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Databases & messaging · #memcached
Learn how Memcached’s Check-And-Set operation lets multiple clients update the same key safely without external locks, with a worked example and practical limits.
Learn how to choose Memcached chunk size and growth factor to avoid premature evictions, reduce internal fragmentation, and validate slab health with stats slabs.
Memcached’s CAS token lets clients detect concurrent changes and safely retry read‑modify‑write updates without server‑side locking.
When scaling Memcached, the classic modulo hashing approach can cause massive cache misses. Consistent hashing, combined with virtual nodes, keeps most keys on their original servers, preserving hit rates. Learn how to implement it and what trade‑offs to watch for.
Learn how to implement Memcached as a distributed caching layer to reduce database load using the cache-aside pattern and consistent hashing.
Learn how Memcached's compare-and-swap (CAS) operation lets you safely update shared counters without lost updates, and see the trade‑offs involved.
Learn how to design a low‑latency, fault‑tolerant cache using Memcached’s client‑side consistent hashing. The guide covers minimal architecture, trust boundaries, operational checks, failure modes, and when to rethink the design.
Cache Backend Selection for High-Traffic State When managing application state in a high-traffic environment using Zend Cache, the choice of backend adapter directly impacts I/O performance and scalability. The Filesystem adapter provides persistence across service restarts but introduces potential file-locking contention and disk I/O overhead. In contrast,
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