Caching in C++: Redis, Data Layer, and Other Tools

Caching in C++: Redis and Other Tools for Accelerating Applications

Caching is one of the key mechanisms for optimizing the performance of C++ applications. In modern systems where data access speed is critical (web servers, games, high-load backends), a properly configured cache can reduce response time by tens of times. In this article, we will look at how to organize caching in C++ using Redis and alternative solutions, including built-in data structures.

Why is caching needed in C++?

The main goal of a cache is to store frequently requested data in fast storage (usually RAM) to avoid repeated expensive operations: database queries, computations, or network calls. This is especially relevant for C++, as the language is used in real-time systems and high-load services.

Redis — Distributed Cache for C++

Redis is an in-memory data store often used as a cache. There are several mature libraries for working with Redis from C++:

• hiredis — a minimalistic C library, the official Redis client. Lightweight, but requires manual memory management and strict typing.
• redis-plus-plus — a modern C++17 wrapper over hiredis. Supports connection pools, transactions, Redis Cluster, and asynchronous operations.
• cpp_redis — another C++11 library with syntax similar to hiredis, but with a more convenient API.

Example of connecting and basic usage of redis-plus-plus:

#include <sw/redis++/redis++.h>

using namespace sw::redis;

int main() {    // Connect to Redis (local server, port 6379)    auto redis = Redis("tcp://127.0.0.1:6379");

    // Write to cache with TTL (time to live 60 seconds)    redis.setex("user:123:profile", 60, "{\\"name\\":\\"Alice\\",\\"age\\":30}");

    // Read from cache    auto val = redis.get("user:123:profile");    if (val) {        std::cout << "Cache found: " << *val << std::endl;    } else {        std::cout << "Cache is empty, need to load data from DB" << std::endl;    }

    return 0;}

Advantages of Redis: distributed nature, support for complex structures (lists, hashes, sets), TTL, pub/sub. Disadvantages: need for a separate server, network latency (though minimal).

Built-in Caches in C++: LRU Cache and Data Layer

If you don't need a distributed system, you can implement a cache directly in the application. The most popular algorithm is LRU (Least Recently Used). It evicts data that has not been used for the longest time.

Example implementation of a simple LRU cache in C++17 using std::unordered_map and std::list:

#include <unordered_map>#include <list>#include <stdexcept>

template<typename Key, typename Value>class LRUCache {public:    LRUCache(size_t capacity) : capacity_(capacity) {}

    Value get(const Key& key) {        auto it = cache_.find(key);        if (it == cache_.end()) {            throw std::runtime_error("Key not found");        }        // Move the element to the front of the list (most recently used)        order_.splice(order_.begin(), order_, it->second.second);        return it->second.first;    }

    void put(const Key& key, const Value& value) {        auto it = cache_.find(key);        if (it != cache_.end()) {            // Update the value and move to the front            it->second.first = value;            order_.splice(order_.begin(), order_, it->second.second);            return;        }        // If the cache is full, remove the last element (LRU)        if (cache_.size() == capacity_) {            auto last = order_.back();            order_.p