Redis从入门到放弃系列(二) Hash

一时失言乱红尘 2022-04-27 23:02 155阅读 0赞

## Redis从入门到放弃系列(二) Hash ##

> 本文例子基于：5.0.4 Hash是Redis中一种比较常见的数据结构,其实现为hashtable/ziplist,默认创建时为ziplist,当到达一定量级时,redis会将ziplist转化为hashtable

[Redis从入门到放弃系列(一) String][Redis_ String]

首先让我们来看一下该如何在redis里面使用Hash类型

//将hash表中key的域field的值设为value
    //如果key不存在，一个新的哈希表被创建并进行HSET操作
    //如果域field已经存在于哈希表中，旧值将被覆盖
    hset key field value
    复制代码

### 代码示例： ###

//创建不存在的field
    >hset user:1 id 1
    (integer) 1
    //覆盖原先的field
    >hset user:1 id 2
    (integer) 0
    >hget user:1 id
    "2"
    //获取不存在的field
    >hget user:1 not_exist
    (nil)
    ----------------------------------
    // hsetnx key field value
    //当不存在该field 设置成功返回1 ，否则返回0
    > hsetnx user:1 id 1
    (integer) 1
    > hsetnx user:1 id 1
    (integer) 0
    > hget user:1 id
    "1"
    ----------------------------------
    // hmset key field value [field value ....]
    //批量设置多个键值对
    >HMSET user:1 id 1 name "黑搜丶D" wechat "black-search"
    OK
    ----------------------------------
    //hget key field
    //获取hash表key中给定的field的值
    >hget user:1 id 
    "1"
    ----------------------------------
    // hmget key field[field...]
    //按照我们输入的field的顺序返回
    >hmget user:1 name wechat id  not_exist
    1) "黑搜丶D"
    2) "black-search"
    3) "1"
    4) (nil)
    ----------------------------------
    // hdel key field 删除返回被成功移除的域的数量 
    > hgetall user:1
    1) "id"
    2) "1"
    3) "name"
    4) "black-search"
    > HDEL user:1 name
    (integer) 1
    > HDEL user:1 name
    (integer) 0
    ----------------------------------
    // HINCRBY key field increment
    // 为hash表某个整数类型的field增加increment ,返回增加increment之后的大小
    > hset user:1 wechat "black-search"
    (integer) 1
    > HINCRBY user:1 wechat 2
    (error) ERR hash value is not an integer
    > HINCRBY user:1 id 21
    (integer) 22
    > hget user:1 id
    "22"
    复制代码

至此,redis hash的用法先告一段落.

--------------------

### debug object key ###

本文开头的时候讲默认创建为ziplist,当达到一定的量级转化为hashtable,那么具体是在什么时候才会转化成hashtable呢？

# Hashes are encoded using a memory efficient data structure when they have a
    # small number of entries, and the biggest entry does not exceed a given
    # threshold. These thresholds can be configured using the following directives.
    hash-max-ziplist-entries 512
    hash-max-ziplist-value 64
    复制代码

从上文我们可以知道，只有当我们满足以下两个条件会将ziplist转化为hashtable结构

1.  保存的所有键值对个数小于 512个 (这个限制是由 hash-max-ziplist-entries 参数控制，默认 512)
2.  保存的所有键值对的长度都小于 64 字节（这个限制是由 hash-max-ziplist-value 参数控制，默认 64）

// 这里测试当键值对小于等于512时，hash的类型
    @RequestMapping("/")
    public void test(){
    	List<Long> list = redisTemplate.executePipelined(new RedisCallback<Long>() {
    		@Override
    		public Long doInRedis(RedisConnection redisConnection) throws DataAccessException {
    			redisConnection.openPipeline();
    			for (int i=0;i<512;i++){
    				redisConnection.hSet("key".getBytes(),("field"+i).getBytes(),"value".getBytes());
    			}
    			return null;
    		}
    	});
    	System.out.println("结束");
    }
    //我们发现这里hash的类型就是ziplist
    > debug object key
    Value at:0xbc6f80 refcount:1 encoding:ziplist serializedlength:2603 lru:14344435 lru_seconds_idle:17
    //让我们调大一下循环的次数，改为513，我们发现
    > debug object key
    Value at:0xbc6f80 refcount:1 encoding:hashtable serializedlength:7587 lru:14344656 lru_seconds_idle:4
    复制代码

