The Case For A Go Worker Pool

Datetime:2016-08-23 05:14:23          Topic: Golang           Share

When it comes to the question of what the right constructs for concurrency that a langauge should expose to developers, I’m a true believer that Go’s channels and goroutines are as good as it gets. They strike a nice balance between power and flexibility, while simultaneously avoiding the perpensity for deadlocks that you’d see in a pthread model, the maintenance hell introduced by callbacks, or the incredible conceptual overhead of promises.

However, there’s one blindspot in Go’s concurrency APIs that I find myself implementing in new Go programs time and time again: the worker pool (or otherwise known as a thread pool ).

Worker pools are a model in which a fixed number of m workers (implemented in Go with goroutines) work there way through n tasks in a work queue (implemented in Go with a channel). Work stays in a queue until a worker finishes up its current task and pulls a new one off.

Traditionally, thread pools have been useful to ammortizing the costs of spinning up new threads. Goroutines are lightweight enough that that’s not a problem in Go, but a worker pool can still be useful in controlling the number of concurrently running tasks, especially when those tasks are accessing resources that can easily be saturated like I/O or remote APIs.

A visualization of a worker pool: few workers working many work items.

Implementing a worker pool in Go is by no means a tremendously difficult feat. In fact, Go By Example describes one implementation that’s only a few dozen lines of code:

package main

import (
	"fmt"
	"time"
)

func worker(id int, jobs <-chan int, results chan<- int) {
	for j := range jobs {
		fmt.Println("worker", id, "processing job", j)
		time.Sleep(time.Second)
		results <- j * 2
	}
}

func main() {
	jobs := make(chan int, 100)
	results := make(chan int, 100)

	for w := 1; w <= 3; w++ {
		go worker(w, jobs, results)
	}

	for j := 1; j <= 9; j++ {
		jobs <- j
	}
	close(jobs)

	for a := 1; a <= 9; a++ {
		<-results
	}
}

In this example, 3 workers are started and 9 work items are in put onto a job channel. Workers have a work loop with a time.Sleep so that each ends up working 3 jobs. close is used on the channel after all the work’s been put onto it, which signals to all 3 workers that they can exit their work loop by dropping them out of their range .

This implementation is meant to show the classical reason that a worker pool doesn’t need to be in Go’s standard library: the language’s concurrency primitives are already so powerful that implementing one is trivial to the point where it doesn’t even need to put into a common utility package.

So if primitives alone already present such an elegant solution, why would anyone ever argue for introducing another unneeded layer of abstraction and complexity?

Well, there’s a simplification in the above example that you may have spotted already. While it’s perfectly fine if the workload for our asynchronous tasks is going to be to multiply an integer by two, it doesn’t stand up quite as well when work items may or may not have to return an error. And in a real world system, you’re always going to have to return an error.

But we can fix it! To get some error handling in the program, we introduce a new channel called errors . Workers will inject an error into it if they encounter one, and otherwise put a value in results as usual.

errors := make(chan error, 100)

...

// check errors before using results
select {
case err := <-errors:
    fmt.Println("finished with error:", err.Error())
default:
}

We need to make one other small change too. Because some threads may now output over the errors channel rather than results , we can no longer use results to know when all work is complete. Instead we introduce a sync.WaitGroup that workers signal when they finish work regardless of the result.

Here’s a complete version of the same program with those changes:

package main

import (
	"fmt"
	"sync"
	"time"
)

func worker(id int, wg *sync.WaitGroup, jobs <-chan int, results chan<- int, errors chan<- error) {
	for j := range jobs {
		fmt.Println("worker", id, "processing job", j)
		time.Sleep(time.Second)

		if j%2 == 0 {
			results <- j * 2
		} else {
			errors <- fmt.Errorf("error on job %v", j)
		}
		wg.Done()
	}
}

func main() {
	jobs := make(chan int, 100)
	results := make(chan int, 100)
	errors := make(chan error, 100)

	var wg sync.WaitGroup
	for w := 1; w <= 3; w++ {
		go worker(w, &wg, jobs, results, errors)
	}

	for j := 1; j <= 9; j++ {
		jobs <- j
		wg.Add(1)
	}
	close(jobs)

	wg.Wait()

	select {
	case err := <-errors:
		fmt.Println("finished with error:", err.Error())
	default:
	}
}

As you can see, it’s fine code, but not quite as elegant as the original.

