Task Queues

Task queues manage background work that must be executed outside the usual HTTP request-response cycle.

Why are task queues necessary?

Tasks are handled asynchronously either because they are not initiated by an HTTP request or because they are long-running jobs that would dramatically reduce the performance of an HTTP response.

For example, a web application could poll the GitHub API every 10 minutes to collect the names of the top 100 starred repositories. A task queue would handle invoking code to call the GitHub API, process the results and store them in a persistent database for later use.

Another example is when a database query would take too long during the HTTP request-response cycle. The query could be performed in the background on a fixed interval with the results stored in the database. When an HTTP request comes in that needs those results a query would simply fetch the precalculated result instead of re-executing the longer query. This precalculation scenario is a form of caching enabled by task queues.

Other types of jobs for task queues include

spreading out large numbers of independent database inserts over time instead of inserting everything at once

aggregating collected data values on a fixed interval, such as every 15 minutes

scheduling periodic jobs such as batch processes

Task queue projects

The defacto standard Python task queue is Celery . The other task queue projects that arise tend to come from the perspective that Celery is overly complicated for simple use cases. My recommendation is to put the effort into Celery's reasonable learning curve as it is worth the time it takes to understand how to use the project.

The Celery distributed task queue is the most commonly used Python library for handling asynchronous tasks and scheduling.

The RQ (Redis Queue) is a simple Python library for queueing jobs and processing them in the background with workers. RQ is backed by Redis and is designed to have a low barrier to entry.

Taskmaster is a lightweight simple distributed queue for handling large volumes of one-off tasks.

Huey is a Redis-based task queue that aims to provide a simple, yet flexible framework for executing tasks. Huey supports task scheduling, crontab-like repeating tasks, result storage and automatic retry in the event of failure.

Kuyruk is simple and easy to use task queue system built on top of RabbitMQ. Although feature set is small, new features can be added by extensions.

Dramatiq is a fast and reliable alternative to Celery. It supports RabbitMQ and Redis as message brokers.

django-carrot is a simple task queue specifically for Django that can serve when Celery is overkill.

tasq is a brokerless task queue for simple use cases. It is not recommended for production unless further testing and development is done.

Hosted message and task queue services

Task queue third party services aim to solve the complexity issues that arise when scaling out a large deployment of distributed task queues.

Iron.io is a distributed messaging service platform that works with many types of task queues such as Celery. It also is built to work with other IaaS and PaaS environments such as Amazon Web Services and Heroku.

Amazon Simple Queue Service (SQS) is a set of five APIs for creating, sending, receiving, modifying and deleting messages.

CloudAMQP is at its core managed servers with RabbitMQ installed and configured. This service is an option if you are using RabbitMQ and do not want to maintain RabbitMQ installations on your own servers.

Open source examples that use task queues

flask-celery-example is a simple Flask application with Celery as a task queue and Redis as the broker.

django_dramatiq_example and flask_dramatiq_example are simple apps that demo how you can use Dramatiq with Django and Flask, respectively.

Task queue resources

International Space Station notifications with Python and Redis Queue (RQ) shows how to combine the RQ task queue library with Flask to send text message notifications every time a condition is met - in this blog post's case that the ISS is currently flying over your location on Earth.

Evaluating persistent, replicated message queues is a detailed comparison of Amazon SQS, MongoDB, RabbitMQ, HornetQ and Kafka's designs and performance.

Why Task Queues is a presentation for what task queues are and why they are needed.

Asynchronous Processing in Web Applications Part One and Part Two are great reads for understanding the difference between a task queue and why you shouldn't use your database as one.

Flask by Example Implementing a Redis Task Queue provides a detailed walkthrough of setting up workers to use RQ with Redis.

Heroku has a clear walkthrough for using RQ for background tasks .

How to use Celery with RabbitMQ is a detailed walkthrough for using these tools on an Ubuntu VPS.

Celery - Best Practices explains things you should not do with Celery and shows some underused features for making task queues easier to work with.

Celery in Production on the Caktus Group blog contains good practices from their experience using Celery with RabbitMQ, monitoring tools and other aspects not often discussed in existing documentation.

A 4 Minute Intro to Celery is a short introductory task queue screencast.

This Celery tasks checklist has some nice tips and resources for using Celery in your applications.

Heroku wrote about how to secure Celery when tasks are otherwise sent over unencrypted networks.

Miguel Grinberg wrote a nice post on using the task queue Celery with Flask . He gives an overview of Celery followed by specific code to set up the task queue and integrate it with Flask.

Ditching the Task Queue for Gevent explains how in some cases you can replace the complexity of a task queue with concurrency. For example, you can remove Celery in favor of gevent .

