Go language concurrent data structure: performance optimization of queues and stacks

In the Go language, the performance of queues and stacks can be achieved through the following optimizations: using sync.Mutex and sync.Cond to implement concurrent queues to ensure the safety of read and write operations. Use sync.Mutex and atomic packages to implement concurrent stacks to ensure atomicity of top pointer updates. In practical cases, efficient concurrent processing is achieved through concurrent queue and stack processing tasks.

Go language concurrent data structure: performance optimization of queues and stacks

In Go, queues and stacks are commonly used data structure. However, in high-concurrency scenarios, the default implementation may not meet performance requirements. This article will introduce how to use the built-in concurrency primitives of the Go language to optimize the performance of queues and stacks.

Optimizing Queue

Go provides sync.Mutex and sync.Cond primitives to implement concurrent queues. Here is a concurrent queue implemented using sync.Mutex and sync.Cond:

type ConcurrentQueue struct {
    m     sync.Mutex
    items []interface{}
    conds sync.Cond
}

func (q *ConcurrentQueue) Enqueue(v interface{}) {
    q.m.Lock()
    defer q.m.Unlock()
    q.items = append(q.items, v)
    q.conds.Signal()
}

func (q *ConcurrentQueue) Dequeue() interface{} {
    q.m.Lock()
    defer q.m.Unlock()
    var v interface{}
    if len(q.items) > 0 {
        v = q.items[0]
        q.items = q.items[1:]
    }
    return v
}

By using sync.Mutex and sync.Cond, we can safely read and write queues in concurrent scenarios. Use the Signal signal to wake up the waiting coroutine, thereby improving efficiency.

Optimized Stack

There is no built-in concurrent stack implementation in Go. Here is a concurrency stack implemented using the sync.Mutex and atomic packages:

type ConcurrentStack struct {
    m sync.Mutex
    top *node
}

type node struct {
    data interface{}
    next *node
}

func (s *ConcurrentStack) Push(v interface{}) {
    s.m.Lock()
    defer s.m.Unlock()
    n := &node{data: v}
    n.next = s.top
    s.top = n
}

func (s *ConcurrentStack) Pop() interface{} {
    s.m.Lock()
    defer s.m.Unlock()
    if s.top == nil {
        return nil
    }
    v := s.top.data
    s.top = s.top.next
    return v
}

Using variables in the atomic package can ensure a concurrent environment The top pointer updates below are atomic.

Practical case

The following is an example of using concurrent queues and stacks to handle concurrent tasks:

func main() {
    q := ConcurrentQueue{}
    s := ConcurrentStack{}

    for i := 0; i < 1000; i++ {
        // 向队列中并发添加任务
        go func(i int) {
            q.Enqueue(i)
        }(i)
    }

    for i := 0; i < 1000; i++ {
        // 从队列中并发获取任务并推入栈中
        go func() {
            if v := q.Dequeue(); v != nil {
                s.Push(v)
            }
        }()
    }

    for i := 0; i < 1000; i++ {
        // 从栈中弹出任务并处理
        go func() {
            if v := s.Pop(); v != nil {
                // 处理任务 v
            }
        }()
    }
}

This example adds 1000 tasks concurrently to queue, and concurrently obtain tasks from the queue and push them onto the stack. Tasks are then concurrently popped from the stack and processed. By using concurrent data structures, this example can efficiently handle large concurrency tasks.

The above is the detailed content of Go language concurrent data structure: performance optimization of queues and stacks. For more information, please follow other related articles on the PHP Chinese website!