pond

Minimalistic and High-performance goroutine worker pool written in Go

Motivation

This library is meant to provide a simple way to limit concurrency when executing some function over a limited resource or service.

Some common scenarios include:

• Executing queries against a Database with a limited no. of connections
• Sending HTTP requests to a a rate/concurrency limited API

Features:

• Zero dependencies
• Create pools with fixed or dynamic size
• Worker goroutines are only created when needed (backpressure detection) and automatically purged after being idle for some time (configurable)
• Minimalistic APIs for:
  • Creating worker pools with fixed or dynamic size
  • Submitting tasks to a pool in a fire-and-forget fashion
  • Submitting tasks to a pool and waiting for them to complete
  • Submitting tasks to a pool with a deadline
  • Submitting a group of related tasks and waiting for them to complete
  • Getting the number of running workers (goroutines)
  • Stopping a worker pool
• Task panics are handled gracefully (configurable panic handler)
• Supports Non-blocking and Blocking task submission modes (buffered / unbuffered)
• Very high performance under heavy workloads (See benchmarks)
• New (since v1.3.0): configurable pool resizing strategy, with 3 presets for common scenarios: Eager, Balanced and Lazy.
• API reference

How to install

go get -u github.com/alitto/pond

How to use

Worker pool with dynamic size

package main

import (
	"fmt"

	"github.com/alitto/pond"
)

func main() {

	// Create a buffered (non-blocking) pool that can scale up to 100 workers
	// and has a buffer capacity of 1000 tasks
	pool := pond.New(100, 1000)

	// Submit 1000 tasks
	for i := 0; i < 1000; i++ {
		n := i
		pool.Submit(func() {
			fmt.Printf("Running task #%d\n", n)
		})
	}

	// Stop the pool and wait for all submitted tasks to complete
	pool.StopAndWait()
}

Worker pool with fixed size

package main

import (
	"fmt"

	"github.com/alitto/pond"
)

func main() {

	// Create an unbuffered (blocking) pool with a fixed 
	// number of workers
	pool := pond.New(10, 0, pond.MinWorkers(10))

	// Submit 1000 tasks
	for i := 0; i < 1000; i++ {
		n := i
		pool.Submit(func() {
			fmt.Printf("Running task #%d\n", n)
		})
	}

	// Stop the pool and wait for all submitted tasks to complete
	pool.StopAndWait()
}

Submitting groups of related tasks

package main

import (
	"fmt"

	"github.com/alitto/pond"
)

func main() {

	// Create a pool
	pool := pond.New(10, 1000)
	defer pool.StopAndWait()

	// Create a task group
	group := pool.Group()

	// Submit a group of related tasks
	for i := 0; i < 20; i++ {
		n := i
		group.Submit(func() {
			fmt.Printf("Running group task #%d\n", n)
		})
	}

	// Wait for all tasks in the group to complete
	group.Wait()
}

Pool Configuration Options

• MinWorkers: Specifies the minimum number of worker goroutines that must be running at any given time. These goroutines are started when the pool is created. The default value is 0. Example:

// This will create a pool with 5 running worker goroutines 
pool := pond.New(10, 1000, pond.MinWorkers(5))

• IdleTimeout: Defines how long to wait before removing idle worker goroutines from the pool. The default value is 5 seconds. Example:

// This will create a pool that will remove workers 100ms after they become idle 
pool := pond.New(10, 1000, pond.IdleTimeout(100 * time.Millisecond))

• PanicHandler: Allows to configure a custom function to handle panics thrown by tasks submitted to the pool. The default handler just writes a message to standard output using fmt.Printf with the following contents: Worker exits from a panic: [panic] \n Stack trace: [stack trace]). Example:

// Custom panic handler function
panicHandler := func(p interface{}) {
	fmt.Printf("Task panicked: %v", p)
}

// This will create a pool that will handle panics using a custom panic handler
pool := pond.New(10, 1000, pond.PanicHandler(panicHandler)))

• Strategy: Configures the strategy used to resize the pool when backpressure is detected. You can create a custom strategy by implementing the pond.ResizingStrategy interface or choose one of the 3 presets:
  • Eager: maximizes responsiveness at the expense of higher resource usage, which can reduce throughput under certain conditions. This strategy is meant for worker pools that will operate at a small percentage of their capacity most of the time and may occasionally receive bursts of tasks.
  • Balanced: tries to find a balance between responsiveness and throughput. It's suitable for general purpose worker pools or those that will operate close to 50% of their capacity most of the time. This is the default strategy.
  • Lazy: maximizes throughput at the expense of responsiveness. This strategy is meant for worker pools that will operate close to their max. capacity most of the time.

// Example: create pools with different resizing strategies
eagerPool := pond.New(10, 1000, pond.Strategy(pond.Eager))
balancedPool := pond.New(10, 1000, pond.Strategy(pond.Balanced))
lazyPool := pond.New(10, 1000, pond.Strategy(pond.Lazy))

API Reference

Full API reference is available at https://pkg.go.dev/github.com/alitto/pond

Benchmarks

We ran a few benchmarks to see how pond's performance compares against some of the most popular worker pool libraries available for Go (ants and gammazero's workerpool), as well as just launching unbounded goroutines and manually creating a goroutine worker pool (inspired by gobyexample.com), using either a buffered or an unbuffered channel to dispatch tasks.

