Sharing insights on technical analysis and exchange, making life better through technology.
This article explains the three major Redis cache issues—cache avalanche, cache breakdown, and cache penetration—how they can overload databases, and provides practical solutions such as high‑availability deployment, appropriate key expiration, local caches, mutex locks, empty‑object caching, request validation, and Bloom filters.
This guide explains how to create a seamless auto‑login feature by persisting a token in a cookie after the first successful login, storing the token in Redis, and validating it on subsequent visits, complete with Java Spring code examples.
This article explains why traditional relational databases falter under high‑concurrency loads, introduces caching as a solution, compares Redis distributed caching with local in‑process caching, and shows how combining them into a multi‑level cache can dramatically improve performance and reliability.
This article explains a divide‑and‑conquer method that splits massive search‑log files, uses multithreaded hashing to count keyword frequencies, and applies a min‑heap to efficiently retrieve the top‑100 most frequent search terms for SEO and recommendation tasks.
Learn practical methods to identify users who exceed three failed login attempts within ten seconds, covering both a MySQL log‑table approach with window functions and a Redis‑based solution with key expiration, plus discussion of performance trade‑offs and atomicity considerations.
Hotlinking attacks steal popular media by fetching resources from major platforms, but developers can protect assets using anti-leech methods such as Nginx referer checks, SpringBoot filters, token validation, timestamp verification, and graphical captchas, each with strengths and limitations against forged requests.
The article explains JVM garbage‑collection techniques, comparing simple reference‑counting and reachability analysis, then details the three‑color marking algorithm—including its phases, color semantics, step‑by‑step process, and common issues like over‑marking and under‑marking—followed by solutions used in CMS and G1 collectors.
This article explains how Java HotSpot uses a card‑table memory set to efficiently detect and handle cross‑generational references during young‑generation GCRoot scans, avoiding full old‑generation scans and significantly improving garbage‑collection performance.
This article explains how Java's garbage collectors work, covering generational memory spaces, major algorithms such as mark‑sweep, mark‑compact, and copy, the most common collectors for young and old generations, safe points, throughput calculations, and tuning parameters.
This article explains common data‑migration strategies such as hash‑modulo, time‑range and ID‑range sharding, and compares practical migration solutions including downtime migration, dual‑write, and an MQ + Redis approach, with detailed workflow diagrams and implementation tips.
