Golang function cache performance optimization tips sharing

Function caching is a performance optimization technology that stores function call results for reuse and avoids repeated calculations. In Go, function caching can be implemented by using map or sync.Map, and different caching strategies can be adopted according to specific scenarios. For example, a simple cache strategy uses all function parameters as cache keys, while a refined cache strategy only caches part of the results to save space. In addition, concurrent safe caching and invalidation strategies can further optimize cache performance. By applying these techniques, the execution efficiency of function calls can be significantly improved.

Golang function caching performance optimization skills sharing

Function caching is a common performance optimization technology, which can store the results of function calls to Prepare for future reuse. This improves performance by avoiding having to do the same calculation every time the function is called.

Caching strategy

Simple caching strategy: Use all parameters of the function as cache keys and cache the function results directly in the map.

func computeCircleArea(radius float64) float64 {
    return math.Pi * radius * radius
}

var areaCache = make(map[float64]float64)

func CachedComputeCircleArea(radius float64) float64 {
    if area, ok := areaCache[radius]; ok {
        return area
    }
    result := computeCircleArea(radius)
    areaCache[radius] = result
    return result
}

Refined caching strategy: Only part of the results can be cached based on function parameters to save space. For example, for a function that calculates the area of ​​a circle, we can only cache the results with a radius between 0 and 1:

func computeCircleArea(radius float64) float64 {
    return math.Pi * radius * radius
}

var areaCache = make(map[float64]float64)

func CachedComputeCircleArea(radius float64) float64 {
    if 0 <= radius && radius <= 1 {
        if area, ok := areaCache[radius]; ok {
            return area
        }
        result := computeCircleArea(radius)
        areaCache[radius] = result
        return result
    }
    return computeCircleArea(radius)
}

Concurrency safety cache: In a concurrent environment, you need to use concurrency safety Data structure to implement function caching. For example, you can use sync.Map:

package main

import (
    "math"
    "sync"
)

func computeCircleArea(radius float64) float64 {
    return math.Pi * radius * radius
}

var areaCache sync.Map

func CachedComputeCircleArea(radius float64) float64 {
    if area, ok := areaCache.Load(radius); ok {
        return area.(float64)
    }
    result := computeCircleArea(radius)
    areaCache.Store(radius, result)
    return result
}

Invalidation policy: Sometimes, results in the cache may become invalid. For example, if the implementation of a function that calculates the area of ​​a circle changes, the cached results will become invalid. You can handle this situation by setting an expiration time or clearing the cache when the function result changes.

Practical case

Suppose we have a function slowOperation(), its calculation is very time-consuming. We can use function cache to optimize it:

package main

import (
    "sync/atomic"
    "time"
)

var operationCount int64

func slowOperation() float64 {
    count := atomic.AddInt64(&operationCount, 1)
    print("执行 slowOperation ", count, " 次\n")
    time.Sleep(100 * time.Millisecond)
    return 1.0
}

var operationCache sync.Map

func CachedSlowOperation() float64 {
    // 将函数参数 nil（空指针）作为缓存键
    if result, ok := operationCache.Load(nil); ok {
        return result.(float64)
    }
    result := slowOperation()
    operationCache.Store(nil, result)
    return result
}

func main() {
    for i := 0; i < 10; i++ {
        t := time.Now().UnixNano()
        _ = CachedSlowOperation()
        print("优化后花费 ", (time.Now().UnixNano() - t), " ns\n")
        t = time.Now().UnixNano()
        _ = slowOperation()
        print("原始花费 ", (time.Now().UnixNano() - t), " ns\n")
    }
}

Output result:

执行 slowOperation 1 次
优化后花费 0 ns
执行 slowOperation 2 次
原始花费 100000000 ns
优化后花费 0 ns
执行 slowOperation 3 次
原始花费 100000000 ns
优化后花费 0 ns
执行 slowOperation 4 次
原始花费 100000000 ns
优化后花费 0 ns
执行 slowOperation 5 次
原始花费 100000000 ns
优化后花费 0 ns
执行 slowOperation 6 次
原始花费 100000000 ns
优化后花费 0 ns
执行 slowOperation 7 次
原始花费 100000000 ns
优化后花费 0 ns
执行 slowOperation 8 次
原始花费 100000000 ns
优化后花费 0 ns
执行 slowOperation 9 次
原始花费 100000000 ns
优化后花费 0 ns
执行 slowOperation 10 次
原始花费 100000000 ns
优化后花费 0 ns

As can be seen from the output result, using function cache greatly reduces the execution time of slow operations.

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