哈希冲突问题
Redis使用哈希表来存放键值对数据，在插入新键值对数据时，会先按照”key“来计算哈希值，再根据哈希值和哈希表的sizemask来计算出该”key“在对于哈希数组中的索引值，然后将键值对数据封装成dictEntry对象并放入到索引值对应的哈希数组中。
不同的Key经过相同哈希函数计算后可能得到相同的哈希值和索引值，当两个或多个Key被分配到哈希数组相同的索引位置时，成为哈希冲突，Redis使用单向链表的方式来解决哈希冲突。最后增加的键值对数据会被放置在单向链表的头部位置。
/* Add an element to the target hash table */
int dictAdd(dict *d, void *key, void *val)
{
/* 使用dictAddRaw来创建一个新的entry */
dictEntry *entry = dictAddRaw(d,key,NULL);
if (!entry) return DICT_ERR;
/* 设置新entry的value数据 */
dictSetVal(d, entry, val);
return DICT_OK;
}
/* Low level add or find:
* This function adds the entry but instead of setting a value returns the
* dictEntry structure to the user, that will make sure to fill the value
* field as he wishes.
*
* This function is also directly exposed to the user API to be called
* mainly in order to store non-pointers inside the hash value, example:
*
* entry = dictAddRaw(dict,mykey,NULL);
* if (entry != NULL) dictSetSignedIntegerVal(entry,1000);
*
* Return values:
*
* If key already exists NULL is returned, and "*existing" is populated
* with the existing entry if existing is not NULL.
*
* If key was added, the hash entry is returned to be manipulated by the caller.
*/
dictEntry *dictAddRaw(dict *d, void *key, dictEntry **existing)
{
long index;
dictEntry *entry;
dictht *ht;
if (dictIsRehashing(d)) _dictRehashStep(d);
/* Get the index of the new element, or -1 if
* the element already exists. */
/* 获取到新增key在哈希数组中的索引位置 */
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. */
/* 找到当前使用的哈希表，
* 如果处于rehash状态则新增的键值对数据放入到ht[1]中
* 否则将新增的键值对数据放入到ht[0]中
*/
ht = dictIsRehashing(d) ? &d->ht[1] : &d->ht[0];
/* 创建新的entry对象 */
entry = zmalloc(sizeof(*entry));
/* 将当前位置上数据赋值给新entry的next指针 */
entry->next = ht->table[index];
/* 将心得entry对象赋值给当前位置，即将新enrty放到单向链表的头部 */
ht->table[index] = entry;
/* 增加哈希表的已使用值 */
ht->used++;
/* Set the hash entry fields. */
/* 设置新entry的key数据 */
dictSetKey(d, entry, key);
return entry;
}
在按照key进行哈希查找时，会先按照key来计算出对应的哈希值和索引值，再按照索引值找到哈希数组对应位置上的单向链表，再依次遍历单向链表中的每个entry对象并使用查找的key和entry的key值进行对比，从而找到特定key的entry对象。
dictEntry *dictFind(dict *d, const void *key)
{
dictEntry *he;
uint64_t h, idx, table;
if (d->ht[0].used + d->ht[1].used == 0) return NULL; /* dict is empty */
/* 如果当前字典处于rehash状态，则辅助完成1条数据迁移工作 */
if (dictIsRehashing(d)) _dictRehashStep(d);
/* 计算key对应的hash值 */
h = dictHashKey(d, key);
for (table = 0; table <= 1; table++) {
/* 计算key对应的索引值 */
/* 在rehash过程中，ht[0]和ht[1]的sizemask不同，因此需要分别计算 */
idx = h & d->ht[table].sizemask;
/* 获取到哈希数组上指定索引位置上的单向链表 */
he = d->ht[table].table[idx];
/* 遍历单向链表 */
while(he) {
/* 针对单向链表上每个entry，对比entry存储的key和要查找的key */
if (key==he->key || dictCompareKeys(d, key, he->key))
/* 找到后立即返回，同一个key只会存在一份数据 */
return he;
he = he->next;
}
/* 如果未处于rehas状态，处理完ht[0]后无需再处理ht[1] */
if (!dictIsRehashing(d)) return NULL;
}
return NULL;
}
解决哈希冲突
在每个哈希表对象中，使用size字段来存放该哈希表的长度，使用used字段来存放哈希表的已使用量：
/* 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 {
/* 使用数组方式来存放哈希表数据 */
dictEntry **table;
/* 哈希表长度 */
unsigned long size;
/* 哈希表长度掩码，用来计算哈希值对应的数组下标 */
unsigned long sizemask;
