给 GPT 的 Prompts

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gopool.go package gopool

import ( "sync" "time" )

// task represents a function that will be executed by a worker. // It returns a result and an error. type task func() (interface{}, error)

// goPool represents a pool of workers. type goPool struct { workers []*worker maxWorkers int minWorkers int workerStack []int taskQueue chan task lock sync.Locker cond *sync.Cond timeout time.Duration resultCallback func(interface{}) errorCallback func(error) }

// NewGoPool creates a new pool of workers. func NewGoPool(maxWorkers int, opts ...Option) *goPool { pool := &goPool{ maxWorkers: maxWorkers, minWorkers: maxWorkers, // Set minWorkers to maxWorkers by default workers: make([]*worker, maxWorkers), workerStack: make([]int, maxWorkers), taskQueue: make(chan task, 1e6), lock: new(sync.Mutex), timeout: 0, } for _, opt := range opts { opt(pool) } if pool.cond == nil { pool.cond = sync.NewCond(pool.lock) } for i := 0; i < pool.minWorkers; i++ { worker := newWorker() pool.workers[i] = worker pool.workerStack[i] = i worker.start(pool, i) } go pool.dispatch() return pool }

// AddTask adds a task to the pool. func (p *goPool) AddTask(t task) { p.taskQueue <- t }

// Release stops all workers and releases resources. func (p *goPool) Release() { close(p.taskQueue) p.cond.L.Lock() for len(p.workerStack) != p.maxWorkers { p.cond.Wait() } p.cond.L.Unlock() for _, worker := range p.workers { close(worker.taskQueue) } p.workers = nil p.workerStack = nil }

func (p *goPool) popWorker() int { p.lock.Lock() workerIndex := p.workerStack[len(p.workerStack)-1] p.workerStack = p.workerStack[:len(p.workerStack)-1] p.lock.Unlock() return workerIndex }

func (p *goPool) pushWorker(workerIndex int) { p.lock.Lock() p.workerStack = append(p.workerStack, workerIndex) p.lock.Unlock() p.cond.Signal() }

func (p *goPool) dispatch() { for t := range p.taskQueue { p.cond.L.Lock() for len(p.workerStack) == 0 { p.cond.Wait() } p.cond.L.Unlock() workerIndex := p.popWorker() p.workers[workerIndex].taskQueue <- t if len(p.taskQueue) > (p.maxWorkers-p.minWorkers)/2+p.minWorkers && len(p.workerStack) < p.maxWorkers { worker := newWorker() p.workers = append(p.workers, worker) p.workerStack = append(p.workerStack, len(p.workers)-1) worker.start(p, len(p.workers)-1) } else if len(p.taskQueue) < p.minWorkers && len(p.workerStack) > p.minWorkers { p.workers = p.workers[:len(p.workers)-1] p.workerStack = p.workerStack[:len(p.workerStack)-1] } } }

worker.go package gopool

import ( "context" "fmt" )

// worker represents a worker in the pool. type worker struct { taskQueue chan task }

func newWorker() *worker { return &worker{ taskQueue: make(chan task, 1), } }

func (w *worker) start(pool *goPool, workerIndex int) { go func() { for t := range w.taskQueue { if t != nil { var result interface{} var err error

            if pool.timeout > 0 {
                // Create a context with timeout
                ctx, cancel := context.WithTimeout(context.Background(), pool.timeout)
                defer cancel()

                // Create a channel to receive the result of the task
                done := make(chan struct{})

                // Run the task in a separate goroutine
                go func() {
                    result, err = t()
                    close(done)
                }()

                // Wait for the task to finish or for the context to timeout
                select {
                case <-done:
                    // The task finished successfully
                    if err != nil && pool.errorCallback != nil {
                        pool.errorCallback(err)
                    } else if pool.resultCallback != nil {
                        pool.resultCallback(result)
                    }
                case <-ctx.Done():
                    // The context timed out, the task took too long
                    if pool.errorCallback != nil {
                        pool.errorCallback(fmt.Errorf("Task timed out"))
                    }
                }
            } else {
                // If timeout is not set or is zero, just run the task
                result, err = t()
                if err != nil && pool.errorCallback != nil {
                    pool.errorCallback(err)
                } else if pool.resultCallback != nil {
                    pool.resultCallback(result)
                }
            }
        }
        pool.pushWorker(workerIndex)
    }
}()

}

option.go package gopool

import ( "sync" "time" )

