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[2]; 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 1; }
/* 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 0; }
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)) == -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. */ 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; }
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 == 0) dictRehash(d,1); }
int dictRehash(dict *d, int n) { int empty_visits = n*10; /* Max number of empty buckets to visit. */ if (!dictIsRehashing(d)) return 0;
while(n-- && d->ht[0].used != 0) { 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[0].size > (unsigned long)d->rehashidx); while(d->ht[0].table[d->rehashidx] == NULL) { d->rehashidx ; if (--empty_visits == 0) return 1; } de = d->ht[0].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[1].sizemask; de->next = d->ht[1].table[h]; d->ht[1].table[h] = de; d->ht[0].used--; d->ht[1].used ; de = nextde; } d->ht[0].table[d->rehashidx] = NULL; d->rehashidx ; }
/* Check if we already rehashed the whole table... */ if (d->ht[0].used == 0) { zfree(d->ht[0].table); d->ht[0] = d->ht[1]; _dictReset(&d->ht[1]); d->rehashidx = -1; return 0; }
/* More to rehash... */ return 1; }
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 -1; for (table = 0; table <= 1; 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 -1; } 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[0].size == 0) 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[0].used >= d->ht[0].size && (dict_can_resize || d->ht[0].used/d->ht[0].size > dict_force_resize_ratio)) { return dictExpand(d, d->ht[0].used*2); } 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[0].used > size) return DICT_ERR;
/* Rehashing to the same table size is not useful. */ if (realsize == d->ht[0].size) return DICT_ERR;
/* Allocate the new hash table and initialize all pointers to NULL */ n.size = realsize; n.sizemask = realsize-1; n.table = zcalloc(realsize*sizeof(dictEntry*)); n.used = 0;
/* 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[0].table == NULL) { d->ht[0] = n; return DICT_OK; }
/* Prepare a second hash table for incremental rehashing */ d->ht[1] = n; d->rehashidx = 0; 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[0].used d->ht[1].used == 0) return NULL; /* dict is empty */ if (dictIsRehashing(d)) _dictRehashStep(d); h = dictHashKey(d, key); for (table = 0; table <= 1; 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]。