/* 哈希表上已使用量，即当前存储的entry总数量 */
unsigned long used;
} dictht;
当 used * 100 / size < HASHTABLE_MIN_FILL(10)时，哈希数组资源浪费严重，需要对哈希表进行缩容，将哈希数表长度扩展为第一个大于等于used*2的2的n次幂。
当 used / size > dict_force_resize_ratio(5) 时，哈希数组上哈希冲突严重，需要对哈希表进行扩容，将哈希数表长度缩小为第一个大于等于used的2的n次幂。
哈希扩容操作
在Redis执行命令请求增加新键时，会调用函数dictAddRaw或dictAdd-->dictAddRaw来进行插入，在函数dictAddRaw中会使用_dictKeyIndex --> _dictExpandIfNeeded 来判断当前哈希表是否需要扩容。
static int dict_can_resize = 1;
static unsigned int dict_force_resize_ratio = 5;
/* 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;
}
哈希表的扩容操作主要由Redis插入新键值对数据的命令来触发，如：
- set类型的增加新键值对时调用setTypeAdd-->dictAddRaw来触发。
- zset类型的增加新键值对时调用zunionInterGenericCommand-->dictAddRaw来触发。
- string类型的增加新键值对时调用setGenericCommand-->setKey-->dbAdd-->dictAdd-->dictAddRaw来触发。
哈希缩容操作
在判断哈希表是否需要缩容时，通过函数tryResizeHashTables来：
- 调用htNeedsResize判断当前dict是否需要缩容
- 对需要缩容的dict调用dictResize来进行缩容，按照当前哈希表的已使用量(used)作为缩容标准。
/* If the percentage of used slots in the HT reaches HASHTABLE_MIN_FILL
* we resize the hash table to save memory */
void tryResizeHashTables(int dbid) {
if (htNeedsResize(server.db[dbid].dict))
dictResize(server.db[dbid].dict);
if (htNeedsResize(server.db[dbid].expires))
dictResize(server.db[dbid].expires);
}
/* This is the initial size of every hash table */
#define DICT_HT_INITIAL_SIZE 4
/* Hash table parameters */
/* Minimal hash table fill 10% */
#define HASHTABLE_MIN_FILL 10
int htNeedsResize(dict *dict) {
long long size, used;
size = dictSlots(dict);
used = dictSize(dict);
/* 当前使用量小于当前长度的10%时，进行缩容 */
return (size > DICT_HT_INITIAL_SIZE &&
(used*100/size < HASHTABLE_MIN_FILL));
}
/* Resize the table to the minimal size that contains all the elements,
* but with the invariant of a USED/BUCKETS ratio near to <= 1 */
int dictResize(dict *d)
{
int minimal;
if (!dict_can_resize || dictIsRehashing(d)) return DICT_ERR;
/* 按照当前使用量进行缩容 */
minimal = d->ht[0].used;
if (minimal < DICT_HT_INITIAL_SIZE)
minimal = DICT_HT_INITIAL_SIZE;
return dictExpand(d, minimal);
}
Redis按照redisServer.hz参数控制的频率执行定时任务执行过程中，会调用serverCron-->databasesCron函数，在databasesCron函数执行过程中，如果当前未执行AOF重写或RDB备份操作，则：
- 判断当前实例上所有redisDB是否需要缩容，需要则调用dictResize进行缩容操作。
- 判断当前实例上是否存在rehash操作，存在则调用incrementallyRehash函数进行增量处理。
/* 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. */
/* 在没有RDB备份或AOF日志重写时，才会触发rehash操作 */
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 */
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;
}
}
}
}
}
渐进式rehash操作
无论时缩容还是扩容，都会调用dictExpand函数来处理，按照新的hash表容量(unsigned long size)计算出一个接近且大于size的2^N的值，并以此值来初始化dictht对象，再赋值给d->ht[1]，同时设置rehash状态值d->rehashidx =0。
/* Expand or create the hash table */
int dictExpand(dict *d, unsigned long 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;
dictht n; /* the new hash table */
unsigned long realsize = _dictNextPower(size);
/* 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;
}
/* Our hash table capability is a power of two */
/* 哈希表的长度必须是2的N次方，因此必须选择一个接近且大于指定长度的2^N值 */
static unsigned long _dictNextPower(unsigned long size)
{
unsigned long i = DICT_HT_INITIAL_SIZE;
