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Concurrency in Golang And WorkerPool [Part 2]

by MD Ahad HasanDecember 8th, 2020

Too Long; Didn't Read

Concurrency in Golang And WorkerPool [Part 2] The first part of this article explored how we can build a workerpool to optimize the performance of the concurrency structs of golang. In this article, we will build a robust solution according to the learning from the first part so that we can use this solution in any application. We make a generic workerpool package that can handle tasks with workers based on the desired concurrency. In themethod, we range over thefor incoming tasks to process inside a goroutine.

Company Mentioned

featured image - Concurrency in Golang And WorkerPool [Part 2]

Project Link: https://github.com/Joker666/goworkerpool

Concurrency in Golang and WorkerPool: [Part 1]

Goroutines and channels are powerful language structs that make golang a powerful concurrent language. In the first part of the article, we explored, how we can build a workerpool to optimize the performance of the concurrency structs of golang namely limiting the resource utilization. But it was a simple example to demonstrate how we can go about it.

Here, we will build a robust solution according to the learning from the first part so that we can use this solution in any application. There are some solutions on the internet with complex architecture using dispatchers and all. In reality, we do not need it, we can do everything using one shared channel. Let's see how we can build that here

Architecture

Here we make a generic workerpool package that can handle tasks with workers based on the desired concurrency. Let's see the directory structure.

workerpool
├── pool.go
├── task.go
└── worker.go

The

workerpool

directory is in the root folder of the project. Let's go over what

Task

is.

Task

is a single unit of work that needs to be processed.

Worker

is a simple worker function that handles running the task. And

Pool

actually handles the creation and managing the workers.

Implementation

Let's code out

Task

first.

// workerpool/task.go

package workerpool

import (
	"fmt"
)

type Task struct {
	Err  error
	Data interface{}
	f    func(interface{}) error
}

func NewTask(f func(interface{}) error, data interface{}) *Task {
	return &Task{f: f, Data: data}
}

func process(workerID int, task *Task) {
	fmt.Printf("Worker %d processes task %v\n", workerID, task.Data)
	task.Err = task.f(task.Data)
}

Task

is a simple stuct that holds everything needed to process a task. We pass it the

Data

and the function

that is supposed to be executed. And

process

function executes the task. The function

takes the Data as a parameter for processing. And we also store the error that is returned. Let's see how

Worker

processes these tasks.

// workerpool/worker.go

package workerpool

import (
	"fmt"
	"sync"
)

// Worker handles all the work
type Worker struct {
	ID       int
	taskChan chan *Task
}

// NewWorker returns new instance of worker
func NewWorker(channel chan *Task, ID int) *Worker {
	return &Worker{
		ID:       ID,
		taskChan: channel,
	}
}

// Start starts the worker
func (wr *Worker) Start(wg *sync.WaitGroup) {
	fmt.Printf("Starting worker %d\n", wr.ID)

	wg.Add(1)
	go func() {
		defer wg.Done()
		for task := range wr.taskChan {
			process(wr.ID, task)
		}
	}()
}

We have a nice little struct

Worker

here. It takes a worker ID and a channel where tasks should be written to. In the

Start

method, we range over the

taskChan

for incoming tasks to process inside a goroutine. We can imagine, multiple of this workers would be running concurrently and handling tasks.

Worker Pool

We have implemented the

Task

and

Worker

to handle tasks but there's a missing piece here. Who's gonna spawn up these workers and send them tasks. The answer: Worker Pool

// workerpoo/pool.go

package workerpool

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

// Pool is the worker pool
type Pool struct {
	Tasks   []*Task

	concurrency   int
	collector     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,
		collector:   make(chan *Task, 1000),
	}
}

// Run runs all work within the pool and blocks until it's
// finished.
func (p *Pool) Run() {
	for i := 1; i <= p.concurrency; i++ {
		worker := NewWorker(p.collector, i)
		worker.Start(&p.wg)
	}

	for i := range p.Tasks {
		p.collector <- p.Tasks[i]
	}
	close(p.collector)

	p.wg.Wait()
}

The worker pool holds all the tasks that it needs to process and takes a concurrency number as input to spawn that many numbers of goroutines to complete the tasks concurrently. It has a buffered channel

collector

which is shared among all the workers.