// Option represents an option for the pool. type Option func(*goPool)

// WithLock sets the lock for the pool. func WithLock(lock sync.Locker) Option { return func(p *goPool) { p.lock = lock p.cond = sync.NewCond(p.lock) } }

// WithMinWorkers sets the minimum number of workers for the pool. func WithMinWorkers(minWorkers int) Option { return func(p *goPool) { p.minWorkers = minWorkers } }

// WithTimeout sets the timeout for the pool. func WithTimeout(timeout time.Duration) Option { return func(p *goPool) { p.timeout = timeout } }

// WithResultCallback sets the result callback for the pool. func WithResultCallback(callback func(interface{})) Option { return func(p *goPool) { p.resultCallback = callback } }

// WithErrorCallback sets the error callback for the pool. func WithErrorCallback(callback func(error)) Option { return func(p *goPool) { p.errorCallback = callback } }

接下来我想增加 task retry 功能，我依旧希望通过 Option 方式来配置，让用户自定义重试次数。当然你要注意当用户不配置这个值的时候，默认行为是不重试

avatar DevChat

首先，我们需要在 goPool 结构体中添加一个新的字段 retryCount 来存储重试次数。然后，我们需要创建一个新的 Option 函数 WithRetryCount 来设置这个值。如果用户没有配置这个值，那么 retryCount 应该默认为 0，表示不进行重试。

在 worker 的 start 方法中，我们需要修改任务执行的逻辑。如果任务执行失败并且 retryCount 大于 0，那么我们应该重试执行任务，直到任务执行成功或者达到最大重试次数。

下面是修改后的代码：

package gopool

import (
    "sync"
    "time"
)

type goPool struct {
    // ...
    retryCount int
    // ...
}

func NewGoPool(maxWorkers int, opts ...Option) *goPool {
    pool := &goPool{
        // ...
        retryCount: 0, // Set retryCount to 0 by default
        // ...
    }
    // ...
    return pool
}
package gopool

import (
    "sync"
    "time"
)

func WithRetryCount(retryCount int) Option {
    return func(p *goPool) {
        p.retryCount = retryCount
    }
}
package gopool

import (
    "context"
    "fmt"
)

func (w *worker) start(pool *goPool, workerIndex int) {
    go func() {
        for t := range w.taskQueue {
            if t != nil {
                var result interface{}
                var err error

                for i := 0; i <= pool.retryCount; i++ {
                    // ...
                    if err != nil && pool.errorCallback != nil {
                        if i < pool.retryCount {
                            continue // Retry the task if it failed and retryCount > 0
                        }
                        pool.errorCallback(err)
                    } else if pool.resultCallback != nil {
                        pool.resultCallback(result)
                    }
                    break // Break the loop if the task succeeded or reached the maximum retry count
                }
                // ...
            }
            pool.pushWorker(workerIndex)
        }
    }()
}

avatar User

worker.go func (w *worker) start(pool *goPool, workerIndex int) { go func() { for t := range w.taskQueue { if t != nil { var result interface{} var err error 
            if pool.timeout > 0 {
                // Create a context with timeout
                ctx, cancel := context.WithTimeout(context.Background(), pool.timeout)
                defer cancel()

                // Create a channel to receive the result of the task
                done := make(chan struct{})

                // Run the task in a separate goroutine
                go func() {
                    result, err = t()
                    close(done)
                }()

                // Wait for the task to finish or for the context to timeout
                select {
                case <-done:
                    // The task finished successfully
                    if err != nil && pool.errorCallback != nil {
                        pool.errorCallback(err)
                    } else if pool.resultCallback != nil {
                        pool.resultCallback(result)
                    }
                case <-ctx.Done():
                    // The context timed out, the task took too long
                    if pool.errorCallback != nil {
                        pool.errorCallback(fmt.Errorf("Task timed out"))
                    }
                }
            } else {
                // If timeout is not set or is zero, just run the task
                result, err = t()
                if err != nil && pool.errorCallback != nil {
                    pool.errorCallback(err)
                } else if pool.resultCallback != nil {
                    pool.resultCallback(result)
                }
            }
        }
        pool.pushWorker(workerIndex)
    }
}()

}

当前 start 函数是这样的，我觉得已经太复杂导致新增 retry 功能有点困难了。你能不能重构当前函数，通过拆分子函数等方式让这个函数变得更加可维护，同时正确地讲 retry 逻辑加进去。

avatar DevChat

首先，我们可以将任务执行和结果处理的逻辑抽取出来，形成两个新的方法：executeTask 和 handleResult。这样可以使 start 方法更加简洁，也更容易理解。

