前言
從概念上講，我只知道redis採用惰性刪除加定期隨機刪除的策略去釋放過期鍵的內存，但是並沒有研究過其中的原理實現，今天追了一下源碼，剖析下惰性刪除和定期取樣刪除的實現過程。
惰性刪除
在server.c文件中查找對應的命令函數，對應的方法是getCommand
struct redisCommand redisCommandTable[] = {
{"module",moduleCommand,-2,
"admin no-script",
0,NULL,0,0,0,0,0,0},
{"get",getCommand,2,
"read-only fast @string",
0,NULL,1,1,1,0,0,0},
......
}
getCommand定義在t_string.c文件中，該方法調用了getGenericCommand方法
void getCommand(client *c) {
getGenericCommand(c);
}
int getGenericCommand(client *c) {
robj *o;
if ((o = lookupKeyReadOrReply(c,c->argv[1],shared.null[c->resp])) == NULL)
return C_OK;
if (o->type != OBJ_STRING) {
addReply(c,shared.wrongtypeerr);
return C_ERR;
} else {
addReplyBulk(c,o);
return C_OK;
}
}
繼續調用了lookupKeyReadOrReply方法，去查找指定key是否存在，第一個參數爲當前client對象，第二個參數爲client對象的第二個參數，爲什麼是第二個參數，這個很容易理解，假如我們的命令是 get why，那麼第一個參數爲當前命令get，第二個參數爲why，也就是當前的key，我們可以看一下server.h中的client結構體源碼：
typedef struct client {
......
int argc; /* Num of arguments of current command. */
robj **argv; /* Arguments of current command. */
......
}
在lookupKeyReadOrReply方法中調用lookupKeyRead方法，lookupKeyRead方法又調用了lookupKeyReadWithFlags方法，在這個方法中調用expireIfNeeded方法去檢測是否過期，如果key過期，此函數返回1。則更新當前redis服務的keyspace_misses計數（關於運維指標監控可以看另一篇文章）。
/* Lookup a key for read operations, or return NULL if the key is not found
* in the specified DB.
*
* As a side effect of calling this function:
* 1. A key gets expired if it reached it's TTL.
* 2. The key last access time is updated.
* 3. The global keys hits/misses stats are updated (reported in INFO).
* 4. If keyspace notifications are enabled, a "keymiss" notification is fired.
*
* This API should not be used when we write to the key after obtaining
* the object linked to the key, but only for read only operations.
*
* Flags change the behavior of this command:
*
* LOOKUP_NONE (or zero): no special flags are passed.
* LOOKUP_NOTOUCH: don't alter the last access time of the key.
*
* Note: this function also returns NULL if the key is logically expired
* but still existing, in case this is a slave, since this API is called only
* for read operations. Even if the key expiry is master-driven, we can
* correctly report a key is expired on slaves even if the master is lagging
* expiring our key via DELs in the replication link. */
robj *lookupKeyReadWithFlags(redisDb *db, robj *key, int flags) {
robj *val;
if (expireIfNeeded(db,key) == 1) {
/* Key expired. If we are in the context of a master, expireIfNeeded()
* returns 0 only when the key does not exist at all, so it's safe
* to return NULL ASAP. */
if (server.masterhost == NULL) {
server.stat_keyspace_misses++;
notifyKeyspaceEvent(NOTIFY_KEY_MISS, "keymiss", key, db->id);
return NULL;
}
/* However if we are in the context of a slave, expireIfNeeded() will
* not really try to expire the key, it only returns information
* about the "logical" status of the key: key expiring is up to the
* master in order to have a consistent view of master's data set.
*
* However, if the command caller is not the master, and as additional
* safety measure, the command invoked is a read-only command, we can
* safely return NULL here, and provide a more consistent behavior
* to clients accessign expired values in a read-only fashion, that
* will say the key as non existing.
