-q是只显示进程 id，-m是输出给main 方法的参数，比如我在配置中加给参数

    然后用 jps -m查看

    -v加上小 v 的话就是打印 jvm 参数

    还是有点东西，然后就继续介绍 jstack 了，然后我们看看 jstack 出来是啥，为了加点内容我加了个死锁

    -
    public static void main(String[] args) throws InterruptedException {
            SpringApplication.run(ThreadDumpDemoApplication.class, args);
            ReentrantLock lock1 = new ReentrantLock();
            ReentrantLock lock2 = new ReentrantLock();
            Thread t1 = new Thread() {
                @Override
                public void run() {
                    try {
                        lock1.lock();
                        TimeUnit.SECONDS.sleep(1);
                        lock2.lock();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
            };
            Thread t2 = new Thread() {
                @Override
                public void run() {
                    try {
                        lock2.lock();
                        TimeUnit.SECONDS.sleep(1);
                        lock1.lock();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
            };
            t1.setName("mythread1");
            t2.setName("mythread2");
            t1.start();
            t2.start();
            Thread.sleep(10000);
        }
    -

    然后看看出来时怎么样的

    -
    2020-08-02 21:50:32
    Full thread dump Java HotSpot(TM) 64-Bit Server VM (25.201-b09 mixed mode):

    "DestroyJavaVM" #147 prio=5 os_prio=31 tid=0x00007fc9dd807000 nid=0x2603 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "mythread2" #140 prio=5 os_prio=31 tid=0x00007fc9dd877000 nid=0x9903 waiting on condition [0x0000700006fb9000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$2.run(ThreadDumpDemoApplication.java:34)

       Locked ownable synchronizers:
            - <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)

    "mythread1" #139 prio=5 os_prio=31 tid=0x00007fc9de873800 nid=0x9a03 waiting on condition [0x0000700006eb6000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$1.run(ThreadDumpDemoApplication.java:22)

       Locked ownable synchronizers:
            - <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)

    "http-nio-8080-Acceptor" #137 daemon prio=5 os_prio=31 tid=0x00007fc9de1ac000 nid=0x9b03 runnable [0x0000700006db3000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.ServerSocketChannelImpl.accept0(Native Method)
            at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:422)
            at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:250)
            - locked <0x000000076f1e4820> (a java.lang.Object)
            at org.apache.tomcat.util.net.NioEndpoint.serverSocketAccept(NioEndpoint.java:469)
            at org.apache.tomcat.util.net.NioEndpoint.serverSocketAccept(NioEndpoint.java:71)
            at org.apache.tomcat.util.net.Acceptor.run(Acceptor.java:95)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-ClientPoller" #136 daemon prio=5 os_prio=31 tid=0x00007fc9dd876800 nid=0x6503 runnable [0x0000700006cb0000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)
            at sun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)
            at sun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)
            at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
            - locked <0x000000076f2978c8> (a sun.nio.ch.Util$3)
            - locked <0x000000076f2978b8> (a java.util.Collections$UnmodifiableSet)
            - locked <0x000000076f297798> (a sun.nio.ch.KQueueSelectorImpl)
            at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
            at org.apache.tomcat.util.net.NioEndpoint$Poller.run(NioEndpoint.java:709)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-10" #135 daemon prio=5 os_prio=31 tid=0x00007fc9de1af000 nid=0x9d03 waiting on condition [0x0000700006bad000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-9" #134 daemon prio=5 os_prio=31 tid=0x00007fc9de1ab800 nid=0x6403 waiting on condition [0x0000700006aaa000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-8" #133 daemon prio=5 os_prio=31 tid=0x00007fc9de873000 nid=0x9f03 waiting on condition [0x00007000069a7000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-7" #132 daemon prio=5 os_prio=31 tid=0x00007fc9df0a1800 nid=0xa103 waiting on condition [0x00007000068a4000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-6" #131 daemon prio=5 os_prio=31 tid=0x00007fc9df242800 nid=0x6103 waiting on condition [0x00007000067a1000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-5" #130 daemon prio=5 os_prio=31 tid=0x00007fc9de872000 nid=0x5f03 waiting on condition [0x000070000669e000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-4" #129 daemon prio=5 os_prio=31 tid=0x00007fc9de1a6000 nid=0x5e03 waiting on condition [0x000070000659b000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-3" #128 daemon prio=5 os_prio=31 tid=0x00007fc9de871800 nid=0x5c03 waiting on condition [0x0000700006498000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-2" #127 daemon prio=5 os_prio=31 tid=0x00007fc9dead9000 nid=0x5b03 waiting on condition [0x0000700006395000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-1" #126 daemon prio=5 os_prio=31 tid=0x00007fc9ddb00000 nid=0x5a03 waiting on condition [0x0000700006292000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-BlockPoller" #125 daemon prio=5 os_prio=31 tid=0x00007fc9df242000 nid=0xa503 runnable [0x000070000618f000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)
            at sun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)
            at sun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)
            at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
            - locked <0x000000076f1eea30> (a sun.nio.ch.Util$3)
            - locked <0x000000076f1ee198> (a java.util.Collections$UnmodifiableSet)
            - locked <0x000000076f1ee010> (a sun.nio.ch.KQueueSelectorImpl)
            at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
            at org.apache.tomcat.util.net.NioBlockingSelector$BlockPoller.run(NioBlockingSelector.java:313)

       Locked ownable synchronizers:
            - None

    "container-0" #124 prio=5 os_prio=31 tid=0x00007fc9df06a000 nid=0x5803 waiting on condition [0x000070000608c000]
       java.lang.Thread.State: TIMED_WAITING (sleeping)
            at java.lang.Thread.sleep(Native Method)
            at org.apache.catalina.core.StandardServer.await(StandardServer.java:570)
            at org.springframework.boot.web.embedded.tomcat.TomcatWebServer$1.run(TomcatWebServer.java:197)

       Locked ownable synchronizers:
            - None

    "Catalina-utility-2" #123 prio=1 os_prio=31 tid=0x00007fc9de886000 nid=0xa80f waiting on condition [0x0000700005f89000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076c88ab58> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1088)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Catalina-utility-1" #122 prio=1 os_prio=31 tid=0x00007fc9de884000 nid=0x5667 waiting on condition [0x0000700005e86000]
       java.lang.Thread.State: TIMED_WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076c88ab58> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "RMI Scheduler(0)" #15 daemon prio=5 os_prio=31 tid=0x00007fc9de9ee000 nid=0x5503 waiting on condition [0x0000700005d83000]
       java.lang.Thread.State: TIMED_WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x00000006c0015410> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Attach Listener" #13 daemon prio=9 os_prio=31 tid=0x00007fc9df149800 nid=0x3c07 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "RMI TCP Accept-0" #11 daemon prio=5 os_prio=31 tid=0x00007fc9df100000 nid=0x4003 runnable [0x0000700005977000]
       java.lang.Thread.State: RUNNABLE
            at java.net.PlainSocketImpl.socketAccept(Native Method)
            at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)
            at java.net.ServerSocket.implAccept(ServerSocket.java:545)
            at java.net.ServerSocket.accept(ServerSocket.java:513)
            at sun.management.jmxremote.LocalRMIServerSocketFactory$1.accept(LocalRMIServerSocketFactory.java:52)
            at sun.rmi.transport.tcp.TCPTransport$AcceptLoop.executeAcceptLoop(TCPTransport.java:405)
            at sun.rmi.transport.tcp.TCPTransport$AcceptLoop.run(TCPTransport.java:377)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Service Thread" #9 daemon prio=9 os_prio=31 tid=0x00007fc9df0ce800 nid=0x4103 runnable [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C1 CompilerThread2" #8 daemon prio=9 os_prio=31 tid=0x00007fc9df0ce000 nid=0x4203 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C2 CompilerThread1" #7 daemon prio=9 os_prio=31 tid=0x00007fc9de0a3800 nid=0x3503 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C2 CompilerThread0" #6 daemon prio=9 os_prio=31 tid=0x00007fc9de89b000 nid=0x3403 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "Monitor Ctrl-Break" #5 daemon prio=5 os_prio=31 tid=0x00007fc9df0ca000 nid=0x3303 runnable [0x0000700005468000]
       java.lang.Thread.State: RUNNABLE
            at java.net.SocketInputStream.socketRead0(Native Method)
            at java.net.SocketInputStream.socketRead(SocketInputStream.java:116)
            at java.net.SocketInputStream.read(SocketInputStream.java:171)
            at java.net.SocketInputStream.read(SocketInputStream.java:141)
            at sun.nio.cs.StreamDecoder.readBytes(StreamDecoder.java:284)
            at sun.nio.cs.StreamDecoder.implRead(StreamDecoder.java:326)
            at sun.nio.cs.StreamDecoder.read(StreamDecoder.java:178)
            - locked <0x00000006c001b760> (a java.io.InputStreamReader)
            at java.io.InputStreamReader.read(InputStreamReader.java:184)
            at java.io.BufferedReader.fill(BufferedReader.java:161)
            at java.io.BufferedReader.readLine(BufferedReader.java:324)
            - locked <0x00000006c001b760> (a java.io.InputStreamReader)
            at java.io.BufferedReader.readLine(BufferedReader.java:389)
            at com.intellij.rt.execution.application.AppMainV2$1.run(AppMainV2.java:64)

       Locked ownable synchronizers:
            - None

    "Signal Dispatcher" #4 daemon prio=9 os_prio=31 tid=0x00007fc9de824000 nid=0x4503 runnable [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "Finalizer" #3 daemon prio=8 os_prio=31 tid=0x00007fc9dd811800 nid=0x4f03 in Object.wait() [0x0000700005262000]
       java.lang.Thread.State: WAITING (on object monitor)
            at java.lang.Object.wait(Native Method)
            - waiting on <0x00000006c0008348> (a java.lang.ref.ReferenceQueue$Lock)
            at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:144)
            - locked <0x00000006c0008348> (a java.lang.ref.ReferenceQueue$Lock)
            at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:165)
            at java.lang.ref.Finalizer$FinalizerThread.run(Finalizer.java:216)

       Locked ownable synchronizers:
            - None

    "Reference Handler" #2 daemon prio=10 os_prio=31 tid=0x00007fc9de02a000 nid=0x5003 in Object.wait() [0x000070000515f000]
       java.lang.Thread.State: WAITING (on object monitor)
            at java.lang.Object.wait(Native Method)
            - waiting on <0x00000006c001b940> (a java.lang.ref.Reference$Lock)
            at java.lang.Object.wait(Object.java:502)
            at java.lang.ref.Reference.tryHandlePending(Reference.java:191)
            - locked <0x00000006c001b940> (a java.lang.ref.Reference$Lock)
            at java.lang.ref.Reference$ReferenceHandler.run(Reference.java:153)

       Locked ownable synchronizers:
            - None

    "VM Thread" os_prio=31 tid=0x00007fc9df00b800 nid=0x2c03 runnable 

    "GC task thread#0 (ParallelGC)" os_prio=31 tid=0x00007fc9de805000 nid=0x1e07 runnable 

    "GC task thread#1 (ParallelGC)" os_prio=31 tid=0x00007fc9de003800 nid=0x2a03 runnable 

    "GC task thread#2 (ParallelGC)" os_prio=31 tid=0x00007fc9df002000 nid=0x5403 runnable 

    "GC task thread#3 (ParallelGC)" os_prio=31 tid=0x00007fc9df002800 nid=0x5203 runnable 

    "VM Periodic Task Thread" os_prio=31 tid=0x00007fc9df11a800 nid=0x3a03 waiting on condition 

    JNI global references: 1087


    Found one Java-level deadlock:
    =============================
    "mythread2":
      waiting for ownable synchronizer 0x000000076f5d4330, (a java.util.concurrent.locks.ReentrantLock$NonfairSync),
      which is held by "mythread1"
    "mythread1":
      waiting for ownable synchronizer 0x000000076f5d4360, (a java.util.concurrent.locks.ReentrantLock$NonfairSync),
      which is held by "mythread2"

    Java stack information for the threads listed above:
    ===================================================
    "mythread2":
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$2.run(ThreadDumpDemoApplication.java:34)
    "mythread1":
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$1.run(ThreadDumpDemoApplication.java:22)

    Found 1 deadlock.
    -

    前面的信息其实上次就看过了，后面就可以发现有个死锁了，

    上面比较长，把主要的截出来，就是这边的，这点就很强大了。

    -

    jmap

    惯例还是看一下帮助信息

    这个相对命令比较多，不过因为现在 dump 下来我们可能会用文件模式，然后将文件下载下来使用 mat 进行分析，所以可以使用
    jmap -dump:live,format=b,file=heap.bin <pid>
    命令照着上面看的就是打印活着的对象，然后以二进制格式，文件名叫 heap.bin 然后最后就是进程 id，打印出来以后可以用 mat 打开

    这样就可以很清晰的看到应用里的各种信息，jmap 直接在命令中还可以看很多信息，比如使用jmap -histo <pid>打印对象的实例数和对象占用的内存

    jmap -finalizerinfo <pid> 打印正在等候回收的对象

    -

    小tips

    对于一些应用内存已经占满了，jstack 和 jmap 可能会连不上的情况，可以使用-F参数强制打印线程或者 dump 文件，但是要注意这两者使用的用户必须与 java 进程启动用户一致，并且使用的 jdk 也要一致

    -]]>     - - Java - Thread dump - 工具 - 问题排查 - - - Java - JPS - JStack - JMap - -     聊聊 RocketMQ 的 Broker 源码 /2020/07/19/%E8%81%8A%E8%81%8A-RocketMQ-%E7%9A%84-Broker-%E6%BA%90%E7%A0%81/ @@ -6627,17 +6691,23 @@ user3: - 聊聊 Sharding-Jdbc 的简单使用 - /2021/12/12/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E4%BD%BF%E7%94%A8/ - 我们在日常工作中还是使用比较多的分库分表组件的，其中比较优秀的就有 Sharding-Jdbc，一开始由当当开源，后来捐献给了 Apache，说一下简单使用，因为原来经常的使用都是基于 xml 跟 properties 组合起来使用，这里主要试下用 Java Config 来配置
    首先是通过 Spring Initializr 创建个带 jdbc 的 Spring Boot 项目，然后引入主要的依赖

    -
    <dependency>
        <groupId>org.apache.shardingsphere</groupId>
        <artifactId>shardingsphere-jdbc-core</artifactId>
        <version>5.0.0-beta</version>
    </dependency>
    -

    因为前面有聊过 Spring Boot 的自动加载，在这里 spring 就会自己去找 DataSource 的配置，所以要在入口把它干掉

    -
    @SpringBootApplication(exclude = { DataSourceAutoConfiguration.class})
    public class ShardingJdbcDemoApplication implements CommandLineRunner {
    -

    然后因为想在入口跑代码，就实现了下 org.springframework.boot.CommandLineRunner 主要是后面的 Java Config 代码

    -

    // 注意这里的注解，可以让 Spring 自动帮忙加载，也就是 Java Config 的核心
    @Configuration
    public class MysqlConfig {

        @Bean
        public DataSource dataSource() throws SQLException {
            // Configure actual data sources
            Map<String, DataSource> dataSourceMap = new HashMap<>();

            
            // Configure the first data source
            // 使用了默认的Hikari连接池的 DataSource
            HikariDataSource dataSource1 = new HikariDataSource();
            dataSource1.setDriverClassName("com.mysql.jdbc.Driver");
            dataSource1.setJdbcUrl("jdbc:mysql://localhost:3306/sharding");
            dataSource1.setUsername("username");
            dataSource1.setPassword("password");
            dataSourceMap.put("ds0", dataSource1);

            // Configure student table rule
            // 这里是配置分表逻辑，逻辑表是 student，对应真实的表是 student_0 到 student_1, 这个配置方式就是有多少表可以用 student_$->{0..n}
            ShardingTableRuleConfiguration studentTableRuleConfig = new ShardingTableRuleConfiguration("student", "ds0.student_$->{0..1}");

            // 设置分表字段
            studentTableRuleConfig.setTableShardingStrategy(new StandardShardingStrategyConfiguration("user_id", "tableShardingAlgorithm"));


            // Configure sharding rule
            // 配置 studentTableRuleConfig
            ShardingRuleConfiguration shardingRuleConfig = new ShardingRuleConfiguration();
            shardingRuleConfig.getTables().add(studentTableRuleConfig);

            // Configure table sharding algorithm
            Properties tableShardingAlgorithmrProps = new Properties();
            // 算法表达式就是根据 user_id 对 2 进行取模
            tableShardingAlgorithmrProps.setProperty("algorithm-expression", "student_${user_id % 2}");
            shardingRuleConfig.getShardingAlgorithms().put("tableShardingAlgorithm", new ShardingSphereAlgorithmConfiguration("INLINE", tableShardingAlgorithmrProps));


            // 然后创建这个 DataSource
            return ShardingSphereDataSourceFactory.createDataSource(dataSourceMap, Collections.singleton(shardingRuleConfig), new Properties());

        }
    }
    -

    然后我们就可以在使用这个 DataSource 了，先看下这两个表的数据

    -
    @Override
        public void run(String... args) {
            LOGGER.info("run here");
            String sql = "SELECT * FROM student WHERE user_id=? ";
            try (
                    Connection conn = dataSource.getConnection();
                    PreparedStatement ps = conn.prepareStatement(sql)) {
                // 参数就是 user_id，然后也是分表键，对 2 取模就是 1，应该是去 student_1 取数据
                ps.setInt(1, 1001);

                ResultSet resultSet = ps.executeQuery();
                while (resultSet.next()) {
                    final int id = resultSet.getInt("id");
                    final String name = resultSet.getString("name");
                    final int userId = resultSet.getInt("user_id");
                    final int age = resultSet.getInt("age");
                    System.out.println("奇数表 id：" + id + " 姓名：" + name
                            + " 用户 id：" + userId + " 年龄：" + age );
                    System.out.println("=============================");
                }
                // 参数就是 user_id，然后也是分表键，对 2 取模就是 0，应该是去 student_0 取数据
                ps.setInt(1, 1000);
                resultSet = ps.executeQuery();
                while (resultSet.next()) {
                    final int id = resultSet.getInt("id");
                    final String name = resultSet.getString("name");
                    final int userId = resultSet.getInt("user_id");
                    final int age = resultSet.getInt("age");
                    System.out.println("偶数表 id：" + id + " 姓名：" + name
                            + " 用户 id：" + userId + " 年龄：" + age );
                    System.out.println("=============================");
                }
            } catch (SQLException e) {
                e.printStackTrace();
            }
        }
    -

    看下查询结果

    + 聊聊 Sharding-Jdbc 分库分表下的分页方案 + /2022/01/09/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E5%88%86%E5%BA%93%E5%88%86%E8%A1%A8%E4%B8%8B%E7%9A%84%E5%88%86%E9%A1%B5%E6%96%B9%E6%A1%88/ + 前面在聊 Sharding-Jdbc 的时候看到了一篇文章，关于一个分页的查询，一直比较直接的想法就是分库分表下的分页是非常不合理的，一般我们的实操方案都是分表加上 ES 搜索做分页，或者通过合表读写分离的方案，因为对于 sharding-jdbc 如果没有带分表键，查询基本都是只能在所有分表都执行一遍，然后再加上分页，基本上是分页越大后续的查询越耗资源，但是仔细的去想这个细节还是这次，就简单说说
    首先就是我的分表结构

    +
    CREATE TABLE `student_time_0` (
      `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
      `user_id` int(11) NOT NULL,
      `name` varchar(200) COLLATE utf8_bin DEFAULT NULL,
      `age` tinyint(3) unsigned DEFAULT NULL,
      `create_time` bigint(20) DEFAULT NULL,
      PRIMARY KEY (`id`)
    ) ENGINE=InnoDB AUTO_INCREMENT=674 DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
    +

    有这样的三个表，student_time_0, student_time_1, student_time_2, 以 user_id 作为分表键，根据表数量取模作为分表依据
    这里先构造点数据，

    +
    insert into student_time (`name`, `user_id`, `age`, `create_time`) values (?, ?, ?, ?)
    +

    主要是为了保证 create_time 唯一比较好说明问题，

    +
    int i = 0;
    try (
            Connection conn = dataSource.getConnection();
            PreparedStatement ps = conn.prepareStatement(insertSql)) {
        do {
            ps.setString(1, localName + new Random().nextInt(100));
            ps.setLong(2, 10086L + (new Random().nextInt(100)));
            ps.setInt(3, 18);
            ps.setLong(4, new Date().getTime());


            int result = ps.executeUpdate();
            LOGGER.info("current execute result: {}", result);
            Thread.sleep(new Random().nextInt(100));
            i++;
        } while (i <= 2000);
    +

    三个表的数据分别是 673，678，650，说明符合预期了，各个表数据不一样，接下来比如我们想要做一个这样的分页查询

    +
    select * from student_time ORDER BY create_time ASC limit 1000, 5;
    +

    student_time 对于我们使用的 sharding-jdbc 来说当然是逻辑表，首先从一无所知去想这个查询如果我们自己来处理应该是怎么做，
    首先是不是可以每个表都从 333 开始取 5 条数据，类似于下面的查询，然后进行 15 条的合并重排序获取前面的 5 条

    +
    select * from student_time_0 ORDER BY create_time ASC limit 333, 5;
    select * from student_time_1 ORDER BY create_time ASC limit 333, 5;
    select * from student_time_2 ORDER BY create_time ASC limit 333, 5;
    +

    忽略前面 limit 差的 1，这个结果除非三个表的分布是绝对的均匀，否则结果肯定会出现一定的偏差，以为每个表的 333 这个位置对于其他表来说都不一定是一样的，这样对于最后整体的结果，就会出现偏差
    因为一直在纠结怎么让这个更直观的表现出来，所以尝试画了个图

    黑色的框代表我从每个表里按排序从 334 到 338 的 5 条数据， 他们在每个表里都是代表了各自正确的排序值，但是对于我们想要的其实是合表后的 1001，1005 这五条，然后我们假设总的排序值位于前 1000 的分布是第 0 个表是 320 条，第 1 个表是 340 条，第 2 个表是 340 条，那么可以明显地看出来我这么查的结果简单合并肯定是不对的。
    那么 sharding-jdbc 是如何保证这个结果的呢，其实就是我在每个表里都查分页偏移量和分页大小那么多的数据，在我这个例子里就是对于 0，1，2 三个分表每个都查 1005 条数据，即使我的数据不平衡到最极端的情况，前 1005 条数据都出在某个分表中，也可以正确获得最后的结果，但是明显的问题就是大分页，数据较多，就会导致非常大的问题，即使如 sharding-jdbc 对于合并排序的优化做得比较好，也还是需要传输那么大量的数据，并且查询也耗时，那么有没有解决方案呢，应该说有两个，或者说主要是想讲后者
    第一个办法是像这种查询，如果业务上不需要进行跳页，而是只给下一页，那么我们就能把前一次的最大偏移量的 create_time 记录下来，下一页就可以拿着这个偏移量进行查询，这个比较简单易懂，就不多说了
    第二个办法是看的58 沈剑的一篇文章，尝试理解讲述一下，
    这个办法的第一步跟前面那个错误的方法或者说不准确的方法一样，先是将分页偏移量平均后在三个表里进行查询

    +
    t0
    334 10158 nick95  18  1641548941767
    335 10098 nick11  18  1641548941879
    336 10167 nick51  18  1641548942089
    337 10167 nick3 18  1641548942119
    338 10170 nick57  18  1641548942169


    t1
    334 10105 nick98  18  1641548939071   最小
    335 10174 nick94  18  1641548939377
    336 10129 nick85  18  1641548939442
    337 10141 nick84  18  1641548939480
    338 10096 nick74  18  1641548939668

    t2
    334 10184 nick11  18  1641548945075
    335 10109 nick93  18  1641548945382
    336 10181 nick41  18  1641548945583
    337 10130 nick80  18  1641548945993
    338 10184 nick19  18  1641548946294  最大
    +

    然后要做什么呢，其实目标比较明白，因为前面那种方法其实就是我知道了前一页的偏移量，所以可以直接当做条件来进行查询，那这里我也想着拿到这个条件，所以我将第一遍查出来的最小的 create_time 和最大的 create_time 找出来，然后再去三个表里查询，其实主要是最小值，因为我拿着最小值去查以后我就能知道这个最小值在每个表里处在什么位置，

    +
    t0
    322 10161 nick81  18  1641548939284
    323 10113 nick16  18  1641548939393
    324 10110 nick56  18  1641548939577
    325 10116 nick69  18  1641548939588
    326 10173 nick51  18  1641548939646

    t1
    334 10105 nick98  18  1641548939071
    335 10174 nick94  18  1641548939377
    336 10129 nick85  18  1641548939442
    337 10141 nick84  18  1641548939480
    338 10096 nick74  18  1641548939668

    t2
    297 10136 nick28  18  1641548939161
    298 10142 nick68  18  1641548939177
    299 10124 nick41  18  1641548939237
    300 10148 nick87  18  1641548939510
    301 10169 nick23  18  1641548939715
    +

    我只贴了前五条数据，为了方便知道偏移量，每个分表都使用了自增主键，我们可以看到前一次查询的最小值分别在其他两个表里的位置分别是 322-1 和 297-1，那么对于总体来说这个时间应该是在 322 - 1 + 333 + 297 - 1 = 951，那这样子我只要对后面的数据最多每个表查 1000 - 951 + 5 = 54 条数据再进行合并排序就可以获得最终正确的结果。
    这个就是传说中的二次查询法。

    ]]>     Java @@ -6648,22 +6718,17 @@ user3:     - 聊聊 Sharding-Jdbc 的简单原理初篇 - /2021/12/26/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E5%8E%9F%E7%90%86%E5%88%9D%E7%AF%87/ - 在上一篇 sharding-jdbc 的介绍中其实碰到过一个问题，这里也引出了一个比较有意思的话题
    就是我在执行 query 的时候犯过一个比较难发现的错误，

    -
    ResultSet resultSet = ps.executeQuery(sql);
    -

    实际上应该是

    -
    ResultSet resultSet = ps.executeQuery();
    -

    而这里的差别就是，是否传 sql 这个参数，首先我们要知道这个 ps 是什么，它也是个接口java.sql.PreparedStatement,而真正的实现类是org.apache.shardingsphere.driver.jdbc.core.statement.ShardingSpherePreparedStatement,我们来看下继承关系

