Set a timeout on
After the timeout has expired, the key will automatically be deleted.
A key with an associated timeout is often said to be volatile in Redis
The timeout will only be cleared by commands that delete or overwrite the
contents of the key, including DEL, SET, GETSET and all the
This means that all the operations that conceptually alter the value stored at
the key without replacing it with a new one will leave the timeout untouched.
For instance, incrementing the value of a key with INCR, pushing a new value
into a list with LPUSH, or altering the field value of a hash with HSET are
all operations that will leave the timeout untouched.
The timeout can also be cleared, turning the key back into a persistent key, using the PERSIST command.
If a key is renamed with RENAME, the associated time to live is transferred to the new key name.
If a key is overwritten by RENAME, like in the case of an existing key
that is overwritten by a call like
RENAME Key_B Key_A, it does not matter if
Key_A had a timeout associated or not, the new key
inherit all the characteristics of
It is possible to call EXPIRE using as argument a key that already has an existing expire set. In this case the time to live of a key is updated to the new value. There are many useful applications for this, an example is documented in the Navigation session pattern section below.
*Differences in Redis prior 2.1.3
In Redis versions prior 2.1.3 altering a key with an expire set using a command altering its value had the effect of removing the key entirely. This semantics was needed because of limitations in the replication layer that are now fixed.
Integer reply, specifically:
1if the timeout was set.
keydoes not exist or the timeout could not be set.
OKredis> EXPIRE mykey 10
(integer) 1redis> TTL mykey
(integer) 10redis> SET mykey "Hello World"
OKredis> TTL mykey
*Pattern: Navigation session
Imagine you have a web service and you are interested in the latest N pages recently visited by your users, such that each adjacent page view was not performed more than 60 seconds after the previous. Conceptually you may consider this set of page views as a Navigation session of your user, that may contain interesting information about what kind of products he or she is looking for currently, so that you can recommend related products.
You can easily model this pattern in Redis using the following strategy: every time the user does a page view you call the following commands:
MULTI RPUSH pagewviews.user:<userid> http://..... EXPIRE pagewviews.user:<userid> 60 EXEC
If the user will be idle more than 60 seconds, the key will be deleted and only subsequent page views that have less than 60 seconds of difference will be recorded.
*Appendix: Redis expires
*Keys with an expire
Normally Redis keys are created without an associated time to live. The key will simply live forever, unless it is removed by the user in an explicit way, for instance using the DEL command.
The EXPIRE family of commands is able to associate an expire to a given key, at the cost of some additional memory used by the key. When a key has an expire set, Redis will make sure to remove the key when the specified amount of time elapsed.
In Redis 2.4 the expire might not be pin-point accurate, and it could be between zero to one seconds out.
Since Redis 2.6 the expire error is from 0 to 1 milliseconds.
*Expires and persistence
Keys expiring information is stored as absolute Unix timestamps (in milliseconds in case of Redis version 2.6 or greater). This means that the time is flowing even when the Redis instance is not active.
For expires to work well, the computer time must be taken stable. If you move an RDB file from two computers with a big desync in their clocks, funny things may happen (like all the keys loaded to be expired at loading time).
Even running instances will always check the computer clock, so for instance if you set a key with a time to live of 1000 seconds, and then set your computer time 2000 seconds in the future, the key will be expired immediately, instead of lasting for 1000 seconds.
*How Redis expires keys
Redis keys are expired in two ways: a passive way, and an active way.
A key is actively expired simply when some client tries to access it, and the key is found to be timed out.
Of course this is not enough as there are expired keys that will never be accessed again. These keys should be expired anyway, so periodically Redis tests a few keys at random among keys with an expire set. All the keys that are already expired are deleted from the keyspace.
Specifically this is what Redis does 10 times per second:
- Test 20 random keys from the set of keys with an associated expire.
- Delete all the keys found expired.
- If more than 25% of keys were expired, start again from step 1.
This is a trivial probabilistic algorithm, basically the assumption is that our sample is representative of the whole key space, and we continue to expire until the percentage of keys that are likely to be expired is under 25%
This means that at any given moment the maximum amount of keys already expired that are using memory is at max equal to max amount of write operations per second divided by 4.
*How expires are handled in the replication link and AOF file
In order to obtain a correct behavior without sacrificing consistency, when a key expires, a DEL operation is synthesized in both the AOF file and gains all the attached slaves. This way the expiration process is centralized in the master instance, and there is no chance of consistency errors.
However while the slaves connected to a master will not expire keys independently (but will wait for the DEL coming from the master), they'll still take the full state of the expires existing in the dataset, so when a slave is elected to a master it will be able to expire the keys independently, fully acting as a master.