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

1. In-Memory Implementation

Redis stores all data directly in RAM, eliminating the disk I/O overhead that traditional disk‑based databases suffer. Memory access speeds are orders of magnitude faster than mechanical disks, SSDs, or even CPU caches, which explains the baseline speed advantage.

Typical read speeds: mechanical disk ~0.1 GB/s, SSD ~1.3 GB/s, RAM ~30 GB/s, L3 cache ~190 GB/s, L2 ~200 GB/s, L1 ~800 GB/s.

2. Efficient Hash Table Structure

Redis uses a global hash table where each bucket holds pointers to keys and values. This design enables O(1) lookup by computing the hash of a key and accessing the corresponding bucket.

When the hash table grows, collisions can occur. Redis resolves them with chaining (linked lists) and employs progressive rehashing to avoid blocking the single thread.

3. Single-Threaded Model

Redis runs a single thread, removing context‑switch overhead, lock contention, and deadlock risk, which further reduces latency.

4. Epoll-Based I/O Multiplexing

Redis uses the epoll event‑driven model (similar to Java NIO) to handle many concurrent connections efficiently.

5. Progressive Rehash

When the hash table needs to expand, Redis allocates a larger second table and migrates entries incrementally, copying a small portion of buckets on each client request. This spreads the copying cost over time, keeping the service responsive.

6. Cached Timestamps

Instead of calling the system clock for every operation, Redis updates a cached timestamp every millisecond via a background task, allowing fast access to the current time without costly system calls.