The test consists of submitting 3 different workloads to each worker pool:

• 1M-10ms: 1 million tasks that sleep for 10 milliseconds (time.Sleep(10*time.Millisecond))
• 100k-500ms: 100 thousand tasks that sleep for 500 milliseconds (time.Sleep(500*time.Millisecond))
• 10k-1000ms: 10 thousand tasks that sleep for 1 second (time.Sleep(1*time.Second))

All pools are configured to use a maximum of 200k workers and initialization times are taken into account.

Here are the results:

goos: linux
goarch: amd64
pkg: github.com/alitto/pond/benchmark
BenchmarkAll/1M-10ms/Pond-Eager-8         	       2	 620347142 ns/op	82768720 B/op	 1086686 allocs/op
BenchmarkAll/1M-10ms/Pond-Balanced-8      	       2	 578973910 ns/op	81339088 B/op	 1083203 allocs/op
BenchmarkAll/1M-10ms/Pond-Lazy-8          	       2	 613344573 ns/op	84347248 B/op	 1084987 allocs/op
BenchmarkAll/1M-10ms/Goroutines-8         	       2	 540765682 ns/op	98457168 B/op	 1060433 allocs/op
BenchmarkAll/1M-10ms/GoroutinePool-8      	       1	1157705614 ns/op	68137088 B/op	 1409763 allocs/op
BenchmarkAll/1M-10ms/BufferedPool-8       	       1	1158068370 ns/op	76426272 B/op	 1412739 allocs/op
BenchmarkAll/1M-10ms/Gammazero-8          	       1	1330312458 ns/op	34524328 B/op	 1029692 allocs/op
BenchmarkAll/1M-10ms/AntsPool-8           	       2	 724231628 ns/op	37870404 B/op	 1077297 allocs/op
BenchmarkAll/100k-500ms/Pond-Eager-8      	       2	 604180003 ns/op	31523028 B/op	  349877 allocs/op
BenchmarkAll/100k-500ms/Pond-Balanced-8   	       1	1060079592 ns/op	35520416 B/op	  398779 allocs/op
BenchmarkAll/100k-500ms/Pond-Lazy-8       	       1	1053705909 ns/op	35040512 B/op	  392696 allocs/op
BenchmarkAll/100k-500ms/Goroutines-8      	       2	 551869174 ns/op	 8000016 B/op	  100001 allocs/op
BenchmarkAll/100k-500ms/GoroutinePool-8   	       2	 635442074 ns/op	20764560 B/op	  299632 allocs/op
BenchmarkAll/100k-500ms/BufferedPool-8    	       2	 641683384 ns/op	21647840 B/op	  299661 allocs/op
BenchmarkAll/100k-500ms/Gammazero-8       	       2	 667449574 ns/op	16241864 B/op	  249664 allocs/op
BenchmarkAll/100k-500ms/AntsPool-8        	       2	 659853037 ns/op	37300372 B/op	  549784 allocs/op
BenchmarkAll/10k-1000ms/Pond-Eager-8      	       1	1014320653 ns/op	12135080 B/op	   39692 allocs/op
BenchmarkAll/10k-1000ms/Pond-Balanced-8   	       1	1015979207 ns/op	12083704 B/op	   39518 allocs/op
BenchmarkAll/10k-1000ms/Pond-Lazy-8       	       1	1036374161 ns/op	12046632 B/op	   39366 allocs/op
BenchmarkAll/10k-1000ms/Goroutines-8      	       1	1007837894 ns/op	  800016 B/op	   10001 allocs/op
BenchmarkAll/10k-1000ms/GoroutinePool-8   	       1	1149536612 ns/op	21393024 B/op	  222458 allocs/op
BenchmarkAll/10k-1000ms/BufferedPool-8    	       1	1127286218 ns/op	20343584 B/op	  219359 allocs/op
BenchmarkAll/10k-1000ms/Gammazero-8       	       1	1023249222 ns/op	 2019688 B/op	   29374 allocs/op
BenchmarkAll/10k-1000ms/AntsPool-8        	       1	1016280850 ns/op	 4155904 B/op	   59487 allocs/op
PASS
ok  	github.com/alitto/pond/benchmark	37.331s

As you can see, pond's resizing strategies (Eager, Balanced or Lazy) behave differently under different workloads and generally one of them outperforms the other worker pool implementations, except for launching unbounded goroutines.

Leaving aside the fact that launching unlimited goroutines defeats the goal of limiting concurrency over a resource, its performance is highly dependant on how much resources (CPU and memory) are available at a given time, which make it unpredictable and likely to cause starvation. In other words, it's generally not a good idea for production applications.

These tests were executed on a laptop with an 8-core CPU (Intel(R) Core(TM) i7-8550U CPU @ 1.80GHz) and 16GB of RAM.

Resources

Here are some of the resources which have served as inspiration when writing this library:

• http://marcio.io/2015/07/handling-1-million-requests-per-minute-with-golang/
• https://brandur.org/go-worker-pool
• https://gobyexample.com/worker-pools
• https://github.com/panjf2000/ants
• https://github.com/gammazero/workerpool

Contribution & Support

Feel free to send a pull request if you consider there's something which can be improved. Also, please open up an issue if you run into a problem when using this library or just have a question.