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Scenario: Exposing System Resource Usage via API When building APIs that expose system metrics like CPU and memory utilization, performance optimization becomes critical. This article explores how the Proxy Pattern can efficiently handle resource monitoring requests. The Challenge Multiple servers may simultaneously call a resource monitoring e ...

Introduction to Caching Caching is a common feature in ORM frameworks, designed to enhance query performance and reduce database load by storing frequently accessed data in memory. Cache Architecture In the MyBatis source code, classes related to caching are located in the cache package. The core of this system is the Cache interface, with the ...

Why caching matters Every request that reaches the database consumes CPU, memory, and disk I/O. When traffic spikes, the database becomes the first bottleneck. A cache layer—placed between the application and the datastore—absorbs the majority of read requests, reduces latency, and acts as a circuit-breaker when the primary store is unavailable ...

Stateless Authentication with RedisIn a distributed architecture, relying on Tomcat's local sessions for user state leads to inconsistencies across instances. Redis serves as a centralized session store to solve this. When a user logs in via SMS, a unique token is generated and stored in Redis as the key, with the serialized user DTO as the val ...

Understanding MyBatis Caching Mechanisms MyBatis incorporates an internal caching system designed to enhance application performance by minimizing redundant database interactions. This caching operates at two distinct levels: a local, session-scoped cache and a global, application-scoped cache. First-Level Cache: SqlSession Scope The first-leve ...

In the previous article, we explored Spring Boot integration with Redis. This article focuses on implementing Redis caching in MyBatis operations. We'll examine four key annotations: @CachePut, @Cacheable, @CacheEvict, and @CacheConfig. Fundamentals @Cacheable The @Cacheable annotation configures method-level caching, storing results based on m ...

Introduction to LFU Caching LFU (Least Frequently Used) is a caching algorithm that removes the least frequently accessed items when the cache reaches its capacity. Unlike LRU (Least Recently Used), which considers only recency, LFU prioritizes access frequency. Comparison of LFU and LRU Consider a cache with capacity 3 and the following access ...

Cache Penetration Cache penetration occurs when a large number of requests target data that exists neither in the cache nor in the database. For example, if users repeatedly request non-existent order numbers like -1, these requests bypass the cache entirely and hit the database directly. This scenario can be exploited maliciously to overwhelm ...

Redis Cache and Database Consistency Caching improves performance and reduces database load, but introduces potential inconsistencies between cached data and the database. Inconsistent data leads to stale reads from the cache. Non-Concurrency Scenarios Update Cache First, Then Database If cache udpate succeeds but database update fails, the cac ...

ReentrantReadWriteLock Introduction and Usage Overview ReentrantReadWriteLock is ideal for scenarios with far more read operations than write operations. It allows concurrent read access while ensuring mutual exclusion between reads and writes, or between writes and writes—similar to database shared locks (select ... from ... lock in share mode ...