Scalability design of golang function concurrent cache

Function concurrency cache can optimize performance in high concurrency scenarios by storing function calculation results in memory. It stores results using a concurrent-safe map and implements cache invalidation strategies as needed. For example, an example of concurrent caching for calculating the Fibonacci sequence demonstrates its advantages in avoiding repeated calculations and increasing execution speed.

Scalability design of function concurrent cache in Go language

Introduction

In high-concurrency scenarios, function calls often become performance issues bottleneck, especially when functions are expensive to process. To address this problem, we can adopt the function concurrent cache strategy to avoid repeated calculations and improve performance by storing function calculation results in memory.

Implementation Principle

1. Concurrent execution:

import "sync"

type concurrentCache struct {
    sync.Mutex
    cache map[interface{}]interface{}
}

func (c *concurrentCache) Get(key interface{}) (interface{}, bool) {
    c.Lock()
    defer c.Unlock()

    val, ok := c.cache[key]
    return val, ok
}

func (c *concurrentCache) Set(key, val interface{}) {
    c.Lock()
    defer c.Unlock()

    c.cache[key] = val
}

concurrentCache maintains a concurrent and safe mapping for storage function calculation result. The Get method gets the result from the map, while the Set method stores the new result.

2. Cache invalidation:

In order to maintain the effectiveness of the cache, we need to consider the cache invalidation strategy according to specific scenarios. For example, we can set an expiration time or use the LRU (Least Recently Used) algorithm to cull less frequently used cache entries.

Practical example

The following is a simple function concurrent cache example based on concurrentCache, used to calculate the Fibonacci sequence:

package main

import "fmt"
import "sync"

var cache = &concurrentCache{cache: make(map[int]int)}

func fibonacci(n int) int {
    if n <= 1 {
        return 1
    }

    if val, ok := cache.Get(n); ok {
        return val.(int)
    }

    result := fibonacci(n-1) + fibonacci(n-2)
    cache.Set(n, result)

    return result
}

func main() {
    wg := sync.WaitGroup{}
    jobs := []int{10, 20, 30, 40, 50, 60}

    for _, n := range jobs {
        wg.Add(1)
        go func(n int) {
            defer wg.Done()
            result := fibonacci(n)
            fmt.Printf("Fibonacci(%d) = %d\n", n, result)
        }(n)
    }

    wg.Wait()
}

In In this example, we cache the Fibonacci calculation function concurrently to avoid repeated calculations. By running this program, we can observe that concurrent calls are significantly faster than sequential execution.

Conclusion

Function concurrent caching is an effective method to improve performance in high concurrency scenarios. By adopting concurrency-safe data structures such as concurrentCache and taking into account cache invalidation strategies, we can design a scalable and efficient function concurrent cache.

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