*
* Notably this covers GETs when slaves are used to scale reads. */
if (server.current_client &&
server.current_client != server.master &&
server.current_client->cmd &&
server.current_client->cmd->flags & CMD_READONLY)
{
server.stat_keyspace_misses++;
notifyKeyspaceEvent(NOTIFY_KEY_MISS, "keymiss", key, db->id);
return NULL;
}
}
val = lookupKey(db,key,flags);
if (val == NULL) {
server.stat_keyspace_misses++;
notifyKeyspaceEvent(NOTIFY_KEY_MISS, "keymiss", key, db->id);
}
else
server.stat_keyspace_hits++;
return val;
}
expireIfNeeded方法中先調用keyIsExpired方法判斷是否過期，如果未過期返回0，如果當前不是主服務器，由於從服務器不允許修改，所以直接返回1，若是主服務器，則更新expired_keys計數。此時根據server.lazyfree_lazy_expire檢查是服務器是否在進行定期隨機刪除，如果沒有執行定期惰性刪除，則調用dbSyncDelete同步刪除當前key，否則調用dbAsyncDelete異步刪除當前key。
int expireIfNeeded(redisDb *db, robj *key) {
if (!keyIsExpired(db,key)) return 0;
if (server.masterhost != NULL) return 1;
server.stat_expiredkeys++;
propagateExpire(db,key,server.lazyfree_lazy_expire);
notifyKeyspaceEvent(NOTIFY_EXPIRED,
"expired",key,db->id);
int retval = server.lazyfree_lazy_expire ? dbAsyncDelete(db,key) :
dbSyncDelete(db,key);
if (retval) signalModifiedKey(db,key);
return retval;
}
異步執行是通過調用bio.c文件裏的bioCreateBackgroundJob方法去創建一個後臺job（Bio = Background I/O ）。後臺job保存在一個鏈表結構中，新加入的都放到尾部。
void bioCreateBackgroundJob(int type, void *arg1, void *arg2, void *arg3) {
struct bio_job *job = zmalloc(sizeof(*job));
job->time = time(NULL);
job->arg1 = arg1;
job->arg2 = arg2;
job->arg3 = arg3;
pthread_mutex_lock(&bio_mutex[type]);
listAddNodeTail(bio_jobs[type],job); //將當前job放到鏈表尾部
bio_pending[type]++;
pthread_cond_signal(&bio_newjob_cond[type]);
pthread_mutex_unlock(&bio_mutex[type]);
}
至此，一個key的惰性刪除整體流程就介紹完了，流程如下：
- 訪問某個key時先檢測key是否存在，如果存在繼續向下執行，否則直接返回空
- 檢查key是否過期，如果沒有過期直接返回對應value
- 如果過期更新key的miss計數
- 根據server.lazyfree_lazy_expire檢查後臺線是否在進行定期隨機刪除，如果不在直接同步刪除，結束
- 如果正在進行定期刪除任務，則調用bio方法創建一個後臺job，放到bio的job鏈表尾部，等待定時任務執行刪除
定期抽樣刪除
明確幾個redisServer中的全局變量和變動規則：
struct redisServer {
...
int active_expire_effort; /* From 1 (default) to 10, active effort. */
double stat_expired_stale_perc; /* Percentage of keys probably expired */
...
}
- active_expire_effort：從1到10，直譯過來爲有效期的活躍度，因爲在一個週期內，隨着時間的前進，過期的鍵數量一般是會逐步減少的，所以用它來控制改變取樣基數、回收任務間隔、週期CPU使用率（用於控制耗時）、過期百分比閥值。
- stat_expired_stale_perc：在每次定期刪除任務中，如果當前過期淘汰的鍵佔抽樣鍵總數的百分比大於當前值，則重複抽樣刪除，每次定時任務結束會根據公式動態調整。
/* Try to expire a few timed out keys. The algorithm used is adaptive and
* will use few CPU cycles if there are few expiring keys, otherwise
* it will get more aggressive to avoid that too much memory is used by
* keys that can be removed from the keyspace.
*
* Every expire cycle tests multiple databases: the next call will start
* again from the next db, with the exception of exists for time limit: in that
* case we restart again from the last database we were processing. Anyway
* no more than CRON_DBS_PER_CALL databases are tested at every iteration.
*
* The function can perform more or less work, depending on the "type"
* argument. It can execute a "fast cycle" or a "slow cycle". The slow
* cycle is the main way we collect expired cycles: this happens with
* the "server.hz" frequency (usually 10 hertz).
*
* However the slow cycle can exit for timeout, since it used too much time.
* For this reason the function is also invoked to perform a fast cycle
* at every event loop cycle, in the beforeSleep() function. The fast cycle
* will try to perform less work, but will do it much more often.
