The article explains how massive simultaneous expiration of Redis keys can overload the single‑threaded server, causing brief request blocking, response‑time spikes, and even memory‑related issues in master‑slave setups.
An in‑depth guide to backend caching covers read‑through and cache‑aside strategies, local versus distributed caches (including Redis and Memcached), eviction algorithms such as FIFO, LRU and LFU, and tackles consistency, avalanche, penetration and breakdown issues with practical mitigation techniques.
Redis uses key expiration commands, periodic scanning, and a combination of lazy and active deletion strategies, and when memory is full it applies one of eight eviction policies—including refined LRU and LFU algorithms with sampling and decay mechanisms—to decide which keys to discard.
This article explains how Redis handles key expiration, the three expiration deletion strategies, the eight memory‑eviction policies configurable via maxmemory, and the inner workings of Redis's LRU and LFU algorithms, including sampling, lru_clock, and counter decay mechanisms.
When expired keys accumulate and lazy deletion can't keep up, Redis relies on configurable memory eviction policies and proper maxmemory settings, and selecting the appropriate strategy along with an optimal cache size (typically 15‑30% of total data) ensures stable performance and avoids out‑of‑memory errors.
This article explains how Redis handles memory pressure using configurable maxmemory limits and a variety of eviction policies—including noeviction, volatile‑lru, volatile‑lfu, allkeys‑lru, and allkeys‑random—while offering guidance on selecting appropriate policies and sizing cache capacity for optimal performance.
Redis handles key expiration using commands like EXPIRE, PEXPIRE, EXPIREAT, and PEXPIREAT, offers TTL queries, and employs three deletion strategies—timed, lazy, and periodic scanning—while its eight eviction policies (e.g., volatile‑lru, allkeys‑lfu, noeviction) and refined LRU/LFU algorithms manage memory pressure efficiently.
This article explains how Redis handles memory exhaustion by using key expiration commands, describes the three expiration strategies, details the eight maxmemory eviction policies, and dives into the internal LRU and LFU algorithms with their configuration parameters and code examples.
Learn how to set Redis's maximum memory usage, understand its various eviction policies—including noeviction, allkeys‑lru, volatile‑lru, allkeys‑random, volatile‑random, volatile‑ttl, and the newer LFU strategies—plus see Java code examples and insights into approximate LRU implementation.
This article explains how Redis handles key expiration, the commands for setting TTL, the three expiration strategies, the eight eviction policies, and the internal LRU and LFU algorithms, including their implementation details, sampling techniques, and configuration parameters for memory management.
This article presents common Redis interview questions covering cache penetration, breakdown, and avalanche, explains RDB and AOF persistence mechanisms with configuration commands, and details memory‑size tuning, LRU/LFU/RANDOM eviction policies, plus a practical flushAll scenario.
This article explains Redis's core data structures—string, hash, list, set, and sorted set—covers why and how data expires, details Redis's periodic and lazy expiration mechanisms, outlines the six memory eviction policies, and provides a Java LinkedHashMap‑based LRU cache implementation.
