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How to solve the problem of task dependency and task scheduling graph of concurrent tasks in Go language?
How to solve the problem of task dependency and task scheduling graph of concurrent tasks in Go language?
How to solve the problem of task dependency and task scheduling graph of concurrent tasks in Go language?
In the Go language, executing tasks in a concurrent manner can significantly improve the performance and efficiency of the program. However, when there are dependencies between tasks and need to be executed in a specific order, we need to solve the problem of task dependencies and task scheduling graphs in concurrent tasks. This article will introduce how to use Go language to solve these problems and give specific code examples.
First, we need to define the structure of the task. Each task should contain a unique identifier, the task's logic code, and other tasks on which the task depends. For example:
type Task struct {
ID int
Logic func()
Dependency []*Task
}Next, we need to create a function that performs a task and its dependent tasks. This function needs to be called recursively according to the dependencies of the task to ensure that all dependent tasks have been executed before executing the current task. The sample code is as follows:
func executeTask(task *Task, tasksCompleted *sync.Map) {
// 检查任务依赖是否已经完成
for _, dependency := range task.Dependency {
dependencyID := dependency.ID
_, dependencyCompleted := tasksCompleted.Load(dependencyID)
if !dependencyCompleted {
// 等待依赖的任务完成
executeTask(dependency, tasksCompleted)
}
}
// 执行当前任务
task.Logic()
// 任务完成标记设为true
tasksCompleted.Store(task.ID, true)
}Next, we need to build the entire task scheduling graph and execute all tasks. We can use a map to store all tasks, and use sync.Map to mark whether the task has been completed. The sample code is as follows:
func main() {
// 创建所有任务和它们的依赖关系
task1 := &Task{
ID: 1,
Logic: func() { fmt.Println("执行任务1") },
}
task2 := &Task{
ID: 2,
Logic: func() { fmt.Println("执行任务2") },
Dependency: []*Task{task1},
}
task3 := &Task{
ID: 3,
Logic: func() { fmt.Println("执行任务3") },
Dependency: []*Task{task1},
}
task4 := &Task{
ID: 4,
Logic: func() { fmt.Println("执行任务4") },
Dependency: []*Task{task2, task3},
}
// 构建任务调度图
tasks := map[int]*Task{
1: task1,
2: task2,
3: task3,
4: task4,
}
// 执行所有任务
tasksCompleted := &sync.Map{}
for _, task := range tasks {
go executeTask(task, tasksCompleted)
}
// 等待所有任务完成
time.Sleep(time.Second)
}Through the above code examples, we have successfully solved the task dependency and task scheduling graph problems of concurrent tasks in the Go language. In practical applications, appropriate modifications and extensions can be made as needed. At the same time, we can also see that by reasonably arranging task dependencies and concurrent execution, the performance and efficiency of the program can be significantly improved.
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