3 Gotchas for Working with Celery are things to keep in mind when you're new to the Celery task queue implementation.

Setting up an asynchronous task queue for Django using Celery and Redis is a straightforward tutorial for setting up the Celery task queue for Django web applications using the Redis broker on the back end.

Asynchronous Tasks with Flask and Redis Queue looks at how to configure Redis Queue to handle long-running tasks in a Flask app.

Developing an Asynchronous Task Queue in Python looks at how to implement several asynchronous task queues using Python's multiprocessing library and Redis.

Task queue learning checklist

Pick a slow function in your project that is called during an HTTP request.

Determine if you can precompute the results on a fixed interval instead of during the HTTP request. If so, create a separate function you can call from elsewhere then store the precomputed value in the database.

Read the Celery documentation and the links in the resources section below to understand how the project works.

Install a message broker such as RabbitMQ or Redis and then add Celery to your project. Configure Celery to work with the installed message broker.

Use Celery to invoke the function from step one on a regular basis.

Have the HTTP request function use the precomputed value instead of the slow running code it originally relied upon.

What's next to learn after task queues?

How do I log errors that occur in my application?

I want to learn more about app users via web analytics.

What tools exist for monitoring a deployed web app?

Table of Contents

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Developing an Asynchronous Task Queue in Python

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This tutorial looks at how to implement several asynchronous task queues using Python's multiprocessing library and Redis .

Queue Data Structures

Following along, multiprocessing pool, multiprocessing queue.

A queue is a First-In-First-Out ( FIFO ) data structure.

  • an item is added at the tail ( enqueue )
  • an item is removed at the head ( dequeue )

queue

You'll see this in practice as you code out the examples in this tutorial.

Let's start by creating a basic task:

So, get_word_counts finds the twenty most frequent words from a given text file and saves them to an output file. It also prints the current process identifier (or pid) using Python's os library.

Create a project directory along with a virtual environment. Then, use pip to install NLTK :

Once installed, invoke the Python shell and download the stopwords corpus :

If you experience an SSL error refer to this article. Example fix: >>> import nltk >>> nltk.download ( 'stopwords' ) [ nltk_data ] Error loading stopwords: <urlopen error [ SSL: [ nltk_data ] CERTIFICATE_VERIFY_FAILED ] certificate verify failed: [ nltk_data ] unable to get local issuer certificate ( _ssl.c:1056 ) > False >>> import ssl >>> try: ... _create_unverified_https_context = ssl._create_unverified_context ... except AttributeError: ... pass ... else : ... ssl._create_default_https_context = _create_unverified_https_context ... >>> nltk.download ( 'stopwords' ) [ nltk_data ] Downloading package stopwords to [ nltk_data ] /Users/michael.herman/nltk_data... [ nltk_data ] Unzipping corpora/stopwords.zip. True

Add the above tasks.py file to your project directory but don't run it quite yet.

We can run this task in parallel using the multiprocessing library:

Here, using the Pool class, we processed four tasks with two processes.

Did you notice the map_async method? There are essentially four different methods available for mapping tasks to processes. When choosing one, you have to take multi-args, concurrency, blocking, and ordering into account:

Without both close and join , garbage collection may not occur, which could lead to a memory leak.

  • close tells the pool not to accept any new tasks
  • join tells the pool to exit after all tasks have completed
Following along? Grab the Project Gutenberg sample text files from the "data" directory in the simple-task-queue repo, and then add an "output" directory. Your project directory should look like this: ├── data │   ├── dracula.txt │   ├── frankenstein.txt │   ├── heart-of-darkness.txt │   └── pride-and-prejudice.txt ├── output ├── simple_pool.py └── tasks.py

It should take less than a second to run:

This script ran on a i9 Macbook Pro with 16 cores.

So, the multiprocessing Pool class handles the queuing logic for us. It's perfect for running CPU-bound tasks or really any job that can be broken up and distributed independently. If you need more control over the queue or need to share data between multiple processes, you may want to look at the Queue class.

For more on this along with the difference between parallelism (multiprocessing) and concurrency (multithreading), review the Speeding Up Python with Concurrency, Parallelism, and asyncio article.

Let's look at a simple example:

The Queue class, also from the multiprocessing library, is a basic FIFO (first in, first out) data structure. It's similar to the queue.Queue class, but designed for interprocess communication. We used put to enqueue an item to the queue and get to dequeue an item.

Check out the Queue source code for a better understanding of the mechanics of this class.

Now, let's look at more advanced example:

Here, we enqueued 40 tasks (ten for each text file) to the queue, created separate processes via the Process class, used start to start running the processes, and, finally, used join to complete the processes.

It should still take less than a second to run.

