Redis Cache

Spring Data Redis provides an implementation of Spring Framework’s Cache Abstraction in the org.springframework.data.redis.cache package. To use Redis as a backing implementation, add RedisCacheManager to your configuration, as follows:

@Bean
public RedisCacheManager cacheManager(RedisConnectionFactory connectionFactory) {
    return RedisCacheManager.create(connectionFactory);
}

RedisCacheManager behavior can be configured with RedisCacheManagerBuilder, letting you set the default RedisCacheConfiguration, transaction behavior, and predefined caches.

RedisCacheManager cacheManager = RedisCacheManager.builder(connectionFactory)
    .cacheDefaults(RedisCacheConfiguration.defaultCacheConfig())
    .transactionAware()
    .withInitialCacheConfigurations(Collections.singletonMap("predefined",
        RedisCacheConfiguration.defaultCacheConfig().disableCachingNullValues()))
    .build();

As shown in the preceding example, RedisCacheManager allows custom configuration on a per-cache basis.

The behavior of RedisCache created by RedisCacheManager is defined with RedisCacheConfiguration. The configuration lets you set key expiration times, prefixes, and RedisSerializer implementations for converting to and from the binary storage format, as shown in the following example:

RedisCacheConfiguration cacheConfiguration = RedisCacheConfiguration.defaultCacheConfig()
    .entryTtl(Duration.ofSeconds(1))
    .disableCachingNullValues();

RedisCacheManager defaults to a lock-free RedisCacheWriter for reading and writing binary values. Lock-free caching improves throughput. The lack of entry locking can lead to overlapping, non-atomic commands for the Cache putIfAbsent and clean operations, as those require multiple commands to be sent to Redis. The locking counterpart prevents command overlap by setting an explicit lock key and checking against presence of this key, which leads to additional requests and potential command wait times.

Locking applies on the cache level, not per cache entry.

It is possible to opt in to the locking behavior as follows:

RedisCacheManager cacheMangager = RedisCacheManager
    .build(RedisCacheWriter.lockingRedisCacheWriter(connectionFactory))
    .cacheDefaults(RedisCacheConfiguration.defaultCacheConfig())
    ...

By default, any key for a cache entry gets prefixed with the actual cache name followed by two colons (::). This behavior can be changed to a static as well as a computed prefix.

The following example shows how to set a static prefix:

// static key prefix
RedisCacheConfiguration.defaultCacheConfig().prefixCacheNameWith("(͡° ᴥ ͡°)");

The following example shows how to set a computed prefix:

// computed key prefix
RedisCacheConfiguration.defaultCacheConfig()
    .computePrefixWith(cacheName -> "¯\_(ツ)_/¯" + cacheName);

The cache implementation defaults to use KEYS and DEL to clear the cache. KEYS can cause performance issues with large keyspaces. Therefore, the default RedisCacheWriter can be created with a BatchStrategy to switch to a SCAN-based batch strategy. The SCAN strategy requires a batch size to avoid excessive Redis command round trips:

RedisCacheManager cacheManager = RedisCacheManager
    .build(RedisCacheWriter.nonLockingRedisCacheWriter(connectionFactory, BatchStrategies.scan(1000)))
    .cacheDefaults(RedisCacheConfiguration.defaultCacheConfig())
    ...

The KEYS batch strategy is fully supported using any driver and Redis operation mode (Standalone, Clustered). SCAN is fully supported when using the Lettuce driver. Jedis supports SCAN only in non-clustered modes.

The following table lists the default settings for RedisCacheManager:

Table 1. `RedisCacheManager` defaults
Setting	Value
Cache Writer	Non-locking, `KEYS` batch strategy
Cache Configuration	`RedisCacheConfiguration#defaultConfiguration`
Initial Caches	None
Transaction Aware	No

The following table lists the default settings for RedisCacheConfiguration:

Table 2. RedisCacheConfiguration defaults
Key Expiration	None
Cache `null`	Yes
Prefix Keys	Yes
Default Prefix	The actual cache name
Key Serializer	`StringRedisSerializer`
Value Serializer	`JdkSerializationRedisSerializer`
Conversion Service	`DefaultFormattingConversionService` with default cache key converters

By default RedisCache, statistics are disabled. Use RedisCacheManagerBuilder.enableStatistics() to collect local hits and misses through RedisCache#getStatistics(), returning a snapshot of the collected data.

Redis Cache Expiration

The implementation of time-to-idle (TTI) as well as time-to-live (TTL) varies in definition and behavior even across different data stores.

In general:

time-to-live (TTL) expiration - TTL is only set and reset by a create or update data access operation. As long as the entry is written before the TTL expiration timeout, including on creation, an entry’s timeout will reset to the configured duration of the TTL expiration timeout. For example, if the TTL expiration timeout is set to 5 minutes, then the timeout will be set to 5 minutes on entry creation and reset to 5 minutes anytime the entry is updated thereafter and before the 5-minute interval expires. If no update occurs within 5 minutes, even if the entry was read several times, or even just read once during the 5-minute interval, the entry will still expire. The entry must be written to prevent the entry from expiring when declaring a TTL expiration policy.
time-to-idle (TTI) expiration - TTI is reset anytime the entry is also read as well as for entry updates, and is effectively and extension to the TTL expiration policy.

Some data stores expire an entry when TTL is configured no matter what type of data access operation occurs on the entry (reads, writes, or otherwise). After the set, configured TTL expiration timeout, the entry is evicted from the data store regardless. Eviction actions (for example: destroy, invalidate, overflow-to-disk (for persistent stores), etc.) are data store specific.

