BestHub
Sign in
Tag

Caffeine Cache

0 views collected around this technical thread.

Top Architect
Top Architect
May 10, 2025 · Backend Development

Optimizing a High‑Concurrency Backend Interface: Reducing Response Time from 30 seconds to 0.8 seconds

This article presents a real‑world case study of a high‑traffic backend API that originally took 30 seconds to process over two million records, detailing problem diagnosis, SQL and Java code analysis, and a series of optimizations—including SQL rewrites, database‑side aggregation, and Caffeine caching—that ultimately cut the response time to under one second.

Caffeine CacheJavaMyBatis
0 likes · 13 min read
Optimizing a High‑Concurrency Backend Interface: Reducing Response Time from 30 seconds to 0.8 seconds
Top Architect
Top Architect
Oct 12, 2024 · Backend Development

Optimizing a High‑Concurrency Interface: Reducing Response Time from 30 seconds to 0.8 seconds

This article presents a real‑world case study of a high‑concurrency data‑processing API that originally took over 30 seconds to respond, explains how the bottleneck was identified in SQL and Java code, and details a series of optimizations—including SQL aggregation, moving logic to the database, and introducing Caffeine caching—that ultimately brought the response time under one second.

Backend DevelopmentCaffeine CacheMyBatis
0 likes · 12 min read
Optimizing a High‑Concurrency Interface: Reducing Response Time from 30 seconds to 0.8 seconds
Top Architect
Top Architect
Sep 30, 2024 · Backend Development

Optimizing a High‑Concurrency Java Backend Interface: Reducing Response Time from 30 s to 0.8 s

This article details a real-world performance optimization of a high‑concurrency Java backend interface, reducing response time from 30 seconds to under 0.8 seconds through SQL analysis, array aggregation, moving logic to PostgreSQL, and introducing Caffeine caching, with code examples and lessons learned.

Backend DevelopmentCaffeine CacheJava
0 likes · 13 min read
Optimizing a High‑Concurrency Java Backend Interface: Reducing Response Time from 30 s to 0.8 s
Code Ape Tech Column
Code Ape Tech Column
Jul 29, 2024 · Backend Development

Using Caffeine Cache in Spring Boot: Features, Algorithms, and Configuration

This article introduces Caffeine Cache, explains its W‑TinyLFU algorithm advantages over traditional FIFO, LRU, and LFU strategies, demonstrates manual, synchronous, and asynchronous loading methods, covers eviction policies, statistics, Spring Boot integration, and provides detailed configuration and code examples for Java developers.

Cache ConfigurationCaffeine CacheJava
0 likes · 30 min read
Using Caffeine Cache in Spring Boot: Features, Algorithms, and Configuration
vivo Internet Technology
vivo Internet Technology
Jul 24, 2024 · Backend Development

Design and Implementation of a Visual Monitoring System for Caffeine Cache

The article presents a visual monitoring system for the Caffeine Java cache that adds instance naming, memory‑usage estimation, dynamic size and expiration configuration, real‑time statistics and data queries, and a Jetty‑based HTTP control panel, enabling global cache management, trend charts, and on‑the‑fly cache invalidation.

Backend DevelopmentCache VisualizationCaffeine Cache
0 likes · 15 min read
Design and Implementation of a Visual Monitoring System for Caffeine Cache
Code Ape Tech Column
Code Ape Tech Column
Jul 18, 2024 · Backend Development

Optimizing a High‑Concurrency Data Processing Interface: Reducing Response Time from 30 s to 0.8 s

This article presents a real‑world case study of a high‑concurrency transaction‑handling API that originally required 30 seconds to process over two million records, and details the step‑by‑step SQL, Java, and caching optimizations that brought the response time down to under one second.

Caffeine CacheMyBatisPostgreSQL
0 likes · 10 min read
Optimizing a High‑Concurrency Data Processing Interface: Reducing Response Time from 30 s to 0.8 s
Top Architect
Top Architect
Jul 8, 2022 · Backend Development

Caffeine Cache: Algorithm Advantages, Usage Strategies, and Spring Boot Integration

This article explains the high‑performance Caffeine Cache library, its W‑TinyLFU eviction algorithm, various loading and eviction strategies, statistics collection, and how to integrate and configure it in Spring Boot using annotations and programmatic beans.

Cache AnnotationsCache EvictionCaffeine Cache
0 likes · 22 min read
Caffeine Cache: Algorithm Advantages, Usage Strategies, and Spring Boot Integration
Top Architect
Top Architect
Dec 27, 2021 · Backend Development

Caffeine Cache in Spring Boot: Algorithm Advantages, Configuration, and Usage

This article introduces Caffeine Cache as a high‑performance local cache for Java, explains its W‑TinyLFU algorithm advantages over Guava, details various eviction and loading strategies, and provides step‑by‑step Spring Boot integration with Maven dependencies, configuration properties, bean definitions, and annotation‑driven usage examples.

Cache ConfigurationCaffeine CacheJava
0 likes · 21 min read
Caffeine Cache in Spring Boot: Algorithm Advantages, Configuration, and Usage
Architect's Tech Stack
Architect's Tech Stack
Jun 24, 2021 · Backend Development

Caffeine Cache in Spring Boot: Algorithms, Configuration, and Practical Usage

This article introduces Caffeine Cache as a high‑performance local caching solution that improves on Guava Cache by using the W‑TinyLFU eviction algorithm, explains its core concepts, demonstrates manual, synchronous, and asynchronous loading strategies, details eviction policies, and provides step‑by‑step integration and configuration examples for Spring Boot applications.

Cache EvictionCaffeine CacheJava Caching
0 likes · 22 min read
Caffeine Cache in Spring Boot: Algorithms, Configuration, and Practical Usage
vivo Internet Technology
vivo Internet Technology
Apr 21, 2021 · Backend Development

Understanding and Configuring Caffeine Cache in Java Applications

Understanding Caffeine Cache in Java involves using its builder for options like expiration, refresh, and weight‑based eviction, recognizing that configuring both expireAfterWrite and expireAfterAccess is redundant, grasping core methods such as isBounded, refreshes, computeIfAbsent, and avoiding common pitfalls like mis‑ordered expiration settings, blocking loaders, cache penetration, and mutable cached objects.

Cache ConfigurationCache EvictionCaffeine Cache
0 likes · 15 min read
Understanding and Configuring Caffeine Cache in Java Applications