Challenge : Check your understanding by adding another queue to hold completed tasks. You can enqueue them within the process_tasks function.

The multiprocessing library provides support for logging as well:

To test, change task_queue.put("dracula.txt") to task_queue.put("drakula.txt") . You should see the following error outputted ten times in the terminal:

Want to log to disc?

Again, cause an error by altering one of the file names, and then run it. Take a look at process.log . It's not quite as organized as it should be since the Python logging library does not use shared locks between processes. To get around this, let's have each process write to its own file. To keep things organized, add a logs directory to your project folder:

Moving right along, instead of using an in-memory queue, let's add Redis into the mix.

Following along? Download and install Redis if you do not already have it installed. Then, install the Python interface : (env)$ pip install redis == 4 .5.5

We'll break the logic up into four files:

  • redis_queue.py creates new queues and tasks via the SimpleQueue and SimpleTask classes, respectively.
  • redis_queue_client enqueues new tasks.
  • redis_queue_worker dequeues and processes tasks.
  • redis_queue_server spawns worker processes.

Here, we defined two classes, SimpleQueue and SimpleTask :

  • SimpleQueue creates a new queue and enqueues, dequeues, and gets the length of the queue.
  • SimpleTask creates new tasks, which are used by the instance of the SimpleQueue class to enqueue new tasks, and processes new tasks.
Curious about lpush() , brpop() , and llen() ? Refer to the Command reference page. ( The brpop() function is particularly cool because it blocks the connection until a value exists to be popped!)

This module will create a new instance of Redis and the SimpleQueue class. It will then enqueue 40 tasks.

If a task is available, the dequeue method is called, which then de-serializes the task and calls the process_task method (in redis_queue.py ).

The run method spawns four new worker processes.

You probably don’t want four processes running at once all the time, but there may be times that you will need four or more processes. Think about how you could programmatically spin up and down additional workers based on demand.

To test, run redis_queue_server.py and redis_queue_client.py in separate terminal windows:

example

Check your understanding again by adding logging to the above application.

In this tutorial, we looked at a number of asynchronous task queue implementations in Python. If the requirements are simple enough, it may be easier to develop a queue in this manner. That said, if you're looking for more advanced features -- like task scheduling, batch processing, job prioritization, and retrying of failed tasks -- you should look into a full-blown solution. Check out Celery , RQ , or Huey .

Grab the final code from the simple-task-queue repo.

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Using Python RQ for Task Queues in Python

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This is a getting started on python-rq tutorial and I will demonstrate how to work with asynchronous tasks using python redis queue (python-rq).

What will we be doing

We want a client to submit 1000's of jobs in a non-blocking asynchronous fashion, and then we will have workers which will consume these jobs from our redis queue, and process those tasks at the rate of what our consumer can handle.

The nice thing about this is that, if our consumer is unavailable for processing the tasks will remain in the queue and once the consumer is ready to consume, the tasks will be executed. It's also nice that its asynchronous, so the client don't have to wait until the task has finished.

We will run a redis server using docker, which will be used to queue all our jobs, then we will go through the basics in python and python-rq such as:

  • Writing a Task
  • Enqueueing a Job
  • Getting information from our queue, listing jobs, job statuses
  • Running our workers to consume from the queue and action our tasks
  • Basic application which queues jobs to the queue, consumes and action them and monitors the queue

Redis Server

You will require docker for this next step, to start the redis server:

Install python-rq:

Create the task which will be actioned by our workers, in our case it will just be a simple function that adds all the numbers from a given string to a list, then adds them up and return the total value.

This is however a very basic task, but its just for demonstration.

Our tasks.py :

To test this locally:

Now, lets import redis and redis-queue, with our tasks and instantiate a queue object:

Submit a Task to the Queue

Let's submit a task to the queue:

We have a couple of properties from result which we can inspect, first let's have a look at the id that we got back when we submitted our task to the queue:

We can also get the status from our task:

We can also view our results in json format:

If we dont have context of the job id, we can use get_jobs to get all the jobs which is queued:

Then we can loop through the results and get the id like below:

Or to get the job id's in a list:

Since we received the job id, we can use fetch_job to get more info about the job:

And as before we can view it in json format:

We can also view the key in redis by passing the job_id:

To view how many jobs are in our queue, we can either do:

Consuming from the Queue

Now that our task is queued, let's fire of our worker to consume the job from the queue and action the task:

Now, when we get the status of our job, you will see that it finished:

And to get the result from our worker:

And like before, if you dont have context of your job id, you can get the job id, then return the result:

Naming Queues

We can namespace our tasks into specific queues, for example if we want to create queue1 :

To verify the queue name:

As we can see our queue is empty:

Let's submit 10 jobs to our queue:

To verify the number of jobs in our queue:

And to count them:

Cleaning the Queue

Cleaning the queue can either be done with:

Then to verify that our queue is clean:

Naming Workers

The same way that we defined a name for our queue, we can define a name for our workers:

Which means you can have different workers consuming jobs from specific queues.

