Using Amazon ElastiCache for Redis To Optimize Your Spring Boot Application

Brandon Klimek AWS, Development Technologies, Java, Spring, Spring Boot 13 Comments

Attention: The following article was published over 4 years ago, and the information provided may be aged or outdated. Please keep that in mind as you read the post.

Has your project gotten to the point when big data sets and/or time-consuming calculations have begun to affect performance? Or are you struggling to optimize your queries and need to cache some information to avoid continually hitting your database? Then caching could be your solution.

For this article, I will demonstrate how to utilize Amazon ElastiCache for Redis to speed up areas of your application. The example application we will build uses Spring Boot 2.x and is available on Github.

What is Amazon ElastiCache for Redis?

ElastiCache for Redis is a super fast, in memory, key-value store database. It supports many different kinds of abstract data structures, such as strings, lists, maps, sets, sorted sets, hyperloglogs, bitmaps, and spatial indexes. At its highest level, ElastiCache for Redis is a fully managed service for a standard Redis installation and uses all the standard Redis APIs. Which means the app we are building can rely not only on the Amazon ElastiCache flavor of Redis, but any other environment matching the version of your choosing.

Setting up ElastiCache for Redis

Begin by navigating to the ElasticCache dashboard, selecting Redis, and create a new cluster. You will be prompted to define a cache name, description, node type (server size), and number of replicates.

I filled in the name, description, and changed the node type to a smaller instance.

VPC & Security Groups

To be able to access your Redis cluster, the instance(s) running our app must have be in the same Virtual Private Network (VPC) and contain the proper security groups. Your EC2 instances must allow the port of your Redis cluster (6379) to be able to communicate. By default, your Redis cluster is only accessible internally from the VPC selected. This is done purposely, as no internet gateway should be connected as it would defeat the purpose of a high efficiency, in-memory cache that Redis provides.

Our app will only be able to access Redis once it is deployed to AWS.

If you wish to run the app locally, consider installing Redis using Docker. The variables outlined in our file below can be modified to run locally.

Launching your Redis Cluster

Once you have properly configured your security groups and VPC, click “create”. ElastiCache will now provision and launch you new Redis cluster. When the status turns to available the cluster is ready to handle connections.

We need the primary endpoint for our new spring boot application.

Building Our Application

For this example application, we will be using Spring Boot 2.x with the Spring-Data-Redis and Jedis (client library for Redis). I first begin by importing them into my project.


These  libraries allow us to setup our caching config in Spring. A important concept to understand is that the Spring Framework provides its own abstraction for transparently adding caching. You do not only have to use Redis, the abstraction provides a list of providers; CouchbaseEhCache 2.xHazelcast, etc. As you will see adding caching to a service method is as simple as providing the appropriate annotation.

Now that we have included the required libraries in our pom file, Spring will try to autoconfigure a RedisCacheManager. I personally do not like magic behind the scenes so we are going to setup and configure our own annotation based RedisCacheManager.

A RedisCacheManager is how we configure and build a cacheManger for Spring to use. Notice that I have defined the redisHostName, redisPort, and redisPrefix for the Jedis (client library for redis) to use to connect to our cluster.

public class RedisConfig {

    private String redisHostName;

    private int redisPort;

    private String redisPrefix;

    JedisConnectionFactory jedisConnectionFactory() {
        RedisStandaloneConfiguration redisStandaloneConfiguration = new RedisStandaloneConfiguration(redisHostName, redisPort);
        return new JedisConnectionFactory(redisStandaloneConfiguration);

    @Bean(value = "redisTemplate")
    public RedisTemplate<String, Object> redisTemplate(RedisConnectionFactory redisConnectionFactory) {
        RedisTemplate<String, Object> redisTemplate = new RedisTemplate<>();
        return redisTemplate;

    @Bean(name = "cacheManager") // Default cache manager is infinite
    public CacheManager cacheManager(RedisConnectionFactory redisConnectionFactory) {
        return RedisCacheManager.builder(redisConnectionFactory).cacheDefaults(RedisCacheConfiguration.defaultCacheConfig().prefixKeysWith(redisPrefix)).build();

    @Bean(name = "cacheManager1Hour")
    public CacheManager cacheManager1Hour(RedisConnectionFactory redisConnectionFactory) {
        Duration expiration = Duration.ofHours(1);
        return RedisCacheManager.builder(redisConnectionFactory)

I have defined two cache managers. One that is infinite (default), and once that will cause all keys to expire in 1 hour called cacheManager1Hour.

The cluster information is passed in from the


Implementing a Simple Service

Now that our Redis cluster is configured in Spring, annotation-based caching is enabled. Let’s assume you have a long-running task that takes 2 seconds to do its work. By annotation the service with @Cacheable the result of the method call will be cached. I have given this cachable a value of getLongRunningTaskResult which will be used in its compound key, a key (by default is generated for you), and a cacheManager (cacheManager1Hour configured above).

@Cacheable(value = "getLongRunningTaskResult", key="{#seconds}", cacheManager = "cacheManager1Hour")
public Optional<TaskDTO> getLongRunningTaskResult(long seconds) {
    try {
        long randomMultiplier = new Random().nextLong();
        long calculatedResult = randomMultiplier * seconds;
        TaskDTO taskDTO = new TaskDTO();
        Thread.sleep(2000); // 2 Second Delay to Simulate Workload
        return Optional.of(taskDTO);
    } catch (InterruptedException e) {
        return Optional.of(null);

Note: It is important that the resulting object of the method is serializable otherwise the cacheManager will not be able to cache the result.

Testing for Performance Improvements

To easy test the API, I have included swagger-ui which make it simple for developers to interact with the api we have built. I have also created a few simple endpoints to create and flush the cache.

        value = "Perform the long running task")
@RequestMapping(value = "/{seconds}", method = RequestMethod.GET)
public ResponseEntity<TaskDTO> longRunningTask(@PathVariable long seconds) {
    Optional<TaskDTO> user = taskService.getLongRunningTaskResult(seconds);
    return ResponseUtil.wrapOrNotFound(user);

        value = "Resets the cache for a key")
@RequestMapping(value = "/reset/{seconds}", method = RequestMethod.DELETE)
public ResponseEntity<?> reset(@PathVariable long seconds) {
    return new ResponseEntity<>(HttpStatus.ACCEPTED);

Once you deploy your app to EC2 navigate to URL path: /swagger-ui.html

From this GUI you can easily test your API performance improvements. Calling the GET endpoint for the first time should take roughly two seconds to return the new calculated result. Calling it subsequently will return an almost instant response as the long-running task is now cached in Redis.

Final Thoughts

Today’s applications demand a responsive user experience. Design your queries and calculations to be as performant as possible, but every once in a while, when you can sacrifice real-time data, just cache it.

Full project code is available at:

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