Caching in Kotlin: Redis, Memcached, and Built-in Solutions

Introduction: Why Caching is Critical for High-Load Kotlin Applications

In modern Kotlin development, especially in the realm of microservices and backend applications, performance is a key requirement. Caching allows you to drastically reduce response time, decrease load on databases and external APIs, and lower infrastructure costs. At an advanced level, a developer must not just implement a cache but choose the optimal strategy depending on the scenario: a distributed cache (Redis, Memcached) or in-memory solutions (Caffeine, Guava). In this article, we will cover advanced techniques for working with caching in Kotlin, including integration with coroutines, TTL configuration, and invalidation patterns.

1. Redis in Kotlin: Advanced Work with Lettuce and Redisson

1.1. Asynchronous Caching with Lettuce

The Lettuce library is the de facto standard for working with Redis from Kotlin due to its full support for reactive and asynchronous streams. At an advanced level, it is important to use the Suspendable API via coroutines.

import io.lettuce.core.RedisClientimport io.lettuce.core.api.coroutinesimport kotlinx.coroutines.flow.Flowimport kotlinx.coroutines.flow.map

class RedisCache(private val client: RedisClient) {    private val connection = client.connect()    private val commands = connection.coroutines() // Coroutine API

    suspend fun get(key: String): String? = commands.get(key)

    suspend fun set(key: String, value: String, ttlSeconds: Long = 60) {        commands.setex(key, ttlSeconds, value)    }

    suspend fun invalidate(pattern: String) {        val keys = commands.keys(pattern)        if (keys.isNotEmpty()) commands.del(*keys.toTypedArray())    }}

Advanced tip: Use SCAN instead of KEYS in production to avoid blocking Redis on large datasets.

1.2. Redisson: Distributed Locks and Cache with a Local Layer

Redisson offers a higher-level API, including distributed locks and a two-level cache (Redis + local Caffeine). This is ideal for scenarios where data consistency is critical.

import org.redisson.Redissonimport org.redisson.config.Configimport org.redisson.api.RLocalCachedMap

class RedissonCache {    private val config = Config().apply {        useSingleServer().address = "redis://localhost:6379"    }    private val client = Redisson.create(config)

    fun getCachedMap<K, V>(name: String): RLocalCachedMap<K, V> {        return client.getLocalCachedMap(name, LocalCachedMapOptions.defaults())    }}

2. Memcached in Kotlin: Lightweight Protocol and High Speed

2.1. Connection via xmemcached

Memcached remains an excellent choice for simple key-value caching without the need for persistence. The xmemcached library provides high performance and support for the text/binary protocol.

import net.rubyeye.xmemcached.MemcachedClientBuilderimport net.rubyeye.xmemcached.XMemcachedClientimport kotlinx.coroutines.Dispatchersimport kotlinx.coroutines.withContext

class MemcachedCache(private val client: XMemcachedClient) {    suspend fun get(key: String): Any? = withContext(Dispatchers.IO) {        client.get(key)    }

    suspend fun set(key: String, value: Any, ttl: Int = 3600) = withContext(Dispatchers.IO) {        client.set(key, ttl, value)    }}

Important: Memcached does not support complex data structures, so for lists or hashes, use Redis or serialize to JSON.

2.2. Optimization with the Binary Protocol

At an advanced level, enable the binary protocol to reduce the overhead of command parsing:

val builder = Memcach