*
* The following are the details of the two expire cycles and their stop
* conditions:
*
* If type is ACTIVE_EXPIRE_CYCLE_FAST the function will try to run a
* "fast" expire cycle that takes no longer than EXPIRE_FAST_CYCLE_DURATION
* microseconds, and is not repeated again before the same amount of time.
* The cycle will also refuse to run at all if the latest slow cycle did not
* terminate because of a time limit condition.
*
* If type is ACTIVE_EXPIRE_CYCLE_SLOW, that normal expire cycle is
* executed, where the time limit is a percentage of the REDIS_HZ period
* as specified by the ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC define. In the
* fast cycle, the check of every database is interrupted once the number
* of already expired keys in the database is estimated to be lower than
* a given percentage, in order to avoid doing too much work to gain too
* little memory.
*
* The configured expire "effort" will modify the baseline parameters in
* order to do more work in both the fast and slow expire cycles.
*/
#define ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP 20 /* Keys for each DB loop. */
#define ACTIVE_EXPIRE_CYCLE_FAST_DURATION 1000 /* Microseconds. */
#define ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC 25 /* Max % of CPU to use. */
#define ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE 10 /* % of stale keys after which
we do extra efforts. */
void activeExpireCycle(int type) {
/* Adjust the running parameters according to the configured expire
* effort. The default effort is 1, and the maximum configurable effort
* is 10. */
unsigned long
effort = server.active_expire_effort-1, /* Rescale from 0 to 9. */
config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort,
config_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort,
config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
2*effort,
config_cycle_acceptable_stale = ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE-
effort;
/* This function has some global state in order to continue the work
* incrementally across calls. */
static unsigned int current_db = 0; /* Last DB tested. */
static int timelimit_exit = 0; /* Time limit hit in previous call? */
static long long last_fast_cycle = 0; /* When last fast cycle ran. */
int j, iteration = 0;
int dbs_per_call = CRON_DBS_PER_CALL;
long long start = ustime(), timelimit, elapsed;
/* When clients are paused the dataset should be static not just from the
* POV of clients not being able to write, but also from the POV of
* expires and evictions of keys not being performed. */
if (clientsArePaused()) return;
if (type == ACTIVE_EXPIRE_CYCLE_FAST) {
/* Don't start a fast cycle if the previous cycle did not exit
* for time limit, unless the percentage of estimated stale keys is
* too high. Also never repeat a fast cycle for the same period
* as the fast cycle total duration itself. */
if (!timelimit_exit &&
server.stat_expired_stale_perc < config_cycle_acceptable_stale)
return;
if (start < last_fast_cycle + (long long)config_cycle_fast_duration*2)
return;
last_fast_cycle = start;
}
/* We usually should test CRON_DBS_PER_CALL per iteration, with
* two exceptions:
*
* 1) Don't test more DBs than we have.
* 2) If last time we hit the time limit, we want to scan all DBs
* in this iteration, as there is work to do in some DB and we don't want
* expired keys to use memory for too much time. */
if (dbs_per_call > server.dbnum || timelimit_exit)
dbs_per_call = server.dbnum;
/* We usually should test CRON_DBS_PER_CALL per iteration, with
* two exceptions:
*
* 1) Don't test more DBs than we have.
* 2) If last time we hit the time limit, we want to scan all DBs
* in this iteration, as there is work to do in some DB and we don't want
* expired keys to use memory for too much time. */
if (dbs_per_call > server.dbnum || timelimit_exit)
dbs_per_call = server.dbnum;
/* We can use at max 'config_cycle_slow_time_perc' percentage of CPU
* time per iteration. Since this function gets called with a frequency of
* server.hz times per second, the following is the max amount of
* microseconds we can spend in this function. */
timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
timelimit_exit = 0;
if (timelimit <= 0) timelimit = 1;
if (type == ACTIVE_EXPIRE_CYCLE_FAST)
timelimit = config_cycle_fast_duration; /* in microseconds. */
/* Accumulate some global stats as we expire keys, to have some idea
* about the number of keys that are already logically expired, but still
* existing inside the database. */
long total_sampled = 0;
long total_expired = 0;
for (j = 0; j < dbs_per_call && timelimit_exit == 0; j++) {
/* Expired and checked in a single loop. */
unsigned long expired, sampled;
redisDb *db = server.db+(current_db % server.dbnum);
/* Increment the DB now so we are sure if we run out of time
* in the current DB we'll restart from the next. This allows to
* distribute the time evenly across DBs. */
current_db++;
/* Continue to expire if at the end of the cycle there are still
* a big percentage of keys to expire, compared to the number of keys
* we scanned. The percentage, stored in config_cycle_acceptable_stale
* is not fixed, but depends on the Redis configured "expire effort". */
do {
unsigned long num, slots;
long long now, ttl_sum;
int ttl_samples;
iteration++;
/* If there is nothing to expire try next DB ASAP. */
if ((num = dictSize(db->expires)) == 0) {
db->avg_ttl = 0;
break;
}
slots = dictSlots(db->expires);
now = mstime();
/* When there are less than 1% filled slots, sampling the key
* space is expensive, so stop here waiting for better times...
