redis中的"HashMap"

redis是一个存储键值对的内存数据库，其存储键值的方式和java中的HashMap相似。

表征redis数据库的结构体是redisDb (在server.h文件中)，redis服务器默认有16个数据库，编号从0到15。

typedef struct redisDb {
    dict *dict;                 /* 键空间 */
    dict *expires;              /* 过期键空间 */
    dict *blocking_keys;        /* 客户端在等待的键 (BLPOP) */
    dict *ready_keys;           /* 接收到 push 的阻塞键 */
    dict *watched_keys;         /* WATCHED keys for MULTI/EXEC CAS */
    struct evictionPoolEntry *eviction_pool;    /* Eviction pool of keys */
    int id;                     /* Database ID */
    long long avg_ttl;          /* Average TTL, just for stats */
} redisDb;

dict 中存储的是 key -> value，而expires存储的 key -> 过期时间

dict是dict.h文件中定义的结构体：

typedef struct dict {
    dictType *type;
    void *privdata;
    dictht ht[];
    long rehashidx; /* rehashing not in progress if rehashidx == -1 */
    unsigned long iterators; /* number of iterators currently running */
} dict;

typedef struct dictht {
    dictEntry **table;
    unsigned long size; //table的大小
    unsigned long sizemask;
    unsigned long used; //table中键值对的数量
} dictht;

typedef struct dictEntry {
    void *key;
    union {
        void *val;
        uint64_t u64;
        int64_t s64;
        double d;
    } v;
    struct dictEntry *next;
} dictEntry;

dict可以类比为java中的HashMap，dictEntry对应java.util.HashMap.Entry<K, V>，稍微不同的是，dict对entry的table做了简单的封装（即dictht），而且dict中有两个table用于rehash。

分析dict的dictReplace(dict.c文件中)，类似于HashMap的put：

/* Add or Overwrite:
 * Add an element, discarding the old value if the key already exists.
 * Return 1 if the key was added from scratch, 0 if there was already an
 * element with such key and dictReplace() just performed a value update
 * operation. */
int dictReplace(dict *d, void *key, void *val)
{
    dictEntry *entry, *existing, auxentry;

    /* Try to add the element. If the key
     * does not exists dictAdd will suceed. */
    entry = dictAddRaw(d,key,&existing);
    if (entry) {
        dictSetVal(d, entry, val);
        return ;
    }

    /* Set the new value and free the old one. Note that it is important
     * to do that in this order, as the value may just be exactly the same
     * as the previous one. In this context, think to reference counting,
     * you want to increment (set), and then decrement (free), and not the
     * reverse. */
    auxentry = *existing;
    dictSetVal(d, existing, val);
    dictFreeVal(d, &auxentry);
    return ;
}

dictEntry *dictAddRaw(dict *d, void *key, dictEntry **existing)
{
    int index;
    dictEntry *entry;
    dictht *ht;

    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)) == -)
        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. */
    ht = dictIsRehashing(d) ? &d->ht[] : &d->ht[];
    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;
}

主要逻辑在dictAddRaw中，也是先取得table中index，然后使用头插法插入到table的链表中。

如果dict处于rehash状态（即rehashidx ！=  -1），则在插入的时候，先调用_dictRehashStep，对于rehash中的dict，使用的table是ht[1]。

static void _dictRehashStep(dict *d) {
    if (d->iterators == ) dictRehash(d,);
}

int dictRehash(dict *d, int n) {
    int empty_visits = n*; /* Max number of empty buckets to visit. */
    if (!dictIsRehashing(d)) return ;

    while(n-- && d->ht[].used != ) {
        dictEntry *de, *nextde;

        /* Note that rehashidx can't overflow as we are sure there are more
         * elements because ht[0].used != 0 */
        assert(d->ht[].size > (unsigned long)d->rehashidx);
        while(d->ht[].table[d->rehashidx] == NULL) {
            d->rehashidx++;
            if (--empty_visits == ) return ;
        }
        de = d->ht[].table[d->rehashidx];
        /* Move all the keys in this bucket from the old to the new hash HT */
        while(de) {
            unsigned int h;

            nextde = de->next;
            /* Get the index in the new hash table */
            h = dictHashKey(d, de->key) & d->ht[].sizemask;
            de->next = d->ht[].table[h];
            d->ht[].table[h] = de;
            d->ht[].used--;
            d->ht[].used++;
            de = nextde;
        }
        d->ht[].table[d->rehashidx] = NULL;
        d->rehashidx++;
    }

    /* Check if we already rehashed the whole table... */
    if (d->ht[].used == ) {
        zfree(d->ht[].table);
        d->ht[] = d->ht[];
        _dictReset(&d->ht[]);
        d->rehashidx = -;
        return ;
    }

