As explained in Why Spring Data Redis?, Spring Data Redis (SDR) provides integration between Spring framework and the Redis key value store. Thus, it is important to become acquainted with both of these frameworks (storages or environments depending on how you want to name them). Throughout the SDR documentation, each section provides links to resources relevant however, it is best to become familiar with these topics beforehand.

If you encounter issues or you are just looking for advice, feel free to use one of the links below:

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You can help make Spring Data best serve the needs of the Spring community by interacting with developers on Stackoverflow at either spring-data or spring-data-redis.

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Spring Redis requires Redis 2.6 or above and Java SE 6.0 or above . In terms of language bindings (or connectors), Spring Redis integrates with Jedis, JRedis (Deprecated since 1.7), SRP (Deprecated since 1.7) and Lettuce, four popular open source Java libraries for Redis. If you are aware of any other connector that we should be integrating with please send us feedback.

The Redis support provides several components (in order of dependencies):

For most tasks, the high-level abstractions and support services are the best choice. Note that at any point, one can move between layers - for example, it’s very easy to get a hold of the low level connection (or even the native library) to communicate directly with Redis.

One of the first tasks when using Redis and Spring is to connect to the store through the IoC container. To do that, a Java connector (or binding) is required. No matter the library one chooses, there is only one set of Spring Data Redis API that one needs to use that behaves consistently across all connectors, namely the org.springframework.data.redis.connection package and its RedisConnection and RedisConnectionFactory interfaces for working with and retrieving active connections to Redis.

For dealing with high available Redis there is support for Redis Sentinel using RedisSentinelConfiguration.

Sometimes direct interaction with the one of the Sentinels is required. Using RedisConnectionFactory.getSentinelConnection() or RedisConnection.getSentinelCommands() gives you access to the first active Sentinel configured.

Most users are likely to use RedisTemplate and its coresponding package org.springframework.data.redis.core - the template is in fact the central class of the Redis module due to its rich feature set. The template offers a high-level abstraction for Redis interactions. While RedisConnection offers low level methods that accept and return binary values (byte arrays), the template takes care of serialization and connection management, freeing the user from dealing with such details.

Moreover, the template provides operations views (following the grouping from Redis command reference) that offer rich, generified interfaces for working against a certain type or certain key (through the KeyBound interfaces) as described below:

Once configured, the template is thread-safe and can be reused across multiple instances.

Out of the box, RedisTemplate uses a Java-based serializer for most of its operations. This means that any object written or read by the template will be serialized/deserialized through Java. The serialization mechanism can be easily changed on the template, and the Redis module offers several implementations available in the org.springframework.data.redis.serializer package - see Serializers for more information. You can also set any of the serializers to null and use RedisTemplate with raw byte arrays by setting the enableDefaultSerializer property to false. Note that the template requires all keys to be non-null - values can be null as long as the underlying serializer accepts them; read the javadoc of each serializer for more information.

For cases where a certain template view is needed, declare the view as a dependency and inject the template: the container will automatically perform the conversion eliminating the opsFor[X] calls:

Since it’s quite common for the keys and values stored in Redis to be java.lang.String, the Redis modules provides two extensions to RedisConnection and RedisTemplate, respectively the StringRedisConnection (and its DefaultStringRedisConnection implementation) and StringRedisTemplate as a convenient one-stop solution for intensive String operations. In addition to being bound to String keys, the template and the connection use the StringRedisSerializer underneath which means the stored keys and values are human readable (assuming the same encoding is used both in Redis and your code). For example:

As with the other Spring templates, RedisTemplate and StringRedisTemplate allow the developer to talk directly to Redis through the RedisCallback interface. This gives complete control to the developer as it talks directly to the RedisConnection. Note that the callback receives an instance of StringRedisConnection when a StringRedisTemplate is used.

From the framework perspective, the data stored in Redis is just bytes. While Redis itself supports various types, for the most part these refer to the way the data is stored rather than what it represents. It is up to the user to decide whether the information gets translated into Strings or any other objects. The conversion between the user (custom) types and raw data (and vice-versa) is handled in Spring Data Redis through the RedisSerializer interface (package org.springframework.data.redis.serializer) which as the name implies, takes care of the serialization process. Multiple implementations are available out of the box, two of which have been already mentioned before in this documentation: the StringRedisSerializer and the JdkSerializationRedisSerializer. However one can use OxmSerializer for Object/XML mapping through Spring 3 OXM support or either JacksonJsonRedisSerializer, Jackson2JsonRedisSerializer or GenericJackson2JsonRedisSerializer for storing data in JSON format. Do note that the storage format is not limited only to values - it can be used for keys, values or hashes without any restrictions.

