Go function performance optimization: in-depth explanation of locks and synchronization primitives

Performance optimization of Go functions relies on the correct use of locks and synchronization primitives. Lock types: 1. Mutex lock (Mutex): guarantees exclusive access to shared resources; 2. Read-write lock (RWMutex): allows concurrent reading but only allows one write. Synchronization primitives: 1. Condition variable (Cond): pause goroutine until the condition is met; 2. Atomic operation (atomic): safely read and write shared variables; 3. Waiting group (WaitGroup): wait for multiple goroutine tasks to complete. Practical case: Concurrent calculation of the sum of slices can be achieved by protecting the shared variable sum with a mutex lock to avoid non-atomic problems caused by concurrent updates and significantly improve performance.

Go function performance optimization: in-depth explanation of locks and synchronization primitives

In high-concurrency scenarios, the concurrency features of the Go language play a vital role role. Understanding the usage of locks and synchronization primitives is crucial to improving the performance of Go functions. This article will provide an in-depth explanation of locks and synchronization primitives, and show how to optimize Go function performance through practical cases.

Lock type

Go language provides multiple types of locks, including:

• Mutex lock (Mutex): Guaranteed once Only one goroutine can access shared resources.
• Read-write lock (RWMutex): Allows multiple goroutines to read shared resources at the same time, but only allows one goroutine to write resources.

Synchronization primitives

In addition to locks, the Go language also provides the following synchronization primitives:

• Condition variable (Cond): Used to pause goroutine until a certain condition is met.
• Atomic operations (atomic): Used to safely read and write shared variables.
• Waiting Group (WaitGroup): Used to wait for multiple goroutines to complete tasks.

Practical case

Scenario: Concurrently sum the elements in a slice

Assume there is a slicenums, the sum of all elements in the slice needs to be calculated concurrently. We can use a mutex to ensure that updates to sum are atomic.

var nums = []int{1, 2, 3, 4, 5}

// 使用互斥锁保护共享变量
var mu sync.Mutex
var sum int

// 求和函数
func sumNums() {
    mu.Lock()
    defer mu.Unlock()
    for _, num := range nums {
        sum += num
    }
}

// 并发求和
func main() {
    // 创建一个 goroutine 数组
    var goroutines []*goroutine
    for i := 0; i < 4; i++ {
        goroutines[i] = goroutine.New(sumNums)
    }

    // 启动 goroutine 并等待完成
    for _, g := range goroutines {
        g.Start()
        g.Wait()
    }

    // 打印计算结果
    fmt.Println(sum)
}

By using mutex locks, multiple goroutines can update sum concurrently while ensuring the atomicity of updates. This significantly improves summation performance.

Notes

• Try to reduce the use of locks, because locks will cause additional overhead.
• Prefer using read-write locks to allow both read and write operations.
• When using synchronization primitives, be sure to call defer Unlock() to unlock the lock.
• Avoid holding locks in goroutine for too long, which may lead to deadlock.

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