    /* More to rehash... */
    return ;
}

从代码中可以看出：rehashidx标记了ht[0]中正在rehash的链表的index。

那么，在什么情况下，redis会对dict进行rehash呢？

调用栈： _dictKeyIndex -> _dictExpandIfNeeded -> dictExpand。在计算键的index时，会判断是否需要扩展dict，如果需要扩展，则把dict的rehashidx置为0。

static int _dictKeyIndex(dict *d, const void *key, unsigned int hash, dictEntry **existing)
{
    unsigned int idx, table;
    dictEntry *he;
    if (existing) *existing = NULL;

    /* Expand the hash table if needed */
    if (_dictExpandIfNeeded(d) == DICT_ERR)
        return -;
    for (table = ; table <= ; table++) {
        idx = hash & d->ht[table].sizemask;
        /* Search if this slot does not already contain the given key */
        he = d->ht[table].table[idx];
        while(he) {
            if (key==he->key || dictCompareKeys(d, key, he->key)) {
                if (existing) *existing = he;
                return -;
            }
            he = he->next;
        }
        if (!dictIsRehashing(d)) break;
    }
    return idx;
}

/* Expand the hash table if needed */
static int _dictExpandIfNeeded(dict *d)
{
    /* Incremental rehashing already in progress. Return. */
    if (dictIsRehashing(d)) return DICT_OK;

    /* If the hash table is empty expand it to the initial size. */
    if (d->ht[].size == ) return dictExpand(d, DICT_HT_INITIAL_SIZE);

    /* If we reached the 1:1 ratio, and we are allowed to resize the hash
     * table (global setting) or we should avoid it but the ratio between
     * elements/buckets is over the "safe" threshold, we resize doubling
     * the number of buckets. */
    if (d->ht[].used >= d->ht[].size &&
        (dict_can_resize ||
         d->ht[].used/d->ht[].size > dict_force_resize_ratio))
    {
        return dictExpand(d, d->ht[].used*);
    }
    return DICT_OK;
}

/* Expand or create the hash table */
int dictExpand(dict *d, unsigned long size)
{
    dictht n; /* the new hash table */
    unsigned long realsize = _dictNextPower(size);

    /* the size is invalid if it is smaller than the number of
     * elements already inside the hash table */
    if (dictIsRehashing(d) || d->ht[].used > size)
        return DICT_ERR;

    /* Rehashing to the same table size is not useful. */
    if (realsize == d->ht[].size) return DICT_ERR;

    /* Allocate the new hash table and initialize all pointers to NULL */
    n.size = realsize;
    n.sizemask = realsize-;
    n.table = zcalloc(realsize*sizeof(dictEntry*));
    n.used = ;

    /* Is this the first initialization? If so it's not really a rehashing
     * we just set the first hash table so that it can accept keys. */
    if (d->ht[].table == NULL) {
        d->ht[] = n;
        return DICT_OK;
    }

    /* Prepare a second hash table for incremental rehashing */
    d->ht[] = n;
    d->rehashidx = ;
    return DICT_OK;
}

从数据结构的角度来看，redis的dict和java的HashMap很像，区别在于rehash：HashMap在resize时是一次性拷贝的，然后使用新的数组，而dict维持了2个dictht，平常使用ht[0]，一旦开始rehash则使用ht[0]和ht[1]，rehash被分摊到每次的dictAdd和dictFind等操作中。

dictEntry *dictFind(dict *d, const void *key)
{
    dictEntry *he;
    unsigned int h, idx, table;

    if (d->ht[].used + d->ht[].used == ) return NULL; /* dict is empty */
    if (dictIsRehashing(d)) _dictRehashStep(d);
    h = dictHashKey(d, key);
    for (table = ; table <= ; table++) { //会遍历d->ht[0]和d->ht[1]
        idx = h & d->ht[table].sizemask;
        he = d->ht[table].table[idx];
        while(he) {
            if (key==he->key || dictCompareKeys(d, key, he->key))
                return he; //找到即返回
            he = he->next;
        }
        if (!dictIsRehashing(d)) return NULL;
    }
    return NULL;
}

redis为什么要如此设计？

试想一下，如果和java的HashMap一样，redis也是一次性拷贝，那么当这个dict非常大时，拷贝就会比较耗时，而在这段时间内，redis就无法对外提供服务了。

这种设计增加了复杂度，开始rehash后，dict的数据分散在ht[0]和ht[1]中，对于查询（dictFind）和删除（dictDelete）和设置（dictReplace），则会遍历ht[0]和ht[1]。