Data can be stored using various data structures within Redis. You already learned about Jackson2JsonRedisSerializer which can convert objects in JSON format. JSON can be ideally stored as value using plain keys. A more sophisticated mapping of structured objects can be achieved using Redis Hashes. Spring Data Redis offers various strategies for mapping data to hashes depending on the use case.

Spring Data provides dedicated messaging integration for Redis, very similar in functionality and naming to the JMS integration in Spring Framework; in fact, users familiar with the JMS support in Spring should feel right at home.

Redis messaging can be roughly divided into two areas of functionality, namely the production or publication and consumption or subscription of messages, hence the shortcut pubsub (Publish/Subscribe). The RedisTemplate class is used for message production. For asynchronous reception similar to Java EE’s message-driven bean style, Spring Data provides a dedicated message listener container that is used to create Message-Driven POJOs (MDPs) and for synchronous reception, the RedisConnection contract.

The package org.springframework.data.redis.connection and org.springframework.data.redis.listener provide the core functionality for using Redis messaging.

Redis provides support for transactions through the multi, exec, and discard commands. These operations are available on RedisTemplate, however RedisTemplate is not guaranteed to execute all operations in the transaction using the same connection.

Spring Data Redis provides the SessionCallback interface for use when multiple operations need to be performed with the same connection, as when using Redis transactions. For example:

RedisTemplate will use its value, hash key, and hash value serializers to deserialize all results of exec before returning. There is an additional exec method that allows you to pass a custom serializer for transaction results.

Redis provides support for pipelining, which involves sending multiple commands to the server without waiting for the replies and then reading the replies in a single step. Pipelining can improve performance when you need to send several commands in a row, such as adding many elements to the same List.

Spring Data Redis provides several RedisTemplate methods for executing commands in a pipeline. If you don’t care about the results of the pipelined operations, you can use the standard execute method, passing true for the pipeline argument. The executePipelined methods will execute the provided RedisCallback or SessionCallback in a pipeline and return the results. For example:

The example above executes a bulk right pop of items from a queue in a pipeline. The results List contains all of the popped items. RedisTemplate uses its value, hash key, and hash value serializers to deserialize all results before returning, so the returned items in the above example will be Strings. There are additional executePipelined methods that allow you to pass a custom serializer for pipelined results.

Note that the value returned from the RedisCallback is required to be null, as this value is discarded in favor of returning the results of the pipelined commands.

Redis versions 2.6 and higher provide support for execution of Lua scripts through the eval and evalsha commands. Spring Data Redis provides a high-level abstraction for script execution that handles serialization and automatically makes use of the Redis script cache.

Scripts can be run through the execute methods of RedisTemplate. RedisTemplate uses a configurable ScriptExecutor to execute the provided script. By default, the ScriptExecutor takes care of serializing the provided keys and arguments and deserializing the script result. This is done with the RedisTemplate key and value serializers. There is an additional execute method that allows you to pass custom serializers for the script arguments and result.

The default ScriptExecutor optimizes performance by retrieving the SHA1 of the script and attempting first to run evalsha, falling back to eval if the script is not yet present in the Redis script cache.

Here’s an example that executes a common "check-and-set" scenario using a Lua script. This is an ideal use case for a Redis script, as it requires that we execute a set of commands atomically and the behavior of one command is influenced by the result of another.

The XML above configures a DefaultRedisScript pointing to a file called checkandset.lua, which is expected to return a boolean value. The script resultType should be one of Long, Boolean, List, or deserialized value type. It can also be null if the script returns a throw-away status (i.e "OK"). It is ideal to configure a single instance of DefaultRedisScript in your application context to avoid re-calculation of the script’s SHA1 on every script execution.

The checkAndSet method above then executes th Scripts can be executed within a SessionCallback as part of a transaction or pipeline. See Redis Transactions and Pipelining for more information.