    这里可以看到继承关系里有org.apache.shardingsphere.driver.jdbc.unsupported.AbstractUnsupportedOperationPreparedStatement
    那么在我上面的写错的代码里

    -
    @Override
    public final ResultSet executeQuery(final String sql) throws SQLException {
        throw new SQLFeatureNotSupportedException("executeQuery with SQL for PreparedStatement");
    }
    -

    这个报错一开始让我有点懵，后来点进去了发现是这么个异常，但是我其实一开始是用的更新语句，以为更新不支持，因为平时使用没有深究过，以为是不是需要使用 Mybatis 才可以执行更新，但是理论上也不应该，再往上看原来这些异常是由 sharding-jdbc 包装的，也就是在上面说的AbstractUnsupportedOperationPreparedStatement，这其实也是一种设计思想，本身 jdbc 提供了一系列接口，由各家去支持，包括 mysql，sql server，oracle 等，而正因为这个设计，所以 sharding-jdbc 也可以在此基础上进行设计，我们可以总体地看下 sharding-jdbc 的实现基础

    看了前面ShardingSpherePreparedStatement的继承关系，应该也能猜到这里的几个类都是实现了 jdbc 的基础接口，

    在前一篇的 demo 中的

    -
    Connection conn = dataSource.getConnection();
    -

    其实就获得了org.apache.shardingsphere.driver.jdbc.core.connection.ShardingSphereConnection#ShardingSphereConnection
    然后获得java.sql.PreparedStatement

    -
    PreparedStatement ps = conn.prepareStatement(sql)
    -

    就是获取了org.apache.shardingsphere.driver.jdbc.core.statement.ShardingSpherePreparedStatement
    然后就是执行

    -
    ResultSet resultSet = ps.executeQuery();
    -

    然后获得结果
    org.apache.shardingsphere.driver.jdbc.core.resultset.ShardingSphereResultSet

    -

    其实像 mybatis 也是基于这样去实现的

    + 聊聊 Sharding-Jdbc 的简单使用 + /2021/12/12/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E4%BD%BF%E7%94%A8/ + 我们在日常工作中还是使用比较多的分库分表组件的，其中比较优秀的就有 Sharding-Jdbc，一开始由当当开源，后来捐献给了 Apache，说一下简单使用，因为原来经常的使用都是基于 xml 跟 properties 组合起来使用，这里主要试下用 Java Config 来配置
    首先是通过 Spring Initializr 创建个带 jdbc 的 Spring Boot 项目，然后引入主要的依赖

    +
    <dependency>
        <groupId>org.apache.shardingsphere</groupId>
        <artifactId>shardingsphere-jdbc-core</artifactId>
        <version>5.0.0-beta</version>
    </dependency>
    +

    因为前面有聊过 Spring Boot 的自动加载，在这里 spring 就会自己去找 DataSource 的配置，所以要在入口把它干掉

    +
    @SpringBootApplication(exclude = { DataSourceAutoConfiguration.class})
    public class ShardingJdbcDemoApplication implements CommandLineRunner {
    +

    然后因为想在入口跑代码，就实现了下 org.springframework.boot.CommandLineRunner 主要是后面的 Java Config 代码

    +

    // 注意这里的注解，可以让 Spring 自动帮忙加载，也就是 Java Config 的核心
    @Configuration
    public class MysqlConfig {

        @Bean
        public DataSource dataSource() throws SQLException {
            // Configure actual data sources
            Map<String, DataSource> dataSourceMap = new HashMap<>();

            
            // Configure the first data source
            // 使用了默认的Hikari连接池的 DataSource
            HikariDataSource dataSource1 = new HikariDataSource();
            dataSource1.setDriverClassName("com.mysql.jdbc.Driver");
            dataSource1.setJdbcUrl("jdbc:mysql://localhost:3306/sharding");
            dataSource1.setUsername("username");
            dataSource1.setPassword("password");
            dataSourceMap.put("ds0", dataSource1);

            // Configure student table rule
            // 这里是配置分表逻辑，逻辑表是 student，对应真实的表是 student_0 到 student_1, 这个配置方式就是有多少表可以用 student_$->{0..n}
            ShardingTableRuleConfiguration studentTableRuleConfig = new ShardingTableRuleConfiguration("student", "ds0.student_$->{0..1}");

            // 设置分表字段
            studentTableRuleConfig.setTableShardingStrategy(new StandardShardingStrategyConfiguration("user_id", "tableShardingAlgorithm"));


            // Configure sharding rule
            // 配置 studentTableRuleConfig
            ShardingRuleConfiguration shardingRuleConfig = new ShardingRuleConfiguration();
            shardingRuleConfig.getTables().add(studentTableRuleConfig);

            // Configure table sharding algorithm
            Properties tableShardingAlgorithmrProps = new Properties();
            // 算法表达式就是根据 user_id 对 2 进行取模
            tableShardingAlgorithmrProps.setProperty("algorithm-expression", "student_${user_id % 2}");
            shardingRuleConfig.getShardingAlgorithms().put("tableShardingAlgorithm", new ShardingSphereAlgorithmConfiguration("INLINE", tableShardingAlgorithmrProps));


            // 然后创建这个 DataSource
            return ShardingSphereDataSourceFactory.createDataSource(dataSourceMap, Collections.singleton(shardingRuleConfig), new Properties());

        }
    }
    +

    然后我们就可以在使用这个 DataSource 了，先看下这两个表的数据

    +
    @Override
        public void run(String... args) {
            LOGGER.info("run here");
            String sql = "SELECT * FROM student WHERE user_id=? ";
            try (
                    Connection conn = dataSource.getConnection();
                    PreparedStatement ps = conn.prepareStatement(sql)) {
                // 参数就是 user_id，然后也是分表键，对 2 取模就是 1，应该是去 student_1 取数据
                ps.setInt(1, 1001);

                ResultSet resultSet = ps.executeQuery();
                while (resultSet.next()) {
                    final int id = resultSet.getInt("id");
                    final String name = resultSet.getString("name");
                    final int userId = resultSet.getInt("user_id");
                    final int age = resultSet.getInt("age");
                    System.out.println("奇数表 id：" + id + " 姓名：" + name
                            + " 用户 id：" + userId + " 年龄：" + age );
                    System.out.println("=============================");
                }
                // 参数就是 user_id，然后也是分表键，对 2 取模就是 0，应该是去 student_0 取数据
                ps.setInt(1, 1000);
                resultSet = ps.executeQuery();
                while (resultSet.next()) {
                    final int id = resultSet.getInt("id");
                    final String name = resultSet.getString("name");
                    final int userId = resultSet.getInt("user_id");
                    final int age = resultSet.getInt("age");
                    System.out.println("偶数表 id：" + id + " 姓名：" + name
                            + " 用户 id：" + userId + " 年龄：" + age );
                    System.out.println("=============================");
                }
            } catch (SQLException e) {
                e.printStackTrace();
            }
        }
    +

    看下查询结果

    ]]>     Java @@ -6707,23 +6772,22 @@ user3:     - 聊聊 Sharding-Jdbc 分库分表下的分页方案 - /2022/01/09/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E5%88%86%E5%BA%93%E5%88%86%E8%A1%A8%E4%B8%8B%E7%9A%84%E5%88%86%E9%A1%B5%E6%96%B9%E6%A1%88/ - 前面在聊 Sharding-Jdbc 的时候看到了一篇文章，关于一个分页的查询，一直比较直接的想法就是分库分表下的分页是非常不合理的，一般我们的实操方案都是分表加上 ES 搜索做分页，或者通过合表读写分离的方案，因为对于 sharding-jdbc 如果没有带分表键，查询基本都是只能在所有分表都执行一遍，然后再加上分页，基本上是分页越大后续的查询越耗资源，但是仔细的去想这个细节还是这次，就简单说说
    首先就是我的分表结构

    -
    CREATE TABLE `student_time_0` (
      `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
      `user_id` int(11) NOT NULL,
      `name` varchar(200) COLLATE utf8_bin DEFAULT NULL,
      `age` tinyint(3) unsigned DEFAULT NULL,
      `create_time` bigint(20) DEFAULT NULL,
      PRIMARY KEY (`id`)
    ) ENGINE=InnoDB AUTO_INCREMENT=674 DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
    -

    有这样的三个表，student_time_0, student_time_1, student_time_2, 以 user_id 作为分表键，根据表数量取模作为分表依据
    这里先构造点数据，

    -
    insert into student_time (`name`, `user_id`, `age`, `create_time`) values (?, ?, ?, ?)
    -

    主要是为了保证 create_time 唯一比较好说明问题，

    -
    int i = 0;
    try (
            Connection conn = dataSource.getConnection();
            PreparedStatement ps = conn.prepareStatement(insertSql)) {
        do {
            ps.setString(1, localName + new Random().nextInt(100));
            ps.setLong(2, 10086L + (new Random().nextInt(100)));
            ps.setInt(3, 18);
            ps.setLong(4, new Date().getTime());


            int result = ps.executeUpdate();
            LOGGER.info("current execute result: {}", result);
            Thread.sleep(new Random().nextInt(100));
            i++;
        } while (i <= 2000);
    -

    三个表的数据分别是 673，678，650，说明符合预期了，各个表数据不一样，接下来比如我们想要做一个这样的分页查询

    -
    select * from student_time ORDER BY create_time ASC limit 1000, 5;
    -

    student_time 对于我们使用的 sharding-jdbc 来说当然是逻辑表，首先从一无所知去想这个查询如果我们自己来处理应该是怎么做，
    首先是不是可以每个表都从 333 开始取 5 条数据，类似于下面的查询，然后进行 15 条的合并重排序获取前面的 5 条

    -
    select * from student_time_0 ORDER BY create_time ASC limit 333, 5;
    select * from student_time_1 ORDER BY create_time ASC limit 333, 5;
    select * from student_time_2 ORDER BY create_time ASC limit 333, 5;
    -

    忽略前面 limit 差的 1，这个结果除非三个表的分布是绝对的均匀，否则结果肯定会出现一定的偏差，以为每个表的 333 这个位置对于其他表来说都不一定是一样的，这样对于最后整体的结果，就会出现偏差
    因为一直在纠结怎么让这个更直观的表现出来，所以尝试画了个图

    黑色的框代表我从每个表里按排序从 334 到 338 的 5 条数据， 他们在每个表里都是代表了各自正确的排序值，但是对于我们想要的其实是合表后的 1001，1005 这五条，然后我们假设总的排序值位于前 1000 的分布是第 0 个表是 320 条，第 1 个表是 340 条，第 2 个表是 340 条，那么可以明显地看出来我这么查的结果简单合并肯定是不对的。
    那么 sharding-jdbc 是如何保证这个结果的呢，其实就是我在每个表里都查分页偏移量和分页大小那么多的数据，在我这个例子里就是对于 0，1，2 三个分表每个都查 1005 条数据，即使我的数据不平衡到最极端的情况，前 1005 条数据都出在某个分表中，也可以正确获得最后的结果，但是明显的问题就是大分页，数据较多，就会导致非常大的问题，即使如 sharding-jdbc 对于合并排序的优化做得比较好，也还是需要传输那么大量的数据，并且查询也耗时，那么有没有解决方案呢，应该说有两个，或者说主要是想讲后者
    第一个办法是像这种查询，如果业务上不需要进行跳页，而是只给下一页，那么我们就能把前一次的最大偏移量的 create_time 记录下来，下一页就可以拿着这个偏移量进行查询，这个比较简单易懂，就不多说了
    第二个办法是看的58 沈剑的一篇文章，尝试理解讲述一下，
    这个办法的第一步跟前面那个错误的方法或者说不准确的方法一样，先是将分页偏移量平均后在三个表里进行查询

    -
    t0
    334 10158 nick95  18  1641548941767
    335 10098 nick11  18  1641548941879
    336 10167 nick51  18  1641548942089
    337 10167 nick3 18  1641548942119
    338 10170 nick57  18  1641548942169


    t1
    334 10105 nick98  18  1641548939071   最小
    335 10174 nick94  18  1641548939377
    336 10129 nick85  18  1641548939442
    337 10141 nick84  18  1641548939480
    338 10096 nick74  18  1641548939668

    t2
    334 10184 nick11  18  1641548945075
    335 10109 nick93  18  1641548945382
    336 10181 nick41  18  1641548945583
    337 10130 nick80  18  1641548945993
    338 10184 nick19  18  1641548946294  最大
    -

    然后要做什么呢，其实目标比较明白，因为前面那种方法其实就是我知道了前一页的偏移量，所以可以直接当做条件来进行查询，那这里我也想着拿到这个条件，所以我将第一遍查出来的最小的 create_time 和最大的 create_time 找出来，然后再去三个表里查询，其实主要是最小值，因为我拿着最小值去查以后我就能知道这个最小值在每个表里处在什么位置，

    -
    t0
    322 10161 nick81  18  1641548939284
    323 10113 nick16  18  1641548939393
    324 10110 nick56  18  1641548939577
    325 10116 nick69  18  1641548939588
    326 10173 nick51  18  1641548939646

    t1
    334 10105 nick98  18  1641548939071
    335 10174 nick94  18  1641548939377
    336 10129 nick85  18  1641548939442
    337 10141 nick84  18  1641548939480
    338 10096 nick74  18  1641548939668

    t2
    297 10136 nick28  18  1641548939161
    298 10142 nick68  18  1641548939177
    299 10124 nick41  18  1641548939237
    300 10148 nick87  18  1641548939510
    301 10169 nick23  18  1641548939715
    -

    我只贴了前五条数据，为了方便知道偏移量，每个分表都使用了自增主键，我们可以看到前一次查询的最小值分别在其他两个表里的位置分别是 322-1 和 297-1，那么对于总体来说这个时间应该是在 322 - 1 + 333 + 297 - 1 = 951，那这样子我只要对后面的数据最多每个表查 1000 - 951 + 5 = 54 条数据再进行合并排序就可以获得最终正确的结果。
    这个就是传说中的二次查询法。

    + 聊聊 Sharding-Jdbc 的简单原理初篇 + /2021/12/26/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E5%8E%9F%E7%90%86%E5%88%9D%E7%AF%87/ + 在上一篇 sharding-jdbc 的介绍中其实碰到过一个问题，这里也引出了一个比较有意思的话题
    就是我在执行 query 的时候犯过一个比较难发现的错误，

    +
    ResultSet resultSet = ps.executeQuery(sql);
    +

    实际上应该是

    +
    ResultSet resultSet = ps.executeQuery();
    +

    而这里的差别就是，是否传 sql 这个参数，首先我们要知道这个 ps 是什么，它也是个接口java.sql.PreparedStatement,而真正的实现类是org.apache.shardingsphere.driver.jdbc.core.statement.ShardingSpherePreparedStatement,我们来看下继承关系

    这里可以看到继承关系里有org.apache.shardingsphere.driver.jdbc.unsupported.AbstractUnsupportedOperationPreparedStatement
    那么在我上面的写错的代码里

    +
    @Override
    public final ResultSet executeQuery(final String sql) throws SQLException {
        throw new SQLFeatureNotSupportedException("executeQuery with SQL for PreparedStatement");
    }
    +

    这个报错一开始让我有点懵，后来点进去了发现是这么个异常，但是我其实一开始是用的更新语句，以为更新不支持，因为平时使用没有深究过，以为是不是需要使用 Mybatis 才可以执行更新，但是理论上也不应该，再往上看原来这些异常是由 sharding-jdbc 包装的，也就是在上面说的AbstractUnsupportedOperationPreparedStatement，这其实也是一种设计思想，本身 jdbc 提供了一系列接口，由各家去支持，包括 mysql，sql server，oracle 等，而正因为这个设计，所以 sharding-jdbc 也可以在此基础上进行设计，我们可以总体地看下 sharding-jdbc 的实现基础

    看了前面ShardingSpherePreparedStatement的继承关系，应该也能猜到这里的几个类都是实现了 jdbc 的基础接口，

    在前一篇的 demo 中的

    +
    Connection conn = dataSource.getConnection();
    +

    其实就获得了org.apache.shardingsphere.driver.jdbc.core.connection.ShardingSphereConnection#ShardingSphereConnection
    然后获得java.sql.PreparedStatement

    +
    PreparedStatement ps = conn.prepareStatement(sql)
    +

    就是获取了org.apache.shardingsphere.driver.jdbc.core.statement.ShardingSpherePreparedStatement
    然后就是执行

    +
    ResultSet resultSet = ps.executeQuery();
    +

    然后获得结果
    org.apache.shardingsphere.driver.jdbc.core.resultset.ShardingSphereResultSet

    +

    其实像 mybatis 也是基于这样去实现的

    ]]>     Java @@ -6744,41 +6808,6 @@ user3:

    SELECT … FOR UPDATE sets an exclusive next-key lock on every record the search encounters. However, only an index record lock is required for statements that lock rows using a unique index to search for a unique row.

    对于一个 for update 查询，在 RR 级别下，会设置一个 next-key lock在每一条被查询到的记录上，next-lock 又是啥呢，其实就是 gap 锁和 record 锁的结合体，比如我在数据库里有 id 是 1，3，5，7，10，对于上面那条查询，查出来的结果就是 1，3，5，7，那么按照文档里描述的，对于这几条记录都会加上next-key lock，也就是(-∞, 1], (1, 3], (3, 5], (5, 7], (7, 10) 这些区间和记录会被锁起来，不让插入，再唠叨一下呢，就是其实如果是只读的事务，光 read view 一致性读就够了，如果是有写操作的呢，就需要锁了。

    -]]>     - - Mysql - C - 数据结构 - 源码 - Mysql - - - mysql - 数据结构 - 源码 - mvcc - read view - gap lock - next-key lock - 幻读 - -     - - 聊聊 mysql 的 MVCC 续续篇之锁分析 - /2020/05/10/%E8%81%8A%E8%81%8A-mysql-%E7%9A%84-MVCC-%E7%BB%AD%E7%BB%AD%E7%AF%87%E4%B9%8B%E5%8A%A0%E9%94%81%E5%88%86%E6%9E%90/ - 看完前面两篇水文之后，感觉不得不来分析下 mysql 的锁了，其实前面说到幻读的时候是有个前提没提到的，比如一个select * from table1 where id = 1这种查询语句其实是不会加传说中的锁的，当然这里是指在 RR 或者 RC 隔离级别下，
    看一段 mysql官方文档

    -
    -

    SELECT ... FROM is a consistent read, reading a snapshot of the database and setting no locks unless the transaction isolation level is set to SERIALIZABLE. For SERIALIZABLE level, the search sets shared next-key locks on the index records it encounters. However, only an index record lock is required for statements that lock rows using a unique index to search for a unique row.

    -
    -

    纯粹的这种一致性读，实际读取的是快照，也就是基于 read view 的读取方式，除非当前隔离级别是SERIALIZABLE
    但是对于以下几类

    -
      -
    • select * from table where ? lock in share mode;
      • -
    • select * from table where ? for update;
      • -
    • insert into table values (...);
      • -
    • update table set ? where ?;
      • -
    • delete from table where ?;
      • -
    -

    除了第一条是 S 锁之外，其他都是 X 排他锁，这边在顺带下，S 锁表示共享锁， X 表示独占锁，同为 S 锁之间不冲突，S 与 X，X 与 S，X 与 X 之间都冲突，也就是加了前者，后者就加不上了
    我们知道对于 RC 级别会出现幻读现象，对于 RR 级别不会出现，主要的区别是 RR 级别下对于以上的加锁读取都根据情况加上了 gap 锁，那么是不是 RR 级别下以上所有的都是要加 gap 锁呢，当然不是
    举个例子，RR 事务隔离级别下，table1 有个主键id 字段
    select * from table1 where id = 10 for update
    这条语句要加 gap 锁吗？
    答案是不需要，这里其实算是我看了这么久的一点自己的理解，啥时候要加 gap 锁，判断的条件是根据我查询的数据是否会因为不加 gap 锁而出现数量的不一致，我上面这条查询语句，在什么情况下会出现查询结果数量不一致呢，只要在这条记录被更新或者删除的时候，有没有可能我第一次查出来一条，第二次变成两条了呢，不可能，因为是主键索引。
    再变更下这个题的条件，当 id 不是主键，但是是唯一索引，这样需要怎么加锁，注意问题是怎么加锁，不是需不需要加 gap 锁，这里呢就是稍微延伸一下，把聚簇索引（主键索引）和二级索引带一下，当 id 不是主键，说明是个二级索引，但是它是唯一索引，体会下，首先对于 id = 10这个二级索引肯定要加锁，要不要锁 gap 呢，不用，因为是唯一索引，id = 10 只可能有这一条记录，然后呢，这样是不是就好了，还不行，因为啥，因为它是二级索引，对应的主键索引的记录才是真正的数据，万一被更新掉了咋办，所以在 id = 10 对应的主键索引上也需要加上锁（默认都是 record lock行锁），那主键索引上要不要加 gap 呢，也不用，也是精确定位到这一条记录
    最后呢，当 id 不是主键，也不是唯一索引，只是个普通的索引，这里就需要大名鼎鼎的 gap 锁了，
    是时候画个图了

    其实核心的目的还是不让这个 id=10 的记录不会出现幻读，那么就需要在 id 这个索引上加上三个 gap 锁，主键索引上就不用了，在 id 索引上已经控制住了id = 10 不会出现幻读，主键索引上这两条对应的记录已经锁了，所以就这样 OK 了

    ]]>     Mysql @@ -6871,29 +6900,38 @@ user3:     - 聊聊Java中的单例模式 - /2019/12/21/%E8%81%8A%E8%81%8AJava%E4%B8%AD%E7%9A%84%E5%8D%95%E4%BE%8B%E6%A8%A1%E5%BC%8F/ - 这是个 Java 面试的高频问题，我也遇到过，以往都是觉得这类题没意思，网上一搜一大堆，也不愿意记，其实说回来，主要还是没静下心来好好去理解，今天无意中看到一个课程，基本帮我把一些疑惑的点讲清楚了，首先单例是啥意思，这个其实是有范围一说，比如我起了个Spring Boot应用，在这个应用范围内，我的常规 bean 是单例的，意味着 getBean 的时候其实永远只会拿到那一个对象，那要怎么来写一个单例呢，首先就是传说中的饿汉模式，也是最简单的

    -

    饿汉模式

    public class Singleton1 {
        // 首先，将构造方法变成私有的
        private Singleton1() {};
        // 创建私有静态实例，这样第一次使用的时候就会进行创建
        private static Singleton instance = new Singleton1();

        // 使用这个对象都是通过这个 getInstance 来获取
        public static Singleton1 getInstance() {
            return instance;
        }
        // 瞎写一个静态方法。这里想说的是，如果我们只是要调用 Singleton.getDate(...)，
        // 本来是不想要生成 Singleton 实例的，不过没办法，已经生成了
        public static Date getDate(String mode) {return new Date();}
    }
    -

    上面借鉴了一些代码，其实这是最基本，也不会错的方法，但是正如其中getDate方法里说的问题，有时候并没有想那这个对象，但是因为我调用了这个类的静态方法，导致对象已经生成了，可能这也是饿汉模式名字的来由，不管三七二十一给你生成个单例就完事了，不管有没有用，但是这种个人觉得也没啥大问题，如果是面试的话最好说出来它的缺点

    -

    饱汉模式

    public class Singleton2 {
        // 首先，也是先堵死 new Singleton() 这条路,将构造方法变成私有
        private Singleton2() {}
        // 和饿汉模式相比，这边不需要先实例化出来，注意这里的 volatile，它是必须的
        private static volatile Singleton2 instance = null;

        private int m = 9;

        public static Singleton getInstance() {
            if (instance == null) {
                // 加锁
                synchronized (Singleton2.class) {
                    // 这一次判断也是必须的，不然会有并发问题
                    if (instance == null) {
                        instance = new Singleton2();
                    }
                }
            }
            return instance;
        }
    }
    -

    这里容易错的有三点，理解了其实就比较好记了

    -

    第一点，为啥不在 getInstance 上整个代码块加 synchronized，这个其实比较容易理解，就是锁的力度太大，性能太差了，这点其实也要去理解，可以举个夸张的例子，比如我一个电商的服务，如果为了避免一个人的订单出现问题，是不是可以从请求入口就把他锁住，到请求结束释放，那么里面做的事情都有保障，然而这显然不可能，因为我们想要这种竞态条件抢占资源的时间尽量减少，防止其他线程等待。
    第二点，为啥synchronized之已经检查了 instance == null,还要在里面再检查一次，这个有个术语，叫 double check lock,但是为啥要这么做呢，其实很简单，想象当有两个线程，都过了第一步为空判断，这个时候只有一个线程能拿到这个锁，另一个线程就等待了，如果不再判断一次，那么第一个线程新建完对象释放锁之后，第二个线程又能拿到锁，再去创建一个对象。
    第三点，为啥要volatile关键字，原先对它的理解是它修饰的变量在 JMM 中能及时将变量值写到主存中，但是它还有个很重要的作用，就是防止指令重排序，instance = new Singleton();这行代码其实在底层是分成三条指令执行的，第一条是在堆上申请了一块内存放这个对象，但是对象的字段啥的都还是默认值，第二条是设置对象的值，比如上面的 m 是 9，然后第三条是将这个对象和虚拟机栈上的指针建立引用关联，那么如果我不用volatile关键字，这三条指令就有可能出现重排，比如变成了 1-3-2 这种顺序，当执行完第二步时，有个线程来访问这个对象了，先判断是不是空，发现不是空的，就拿去直接用了，是不是就出现问题了，所以这个volatile也是不可缺少的

    -

    嵌套类

    public class Singleton3 {

        private Singleton3() {}
        // 主要是使用了 嵌套类可以访问外部类的静态属性和静态方法 的特性
        private static class Holder {
            private static Singleton3 instance = new Singleton3();
        }
        public static Singleton3 getInstance() {
            return Holder.instance;
        }
    }
    -

    这个我个人感觉是饿汉模式的升级版，可以在调用getInstance的时候去实例化对象，也是比较推荐的

    -

    枚举单例

    public enum Singleton {
        INSTANCE;
        
        public void doSomething(){
            //todo doSomething
        }
    }
    -

    枚举很特殊，它在类加载的时候会初始化里面的所有的实例，而且 JVM 保证了它们不会再被实例化，所以它天生就是单例的。

    + 聊聊 mysql 的 MVCC 续续篇之锁分析 + /2020/05/10/%E8%81%8A%E8%81%8A-mysql-%E7%9A%84-MVCC-%E7%BB%AD%E7%BB%AD%E7%AF%87%E4%B9%8B%E5%8A%A0%E9%94%81%E5%88%86%E6%9E%90/ + 看完前面两篇水文之后，感觉不得不来分析下 mysql 的锁了，其实前面说到幻读的时候是有个前提没提到的，比如一个select * from table1 where id = 1这种查询语句其实是不会加传说中的锁的，当然这里是指在 RR 或者 RC 隔离级别下，
    看一段 mysql官方文档

    +
    +

    SELECT ... FROM is a consistent read, reading a snapshot of the database and setting no locks unless the transaction isolation level is set to SERIALIZABLE. For SERIALIZABLE level, the search sets shared next-key locks on the index records it encounters. However, only an index record lock is required for statements that lock rows using a unique index to search for a unique row.