然后，我们在 executeTask 方法中添加重试逻辑。如果任务执行失败并且 retryCount 大于 0，那么我们应该重试执行任务，直到任务执行成功或者达到最大重试次数。

下面是修改后的代码：

package gopool

import (
    "context"
    "fmt"
)

func (w *worker) executeTask(t task, pool *goPool) (result interface{}, err error) {
    for i := 0; i <= pool.retryCount; i++ {
        if pool.timeout > 0 {
            // Create a context with timeout
            ctx, cancel := context.WithTimeout(context.Background(), pool.timeout)
            defer cancel()

            // Create a channel to receive the result of the task
            done := make(chan struct{})

            // Run the task in a separate goroutine
            go func() {
                result, err = t()
                close(done)
            }()

            // Wait for the task to finish or for the context to timeout
            select {
            case <-done:
                // The task finished successfully
                if err == nil || i == pool.retryCount {
                    return result, err
                }
            case <-ctx.Done():
                // The context timed out, the task took too long
                err = fmt.Errorf("Task timed out")
                if i == pool.retryCount {
                    return nil, err
                }
            }
        } else {
            // If timeout is not set or is zero, just run the task
            result, err = t()
            if err == nil || i == pool.retryCount {
                return result, err
            }
        }
    }
    return
}

func (w *worker) handleResult(result interface{}, err error, pool *goPool) {
    if err != nil && pool.errorCallback != nil {
        pool.errorCallback(err)
    } else if pool.resultCallback != nil {
        pool.resultCallback(result)
    }
}

func (w *worker) start(pool *goPool, workerIndex int) {
    go func() {
        for t := range w.taskQueue {
            if t != nil {
                result, err := w.executeTask(t, pool)
                w.handleResult(result, err, pool)
            }
            pool.pushWorker(workerIndex)
        }
    }()
}

avatar User

worker.go func (w *worker) executeTask(t task, pool *goPool) (result interface{}, err error) { for i := 0; i <= pool.retryCount; i++ { if pool.timeout > 0 { // Create a context with timeout ctx, cancel := context.WithTimeout(context.Background(), pool.timeout) defer cancel() 
        // Create a channel to receive the result of the task
        done := make(chan struct{})

        // Run the task in a separate goroutine
        go func() {
            result, err = t()
            close(done)
        }()

        // Wait for the task to finish or for the context to timeout
        select {
        case <-done:
            // The task finished successfully
            if err == nil || i == pool.retryCount {
                return result, err
            }
        case <-ctx.Done():
            // The context timed out, the task took too long
            err = fmt.Errorf("Task timed out")
            if i == pool.retryCount {
                return nil, err
            }
        }
    } else {
        // If timeout is not set or is zero, just run the task
        result, err = t()
        if err == nil || i == pool.retryCount {
            return result, err
        }
    }
}
return

}

请继续拆分这个函数，进一步提升可读性

avatar DevChat

我们可以将 executeTask 函数进一步拆分为两个子函数：executeTaskWithTimeout 和 executeTaskWithoutTimeout。这样可以使 executeTask 函数更加简洁，也更容易理解。

下面是修改后的代码：

package gopool

import (
    "context"
    "fmt"
)

func (w *worker) executeTaskWithTimeout(t task, pool *goPool) (result interface{}, err error) {
    // Create a context with timeout
    ctx, cancel := context.WithTimeout(context.Background(), pool.timeout)
    defer cancel()

    // Create a channel to receive the result of the task
    done := make(chan struct{})

    // Run the task in a separate goroutine
    go func() {
        result, err = t()
        close(done)
    }()

    // Wait for the task to finish or for the context to timeout
    select {
    case <-done:
        // The task finished successfully
        return result, err
    case <-ctx.Done():
        // The context timed out, the task took too long
        return nil, fmt.Errorf("Task timed out")
    }
}

func (w *worker) executeTaskWithoutTimeout(t task, pool *goPool) (result interface{}, err error) {
    // If timeout is not set or is zero, just run the task
    return t()
}

func (w *worker) executeTask(t task, pool *goPool) (result interface{}, err error) {
    for i := 0; i <= pool.retryCount; i++ {
        if pool.timeout > 0 {
            result, err = w.executeTaskWithTimeout(t, pool)
        } else {
            result, err = w.executeTaskWithoutTimeout(t, pool)
        }
        if err == nil || i == pool.retryCount {
            return result, err
        }
    }
    return
}

