Caching in C++: Redis, Memcached, and Built-in Solutions: From A to Z

Introduction: Why is caching needed in C++?

In modern high-load software, data access speed is critical. Whether it's a web server processing millions of requests or a high-frequency trading terminal, every extra database call or slow disk access can become a bottleneck. This is where caching comes to the rescue — a technique of storing frequently requested data in fast memory (RAM) for subsequent instant access.

The C++ language provides developers with unique opportunities for building efficient caching systems: from fine-grained memory control to using powerful external tools. In this article, we will journey from simple built-in solutions to industrial systems like Redis and Memcached, and analyze when and which approach to choose.

You will learn:

• How to implement a simple cache in C++ without third-party libraries.
• How to connect and use Redis and Memcached.
• A comparison of performance and use cases for each approach.

Built-in Solutions: Your Own Cache in C++

Before connecting external services, it's worth understanding that for many tasks, a built-in cache is sufficient. The main advantages: zero network latency, ease of deployment, and full control over data.

Simplest Cache with std::unordered_map

The most basic option is to use an associative array. However, it has no eviction policy and will grow indefinitely, which can lead to memory overflow.

#include <unordered_map>#include <string>

class SimpleCache {    std::unordered_map<std::string, std::string> cache_;public:    void put(const std::string& key, const std::string& value) {        cache_[key] = value;    }    std::string get(const std::string& key) {        auto it = cache_.find(key);        if (it != cache_.end()) {            return it->second;        }        return ""; // or throw an exception    }};

LRU Cache (Least Recently Used)

A more advanced solution is an LRU cache, which removes the least recently used items when the limit is reached. The implementation requires a combination of a hash table and a doubly linked list.

#include <list>#include <unordered_map>

template<typename K, typename V>class LRUCache {    size_t capacity_;    std::list<std::pair<K, V>> items_;    std::unordered_map<K, typename std::list<std::pair<K, V>>::iterator> cache_;

public:    LRUCache(size_t cap) : capacity_(cap) {}

    V get(const K& key) {        auto it = cache_.find(key);        if (it == cache_.end()) {            throw std::runtime_error("Key not found");        }        items_.splice(items_.begin(), items_, it->second);        return it->second->second;    }

    void put(const K& key, const V& value) {        auto it = cache_.find(key);        if (it != cache_.end()) {            it->second->second = value;            items_.splice(items_.begin(), items_, it->second);            return;        }        if (items_.size() == capacity_) {            auto last = items_.back();            cache_.erase(last.first);            items_.pop_back();        }        items_.emplace_front(key, value);        cache_[key] = items_.begin();    }};

Such a cache is ideal for working within a single process, when the data belongs only to that process and does not require synchronization.

Memcached: A Distributed Cache for Simple Data

Memcached is a lightweight, high-performance caching system that operates over the TCP/UDP protocol. It stores data in RAM and does not support persistence. An excellent choice for caching res