    +
    +

    纯粹的这种一致性读，实际读取的是快照，也就是基于 read view 的读取方式，除非当前隔离级别是SERIALIZABLE
    但是对于以下几类

    +
      +
    • select * from table where ? lock in share mode;
      • +
    • select * from table where ? for update;
      • +
    • insert into table values (...);
      • +
    • update table set ? where ?;
      • +
    • delete from table where ?;
      • +
    +

    除了第一条是 S 锁之外，其他都是 X 排他锁，这边在顺带下，S 锁表示共享锁， X 表示独占锁，同为 S 锁之间不冲突，S 与 X，X 与 S，X 与 X 之间都冲突，也就是加了前者，后者就加不上了
    我们知道对于 RC 级别会出现幻读现象，对于 RR 级别不会出现，主要的区别是 RR 级别下对于以上的加锁读取都根据情况加上了 gap 锁，那么是不是 RR 级别下以上所有的都是要加 gap 锁呢，当然不是
    举个例子，RR 事务隔离级别下，table1 有个主键id 字段
    select * from table1 where id = 10 for update
    这条语句要加 gap 锁吗？
    答案是不需要，这里其实算是我看了这么久的一点自己的理解，啥时候要加 gap 锁，判断的条件是根据我查询的数据是否会因为不加 gap 锁而出现数量的不一致，我上面这条查询语句，在什么情况下会出现查询结果数量不一致呢，只要在这条记录被更新或者删除的时候，有没有可能我第一次查出来一条，第二次变成两条了呢，不可能，因为是主键索引。
    再变更下这个题的条件，当 id 不是主键，但是是唯一索引，这样需要怎么加锁，注意问题是怎么加锁，不是需不需要加 gap 锁，这里呢就是稍微延伸一下，把聚簇索引（主键索引）和二级索引带一下，当 id 不是主键，说明是个二级索引，但是它是唯一索引，体会下，首先对于 id = 10这个二级索引肯定要加锁，要不要锁 gap 呢，不用，因为是唯一索引，id = 10 只可能有这一条记录，然后呢，这样是不是就好了，还不行，因为啥，因为它是二级索引，对应的主键索引的记录才是真正的数据，万一被更新掉了咋办，所以在 id = 10 对应的主键索引上也需要加上锁（默认都是 record lock行锁），那主键索引上要不要加 gap 呢，也不用，也是精确定位到这一条记录
    最后呢，当 id 不是主键，也不是唯一索引，只是个普通的索引，这里就需要大名鼎鼎的 gap 锁了，
    是时候画个图了

    其实核心的目的还是不让这个 id=10 的记录不会出现幻读，那么就需要在 id 这个索引上加上三个 gap 锁，主键索引上就不用了，在 id 索引上已经控制住了id = 10 不会出现幻读，主键索引上这两条对应的记录已经锁了，所以就这样 OK 了

    ]]>     - Java - Design Patterns - Singleton + Mysql + C + 数据结构 + 源码 + Mysql - 设计模式 - Design Patterns - 单例 - Singleton + mysql + 数据结构 + 源码 + mvcc + read view + gap lock + next-key lock + 幻读     @@ -6927,6 +6965,32 @@ user3: 互斥锁 + + 聊聊Java中的单例模式 + /2019/12/21/%E8%81%8A%E8%81%8AJava%E4%B8%AD%E7%9A%84%E5%8D%95%E4%BE%8B%E6%A8%A1%E5%BC%8F/ + 这是个 Java 面试的高频问题，我也遇到过，以往都是觉得这类题没意思，网上一搜一大堆，也不愿意记，其实说回来，主要还是没静下心来好好去理解，今天无意中看到一个课程，基本帮我把一些疑惑的点讲清楚了，首先单例是啥意思，这个其实是有范围一说，比如我起了个Spring Boot应用，在这个应用范围内，我的常规 bean 是单例的，意味着 getBean 的时候其实永远只会拿到那一个对象，那要怎么来写一个单例呢，首先就是传说中的饿汉模式，也是最简单的

    +

    饿汉模式

    public class Singleton1 {
        // 首先，将构造方法变成私有的
        private Singleton1() {};
        // 创建私有静态实例，这样第一次使用的时候就会进行创建
        private static Singleton instance = new Singleton1();

        // 使用这个对象都是通过这个 getInstance 来获取
        public static Singleton1 getInstance() {
            return instance;
        }
        // 瞎写一个静态方法。这里想说的是，如果我们只是要调用 Singleton.getDate(...)，
        // 本来是不想要生成 Singleton 实例的，不过没办法，已经生成了
        public static Date getDate(String mode) {return new Date();}
    }
    +

    上面借鉴了一些代码，其实这是最基本，也不会错的方法，但是正如其中getDate方法里说的问题，有时候并没有想那这个对象，但是因为我调用了这个类的静态方法，导致对象已经生成了，可能这也是饿汉模式名字的来由，不管三七二十一给你生成个单例就完事了，不管有没有用，但是这种个人觉得也没啥大问题，如果是面试的话最好说出来它的缺点

    +

    饱汉模式

    public class Singleton2 {
        // 首先，也是先堵死 new Singleton() 这条路,将构造方法变成私有
        private Singleton2() {}
        // 和饿汉模式相比，这边不需要先实例化出来，注意这里的 volatile，它是必须的
        private static volatile Singleton2 instance = null;

        private int m = 9;

        public static Singleton getInstance() {
            if (instance == null) {
                // 加锁
                synchronized (Singleton2.class) {
                    // 这一次判断也是必须的，不然会有并发问题
                    if (instance == null) {
                        instance = new Singleton2();
                    }
                }
            }
            return instance;
        }
    }
    +

    这里容易错的有三点，理解了其实就比较好记了

    +

    第一点，为啥不在 getInstance 上整个代码块加 synchronized，这个其实比较容易理解，就是锁的力度太大，性能太差了，这点其实也要去理解，可以举个夸张的例子，比如我一个电商的服务，如果为了避免一个人的订单出现问题，是不是可以从请求入口就把他锁住，到请求结束释放，那么里面做的事情都有保障，然而这显然不可能，因为我们想要这种竞态条件抢占资源的时间尽量减少，防止其他线程等待。
    第二点，为啥synchronized之已经检查了 instance == null,还要在里面再检查一次，这个有个术语，叫 double check lock,但是为啥要这么做呢，其实很简单，想象当有两个线程，都过了第一步为空判断，这个时候只有一个线程能拿到这个锁，另一个线程就等待了，如果不再判断一次，那么第一个线程新建完对象释放锁之后，第二个线程又能拿到锁，再去创建一个对象。
    第三点，为啥要volatile关键字，原先对它的理解是它修饰的变量在 JMM 中能及时将变量值写到主存中，但是它还有个很重要的作用，就是防止指令重排序，instance = new Singleton();这行代码其实在底层是分成三条指令执行的，第一条是在堆上申请了一块内存放这个对象，但是对象的字段啥的都还是默认值，第二条是设置对象的值，比如上面的 m 是 9，然后第三条是将这个对象和虚拟机栈上的指针建立引用关联，那么如果我不用volatile关键字，这三条指令就有可能出现重排，比如变成了 1-3-2 这种顺序，当执行完第二步时，有个线程来访问这个对象了，先判断是不是空，发现不是空的，就拿去直接用了，是不是就出现问题了，所以这个volatile也是不可缺少的

    +

    嵌套类

    public class Singleton3 {

        private Singleton3() {}
        // 主要是使用了 嵌套类可以访问外部类的静态属性和静态方法 的特性
        private static class Holder {
            private static Singleton3 instance = new Singleton3();
        }
        public static Singleton3 getInstance() {
            return Holder.instance;
        }
    }
    +

    这个我个人感觉是饿汉模式的升级版，可以在调用getInstance的时候去实例化对象，也是比较推荐的

    +

    枚举单例

    public enum Singleton {
        INSTANCE;
        
        public void doSomething(){
            //todo doSomething
        }
    }
    +

    枚举很特殊，它在类加载的时候会初始化里面的所有的实例，而且 JVM 保证了它们不会再被实例化，所以它天生就是单例的。

    +]]>     + + Java + Design Patterns + Singleton + + + 设计模式 + Design Patterns + 单例 + Singleton + +     聊聊 SpringBoot 自动装配 /2021/07/11/%E8%81%8A%E8%81%8ASpringBoot-%E8%87%AA%E5%8A%A8%E8%A3%85%E9%85%8D/ @@ -6948,118 +7012,48 @@ user3:

    我们可以看下 spring-boot-autoconfigure 的 META-INF/spring.factories

    # Initializers
    org.springframework.context.ApplicationContextInitializer=\
    org.springframework.boot.autoconfigure.SharedMetadataReaderFactoryContextInitializer,\
    org.springframework.boot.autoconfigure.logging.ConditionEvaluationReportLoggingListener

    # Application Listeners
    org.springframework.context.ApplicationListener=\
    org.springframework.boot.autoconfigure.BackgroundPreinitializer

    # Environment Post Processors
    org.springframework.boot.env.EnvironmentPostProcessor=\
    org.springframework.boot.autoconfigure.integration.IntegrationPropertiesEnvironmentPostProcessor

    # Auto Configuration Import Listeners
    org.springframework.boot.autoconfigure.AutoConfigurationImportListener=\
    org.springframework.boot.autoconfigure.condition.ConditionEvaluationReportAutoConfigurationImportListener

    # Auto Configuration Import Filters
    org.springframework.boot.autoconfigure.AutoConfigurationImportFilter=\
    org.springframework.boot.autoconfigure.condition.OnBeanCondition,\
    org.springframework.boot.autoconfigure.condition.OnClassCondition,\
    org.springframework.boot.autoconfigure.condition.OnWebApplicationCondition

    # 注意这里，其实就是类似于 dubbo spi 的通过 org.springframework.boot.autoconfigure.EnableAutoConfiguration作为 key
    # 获取下面所有的 AutoConfiguration 配置类
    # Auto Configure
    org.springframework.boot.autoconfigure.EnableAutoConfiguration=\
    org.springframework.boot.autoconfigure.admin.SpringApplicationAdminJmxAutoConfiguration,\
    org.springframework.boot.autoconfigure.aop.AopAutoConfiguration,\
    org.springframework.boot.autoconfigure.amqp.RabbitAutoConfiguration,\
    org.springframework.boot.autoconfigure.batch.BatchAutoConfiguration,\
    org.springframework.boot.autoconfigure.cache.CacheAutoConfiguration,\
    org.springframework.boot.autoconfigure.cassandra.CassandraAutoConfiguration,\
    org.springframework.boot.autoconfigure.context.ConfigurationPropertiesAutoConfiguration,\
    org.springframework.boot.autoconfigure.context.LifecycleAutoConfiguration,\
    org.springframework.boot.autoconfigure.context.MessageSourceAutoConfiguration,\
    org.springframework.boot.autoconfigure.context.PropertyPlaceholderAutoConfiguration,\
    org.springframework.boot.autoconfigure.couchbase.CouchbaseAutoConfiguration,\
    org.springframework.boot.autoconfigure.dao.PersistenceExceptionTranslationAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.cassandra.CassandraDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.cassandra.CassandraReactiveDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.cassandra.CassandraReactiveRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.cassandra.CassandraRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.couchbase.CouchbaseDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.couchbase.CouchbaseReactiveDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.couchbase.CouchbaseReactiveRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.couchbase.CouchbaseRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.elasticsearch.ElasticsearchDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.elasticsearch.ElasticsearchRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.elasticsearch.ReactiveElasticsearchRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.elasticsearch.ReactiveElasticsearchRestClientAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.jdbc.JdbcRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.jpa.JpaRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.ldap.LdapRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.mongo.MongoDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.mongo.MongoReactiveDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.mongo.MongoReactiveRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.mongo.MongoRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.neo4j.Neo4jDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.neo4j.Neo4jReactiveDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.neo4j.Neo4jReactiveRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.neo4j.Neo4jRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.r2dbc.R2dbcDataAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.r2dbc.R2dbcRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.redis.RedisAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.redis.RedisReactiveAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.redis.RedisRepositoriesAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.rest.RepositoryRestMvcAutoConfiguration,\
    org.springframework.boot.autoconfigure.data.web.SpringDataWebAutoConfiguration,\
    org.springframework.boot.autoconfigure.elasticsearch.ElasticsearchRestClientAutoConfiguration,\
    org.springframework.boot.autoconfigure.flyway.FlywayAutoConfiguration,\
    org.springframework.boot.autoconfigure.freemarker.FreeMarkerAutoConfiguration,\
    org.springframework.boot.autoconfigure.groovy.template.GroovyTemplateAutoConfiguration,\
    org.springframework.boot.autoconfigure.gson.GsonAutoConfiguration,\
    org.springframework.boot.autoconfigure.h2.H2ConsoleAutoConfiguration,\
    org.springframework.boot.autoconfigure.hateoas.HypermediaAutoConfiguration,\
    org.springframework.boot.autoconfigure.hazelcast.HazelcastAutoConfiguration,\
    org.springframework.boot.autoconfigure.hazelcast.HazelcastJpaDependencyAutoConfiguration,\
    org.springframework.boot.autoconfigure.http.HttpMessageConvertersAutoConfiguration,\
    org.springframework.boot.autoconfigure.http.codec.CodecsAutoConfiguration,\
    org.springframework.boot.autoconfigure.influx.InfluxDbAutoConfiguration,\
    org.springframework.boot.autoconfigure.info.ProjectInfoAutoConfiguration,\
    org.springframework.boot.autoconfigure.integration.IntegrationAutoConfiguration,\
    org.springframework.boot.autoconfigure.jackson.JacksonAutoConfiguration,\
    org.springframework.boot.autoconfigure.jdbc.DataSourceAutoConfiguration,\
    org.springframework.boot.autoconfigure.jdbc.JdbcTemplateAutoConfiguration,\
    org.springframework.boot.autoconfigure.jdbc.JndiDataSourceAutoConfiguration,\
    org.springframework.boot.autoconfigure.jdbc.XADataSourceAutoConfiguration,\
    org.springframework.boot.autoconfigure.jdbc.DataSourceTransactionManagerAutoConfiguration,\
    org.springframework.boot.autoconfigure.jms.JmsAutoConfiguration,\
    org.springframework.boot.autoconfigure.jmx.JmxAutoConfiguration,\
    org.springframework.boot.autoconfigure.jms.JndiConnectionFactoryAutoConfiguration,\
    org.springframework.boot.autoconfigure.jms.activemq.ActiveMQAutoConfiguration,\
    org.springframework.boot.autoconfigure.jms.artemis.ArtemisAutoConfiguration,\
    org.springframework.boot.autoconfigure.jersey.JerseyAutoConfiguration,\
    org.springframework.boot.autoconfigure.jooq.JooqAutoConfiguration,\
    org.springframework.boot.autoconfigure.jsonb.JsonbAutoConfiguration,\
    org.springframework.boot.autoconfigure.kafka.KafkaAutoConfiguration,\
    org.springframework.boot.autoconfigure.availability.ApplicationAvailabilityAutoConfiguration,\
    org.springframework.boot.autoconfigure.ldap.embedded.EmbeddedLdapAutoConfiguration,\
    org.springframework.boot.autoconfigure.ldap.LdapAutoConfiguration,\
    org.springframework.boot.autoconfigure.liquibase.LiquibaseAutoConfiguration,\
    org.springframework.boot.autoconfigure.mail.MailSenderAutoConfiguration,\
    org.springframework.boot.autoconfigure.mail.MailSenderValidatorAutoConfiguration,\
    org.springframework.boot.autoconfigure.mongo.embedded.EmbeddedMongoAutoConfiguration,\
    org.springframework.boot.autoconfigure.mongo.MongoAutoConfiguration,\
    org.springframework.boot.autoconfigure.mongo.MongoReactiveAutoConfiguration,\
    org.springframework.boot.autoconfigure.mustache.MustacheAutoConfiguration,\
    org.springframework.boot.autoconfigure.neo4j.Neo4jAutoConfiguration,\
    org.springframework.boot.autoconfigure.netty.NettyAutoConfiguration,\
    org.springframework.boot.autoconfigure.orm.jpa.HibernateJpaAutoConfiguration,\
    org.springframework.boot.autoconfigure.quartz.QuartzAutoConfiguration,\
    org.springframework.boot.autoconfigure.r2dbc.R2dbcAutoConfiguration,\
    org.springframework.boot.autoconfigure.r2dbc.R2dbcTransactionManagerAutoConfiguration,\
    org.springframework.boot.autoconfigure.rsocket.RSocketMessagingAutoConfiguration,\
    org.springframework.boot.autoconfigure.rsocket.RSocketRequesterAutoConfiguration,\
    org.springframework.boot.autoconfigure.rsocket.RSocketServerAutoConfiguration,\
    org.springframework.boot.autoconfigure.rsocket.RSocketStrategiesAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.servlet.SecurityAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.servlet.UserDetailsServiceAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.servlet.SecurityFilterAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.reactive.ReactiveSecurityAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.reactive.ReactiveUserDetailsServiceAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.rsocket.RSocketSecurityAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.saml2.Saml2RelyingPartyAutoConfiguration,\
    org.springframework.boot.autoconfigure.sendgrid.SendGridAutoConfiguration,\
    org.springframework.boot.autoconfigure.session.SessionAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.oauth2.client.servlet.OAuth2ClientAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.oauth2.client.reactive.ReactiveOAuth2ClientAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.oauth2.resource.servlet.OAuth2ResourceServerAutoConfiguration,\
    org.springframework.boot.autoconfigure.security.oauth2.resource.reactive.ReactiveOAuth2ResourceServerAutoConfiguration,\
    org.springframework.boot.autoconfigure.solr.SolrAutoConfiguration,\
    org.springframework.boot.autoconfigure.sql.init.SqlInitializationAutoConfiguration,\
    org.springframework.boot.autoconfigure.task.TaskExecutionAutoConfiguration,\
    org.springframework.boot.autoconfigure.task.TaskSchedulingAutoConfiguration,\
    org.springframework.boot.autoconfigure.thymeleaf.ThymeleafAutoConfiguration,\
    org.springframework.boot.autoconfigure.transaction.TransactionAutoConfiguration,\
    org.springframework.boot.autoconfigure.transaction.jta.JtaAutoConfiguration,\
    org.springframework.boot.autoconfigure.validation.ValidationAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.client.RestTemplateAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.embedded.EmbeddedWebServerFactoryCustomizerAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.reactive.HttpHandlerAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.reactive.ReactiveWebServerFactoryAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.reactive.WebFluxAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.reactive.error.ErrorWebFluxAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.reactive.function.client.ClientHttpConnectorAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.reactive.function.client.WebClientAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.servlet.DispatcherServletAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.servlet.ServletWebServerFactoryAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.servlet.error.ErrorMvcAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.servlet.HttpEncodingAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.servlet.MultipartAutoConfiguration,\
    org.springframework.boot.autoconfigure.web.servlet.WebMvcAutoConfiguration,\
    org.springframework.boot.autoconfigure.websocket.reactive.WebSocketReactiveAutoConfiguration,\
    org.springframework.boot.autoconfigure.websocket.servlet.WebSocketServletAutoConfiguration,\
    org.springframework.boot.autoconfigure.websocket.servlet.WebSocketMessagingAutoConfiguration,\
    org.springframework.boot.autoconfigure.webservices.WebServicesAutoConfiguration,\
    org.springframework.boot.autoconfigure.webservices.client.WebServiceTemplateAutoConfiguration

    # Failure analyzers
    org.springframework.boot.diagnostics.FailureAnalyzer=\
    org.springframework.boot.autoconfigure.data.redis.RedisUrlSyntaxFailureAnalyzer,\
    org.springframework.boot.autoconfigure.diagnostics.analyzer.NoSuchBeanDefinitionFailureAnalyzer,\
    org.springframework.boot.autoconfigure.flyway.FlywayMigrationScriptMissingFailureAnalyzer,\
    org.springframework.boot.autoconfigure.jdbc.DataSourceBeanCreationFailureAnalyzer,\
    org.springframework.boot.autoconfigure.jdbc.HikariDriverConfigurationFailureAnalyzer,\
    org.springframework.boot.autoconfigure.r2dbc.ConnectionFactoryBeanCreationFailureAnalyzer,\
    org.springframework.boot.autoconfigure.session.NonUniqueSessionRepositoryFailureAnalyzer

    # Template availability providers
    org.springframework.boot.autoconfigure.template.TemplateAvailabilityProvider=\
    org.springframework.boot.autoconfigure.freemarker.FreeMarkerTemplateAvailabilityProvider,\
    org.springframework.boot.autoconfigure.mustache.MustacheTemplateAvailabilityProvider,\
    org.springframework.boot.autoconfigure.groovy.template.GroovyTemplateAvailabilityProvider,\
    org.springframework.boot.autoconfigure.thymeleaf.ThymeleafTemplateAvailabilityProvider,\
    org.springframework.boot.autoconfigure.web.servlet.JspTemplateAvailabilityProvider

    # DataSource initializer detectors
    org.springframework.boot.sql.init.dependency.DatabaseInitializerDetector=\
    org.springframework.boot.autoconfigure.flyway.FlywayMigrationInitializerDatabaseInitializerDetector

    -

    上面根据 org.springframework.boot.autoconfigure.EnableAutoConfiguration 获取的各个配置类，在通过反射加载就能得到一堆 JavaConfig配置类，然后再根据 ConditionalOnProperty等条件配置加载具体的 bean，大致就是这么个逻辑

    -]]> - - Java - SpringBoot - - - Java - Spring - SpringBoot - 自动装配 - AutoConfiguration - -     - - 聊一聊 https 的逻辑 - /2024/03/10/%E8%81%8A%E4%B8%80%E8%81%8A-https-%E7%9A%84%E9%80%BB%E8%BE%91/ - https 和 tls

    https 我原本的理解来自于几个概念

    -

    第一个是非对称加密

    -

    也就是公钥可以公开在网上，然后用公钥加密，由私有的私钥进行解密，然后就对 https 有了初步的偏颇的概念，就是浏览器用公钥进行加密，服务器端用私钥进行解密，这里再仔细想想会延伸出来几个问题，也是逐步完善我的后续理解

    -

    第二个就是非对称加密以后的对称加密

    -

    因为对加密的理解是超大的质数（这个还来源于大学的数据结构老师）相乘后会很难做质因数分解，比如最大的质数2^82,589,933 − 1，用十进制表示有24,862,048位，用现在计算机来算也是非常难的，那如果一直采用非对称加密，这个效率也会比较低，所以在网上查了一番之后知道了是先采用非对称加密，然后再用对称加密，因为非对称加密先有了安全保证，后续的消息就可以用对称加密的方式来进行传输安全了

    -

    第三个是最近一次看到的

    -

    也是一直缺乏思考的问题，因为 https 我们在使用的时候都会购买证书或者使用免费的证书，那这个证书和前面说的公私钥是什么关系，好像一直是悬空的，没想过这中间的相关性，证书究竟是怎么起作用的

    -

    下面是摘自 cloudflare 的 tls 握手流程，把证书的使用方式说的比较明白了

    -

    TLS 握手是由客户端和服务器交换的一系列数据报或消息。TLS 握手涉及多个步骤，因为客户端和服务器要交换完成握手和进行进一步对话所需的信息。

    -

    TLS 握手中的确切步骤将根据所使用的密钥交换算法的种类和双方支持的密码套件而有所不同。RSA 密钥交换算法虽然现在被认为不安全，但曾在 1.3 之前的 TLS 版本中使用。大致如下：

    -
      -
    1. “客户端问候（client hello）” 消息： 客户端通过向服务器发送“问候”消息来开始握手。该消息将包含客户端支持的 TLS 版本，支持的密码套件，以及称为一串称为“客户端随机数（client random）”的随机字节。
      2. -
    2. “服务器问候（server hello）”消息： 作为对 client hello 消息的回复，服务器发送一条消息，内含服务器的 SSL 证书、服务器选择的密码套件，以及“服务器随机数（server random）”，即由服务器生成的另一串随机字节。
      3. -
    3. 身份验证： 客户端使用颁发该证书的证书颁发机构验证服务器的 SSL 证书。此举确认服务器是其声称的身份，且客户端正在与该域的实际所有者进行交互。
      4. -
    4. 预主密钥： 客户端再发送一串随机字节，即“预主密钥（premaster secret）”。预主密钥是使用公钥加密的，只能使用服务器的私钥解密。（客户端从服务器的 SSL 证书中获得公钥。）
      5. -
    5. 私钥被使用：服务器对预主密钥进行解密。
      6. -
    6. 生成会话密钥：客户端和服务器均使用客户端随机数、服务器随机数和预主密钥生成会话密钥。双方应得到相同的结果。
      7. -
    7. 客户端就绪：客户端发送一条“已完成”消息，该消息用会话密钥加密。
      8. -
    8. 服务器就绪：服务器发送一条“已完成”消息，该消息用会话密钥加密。
      9. -
    9. 实现安全对称加密：已完成握手，并使用会话密钥继续进行通信。
      10. -
    -