Documentation:

  • https://python-rq.org/docs/
  • https://python-rq.org/docs/workers/
  • https://python-rq.org/docs/monitoring/

Thanks for reading, feel free to check out my website , and subscrube to my newsletter or follow me at @ruanbekker on Twitter.

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Task queues

Task queues manage background work that must be executed outside the usual HTTP request-response cycle.

Why are task queues necessary?

Tasks are handled asynchronously either because they are not initiated by an HTTP request or because they are long-running jobs that would dramatically reduce the performance of an HTTP response.

For example, a web application could poll the GitHub API every 10 minutes to collect the names of the top 100 starred repositories. A task queue would handle invoking code to call the GitHub API, process the results and store them in a persistent database for later use.

Another example is when a database query would take too long during the HTTP request-response cycle. The query could be performed in the background on a fixed interval with the results stored in the database. When an HTTP request comes in that needs those results a query would simply fetch the precalculated result instead of re-executing the longer query. This precalculation scenario is a form of caching enabled by task queues.

Other types of jobs for task queues include

spreading out large numbers of independent database inserts over time instead of inserting everything at once

aggregating collected data values on a fixed interval, such as every 15 minutes

scheduling periodic jobs such as batch processes

Task queue projects

The defacto standard Python task queue is Celery. The other task queue projects that arise tend to come from the perspective that Celery is overly complicated for simple use cases. My recommendation is to put the effort into Celery's reasonable learning curve as it is worth the time it takes to understand how to use the project.

The Celery distributed task queue is the most commonly used Python library for handling asynchronous tasks and scheduling.

The RQ (Redis Queue) is a simple Python library for queueing jobs and processing them in the background with workers. RQ is backed by Redis and is designed to have a low barrier to entry. The intro post contains information on design decisions and how to use RQ.

Taskmaster is a lightweight simple distributed queue for handling large volumes of one-off tasks.

Huey is a simple task queue that uses Redis on the backend but otherwise does not depend on other libraries. The project was previously known as Invoker and the author changed the name.

Huey is a Redis-based task queue that aims to provide a simple, yet flexible framework for executing tasks. Huey supports task scheduling, crontab-like repeating tasks, result storage and automatic retry in the event of failure.

Hosted message and task queue services

Task queue third party services aim to solve the complexity issues that arise when scaling out a large deployment of distributed task queues.

Iron.io is a distributed messaging service platform that works with many types of task queues such as Celery. It also is built to work with other IaaS and PaaS environments such as Amazon Web Services and Heroku.

Amazon Simple Queue Service (SQS) is a set of five APIs for creating, sending, receiving, modifying and deleting messages.

CloudAMQP is at its core managed servers with RabbitMQ installed and configured. This service is an option if you are using RabbitMQ and do not want to maintain RabbitMQ installations on your own servers.

Open source examples that use task queues

Take a look at the code in this open source Flask application and this Django application for examples of how to use and deploy Celery with a Redis broker to send text messages with these frameworks.

flask-celery-example is a simple Flask application with Celery as a task queue and Redis as the broker.

Task queue resources

Getting Started Scheduling Tasks with Celery is a detailed walkthrough for setting up Celery with Django (although Celery can also be used without a problem with other frameworks).

International Space Station notifications with Python and Redis Queue (RQ) shows how to combine the RQ task queue library with Flask to send text message notifications every time a condition is met - in this blog post's case that the ISS is currently flying over your location on Earth.

Evaluating persistent, replicated message queues is a detailed comparison of Amazon SQS, MongoDB, RabbitMQ, HornetQ and Kafka's designs and performance.

Queues.io is a collection of task queue systems with short summaries for each one. The task queues are not all compatible with Python but ones that work with it are tagged with the "Python" keyword.

Why Task Queues is a presentation for what task queues are and why they are needed.

Flask by Example Implementing a Redis Task Queue provides a detailed walkthrough of setting up workers to use RQ with Redis.

How to use Celery with RabbitMQ is a detailed walkthrough for using these tools on an Ubuntu VPS.

Heroku has a clear walkthrough for using RQ for background tasks .

Introducing Celery for Python+Django provides an introduction to the Celery task queue.

Celery - Best Practices explains things you should not do with Celery and shows some underused features for making task queues easier to work with.

The "Django in Production" series by Rob Golding contains a post specifically on Background Tasks .

Asynchronous Processing in Web Applications Part One and Part Two are great reads for understanding the difference between a task queue and why you shouldn't use your database as one.

