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Redis存储键值方式详解

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,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 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]。

本文永久更新链接地址:https://www.linuxidc.com/Linux/2018-03/151173.htm

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