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 and efficient resource usage under heavy workloads, even outperforming unbounded goroutines in some scenarios (See benchmarks)
• New (since v1.3.0): configurable pool resizing strategy, with 3 presets for common scenarios: Eager, Balanced and Lazy.
• New (since v1.5.0): complete pool metrics such as number of running workers, tasks waiting in the queue and more.
• 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. This is the default strategy.
  • 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.
  • 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()))

Resizing strategies

The following chart illustrates the behaviour of the different pool resizing strategies as the number of submitted tasks increases. Each line represents the number of worker goroutines in the pool (pool size) and the x-axis reflects the number of submitted tasks (cumulative).

As the name suggests, the "Eager" strategy always spawns an extra worker when there are no idles, which causes the pool to grow almost linearly with the number of submitted tasks. On the other end, the "Lazy" strategy creates one worker every N submitted tasks, where N is the maximum number of available CPUs (GOMAXPROCS). The "Balanced" strategy represents a middle ground between the previous two because it creates a worker every N/2 submitted tasks.

Metrics & monitoring

Each worker pool instance exposes useful metrics that can be queried through the following methods:

• pool.RunningWorkers() int: Current number of running workers
• pool.IdleWorkers() int: Current number of idle workers
• pool.MinWorkers() int: Minimum number of worker goroutines
• pool.MaxWorkers() int: Maxmimum number of worker goroutines
• pool.MaxCapacity() int: Maximum number of tasks that can be waiting in the queue at any given time (queue capacity)
• pool.SubmittedTasks() uint64: Total number of tasks submitted since the pool was created
• pool.WaitingTasks() uint64: Current number of tasks in the queue that are waiting to be executed
• pool.SuccessfulTasks() uint64: Total number of tasks that have successfully completed their exection since the pool was created
• pool.FailedTasks() uint64: Total number of tasks that completed with panic since the pool was created
• pool.CompletedTasks() uint64: Total number of tasks that have completed their exection either successfully or with panic since the pool was created

In our Prometheus example we showcase how to configure collectors for these metrics and expose them to Prometheus.

API Reference

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

Benchmarks

See Benchmarks.

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.