


Application tips for decrypting Guava cache: an artifact to improve application performance
Guava cache usage tips
Guava cache is a high-performance memory cache that can significantly improve application performance. It provides a variety of caching strategies, and you can choose the most appropriate caching strategy according to different scenarios.
Basic use of Guava cache
The basic use of Guava cache is very simple and only requires a few lines of code.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
|
Caching strategy of Guava cache
Guava cache provides a variety of caching strategies, and you can choose the most appropriate caching strategy according to different scenarios.
Size-based caching strategy
Size-based caching strategy refers to deciding whether to put data into the cache based on the size of the cache. Guava cache provides two size-based caching strategies:
- maximumSize(): Set the maximum capacity of the cache. When the cache reaches maximum capacity, the oldest data put into the cache will be evicted.
- weigher(): Set the cache weight function. The weight function can calculate the weight of data based on its size or other factors. When the cache reaches its maximum capacity, the data with the greatest weight will be evicted.
Time-based caching strategy
The time-based caching strategy refers to deciding whether to put data into the cache based on the expiration time of the data. Guava cache provides two time-based caching strategies:
- expireAfterWrite(): Set the expiration time of data in the cache. When the data stored in the cache exceeds the expiration time, it will be eliminated.
- expireAfterAccess(): Set the expiration time after the data is accessed in the cache. When the data is accessed in the cache, the expiration time is recalculated.
Reference-based caching strategy
Reference-based caching strategy refers to deciding whether to put data into the cache based on the reference count of the data. Guava cache provides two reference-based caching strategies:
- weakKeys(): Set cache keys to weak references. When a key is garbage collected, the data in the cache will be evicted.
- softValues(): Set cached values to soft references. When a value is garbage collected, the data in the cache is evicted.
Tips for using Guava cache
When using Guava cache, you can pay attention to the following points:
- Choose the appropriate caching strategy: Choose the most appropriate caching strategy according to different scenarios.
- Set the cache capacity reasonably: The cache capacity should not be too large, otherwise it will occupy too much memory.
- Set the expiration time reasonably: The expiration time should not be too long, otherwise the data in the cache may become invalid.
- Pay attention to concurrent access to the cache: If the cache is accessed by multiple threads at the same time, you need to consider the concurrency control of the cache.
- Clear the cache regularly: Cleaning the cache regularly can prevent the data in the cache from becoming outdated.
Conclusion
Guava cache is a high-performance memory cache that can significantly improve application performance. Through reasonable use of Guava cache, the performance and scalability of the application can be effectively improved.
The above is the detailed content of Application tips for decrypting Guava cache: an artifact to improve application performance. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

Performance comparison of different Java frameworks: REST API request processing: Vert.x is the best, with a request rate of 2 times SpringBoot and 3 times Dropwizard. Database query: SpringBoot's HibernateORM is better than Vert.x and Dropwizard's ORM. Caching operations: Vert.x's Hazelcast client is superior to SpringBoot and Dropwizard's caching mechanisms. Suitable framework: Choose according to application requirements. Vert.x is suitable for high-performance web services, SpringBoot is suitable for data-intensive applications, and Dropwizard is suitable for microservice architecture.

In PHP development, the caching mechanism improves performance by temporarily storing frequently accessed data in memory or disk, thereby reducing the number of database accesses. Cache types mainly include memory, file and database cache. Caching can be implemented in PHP using built-in functions or third-party libraries, such as cache_get() and Memcache. Common practical applications include caching database query results to optimize query performance and caching page output to speed up rendering. The caching mechanism effectively improves website response speed, enhances user experience and reduces server load.

Effective techniques for optimizing C++ multi-threaded performance include limiting the number of threads to avoid resource contention. Use lightweight mutex locks to reduce contention. Optimize the scope of the lock and minimize the waiting time. Use lock-free data structures to improve concurrency. Avoid busy waiting and notify threads of resource availability through events.

Pitfalls in Go Language When Designing Distributed Systems Go is a popular language used for developing distributed systems. However, there are some pitfalls to be aware of when using Go, which can undermine the robustness, performance, and correctness of your system. This article will explore some common pitfalls and provide practical examples on how to avoid them. 1. Overuse of concurrency Go is a concurrency language that encourages developers to use goroutines to increase parallelism. However, excessive use of concurrency can lead to system instability because too many goroutines compete for resources and cause context switching overhead. Practical case: Excessive use of concurrency leads to service response delays and resource competition, which manifests as high CPU utilization and high garbage collection overhead.

DeepSeek: How to deal with the popular AI that is congested with servers? As a hot AI in 2025, DeepSeek is free and open source and has a performance comparable to the official version of OpenAIo1, which shows its popularity. However, high concurrency also brings the problem of server busyness. This article will analyze the reasons and provide coping strategies. DeepSeek web version entrance: https://www.deepseek.com/DeepSeek server busy reason: High concurrent access: DeepSeek's free and powerful features attract a large number of users to use at the same time, resulting in excessive server load. Cyber Attack: It is reported that DeepSeek has an impact on the US financial industry.

When developing high-performance applications, C++ outperforms other languages, especially in micro-benchmarks. In macro benchmarks, the convenience and optimization mechanisms of other languages such as Java and C# may perform better. In practical cases, C++ performs well in image processing, numerical calculations and game development, and its direct control of memory management and hardware access brings obvious performance advantages.

In the Go distributed system, caching can be implemented using the groupcache package. This package provides a general caching interface and supports multiple caching strategies, such as LRU, LFU, ARC and FIFO. Leveraging groupcache can significantly improve application performance, reduce backend load, and enhance system reliability. The specific implementation method is as follows: Import the necessary packages, set the cache pool size, define the cache pool, set the cache expiration time, set the number of concurrent value requests, and process the value request results.

In Java concurrent programming, race conditions and race conditions can lead to unpredictable behavior. A race condition occurs when multiple threads access shared data at the same time, resulting in inconsistent data states, which can be resolved by using locks for synchronization. A race condition is when multiple threads execute the same critical part of the code at the same time, leading to unexpected results. Atomic operations can be ensured by using atomic variables or locks.
