Why Is Redis So Fast? Uncover the Secrets Behind Its Performance

Introduction

As a backend software engineer, you have probably worked with Redis. Many can say Redis is fast because it is in‑memory, but there are additional reasons.

1. In‑Memory Implementation

Redis stores data directly in RAM, avoiding the I/O overhead of reading from disk. Memory access can be thousands of times faster than mechanical disks, giving Redis a huge performance advantage.

2. Efficient Data Structure

Redis uses a hash table (an array of buckets) to store all key‑value pairs, allowing O(1) lookup. The global hash table holds pointers to keys and values, enabling fast access even for complex values.

Hash collisions are resolved with chaining, where each bucket contains a linked list of entries.

Global hash table illustration.

Hash collision example.

3. Single‑Threaded Model

Running in a single thread eliminates context switches, lock contention, and deadlocks, reducing CPU overhead.

4. I/O Multiplexing with epoll

Redis uses the epoll model (similar to Java NIO) for handling many concurrent connections efficiently.

epoll model diagram.

5. Incremental Rehash

When the hash table grows, Redis allocates a larger second hash table and migrates entries bucket by bucket as client requests arrive, avoiding long pauses.

This gradual rehash keeps operations near O(1) even during resizing.

Incremental rehash illustration.

6. Cached Timestamps

Instead of calling the system clock for every request, Redis updates a cached timestamp every millisecond, reducing costly system calls.

Original article: https://www.cnblogs.com/fhey/p/17663668.html