Key-Value Stores (Redis, DynamoDB, In-Memory Mechanics)
Explore ultra-fast key-value engines: Redis data structures (Strings, Hashes, Lists, Sets, Sorted Sets, HyperLogLog, Bitmaps), single-threaded event loop, and persistence (RDB vs AOF).
01.1. Why Redis is Blistering Fast: The Single-Threaded Architecture
Redis (Remote Dictionary Server) consistently delivers 100,000+ operations per second per CPU core with sub-millisecond latencies (<500ΞΌs).
Why is Redis so fast despite running primarily on a single main thread?
- In-Memory Execution: All primary datasets reside directly in physical DRAM, eliminating slow disk seek and block transfer overhead (~100ns access time).
- Zero Mutex Contention: Because command execution is single-threaded, Redis requires zero locks, zero mutexes, zero semaphores, and zero thread synchronization, completely avoiding thread context-switching overhead.
- I/O Multiplexing (
epoll/kqueue): The single thread uses non-blocking kernel event notifications to service tens of thousands of client TCP connections concurrently. - Optimized C Data Structures: Data is not stored as generic text blobs; it is backed by memory-optimized C structures (SkipLists, QuickLists, ZipLists, IntSets).
Redis Internal Data Structures & Dual Persistence Subsystems (RDB / AOF) β‘
Redis Internal Data Structures & Dual Persistence Subsystems (RDB / AOF) β‘
How Redis combines a single-threaded epoll event loop, native C data structures (SkipLists, QuickLists), and dual persistence engines (RDB snapshots and AOF logging).
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