The scripting support provided by Spring Data Redis also allows you to schedule Redis scripts for periodic execution using the Spring Task and Scheduler abstractions. See the Spring Framework documentation for more details.

Package org.springframework.data.redis.support offers various reusable components that rely on Redis as a backing store. Currently the package contains various JDK-based interface implementations on top of Redis such as atomic counters and JDK Collections.

The atomic counters make it easy to wrap Redis key incrementation while the collections allow easy management of Redis keys with minimal storage exposure or API leakage: in particular the RedisSet and RedisZSet interfaces offer easy access to the set operations supported by Redis such as intersection and union while RedisList implements the List, Queue and Deque contracts (and their equivalent blocking siblings) on top of Redis, exposing the storage as a FIFO (First-In-First-Out), LIFO (Last-In-First-Out) or capped collection with minimal configuration:

As shown in the example above, the consuming code is decoupled from the actual storage implementation - in fact there is no indication that Redis is used underneath. This makes moving from development to production environments transparent and highly increases testability (the Redis implementation can just as well be replaced with an in-memory one).

Cluster support is based on the very same building blocks as non clustered communication. RedisClusterConnection an extension to RedisConnection handles the communication with the Redis Cluster and translates errors into the Spring DAO exception hierarchy. RedisClusterConnection 's are created via the RedisConnectionFactory which has to be set up with the according RedisClusterConfiguration.

As mentioned above Redis Cluster behaves different from single node Redis or even a Sentinel monitored master slave environment. This is reasoned by the automatic sharding that maps a key to one of 16384 slots which are distributed across the nodes. Therefore commands that involve more than one key must assert that all keys map to the exact same slot in order to avoid cross slot execution errors. Further on, hence a single cluster node, only serves a dedicated set of keys, commands issued against one particular server only return results for those keys served by the server. As a very simple example take the KEYS command. When issued to a server in cluster environment it only returns the keys served by the node the request is sent to and not necessarily all keys within the cluster. So to get all keys in cluster environment it is necessary to read the keys from at least all known master nodes.

While redirects for to a specific keys to the corresponding slot serving node are handled by the driver libraries, higher level functions like collecting information across nodes, or sending commands to all nodes in the cluster that are covered by RedisClusterConnection. Picking up the keys example from just before, this means, that the keys(pattern) method picks up every master node in cluster and simultaneously executes the KEYS command on every single one, while picking up the results and returning the cumulated set of keys. To just request the keys of a single node RedisClusterConnection provides overloads for those (like keys(node, pattern) ).

A RedisClusterNode can be obtained from RedisClusterConnection.clusterGetNodes or it can be constructed using either host and port or the node Id.

Cross slot requests such as MGET are automatically served by the native driver library when all keys map to the same slot. However once this is not the case RedisClusterConnection executes multiple parallel GET commands against the slot serving nodes and again returns a cumulated result. Obviously this is less performing than the single slot execution and therefore should be used with care. In doubt please consider pinning keys to the same slot by providing a prefix in curly brackets like {my-prefix}.foo and {my-prefix}.bar which will both map to the same slot number.

Please refer to the section Working with Objects through RedisTemplate to read about general purpose, configuration and usage of RedisTemplate.

RedisTemplate provides access to cluster specific operations via the ClusterOperations interface that can be obtained via RedisTemplate.opsForCluster(). This allows to execute commands explicitly on a single node within the cluster while retaining de-/serialization features configured for the template and provides administrative commands such as CLUSTER MEET or more high level operations for eg. resharding.

To access domain entities stored in a Redis you can leverage repository support that eases implementing those quite significantly.

We have a pretty simple domain object here. Note that it has a property named id annotated with org.springframework.data.annotation.Id and a @RedisHash annotation on its type. Those two are responsible for creating the actual key used to persist the hash.

To now actually have a component responsible for storage and retrieval we need to define a repository interface.

As our repository extends CrudRepository it provides basic CRUD and finder operations. The thing we need in between to glue things together is the according Spring configuration.

Given the setup above we can go on and inject PersonRepository into our components.

The Redis Repository support persists Objects in Hashes. This requires an Object to Hash conversion which is done by a RedisConverter. The default implementation uses Converter for mapping property values to and from Redis native byte[].