    为了学习这个过程，我们尝试用 Chrome 的自带抓包工具，以往这是可以通过在 Chrome 地址栏中输入 chrome://net-internals/#events 来打开，现在变成了两部分，chrome://net-export/ 这里可以开始抓包，然后会记录下一个抓包日志文件里，

    -

    -

    然后再打开

    -

    https://netlog-viewer.appspot.com/#events 来查看具体的日志

    -

    这里我们以打开百度为例，即在打开 chrome://net-export/ 并启动抓包后，再在一个新 tab 打开 baidu.com，然后关闭

    -

    将日志文件在 https://netlog-viewer.appspot.com/#events 打开

    -

    -

    这里可以看到

    -

    -

    --> type 1 表示现在 tls 握手进行到哪一步了, 对应的值表示不同的阶段

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    代码(type)释义
    0HelloRequest
    1ClientHello
    2ServerHello
    11Certificate
    12ServerKeyExchange
    13CertificateRequest
    14ServerHelloDone
    15CertificateVerify
    16ClientKeyExchange
    20Finished
    -

    具体的对应关系是在这边

    -

    比如 11 之后表示我们从服务端收到了证书

    -

    -

    然后就要去验证证书的可靠性

    -

    -

    后面是验证后的结果

    -

    -

    -

    最后就是交换对称会话秘钥然后完成握手

    -

    +

    上面根据 org.springframework.boot.autoconfigure.EnableAutoConfiguration 获取的各个配置类，在通过反射加载就能得到一堆 JavaConfig配置类，然后再根据 ConditionalOnProperty等条件配置加载具体的 bean，大致就是这么个逻辑

    ]]>     - 网络 + Java + SpringBoot - http + Java + Spring + SpringBoot + 自动装配 + AutoConfiguration + +     + + 聊聊传说中的 ThreadLocal + /2021/05/30/%E8%81%8A%E8%81%8A%E4%BC%A0%E8%AF%B4%E4%B8%AD%E7%9A%84-ThreadLocal/ + 说来也惭愧，这个 ThreadLocal 其实一直都是一知半解，而且看了一下之后还发现记错了，所以还是记录下
    原先记忆里的都是反过来，一个 ThreadLocal 是里面按照 thread 作为 key，存储线程内容的，真的是半解都米有，完全是错的，这样就得用 concurrentHashMap 这种去存储并且要锁定线程了，然后内容也只能存一个了，想想简直智障

    +

    究竟是啥结构

    比如我们在代码中 new 一个 ThreadLocal，

    +
    public static void main(String[] args) {
            ThreadLocal<Man> tl = new ThreadLocal<>();

            new Thread(() -> {
                try {
                    TimeUnit.SECONDS.sleep(2);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
                System.out.println(tl.get());
            }).start();
            new Thread(() -> {
                try {
                    TimeUnit.SECONDS.sleep(1);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
                tl.set(new Man());
            }).start();
        }

        static class Man {
            String name = "nick";
        }
    +

    这里构造了两个线程，一个先往里设值，一个后从里取，运行看下结果，

    知道这个用法的话肯定知道是取不到值的，只是具体的原理原来搞错了，我们来看下设值 set 方法

    +
    public void set(T value) {
        Thread t = Thread.currentThread();
        ThreadLocalMap map = getMap(t);
        if (map != null)
            map.set(this, value);
        else
            createMap(t, value);
    }
    +

    写博客这会我才明白我原来咋会错得这么离谱，看到第一行代码 t 就是当前线程，然后第二行就是用这个线程去getMap，然后我是把这个当成从 map 里取值了，其实这里是

    +
    ThreadLocalMap getMap(Thread t) {
        return t.threadLocals;
    }
    +

    获取 t 的 threadLocals 成员变量，那这个 threadLocals 又是啥呢

    它其实是线程 Thread 中的一个类型是java.lang.ThreadLocal.ThreadLocalMap的成员变量
    这是 ThreadLocal 的一个静态成员变量

    +
    static class ThreadLocalMap {

            /**
             * The entries in this hash map extend WeakReference, using
             * its main ref field as the key (which is always a
             * ThreadLocal object).  Note that null keys (i.e. entry.get()
             * == null) mean that the key is no longer referenced, so the
             * entry can be expunged from table.  Such entries are referred to
             * as "stale entries" in the code that follows.
             */
            static class Entry extends WeakReference<ThreadLocal<?>> {
                /** The value associated with this ThreadLocal. */
                Object value;

                Entry(ThreadLocal<?> k, Object v) {
                    super(k);
                    value = v;
                }
            }
        }
    +

    全部代码有点长，只截取了一小部分，然后我们再回头来分析前面说的 set 过程，再 copy 下代码

    +
    public void set(T value) {
        Thread t = Thread.currentThread();
        ThreadLocalMap map = getMap(t);
        if (map != null)
            map.set(this, value);
        else
            createMap(t, value);
    }
    +

    获取到 map 以后呢，如果 map 不为空，就往 map 里 set，这里注意 key 是啥，其实是当前这个 ThreadLocal，这里就比较明白了究竟是啥结构，每个线程都会维护自身的 ThreadLocalMap，它是线程的一个成员变量，当创建 ThreadLocal 的时候，进行设值的时候其实是往这个 map 里以 ThreadLocal 作为 key，往里设 value。

    +

    内存泄漏是什么鬼

    这里又要看下前面的 ThreadLocalMap 结构了，类似 HashMap，它有个 Entry 结构，在设置的时候会先包装成一个 Entry

    +
    private void set(ThreadLocal<?> key, Object value) {

            // We don't use a fast path as with get() because it is at
            // least as common to use set() to create new entries as
            // it is to replace existing ones, in which case, a fast
            // path would fail more often than not.

            Entry[] tab = table;
            int len = tab.length;
            int i = key.threadLocalHashCode & (len-1);

            for (Entry e = tab[i];
                 e != null;
                 e = tab[i = nextIndex(i, len)]) {
                ThreadLocal<?> k = e.get();

                if (k == key) {
                    e.value = value;
                    return;
                }

                if (k == null) {
                    replaceStaleEntry(key, value, i);
                    return;
                }
            }

            tab[i] = new Entry(key, value);
            int sz = ++size;
            if (!cleanSomeSlots(i, sz) && sz >= threshold)
                rehash();
    }
    +

    这里其实比较重要的就是前面的 Entry 的构造方法，Entry 是个 WeakReference 的子类，然后在构造方法里可以看到 key 会被包装成一个弱引用，这里为什么使用弱引用，其实是方便这个 key 被回收，如果前面的 ThreadLocal tl实例被设置成 null 了，如果这里是直接的强引用的话，就只能等到线程整个回收了，但是其实是弱引用也会有问题，主要是因为这个 value，如果在 ThreadLocal tl 被设置成 null 了，那么其实这个 value 就会没法被访问到，所以最好的操作还是在使用完了就 remove 掉

    +]]>     + + Java + + + Java + ThreadLocal + 弱引用 + 内存泄漏 + WeakReference     @@ -7104,6 +7098,53 @@ user3: zsh + + 聊聊厦门旅游的好与不好 + /2021/04/11/%E8%81%8A%E8%81%8A%E5%8E%A6%E9%97%A8%E6%97%85%E6%B8%B8%E7%9A%84%E5%A5%BD%E4%B8%8E%E4%B8%8D%E5%A5%BD/ + 这几天去了趟厦门，原来几年前就想去了，本来都请好假了，后面因为一些事情没去成，这次刚好公司组织，就跟 LD 一起去了厦门，也不洋洋洒洒地写游记了，后面可能会有，今天先来总结下好的地方和比较坑的地方。
    这次主要去了中山路、鼓浪屿、曾厝(cuo)垵、植物园、灵玲马戏团，因为住的离环岛路比较近，还有幸现场看了下厦门马拉松，其中

    +

    中山路

    这里看上去是有点民国时期的建筑风格，部分像那种电视里的租界啥的，不过这次去的时候都在翻修，路一大半拦起来了，听导游说这里往里面走有个局口街，然后上次听前同事说厦门比较有名的就是沙茶面和海蛎煎，不出意料的不太爱吃，沙茶面比较普通，可能是没吃到正宗的，海蛎煎吃不惯，倒是有个大叔的沙茶里脊还不错，在局口街那，还有小哥在那拍，应该也算是个网红打卡点了，然后吃了个油条麻糍也还不错，总体如果是看建筑的话可能最近不是个好时间，个人也没这方面爱好，吃的话最好多打听打听沙茶面跟海蛎煎哪里正宗。如果不知道哪家好吃，也不爱看这类建筑的可以排个坑。

    +

    鼓浪屿

    鼓浪屿也是完全没啥概念，需要乘船过去，但是只要二十分钟，岛上没有机动车，基本都靠走，有几个比较有名的地方，菽庄花园，里面有钢琴博物馆，对这个感兴趣的可以去看看，旁边是沙滩还可以逛逛，然后有各种博物馆，风琴啥的，岛上最大的特色是巷子多，道听途说有三百多条小巷，还有几个网红打卡点，周杰伦晴天墙，还有个最美转角，都是挤满了人排队打卡拍照，不过如果不着急，慢慢悠悠逛逛还是不错的，比较推荐，推荐值☆☆

    +

    曾厝垵

    一直读不对这个字，都是叫：那个曾什么垵，愧对语文老师，这里到算是意外之喜，鼓浪屿回来已经挺累了，不过由于比较饿（什么原因后面说），并且离住的地方不远，就过去逛了逛，东西还蛮好吃的，芒果挺便宜，一大杯才十块，无骨鸡爪很贵，不是特别爱，臭豆腐不错的，也不算很贵，这里想起来，那边八婆婆的豆乳烧仙草还不错的，去中山路那会喝了，来曾厝垵也买了，奶茶爱好者可以试试，含糖量应该很高，不爱甜食或者减肥的同学慎重考虑好了再尝试，晚上那边从牌坊出来，沿着环岛路挺多夜宵店什么的，非常推荐，推荐值☆☆☆☆

    +

    植物园

    植物园还是挺名副其实的，有热带植物，沙漠多肉，因为赶时间逛得不多，热带雨林植物那太多人了，都是在那拍照，而且我指的拍照都是拍人照，本身就很小的路，各种十八线网红，或者普通游客在那摆 pose 拍照，挺无语的；沙漠多肉比较惊喜，好多比人高的仙人掌，一大片的仙人球，很可恶的是好多大仙人掌上都有人刻字，越来越体会到，我们社会人多了，什么样的都有，而且不少；还看了下百花厅，但没什么特别的，可能赶时间比较着急，没仔细看，比较推荐，推荐值☆☆☆

    +

    灵玲马戏团

    对这个其实比较排斥，主要是比较晚了，跑的有点远（我太懒了），一开始真的挺拉低体验感受的，上来个什么书法家，现场画马，卖画；不过后面的还算值回票价，主题是花木兰，空中动作应该很考验基本功，然后那些老外的飞轮还跳绳（不知道学名叫啥），动物那块不太忍心看，应该是吃了不少苦头，不过人都这样就往后点再心疼动物吧。

    +

    总结

    厦门是个非常适合干饭人的地方，吃饭的地方大部分是差不多一桌菜十个左右就完了，而且上来就一大碗饭，一瓶雪碧一瓶可乐，对于经常是家里跟亲戚吃饭都得十几二十个菜的乡下人来说，不太吃得惯这样的🤦‍♂️，当然很有可能是我们预算不足，点的差。但是有一点是我回杭州深有感触的，感觉杭州司机的素质真的是跟厦门的司机差了比较多，杭州除非公交车停了，否则人行道很难看到主动让人的，当然这里拿厦门这个旅游城市来对比也不是很公平，不过这也是体现城市现代化文明水平的一个维度吧。

    +]]>     + + 生活 + 旅游 + + + 生活 + 杭州 + 旅游 + 厦门 + 中山路 + 局口街 + 鼓浪屿 + 曾厝垵 + 植物园 + 马戏团 + 沙茶面 + 海蛎煎 + +     + + 聊聊如何识别和意识到日常生活中的各类危险 + /2021/06/06/%E8%81%8A%E8%81%8A%E5%A6%82%E4%BD%95%E8%AF%86%E5%88%AB%E5%92%8C%E6%84%8F%E8%AF%86%E5%88%B0%E6%97%A5%E5%B8%B8%E7%94%9F%E6%B4%BB%E4%B8%AD%E7%9A%84%E5%90%84%E7%B1%BB%E5%8D%B1%E9%99%A9/ + 这篇博客的灵感又是来自于我从绍兴来杭州的路上，在我们进站以后上电梯快到的时候，突然前面不动了，右边我能看到的是有个人的行李箱一时拎不起来，另一边后面看到其实是个小孩子在那哭闹，一位妈妈就在那停着安抚或者可能有点手足无措，其实这一点应该是在几年前慢慢意识到是个非常危险的场景，特别是像绍兴北站这样上去站台是非常长的电梯，因为最近扩建改造，车次减少了很多，所以每一班都有很多人，检票上站台的电梯都是满员运转，试想这种情况，如果刚才那位妈妈再多停留一点时间，很可能就会出现后面的人上不来被挤下去，再严重点就是踩踏事件，但是这类情况很少人真的意识到，非常明显的例子就是很多人拿着比较大比较重的行李箱，不走垂梯，并且在快到的时候没有提前准备好，有可能在玩手机啥的，如果提不动，后面又是挤满人了，就很可能出现前面说的这种情况，并且其实这种是非紧急情况，大多数人都没有心理准备，一旦发生后果可能就会很严重，例如火灾地震疏散大部分人或者说负责引导的都是指示要有序撤离，防止踩踏，但是普通坐个扶梯，一般都不会有这个意识，但是如果这个时间比较长，出现了人员站不住往后倒了，真的会很严重。所以如果自己是带娃的或者带了很重的行李箱的，请提前做好准备，看到前面有人带的，最好也保持一定距离。
    还有比如日常走路，旁边有车子停着的情况，比较基本的看车灯有没有亮着，亮着的是否是倒车灯，这种应该特别注意远离，至少保持距离，不能挨着走，很多人特别是一些老年人，在一些人比较多的路上，往往完全无视旁边这些车的状态，我走我的路，谁敢阻拦我，管他车在那动不动，其实真的非常危险，车子本身有视线死角，再加上司机的驾驶习惯和状态，想去送死跟碰瓷的除外，还有就是有一些车会比较特殊，车子发动着，但是没灯，可能是车子灯坏了或者司机通过什么方式关了灯，这种比较难避开，不过如果车子打着了，一般会有比较大的热量散发，车子刚灭了也会有，反正能远离点尽量远离，从轿车的车前面走过挨着走要比从屁股后面挨着走稍微安全一些，但也最好不要挨着车走。
    最后一点其实是我觉得是我自己比较怕死，一般对来向的车或者从侧面出来的车会做更长的预判距离，特别是电瓶车，一般是不让人的，像送外卖的小哥，的确他们不太容易，但是真的很危险啊，基本就生死看刹车，能刹住就赚了，刹不住就看身子骨扛不扛撞了，只是这里要多说点又要谈到资本的趋利性了，总是想法设法的压榨以获取更多的利益，也不扯远了，能远离就远离吧。

    +]]>     + + 生活 + + + 生活 + 糟心事 + 扶梯 + 踩踏 + 安全 + 电瓶车 + +     聊聊对 FunctionalInterface 注解的一些理解 /2023/10/15/%E8%81%8A%E8%81%8A%E5%AF%B9-FunctionalInterface-%E6%B3%A8%E8%A7%A3%E7%9A%84%E4%B8%80%E4%BA%9B%E7%90%86%E8%A7%A3/ @@ -7126,20 +7167,21 @@ user3: - 聊聊如何识别和意识到日常生活中的各类危险 - /2021/06/06/%E8%81%8A%E8%81%8A%E5%A6%82%E4%BD%95%E8%AF%86%E5%88%AB%E5%92%8C%E6%84%8F%E8%AF%86%E5%88%B0%E6%97%A5%E5%B8%B8%E7%94%9F%E6%B4%BB%E4%B8%AD%E7%9A%84%E5%90%84%E7%B1%BB%E5%8D%B1%E9%99%A9/ - 这篇博客的灵感又是来自于我从绍兴来杭州的路上，在我们进站以后上电梯快到的时候，突然前面不动了，右边我能看到的是有个人的行李箱一时拎不起来，另一边后面看到其实是个小孩子在那哭闹，一位妈妈就在那停着安抚或者可能有点手足无措，其实这一点应该是在几年前慢慢意识到是个非常危险的场景，特别是像绍兴北站这样上去站台是非常长的电梯，因为最近扩建改造，车次减少了很多，所以每一班都有很多人，检票上站台的电梯都是满员运转，试想这种情况，如果刚才那位妈妈再多停留一点时间，很可能就会出现后面的人上不来被挤下去，再严重点就是踩踏事件，但是这类情况很少人真的意识到，非常明显的例子就是很多人拿着比较大比较重的行李箱，不走垂梯，并且在快到的时候没有提前准备好，有可能在玩手机啥的，如果提不动，后面又是挤满人了，就很可能出现前面说的这种情况，并且其实这种是非紧急情况，大多数人都没有心理准备，一旦发生后果可能就会很严重，例如火灾地震疏散大部分人或者说负责引导的都是指示要有序撤离，防止踩踏，但是普通坐个扶梯，一般都不会有这个意识，但是如果这个时间比较长，出现了人员站不住往后倒了，真的会很严重。所以如果自己是带娃的或者带了很重的行李箱的，请提前做好准备，看到前面有人带的，最好也保持一定距离。
    还有比如日常走路，旁边有车子停着的情况，比较基本的看车灯有没有亮着，亮着的是否是倒车灯，这种应该特别注意远离，至少保持距离，不能挨着走，很多人特别是一些老年人，在一些人比较多的路上，往往完全无视旁边这些车的状态，我走我的路，谁敢阻拦我，管他车在那动不动，其实真的非常危险，车子本身有视线死角，再加上司机的驾驶习惯和状态，想去送死跟碰瓷的除外，还有就是有一些车会比较特殊，车子发动着，但是没灯，可能是车子灯坏了或者司机通过什么方式关了灯，这种比较难避开，不过如果车子打着了，一般会有比较大的热量散发，车子刚灭了也会有，反正能远离点尽量远离，从轿车的车前面走过挨着走要比从屁股后面挨着走稍微安全一些，但也最好不要挨着车走。
    最后一点其实是我觉得是我自己比较怕死，一般对来向的车或者从侧面出来的车会做更长的预判距离，特别是电瓶车，一般是不让人的，像送外卖的小哥，的确他们不太容易，但是真的很危险啊，基本就生死看刹车，能刹住就赚了，刹不住就看身子骨扛不扛撞了，只是这里要多说点又要谈到资本的趋利性了，总是想法设法的压榨以获取更多的利益，也不扯远了，能远离就远离吧。

    + 聊聊我刚学会的应用诊断方法 + /2020/05/22/%E8%81%8A%E8%81%8A%E6%88%91%E5%88%9A%E5%AD%A6%E4%BC%9A%E7%9A%84%E5%BA%94%E7%94%A8%E8%AF%8A%E6%96%AD%E6%96%B9%E6%B3%95/ + 因为传说中的出身问题，我以前写的是PHP，在使用 swoole 之前，基本的应用调试手段就是简单粗暴的 var_dump，exit，对于单进程模型的 PHP 也是简单有效，技术栈换成 Java 之后，就变得没那么容易，一方面是需要编译，另一方面是一般都是基于 spring 的项目，如果问题定位比较模糊，那框架层的是很难靠简单的 System.out.println 或者打 log 解决，(PS：我觉得可能我写的东西比较适合从 PHP 这种弱类型语言转到 Java 的小白同学)这个时候一方面因为是 Java，有了非常好用的 idea IDE 的支持，可以各种花式调试，条件断点尤其牛叉，但是又因为有 Spring+Java 的双重原因，有些情况下单步调试可以把手按废掉，这也是我之前一直比较困惑苦逼的点，后来随着慢慢精（jiang）进（you）之后，比如对于一个 oom 的情况，我们可以通过启动参数加上-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=xx/xx 来配置溢出时的堆dump 日志，获取到这个文件后，我们可以通过像 Memory Analyzer (MAT)[https://www.eclipse.org/mat/] (The Eclipse Memory Analyzer is a fast and feature-rich Java heap analyzer that helps you find memory leaks and reduce memory consumption.)来查看诊断问题所在，之前用到的时候是因为有个死循环一直往链表里塞数据，属于比较简单的，后来一次是由于运维进行应用迁移时按默认的统一配置了堆内存大小，导致内存的确不够用，所以溢出了，
    今天想说的其实主要是我们的 thread dump，这也是我最近才真正用的一个方法，可能真的很小白了，用过 ide 的单步调试其实都知道会有一个一层层的玩意，比如函数从 A，调用了 B，再从 B 调用了 C，一直往下(因为是 Java，所以还有很多🤦‍♂️)，这个其实也是大部分语言的调用模型，利用了栈这个数据结构，通过这个结构我们可以知道代码的调用链路，由于对于一个 spring 应用，在本身框架代码量非常庞大的情况下，外加如果应用代码也是非常多的时候，有时候通过单步调试真的很难短时间定位到问题，需要非常大的耐心和仔细观察，当然不是说完全不行，举个例子当我的应用经常启动需要非常长的时间，因为本身应用有非常多个 bean，比较难说究竟是 bean 的加载的确很慢还是有什么异常原因，这种时候就可以使用 thread dump 了，具体怎么操作呢

    如果在idea 中运行或者调试时，可以直接点击这个照相机一样的按钮，右边就会出现了左边会显示所有的线程，右边会显示线程栈，

    +
    "main@1" prio=5 tid=0x1 nid=NA runnable
      java.lang.Thread.State: RUNNABLE
    	  at TreeDistance.treeDist(TreeDistance.java:64)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.main(TreeDistance.java:45)
    +

    这就是我们主线程的堆栈信息了，main 表示这个线程名，prio表示优先级，默认是 5，tid 表示线程 id，nid 表示对应的系统线程，后面的runnable 表示目前线程状态，因为是被我打了断点，所以是就许状态，然后下面就是对应的线程栈内容了，在TreeDistance类的 treeDist方法中，对应的文件行数是 64 行。
    这里使用 thread dump一般也不会是上面我截图代码里的这种代码量很少的，一般是大型项目，有时候跑着跑着没反应，又不知道跑到哪了，特别是一些刚接触的大项目或者需要定位一个大项目的一个疑难问题，一时没思路时，可以使用这个方法，个人觉得非常有帮助。

    ]]>     - 生活 + Java + Thread dump + 工具 + 问题排查 - 生活 - 糟心事 - 扶梯 - 踩踏 - 安全 - 电瓶车 + Java + Thread dump     @@ -7199,84 +7241,6 @@ user3: 远程办公 - - 聊聊厦门旅游的好与不好 - /2021/04/11/%E8%81%8A%E8%81%8A%E5%8E%A6%E9%97%A8%E6%97%85%E6%B8%B8%E7%9A%84%E5%A5%BD%E4%B8%8E%E4%B8%8D%E5%A5%BD/ - 这几天去了趟厦门，原来几年前就想去了，本来都请好假了，后面因为一些事情没去成，这次刚好公司组织，就跟 LD 一起去了厦门，也不洋洋洒洒地写游记了，后面可能会有，今天先来总结下好的地方和比较坑的地方。
    这次主要去了中山路、鼓浪屿、曾厝(cuo)垵、植物园、灵玲马戏团，因为住的离环岛路比较近，还有幸现场看了下厦门马拉松，其中

    -

    中山路

    这里看上去是有点民国时期的建筑风格，部分像那种电视里的租界啥的，不过这次去的时候都在翻修，路一大半拦起来了，听导游说这里往里面走有个局口街，然后上次听前同事说厦门比较有名的就是沙茶面和海蛎煎，不出意料的不太爱吃，沙茶面比较普通，可能是没吃到正宗的，海蛎煎吃不惯，倒是有个大叔的沙茶里脊还不错，在局口街那，还有小哥在那拍，应该也算是个网红打卡点了，然后吃了个油条麻糍也还不错，总体如果是看建筑的话可能最近不是个好时间，个人也没这方面爱好，吃的话最好多打听打听沙茶面跟海蛎煎哪里正宗。如果不知道哪家好吃，也不爱看这类建筑的可以排个坑。

    -

    鼓浪屿

    鼓浪屿也是完全没啥概念，需要乘船过去，但是只要二十分钟，岛上没有机动车，基本都靠走，有几个比较有名的地方，菽庄花园，里面有钢琴博物馆，对这个感兴趣的可以去看看，旁边是沙滩还可以逛逛，然后有各种博物馆，风琴啥的，岛上最大的特色是巷子多，道听途说有三百多条小巷，还有几个网红打卡点，周杰伦晴天墙，还有个最美转角，都是挤满了人排队打卡拍照，不过如果不着急，慢慢悠悠逛逛还是不错的，比较推荐，推荐值☆☆

    -

    曾厝垵

    一直读不对这个字，都是叫：那个曾什么垵，愧对语文老师，这里到算是意外之喜，鼓浪屿回来已经挺累了，不过由于比较饿（什么原因后面说），并且离住的地方不远，就过去逛了逛，东西还蛮好吃的，芒果挺便宜，一大杯才十块，无骨鸡爪很贵，不是特别爱，臭豆腐不错的，也不算很贵，这里想起来，那边八婆婆的豆乳烧仙草还不错的，去中山路那会喝了，来曾厝垵也买了，奶茶爱好者可以试试，含糖量应该很高，不爱甜食或者减肥的同学慎重考虑好了再尝试，晚上那边从牌坊出来，沿着环岛路挺多夜宵店什么的，非常推荐，推荐值☆☆☆☆

    -

    植物园

    植物园还是挺名副其实的，有热带植物，沙漠多肉，因为赶时间逛得不多，热带雨林植物那太多人了，都是在那拍照，而且我指的拍照都是拍人照，本身就很小的路，各种十八线网红，或者普通游客在那摆 pose 拍照，挺无语的；沙漠多肉比较惊喜，好多比人高的仙人掌，一大片的仙人球，很可恶的是好多大仙人掌上都有人刻字，越来越体会到，我们社会人多了，什么样的都有，而且不少；还看了下百花厅，但没什么特别的，可能赶时间比较着急，没仔细看，比较推荐，推荐值☆☆☆