### 源码解析 ###

//首先我们来看一下dict的结构
    typedef struct dict { 
        dictType *type;
        void *privdata;
        dictht ht[2];
        long rehashidx; /* rehashing not in progress if rehashidx == -1 */
        unsigned long iterators; /* number of iterators currently running */
    } dict;
    typedef struct dictType { 
        uint64_t (*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 dictEntry { 
        void *key;
        union {
            void *val;
            uint64_t u64;
            int64_t s64;
            double d;
        } v;
        struct dictEntry *next;
    } dictEntry;
    复制代码

从以上我们可以知道，dict里面包含了两个dictht(ps:hashtable),通常情况下只有一个dictht有值.但是当dict扩容/缩容的时候,需要分配新的dictht，然后渐进式搬迁，当迁移结束之后，旧的dictht被删除，只保留新的dictht dict如何解决hash冲突呢？其实原理跟Java的HashMap是一样的，采用数组+链表的方式去解决

#### 渐进式rehash ####

我们知道，redis是单进程的,如果要将一个大的字典扩容是会比较耗时的,那么有可能就会将其他请求挂起。所以redis采用渐进式rehash来完成这一项艰巨任务~

dictEntry *dictAddRaw(dict *d, void *key, dictEntry **existing) {
        long index;
        dictEntry *entry;
        dictht *ht;
        //这里每次都会进行搬迁~
        if (dictIsRehashing(d)) _dictRehashStep(d);
    
        /* Get the index of the new element, or -1 if * the element already exists. */
        if ((index = _dictKeyIndex(d, key, dictHashKey(d,key), existing)) == -1)
            return NULL;
    
        /* Allocate the memory and store the new entry. * Insert the element in top, with the assumption that in a database * system it is more likely that recently added entries are accessed * more frequently. */
        //当字典处于搬迁中，将新添加的元素挂到新的数组下面
        ht = dictIsRehashing(d) ? &d->ht[1] : &d->ht[0];
        entry = zmalloc(sizeof(*entry));
        entry->next = ht->table[index];
        ht->table[index] = entry;
        ht->used++;
    
        /* Set the hash entry fields. */
        dictSetKey(d, entry, key);
        return entry;
    }
    复制代码

这样,在客户端每次请求(hset/hdel等)都会去判断是否需要搬迁,那么当客户端不请求我们的时候,有可能没有完整的搬迁？no no no redis会在定时任务里面扫描处于rehash的dict，然后完成剩余的搬迁~代码如下

/* 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. */
        if (server.active_defrag_enabled)
            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 (server.rdb_child_pid == -1 && server.aof_child_pid == -1) {
            /* 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 */
            //重点在这里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;
                    }
                }
            }
        }
    }
    复制代码

### 应用场景 ###

储存业务数据,我们发现其实hset的用法很简单,回顾上一讲最后的应用场景

//上一讲使用string 
    >set user:1 '{"id":1,"name":"黑搜丶D","wechat":"black-search"}'
    //让我们使用hash来实现相似的做法
    > HMSET user:1 id 1 name "黑搜丶D" wechat "black-search"
    OK
    //获取key的某个field的值
    >hget user:1 wechat
    "black-search"
    //获取到key的所有 field:value组合
    > HGETALL user:1
    1) "id"
    2) "1"
    3) "name"
    4) "\xe9\xbb\x91\xe6\x90\x9c\xe4\xb8\xb6D"
    5) "wechat"
    6) "black-search"
    复制代码

相对于string的用法,我们使用hash get某个field或者set某个field会省很多带宽~

[Redis_ String]: https://link.juejin.im?target=https%3A%2F%2Fmp.weixin.qq.com%2Fs%2F1B9sxvq0_6iiz2FIwsKU-A