So, when we run this worker pool, we spawn the desired number of workers that take the shared channel

collector

. Next, we range over the tasks and write it to the

collector

channel. We synchronize everything with waitgoup. Now that we have a nice solution, let's test it out

// main.go

package main

import (
	"fmt"
	"time"

	"github.com/Joker666/goworkerpool/workerpool"
)

func main() {
	var allTask []*workerpool.Task
	for i := 1; i <= 100; i++ {
		task := workerpool.NewTask(func(data interface{}) error {
			taskID := data.(int)
			time.Sleep(100 * time.Millisecond)
			fmt.Printf("Task %d processed\n", taskID)
			return nil
		}, i)
		allTask = append(allTask, task)
	}

	pool := workerpool.NewPool(allTask, 5)
	pool.Run()
}

Here, we create 100 tasks and use 5 as concurrency to process them. And see the output

Worker 3 processes task 98
Task 92 processed
Worker 2 processes task 99
Task 98 processed
Worker 5 processes task 100
Task 99 processed
Task 100 processed
Took ===============> 2.0056295s

It takes us two seconds to process 100 tasks, if we increase the concurrency to 10, we would see that it would take just about one second to process all the tasks.

We have built a robust solution for worker pool that can handle concurrency, store errors to task, send them data to process. This package is generic and not coupled to a specific implementation. We can use this to tackle large problems as well

Extending Further: Handling Tasks In Background

We can actually extend our solution further, so that, the workers keep waiting for new tasks in the background and we can send them new tasks to process. For that, Task stays as is, but we would need to modify

Worker

a bit. Let's see the changes

// workerpool/worker.go

// Worker handles all the work
type Worker struct {
	ID       int
	taskChan chan *Task
	quit     chan bool
}

// NewWorker returns new instance of worker
func NewWorker(channel chan *Task, ID int) *Worker {
	return &Worker{
		ID:       ID,
		taskChan: channel,
		quit:     make(chan bool),
	}
}

....

// StartBackground starts the worker in background waiting
func (wr *Worker) StartBackground() {
	fmt.Printf("Starting worker %d\n", wr.ID)

	for {
		select {
		case task := <-wr.taskChan:
			process(wr.ID, task)
		case <-wr.quit:
			return
		}
	}
}

// Stop quits the worker
func (wr *Worker) Stop() {
	fmt.Printf("Closing worker %d\n", wr.ID)
	go func() {
		wr.quit <- true
	}()
}

We add a

quit

channel to

Worker

struct and two new methods.

StartBackgorund

would start an infinite for loop with

select

to read from

taskChan

and process the task. If it reads from

quit

channel it, returns from the function.

Stop

method writes to the

quit

channel.

Armed with these two new methods let's add some new things to

Pool

// workerpool/pool.go

type Pool struct {
	Tasks   []*Task
	Workers []*Worker

	concurrency   int
	collector     chan *Task
	runBackground chan bool
	wg            sync.WaitGroup
}

// AddTask adds a task to the pool
func (p *Pool) AddTask(task *Task) {
	p.collector <- task
}

// RunBackground runs the pool in background
func (p *Pool) RunBackground() {
	go func() {
		for {
			fmt.Print("⌛ Waiting for tasks to come in ...\n")
			time.Sleep(10 * time.Second)
		}
	}()

	for i := 1; i <= p.concurrency; i++ {
		worker := NewWorker(p.collector, i)
		p.Workers = append(p.Workers, worker)
		go worker.StartBackground()
	}

	for i := range p.Tasks {
		p.collector <- p.Tasks[i]
	}

	p.runBackground = make(chan bool)
	<-p.runBackground
}

// Stop stops background workers
func (p *Pool) Stop() {
	for i := range p.Workers {
		p.Workers[i].Stop()
	}
	p.runBackground <- true
}

The

Pool

struct now holds the workers and has a

runBackground

channel that can help it to stay alive. We have 3 new methods,

AddTask

can add to

collector

channel anytime now.

The

RunBackground

method spawns a goroutine that runs infinitely to keep the Pool alive along with

runBackground

channel. This is a technique to run the execution forever to read from an empty channel. We spin up the workers in goroutines. Stop method, stops the workers, it writes to

runBackground

to finish the

RunBackground

method. Let's see how it works now.

If we had a real world scenario, this would be running alongside a HTTP server and consuming tasks. We would replicate similar behavior with an infinite loop and it would stop if it matches a certain condition

// main.go

...

pool := workerpool.NewPool(allTask, 5)
go func() {
	for {
		taskID := rand.Intn(100) + 20

		if taskID%7 == 0 {
			pool.Stop()
		}

		time.Sleep(time.Duration(rand.Intn(5)) * time.Second)
		task := workerpool.NewTask(func(data interface{}) error {
			taskID := data.(int)
			time.Sleep(100 * time.Millisecond)
			fmt.Printf("Task %d processed\n", taskID)
			return nil
		}, taskID)
		pool.AddTask(task)
	}
}()
pool.RunBackground()

When we run this, we would see a random task is getting inserted while the workers are running in the background, and one of the workers picking the task up. It would eventually stop when it matches the condition to stop.

Conclusion

We explored how we can build a robust solution with worker pool from the naive one in the first part. Also, we extended further to implement the worker pool running in the background to handle further incoming tasks.