Caching in Kotlin: Redis, Caffeine and Ehcache — A Complete Guide

Caching in Kotlin: Redis and Other Tools for Speeding Up Applications

Caching is one of the key mechanisms for improving the performance of modern applications. In the Kotlin ecosystem (as well as Java), several powerful libraries are available for organizing caching: from simple in-memory solutions to distributed systems based on Redis. In this article, we will review popular tools, their configuration, and usage examples in the Kotlin language.

Why is caching needed?

A cache allows you to reduce application response time, lower the load on the database and external APIs. Main scenarios:

• Repeated requests to the same data (e.g., reference data, settings);
• Computations with long execution times (report generation, complex aggregations);
• Working with external services (rate limiting, network delays).

1. Redis — Distributed Caching

Redis is an in-memory data store often used as a distributed cache. In the Kotlin/Java ecosystem, the main clients are Lettuce and Jedis. For Spring Boot, it is recommended to use spring-boot-starter-data-redis.

Example of Redis Integration with Kotlin and Spring Boot

// build.gradle.ktsimplementation("org.springframework.boot:spring-boot-starter-data-redis")implementation("org.springframework.boot:spring-boot-starter-cache")

// application.ymlspring:  cache:    type: redis  redis:    host: localhost    port: 6379

// Service with caching@Serviceclass UserService(private val userRepository: UserRepository) {

    @Cacheable("users")    fun getUserById(id: Long): User {        // Simulating a long request        Thread.sleep(2000)        return userRepository.findById(id).orElseThrow()    }

    @CacheEvict("users", key = "#id")    fun deleteUser(id: Long) {        userRepository.deleteById(id)    }}

The @Cacheable annotation automatically saves the result in Redis, while @CacheEvict removes the entry when data changes.

2. Caffeine — High-Performance In-Memory Cache

Caffeine is a modern library for local caching that significantly surpasses Guava Cache in speed and functionality. It is ideal for single-threaded or microservice architectures.

Configuring Caffeine in Kotlin

// Dependencyimplementation("com.github.ben-manes.caffeine:caffeine:3.1.8")

// Creating a cacheval cache: Cache<String, User> = Caffeine.newBuilder()    .expireAfterWrite(10, TimeUnit.MINUTES)    .maximumSize(1000)    .recordStats()    .build()

// Usagefun getUser(id: String): User {    return cache.get(id) { key ->        // Load function if key is absent        userRepository.findById(key).orElseThrow()    }}

// Getting statisticsprintln("Hit rate: ${cache.stats().hitRate()}")

Caffeine supports automatic eviction of old entries (expire after write/access), size limits, and statistics collection for monitoring.

3. Ehcache — Mature Solution with Disk Storage Support

Ehcache is one of the oldest caching libraries for Java. It supports multi-tier storage (heap, off-heap, disk) and clustering. For Kotlin, the standard Java API is used.

Example with Ehcache 3 and XML Configuration

// Dependencyimplementation("org.ehcache:ehcache:3.10.8")

// Configuration (ehcache.xml)<config xmlns="http://www.ehcache.org/v3">    <cache alias="users">        <key-type>java.lang.Long</key-type>        <value-type>com.example.User</value-type>        <resources>            <heap unit="entries">100</heap>            <offheap unit="MB">10</offheap>        </res