Practice of combining golang function cache and third-party storage

Function caching is an optimization technology used to avoid repeated calculations and improve performance. When the cache size exceeds the memory limit, the cache capacity can be expanded by combining third-party storage, such as using Redis. In practice, a large number of query results can be cached in Redis, thereby significantly improving performance.

Practice of combining Golang function cache and third-party storage

Function cache is an optimization technology used to avoid repeated calculations , improve application performance. In Golang, the sync/syncmap package provides a simple function cache implementation. However, for cache-intensive applications, leveraging third-party storage to expand cache capacity may be necessary.

Use sync/syncmap for simple caching

import (
    "sync"
)

var cache = sync.Map{}

func Get(key string) (interface{}, bool) {
    return cache.Load(key)
}

func Set(key string, value interface{}) {
    cache.Store(key, value)
}

Expand to third-party storage

When the cache size exceeds the memory limit, the cache capacity can be expanded by combining third-party storage. Here is an example of using Redis as the storage backend:

import (
    "context"
    "sync"
    "time"

    "github.com/go-redis/redis/v8"
)

// 将 sync/syncmap 作为一级缓存
var cache = sync.Map{}

// 使用 Redis 作为二级缓存
var redisClient = redis.NewClient(&redis.Options{
    Addr:     "localhost:6379",
    Password: "",
    DB:       0,
})

// 设置缓存超时时间（秒）
var cacheTimeout = 600

// 从一级缓存获取数据，如果没有则从 Redis 获取并设置到一级缓存中
func Get(key string) (interface{}, bool) {
    if val, ok := cache.Load(key); ok {
        return val, true
    }

    val, err := redisClient.Get(context.Background(), key).Result()
    if err != nil {
        return nil, false
    }

    cache.Store(key, val)
    return val, true
}

// 设置缓存数据，同时存储到 Redis 中
func Set(key string, value interface{}) {
    cache.Store(key, value)
    expireCtx := context.Background()
    if err := redisClient.Set(expireCtx, key, value, cacheTimeout*time.Second).Err(); err != nil {
        // 处理可能的错误
    }
}

Practical case: caching a large number of query results

Suppose there is an application that needs to perform a large number of the same database queries . To optimize performance, function caching can be leveraged to avoid repeated queries. However, due to the large query result set, storing all results in memory would exceed available memory.

Using function cache combined with third-party storage, the results of frequent queries can be stored in Redis. This way, even if memory limits are exceeded, applications can still access these results quickly, significantly improving performance.

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