Celery in Production on the Caktus Group blog contains good practices from their experience using Celery with RabbitMQ, monitoring tools and other aspects not often discussed in existing documentation.

A 4 Minute Intro to Celery is a short introductory task queue screencast.

Heroku wrote about how to secure Celery when tasks are otherwise sent over unencrypted networks.

Miguel Grinberg wrote a nice post on using the task queue Celery with Flask . He gives an overview of Celery followed by specific code to set up the task queue and integrate it with Flask.

3 Gotchas for Working with Celery are things to keep in mind when you're new to the Celery task queue implementation.

Deferred Tasks and Scheduled Jobs with Celery 3.1, Django 1.7 and Redis is a video along with code that shows how to set up Celery with Redis as the broker in a Django application.

Setting up an asynchronous task queue for Django using Celery and Redis is a straightforward tutorial for setting up the Celery task queue for Django web applications using the Redis broker on the back end.

Background jobs with Django and Celery shows the code and a simple explanation of how to use Celery with Django .

Asynchronous Tasks With Django and Celery shows how to integrate Celery with Django and create Periodic Tasks.

Three quick tips from two years with Celery provides some solid advice on retry delays, the -Ofair flag and global task timeouts for Celery.

Task queue learning checklist

Pick a slow function in your project that is called during an HTTP request.

Determine if you can precompute the results on a fixed interval instead of during the HTTP request. If so, create a separate function you can call from elsewhere then store the precomputed value in the database.

Read the Celery documentation and the links in the resources section below to understand how the project works.

Install a message broker such as RabbitMQ or Redis and then add Celery to your project. Configure Celery to work with the installed message broker.

Use Celery to invoke the function from step one on a regular basis.

Have the HTTP request function use the precomputed value instead of the slow running code it originally relied upon.

What's next to learn after task queues?

How do I log errors that occur in my application?

I want to learn more about app users via web analytics.

What tools exist for monitoring a deployed web app?

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Sign up to get a monthly email with python tutorials and major updates to this site., table of contents, task queues.

Huey: A little task queue for python

http://media.charlesleifer.com/blog/photos/huey2-logo.png

a lightweight alternative .

  • a task queue ( 2019-04-01 : version 2.0 released )
  • written in python (2.7+, 3.4+)
  • clean and simple API
  • redis, sqlite, or in-memory storage
  • example code .

huey supports:

  • multi-process, multi-thread or greenlet task execution models
  • schedule tasks to execute at a given time, or after a given delay
  • schedule recurring tasks, like a crontab
  • automatically retry tasks that fail
  • task prioritization
  • task result storage
  • task locking
  • task pipelines and chains

http://i.imgur.com/2EpRs.jpg

At a glance

Calling a task -decorated function will enqueue the function call for execution by the consumer. A special result handle is returned immediately, which can be used to fetch the result once the task is finished:

Tasks can be scheduled to run in the future:

For much more, check out the guide or take a look at the example code .

Running the consumer

Run the consumer with four worker processes:

To run the consumer with a single worker thread (default):

If your work-loads are mostly IO-bound, you can run the consumer with threads or greenlets instead. Because greenlets are so lightweight, you can run quite a few of them efficiently:

Huey’s design and feature-set were informed by the capabilities of the Redis database. Redis is a fantastic fit for a lightweight task queueing library like Huey: it’s self-contained, versatile, and can be a multi-purpose solution for other web-application tasks like caching, event publishing, analytics, rate-limiting, and more.

Although Huey was designed with Redis in mind, the storage system implements a simple API and many other tools could be used instead of Redis if that’s your preference.

Huey comes with builtin support for Redis, Sqlite and in-memory storage.

Documentation

See Huey documentation .

Project page

See source code and issue tracker on Github .

Huey is named in honor of my cat:

http://m.charlesleifer.com/t/800x-/blog/photos/p1473037658.76.jpg?key=mD9_qMaKBAuGPi95KzXYqg

https://github.com/coleifer/huey

John

A fast and reliable background task processing library for Python 3

A distributed task queue for python built on top of mongo, redis and gevent, you might also like..., a distributed task queue with asyncio and redis, metalsite: alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning, a type-hint complant, pluggable, python task runner, aliases and scripts to make common tasks easier, brain agent for large-scale and multi-task agent learning, bevfusion: multi-task multi-sensor fusion with unified bird's-eye view representation, the nlp task to classify empathetic dialogues datasets using roberta, ernie-2.0 and xlnet with different preprocessing method.

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  • Contributing

RQ ( Redis Queue ) is a simple Python library for queueing jobs and processing them in the background with workers. It is backed by Redis and it is designed to have a low barrier to entry. It can be integrated in your web stack easily.

