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Redis会因为内存不足而产生错误，也会因为回收过久而导致系统长期的停顿，因此了解掌握Redis的回收策略十分重要。当Redis的内存达到规定的最大值时，可以进行配置进行淘汰键值，并且将一些键值对进行回收。

我们打开Redis安装目录下的redis.conf文件。配置文件中有这么一段话

# Set a memory usage limit to the specified amount of bytes.
# When the memory limit is reached Redis will try to remove keys
# according to the eviction policy selected (see maxmemory-policy).
#
# If Redis can't remove keys according to the policy, or if the policy is
# set to 'noeviction', Redis will start to reply with errors to commands
# that would use more memory, like SET, LPUSH, and so on, and will continue
# to reply to read-only commands like GET.
#
# This option is usually useful when using Redis as an LRU or LFU cache, or to
# set a hard memory limit for an instance (using the 'noeviction' policy).
#
# WARNING: If you have slaves attached to an instance with maxmemory on,
# the size of the output buffers needed to feed the slaves are subtracted
# from the used memory count, so that network problems / resyncs will
# not trigger a loop where keys are evicted, and in turn the output
# buffer of slaves is full with DELs of keys evicted triggering the deletion
# of more keys, and so forth until the database is completely emptied.
#
# In short... if you have slaves attached it is suggested that you set a lower
# limit for maxmemory so that there is some free RAM on the system for slave
# output buffers (but this is not needed if the policy is 'noeviction').
#
# maxmemory <bytes># MAXMEMORY POLICY: how Redis will select what to remove when maxmemory
# is reached. You can select among five behaviors:
#
# volatile-lru -> Evict using approximated LRU among the keys with an expire set.
# allkeys-lru -> Evict any key using approximated LRU.
# volatile-lfu -> Evict using approximated LFU among the keys with an expire set.
# allkeys-lfu -> Evict any key using approximated LFU.
# volatile-random -> Remove a random key among the ones with an expire set.
# allkeys-random -> Remove a random key, any key.
# volatile-ttl -> Remove the key with the nearest expire time (minor TTL)
# noeviction -> Don't evict anything, just return an error on write operations.
#
# LRU means Least Recently Used
# LFU means Least Frequently Used
#
# Both LRU, LFU and volatile-ttl are implemented using approximated
# randomized algorithms.
#
# Note: with any of the above policies, Redis will return an error on write
#       operations, when there are no suitable keys for eviction.
#
#       At the date of writing these commands are: set setnx setex append
#       incr decr rpush lpush rpushx lpushx linsert lset rpoplpush sadd
#       sinter sinterstore sunion sunionstore sdiff sdiffstore zadd zincrby
#       zunionstore zinterstore hset hsetnx hmset hincrby incrby decrby
#       getset mset msetnx exec sort
#
# The default is:
#
# maxmemory-policy noeviction# LRU, LFU and minimal TTL algorithms are not precise algorithms but approximated
# algorithms (in order to save memory), so you can tune it for speed or
# accuracy. For default Redis will check five keys and pick the one that was
# used less recently, you can change the sample size using the following
# configuration directive.
#
# The default of 5 produces good enough results. 10 Approximates very closely
# true LRU but costs more CPU. 3 is faster but not very accurate.
#
# maxmemory-samples 5

• volatile-lru：采用最近使用最少的淘汰策略，Redis将回收那些超时的（仅仅是超时的）键值对，也就是它只淘汰那些超时的键值对。

• allkeys-lru：采用最近最少使用的淘汰策略，Redis将对所有（不仅仅是超时的）的键值对采用最近最少使用的淘汰策略。

• volatile-lfu：采用最近最不常用的淘汰策略，所谓最近最不常用，也就是一定时期内被访问次数最少的。Redis将回收超时的键值对。

• allkeys-lfu：采用最近最不常用的淘汰策略，Redis将对所有的键值对采用最近最不常用的淘汰策略。

• volatile-random：采用随机淘汰策略删除超时的键值对。

• allkeys-random：采用随机淘汰策略删除所有的键值对，这个策略不常用。

• volatile-ttl：采用删除存活时间最短的键值对策略。

• noeviction：不淘汰任何键值对，当内存满时，如果进行读操作，例如get命令，它将正常工作，而做写操作，它将返回错误，也就是说，当Redis采用这个策略内存达到最大的时候，它就只能读不能写了。

Redis默认采用noeviction策略。

LRU算法或者TTL算法都是不精确的算法，而是一个近似算法。

Redis不会通过对全部的键值对进行比较来确定最精确的时间值，因为这太消耗时间，导致回收垃圾占用的时间太多造成服务器卡顿。在配置文件中，有一个参数maxmemory-samples，它的默认值是5,如果采用volatile-ttl算法，我们可以看看下面这个过程，假设有7个即将超时的键值对

当设置maxmemory-samples越大，则Redis删除的就越精确，但消耗CPU。

回收超时策略的缺点是必须指明超时的键值对，这会给程序开发带来一些设置超时的代码，增加刘开发者的工作量。对所有的键值对进行回收，有可能把正在使用的键值对删掉，增加了存储的不稳定性。对于垃圾回收的策略，还需要控制回收的时间。