This article explains the internal structures of Redis data types—including strings, lists, sets, hashes, and sorted sets—detailing their memory representations, evolution across versions, time‑complexity characteristics, and the code implementations that enable efficient in‑memory operations.
Async‑fork shifts the costly page‑table copying from Redis’s parent process to its child, allowing the parent to resume handling queries instantly and cutting snapshot‑induced latency spikes by over 98%, thereby dramatically improving tail latency during AOF rewrites, RDB backups, and master‑slave synchronizations.
This article explores the concurrency control strategies, persistence mechanisms, and compiled query processing techniques used by modern memory databases, comparing systems like Hekaton, Hyper, HANA, and others while highlighting performance trade‑offs and architectural considerations.
This article examines the fundamental differences between traditional disk‑based DBMS and modern in‑memory databases, covering buffer management, lock versus latch mechanisms, logging and recovery, performance overhead, historical evolution, and architectural innovations that enable high‑performance memory‑resident data processing.