RQ requires Redis >= 3.0.0.

Getting started

First, run a Redis server. You can use an existing one. To put jobs on queues, you don’t have to do anything special, just define your typically lengthy or blocking function:

Then, create a RQ queue:

And enqueue the function call:

Scheduling jobs are similarly easy:

You can also ask RQ to retry failed jobs:

To start executing enqueued function calls in the background, start a worker from your project’s directory:

That’s about it.

Installation

Simply use the following command to install the latest released version:

If you want the cutting edge version (that may well be broken), use this:

Project history

This project has been inspired by the good parts of Celery , Resque and this snippet , and has been created as a lightweight alternative to existing queueing frameworks, with a low barrier to entry.

How to Run Your First Task with RQ, Redis, and Python

Time to read: 7 minutes

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header - How to Run Your First Task with RQ, Redis, and Python

As a developer, it can be very useful to learn how to run functions in the background while being able to monitor the queue in another tab or different system. This is incredibly helpful when managing heavy workloads that might not work efficiently when called all at once, or when making large numbers of calls to a database that returns data slowly over time rather than all at once.

In this tutorial we will implement a RQ queue in Python with the help of Redis to schedule and execute tasks in a timely manner.

Tutorial Requirements

  • Python 3.6  or newer. If your operating system does not provide a Python interpreter, you can go to python.org  to download an installer.

Let’s talk about task queues

Task queues are a great way to allow tasks to work asynchronously outside of the main application flow. There are many task queues in Python to assist you in your project, however, we’ll be discussing a solution today known as RQ.

RQ, also known as Redis Queue , is a Python library that allows developers to enqueue jobs to be processed in the background with workers . The RQ workers will be called when it's time to execute the queue in the background. Using a connection to Redis , it’s no surprise that this library is super lightweight and offers support for those getting started for the first time.

By using this particular task queue, it is possible to process jobs in the background with little to no hassle.

Set up the environment

Create a project directory in your terminal called “rq-test” to follow along.

Install a virtual environment and copy and paste the commands to install rq and related packages. If you are using a Unix or MacOS system, enter the following commands:

If you are on a Windows machine, enter the following commands in a prompt window:

RQ requires a Redis installation  on your machine which can be done using the following commands using wget . Redis is on version 6.0.6 at the time of this article publication.

If you are using a Unix or MacOS system, enter these commands to install Redis. This is my personal favorite way to install Redis, but there are alternatives below:

If you have Homebrew installed, you can type brew install redis in the terminal and refer to this GitHub gist to install Redis on the Mac . For developers using Ubuntu Linux, the command sudo apt-get install redis would get the job done as well.

Run the Redis server in a separate terminal window on the default port with the command src/redis-server from the directory where it's installed.

For Windows users, you would have to follow a separate tutorial to run Redis on Windows . Download the latest zip file on GitHub  and extract the contents. Run the redis-server.exe file that was extracted from the zip file to start the Redis server.

The output should look similar to the following after running Redis:

Build out the tasks

In this case, a task for Redis Queue is merely a Python function. For this article, we’ll tell the task to print a message to the terminal for a “x” amount of seconds to demonstrate the use of RQ.

Copy and paste the following code to a file named “tasks.py” in your directory.

These are simple tasks that print out numbers and text on the terminal so that we can see if the tasks are executed properly. Using the time.sleep(1) function from the Python time library  will allow your task to be suspended for the given number of seconds and overall extend the time of the task so that we can examine their progress.

Feel free to alter this code after the tutorial and create your own tasks. Some other popular tasks are sending a fax message or email by connecting to your email client.

Create your queue

Create another file in the root directory and name it “app.py”. Copy and paste the following code:

The queue object sets up a connection to Redis and initializes a queue based on that connection. This queue can hold all the jobs required to run in the background with workers.

As seen in the code, the tasks.print_task function is added using the enqueue function. This means that the task added to the queue will be executed immediately

The enqueue_in function is another nifty RQ function because it expects a timedelta in order to schedule the specified job. In this case, seconds is specified, but this variable can be changed according to the time schedule expected for your usage. Check out other ways to schedule a job  on this GitHub README.

Since we are testing out the RQ queue, I have enqueued both the tasks.print_task and tasks.print_numbers functions so that we can see their output on the terminal. The third argument passed in is a "5" which also stands for the argument passed into the respective functions. In this case, we are expecting to see print_task() print "Hello World!" five times and for print_numbers() to print 5 numbers in order.

If you have created any additional task, be sure to import your tasks at the top of the file so that all the tasks in your Python file can be accessed.

Run the queue

For the purposes of this article, the gif demo below will show a perfect execution of the tasks in queue so no exceptions will be raised.

