Senior engineer at a top internet giant, sharing Java, AI, tech knowledge, growth insights, and interview experiences.
The article outlines five common cache design problems—penetration, concurrency, avalanche, hot‑data management, and business‑cache coupling—illustrates each with a diagram, and provides concrete Redis‑based solutions such as placeholder values, setNX locks, random TTLs, sorted‑set queues, and a Binlog‑Canal‑MQ pipeline.
This article breaks down Redis’s core mechanisms—including its single‑threaded performance tricks, AOF and RDB persistence designs, and the evolution of high‑availability from replication to Sentinel and Cluster—using four clear diagrams to help readers master the system.
The article breaks down MySQL index mechanics into four visual sections, explaining B+Tree structure, why it outperforms B‑tree, hash and binary trees, common index‑failure scenarios, and three practical optimization techniques, giving interviewees a clear framework to answer index‑related questions confidently.
This article explains the three fundamental challenges of distributed locks—availability, deadlock, and split‑brain—compares database and Redis implementations, walks through the five evolutionary steps of Redis locking, and provides a structured interview answer framework to demonstrate deep understanding.
The article breaks down a complete flash‑sale system into four phases and explains how Redis distributed locks, CDN static pages, Nginx rate limiting, message‑queue peak shaving, and sharding together prevent overselling, crashes, and lost orders even when traffic surges dozens of times.
The article explains why reciting the 2PC protocol fails in real‑world interviews, then walks through the two dominant solutions—two‑phase commit and MQ‑based eventual consistency—detailing their mechanisms, trade‑offs, and a concrete interview response template that showcases practical experience and theoretical depth.
The article walks through five interview‑style steps—modulo hashing, consistent hashing, range sharding, metadata routing with etcd, and choosing Raft or Gossip—to design a highly scalable, hot‑item‑aware, and consistent distributed storage system for billions of products.
The article walks through Elasticsearch’s document retrieval workflow, covering single‑document GET, multi‑document mget, the two‑stage query_then_fetch mechanism, shard routing, load balancing, query and fetch phases, collectors, rescore, and pagination optimizations.
This article walks through Elasticsearch’s document indexing pipeline—from single‑document and bulk request routing, through the write‑refresh‑flush‑merge cycle, to the roles of Lucene, the translog, primary and replica shards, and how reliability, consistency, atomicity, isolation, real‑time visibility and performance are achieved.
This article explains Elasticsearch’s overall structure, the underlying Lucene index layout, the indexing and search processing flow, and how built‑in analyzers work, illustrating each concept with diagrams, token examples, and query scenarios.
