How to optimize Golang cache performance?

Tips to improve Golang cache performance include: choosing an appropriate cache library, such as sync.Map, github.com/patrickmn/go-cache and github.com/go-cache/cache. Optimize the data structure, use map to store data, and consider using jump tables to implement hierarchical cache storage. Take advantage of concurrency control, using read-write locks, sync.Map, or channels to manage concurrency.

Tips to improve Golang cache performance

Optimizing cache performance is crucial, it can greatly improve the response time of the application and throughput. This article will explore practical techniques for optimizing cache performance in Golang and explain them through practical cases.

1. Choose an appropriate cache library

• sync.Map: suitable for small-scale concurrent caching scenarios.
• github.com/patrickmn/go-cache: Provides functions such as expiration time, synchronous and asynchronous operations.
• github.com/go-cache/cache: Supports TTL, multiple storage backends and concurrency control.

2. Optimize the data structure

• Use mapping to store cached data and quickly find values ​​based on keys.
• Consider using skiplist to implement hierarchical cache storage and achieve fast range search and update.

3. Utilize concurrency control

• The go-cache library provides read-write locks to allow concurrent reading and writing.
• sync.Map provides safe concurrent operations and is suitable for lock-free environments.
• Use channels to manage concurrent writes to avoid data competition.

Practical case: using go-cache library

import (
    "context"
    "fmt"
    "time"

    "github.com/patrickmn/go-cache"
)

func main() {
    // 创建一个缓存，过期时间为 5 分钟
    c := cache.New(5 * time.Minute, 10 * time.Minute)

    // 设置缓存值
    c.Set("key", "value", cache.DefaultExpiration)

    // 获取缓存值
    value, ok := c.Get("key")
    if ok {
        fmt.Println(value)
    }

    // 定期清理过期的缓存值
    go func() {
        for {
            c.DeleteExpired()
            time.Sleep(5 * time.Second)
        }
    }()

    // 等待缓存处理器退出后再退出程序
    ctx := context.Background()
    <-ctx.Done()
}

Other optimization techniques

• Use LRU The cache policy evicts the least used items.
• Consider using a distributed cache such as Redis or Memcached to meet large-scale caching needs.
• Use cache hit rate monitoring to evaluate cache efficiency.

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