Given the Person type from the previous sections the default mapping looks like the following:

Mapping behavior can be customized by registering the according Converter in CustomConversions. Those converters can take care of converting from/to a single byte[] as well as Map<String,byte[]> whereas the first one is suitable for eg. converting one complex type to eg. a binary JSON representation that still uses the default mappings hash structure. The second option offers full control over the resulting hash. Writing objects to a Redis hash will delete the content from the hash and re-create the whole hash, so not mapped data will be lost.

Using the above byte[] Converter produces eg.

Using the above Map Converter produces eg.

Keyspaces define prefixes used to create the actual key for the Redis Hash. By default the prefix is set to getClass().getName(). This default can be altered via @RedisHash on aggregate root level or by setting up a programmatic configuration. However, the annotated keyspace supersedes any other configuration.

Secondary indexes are used to enable lookup operations based on native Redis structures. Values are written to the according indexes on every save and are removed when objects are deleted or expire.

Objects stored in Redis may only be valid for a certain amount of time. This is especially useful for persisting short lived objects in Redis without having to remove them manually when they reached their end of life. The expiration time in seconds can be set via @RedisHash(timeToLive=…​) as well as via KeyspaceSettings (see Keyspaces).

More flexible expiration times can be set by using the @TimeToLive annotation on either a numeric property or method. However do not apply @TimeToLive on both a method and a property within the same class.

The repository implementation ensures subscription to Redis keyspace notifications via RedisMessageListenerContainer.

When the expiration is set to a positive value the according EXPIRE command is executed. Additionally to persisting the original, a phantom copy is persisted in Redis and set to expire 5 minutes after the original one. This is done to enable the Repository support to publish RedisKeyExpiredEvent holding the expired value via Springs ApplicationEventPublisher whenever a key expires even though the original values have already been gone. Expiry events will be received on all connected applications using Spring Data Redis repositories.

By default, the key expiry listener is disabled when initializing the application. The startup mode can be adjusted in @EnableRedisRepositories or RedisKeyValueAdapter to start the listener with the application or upon the first insert of an entity with a TTL. See EnableKeyspaceEvents for possible values.

The RedisKeyExpiredEvent will hold a copy of the actually expired domain object as well as the key.

Marking properties with @Reference allows storing a simple key reference instead of copying values into the hash itself. On loading from Redis, references are resolved automatically and mapped back into the object.

In some cases it is not necessary to load and rewrite the entire entity just to set a new value within it. A session timestamp for last active time might be such a scenario where you just want to alter one property. PartialUpdate allows to define set and delete actions on existing objects while taking care of updating potential expiration times of the entity itself as well as index structures.

Query methods allow automatic derivation of simple finder queries from the method name.

Using derived query methods might not always be sufficient to model the queries to execute. RedisCallback offers more control over the actual matching of index structures or even custom added ones. All it takes is providing a RedisCallback that returns a single or Iterable set of id values.

Here’s an overview of the keywords supported for Redis and what a method containing that keyword essentially translates to.

Using the Redis repository support in a clustered Redis environment is fine. Please see the Redis Cluster section for ConnectionFactory configuration details. Still some considerations have to be done as the default key distribution will spread entities and secondary indexes through out the whole cluster and its slots.

Instances of the repository interfaces are usually created by a container, which Spring is the most natural choice when working with Spring Data. There’s sophisticated support to easily set up Spring to create bean instances. Spring Data Redis ships with a custom CDI extension that allows using the repository abstraction in CDI environments. The extension is part of the JAR so all you need to do to activate it is dropping the Spring Data Redis JAR into your classpath.

You can now set up the infrastructure by implementing a CDI Producer for the RedisConnectionFactory and RedisOperations:

The necessary setup can vary depending on the JavaEE environment you run in.

The Spring Data Redis CDI extension will pick up all Repositories available as CDI beans and create a proxy for a Spring Data repository whenever a bean of a repository type is requested by the container. Thus obtaining an instance of a Spring Data repository is a matter of declaring an @Injected property:

A Redis Repository requires RedisKeyValueAdapter and RedisKeyValueTemplate instances. These beans are created and managed by the Spring Data CDI extension if no provided beans are found. You can however supply your own beans to configure the specific properties of RedisKeyValueAdapter and RedisKeyValueTemplate.