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## redis系列介绍七-过期策略

Posted on 2020-04-12 In Redis , 数据结构 , C , 源码 , Redis Views: Disqus:

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

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    /* 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

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    /* 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

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    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

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    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

## Comparator使用小记

Posted on 2020-04-05 In Java , 集合 Views: Disqus:

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

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    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

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    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

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    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

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    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

## docker使用中发现的echo命令的一个小技巧及其他

Posted on 2020-03-29 In Linux , Docker , 命令 , echo , 发行版本 Views: Disqus:

最近做 docker 系列，会经常进到 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，因为你懂的原因，在内部想编辑下东西要安装 vim 就会很慢很慢，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
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

## docker比一般多一点的初学者介绍三

Posted on 2020-03-21 In Docker , 介绍 Views: Disqus:

## 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

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    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

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    <?php
    echo "Hello World!";

然后在 dynamic_web 目录下创建 Dockerfile，

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    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比一般多一点的初学者介绍二

Posted on 2020-03-15 Edited on 2020-03-16 In Docker , 介绍 Views: Disqus:

## 限制下 docker 的 cpu 使用率

这里我们开始玩一点有意思的，我们在容器里装下 vim 和 gcc，然后写这样一段 c 代码

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    #include <stdio.h>
    int main(void)
    {
        int i = 0;
        for(;;) i++;
        return 0;
    }

就是一个最简单的死循环，然后在容器里跑起来

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    $ gcc 1.c
    $ ./a.out

然后我们来看下系统资源占用（CPU）
上图是在容器里的，可以看到 cpu 已经 100%了
然后看看容器外面的
可以看到一个核的 cpu 也被占满了，因为是个双核的机器，并且代码是单线程的
然后呢我们要做点啥
因为已经在这个 ubuntu 容器中装了 vim 和 gcc，考虑到国内的网络，所以我们先把这个容器 commit 一下，

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    docker commit -a "nick" -m "my ubuntu" f63c5607df06 my_ubuntu:v1

然后再运行起来

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    docker run -it --cpus=0.1 my_ubuntu:v1 bash

我们的代码跟可执行文件都还在，要的就是这个效果，然后再运行一下
结果是这个样子的，有点神奇是不，关键就在于 run 的时候的`--cpus=0.1`这个参数，它其实就是基于我前一篇说的 cgroup 技术，能将进程之间的cpu，内存等资源进行隔离

## 开始第一个 Dockerfile

上一面为了复用那个我装了 vim 跟 gcc 的容器，我把它提交到了本地，使用了`docker commit`命令，有点类似于 git 的 commit，但是这个不是个很好的操作方式，需要手动介入，这里更推荐使用 Dockerfile 来构建镜像

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    From ubuntu:latest
    MAINTAINER Nicksxs "nicksxs@hotmail.com"
    RUN  sed -i s@/archive.ubuntu.com/@/mirrors.aliyun.com/@g /etc/apt/sources.list
    RUN apt-get clean
    RUN apt-get update && apt install -y nginx
    RUN echo 'Hi, i am in container' \
        > /usr/share/nginx/html/index.html
    EXPOSE 80

先解释下这是在干嘛，首先是这个`From ubuntu:latest`基于的 ubuntu 的最新版本的镜像，然后第二行是维护人的信息，第三四行么作为墙内人你懂的，把 ubuntu 的源换成阿里云的，不然就有的等了，然后就是装下 nginx，往默认的 nginx 的入口 html 文件里输入一行欢迎语，然后暴露 80 端口
然后我们使用`sudo docker build -t="nicksxs/static_web" .`命令来基于这个 Dockerfile 构建我们自己的镜像，过程中是这样的
可以看到图中，我的 Dockerfile 是 7 行，里面就执行了 7 步，并且每一步都有一个类似于容器 id 的层 id 出来，这里就是一个比较重要的东西，docker 在构建的时候其实是有这个层的概念，Dockerfile 里的每一行都会往上加一层，这里有还注意下命令后面的`.`，代表当前目录下会自行去寻找 Dockerfile 进行构建，构建完了之后我们再看下我们的本地镜像
我们自己的镜像出现啦
然后有个问题，如果这个构建中途报了错咋办呢，来试试看，我们把 nginx 改成随便的一个错误名，nginxx(不知道会不会运气好真的有这玩意)，再来 build 一把
找不到 nginxx 包，是不是这个镜像就完全不能用呢，当然也不是，因为前面说到了，docker 是基于层去构建的，可以看到前面的 4 个 step 都没报错，那我们基于最后的成功步骤创建下容器看看
也就是`sudo docker run -t -i bd26f991b6c8 /bin/bash`
答案是可以的，只是没装成功 nginx
还有一点注意到没，前面的几个 step 都有一句 `Using cache`，说明 docker 在构建镜像的时候是有缓存的，这也更能说明 docker 是基于层去构建镜像，同样的底包，同样的步骤，这些层是可以被复用的，这就是 docker 的构建缓存，当然我们也可以在 build 的时候加上`--no-cache`去把构建缓存禁用掉。