The Redis server should still be running in a tab from earlier in the tutorial at this point. If it stopped, run the command src/redis-server inside the redis-6.0.6 folder on one tab, or for developers with a Windows machine, start redis-cli.exe . Open another tab solely to run the RQ scheduler with the command rq worker --with-scheduler .

This should be the output after running the command above.

The worker command activated a worker process in order to connect to Redis and look for any jobs assigned to the queue from the code in app.py .

Lastly, open a third tab in the terminal for the root project directory. Start up the virtual environment again with the command source venv/bin/activate . Then type python app.py to run the project.

Go back to the tab that is running rq worker --with-scheduler . Wait 5 more seconds after the first task is executed to see the next task. Although the live demo gif below wasn’t able to capture the best timing due to having to run the program and record, it is noticeable that there was a pause between tasks until execution and that both tasks were completed within 15 seconds.

Here’s the sample output inside of the rqworker tab:

As seen in the output above, if the tasks written in task.py  had a line to return anything, then the result of both tasks are kept for 500 seconds which is the default. A developer can  alter the return value's time to live by passing in a result_ttl parameter when adding tasks to the queue.

Handle exceptions and try again

If a job were to fail, you can always set up a log to keep track of the error messages, or you can use the RQ queue to enqueue and retry failed jobs. By using RQ's FailedJobRegistry package, you can keep track of the jobs that failed during runtime. The RQ documentation discusses how it handles the exceptions  and how data regarding the job can help the developer figure out how to resubmit the job.  

However, RQ also supports developers in handling exceptions in their own way by injecting your own logic to the rq workers . This may be a helpful option for you if you are executing many tasks in your project and those that failed are not worth retrying.

Force a failed task to retry

Since this is an introductory article to run your first task with RQ, let's try to purposely fail one of the tasks from earlier to test out RQ's retry object.

Go to the tasks.py  file and alter the print_task() function so that random numbers can be generated and determine if the function will be executed or not. We will be using the random Python library  to assist us in generating numbers. Don't forget to include the import random at the top of the file.

Copy and paste the following lines of code to change the print_task() function in the tasks.py  file.

Go back to the app.py  file to change the queue. Instead of using the enqueue_in function to execute the tasks.print_task function, delete the line and replace it with queue.enqueue(tasks.print_task, 5, retry=Retry(max=2)) .

The retry object is imported with rq so make sure you add from rq import Retry at the top of the file as well in order to use this functionality. This object accepts max and interval arguments to specify when the particular function will be retried. In the newly changed line, the tasks.print_task function will pass in the function we want to retry, the argument parameter "5" which stands for the seconds of execution, and lastly the maximum amount of times we want the queue to retry.

The tasks in queue should now look like this:

When running the print_task task, there is a 50/50 chance that tasks.print_task() will execute properly since we're only generating a 1 or 2, and the print statement will only happen if you generate a 1. A RuntimeError will be raised otherwise and the queue will retry the task immediately as many times as it takes to successfully print "Hello World!".

What’s next for task queues?

Congratulations! You have successfully learned and implemented the basics of scheduling tasks in the RQ queue. Perhaps now you can tell the worker command to add a task that prints out an infinite number of "Congratulations" messages in a timely manner!

Otherwise, check out these different tasks that you can build in to your Redis Queue:

  • Schedule Twilio SMS to a list of contacts  quickly!
  • Use Redis Queue to generate a fan fiction with OpenAI GPT-3
  • Queue Emails with Twilio SendGrid using Redis Queue

Let me know what you have been building by reaching out to me over email!

Diane Phan is a developer on the Developer Voices team. She loves to help programmers tackle difficult challenges that might prevent them from bringing their projects to life. She can be reached at dphan [at] twilio.com or LinkedIn .

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task-queue 2.14.0

pip install task-queue Copy PIP instructions

Released: May 7, 2024

Multithreaded cloud queue client.

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License: BSD License (BSD)

Author: Ignacio Tartavull, William Silversmith, and others

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python-task-queue

A client and system for generating, uploading, leasing, and executing dependency free tasks both locally and in the cloud using AWS SQS or on a single machine or cluster with a common file system using file based queues. Of note, file queue requires no setup or queue service and can be used in a distributed fashion on a network filesystem.

The task queue uses JSON as a messaging medium, so be aware that e.g. integer dictionary keys can be turned into strings when bound as a parameter.

Installation

The task queue uses your CloudVolume secrets located in $HOME/.cloudvolume/secrets/ . When using AWS SQS as your queue backend, you must provide $HOME/.cloudvolume/secrets/aws-secret.json . See the CloudVolume repo for additional instructions.

