A Guide to Using Redis in Spring Boot: Custom CacheManager

JackyNote ⭐️
4 min readSep 26, 2023

Working With Redis

Working With Spring Boot and Redis

As a junior developer, you’ve likely heard about Redis and Spring Boot, two powerful technologies that can supercharge your applications. Redis is an in-memory data store known for its speed and versatility, while Spring Boot simplifies Java application development. In this guide, we’ll explore how to integrate Redis into Spring Boot and create a custom CacheManager with Time-To-Live (TTL) functionality for prefix keys. We’ll also delve into the various types of values you can store in Redis and the use cases for each.


Before we dive into Redis and Spring Boot integration, make sure you have the following tools and knowledge:

Java and Spring Boot Knowledge: Familiarity with Java and Spring Boot is essential. If you’re new to Spring Boot, consider going through some introductory tutorials first.

Maven or Gradle: A basic understanding of build tools like Maven or Gradle is helpful for managing dependencies.

Redis: Install and configure a Redis server on your local machine or use a cloud-based Redis service. You can find installation instructions for Redis on the official website.

Spring Boot: Have a Spring Boot application set up. If you don’t have one, you can quickly generate a Spring Boot project using Spring Initializr.

Integrating Redis with Spring Boot

To use Redis as a caching mechanism in your Spring Boot application, you need to include the Spring Data Redis library as a dependency in your project. If you’re using Maven, add the following dependency to your pom.xml:


If you’re using Gradle, add this to your build.gradle:

implementation 'org.springframework.boot:spring-boot-starter-data-redis'

Next, configure your Redis connection in application.properties or application.yml:


Now, you’re ready to create a custom CacheManager with TTL functionality.

Custom CacheManager with TTL

In a Spring Boot application, caching is typically managed using the @Cacheable annotation. To add TTL functionality to cached items, you can create a custom CacheManager that supports TTL per prefix key.

Here’s a step-by-step guide to creating a custom CacheManager:

1. Create a RedisCacheConfiguration Bean:

import org.springframework.boot.autoconfigure.cache.RedisCacheManagerBuilderCustomizer;
import org.springframework.cache.CacheManager;
import org.springframework.cache.annotation.EnableCaching;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.data.redis.cache.RedisCacheConfiguration;

public class CacheConfig {
public CacheManager cacheManager(RedisConnectionFactory connectionFactory) {
return RedisCacheManagerBuilder
.entryTtl(Duration.ofMinutes(10)) // TTL set to 10 minutes
public RedisCacheManagerBuilderCustomizer redisCacheManagerBuilderCustomizer() {
return (builder) -> builder
.entryTtl(Duration.ofMinutes(5)) // TTL set to 5 minutes

2. Add Caching Annotations to Your Service Methods:

import org.springframework.cache.annotation.Cacheable;
import org.springframework.stereotype.Service;

public class MyService {
@Cacheable(value = "myCache", key = "'user_' + #id")
public User getUserById(Long id) {
// Retrieve user data from a database or external source
return userRepository.findById(id).orElse(null);

In this example, we’re caching user objects with a custom TTL for each prefix key.

Types of Values to Store in Redis

Redis is versatile and can store various types of data. Here are some common data types and their use cases:

  • Strings: Use for simple key-value storage, such as caching, configuration, or counters.
  • Lists: Ideal for storing ordered collections of data, like logs, notifications, and queues.
  • Sets: Store unique, unordered values. Use cases include tracking tags, followers, and likes.
  • Hashes: Great for storing objects or structured data. Useful in scenarios like user profiles and storing JSON data.
  • Sorted Sets: Similar to sets but with an associated score. Suitable for leaderboards, rankings, and time-based data.
  • Bitmaps: Efficient for tracking binary data or flags, like user activity or online status.
  • Geospatial Data: Store and query geographical information, such as locations of restaurants or points of interest.

Use Cases for Different Value Types

Now, let’s explore some common use cases for each Redis data type:

  • Strings: Caching frequently accessed data, storing session information, or managing feature toggles.
  • Lists: Logging and audit trails, message queues for background processing, and managing user activity streams.
  • Sets: Implementing social features like followers and likes, tracking tags in a content management system, or managing access control lists (ACLs).
  • Hashes: Storing user profiles, managing configuration settings, or caching complex objects.
  • Sorted Sets: Creating leaderboards, rankings, and implementing features that require sorting by score.
  • Bitmaps: Tracking user activity patterns, online/offline status, or implementing recommendations based on user behavior.
  • Geospatial Data: Building location-based features like location-aware search, geofencing, and proximity alerts.

In conclusion, integrating Redis with Spring Boot can significantly enhance your application’s performance and functionality. By creating a custom CacheManager with TTL for prefix keys and leveraging the various Redis data types, you can optimize data storage and retrieval for different use cases. Redis is a versatile tool that, when used effectively, can elevate your application’s capabilities and responsiveness. Happy coding!

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JackyNote ⭐️

🚀 Software Engineer | Full Stack Java 7 Years of Experience | Tech Enthusiast | Startup Lover | Founder of helik.co - URL Shorten | Coffee