    -

    灵玲马戏团

    对这个其实比较排斥，主要是比较晚了，跑的有点远（我太懒了），一开始真的挺拉低体验感受的，上来个什么书法家，现场画马，卖画；不过后面的还算值回票价，主题是花木兰，空中动作应该很考验基本功，然后那些老外的飞轮还跳绳（不知道学名叫啥），动物那块不太忍心看，应该是吃了不少苦头，不过人都这样就往后点再心疼动物吧。

    -

    总结

    厦门是个非常适合干饭人的地方，吃饭的地方大部分是差不多一桌菜十个左右就完了，而且上来就一大碗饭，一瓶雪碧一瓶可乐，对于经常是家里跟亲戚吃饭都得十几二十个菜的乡下人来说，不太吃得惯这样的🤦‍♂️，当然很有可能是我们预算不足，点的差。但是有一点是我回杭州深有感触的，感觉杭州司机的素质真的是跟厦门的司机差了比较多，杭州除非公交车停了，否则人行道很难看到主动让人的，当然这里拿厦门这个旅游城市来对比也不是很公平，不过这也是体现城市现代化文明水平的一个维度吧。

    -]]>     - - 生活 - 旅游 - - - 生活 - 杭州 - 旅游 - 厦门 - 中山路 - 局口街 - 鼓浪屿 - 曾厝垵 - 植物园 - 马戏团 - 沙茶面 - 海蛎煎 - -     - - 聊聊传说中的 ThreadLocal - /2021/05/30/%E8%81%8A%E8%81%8A%E4%BC%A0%E8%AF%B4%E4%B8%AD%E7%9A%84-ThreadLocal/ - 说来也惭愧，这个 ThreadLocal 其实一直都是一知半解，而且看了一下之后还发现记错了，所以还是记录下
    原先记忆里的都是反过来，一个 ThreadLocal 是里面按照 thread 作为 key，存储线程内容的，真的是半解都米有，完全是错的，这样就得用 concurrentHashMap 这种去存储并且要锁定线程了，然后内容也只能存一个了，想想简直智障

    -

    究竟是啥结构

    比如我们在代码中 new 一个 ThreadLocal，

    -
    public static void main(String[] args) {
            ThreadLocal<Man> tl = new ThreadLocal<>();

            new Thread(() -> {
                try {
                    TimeUnit.SECONDS.sleep(2);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
                System.out.println(tl.get());
            }).start();
            new Thread(() -> {
                try {
                    TimeUnit.SECONDS.sleep(1);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
                tl.set(new Man());
            }).start();
        }

        static class Man {
            String name = "nick";
        }
    -

    这里构造了两个线程，一个先往里设值，一个后从里取，运行看下结果，

    知道这个用法的话肯定知道是取不到值的，只是具体的原理原来搞错了，我们来看下设值 set 方法

    -
    public void set(T value) {
        Thread t = Thread.currentThread();
        ThreadLocalMap map = getMap(t);
        if (map != null)
            map.set(this, value);
        else
            createMap(t, value);
    }
    -

    写博客这会我才明白我原来咋会错得这么离谱，看到第一行代码 t 就是当前线程，然后第二行就是用这个线程去getMap，然后我是把这个当成从 map 里取值了，其实这里是

    -
    ThreadLocalMap getMap(Thread t) {
        return t.threadLocals;
    }
    -

    获取 t 的 threadLocals 成员变量，那这个 threadLocals 又是啥呢

    它其实是线程 Thread 中的一个类型是java.lang.ThreadLocal.ThreadLocalMap的成员变量
    这是 ThreadLocal 的一个静态成员变量

    -
    static class ThreadLocalMap {

            /**
             * The entries in this hash map extend WeakReference, using
             * its main ref field as the key (which is always a
             * ThreadLocal object).  Note that null keys (i.e. entry.get()
             * == null) mean that the key is no longer referenced, so the
             * entry can be expunged from table.  Such entries are referred to
             * as "stale entries" in the code that follows.
             */
            static class Entry extends WeakReference<ThreadLocal<?>> {
                /** The value associated with this ThreadLocal. */
                Object value;

                Entry(ThreadLocal<?> k, Object v) {
                    super(k);
                    value = v;
                }
            }
        }
    -

    全部代码有点长，只截取了一小部分，然后我们再回头来分析前面说的 set 过程，再 copy 下代码

    -
    public void set(T value) {
        Thread t = Thread.currentThread();
        ThreadLocalMap map = getMap(t);
        if (map != null)
            map.set(this, value);
        else
            createMap(t, value);
    }
    -

    获取到 map 以后呢，如果 map 不为空，就往 map 里 set，这里注意 key 是啥，其实是当前这个 ThreadLocal，这里就比较明白了究竟是啥结构，每个线程都会维护自身的 ThreadLocalMap，它是线程的一个成员变量，当创建 ThreadLocal 的时候，进行设值的时候其实是往这个 map 里以 ThreadLocal 作为 key，往里设 value。

    -

    内存泄漏是什么鬼

    这里又要看下前面的 ThreadLocalMap 结构了，类似 HashMap，它有个 Entry 结构，在设置的时候会先包装成一个 Entry

    -
    private void set(ThreadLocal<?> key, Object value) {

            // We don't use a fast path as with get() because it is at
            // least as common to use set() to create new entries as
            // it is to replace existing ones, in which case, a fast
            // path would fail more often than not.

            Entry[] tab = table;
            int len = tab.length;
            int i = key.threadLocalHashCode & (len-1);

            for (Entry e = tab[i];
                 e != null;
                 e = tab[i = nextIndex(i, len)]) {
                ThreadLocal<?> k = e.get();

                if (k == key) {
                    e.value = value;
                    return;
                }

                if (k == null) {
                    replaceStaleEntry(key, value, i);
                    return;
                }
            }

            tab[i] = new Entry(key, value);
            int sz = ++size;
            if (!cleanSomeSlots(i, sz) && sz >= threshold)
                rehash();
    }
    -

    这里其实比较重要的就是前面的 Entry 的构造方法，Entry 是个 WeakReference 的子类，然后在构造方法里可以看到 key 会被包装成一个弱引用，这里为什么使用弱引用，其实是方便这个 key 被回收，如果前面的 ThreadLocal tl实例被设置成 null 了，如果这里是直接的强引用的话，就只能等到线程整个回收了，但是其实是弱引用也会有问题，主要是因为这个 value，如果在 ThreadLocal tl 被设置成 null 了，那么其实这个 value 就会没法被访问到，所以最好的操作还是在使用完了就 remove 掉

    -]]>     - - Java - - - Java - ThreadLocal - 弱引用 - 内存泄漏 - WeakReference - -     - - 聊聊我刚学会的应用诊断方法 - /2020/05/22/%E8%81%8A%E8%81%8A%E6%88%91%E5%88%9A%E5%AD%A6%E4%BC%9A%E7%9A%84%E5%BA%94%E7%94%A8%E8%AF%8A%E6%96%AD%E6%96%B9%E6%B3%95/ - 因为传说中的出身问题，我以前写的是PHP，在使用 swoole 之前，基本的应用调试手段就是简单粗暴的 var_dump，exit，对于单进程模型的 PHP 也是简单有效，技术栈换成 Java 之后，就变得没那么容易，一方面是需要编译，另一方面是一般都是基于 spring 的项目，如果问题定位比较模糊，那框架层的是很难靠简单的 System.out.println 或者打 log 解决，(PS：我觉得可能我写的东西比较适合从 PHP 这种弱类型语言转到 Java 的小白同学)这个时候一方面因为是 Java，有了非常好用的 idea IDE 的支持，可以各种花式调试，条件断点尤其牛叉，但是又因为有 Spring+Java 的双重原因，有些情况下单步调试可以把手按废掉，这也是我之前一直比较困惑苦逼的点，后来随着慢慢精（jiang）进（you）之后，比如对于一个 oom 的情况，我们可以通过启动参数加上-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=xx/xx 来配置溢出时的堆dump 日志，获取到这个文件后，我们可以通过像 Memory Analyzer (MAT)[https://www.eclipse.org/mat/] (The Eclipse Memory Analyzer is a fast and feature-rich Java heap analyzer that helps you find memory leaks and reduce memory consumption.)来查看诊断问题所在，之前用到的时候是因为有个死循环一直往链表里塞数据，属于比较简单的，后来一次是由于运维进行应用迁移时按默认的统一配置了堆内存大小，导致内存的确不够用，所以溢出了，
    今天想说的其实主要是我们的 thread dump，这也是我最近才真正用的一个方法，可能真的很小白了，用过 ide 的单步调试其实都知道会有一个一层层的玩意，比如函数从 A，调用了 B，再从 B 调用了 C，一直往下(因为是 Java，所以还有很多🤦‍♂️)，这个其实也是大部分语言的调用模型，利用了栈这个数据结构，通过这个结构我们可以知道代码的调用链路，由于对于一个 spring 应用，在本身框架代码量非常庞大的情况下，外加如果应用代码也是非常多的时候，有时候通过单步调试真的很难短时间定位到问题，需要非常大的耐心和仔细观察，当然不是说完全不行，举个例子当我的应用经常启动需要非常长的时间，因为本身应用有非常多个 bean，比较难说究竟是 bean 的加载的确很慢还是有什么异常原因，这种时候就可以使用 thread dump 了，具体怎么操作呢

    如果在idea 中运行或者调试时，可以直接点击这个照相机一样的按钮，右边就会出现了左边会显示所有的线程，右边会显示线程栈，

    -
    "main@1" prio=5 tid=0x1 nid=NA runnable
      java.lang.Thread.State: RUNNABLE
    	  at TreeDistance.treeDist(TreeDistance.java:64)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.main(TreeDistance.java:45)
    -

    这就是我们主线程的堆栈信息了，main 表示这个线程名，prio表示优先级，默认是 5，tid 表示线程 id，nid 表示对应的系统线程，后面的runnable 表示目前线程状态，因为是被我打了断点，所以是就许状态，然后下面就是对应的线程栈内容了，在TreeDistance类的 treeDist方法中，对应的文件行数是 64 行。
    这里使用 thread dump一般也不会是上面我截图代码里的这种代码量很少的，一般是大型项目，有时候跑着跑着没反应，又不知道跑到哪了，特别是一些刚接触的大项目或者需要定位一个大项目的一个疑难问题，一时没思路时，可以使用这个方法，个人觉得非常有帮助。

    -]]>     - - Java - Thread dump - 工具 - 问题排查 - - - Java - Thread dump - -     聊聊最近平淡的生活之又聊通勤 /2021/11/07/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB/ @@ -7311,21 +7275,13 @@ user3: - 聊一下 Linux 中 dmesg 跟 journal 的差别 - /2024/07/14/%E8%81%8A%E4%B8%80%E4%B8%8B-Linux-%E4%B8%AD-dmesg-%E8%B7%9F-journal-%E7%9A%84%E5%B7%AE%E5%88%AB/ - 最近在迁移一个自己用的mysql实例，发现用 portainer 安装 mysql 一直失败，还以为是配置了自定义端口映射被系统防火墙限制，但后面不映射端口也是不行，一开始查看 journal日志也没什么发现，因为之前在腾讯云的小机器也是正常的部署，所以没觉得是可能会oom，一时间就有点没头绪，结果也是好奇就看了下dmesg，发现里面都是oom，给了1g的内存的ubuntu虚拟机，安装了一两个docker就不行了，不过这里比较重要的就是记录下dmesg 跟 journal日志的这点区别
    dmesg 打印了内核的日志缓冲，所以这个跟journal差别的第一点就是一个是个会被刷掉的缓冲区，另一个是记在文件，但更重要的是dmesg是打印的内核日志，而journal是会通过systemd收集日志，journal可以收集内核日志，但是可以进行配置，并且可能会晚于dmesg

    而对于这个oom，如果是系统的oom killer，那的确是有可能dmesg会能查看到，而journal看不到
    简单记录下，PS：奶娃是有点累，一天都没啥时间了

    -]]>     - - linux - - - linux - -     - - 聊聊最近平淡的生活之看《神探狄仁杰》 - /2021/12/19/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB%E4%B9%8B%E7%9C%8B%E3%80%8A%E7%A5%9E%E6%8E%A2%E7%8B%84%E4%BB%81%E6%9D%B0%E3%80%8B/ - 其实最近看的不止这一部，前面看了《继承者们》，《少年包青天》这些，就一起聊下，其中看《继承者们》算是个人比较喜欢，以前就有这种看剧的习惯，这个跟《一生一世》里任嘉伦说自己看《寻秦记》看了几十遍一样，我看喜欢的剧也基本上会看不止五遍，继承者们是有帅哥美女看，而且印象中剧情也挺甜的，一般情况下最好是已经有点遗忘剧情了，因为我个人觉得看剧分两种，无聊了又心情不太好，可以看些这类轻松又看过的剧，可以不完全专心地看剧，另外有心情专心看的时候，可以看一些需要思考，一些探案类的或者烧脑类。
    最近看了《神探狄仁杰》，因为跟前面看的《少年包青天》都是这类古装探案剧，正好有些感想，《少年包青天》算是儿时阴影，本来是不太会去看的，正好有一次有机会跟 LD 一起看了会就也觉得比较有意思就看了下去，不得不说，以前的这些剧还是很不错的，包括剧情和演员，第一部一共是 40 集，看的过程中也发现了大概是五个案子，平均八集一个案子，整体节奏还是比较慢的，但是基本每个案子其实都是构思得很巧妙，很久以前看过但是现在基本不太记得剧情了，每个案子在前面几集的时候基本都猜不到犯案逻辑，但是在看了狄仁杰之后，发现两部剧也有比较大的差别，少年包青天相对来说逻辑性会更强一些，个人主观觉得推理的严谨性更高，可能剧本打磨上更将就一下，而狄仁杰因为要提现他的个人强项，不比少年包青天中有公孙策一时瑜亮的情节，狄仁杰中分工明确，李元芳是个武力担当，曾泰是捧哏的，相对来说是狄仁杰在案子里从始至终地推进案情，有些甚至有些玄乎，会有一些跳脱跟不合理，有些像是狄仁杰的奇遇，不过这些想法是私人的观点，并不是想要评孰优孰劣；第二个感受是不知道是不是年代关系，特别是少年包青天，每个案件的大 boss 基本都不是个完全的坏人，甚至都是比较情有可原的可怜人，因为一些特殊原因，而好几个都是包拯身边的人，这一点其实跟狄仁杰里第一个使团惊魂案件比较类似，虎敬晖也是个人物形象比较丰满的角色，不是个标签化的淡薄形象，跟金木兰的感情和反叛行动在最后都说明的缘由，而且也有随着跟狄仁杰一起办案被其影响感化，最终为了救狄仁杰而被金木兰所杀，只是这样金木兰这个角色就会有些偏执和符号化，当然剧本肯定不是能面面俱到，这样的剧本已经比现在很多流量剧的好很多了。还想到了前阵子看的《指环王》中的白袍萨鲁曼在剧中也是个比较单薄的角色，这样的电影彪炳影史也没办法把个个人物都设计得完整有血有肉，或者说这本来也是应该有侧重点，当然其实我也不觉得指环王就是绝对的最好的，因为相对来说故事情节的复杂性等真的不如西游记，只是在 86 版之后的各种乱七八糟的翻牌和乱拍已经让这个真正的王者神话故事有点力不从心，这里边有部西游记后传是个人还比较喜欢的，虽然武打动作比较鬼畜，但是剧情基本是无敌的，在西游的架构上衍生出来这么完整丰富的故事，人物角色也都有各自的出彩点。
    说回狄仁杰，在这之前也看过徐克拍的几部狄仁杰的电影版，第一部刘德华拍得相对完成度更高，故事性也可圈可点，后面几部就是剧情拉胯，靠特效拉回来一点分，虽说这个也是所谓的狄仁杰宇宙的构思在里面但是现在来看基本是跟西游那些差不多，完全没有整体性可言，打一枪换一个地方，演员也没有延续性，剧情也是前后跳脱，没什么关联跟承上启下，导致质量层次不一，更不用谈什么狄仁杰宇宙了，不过这个事情也是难说，原因很多，现在资本都是更加趋利的，一些需要更长久时间才能有回报的投资是很难获得资本青睐，所以只能将重心投给选择一些流量明星，而本来应该将资源投给剧本打磨的基本就没了，再深入说也没意义了，社会现状就是这样。
    还有一点感想是，以前的剧里的拍摄环境还是比较惨的，看着一些房子，甚至皇宫都是比较破旧的，地面还是石板这种，想想以前的演员的环境再想想现在的，比如成龙说的，以前他拍剧就是啪摔了，问这条有没有过，过了就直接送医院，而不是现在可能手蹭破点皮就大叫，甚至还有饭圈这些破事。

    + 聊聊最近平淡的生活之看看老剧 + /2021/11/21/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB%E4%B9%8B%E7%9C%8B%E7%9C%8B%E8%80%81%E5%89%A7/ + 最近因为也没什么好看的新剧和综艺所以就看看一些以前看过的老剧，我是个非常念旧的人吧，很多剧都会反反复复地看，一方面之前看过觉得好看的的确是一直记着，还有就是平时工作完了回来就想能放松下，剧情太纠结的，太烧脑的都不喜欢，也就是我常挂在口头的不喜欢看费脑子的剧，跟我不喜欢狼人杀的原因也类似。

    +

    前面其实是看的太阳的后裔，跟 LD 一起看的，之前其实算是看过一点，但是没有看的很完整，并且很多剧情也忘了，只是这个我我可能看得更少一点，因为最开始的时候觉得男主应该是男二，可能对长得这样的男主并且是这样的人设有点失望，感觉不是特别像个特种兵，但是由于本来也比较火，而且 LD 比较喜欢就从这个开始看了，有两个点是比较想说的

    +

    韩剧虽然被吐槽的很多，但是很多剧的质量，情节把控还是优于目前非常多国内剧的，相对来说剧情发展的前后承接不是那么硬凹出来的，而且人设都立得住，这个是非常重要的，很多国内剧怎么说呢，就是当爹的看起来就比儿子没大几岁，三四十岁的人去演一个十岁出头的小姑娘，除非容貌异常，比如刘晓庆这种，不然就会觉得导演在把我们观众当傻子。瞬间就没有想看下去的欲望了。

    +

    再一点就是情节是大众都能接受度比较高的，现在有很多普遍会找一些新奇的视角，比如卖腐，想某某令，两部都叫某某令，这其实是一个点，延伸出去就是跟前面说的一点有点类似，xx 老祖，人看着就二三十，叫 xx 老祖，（喜欢的人轻喷哈）然后名字有一堆，同一个人物一会叫这个名字，一会又叫另一个名字，然后一堆死表情。

    +

    因为今天有个特殊的事情发生，所以简短的写（shui）一篇了

    ]]>     生活 @@ -7336,13 +7292,9 @@ user3:     - 聊聊最近平淡的生活之看看老剧 - /2021/11/21/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB%E4%B9%8B%E7%9C%8B%E7%9C%8B%E8%80%81%E5%89%A7/ - 最近因为也没什么好看的新剧和综艺所以就看看一些以前看过的老剧，我是个非常念旧的人吧，很多剧都会反反复复地看，一方面之前看过觉得好看的的确是一直记着，还有就是平时工作完了回来就想能放松下，剧情太纠结的，太烧脑的都不喜欢，也就是我常挂在口头的不喜欢看费脑子的剧，跟我不喜欢狼人杀的原因也类似。

    -

    前面其实是看的太阳的后裔，跟 LD 一起看的，之前其实算是看过一点，但是没有看的很完整，并且很多剧情也忘了，只是这个我我可能看得更少一点，因为最开始的时候觉得男主应该是男二，可能对长得这样的男主并且是这样的人设有点失望，感觉不是特别像个特种兵，但是由于本来也比较火，而且 LD 比较喜欢就从这个开始看了，有两个点是比较想说的

    -

    韩剧虽然被吐槽的很多，但是很多剧的质量，情节把控还是优于目前非常多国内剧的，相对来说剧情发展的前后承接不是那么硬凹出来的，而且人设都立得住，这个是非常重要的，很多国内剧怎么说呢，就是当爹的看起来就比儿子没大几岁，三四十岁的人去演一个十岁出头的小姑娘，除非容貌异常，比如刘晓庆这种，不然就会觉得导演在把我们观众当傻子。瞬间就没有想看下去的欲望了。

    -

    再一点就是情节是大众都能接受度比较高的，现在有很多普遍会找一些新奇的视角，比如卖腐，想某某令，两部都叫某某令，这其实是一个点，延伸出去就是跟前面说的一点有点类似，xx 老祖，人看着就二三十，叫 xx 老祖，（喜欢的人轻喷哈）然后名字有一堆，同一个人物一会叫这个名字，一会又叫另一个名字，然后一堆死表情。

    -

    因为今天有个特殊的事情发生，所以简短的写（shui）一篇了

    + 聊聊最近平淡的生活之看《神探狄仁杰》 + /2021/12/19/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB%E4%B9%8B%E7%9C%8B%E3%80%8A%E7%A5%9E%E6%8E%A2%E7%8B%84%E4%BB%81%E6%9D%B0%E3%80%8B/ + 其实最近看的不止这一部，前面看了《继承者们》，《少年包青天》这些，就一起聊下，其中看《继承者们》算是个人比较喜欢，以前就有这种看剧的习惯，这个跟《一生一世》里任嘉伦说自己看《寻秦记》看了几十遍一样，我看喜欢的剧也基本上会看不止五遍，继承者们是有帅哥美女看，而且印象中剧情也挺甜的，一般情况下最好是已经有点遗忘剧情了，因为我个人觉得看剧分两种，无聊了又心情不太好，可以看些这类轻松又看过的剧，可以不完全专心地看剧，另外有心情专心看的时候，可以看一些需要思考，一些探案类的或者烧脑类。
    最近看了《神探狄仁杰》，因为跟前面看的《少年包青天》都是这类古装探案剧，正好有些感想，《少年包青天》算是儿时阴影，本来是不太会去看的，正好有一次有机会跟 LD 一起看了会就也觉得比较有意思就看了下去，不得不说，以前的这些剧还是很不错的，包括剧情和演员，第一部一共是 40 集，看的过程中也发现了大概是五个案子，平均八集一个案子，整体节奏还是比较慢的，但是基本每个案子其实都是构思得很巧妙，很久以前看过但是现在基本不太记得剧情了，每个案子在前面几集的时候基本都猜不到犯案逻辑，但是在看了狄仁杰之后，发现两部剧也有比较大的差别，少年包青天相对来说逻辑性会更强一些，个人主观觉得推理的严谨性更高，可能剧本打磨上更将就一下，而狄仁杰因为要提现他的个人强项，不比少年包青天中有公孙策一时瑜亮的情节，狄仁杰中分工明确，李元芳是个武力担当，曾泰是捧哏的，相对来说是狄仁杰在案子里从始至终地推进案情，有些甚至有些玄乎，会有一些跳脱跟不合理，有些像是狄仁杰的奇遇，不过这些想法是私人的观点，并不是想要评孰优孰劣；第二个感受是不知道是不是年代关系，特别是少年包青天，每个案件的大 boss 基本都不是个完全的坏人，甚至都是比较情有可原的可怜人，因为一些特殊原因，而好几个都是包拯身边的人，这一点其实跟狄仁杰里第一个使团惊魂案件比较类似，虎敬晖也是个人物形象比较丰满的角色，不是个标签化的淡薄形象，跟金木兰的感情和反叛行动在最后都说明的缘由，而且也有随着跟狄仁杰一起办案被其影响感化，最终为了救狄仁杰而被金木兰所杀，只是这样金木兰这个角色就会有些偏执和符号化，当然剧本肯定不是能面面俱到，这样的剧本已经比现在很多流量剧的好很多了。还想到了前阵子看的《指环王》中的白袍萨鲁曼在剧中也是个比较单薄的角色，这样的电影彪炳影史也没办法把个个人物都设计得完整有血有肉，或者说这本来也是应该有侧重点，当然其实我也不觉得指环王就是绝对的最好的，因为相对来说故事情节的复杂性等真的不如西游记，只是在 86 版之后的各种乱七八糟的翻牌和乱拍已经让这个真正的王者神话故事有点力不从心，这里边有部西游记后传是个人还比较喜欢的，虽然武打动作比较鬼畜，但是剧情基本是无敌的，在西游的架构上衍生出来这么完整丰富的故事，人物角色也都有各自的出彩点。
    说回狄仁杰，在这之前也看过徐克拍的几部狄仁杰的电影版，第一部刘德华拍得相对完成度更高，故事性也可圈可点，后面几部就是剧情拉胯，靠特效拉回来一点分，虽说这个也是所谓的狄仁杰宇宙的构思在里面但是现在来看基本是跟西游那些差不多，完全没有整体性可言，打一枪换一个地方，演员也没有延续性，剧情也是前后跳脱，没什么关联跟承上启下，导致质量层次不一，更不用谈什么狄仁杰宇宙了，不过这个事情也是难说，原因很多，现在资本都是更加趋利的，一些需要更长久时间才能有回报的投资是很难获得资本青睐，所以只能将重心投给选择一些流量明星，而本来应该将资源投给剧本打磨的基本就没了，再深入说也没意义了，社会现状就是这样。
    还有一点感想是，以前的剧里的拍摄环境还是比较惨的，看着一些房子，甚至皇宫都是比较破旧的，地面还是石板这种，想想以前的演员的环境再想想现在的，比如成龙说的，以前他拍剧就是啪摔了，问这条有没有过，过了就直接送医院，而不是现在可能手蹭破点皮就大叫，甚至还有饭圈这些破事。