* The dictionary will be resized asap. */
if (num && slots > DICT_HT_INITIAL_SIZE &&
(num*100/slots < 1)) break;
/* The main collection cycle. Sample random keys among keys
* with an expire set, checking for expired ones. */
expired = 0;
sampled = 0;
ttl_sum = 0;
ttl_samples = 0;
if (num > config_keys_per_loop)
num = config_keys_per_loop;
/* Here we access the low level representation of the hash table
* for speed concerns: this makes this code coupled with dict.c,
* but it hardly changed in ten years.
*
* Note that certain places of the hash table may be empty,
* so we want also a stop condition about the number of
* buckets that we scanned. However scanning for free buckets
* is very fast: we are in the cache line scanning a sequential
* array of NULL pointers, so we can scan a lot more buckets
* than keys in the same time. */
long max_buckets = num*20;
long checked_buckets = 0;
while (sampled < num && checked_buckets < max_buckets) {
for (int table = 0; table < 2; table++) {
if (table == 1 && !dictIsRehashing(db->expires)) break;
unsigned long idx = db->expires_cursor;
idx &= db->expires->ht[table].sizemask;
dictEntry *de = db->expires->ht[table].table[idx];
long long ttl;
/* Scan the current bucket of the current table. */
checked_buckets++;
while(de) {
/* Get the next entry now since this entry may get
* deleted. */
dictEntry *e = de;
de = de->next;
ttl = dictGetSignedIntegerVal(e)-now;
if (activeExpireCycleTryExpire(db,e,now)) expired++;
if (ttl > 0) {
/* We want the average TTL of keys yet
* not expired. */
ttl_sum += ttl;
ttl_samples++;
}
sampled++;
}
}
db->expires_cursor++;
}
total_expired += expired;
total_sampled += sampled;
/* Update the average TTL stats for this database. */
if (ttl_samples) {
long long avg_ttl = ttl_sum/ttl_samples;
/* Do a simple running average with a few samples.
* We just use the current estimate with a weight of 2%
* and the previous estimate with a weight of 98%. */
if (db->avg_ttl == 0) db->avg_ttl = avg_ttl;
db->avg_ttl = (db->avg_ttl/50)*49 + (avg_ttl/50);
}
/* We can't block forever here even if there are many keys to
* expire. So after a given amount of milliseconds return to the
* caller waiting for the other active expire cycle. */
if ((iteration & 0xf) == 0) { /* check once every 16 iterations. */
elapsed = ustime()-start;
if (elapsed > timelimit) {
timelimit_exit = 1;
server.stat_expired_time_cap_reached_count++;
break;
}
}
/* We don't repeat the cycle for the current database if there are
* an acceptable amount of stale keys (logically expired but yet
* not reclaimed). */
} while (sampled == 0 ||
(expired*100/sampled) > config_cycle_acceptable_stale);
}
elapsed = ustime()-start;
server.stat_expire_cycle_time_used += elapsed;
latencyAddSampleIfNeeded("expire-cycle",elapsed/1000);
/* Update our estimate of keys existing but yet to be expired.