if (size >= LONG_MAX) return LONG_MAX + 1LU;
while(1) {
if (i >= size)
return i;
i *= 2;
}
}
函数dictExpand仅仅是初始化新哈希表ht[1]并设置dict的状态为rehash状态(rehashidx = 0)，为不严重阻塞正常命令请求，采用渐进式哈希(rehashing)的机制来完成数据从ht[0]到ht[1]的迁移。rehash操作以哈希桶为基本单位调用dictRehash函数来完成。
rehash操作按照触发方式可以分为：
- active rehashing(主动rehash操作)，Redis实例的定时任务通过serverCron-->databasesCron-->incrementallyRehash来触发，按照每次处理100个哈希桶+循环执行1秒的方式来处理。
- lazy rehashing(惰性rehah操作)，在对dict做增删改查的过程中，如dictFind、dictAdd、dictDelete等操作时触发，按照每次处理1个哈希桶的方式进行处理。
PS1: 虽然databasesCron中采用for循环遍历dbs_per_call个RedisDB，但只有在incrementallyRehash函数中成功调用1次dictRehashMilliseconds(dict,1),就会跳出循环，因此能严格保证databasesCron函数中rehash操作最多消耗1ms
主动Rehash操作
在函数incrementallyRehash中，当哈希表处于rehash状态(rehashidx不等于-1)时，会调用dictRehashMilliseconds来进行增量处理，按照每批次出100个键的方式循环执行1ms(硬编码在程序中)，避免rehash操作执行时间过长阻塞其他客户请求。
/* Our hash table implementation performs rehashing incrementally while
* we write/read from the hash table. Still if the server is idle, the hash
* table will use two tables for a long time. So we try to use 1 millisecond
* of CPU time at every call of this function to perform some rehahsing.
*
* The function returns 1 if some rehashing was performed, otherwise 0
* is returned. */
int incrementallyRehash(int dbid) {
/* Keys dictionary */
if (dictIsRehashing(server.db[dbid].dict)) {
dictRehashMilliseconds(server.db[dbid].dict,1);
return 1; /* already used our millisecond for this loop... */
}
/* Expires */
if (dictIsRehashing(server.db[dbid].expires)) {
dictRehashMilliseconds(server.db[dbid].expires,1);
return 1; /* already used our millisecond for this loop... */
}
return 0;
}
/* 按照rehashidx是否不等于-1来判断当前dict对象是否处于rehas状态 */
#define dictIsRehashing(d) ((d)->rehashidx != -1)
/* Rehash for an amount of time between ms milliseconds and ms+1 milliseconds */
int dictRehashMilliseconds(dict *d, int ms) {
long long start = timeInMilliseconds();
int rehashes = 0;
while(dictRehash(d,100)) {
rehashes += 100;
if (timeInMilliseconds()-start > ms) break;
}
return rehashes;
}
惰性rehah操作
在dictFind等对dict的增删改查的操作过程中，如果当前dict处于rehash状态，则调用_dictRehashStep(d)来迁移1个哈希桶的数据。
dictEntry *dictFind(dict *d, const void *key)
{
if (dictIsRehashing(d)) _dictRehashStep(d);
}
/* This function performs just a step of rehashing, and only if there are
* no safe iterators bound to our hash table. When we have iterators in the
* middle of a rehashing we can't mess with the two hash tables otherwise
* some element can be missed or duplicated.
*
* This function is called by common lookup or update operations in the
* dictionary so that the hash table automatically migrates from H1 to H2
* while it is actively used. */
static void _dictRehashStep(dict *d) {
if (d->iterators == 0) dictRehash(d,1);
}
由于惰性rehash的操作仅迁移1个哈希桶的数据，因此不会对操作产生明显的性能影响。
Rehash操作阻塞问题
无论时主动rehash还是惰性rehash操作，都会严格控制rehash操作的耗时，不会阻塞正常命令请求，但：
- 在rehash的初始阶段，需要按照
zcalloc(realsize*sizeof(dictEntry*))来为新的哈希表ht[1]申请内存。 - 在rehash的结尾阶段，需要对老的哈希表ht[0]进行回收并释放内存。
当涉及到的哈希表size较大时，需要申请或释放较大的内存资源，造成Redis服务器的内存使用量明显变化，同时影响到客户端命令请求的执行时间。如果请求执行过程中触发，则可能导致简单命令耗时较长被记录到慢日志中。