Node js Thread Pool Implementation

栏目: IT技术 · 发布时间: 6年前

内容简介:Node pool contains twoThe first implementation is a static thread pool , with a defined number of threads that are started at creation time and will be reused.

Node Thread Pool :arrow_double_up: :on:

Contents

· · · · · ·

Overview

Node pool contains two worker-threads pool implementations , you don' t have to deal with worker-threads complexity.

The first implementation is a static thread pool , with a defined number of threads that are started at creation time and will be reused.

The second implementation is a dynamic thread pool with a number of threads started at creation time ( these threads will be always active and reused) and other threads created when the load will increase ( with an upper limit, these threads will be reused when active ), the new created threads will be stopped after a configurable period of inactivity.

You have to implement your worker extending the ThreadWorker class

Installation

npm install poolifier --save

Usage

You can implement a worker in a simple way , extending the class ThreadWorker :

'use strict'
const { ThreadWorker } = require('poolifier')

function yourFunction (data) {
  // this will be executed in the worker thread,
  // the data will be received by using the execute method
  return { ok: 1 }
}

class MyWorker extends ThreadWorker {
  constructor () {
    super(yourFunction, { maxInactiveTime: 1000 * 60})
  }
}
module.exports = new MyWorker()

Instantiate your pool based on your needed :

'use strict'
const { FixedThreadPool, DynamicThreadPool } = require('poolifier')

// a fixed thread pool
const pool = new FixedThreadPool(15,
  './yourWorker.js',
  { errorHandler: (e) => console.error(e), onlineHandler: () => console.log('worker is online') })

// or a dynamic thread pool
const pool = new DynamicThreadPool(10, 100,
  './yourWorker.js',
  { errorHandler: (e) => console.error(e), onlineHandler: () => console.log('worker is online') })

pool.emitter.on('FullPool', () => console.log('Pool is full'))

// the execute method signature is the same for both implementations,
// so you can easy switch from one to another
pool.execute({}).then(res => {
  console.log(res)
}).catch ....

See examples folder for more details ( in particular if you want to use a pool for multiple functions ).

Node versions

You can use node versions 12.x , 13.x

API

pool = new FixedThreadPool(numThreads, filePath, opts)

numThreads (mandatory) Num of threads for this worker pool

filePath (mandatory) Path to a file with a worker implementation

opts (optional) An object with these properties :

  • errorHandler - A function that will listen for error event on each worker thread
  • onlineHandler - A function that will listen for online event on each worker thread
  • exitHandler - A function that will listen for exit event on each worker thread
  • maxTasks - This is just to avoid not useful warnings message, is used to set maxListeners on event emitters ( workers are event emitters)

pool = new DynamicThreadPool(min, max, filePath, opts)

min (mandatory) Same as FixedThreadPool numThreads , this number of threads will be always active

max (mandatory) Max number of workers that this pool can contain, the new created threads will die after a threshold ( default is 1 minute , you can override it in your worker implementation).

filePath (mandatory) Same as FixedThreadPool

opts (optional) Same as FixedThreadPool

pool.execute(data)

Execute method is available on both pool implementations ( return type : Promise):

data (mandatory) An object that you want to pass to your worker implementation

pool.destroy()

Destroy method is available on both pool implementations.

This method will call the terminate method on each worker.

class YourWorker extends ThreadWorker

fn (mandatory) The function that you want to execute on the worker thread

opts (optional) An object with these properties :

  • maxInactiveTime - Max time to wait tasks to work on ( in ms) , after this period the new worker threads will die.

Choose your pool

Performance is one of the main target of these thread pool implementations, we want to have a strong focus on this.

We already have a bench folder where you can find some comparisons. To choose your pool consider that with a FixedThreadPool or a DynamicThreadPool ( in this case is important the min parameter passed to the constructor) your application memory footprint will increase .

Increasing the memory footprint, your application will be ready to accept more CPU bound tasks, but during idle time your application will consume more memory.

One good choose from my point of view is to profile your application using Fixed/Dynamic thread pool , and to see your application metrics when you increase/decrease the num of threads.

For example you could keep the memory footprint low choosing a DynamicThreadPool with 5 threads, and allow to create new threads until 50/100 when needed, this is the advantage to use the DynamicThreadPool.

But in general , always profile your application

Contribute

See guidelines CONTRIBUTING

License

MIT


以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网

查看所有标签

猜你喜欢:

本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们

鲜活的数据

鲜活的数据

[美] Nathan Yau / 向怡宁 / 人民邮电出版社 / 2012-10-1 / 69.00元

在生活中,数据几乎无处不在,任我们取用。然而,同样的数据给人的感觉可能会千差万别:或冰冷枯燥,让人望而生畏、百思不解其意;或生动有趣,让人一目了然、豁然开朗。为了达到后一种效果,我们需要采用一种特别的方式来展示数据,来解释、分析和应用它。这就是数据可视化技术。Nath an Yau是这一创新领域的先锋。在本书中,他根据数据可视化的工作流程,先后介绍了如何获取数据,将数据格式化,用可视化工具(如R)......一起来看看 《鲜活的数据》 这本书的介绍吧!

HTML 压缩/解压工具
HTML 压缩/解压工具

在线压缩/解压 HTML 代码

XML 在线格式化
XML 在线格式化

在线 XML 格式化压缩工具

UNIX 时间戳转换
UNIX 时间戳转换

UNIX 时间戳转换