Time-To-Live (TTL) Expiration

Spring Data Redis’s Cache implementation supports time-to-live (TTL) expiration on cache entries. Users can either configure the TTL expiration timeout with a fixed Duration or a dynamically computed Duration per cache entry by supplying an implementation of the new RedisCacheWriter.TtlFunction interface.

The RedisCacheWriter.TtlFunction interface was introduced in Spring Data Redis 3.2.0.

If all cache entries should expire after a set duration of time, then simply configure a TTL expiration timeout with a fixed Duration, as follows:

RedisCacheConfiguration fiveMinuteTtlExpirationDefaults =
    RedisCacheConfiguration.defaultCacheConfig().enableTtl(Duration.ofMinutes(5));

However, if the TTL expiration timeout should vary by cache entry, then you must provide a custom implementation of the RedisCacheWriter.TtlFunction interface:

enum MyCustomTtlFunction implements TtlFunction {

    INSTANCE;

    @Override
    public Duration getTimeToLive(Object key, @Nullable Object value) {
        // compute a TTL expiration timeout (Duration) based on the cache entry key and/or value
    }
}

Under-the-hood, a fixed Duration TTL expiration is wrapped in a TtlFunction implementation returning the provided Duration.

Then, you can either configure the fixed Duration or the dynamic, per-cache entry Duration TTL expiration on a global basis using:

Global fixed Duration TTL expiration timeout

RedisCacheManager cacheManager = RedisCacheManager.builder(redisConnectionFactory)
    .cacheDefaults(fiveMinuteTtlExpirationDefaults)
    .build();

Or, alternatively:

Global, dynamically computed per-cache entry Duration TTL expiration timeout

RedisCacheConfiguration defaults = RedisCacheConfiguration.defaultCacheConfig()
        .entryTtl(MyCustomTtlFunction.INSTANCE);

RedisCacheManager cacheManager = RedisCacheManager.builder(redisConnectionFactory)
    .cacheDefaults(defaults)
    .build();

Of course, you can combine both global and per-cache configuration using:

Global fixed Duration TTL expiration timeout

RedisCacheConfiguration predefined = RedisCacheConfiguration.defaultCacheConfig()
                                         .entryTtl(MyCustomTtlFunction.INSTANCE);

Map<String, RedisCacheConfiguration> initialCaches = Collections.singletonMap("predefined", predefined);

RedisCacheManager cacheManager = RedisCacheManager.builder(redisConnectionFactory)
    .cacheDefaults(fiveMinuteTtlExpirationDefaults)
    .withInitialCacheConfigurations(initialCaches)
    .build();

Time-To-Idle (TTI) Expiration

Redis itself does not support the concept of true, time-to-idle (TTI) expiration. Still, using Spring Data Redis’s Cache implementation, it is possible to achieve time-to-idle (TTI) expiration-like behavior.

The configuration of TTI in Spring Data Redis’s Cache implementation must be explicitly enabled, that is, is opt-in. Additionally, you must also provide TTL configuration using either a fixed Duration or a custom implementation of the TtlFunction interface as described above in Redis Cache Expiration.

For example:

@Configuration
@EnableCaching
class RedisConfiguration {

    @Bean
    RedisConnectionFactory redisConnectionFactory() {
        // ...
    }

    @Bean
    RedisCacheManager cacheManager(RedisConnectionFactory connectionFactory) {

        RedisCacheConfiguration defaults = RedisCacheConfiguration.defaultCacheConfig()
            .entryTtl(Duration.ofMinutes(5))
            .enableTimeToIdle();

        return RedisCacheManager.builder(connectionFactory)
            .cacheDefaults(defaults)
            .build();
    }
}

Because Redis servers do not implement a proper notion of TTI, then TTI can only be achieved with Redis commands accepting expiration options. In Redis, the "expiration" is technically a time-to-live (TTL) policy. However, TTL expiration can be passed when reading the value of a key thereby effectively resetting the TTL expiration timeout, as is now the case in Spring Data Redis’s Cache.get(key) operation.

RedisCache.get(key) is implemented by calling the Redis GETEX command.

The Redis GETEX command is only available in Redis version 6.2.0 and later. Therefore, if you are not using Redis 6.2.0 or later, then it is not possible to use Spring Data Redis’s TTI expiration. A command execution exception will be thrown if you enable TTI against an incompatible Redis (server) version. No attempt is made to determine if the Redis server version is correct and supports the GETEX command.

In order to achieve true time-to-idle (TTI) expiration-like behavior in your Spring Data Redis application, then an entry must be consistently accessed with (TTL) expiration on every read or write operation. There are no exceptions to this rule. If you are mixing and matching different data access patterns across your Spring Data Redis application (for example: caching, invoking operations using RedisTemplate and possibly, or especially when using Spring Data Repository CRUD operations), then accessing an entry may not necessarily prevent the entry from expiring if TTL expiration was set. For example, an entry maybe "put" in (written to) the cache during a @Cacheable service method invocation with a TTL expiration (i.e. SET <expiration options>) and later read using a Spring Data Redis Repository before the expiration timeout (using GET without expiration options). A simple GET without specifying expiration options will not reset the TTL expiration timeout on an entry. Therefore, the entry may expire before the next data access operation, even though it was just read. Since this cannot be enforced in the Redis server, then it is the responsibility of your application to consistently access an entry when time-to-idle expiration is configured, in and outside of caching, where appropriate.