Introducing full-stack Internet architecture technologies centered on Java
This article explains MySQL's logical architecture, detailing the four main layers—connection, core service, storage engine, and data storage—while providing code examples and insights into each component's role in the widely used open‑source relational database.
This article explains how to implement distributed locks in Redis using the SETNX command, atomic Lua scripts, the Redisson Java client, and the RedLock algorithm, providing code examples and discussing their advantages and limitations in distributed backend systems.
This article explains why Redis achieves extremely high performance, reaching up to 100,000 QPS, by leveraging its in‑memory design, I/O multiplexing, optimized data structures such as SDS, ziplist and skiplist, and a single‑threaded event loop, each detailed with examples and code.
This article explains how Redis as a distributed cache can cause data inconsistency with relational databases during write operations, outlines read and update workflows, compares five cache‑update strategies, and presents a delayed double‑delete solution with sample code to maintain consistency.
This article reviews the four common Redis deployment modes—single‑instance, master‑slave replication, Sentinel, and cluster—explaining their architectures, advantages, drawbacks, and suitable application scenarios, and provides a comparative table to help readers choose the appropriate setup.
This article explains Redis Cluster's sharding architecture, demonstrates how to configure multiple Redis nodes on different ports, shows commands for creating and testing the cluster, and illustrates failover behavior, highlighting its scalability and high‑availability advantages over Sentinel mode for large‑scale data workloads.
This article warns developers about several Redis commands—such as KEYS, FLUSHDB/FLUSHALL, SAVE/BGSAVE, and DEBUG SLEEP—that can cause performance degradation, data loss, or security issues in production environments, and encourages careful usage to avoid serious operational risks.
The article warns developers against swallowing exceptions in Java, explains the risks of empty catch blocks using a sample code snippet, and emphasizes the need for proper exception handling to avoid hidden errors and potential termination.
This article explains how to set key expiration times in Redis, the lazy and periodic deletion mechanisms for expired data, and the various memory eviction policies that Redis employs when the maxmemory limit is reached, providing a comprehensive guide for interview preparation.
The article explains the concept of idempotence in computing, illustrates typical duplicate‑request scenarios, and presents three practical backend solutions—unique database indexes, optimistic locking with version control, and a Redis‑based token mechanism—along with their advantages, trade‑offs, and implementation details.