* Running average with this sample accounting for 5%. */
double current_perc;
if (total_sampled) {
current_perc = (double)total_expired/total_sampled;
} else
current_perc = 0;
server.stat_expired_stale_perc = (current_perc*0.05)+
(server.stat_expired_stale_perc*0.95);
}
怎麼樣，代碼是不是看懵了，其實我剛看也蒙了，哈哈哈，到現在也沒完全搞清楚細節，但是基本捋明白了整體邏輯，我只說重點：
1、改變有效期活躍值：
effort = server.active_expire_effort-1
2、每次任務開始前需要根據公式改變抽樣大小（初始值20）：
config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP + ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort
3、每次任務開始前需要根據公式改變定時任務間隔時間（初始值1000微秒）：
config_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION + ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort
4、每次任務開始前增大CPU使用率閥值
config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC + 2*effort
5、每次任務開始前需要根據公式改變是否執行抽樣刪除的閥值百分比：
config_cycle_acceptable_stale = ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE - effort
6、如果上次未因時間間隔過短提前結束，並且當前全局過期百分比閥值小於本次遞減後的閥值，則當前任務提前結束
if (!timelimit_exit && server.stat_expired_stale_perc < config_cycle_acceptable_stale)
return;
7、如果上次開始時間 + 2倍的任務間隔仍大於當前時間，則該當前任務提前結束
if (start < last_fast_cycle + (long long)config_cycle_fast_duration*2)
return;
8、 根據config_cycle_slow_time_perc和當前服務器CPU執行頻率hz，計算收集過期鍵中16次迭代的限制時間，單位微秒，當超過這個時間限制馬上停止當前任務，並記錄提前結束次數。（我理解這裏是控制定期隨機回收佔用CPU的時間，如果不對，還希望大佬指點）
timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
。。。
/* We can't block forever here even if there are many keys to
* expire. So after a given amount of milliseconds return to the
* caller waiting for the other active expire cycle. */
if ((iteration & 0xf) == 0) { /* check once every 16 iterations. */
elapsed = ustime()-start;
if (elapsed > timelimit) {
timelimit_exit = 1;
server.stat_expired_time_cap_reached_count++;
break;
}
}
9、在每次定時任務中按照數據庫從0-16的順序依次處理，通過全局變量current_db記錄進度，下次定時任務繼續處理
redisDb *db = server.db+(current_db % server.dbnum);
/* Increment the DB now so we are sure if we run out of time
* in the current DB we'll restart from the next. This allows to
* distribute the time evenly across DBs. */
current_db++;
10、從設置過期時間的鍵（redisDb.expires字典）中隨機取config_keys_per_loop個鍵進行過期檢測，每個hashtable都會有一個備用hashtable用於rehash，如果當前expires字典沒有正在進行rehash則跳過備用hash table。
for (int table = 0; table < 2; table++) {
if (table == 1 && !dictIsRehashing(db->expires)) break;
unsigned long idx = db->expires_cursor;
idx &= db->expires->ht[table].sizemask;
dictEntry *de = db->expires->ht[table].table[idx];
...
}
11、檢查並刪除過期的key（函數activeExpireCycleTryExpire），並記錄存活ttl和過期key數量，用於計算avg_ttl
while(de) {
/* Get the next entry now since this entry may get
* deleted. */
dictEntry *e = de;
de = de->next;
ttl = dictGetSignedIntegerVal(e)-now;
if (activeExpireCycleTryExpire(db,e,now)) expired++;
if (ttl > 0) {
/* We want the average TTL of keys yet
* not expired. */
ttl_sum += ttl;
ttl_samples++;
}
sampled++;
}
12、如果樣本未監測到過期數據，或者當前過期佔比大於config_cycle_acceptable_stale閥值，則繼續下一輪檢測。
do {
...
} while (sampled == 0 || (expired*100/sampled) > config_cycle_acceptable_stale);
13、改變服務器的stat_expired_stale_perc百分比.
公式：server.stat_expired_stale_perc = (current_perc*0.05) + (server.stat_expired_stale_perc*0.95)
/* Update our estimate of keys existing but yet to be expired.
* Running average with this sample accounting for 5%. */
double current_perc;
if (total_sampled) {
current_perc = (double)total_expired/total_sampled;
} else
current_perc = 0;
server.stat_expired_stale_perc = (current_perc*0.05)+
(server.stat_expired_stale_perc*0.95);
怎麼樣，是不是感覺並沒有想象中的那麼難，可能我們日常做的業務比這些還要複雜。我本身c也基礎也一般，就只是本科在大學裏學的一點基礎，遇到不會的關鍵字也是一點點去查，只要靜下心來一行行看，總能看懂。