    ]]>     生活 @@ -7371,23 +7323,6 @@ user3: 修电脑的     - - 聊聊那些加塞狗 - /2021/01/17/%E8%81%8A%E8%81%8A%E9%82%A3%E4%BA%9B%E5%8A%A0%E5%A1%9E%E7%8B%97/ - 今天真的是被气得不轻，情况是碰到一个有 70 多秒的直行红灯，然后直行就排了很长的队，但是左转车道没车，就有好几辆车占着左转车道，准备往直行车道插队加塞，一般这种加塞的，会挑个不太计较的，如果前面一辆不让的话就再等等，我因为赶着回家，就不想让，结果那辆车几次车头直接往里冲，当时怒气值基本已经蓄满了，我真的是分毫都不想让，如果路上都是让着这种人的，那么这种情况只会越来越严重，我理解的这种心态，就赌你怕麻烦，多一事不如少一事，结果就是每次都能顺利插队加塞，其实延伸到我们社会中的种种实质性的排队或者等同于排队的情况，都已经有这种惯有思维，一方面这种不符合规则，可能在严重程度上容易被很多人所忽视，基本上已经被很多人当成是“合理”行为，另一方面，对于这些“微小”的违规行为，本身管理层面也基本没有想要管的意思，就更多的成为了纵容这些行为的导火索，并且大多数人都是想着如果不让，发生点小剐小蹭的要浪费很多时间精力来处理，甚至会觉得会被别人觉得自己太小气等等，诸多内外成本结合起来，会真的去硬刚的可能少之又少了，这样也就让更多的人觉得这种行为是被默许的，再举个非常小的例子，以我们公司疫情期间的盒饭发放为例，有两个比较“有意思”的事情，第一个就是因为疫情，本来是让排队要间隔一米，但是可能除了我比较怕死会跟前面的人保持点距离基本没别人会不挨着前面的人，甚至我跟我前面的人保持点距离，后面的同学会推着我让我上去；第二个是关于拿饭，这么多人排着队拿饭，然后有部分同学，一个人拿好几份，帮组里其他人的都拿了，有些甚至一个人拿十份，假如这个盒饭发放是说明了可以按部门直接全领了那就没啥问题，但是当时的状况是个人排队领自己的那一份，如果一个同学直接帮着组里十几个人都拿了，后面排队的人是什么感受呢，甚至有些是看到队伍排长了，就找队伍里自己认识的比较靠前的人说你帮我也拿一份，其实作为我这个比较按规矩办事的“愣头青”来说，我是比较不能接受这两件小事里的行为的，再往下说可能就有点偏激了，先说到这~

    -]]>     - - 生活 - 开车 - - - 生活 - 开车 - 加塞 - 糟心事 - 规则 - -     聊聊这次换车牌及其他 /2022/02/20/%E8%81%8A%E8%81%8A%E8%BF%99%E6%AC%A1%E6%8D%A2%E8%BD%A6%E7%89%8C%E5%8F%8A%E5%85%B6%E4%BB%96/ @@ -7405,31 +7340,34 @@ user3: - 聊聊部分公交车的设计bug - /2021/12/05/%E8%81%8A%E8%81%8A%E9%83%A8%E5%88%86%E5%85%AC%E4%BA%A4%E8%BD%A6%E7%9A%84%E8%AE%BE%E8%AE%A1bug/ - 今天惯例坐公交回住的地方，不小心撞了头，原因是我们想坐倒数第二排，然后LD 走在我后面，我就走到最后一排中间等着，但是最后一排是高一截的，等 LD 坐进去以后，我就往前走，结果撞到了车顶的扶手杆子的一端，差点撞昏了去，这里我觉得其实杆子长度应该短一点，不然从最后一排出来，还是有比较大概率因为没注意看而撞到头，特别是没注意看的情况，发力其实会比较大，一头撞上就会像我这样，眼前一黑，又痛得要死。
    还有一点就是座位设计了，先来看个图

    图里大致画了两条线，因为可能是轮胎还是什么原因，后排中间会有那么大的突起，但是看两条红线可以发现，靠近过道的座位边缘跟地面突起的边缘不是一样宽的，这样导致的结果就是坐着的时候有一个脚没地儿搁，要不就得侧着斜着坐，或者就是一个脚悬空，短程的可能还好，路程远一点还是比较难受的，特别是像我现在这样，大腿外侧有点难受的情况，就会更难受。
    虽然说这两个点，基本是屁用没有，但是我也是在自己这个博客说说，也当是个树洞了。

    + 聊聊那些加塞狗 + /2021/01/17/%E8%81%8A%E8%81%8A%E9%82%A3%E4%BA%9B%E5%8A%A0%E5%A1%9E%E7%8B%97/ + 今天真的是被气得不轻，情况是碰到一个有 70 多秒的直行红灯，然后直行就排了很长的队，但是左转车道没车，就有好几辆车占着左转车道，准备往直行车道插队加塞，一般这种加塞的，会挑个不太计较的，如果前面一辆不让的话就再等等，我因为赶着回家，就不想让，结果那辆车几次车头直接往里冲，当时怒气值基本已经蓄满了，我真的是分毫都不想让，如果路上都是让着这种人的，那么这种情况只会越来越严重，我理解的这种心态，就赌你怕麻烦，多一事不如少一事，结果就是每次都能顺利插队加塞，其实延伸到我们社会中的种种实质性的排队或者等同于排队的情况，都已经有这种惯有思维，一方面这种不符合规则，可能在严重程度上容易被很多人所忽视，基本上已经被很多人当成是“合理”行为，另一方面，对于这些“微小”的违规行为，本身管理层面也基本没有想要管的意思，就更多的成为了纵容这些行为的导火索，并且大多数人都是想着如果不让，发生点小剐小蹭的要浪费很多时间精力来处理，甚至会觉得会被别人觉得自己太小气等等，诸多内外成本结合起来，会真的去硬刚的可能少之又少了，这样也就让更多的人觉得这种行为是被默许的，再举个非常小的例子，以我们公司疫情期间的盒饭发放为例，有两个比较“有意思”的事情，第一个就是因为疫情，本来是让排队要间隔一米，但是可能除了我比较怕死会跟前面的人保持点距离基本没别人会不挨着前面的人，甚至我跟我前面的人保持点距离，后面的同学会推着我让我上去；第二个是关于拿饭，这么多人排着队拿饭，然后有部分同学，一个人拿好几份，帮组里其他人的都拿了，有些甚至一个人拿十份，假如这个盒饭发放是说明了可以按部门直接全领了那就没啥问题，但是当时的状况是个人排队领自己的那一份，如果一个同学直接帮着组里十几个人都拿了，后面排队的人是什么感受呢，甚至有些是看到队伍排长了，就找队伍里自己认识的比较靠前的人说你帮我也拿一份，其实作为我这个比较按规矩办事的“愣头青”来说，我是比较不能接受这两件小事里的行为的，再往下说可能就有点偏激了，先说到这~

    ]]>     生活 + 开车 生活 - 公交 - 杭州 + 开车 + 加塞 + 糟心事 + 规则     - 解决 网络文件夹目前是以其他用户名和密码进行映射的 问题 - /2023/04/09/%E8%A7%A3%E5%86%B3-%E7%BD%91%E7%BB%9C%E6%96%87%E4%BB%B6%E5%A4%B9%E7%9B%AE%E5%89%8D%E6%98%AF%E4%BB%A5%E5%85%B6%E4%BB%96%E7%94%A8%E6%88%B7%E5%90%8D%E5%92%8C%E5%AF%86%E7%A0%81%E8%BF%9B%E8%A1%8C%E6%98%A0%E5%B0%84%E7%9A%84/ - 之前在使用 smb 协议在 Windows 中共享磁盘使用映射网络驱动器的时候，如果前一次登录过账号密码后面有了改动，或者前一次改错了，
    就会出现这样的提示

    应该是 Windows 已经把之前的连接记录下来了，即使是链接不成功的
    可以通过在 cmd 或者 powershell 执行 net use 命令查看当前已经连接的

    这样就可以用命令来把这个删除
    net use [NETNAME] /delete
    比如这边就可以
    net use \\xxxxxxxx\f /delete
    然后再重新输入账号密码就好了
    关于net use的命令使用方式可以参考

    -
    net use [{<DeviceName> | *}] [\\<ComputerName>\<ShareName>[\<volume>]] [{<Password> | *}]] [/user:[<DomainName>\]<UserName] >[/user:[<DottedDomainName>\]<UserName>] [/user: [<UserName@DottedDomainName>] [/savecred] [/smartcard] [{/delete | /persistent:{yes | no}}]
    net use [<DeviceName> [/home[{<Password> | *}] [/delete:{yes | no}]]
    net use [/persistent:{yes | no}]
    -

    官方链接

    + 聊聊部分公交车的设计bug + /2021/12/05/%E8%81%8A%E8%81%8A%E9%83%A8%E5%88%86%E5%85%AC%E4%BA%A4%E8%BD%A6%E7%9A%84%E8%AE%BE%E8%AE%A1bug/ + 今天惯例坐公交回住的地方，不小心撞了头，原因是我们想坐倒数第二排，然后LD 走在我后面，我就走到最后一排中间等着，但是最后一排是高一截的，等 LD 坐进去以后，我就往前走，结果撞到了车顶的扶手杆子的一端，差点撞昏了去，这里我觉得其实杆子长度应该短一点，不然从最后一排出来，还是有比较大概率因为没注意看而撞到头，特别是没注意看的情况，发力其实会比较大，一头撞上就会像我这样，眼前一黑，又痛得要死。
    还有一点就是座位设计了，先来看个图

    图里大致画了两条线，因为可能是轮胎还是什么原因，后排中间会有那么大的突起，但是看两条红线可以发现，靠近过道的座位边缘跟地面突起的边缘不是一样宽的，这样导致的结果就是坐着的时候有一个脚没地儿搁，要不就得侧着斜着坐，或者就是一个脚悬空，短程的可能还好，路程远一点还是比较难受的，特别是像我现在这样，大腿外侧有点难受的情况，就会更难受。
    虽然说这两个点，基本是屁用没有，但是我也是在自己这个博客说说，也当是个树洞了。

    ]]>     - 技巧 + 生活 - windows + 生活 + 公交 + 杭州     @@ -7451,21 +7389,23 @@ user3: - 记录一次折腾自组 nas 的失败经历-续篇 - /2023/05/14/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86-%E7%BB%AD%E7%AF%87/ - 上次记录了前面的一些失败经验，最重要的点还没提到，先发一下配置单
    cpu i7-8700k
    主板 技嘉 z370m-ds3h
    内存 光威 ddr4-3200Mhz
    硬盘 京东京造 512g
    散热 利民 PA120
    电源 先马平头哥额定 550w
    机箱 爱国者半岛铁盒 F10
    cpu 跟主板是板 U 套装某鱼买的二手的，说实话如果不是后面的网卡问题，这个板 U 套装还是比较良心的，一次点亮（以前没组装过，还不知道有点不亮的情况，后面就体验到了），但是这里就出现了一个很大的坑，因为我这次是想要在裸机上装 exsi，然后看到了群里苏大的一篇 exsi 最新版本 8 的镜像构建文章，硬件也不是很旧，就想着用最新的系统，镜像写进 ventoy 后启动发现报错找不到网卡，这会我还没发现问题的严重性，想着按一些教程打个驱动进去就好了，而且我还以为驱动只要跟镜像 iso 放一块就行了，后面随着深入了解就知道要把驱动打进 iso 镜像里，但是找了一通发现我的网卡是瑞昱的 RTL8168，这个型号的板载网卡，走的是 PCIE 通道，有驱动的最后支持的系统是 exsi6.7，再往后就没有完整打包好的社区版驱动可以使用了，所以这是踩的第一个大坑，照理这个事情也没这么大问题，退回来 6.7 不就行了，问题恰恰是我那时候还不懂，又想用更新的系统，所以就在网上搜了半天，发现华硕的 z370 tuf gaming 系列是用的 intel 的网卡，社区的网卡驱动对 intel 的网卡支持比较好，所以想着还是换个主板算了，其实还有不少选择，买个 pcie 的 intel 网卡或者 usb 的其他千兆网卡，有个说出来可能比较难理解的，usb 的社区版驱动反而比 pcie 的支持得广，pcie 的还是只支持 intel 的。
    在某多多上买了个二手的 z370 tuf gaming 主板，结果踩到了第二个坑，可能比较小白的经验是，前面因为买的板 U 套装，他 cpu 是直接装在主板上邮给我的，所以我没装过 cpu，这回买来这块二手的华硕主板对我来说是第一次装 cpu，不过好像难度不大，一下就装好了，但结果就很惨，就是点不亮，散热器风扇会转，但是键盘灯不亮，而且散热风扇还转得很快，我还试着把内存换个槽，结果四个槽都不行，这个时候就很害怕了，看上去这家店也不像是太坑的，毕竟大量地在卖，所以我就很担心是不是前面 cpu 装的不对，把针脚什么的搞坏了，这个时候已经搞到晚上很迟了，但还是忍不住又装回原来的技嘉主板试了下，幸好能正常点亮，算了，还是就用技嘉这块主板吧，接口配置稍微差了点，网卡也不支持最新版的 exsi，所以我就用 vmware workstation 了，在 win10 的 lstc 上装一个，有点性能损耗就损耗吧，反正我也不暴力使用，能跑跑其他 Ubuntu 虚拟机啥的就可以了，或者回到前面的结果，可以装 6.7 的，网上带了瑞昱网卡驱动的 exsi6.7 的镜像挺多的，可以自己打一个或者用别人打包好的。折腾不止踩坑不止呐。

    + 解决 网络文件夹目前是以其他用户名和密码进行映射的 问题 + /2023/04/09/%E8%A7%A3%E5%86%B3-%E7%BD%91%E7%BB%9C%E6%96%87%E4%BB%B6%E5%A4%B9%E7%9B%AE%E5%89%8D%E6%98%AF%E4%BB%A5%E5%85%B6%E4%BB%96%E7%94%A8%E6%88%B7%E5%90%8D%E5%92%8C%E5%AF%86%E7%A0%81%E8%BF%9B%E8%A1%8C%E6%98%A0%E5%B0%84%E7%9A%84/ + 之前在使用 smb 协议在 Windows 中共享磁盘使用映射网络驱动器的时候，如果前一次登录过账号密码后面有了改动，或者前一次改错了，
    就会出现这样的提示

    应该是 Windows 已经把之前的连接记录下来了，即使是链接不成功的
    可以通过在 cmd 或者 powershell 执行 net use 命令查看当前已经连接的

    这样就可以用命令来把这个删除
    net use [NETNAME] /delete
    比如这边就可以
    net use \\xxxxxxxx\f /delete
    然后再重新输入账号密码就好了
    关于net use的命令使用方式可以参考

    +
    net use [{<DeviceName> | *}] [\\<ComputerName>\<ShareName>[\<volume>]] [{<Password> | *}]] [/user:[<DomainName>\]<UserName] >[/user:[<DottedDomainName>\]<UserName>] [/user: [<UserName@DottedDomainName>] [/savecred] [/smartcard] [{/delete | /persistent:{yes | no}}]
    net use [<DeviceName> [/home[{<Password> | *}] [/delete:{yes | no}]]
    net use [/persistent:{yes | no}]
    +

    官方链接

    ]]>     - nas + 技巧 - nas + windows     - 记录一次折腾自组 nas 的失败经历-续续篇 - /2023/05/28/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86-%E7%BB%AD%E7%BB%AD%E7%AF%87/ - 之前这个机器已经算是跑起来了，虽然不是很完善也不是最佳实践，不过这篇可能也不算是失败经历了，因为最后成功跑起来了，在没法装最新版的 exsi 的情况下，并且我后面买的华硕 z370 主板点不亮，所以我也有点死心就直接用Windows 下装 vmware workstation 装虚拟机，然后直通硬盘来做 nas，这样可能对于其他人来说是很垃圾的方案，不过因为我很多常用软件的都是在 Windows 环境下的，并且纯黑裙的环境会比较浪费，相比一些同学在群晖里安装 Windows 虚拟机，我觉得还是反过来比较好，毕竟 vmware 做虚拟机应该是比群晖专业点，不过这个方案也有一些问题
    第一种方式是直接找网上同学分享的处理好引导的 vmx，这种我碰到了一个问题就是在打开虚拟机安装群晖系统.pat的时候会提示“无法安装此文件，文件可能已损坏”，这其实不是真的文件已损坏，应该是群晖在做校验的时候存在什么条件没有通过，尝试了断网等方式都不成功，所以后来就用了比较釜底抽薪的方案，直接使用大佬开源的 arpl 引导制作工具
    第二种方式一开始是躺在我 B 站收藏夹里，有个 up 制作的，做得很细致，也把很多细节也解释了，过程其实不难，就是按步骤一步步执行，但是一开始选择了 918+的系统在我的方案里安装不了，会提示无法安装，经过视频下的评论的知道，尝试使用 920+的系统就顺利安装成功了，这里唯一的区别就是在添加硬盘的时候要选择物理磁盘，然后 vmware 给出的硬盘选项是Physical0， Physical1，记得别选错了，然后在启动后我租了 raid5,4 块 4T 的盘，可以组成一个 10T 多一点的存储空间，打算用来作为比较长读写的区域，更大的盘可能就会作为只读区域，减小写入量。后面还有一些问题待解决，一个是电源，考虑换个稍好一点，因为目前看下来电源风扇的噪音比较大，还有就是主板，最近看中了微星的 z390，不过价格比较贵，打算慢慢蹲蹲看。

    + 记录一次折腾自组 nas 的失败经历-续篇 + /2023/05/14/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86-%E7%BB%AD%E7%AF%87/ + 上次记录了前面的一些失败经验，最重要的点还没提到，先发一下配置单
    cpu i7-8700k
    主板 技嘉 z370m-ds3h
    内存 光威 ddr4-3200Mhz
    硬盘 京东京造 512g
    散热 利民 PA120
    电源 先马平头哥额定 550w
    机箱 爱国者半岛铁盒 F10
    cpu 跟主板是板 U 套装某鱼买的二手的，说实话如果不是后面的网卡问题，这个板 U 套装还是比较良心的，一次点亮（以前没组装过，还不知道有点不亮的情况，后面就体验到了），但是这里就出现了一个很大的坑，因为我这次是想要在裸机上装 exsi，然后看到了群里苏大的一篇 exsi 最新版本 8 的镜像构建文章，硬件也不是很旧，就想着用最新的系统，镜像写进 ventoy 后启动发现报错找不到网卡，这会我还没发现问题的严重性，想着按一些教程打个驱动进去就好了，而且我还以为驱动只要跟镜像 iso 放一块就行了，后面随着深入了解就知道要把驱动打进 iso 镜像里，但是找了一通发现我的网卡是瑞昱的 RTL8168，这个型号的板载网卡，走的是 PCIE 通道，有驱动的最后支持的系统是 exsi6.7，再往后就没有完整打包好的社区版驱动可以使用了，所以这是踩的第一个大坑，照理这个事情也没这么大问题，退回来 6.7 不就行了，问题恰恰是我那时候还不懂，又想用更新的系统，所以就在网上搜了半天，发现华硕的 z370 tuf gaming 系列是用的 intel 的网卡，社区的网卡驱动对 intel 的网卡支持比较好，所以想着还是换个主板算了，其实还有不少选择，买个 pcie 的 intel 网卡或者 usb 的其他千兆网卡，有个说出来可能比较难理解的，usb 的社区版驱动反而比 pcie 的支持得广，pcie 的还是只支持 intel 的。
    在某多多上买了个二手的 z370 tuf gaming 主板，结果踩到了第二个坑，可能比较小白的经验是，前面因为买的板 U 套装，他 cpu 是直接装在主板上邮给我的，所以我没装过 cpu，这回买来这块二手的华硕主板对我来说是第一次装 cpu，不过好像难度不大，一下就装好了，但结果就很惨，就是点不亮，散热器风扇会转，但是键盘灯不亮，而且散热风扇还转得很快，我还试着把内存换个槽，结果四个槽都不行，这个时候就很害怕了，看上去这家店也不像是太坑的，毕竟大量地在卖，所以我就很担心是不是前面 cpu 装的不对，把针脚什么的搞坏了，这个时候已经搞到晚上很迟了，但还是忍不住又装回原来的技嘉主板试了下，幸好能正常点亮，算了，还是就用技嘉这块主板吧，接口配置稍微差了点，网卡也不支持最新版的 exsi，所以我就用 vmware workstation 了，在 win10 的 lstc 上装一个，有点性能损耗就损耗吧，反正我也不暴力使用，能跑跑其他 Ubuntu 虚拟机啥的就可以了，或者回到前面的结果，可以装 6.7 的，网上带了瑞昱网卡驱动的 exsi6.7 的镜像挺多的，可以自己打一个或者用别人打包好的。折腾不止踩坑不止呐。

    ]]>     nas @@ -7475,9 +7415,9 @@ user3:     - 记录一次折腾自组 nas 的失败经历 - /2023/05/07/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86/ - 鉴于现在市面上的成品 nas 对于我来说要不就是太贵了，要不就是便宜的盘位少，性能比较差，很多 nas 主打还有用 docker 什么的，但是性能对于我个人比较特殊的使用方式来说还是不太够用的，比如现在比较性能好的 nas 像绿联新出的 DX4600，用的是N5105，passmark 分数还不如我 15 年买的 pc 上的 i5 4590，当然很多人是考虑功耗，这也是萝卜青菜各有所爱，可能我算下来还是觉得没多大必要
    然后就是考虑用什么硬件配置了，这个流派也有很多种，
    用蜗牛星际的原版硬件其实对于需求不大的也是挺好的，整套的都解决了，cpu 用 j1900 如果就做 nas 应该也够了，我没选的原因一方面是性能不符合我的要求，另一方面是现在市面上的机器大部分都是战损成色，而且也不太便宜，如果成色比较好的能够 400 以内拿下整机的话感觉还算可以，cpu 换成 j4125 或者 j3455 再加个 100 也能接受，但基本比较少有这种价格，之前看到一个换了 j3455 的只要 360，犹豫了下没下手，其他很多的都是 j1900 的都要 600 左右
    然后是各类 E3，E5 和商用服务器类型的，这种的特点是功耗大，其实 cpu 很便宜，E5 2630V3 跟 2630V4 都只要十几块钱，性能过得去，没有选的主要是机箱占地方也比较贵，还有是商用的怕很多系统需要自己找驱动什么的，配件比如 HBA 卡这种，买的不兼容什么的还是挺麻烦的，另外噪音也是个比较大的问题，租的房子比较小，即使放客厅也是靠着卧室的墙边，如果以后换大一点的房子倒是可以考虑
    最后就是我目前选的方案，就是普通的民用机器，找盘位多一点的机箱，我原来的 4590 的机器的机箱就不错，但是已经停产了，二手的太重了闲鱼都不出外地，cpu 跟主板其实考虑了很久，因为从 4590 开始核显就能硬解 H264 这种常规的视频了，考虑用intel 四代的 i3 或者 i5 应该纯 nas 来讲是足够用了，但是这样就跟我现在已经有的 4590 有点重叠了，而且也觉得最好是能性能好一些的，就开始看一些稍新一点的，很多用的多的有 i3-8100，跟 i3-10100 这种，但是这些已经被炒的价格比较高，寻寻觅觅了很久看中了 i5-8600，这个价格跟 8500 差不多，性能还好一些，主板就是我标题说的最“失败”的一个点了，主要是因为主板自带的网卡是 Realtek 的，至于更具体的后面会专门介绍，机箱是图便宜买的爱国者半岛铁盒的 F10，内存就买了一根光威的 32g 的，装系统的硬盘是用了以前囤的京东京造的麒麟系列，但是现在对这个系列挺不看好，之前有个盘就掉盘了，维修体验一开始也不好，半个多月维修，电源也是图便宜买的一个先马的平头哥系列，额定 550w 只要 140 左右，整体的机器就攒齐了，但是很多问题也随之出现了
    第一个问题是买的二手的板 U 套装，结果寄过来的时候没带挡板，导致一开始装上了又要拆下来；第二个问题是主机贪便宜，主机上固定主板螺丝的螺柱拧不进去，后来店家告诉我让我可以用电源固定的螺丝先拧一下才把螺柱拧进去；第三个问题是因为没什么装机经验导致的，散热装的太累了，因为要兼容各种主板，还有各种螺丝，装的时候也着急；第四个问题是电源的比较便宜，一方面比较不放心安全性，另一方面是我想多装几个硬盘，电源直出的只有四个 sata 口，需要买转接线，从 D 型的 4pin 口子转出来；第五个问题也是电源，主板电源线有点不太够，走背线就比较困难
    以上主要是装机的困难，下一篇介绍作为 nas 的各种问题吧

    + 记录一次折腾自组 nas 的失败经历-续续篇 + /2023/05/28/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86-%E7%BB%AD%E7%BB%AD%E7%AF%87/ + 之前这个机器已经算是跑起来了，虽然不是很完善也不是最佳实践，不过这篇可能也不算是失败经历了，因为最后成功跑起来了，在没法装最新版的 exsi 的情况下，并且我后面买的华硕 z370 主板点不亮，所以我也有点死心就直接用Windows 下装 vmware workstation 装虚拟机，然后直通硬盘来做 nas，这样可能对于其他人来说是很垃圾的方案，不过因为我很多常用软件的都是在 Windows 环境下的，并且纯黑裙的环境会比较浪费，相比一些同学在群晖里安装 Windows 虚拟机，我觉得还是反过来比较好，毕竟 vmware 做虚拟机应该是比群晖专业点，不过这个方案也有一些问题
    第一种方式是直接找网上同学分享的处理好引导的 vmx，这种我碰到了一个问题就是在打开虚拟机安装群晖系统.pat的时候会提示“无法安装此文件，文件可能已损坏”，这其实不是真的文件已损坏，应该是群晖在做校验的时候存在什么条件没有通过，尝试了断网等方式都不成功，所以后来就用了比较釜底抽薪的方案，直接使用大佬开源的 arpl 引导制作工具
    第二种方式一开始是躺在我 B 站收藏夹里，有个 up 制作的，做得很细致，也把很多细节也解释了，过程其实不难，就是按步骤一步步执行，但是一开始选择了 918+的系统在我的方案里安装不了，会提示无法安装，经过视频下的评论的知道，尝试使用 920+的系统就顺利安装成功了，这里唯一的区别就是在添加硬盘的时候要选择物理磁盘，然后 vmware 给出的硬盘选项是Physical0， Physical1，记得别选错了，然后在启动后我租了 raid5,4 块 4T 的盘，可以组成一个 10T 多一点的存储空间，打算用来作为比较长读写的区域，更大的盘可能就会作为只读区域，减小写入量。后面还有一些问题待解决，一个是电源，考虑换个稍好一点，因为目前看下来电源风扇的噪音比较大，还有就是主板，最近看中了微星的 z390，不过价格比较贵，打算慢慢蹲蹲看。

    ]]>     nas @@ -7499,24 +7439,15 @@ user3:     - 记录下 phpunit 的入门使用方法 - /2022/10/16/%E8%AE%B0%E5%BD%95%E4%B8%8B-phpunit-%E7%9A%84%E5%85%A5%E9%97%A8%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95/ - 这周开始打算写个比较简单的php工具包，然后顺带学习使用下php的单元测试，通过phpunit还是比较方便的，首先就composer require phpunit/phpunit
    安装下 phpunit, 前面包就是通过 composer init 创建，装完依赖后就可以把自动加载代码生成下 composer dump-autoload
    目录结构差不多这样

    -
    .
    ├── composer.json
    ├── composer.lock
    ├── oldfile.txt
    ├── phpunit.xml
    ├── src
    │   └── Rename.php
    └── tests
        └── RenameTest.php

    2 directories, 6 files
    -

    src/是源码，tests/是放的单测，比较重要的是phpunit.xml

    -
    <?xml version="1.0" encoding="UTF-8"?>
    <phpunit colors="true" bootstrap="vendor/autoload.php">
        <testsuites>
            <testsuite name="php-rename">
                <directory>./tests/</directory>
            </testsuite>
        </testsuites>
    </phpunit>
    -

    其中bootstrap就是需要把依赖包的自动加载入口配上，因为这个作为一个package，也会指出命名空间
    然后就是testsuite的路径，源码中

    -
    <?php
    namespace Nicksxs\PhpRename;

    class Rename
    {
        public static function renameSingleFile($file, $newFileName): bool
        {
            if(!is_file($file)) {
                echo "it's not a file";
                return false;
            }
            $fileInfo = pathinfo($file);
            return rename($file, $fileInfo["dirname"] . DIRECTORY_SEPARATOR . $newFileName . "." . $fileInfo["extension"]);
        }
    }
    -

    就是一个简单的重命名
    然后test代码是这样，

    -
    <?php

    // require_once 'vendor/autoload.php';

    use PHPUnit\Framework\TestCase;
    use Nicksxs\PhpRename\Rename;
    use function PHPUnit\Framework\assertEquals;

    class RenameTest extends TestCase 
    {
        public function setUp() :void
        {
            $myfile = fopen(__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt", "w") or die("Unable to open file!");
            $txt = "file test1\n";
            fwrite($myfile, $txt);
            fclose($myfile);
        }
        public function testRename()
        {
            Rename::renameSingleFile(__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt", "newfile");
            assertEquals(is_file(__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt"), true);
        }

        protected function tearDown(): void
        {
            unlink(__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt");
        }
    }
    -

    setUp 跟 tearDown 就是初始化跟结束清理的，但是注意如果不指明 __DIR__ ,待会的目录就会在执行 vendor/bin/phpunit 下面，
    或者也可以指定在一个 tmp/ 目录下
    最后就可以通过vendor/bin/phpunit 来执行测试
    执行结果

    -
    ❯ vendor/bin/phpunit
    PHPUnit 9.5.25 by Sebastian Bergmann and contributors.

