go-taskflow is a general-purpose task-parallel programming framework for Go, inspired by taskflow-cpp. It leverages Go's native capabilities and simplicity, making it ideal for managing complex dependencies in concurrent tasks.

• High Extensibility: Easily extend the framework to adapt to various specific use cases.

• Native Go Concurrency Model: Leverages Go's goroutines for efficient concurrent task execution.

• User-Friendly Programming Interface: Simplifies complex task dependency management in Go.

• Static, Subflow, Conditional, and Cyclic Tasking: Define static tasks, condition nodes, nested subflows, and cyclic flows to enhance modularity and programmability.

  Static	Subflow	Condition	Cyclic

• Priority Task Scheduling: Assign task priorities to ensure higher-priority tasks are executed first.

• Built-in Visualization and Profiling Tools: Generate visual representations of tasks and profile task execution performance using integrated tools, simplifying debugging and optimization.

• Data Pipelines: Orchestrate data processing stages with complex dependencies.
• AI Agent Workflow Automation: Define and execute AI agent workflows with clear sequences and dependency structures.
• Parallel Graph Tasking: Execute graph-based tasks concurrently to maximize CPU utilization.

Import the latest version of go-taskflow using:

go get -u github.com/noneback/go-taskflow

DeepWiki Page

Below is an example of using go-taskflow to implement a parallel merge sort:

package main

import (
    "fmt"
    "log"
    "math/rand"
    "os"
    "slices"
    "strconv"
    "sync"

    gtf "github.com/noneback/go-taskflow"
)

// mergeInto merges a sorted source array into a sorted destination array.
func mergeInto(dest, src []int) []int {
    size := len(dest) + len(src)
    tmp := make([]int, 0, size)
    i, j := 0, 0
    for i < len(dest) && j < len(src) {
        if dest[i] < src[j] {
            tmp = append(tmp, dest[i])
            i++
        } else {
            tmp = append(tmp, src[j])
            j++
        }
    }

    if i < len(dest) {
        tmp = append(tmp, dest[i:]...)
    } else {
        tmp = append(tmp, src[j:]...)
    }

    return tmp
}

func main() {
    size := 100
    randomArr := make([][]int, 10)
    sortedArr := make([]int, 0, 10*size)
    mutex := &sync.Mutex{}

    for i := 0; i < 10; i++ {
        for j := 0; j < size; j++ {
            randomArr[i] = append(randomArr[i], rand.Int())
        }
    }

    sortTasks := make([]*gtf.Task, 10)
    tf := gtf.NewTaskFlow("merge sort")
    done := tf.NewTask("Done", func() {
        if !slices.IsSorted(sortedArr) {
            log.Fatal("Sorting failed")
        }
        fmt.Println("Sorted successfully")
        fmt.Println(sortedArr[:1000])
    })

    for i := 0; i < 10; i++ {
        sortTasks[i] = tf.NewTask("sort_"+strconv.Itoa(i), func() {
            arr := randomArr[i]
            slices.Sort(arr)
            mutex.Lock()
            defer mutex.Unlock()
            sortedArr = mergeInto(sortedArr, arr)
        })
    }
    done.Succeed(sortTasks...)

    executor := gtf.NewExecutor(1000)

    executor.Run(tf).Wait()

    if err := tf.Dump(os.Stdout); err != nil {
        log.Fatal("Error dumping taskflow:", err)
    }

    if err := executor.Profile(os.Stdout); err != nil {
        log.Fatal("Error profiling taskflow:", err)
    }
}

For more examples, visit the examples directory.

The following benchmark provides a rough estimate of performance. Note that most realistic workloads are I/O-bound, and their performance cannot be accurately reflected by these results. For CPU-intensive tasks, consider using taskflow-cpp.

$ go test -bench=. -benchmem
goos: linux
goarch: amd64
pkg: github.com/noneback/go-taskflow/benchmark
cpu: Intel(R) Xeon(R) Platinum 8269CY CPU @ 2.50GHz
BenchmarkC32-4    	   23282	     51891 ns/op	    7295 B/op	     227 allocs/op
BenchmarkS32-4    	    7047	    160199 ns/op	    6907 B/op	     255 allocs/op
BenchmarkC6-4     	   66397	     18289 ns/op	    1296 B/op	      47 allocs/op
BenchmarkC8x8-4   	    7946	    143474 ns/op	   16914 B/op	     504 allocs/op
PASS
ok  	github.com/noneback/go-taskflow/benchmark	5.606s

Conditional nodes in go-taskflow behave similarly to those in taskflow-cpp. They participate in both conditional control and looping. To avoid common pitfalls, refer to the Conditional Tasking documentation.

In Go, errors are values, and it is the user's responsibility to handle them appropriately. Only unrecovered panic events are managed by the framework. If a panic occurs, the entire parent graph is canceled, leaving the remaining tasks incomplete. This behavior may evolve in the future. If you have suggestions, feel free to share them.

To prevent interruptions caused by panic, you can handle them manually when registering tasks:

tf.NewTask("not interrupt", func() {
    defer func() {
        if r := recover(); r != nil {
            // Handle the panic.
        }
    }()
    // User-defined logic.
})

To generate a visual representation of a taskflow, use the Dump method:

if err := tf.Dump(os.Stdout); err != nil {
    log.Fatal(err)
}

The Dump method generates raw strings in DOT format. Use the dot tool to create a graph SVG.

To profile a taskflow, use the Profile method:

if err := executor.Profile(os.Stdout); err != nil {
    log.Fatal(err)
}

The Profile method generates raw strings in flamegraph format. Use the flamegraph tool to create a flamegraph SVG.