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Tagged articles

Multi-level Caching

6 articles · Page 1 of 1
Code Farmer Manor Chronicle
Code Farmer Manor Chronicle
Oct 7, 2026 · Backend Development

Mastering High-Concurrency Caching: Penetration, Breakdown, Avalanche & Hot Keys

This article explains four classic high-concurrency caching problems—cache penetration, breakdown, avalanche, and hot keys—their root causes, differences, and practical solutions including empty-value caching, Bloom filters, mutex locks, logical expiration, TTL jitter, multi-level caching, and cache-database consistency patterns.

Bloom filterCache AsideConsistency
0 likes · 12 min read
Mastering High-Concurrency Caching: Penetration, Breakdown, Avalanche & Hot Keys
Xiaolin Talks Programming
Xiaolin Talks Programming
Jun 19, 2026 · Backend Development

Spring Boot Multi-Level Cache: Caffeine + Redis Design with Consistency & 78K QPS

This article details a production-grade Spring Boot multi-level caching architecture using Caffeine L1 and Redis L2 to solve database IO bottlenecks in read-heavy scenarios, covering consistency solutions via MQ async with idempotency, defenses against cache penetration, breakdown, and avalanche, and load test results showing 78K QPS with 5.2ms P99 latency.

CaffeineDistributed lockingMicroMeter
0 likes · 19 min read
Spring Boot Multi-Level Cache: Caffeine + Redis Design with Consistency & 78K QPS
MaGe Linux Operations
MaGe Linux Operations
Sep 6, 2023 · Backend Development

Mastering JetCache: Multi‑Level Caching with Spring Boot and Redis

This guide explains how to combine local and remote caches using Alibaba's JetCache in a Spring Boot project, covering dependency setup, configuration files, annotation‑based usage, advanced API methods, testing procedures, and common troubleshooting tips for a robust multi‑level caching solution.

CacheJavaJetCache
0 likes · 11 min read
Mastering JetCache: Multi‑Level Caching with Spring Boot and Redis
Ctrip Technology
Ctrip Technology
Jun 9, 2022 · Databases

Ctrip Order Database Architecture Optimization and Sharding Case Study

This article details Ctrip's comprehensive redesign of its airline ticket order database, covering the background performance bottlenecks, vertical and hot‑cold data splitting, sharding key selection, multi‑level caching, cross‑shard query optimization, dual‑write mechanisms, fault‑tolerance strategies, project planning, and the resulting improvements in scalability and cost.

Dual WriteMulti-level Cachingdatabase sharding
0 likes · 37 min read
Ctrip Order Database Architecture Optimization and Sharding Case Study