    .                                                                   1 / 1 (100%)

    Time: 00:00.005, Memory: 6.00 MB

    OK (1 test, 1 assertion)
    + 记录一次折腾自组 nas 的失败经历 + /2023/05/07/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86/ + 鉴于现在市面上的成品 nas 对于我来说要不就是太贵了，要不就是便宜的盘位少，性能比较差，很多 nas 主打还有用 docker 什么的，但是性能对于我个人比较特殊的使用方式来说还是不太够用的，比如现在比较性能好的 nas 像绿联新出的 DX4600，用的是N5105，passmark 分数还不如我 15 年买的 pc 上的 i5 4590，当然很多人是考虑功耗，这也是萝卜青菜各有所爱，可能我算下来还是觉得没多大必要
    然后就是考虑用什么硬件配置了，这个流派也有很多种，
    用蜗牛星际的原版硬件其实对于需求不大的也是挺好的，整套的都解决了，cpu 用 j1900 如果就做 nas 应该也够了，我没选的原因一方面是性能不符合我的要求，另一方面是现在市面上的机器大部分都是战损成色，而且也不太便宜，如果成色比较好的能够 400 以内拿下整机的话感觉还算可以，cpu 换成 j4125 或者 j3455 再加个 100 也能接受，但基本比较少有这种价格，之前看到一个换了 j3455 的只要 360，犹豫了下没下手，其他很多的都是 j1900 的都要 600 左右
    然后是各类 E3，E5 和商用服务器类型的，这种的特点是功耗大，其实 cpu 很便宜，E5 2630V3 跟 2630V4 都只要十几块钱，性能过得去，没有选的主要是机箱占地方也比较贵，还有是商用的怕很多系统需要自己找驱动什么的，配件比如 HBA 卡这种，买的不兼容什么的还是挺麻烦的，另外噪音也是个比较大的问题，租的房子比较小，即使放客厅也是靠着卧室的墙边，如果以后换大一点的房子倒是可以考虑
    最后就是我目前选的方案，就是普通的民用机器，找盘位多一点的机箱，我原来的 4590 的机器的机箱就不错，但是已经停产了，二手的太重了闲鱼都不出外地，cpu 跟主板其实考虑了很久，因为从 4590 开始核显就能硬解 H264 这种常规的视频了，考虑用intel 四代的 i3 或者 i5 应该纯 nas 来讲是足够用了，但是这样就跟我现在已经有的 4590 有点重叠了，而且也觉得最好是能性能好一些的，就开始看一些稍新一点的，很多用的多的有 i3-8100，跟 i3-10100 这种，但是这些已经被炒的价格比较高，寻寻觅觅了很久看中了 i5-8600，这个价格跟 8500 差不多，性能还好一些，主板就是我标题说的最“失败”的一个点了，主要是因为主板自带的网卡是 Realtek 的，至于更具体的后面会专门介绍，机箱是图便宜买的爱国者半岛铁盒的 F10，内存就买了一根光威的 32g 的，装系统的硬盘是用了以前囤的京东京造的麒麟系列，但是现在对这个系列挺不看好，之前有个盘就掉盘了，维修体验一开始也不好，半个多月维修，电源也是图便宜买的一个先马的平头哥系列，额定 550w 只要 140 左右，整体的机器就攒齐了，但是很多问题也随之出现了
    第一个问题是买的二手的板 U 套装，结果寄过来的时候没带挡板，导致一开始装上了又要拆下来；第二个问题是主机贪便宜，主机上固定主板螺丝的螺柱拧不进去，后来店家告诉我让我可以用电源固定的螺丝先拧一下才把螺柱拧进去；第三个问题是因为没什么装机经验导致的，散热装的太累了，因为要兼容各种主板，还有各种螺丝，装的时候也着急；第四个问题是电源的比较便宜，一方面比较不放心安全性，另一方面是我想多装几个硬盘，电源直出的只有四个 sata 口，需要买转接线，从 D 型的 4pin 口子转出来；第五个问题也是电源，主板电源线有点不太够，走背线就比较困难
    以上主要是装机的困难，下一篇介绍作为 nas 的各种问题吧

    ]]>     - php + nas - php + nas     @@ -7567,30 +7498,6 @@ user3: php - - 聊一下 Spring 的 SmartLifecycle 使用 - /2024/07/07/%E8%81%8A%E4%B8%80%E4%B8%8B-Spring-%E7%9A%84-SmarLifecycle-%E4%BD%BF%E7%94%A8/ - 最近在学习过程中碰到一个比较有意思的Spring特性，就是 SmartLifecycle ，这个可以很轻松的融合进 Spring 生命周期

    -
    public interface SmartLifecycle extends Lifecycle, Phased {
        int DEFAULT_PHASE = Integer.MAX_VALUE;

        default boolean isAutoStartup() {
            return true;
        }

        default void stop(Runnable callback) {
            this.stop();
            callback.run();
        }

        default int getPhase() {
            return Integer.MAX_VALUE;
        }
    }
    -

    接口继承了 org.springframework.context.Lifecycle 跟 org.springframework.context.Phased
    其中默认实现了 isAutoStartup , 并且默认是返回 true，所以相对于 Lifecycle 能更智能些自动启动
    然后 Lifecycle 这个接口就比较简单，只有几个接口

    -
    public interface Lifecycle {
        void start();

        void stop();

        boolean isRunning();
    }
    -

    我们可以是实现下这个接口

    -
    @Component
    public class MySmartLifecycle implements SmartLifecycle {

        private volatile boolean isRunning;

        @Override
        public void start() {
            System.out.println("my smart life cycle start");
            isRunning = true;
        }

        @Override
        public void stop() {
            System.out.println("my smart life cycle end");
            isRunning = false;
        }

        @Override
        public boolean isRunning() {
            return isRunning;
        }
    }
    -

    这个 bean 会在 Spring 启动后被自动调用 start 方法

    这个就是在不深入 bean 的生命周期，并且是在 bean 已经都初始化以后调用
    同样会在 spring 容器关闭时调用 stop 方法

    如果有此类的需求就可以扩展此接口实现
    而这个其实是在 spring 生命周期中的 finishRefresh 方法中进行调用

    -
    protected void finishRefresh() {
        this.clearResourceCaches();
        this.initLifecycleProcessor();
        this.getLifecycleProcessor().onRefresh();
        this.publishEvent((ApplicationEvent)(new ContextRefreshedEvent(this)));
        if (!NativeDetector.inNativeImage()) {
            LiveBeansView.registerApplicationContext(this);
        }

    }
    -

    调用了 LifecycleProcessor 的 onRefresh 方法

    -
    public void onRefresh() {
        this.startBeans(true);
        this.running = true;
    }
    -

    这其中就会调用 start 方法

    -
    private void startBeans(boolean autoStartupOnly) {
        // 获取这些bean
        Map<String, Lifecycle> lifecycleBeans = this.getLifecycleBeans();
        Map<Integer, LifecycleGroup> phases = new TreeMap();
        lifecycleBeans.forEach((beanName, bean) -> {
            if (!autoStartupOnly || bean instanceof SmartLifecycle && ((SmartLifecycle)bean).isAutoStartup()) {
                int phase = this.getPhase(bean);
                ((LifecycleGroup)phases.computeIfAbsent(phase, (p) -> {
                    return new LifecycleGroup(phase, this.timeoutPerShutdownPhase, lifecycleBeans, autoStartupOnly);
                })).add(beanName, bean);
            }

        });
        if (!phases.isEmpty()) {
            phases.values().forEach(LifecycleGroup::start);
        }

    }
    -

    会先判断 isAutoStartup，然后将不同周期加入分组，然后再循环调用start

    -]]>     - - java - - - java - -     记录下 redis 的一些使用方法 /2022/10/30/%E8%AE%B0%E5%BD%95%E4%B8%8B-redis-%E7%9A%84%E4%B8%80%E4%BA%9B%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95/ @@ -7606,37 +7513,24 @@ user3: - 记录下一次服务器迁移 - /2023/11/12/%E8%AE%B0%E5%BD%95%E4%B8%8B%E4%B8%80%E6%AC%A1%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%81%E7%A7%BB/ - 因为服务器续费即使是这次双十一还是太贵了，之前在 tx 买的三年 2c4g 3m 的一台服务器，原来价格是三年 800+，现在续费三年要 3700，因为之前还买了其他的服务器，所以感觉再这么贵地续一个有点不划算就考虑迁移了服务器
    主要是分为四块内容，先大致记录下，后续会慢慢展开讲
    第一部分就是 headscale 的迁移，本身 headscale 的部署其实还好，原来的服务器是因为权限搞得几个文件都有问题，这次就按着第一次已经修改好的文件进行部署就好了，问题不大，可以参考 Headscale初体验以及踩坑记 -,主要的一点是要考虑 derper 的部署，以及客户端认证逻辑
    第二部分是 traefik 以及背后的 gitea，WordPress 的迁移，这个的部署方式都是使用 docker-compose 的，已经算比较方便了的，但是要注意两点，第一个是对于这些有状态的需要先停止 docker-compose ，然后在进行数据迁移，第二步数据迁移可能就需要使用到 docker 命令，执行 mysqldump
    如

    -
    docker exec -it  mysql_server【docker容器名称/ID】 mysqldump -uroot -p123456【数据库密码】 test_db【数据库名称】 > /opt/sql_bak/test_db.sql【导出表格路径】
    -

    然后再在新的 mysql 容器中先 cp 到容器内，然后在 docker 内用 mysql 命令连接 mysql-server 以后用 source 将 sql 导进来
    而像 traefik 本身其实是无状态的，只需要配置文件没有什么机器依赖就好，还有就是证书的更新，最后就是域名解析问题，修改解析就好
    第三部分是 rustdesk 的迁移，这个其实就按官方文档来跑两个容器就好，hbbr 跟 hbbs 就好了，一个是 relay，一个是 id 服务器，不过这里有个问题我目前没看到怎么识别是否已经用到了这个，有种部署了但是不知道有没有用的尴尬，有知道的可以指导下
    第四部分是 qinglong 的部署，这个也挺简单了，就是有个小问题，如果部署的订阅链接有 github 的就可以去站长的 ping 连接速度看看，自己配个 hosts 就会比较方便，不然经常容易拉不下来，还有就是要注意安全，至少要改强密码

    -]]>     - - 与服务器 - - - 云服务器 - -     - - 这周末我又在老丈人家打了天小工 - /2020/08/30/%E8%BF%99%E5%91%A8%E6%9C%AB%E6%88%91%E5%8F%88%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E6%89%93%E4%BA%86%E5%A4%A9%E5%B0%8F%E5%B7%A5/ - 因为活实在比较多，也不太好叫大工(活比较杂散)，相比上一次我跟 LD 俩人晚起了一点，我真的是只要有事，早上就醒的很早，准备八点出发的，六点就醒了，然后想继续睡就一直做梦🤦‍♂️，差不多八点半多到的丈人家，他们应该已经干了有一会了，我们到了以后就分配给我撬地板的活，上次说的那个敲掉柜子的房间里，还铺着质地还不错的木地板，但是也不想要了，得撬掉重新铺。
    拿着撬棍和榔头就上楼去干了，浙江这几天的天气，最高温度一般 38、9，楼上那个房间也没风扇，有了也不能用，都是灰尘，撬了两下，我感觉我体内的水就像真气爆发一样变成汗炸了出来，眼睛全被汗糊住了，可能大部分人不太了解地板是怎么铺的，一般是在地面先铺一层混凝土，混凝土中间嵌进去规则的长条木条，然后真正的地板一块块的都是钉在那个木条上，用那种气枪钉和普通的钉子，并且块跟块之前还有一个木头的槽结构相互耦合，然后边缘的一圈在用较薄的木板将整个木地板封边(这些词都是我现造的)，边缘的用的钉子会更多，所以那几下真的很用力，而且撬地板，得蹲下起来，如此反复，对于我这个体重快超过身高的中年人来说的确是非常大的挑战，接下来继续撬了几个，已经有种要虚脱晕倒的感觉了，及时去喝水擦了汗，又歇了一会，为啥一上来就这么拼呢，主要是因为那个房间丈人在干活的时候是直接看得到的🤦‍♂️，后来被 LD 一顿教育，本来就是去帮忙的，又不是专业做这个的，急啥。
    喝了水之后，又稍稍歇了一会，就开始继续撬了，本来觉得这个地板撬着好像还行，房间不大，没多久就撬完了，撬完之后喝了点饮料(补充点糖分，早餐吃得少，有点低血糖)，然后看到 LD 在撬下面的木条了，这个动作开始了那天最大的经验值收集行动，前面说了这个木条一般是跟混凝土一块铺上去的，但是谁也没想到，这个混凝土铺上去的时候竟然处理的这么随意，根本没考虑跟下面的贴合，所以撬木条的时候直接把木条跟木条中间大块大块的混凝土一块撬起来了，想想那重量，于是我这靠蛮力干活的，就用力把木条带着混凝土一块撬了起来，还沾沾自喜，但是发现结果是撬起来一块之后，体力值瞬间归零，上一篇我也提到了，其实干这类活也是很有技巧性的，但是上次的是我没学会，可能需要花时间学的，但是这次是LD 用她的纤细胳膊教会我的，我在撬的时候，屏住一口气，双手用力，起，大概是吃好几口奶的力气都用出来了，但是 LD 在我休息的时候，慢慢悠悠的，先把撬棍挤到木条或者混凝土跟下层的缝里，然后往下垫一小块混凝土碎石，然后轻轻松松的扳两下，就撬开了，亏我高中的时候引以为傲的物理成绩，作为物理课代表，这么浅显易懂的杠杆原理都完全不会用到生活里，后面在用这个技巧撬的过程中，真的觉得自己蠢到家了，当然在明白了用点杠杆原理之后，撬地板的活就变得慢慢悠悠，悠哉悠哉的了(其实还是很热的，披着毛巾擦眼睛)。
    上午的活差不多完了，后面就是把撬出来的混凝土和地板条丢下去，地上铺着不用了的被子，然后就是午饭和午休环节了，午饭换了一家快餐，味道非常可以，下午的活就比较单调了，帮忙清理了上去扔下来的混凝土碎块跟木条，然后稍微打扫了下，老丈人就让我们回家了，接着上次说的，还是觉得比跑步啥的消耗大太多了，那汗流的，一口就能喝完一瓶 500 毫升左右的矿泉水。

    + 记录下 phpunit 的入门使用方法 + /2022/10/16/%E8%AE%B0%E5%BD%95%E4%B8%8B-phpunit-%E7%9A%84%E5%85%A5%E9%97%A8%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95/ + 这周开始打算写个比较简单的php工具包，然后顺带学习使用下php的单元测试，通过phpunit还是比较方便的，首先就composer require phpunit/phpunit
    安装下 phpunit, 前面包就是通过 composer init 创建，装完依赖后就可以把自动加载代码生成下 composer dump-autoload
    目录结构差不多这样

    +
    .
    ├── composer.json
    ├── composer.lock
    ├── oldfile.txt
    ├── phpunit.xml
    ├── src
    │   └── Rename.php
    └── tests
        └── RenameTest.php

    2 directories, 6 files
    +

    src/是源码，tests/是放的单测，比较重要的是phpunit.xml

    +
    <?xml version="1.0" encoding="UTF-8"?>
    <phpunit colors="true" bootstrap="vendor/autoload.php">
        <testsuites>
            <testsuite name="php-rename">
                <directory>./tests/</directory>
            </testsuite>
        </testsuites>
    </phpunit>
    +

    其中bootstrap就是需要把依赖包的自动加载入口配上，因为这个作为一个package，也会指出命名空间
    然后就是testsuite的路径，源码中

    +
    <?php
    namespace Nicksxs\PhpRename;

    class Rename
    {
        public static function renameSingleFile($file, $newFileName): bool
        {
            if(!is_file($file)) {
                echo "it's not a file";
                return false;
            }
            $fileInfo = pathinfo($file);
            return rename($file, $fileInfo["dirname"] . DIRECTORY_SEPARATOR . $newFileName . "." . $fileInfo["extension"]);
        }
    }
    +

    就是一个简单的重命名
    然后test代码是这样，

    +
    <?php

    // require_once 'vendor/autoload.php';

    use PHPUnit\Framework\TestCase;
    use Nicksxs\PhpRename\Rename;
    use function PHPUnit\Framework\assertEquals;

    class RenameTest extends TestCase 
    {
        public function setUp() :void
        {
            $myfile = fopen(__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt", "w") or die("Unable to open file!");
            $txt = "file test1\n";
            fwrite($myfile, $txt);
            fclose($myfile);
        }
        public function testRename()
        {
            Rename::renameSingleFile(__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt", "newfile");
            assertEquals(is_file(__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt"), true);
        }

        protected function tearDown(): void
        {
            unlink(__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt");
        }
    }
    +

    setUp 跟 tearDown 就是初始化跟结束清理的，但是注意如果不指明 __DIR__ ,待会的目录就会在执行 vendor/bin/phpunit 下面，
    或者也可以指定在一个 tmp/ 目录下
    最后就可以通过vendor/bin/phpunit 来执行测试
    执行结果

    +
    ❯ vendor/bin/phpunit
    PHPUnit 9.5.25 by Sebastian Bergmann and contributors.

    .                                                                   1 / 1 (100%)

    Time: 00:00.005, Memory: 6.00 MB

    OK (1 test, 1 assertion)
    ]]>     - 生活 - 运动 - 跑步 - 干活 + php - 生活 - 运动 - 减肥 - 跑步 - 干活 + php     @@ -7667,25 +7561,18 @@ user3: - 闲聊下乘公交的用户体验 - /2021/02/28/%E9%97%B2%E8%81%8A%E4%B8%8B%E4%B9%98%E5%85%AC%E4%BA%A4%E7%9A%84%E7%94%A8%E6%88%B7%E4%BD%93%E9%AA%8C/ - 新年开工开车来杭州，因为没有车位加限行今天来就没开车来了，从东站做公交回住的地方，这班神奇的车我之前也吐槽过了，有神奇的乘客和神奇的司机，因为基本上这班车是从我毕业就开始乘了，所以也算是比较熟悉了，以前总体感觉不太好的是乘坐时间太长了，不过这个也不能怪车，是我自己住得远（离东站），后来住到了现在的地方，也算是直达，并且 LD 比较喜欢直达的，不爱更快却要换乘的地铁，所以坐的频率比较高，也说过前面那些比较气人的乘客，自己不好好戴口罩，反而联合一起上车的乘客诽谤司机，说他要吃人了要打人了，也正是这个司机比较有意思，上车就让戴好口罩，还给大家讲，哪里哪里又有疫情了，我觉得其实这个司机还是不错的，特殊时期，对于这种公共交通，这样的确是比较负责任的做法，只是说话方式，语气这个因人而异，他也不是来伺候人的，而且这么一大车人，说了一遍不行，再说一遍，三遍以上了，嗓门大一点也属于正常的人的行为。
    还是说回今天要说的，今天这位司机我看着跟前面说的那位有点像，因为上车的时候比较暗没看清脸，主要原因是这位司机开车比较猛，比较急，然后车上因为这个时间点，比较多大学开学来的学生，拎着个行李箱，一开始是前面已经都站满了人，后面还有很多空位，因为后面没地方放行李箱，就因为这样前面站着的有几个就在说司机开慢点，结果司机貌似也没听进去，还是我行我素，过了会又有人说司机开稳一点，就在这个人说完没一会，停在红绿灯路口的车里，就有人问有没有垃圾桶，接着又让司机开门，说晕车太严重了，要下车，司机开了门，我望出去两个妹子下了车，好像在路边草丛吐了，前面开门下车的时候就有人说她们第一次来杭州，可能有点责怪司机开的不稳，也影响了杭州交通给新来杭州的人的感受，说完了事情经过，其实我有蛮多感触，对于杭州公交司机，我大概是大一来了没多久，陪室友去文三路买电脑就晕车，下车的时候在公交车站吐了，可能是从大学开始缺乏锻炼，又饮食不规律，更加容易晕车，大部分晕车我觉得都是我自己的原因，有时候是上车前吃太多了，或者早上起太早，没睡好，没吃东西，反正自己也是挺多原因的，说到司机的原因的话，我觉得可能这班车还算好的，最让我难受的还是上下班高峰的时候，因为经过的那条路是比较重要的主干道，路比较老比较窄，并且还有很多人行道，所以经常一脚油门连带着一脚刹车，真的很难受，这种算是我觉得真的是公交体验比较差的一点，但是这一点呢也不能完全怪公交司机，杭州的路政规划是很垃圾，没看错，是垃圾，所以总体结论是公交还行，主要是路政规划就是垃圾，包括这条主干道这么多人行道，并且两边都是老小区，老年人在上班高峰可能要买菜送娃或者其他事情，在通畅的情况下可能只需要六分钟的路程，有时候因为各种原因，半小时都开不完，扯开去一点，杭州的路，核心的高速说封就封，本来是高架可以直接通到城西，结果没造，到了路本已经很拥挤的时候开始来造隧道，各种破坏，隧道接高架的地方，无尽的加塞，对于我这样的小白司机来说真的是太恶心了，所以我一直想说的就是杭州这个地方房价领先基础设施十年，地铁，高架，高速通通不行，地面道路就更不行了。
    总结下，其实杭州的真正的公交体验差，应该还是路造成的，对于前面的那两位妹子来说，有可能是她们来自于公交司机都是开的特别稳，并且路况也很好的地方，也或者是我被虐习惯了🤦‍♂️

    + 记录下一次服务器迁移 + /2023/11/12/%E8%AE%B0%E5%BD%95%E4%B8%8B%E4%B8%80%E6%AC%A1%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%81%E7%A7%BB/ + 因为服务器续费即使是这次双十一还是太贵了，之前在 tx 买的三年 2c4g 3m 的一台服务器，原来价格是三年 800+，现在续费三年要 3700，因为之前还买了其他的服务器，所以感觉再这么贵地续一个有点不划算就考虑迁移了服务器
    主要是分为四块内容，先大致记录下，后续会慢慢展开讲
    第一部分就是 headscale 的迁移，本身 headscale 的部署其实还好，原来的服务器是因为权限搞得几个文件都有问题，这次就按着第一次已经修改好的文件进行部署就好了，问题不大，可以参考 Headscale初体验以及踩坑记 +,主要的一点是要考虑 derper 的部署，以及客户端认证逻辑
    第二部分是 traefik 以及背后的 gitea，WordPress 的迁移，这个的部署方式都是使用 docker-compose 的，已经算比较方便了的，但是要注意两点，第一个是对于这些有状态的需要先停止 docker-compose ，然后在进行数据迁移，第二步数据迁移可能就需要使用到 docker 命令，执行 mysqldump
    如

    +
    docker exec -it  mysql_server【docker容器名称/ID】 mysqldump -uroot -p123456【数据库密码】 test_db【数据库名称】 > /opt/sql_bak/test_db.sql【导出表格路径】
    +