In our example above, we’ve accidentally introduced a fairly insidious problem in that if our error channel is smaller than the number of work items that will produce an error, then workers will block as they try to put an error into it. This will cause a deadlock.

We can simulate this easily by changing the size of our error channel to 1:

errors := make(chan error, 1)

And now when the program is run, the runtime detects a deadlock:

$ go run worker_pool_err.go
worker 3 processing job 1
worker 1 processing job 2
worker 2 processing job 3
worker 2 processing job 5
worker 1 processing job 4
worker 1 processing job 6
worker 1 processing job 7
fatal error: all goroutines are asleep - deadlock!

It’s quite possible to address that problem as well, but it helps to show that developing a useful and bug-free worker pool in Go isn’t quite as simple as it’s often made out to be.

Implementing A Robust Worker Pool

Putting together a good worker pool abstraction is pretty simple, and can even be done reliably with a minimal amount of code. Here’s the worker pool implementation that builds this website for example:

import (
	"sync"
)

// Pool is a worker group that runs a number of tasks at a
// configured concurrency.
type Pool struct {
	Tasks []*Task

	concurrency int
	tasksChan   chan *Task
	wg          sync.WaitGroup
}

// NewPool initializes a new pool with the given tasks and
// at the given concurrency.
func NewPool(tasks []*Task, concurrency int) *Pool {
	return &Pool{
		Tasks:       tasks,
		concurrency: concurrency,
		tasksChan:   make(chan *Task),
	}
}

// Run runs all work within the pool and blocks until it's
// finished.
func (p *Pool) Run() {
	for i := 0; i < p.concurrency; i++ {
		go p.work()
	}

	p.wg.Add(len(p.Tasks))
	for _, task := range p.Tasks {
		p.tasksChan <- task
	}

	// all workers return
	close(p.tasksChan)

	p.wg.Wait()
}

// The work loop for any single goroutine.
func (p *Pool) work() {
	for task := range p.tasksChan {
		task.Run(&p.wg)
	}
}

And also simple implementation for the Task that goes with it. Note that we store errors on the task itself to avoid the problem of a saturated Go channel above:

// Task encapsulates a work item that should go in a work
// pool.
type Task struct {
	// Err holds an error that occurred during a task. Its
	// result is only meaningful after Run has been called
	// for the pool that holds it.
	Err error

	f func() error
}

// NewTask initializes a new task based on a given work
// function.
func NewTask(f func() error) *Task {
	return &Task{f: f}
}

// Run runs a Task and does appropriate accounting via a
// given sync.WorkGroup.
func (t *Task) Run(wg *sync.WaitGroup) {
	t.Err = t.f()
	wg.Done()
}

And here’s how to run and performing error handling on it:

tasks := []*Task{
    NewTask(func() error { return nil }),
    NewTask(func() error { return nil }),
    NewTask(func() error { return nil }),
}

p := pool.NewPool(tasks, conf.Concurrency)
p.Run()

var numErrors int
for _, task := range p.Tasks {
    if task.Err != nil {
        log.Error(task.Err)
        numErrors++
    }
    if numErrors >= 10 {
        log.Error("Too many errors.")
        break
    }
}

Even though putting together a robust worker pool isn’t overly problematic, right now it’s something that every project needs to handle on its own. The size of the pattern is also almost a little too simple for an external package, as evidenced by the dozens (if not hundreds) of Go worker pool implementations that you can find on GitHub. Coming to community consensus at this point on a single preferred third party package would be nigh impossible.

It seems to me that this is one easy place that the Go maintainers team could help guide developers and prevent a wild amount fracturing by introducing a One True Path. I’d love to see a worker pool in core.

The Case For A Go Worker Poolwas published on August 19, 2016 from . Find me on Twitter at @brandur .

Find an error? Please consider sending a pull request .





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