func (w *worker) handleResult(result interface{}, err error, pool *goPool) {
    if err != nil && pool.errorCallback != nil {
        pool.errorCallback(err)
    } else if pool.resultCallback != nil {
        pool.resultCallback(result)
    }
}

func (w *worker) start(pool *goPool, workerIndex int) {
    go func() {
        for t := range w.taskQueue {
            if t != nil {
                result, err := w.executeTask(t, pool)
                w.handleResult(result, err, pool)
            }
            pool.pushWorker(workerIndex)
        }
    }()
}

avatar User

gopool.go func (p *goPool) dispatch() { for t := range p.taskQueue { p.cond.L.Lock() for len(p.workerStack) == 0 { p.cond.Wait() } p.cond.L.Unlock() workerIndex := p.popWorker() p.workers[workerIndex].taskQueue <- t if len(p.taskQueue) > (p.maxWorkers-p.minWorkers)/2+p.minWorkers && len(p.workerStack) < p.maxWorkers { worker := newWorker() p.workers = append(p.workers, worker) p.workerStack = append(p.workerStack, len(p.workers)-1) worker.start(p, len(p.workers)-1) } else if len(p.taskQueue) < p.minWorkers && len(p.workerStack) > p.minWorkers { p.workers = p.workers[:len(p.workers)-1] p.workerStack = p.workerStack[:len(p.workerStack)-1] } } }

这段代码先使用了Worker，分发了task，然后再判断扩缩容是否要执行，看起来逻辑反了。另外扩容我希望是不超过上限的时候成倍扩容，直到到达最大值；缩容是不小于最小值的时候减半缩容，直到缩到最小值；而不是每次扩缩1；是不是应该在一个单独的 goroutine 里定时检测并执行扩缩容操作，然后通过 Cond 和 dispatch 同步更合理

avatar DevChat

你的建议很好，我们可以将扩缩容的逻辑放在一个单独的 goroutine 中，并通过定时器定时检查是否需要进行扩缩容。这样可以使 dispatch 函数更加简洁，也更容易理解。

我们可以创建一个新的方法 adjustWorkers 来处理扩缩容的逻辑。在 NewGoPool 函数中，我们启动这个 goroutine。

下面是修改后的代码：

package gopool

import (
    "sync"
    "time"
)

type goPool struct {
    // ...
    adjustInterval time.Duration
    // ...
}

func NewGoPool(maxWorkers int, opts ...Option) *goPool {
    pool := &goPool{
        // ...
        adjustInterval: time.Second, // Set adjustInterval to 1 second by default
        // ...
    }
    // ...
    go pool.adjustWorkers()
    return pool
}

func (p *goPool) adjustWorkers() {
    ticker := time.NewTicker(p.adjustInterval)
    defer ticker.Stop()

    for range ticker.C {
        p.cond.L.Lock()
        if len(p.taskQueue) > (p.maxWorkers-p.minWorkers)/2+p.minWorkers && len(p.workerStack) < p.maxWorkers {
            // Double the number of workers until it reaches the maximum
            newWorkers := min(len(p.workerStack)*2, p.maxWorkers) - len(p.workerStack)
            for i := 0; i < newWorkers; i++ {
                worker := newWorker()
                p.workers = append(p.workers, worker)
                p.workerStack = append(p.workerStack, len(p.workers)-1)
                worker.start(p, len(p.workers)-1)
            }
        } else if len(p.taskQueue) < p.minWorkers && len(p.workerStack) > p.minWorkers {
            // Halve the number of workers until it reaches the minimum
            removeWorkers := max((len(p.workerStack)-p.minWorkers)/2, p.minWorkers)
            p.workers = p.workers[:len(p.workers)-removeWorkers]
            p.workerStack = p.workerStack[:len(p.workerStack)-removeWorkers]
        }
        p.cond.L.Unlock()
    }
}

func (p *goPool) dispatch() {
    for t := range p.taskQueue {
        p.cond.L.Lock()
        for len(p.workerStack) == 0 {
            p.cond.Wait()
        }
        p.cond.L.Unlock()
        workerIndex := p.popWorker()
        p.workers[workerIndex].taskQueue <- t
    }
}

这里，min 和 max 是两个辅助函数，用于获取两个整数中的最小值和最大值。你可能需要在你的代码中实现这两个函数。