Available since 1.0.0.
Time complexity: O(1)

Set a timeout on key. 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 terminology.

The timeout will only be cleared by commands that delete or overwrite the contents of the key, including DEL, SET, GETSET and all the *STORE commands. 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 Key_A that is overwritten by a call like RENAME Key_B Key_A, it does not matter if the original Key_A had a timeout associated or not, the new key Key_A will inherit all the characteristics of Key_B.

Note that calling EXPIRE/PEXPIRE with a non-positive timeout or EXPIREAT/PEXPIREAT with a time in the past will result in the key being deleted rather than expired (accordingly, the emitted key event will be del, not expired).

Refreshing expires

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.

EXPIRE would return 0 and not alter the timeout for a key with a timeout set.

Return value

Integer reply, specifically:

1 if the timeout was set.
0 if key does not exist.

Examplesredis> SET mykey “Hello”

 redis> SET mykey "Hello"
 "OK"
 redis> EXPIRE mykey 10
 (integer) 1
 redis> TTL mykey
 (integer) 10
 redis> SET mykey "Hello World"
 "OK"
 redis> TTL mykey(integer)
  -1
 redis>

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.

This pattern is easily modified to use counters using INCR instead of lists using RPUSH.

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.

The key time to live can be updated or entirely removed using the EXPIRE and PERSIST command (or other strictly related commands).

Expire accuracy

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 passively 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 replicas nodes. This way the expiration process is centralized in the master instance, and there is no chance of consistency errors.

However while the replicas 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 replica is elected to master it will be able to expire the keys independently, fully acting as a master.

上面的内容来自于redis官网，其实如果我们在java代码中也是可以有所发现的，下面是Jedis的expire() 及API.

public Long expire(final String key, final int seconds) {
	checkIsInMulti();
	client.expire(key, seconds);
	return client.getIntegerReply();
}

Set a timeout on the specified key. After the timeout the key will be automatically deleted by the server. A key with an associated timeout is said to be volatile in Redis terminology.
Voltile keys are stored on disk like the other keys, the timeout is persistent too like all the other aspects of the dataset. Saving a dataset containing expires and stopping the server does not stop the flow of time as Redis stores on disk the time when the key will no longer be available as Unix time, and not the remaining seconds.
Since Redis 2.1.3 you can update the value of the timeout of a key already having an expire set. It is also possible to undo the expire at all turning the key into a normal key using the PERSIST command.
Time complexity: O(1)

Integer reply, specifically: 1: the timeout was set. 0: the timeout was not set since the key already has an associated timeout (this may happen only in Redis versions < 2.1.3, Redis >= 2.1.3 will happily update the timeout), or the key does not exist.

标题尽管有点长，但是确实意义明确的。可以先看一下两个简化后对象。Video 和 Star，它们的关系是一部Video有多个Star,这里的Star不是github上的star,而是actor or actress。

@Data
@Document
public class Video{
    @Id
    protected String id;
    @DBRef
    private Set<Star> stars = new LinkedHashSet<>();
}

@Data
@Document
public class Star{
    @Id
    protected String id;
    @Indexed
    private String name;
}

现在有个查询需求，根据Star的id去查询他(她)的所有参演Video.目前我们的思路是：
(1)根据id查询出某个Star star;
(2)然后查询所有的Video,然后遍历的时候用stars.contains(star)晒选；
(3)筛选的结果就是该Star所参演的Video.

public List<Video> findAllByStarId(String starId) {
        Star star = starService.findById(starId);
        List<Video> all = repo.findAll();
        if (CollectionUtils.isEmpty(all)) {
            return Collections.emptyList();
        }
        return all.stream()
                .filter(t -> t.getStars().contains(star))
               .sorted(Comparator.comparing(Video::getRelease).reversed())
               .collect(Collectors.toList());
}

就功能型，上面是满足的，但是你实际运行起来就会发现查询很慢，估计20-30s,显然不符合我们的预期。

所有上面的代码有一个致命的隐患，那就是List all = repo.findAll();这是一个超级大隐患，查询的时候一定不能一次查询所有的数据，然后很有可能程序一下子就卡了，这个和数据的多少及对象大小有直接关系。在生产环境绝对不能这么写代码的。

那么JPA有没有为我们提供一个比较好的方式。当然是有的，那就是把我们在后面filter(t -> t.getStars().contains(star))这段代码直接在查询的时候就搞定。

public interface VideoRepository extends MongoRepository<Video, BigInteger> {
    
    List<Video> findAllByStarsContains(Star star, Sort sort);
    
}

修改后的代码是下面这样的：

    public List<Video> findAllByStarId(String starId) {
        Star star = starService.findById(starId);
        List<Video> all = repo.findAllByStarsContains(star, Sort.by(Sort.Direction.DESC, "release"));
        return CollectionUtils.isEmpty(all) ? Collections.emptyList() : all;
}

后记：
(1) 如果我们建模的时候是双向建模，Star 也有多部Video到还可以一次可以查询出来，可能我们由于数据原因，没有建立这样的关联，再讲白一点，就是Star只是包含一个简单的属性(name)，只是为了扩展，慢慢有了birth,height…属性独立出去了。
(2) JPA的强大之处就是帮省去了写实现的时间，你只要按照规范命名就可以了。至于规范，在IDEA中的提示真的很友好。

You worth it.

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Redis expire原理