    然后再在新的 mysql 容器中先 cp 到容器内，然后在 docker 内用 mysql 命令连接 mysql-server 以后用 source 将 sql 导进来
    而像 traefik 本身其实是无状态的，只需要配置文件没有什么机器依赖就好，还有就是证书的更新，最后就是域名解析问题，修改解析就好
    第三部分是 rustdesk 的迁移，这个其实就按官方文档来跑两个容器就好，hbbr 跟 hbbs 就好了，一个是 relay，一个是 id 服务器，不过这里有个问题我目前没看到怎么识别是否已经用到了这个，有种部署了但是不知道有没有用的尴尬，有知道的可以指导下
    第四部分是 qinglong 的部署，这个也挺简单了，就是有个小问题，如果部署的订阅链接有 github 的就可以去站长的 ping 连接速度看看，自己配个 hosts 就会比较方便，不然经常容易拉不下来，还有就是要注意安全，至少要改强密码

    ]]>     - 生活 - 公交 + 与服务器 - 生活 - 开车 - 加塞 - 糟心事 - 规则 - 公交 - 路政规划 - 基础设施 - 杭州 - 高速 + 云服务器     @@ -7700,6 +7587,64 @@ user3: 路由器 + + 这周末我又在老丈人家打了天小工 + /2020/08/30/%E8%BF%99%E5%91%A8%E6%9C%AB%E6%88%91%E5%8F%88%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E6%89%93%E4%BA%86%E5%A4%A9%E5%B0%8F%E5%B7%A5/ + 因为活实在比较多，也不太好叫大工(活比较杂散)，相比上一次我跟 LD 俩人晚起了一点，我真的是只要有事，早上就醒的很早，准备八点出发的，六点就醒了，然后想继续睡就一直做梦🤦‍♂️，差不多八点半多到的丈人家，他们应该已经干了有一会了，我们到了以后就分配给我撬地板的活，上次说的那个敲掉柜子的房间里，还铺着质地还不错的木地板，但是也不想要了，得撬掉重新铺。
    拿着撬棍和榔头就上楼去干了，浙江这几天的天气，最高温度一般 38、9，楼上那个房间也没风扇，有了也不能用，都是灰尘，撬了两下，我感觉我体内的水就像真气爆发一样变成汗炸了出来，眼睛全被汗糊住了，可能大部分人不太了解地板是怎么铺的，一般是在地面先铺一层混凝土，混凝土中间嵌进去规则的长条木条，然后真正的地板一块块的都是钉在那个木条上，用那种气枪钉和普通的钉子，并且块跟块之前还有一个木头的槽结构相互耦合，然后边缘的一圈在用较薄的木板将整个木地板封边(这些词都是我现造的)，边缘的用的钉子会更多，所以那几下真的很用力，而且撬地板，得蹲下起来，如此反复，对于我这个体重快超过身高的中年人来说的确是非常大的挑战，接下来继续撬了几个，已经有种要虚脱晕倒的感觉了，及时去喝水擦了汗，又歇了一会，为啥一上来就这么拼呢，主要是因为那个房间丈人在干活的时候是直接看得到的🤦‍♂️，后来被 LD 一顿教育，本来就是去帮忙的，又不是专业做这个的，急啥。
    喝了水之后，又稍稍歇了一会，就开始继续撬了，本来觉得这个地板撬着好像还行，房间不大，没多久就撬完了，撬完之后喝了点饮料(补充点糖分，早餐吃得少，有点低血糖)，然后看到 LD 在撬下面的木条了，这个动作开始了那天最大的经验值收集行动，前面说了这个木条一般是跟混凝土一块铺上去的，但是谁也没想到，这个混凝土铺上去的时候竟然处理的这么随意，根本没考虑跟下面的贴合，所以撬木条的时候直接把木条跟木条中间大块大块的混凝土一块撬起来了，想想那重量，于是我这靠蛮力干活的，就用力把木条带着混凝土一块撬了起来，还沾沾自喜，但是发现结果是撬起来一块之后，体力值瞬间归零，上一篇我也提到了，其实干这类活也是很有技巧性的，但是上次的是我没学会，可能需要花时间学的，但是这次是LD 用她的纤细胳膊教会我的，我在撬的时候，屏住一口气，双手用力，起，大概是吃好几口奶的力气都用出来了，但是 LD 在我休息的时候，慢慢悠悠的，先把撬棍挤到木条或者混凝土跟下层的缝里，然后往下垫一小块混凝土碎石，然后轻轻松松的扳两下，就撬开了，亏我高中的时候引以为傲的物理成绩，作为物理课代表，这么浅显易懂的杠杆原理都完全不会用到生活里，后面在用这个技巧撬的过程中，真的觉得自己蠢到家了，当然在明白了用点杠杆原理之后，撬地板的活就变得慢慢悠悠，悠哉悠哉的了(其实还是很热的，披着毛巾擦眼睛)。
    上午的活差不多完了，后面就是把撬出来的混凝土和地板条丢下去，地上铺着不用了的被子，然后就是午饭和午休环节了，午饭换了一家快餐，味道非常可以，下午的活就比较单调了，帮忙清理了上去扔下来的混凝土碎块跟木条，然后稍微打扫了下，老丈人就让我们回家了，接着上次说的，还是觉得比跑步啥的消耗大太多了，那汗流的，一口就能喝完一瓶 500 毫升左右的矿泉水。

    +]]>     + + 生活 + 运动 + 跑步 + 干活 + + + 生活 + 运动 + 减肥 + 跑步 + 干活 + +     + + 远程桌面工具rustdesk的私有化部署-mac锁定问题 + /2024/05/19/%E8%BF%9C%E7%A8%8B%E6%A1%8C%E9%9D%A2%E5%B7%A5%E5%85%B7rustdesk%E7%9A%84%E7%A7%81%E6%9C%89%E5%8C%96%E9%83%A8%E7%BD%B2-mac%E9%94%81%E5%AE%9A%E9%97%AE%E9%A2%98/ + 上次分享了rustdesk如何私有化部署，但是在实际使用中存在一个小问题，就是如果被连接端是mac的话，在mac进入超时自动锁定或者被主动锁定之后，连接就会进入一个“已连接，等待画面传输”的状态，但实际后续并不会传输画面，而是继续卡在这个无法实际连接的状态

    这个问题目前最新版的rustdesk还没有很好的解决，不过可以通过一个小技巧来解决，只是会有一个限制
    就是需要能够使用vnc连接这个被连接端的mac或者ssh连接
    第一种是如果能用vnc连接的话，就先连接，然后稍微动下鼠标，然后rustdesk就能获取画面进行下一步操作
    第二种是通过ssh连接到mac以后，通过一个mac下的小命令 caffeinate 这个命令是mac自带的，可以防止mac
    进入睡眠状态，具体命令如下

    +
    caffeinate -u -t 2
    +

    -u: 表示用户处于活动状态，此参数将打开屏幕并禁止屏幕进入空闲休眠。

    +

    -d：禁止屏幕休眠。

    +

    -i：禁止系统空闲休眠。

    +

    -m：禁止硬盘进入休眠。

    +

    -s：禁止系统进入睡眠状态，此参数仅在插上电源的时候才有效。

    +

    -t：指定命令有效的超时值，以秒为单位。
    这样我们就能从rustdesk获得画面进行操作了，希望后面rustdesk能解决掉这个问题，因为这两种方式也不是很完善，如果只有纯远程的话就没办法了

    +]]>     + + 工具 + + + 工具 + +     + + 远程桌面工具rustdesk的私有化部署 + /2024/05/12/%E8%BF%9C%E7%A8%8B%E6%A1%8C%E9%9D%A2%E5%B7%A5%E5%85%B7rustdesk%E7%9A%84%E7%A7%81%E6%9C%89%E5%8C%96%E9%83%A8%E7%BD%B2/ + 最近因为利用rustdesk远程操作诈骗太多，所以rustdesk官方直接关闭了国内的公有服务，相对其他比如teamviewer来说，rustdesk还是个比较不错的选择，性能考量也过得去，相对来说在双方都是mac的情况下，不付费的体验来说，比自带的vnc好了很多，vnc目测是直接全量传实时画面数据，带宽占用大，体验差。正巧最近有需求就使用了rustdesk所以记录下部署过程
    我使用的较早先版本的部署方式，
    首先需要一台有外网ip的服务器，并且需要使用到以下几个端口，如果使用云服务器，需要在控制台开启这些端口
    核心端口:

    +
    TCP 21114-21119
    UDP 21116
    +

    测试有没有开启可以使用这个工具网站 CanYouSeeMe.org ，或者用另一台机器telnet下
    需要部署的服务有两个
    hbbs - RustDesk ID/Rendezvous server id服务
    hbbr - RustDesk Relay server 中继服务
    这里使用docker来部署，比较简单，先拉取镜像，没错就一个

    +
    sudo docker image pull rustdesk/rustdesk-server
    +

    这边建议创建个rustdesk目录，进入目录后再启动下面的服务
    先启动hbbs

    +
    sudo docker run --name hbbs -p 21115:21115 -p 21116:21116 -p 21116:21116/udp -p 21118:21118 -v `pwd`:/root -td --net=host rustdesk/rustdesk-server hbbs
    +

    使用 --net=host 是为了让server拿到连接进入的真实来源ip
    然后再启动hbbr

    +
    sudo docker run --name hbbr -p 21117:21117 -p 21119:21119 -v `pwd`:/root -td --net=host rustdesk/rustdesk-server hbbr
    +

    接下来进到刚才建的目录，会看到有个一对公私钥，把公钥内容保存下来
    然后就是客户端登录了，以mac为例

    点击这边三点，再点击修改中继设置

    在弹出窗口

    ID服务器跟中继服务器框填入前面部署服务的服务器外网ip，在Key框里把刚才保存的公钥填进去
    每个需要连接的客户端都需要这么设置，然后就可以使用自己的服务器作为ID跟中继服务器了，不会受官方关停国内服务影响

    +]]>     + + 工具 + + + 工具 + +     重看了下《蛮荒记》说说感受 /2021/10/10/%E9%87%8D%E7%9C%8B%E4%BA%86%E4%B8%8B%E3%80%8A%E8%9B%AE%E8%8D%92%E8%AE%B0%E3%80%8B%E8%AF%B4%E8%AF%B4%E6%84%9F%E5%8F%97/ @@ -7716,6 +7661,28 @@ user3: 看书 + + 闲聊下乘公交的用户体验 + /2021/02/28/%E9%97%B2%E8%81%8A%E4%B8%8B%E4%B9%98%E5%85%AC%E4%BA%A4%E7%9A%84%E7%94%A8%E6%88%B7%E4%BD%93%E9%AA%8C/ + 新年开工开车来杭州，因为没有车位加限行今天来就没开车来了，从东站做公交回住的地方，这班神奇的车我之前也吐槽过了，有神奇的乘客和神奇的司机，因为基本上这班车是从我毕业就开始乘了，所以也算是比较熟悉了，以前总体感觉不太好的是乘坐时间太长了，不过这个也不能怪车，是我自己住得远（离东站），后来住到了现在的地方，也算是直达，并且 LD 比较喜欢直达的，不爱更快却要换乘的地铁，所以坐的频率比较高，也说过前面那些比较气人的乘客，自己不好好戴口罩，反而联合一起上车的乘客诽谤司机，说他要吃人了要打人了，也正是这个司机比较有意思，上车就让戴好口罩，还给大家讲，哪里哪里又有疫情了，我觉得其实这个司机还是不错的，特殊时期，对于这种公共交通，这样的确是比较负责任的做法，只是说话方式，语气这个因人而异，他也不是来伺候人的，而且这么一大车人，说了一遍不行，再说一遍，三遍以上了，嗓门大一点也属于正常的人的行为。
    还是说回今天要说的，今天这位司机我看着跟前面说的那位有点像，因为上车的时候比较暗没看清脸，主要原因是这位司机开车比较猛，比较急，然后车上因为这个时间点，比较多大学开学来的学生，拎着个行李箱，一开始是前面已经都站满了人，后面还有很多空位，因为后面没地方放行李箱，就因为这样前面站着的有几个就在说司机开慢点，结果司机貌似也没听进去，还是我行我素，过了会又有人说司机开稳一点，就在这个人说完没一会，停在红绿灯路口的车里，就有人问有没有垃圾桶，接着又让司机开门，说晕车太严重了，要下车，司机开了门，我望出去两个妹子下了车，好像在路边草丛吐了，前面开门下车的时候就有人说她们第一次来杭州，可能有点责怪司机开的不稳，也影响了杭州交通给新来杭州的人的感受，说完了事情经过，其实我有蛮多感触，对于杭州公交司机，我大概是大一来了没多久，陪室友去文三路买电脑就晕车，下车的时候在公交车站吐了，可能是从大学开始缺乏锻炼，又饮食不规律，更加容易晕车，大部分晕车我觉得都是我自己的原因，有时候是上车前吃太多了，或者早上起太早，没睡好，没吃东西，反正自己也是挺多原因的，说到司机的原因的话，我觉得可能这班车还算好的，最让我难受的还是上下班高峰的时候，因为经过的那条路是比较重要的主干道，路比较老比较窄，并且还有很多人行道，所以经常一脚油门连带着一脚刹车，真的很难受，这种算是我觉得真的是公交体验比较差的一点，但是这一点呢也不能完全怪公交司机，杭州的路政规划是很垃圾，没看错，是垃圾，所以总体结论是公交还行，主要是路政规划就是垃圾，包括这条主干道这么多人行道，并且两边都是老小区，老年人在上班高峰可能要买菜送娃或者其他事情，在通畅的情况下可能只需要六分钟的路程，有时候因为各种原因，半小时都开不完，扯开去一点，杭州的路，核心的高速说封就封，本来是高架可以直接通到城西，结果没造，到了路本已经很拥挤的时候开始来造隧道，各种破坏，隧道接高架的地方，无尽的加塞，对于我这样的小白司机来说真的是太恶心了，所以我一直想说的就是杭州这个地方房价领先基础设施十年，地铁，高架，高速通通不行，地面道路就更不行了。
    总结下，其实杭州的真正的公交体验差，应该还是路造成的，对于前面的那两位妹子来说，有可能是她们来自于公交司机都是开的特别稳，并且路况也很好的地方，也或者是我被虐习惯了🤦‍♂️

    +]]>     + + 生活 + 公交 + + + 生活 + 开车 + 加塞 + 糟心事 + 规则 + 公交 + 路政规划 + 基础设施 + 杭州 + 高速 + +     闲话篇-也算碰到了为老不尊和坏人变老了的典型案例 /2022/05/22/%E9%97%B2%E8%AF%9D%E7%AF%87-%E4%B9%9F%E7%AE%97%E7%A2%B0%E5%88%B0%E4%BA%86%E4%B8%BA%E8%80%81%E4%B8%8D%E5%B0%8A%E5%92%8C%E5%9D%8F%E4%BA%BA%E5%8F%98%E8%80%81%E4%BA%86%E7%9A%84%E5%85%B8%E5%9E%8B%E6%A1%88%E4%BE%8B/ @@ -7773,43 +7740,4 @@ user3: mysql - - 远程桌面工具rustdesk的私有化部署-mac锁定问题 - /2024/05/19/%E8%BF%9C%E7%A8%8B%E6%A1%8C%E9%9D%A2%E5%B7%A5%E5%85%B7rustdesk%E7%9A%84%E7%A7%81%E6%9C%89%E5%8C%96%E9%83%A8%E7%BD%B2-mac%E9%94%81%E5%AE%9A%E9%97%AE%E9%A2%98/ - 上次分享了rustdesk如何私有化部署，但是在实际使用中存在一个小问题，就是如果被连接端是mac的话，在mac进入超时自动锁定或者被主动锁定之后，连接就会进入一个“已连接，等待画面传输”的状态，但实际后续并不会传输画面，而是继续卡在这个无法实际连接的状态

    这个问题目前最新版的rustdesk还没有很好的解决，不过可以通过一个小技巧来解决，只是会有一个限制
    就是需要能够使用vnc连接这个被连接端的mac或者ssh连接
    第一种是如果能用vnc连接的话，就先连接，然后稍微动下鼠标，然后rustdesk就能获取画面进行下一步操作
    第二种是通过ssh连接到mac以后，通过一个mac下的小命令 caffeinate 这个命令是mac自带的，可以防止mac
    进入睡眠状态，具体命令如下

    -
    caffeinate -u -t 2
    -

    -u: 表示用户处于活动状态，此参数将打开屏幕并禁止屏幕进入空闲休眠。

    -

    -d：禁止屏幕休眠。

    -

    -i：禁止系统空闲休眠。

    -

    -m：禁止硬盘进入休眠。

    -

    -s：禁止系统进入睡眠状态，此参数仅在插上电源的时候才有效。

    -

    -t：指定命令有效的超时值，以秒为单位。
    这样我们就能从rustdesk获得画面进行操作了，希望后面rustdesk能解决掉这个问题，因为这两种方式也不是很完善，如果只有纯远程的话就没办法了

    -]]>     - - 工具 - - - 工具 - -     - - 远程桌面工具rustdesk的私有化部署 - /2024/05/12/%E8%BF%9C%E7%A8%8B%E6%A1%8C%E9%9D%A2%E5%B7%A5%E5%85%B7rustdesk%E7%9A%84%E7%A7%81%E6%9C%89%E5%8C%96%E9%83%A8%E7%BD%B2/ - 最近因为利用rustdesk远程操作诈骗太多，所以rustdesk官方直接关闭了国内的公有服务，相对其他比如teamviewer来说，rustdesk还是个比较不错的选择，性能考量也过得去，相对来说在双方都是mac的情况下，不付费的体验来说，比自带的vnc好了很多，vnc目测是直接全量传实时画面数据，带宽占用大，体验差。正巧最近有需求就使用了rustdesk所以记录下部署过程
    我使用的较早先版本的部署方式，
    首先需要一台有外网ip的服务器，并且需要使用到以下几个端口，如果使用云服务器，需要在控制台开启这些端口
    核心端口:

    -
    TCP 21114-21119
    UDP 21116
    -

    测试有没有开启可以使用这个工具网站 CanYouSeeMe.org ，或者用另一台机器telnet下
    需要部署的服务有两个
    hbbs - RustDesk ID/Rendezvous server id服务
    hbbr - RustDesk Relay server 中继服务
    这里使用docker来部署，比较简单，先拉取镜像，没错就一个

    -
    sudo docker image pull rustdesk/rustdesk-server
    -

    这边建议创建个rustdesk目录，进入目录后再启动下面的服务
    先启动hbbs

    -
    sudo docker run --name hbbs -p 21115:21115 -p 21116:21116 -p 21116:21116/udp -p 21118:21118 -v `pwd`:/root -td --net=host rustdesk/rustdesk-server hbbs
    -

    使用 --net=host 是为了让server拿到连接进入的真实来源ip
    然后再启动hbbr

    -
    sudo docker run --name hbbr -p 21117:21117 -p 21119:21119 -v `pwd`:/root -td --net=host rustdesk/rustdesk-server hbbr
    -

    接下来进到刚才建的目录，会看到有个一对公私钥，把公钥内容保存下来
    然后就是客户端登录了，以mac为例

    点击这边三点，再点击修改中继设置

    在弹出窗口

    ID服务器跟中继服务器框填入前面部署服务的服务器外网ip，在Key框里把刚才保存的公钥填进去
    每个需要连接的客户端都需要这么设置，然后就可以使用自己的服务器作为ID跟中继服务器了，不会受官方关停国内服务影响

    -]]>     - - 工具 - - - 工具 - -     diff --git a/sitemap.xml b/sitemap.xml index 5adab149c0..d0b67aa742 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1172,7 +1172,7 @@ - https://nicksxs.me/2021/05/01/Leetcode-48-%E6%97%8B%E8%BD%AC%E5%9B%BE%E5%83%8F-Rotate-Image-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + https://nicksxs.me/2021/07/04/Leetcode-42-%E6%8E%A5%E9%9B%A8%E6%B0%B4-Trapping-Rain-Water-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ 2022-06-11 @@ -1181,7 +1181,7 @@ - https://nicksxs.me/2021/07/04/Leetcode-42-%E6%8E%A5%E9%9B%A8%E6%B0%B4-Trapping-Rain-Water-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + https://nicksxs.me/2021/05/01/Leetcode-48-%E6%97%8B%E8%BD%AC%E5%9B%BE%E5%83%8F-Rotate-Image-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ 2022-06-11 @@ -1217,7 +1217,7 @@ - https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/ + https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0-%E6%89%80%E6%9C%89%E6%9D%83%E4%BA%8C/ 2022-06-11 @@ -1226,7 +1226,7 @@ - https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0-%E6%89%80%E6%9C%89%E6%9D%83%E4%BA%8C/ + https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/ 2022-06-11 @@ -1244,7 +1244,7 @@ - https://nicksxs.me/2021/03/07/%E3%80%8A%E5%9E%83%E5%9C%BE%E5%9B%9E%E6%94%B6%E7%AE%97%E6%B3%95%E6%89%8B%E5%86%8C%E8%AF%BB%E4%B9%A6%E3%80%8B%E7%AC%94%E8%AE%B0%E4%B9%8B%E6%95%B4%E7%90%86%E7%AE%97%E6%B3%95/ + https://nicksxs.me/2021/12/05/wordpress-%E5%BF%98%E8%AE%B0%E5%AF%86%E7%A0%81%E7%9A%84%E4%B8%80%E7%A7%8D%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95/ 2022-06-11 @@ -1253,7 +1253,7 @@ - https://nicksxs.me/2021/12/05/wordpress-%E5%BF%98%E8%AE%B0%E5%AF%86%E7%A0%81%E7%9A%84%E4%B8%80%E7%A7%8D%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95/ + https://nicksxs.me/2021/03/07/%E3%80%8A%E5%9E%83%E5%9C%BE%E5%9B%9E%E6%94%B6%E7%AE%97%E6%B3%95%E6%89%8B%E5%86%8C%E8%AF%BB%E4%B9%A6%E3%80%8B%E7%AC%94%E8%AE%B0%E4%B9%8B%E6%95%B4%E7%90%86%E7%AE%97%E6%B3%95/ 2022-06-11 @@ -1289,7 +1289,7 @@ - https://nicksxs.me/2021/09/04/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8/ + https://nicksxs.me/2021/10/03/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%B8%89/ 2022-06-11 @@ -1298,7 +1298,7 @@ - https://nicksxs.me/2021/10/03/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%B8%89/ + https://nicksxs.me/2021/09/12/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%BA%8C/ 2022-06-11 @@ -1307,7 +1307,7 @@ - https://nicksxs.me/2021/09/12/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%BA%8C/ + https://nicksxs.me/2021/09/04/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8/ 2022-06-11 @@ -1343,7 +1343,7 @@ - https://nicksxs.me/2021/06/27/%E8%81%8A%E8%81%8A-Java-%E4%B8%AD%E7%BB%95%E4%B8%8D%E5%BC%80%E7%9A%84-Synchronized-%E5%85%B3%E9%94%AE%E5%AD%97-%E4%BA%8C/ + 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(max-width:767px){.main{width:auto}}@media (min-width:1200px){.main{width:1160px}}@media (min-width:1600px){.main{width:73%}}@media (max-width:991px){header.header{border-radius:initial}.main{display:block;width:auto}}.main-inner{border-radius:initial;box-sizing:border-box;width:calc(100% - 252px)}.footer-inner{padding-left:252px}@media (max-width:991px){.main-inner{border-radius:initial;width:100%}.footer-inner{padding-left:0;padding-right:0;width:auto}}.column{width:240px}.site-brand-container{background:var(--theme-color)}.site-meta{padding:20px 0}.site-nav-right .toggle,.site-nav-toggle .toggle{color:#fff}.site-nav-right .toggle .toggle-line,.site-nav-toggle .toggle .toggle-line{background:#fff}@media (min-width:768px) and (max-width:991px){.site-nav-right,.site-nav-toggle{display:flex;flex-direction:column;justify-content:center}.site-nav{--scroll-height:0;height:0;overflow:hidden;transition:height .2s ease-in-out,visibility .2s ease-in-out;visibility:hidden}body:not(.site-nav-on) .site-nav .animated{animation:none}body.site-nav-on .site-nav{height:var(--scroll-height);visibility:unset}}.menu .menu-item{display:block;margin:0}.menu .menu-item a{padding:5px 20px;position:relative;text-align:left;transition-property:background-color}.menu .menu-item .badge{background:#ccc;border-radius:10px;color:var(--content-bg-color);float:right;padding:2px 5px;text-shadow:1px 1px 0 rgba(0,0,0,.1)}.main-menu .menu-item-active::after{background:#bbb;border-radius:50%;content:' ';height:6px;margin-top:-3px;position:absolute;right:15px;top:50%;width:6px}.sub-menu{margin:0;padding:6px 0}.sub-menu .menu-item{display:inline-block}.sub-menu .menu-item a{background:0 0;margin:5px 10px;padding:initial}.sub-menu .menu-item a:hover{background:0 0;color:#fc6423}.sub-menu .menu-item-active{border-bottom-color:#fc6423;color:#fc6423}.sub-menu .menu-item-active:hover{border-bottom-color:#fc6423}.sidebar{position:-webkit-sticky;position:sticky;top:12px}@media (max-width:991px){.column{width:auto}.site-nav-on .site-brand-container{box-shadow:0 0 16px rgba(0,0,0,.5)}.menu .menu-item.menu-item-search,.sidebar{display:none}}.sidebar-inner{background:var(--content-bg-color);border-radius:initial;box-shadow:initial;box-sizing:border-box;color:var(--text-color);margin-top:12px;max-height:calc(100vh - 24px);visibility:hidden}.site-state-item{padding:0 10px}.sidebar .sidebar-button{border-bottom:1px dotted #ccc;border-top:1px dotted #ccc}.sidebar .sidebar-button button{border:0;color:#fc6423;display:block;width:100%}.sidebar .sidebar-button button:hover{background:0 0;border:0;color:#e34603}.links-of-author{display:flex;flex-wrap:wrap;justify-content:center}.links-of-author-item{margin:5px 0 0;width:50%}.links-of-author-item a{box-sizing:border-box;max-width:100%;overflow:hidden;padding:0 5px;text-overflow:ellipsis;white-space:nowrap;border-bottom:0;border-radius:4px;display:block}.links-of-author-item a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: Linked List | Nicksxs's Blog

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