As of version 2.7.0, there are two ways to create a queueable task. The new way is simpler and probably preferred.

MacOS Only: Note that proxy servers are disabled for parallel operation due to libdispatch being not fork-safe.

New School: Queueable Functions

Designate a function as queueable using the @queueable decorator. Currently variable positional arguments ( *args ) and variable keyword arguments ( **kwargs ) are not yet supported. If a function is not marked with the decorator, it cannot be executed via the queue.

You then create queueable instantiations of these functions by using the standard library partial function to create a concrete binding.

Old School: RegisteredTask Subclasses

Define a class that inherits from taskqueue.RegisteredTask and implements the execute method. RegisteredTasks contain logic that will render their attributes into a JSON payload and can be reconstituted into a live class on the other side of a task queue.

Tasks can be loaded into queues locally or in the cloud and executed later. Here's an example implementation of a trivial PrintTask . The attributes of your container class should be simple values that can be easily encoded into JSON such as ints, floats, strings, and numpy arrays. Let the execute method download and manipulate heavier data. If you're feeling curious, you can see what JSON a task will turn into by calling task.payload() .

Local Usage

For small jobs, you might want to use one or more processes to execute the tasks.

This will load the queue with 1000 print tasks then execute them across five processes.

Cloud and Cluster Usage

Set up an SQS queue and acquire an aws-secret.json that is compatible with CloudVolume. Generate the tasks and insert them into the cloud queue.

You can alternatively set up a file based queue that has the same time-based leasing property of an SQS queue.

IMPORTANT: You must import the tasks that will be executed, otherwise the code to execute them has not been loaded.

This inserts 1000 PrintTask JSON descriptions into your SQS queue.

Somewhere else, you'll do the following (probably across multiple workers):

Poll will check the queue for a new task periodically. If a task is found, it will execute it immediately, delete the task from the queue, and request another. If no task is found, a random exponential backoff of up to 120sec is built in to prevent workers from attempting to DDOS the queue. If the task fails to complete, the task will eventually recirculate within the queue, ensuring that all tasks will eventually complete provided they are not fundementally flawed in some way.

Local Container testing

If there is a AWS compatible queue running on a local cluster, e.g. alpine-sqs , the underlying connection client needs additional parameters. These can be passed into the TaskQueue constructor.

The following code on a worker will work in local and production contexts:

Example docker-compose.yml for local testing:

Example docker-compose.yml for production:

Notes on Google PubSub

TaskQueue will try to connect to pubsub using the credentials it finds at ~/.cloudvolume/secrets/google-secret.json

You must first make both a topic and a subscription that is subscribed to that topic.

Then you can specify a taskqueue using this url format (which we invented to include both the project_id, topic and subscription)

Note that Google PubSub doesn't have all the same features as Amazon SQS, including statistics reporting, and so some features do not function properly with this backend.

Also, google pubsub libraries are not installed by default and so if you want to use this backend install with the pubsub option

Notes on File Queue

FileQueue ( fq:// ) is designed to simulate the timed task leasing feature from SQS and exploits a common filesystem to avoid requiring an additional queue server. You can read in detail about its design on the wiki .

There are a few things FileQueue can do that SQS can't and also some quirks you should be aware of. For one, FileQueue can track the number of task completions ( tq.completions , tq.poll(..., tally=True) ), but it does so by appending a byte to a file called completions for each completion. The size of the file in bytes is the number of completions. This design is an attempt to avoid problems with locking and race conditions. FileQueue also tracks insertions ( tq.insertions ) in a more typical way in an insertions file. Also unlike SQS, FileQueue allows listing all tasks at once.

FileQueue also allows releasing all current tasks from their leases, something impossible in SQS. Sometimes a few tasks will die immediately after leasing, but with a long lease, and you'll figure out how to fix them. Instead of starting over or waiting possibly hours, you can set the queue to be made available again ( tq.release_all() ).

As FileQueue is based on the filesystem, it can be managed somewhat via the command line. To delete a queue, just rm -r $QUEUE_PATH . To reset a counter: rm $QUEUE_PATH/completions (e.g.). If you are brave, you could even use the mv command to reassign a task's availability.

We also discovered that FileQueues are also amenable to fixing problems on the fly. In one case, we generated a set of tasks that took 4.5 hours of computation time and decided to run those tasks on a different cluster. The 500k tasks each contained a path to the old storage cluster. Using find , xargs , and sed we were able to fix them efficiently.

Bundled ptq CLI Tool

As of 2.5.0, we now bundle a command line tool ptq to make managing running FileQueues easier.

Distributed dependency free task execution engines (such as Igneous ) often make use of cloud based queues like Amazon Simple Queue Service (SQS). In the connectomics field we process petascale images which requires generating hundreds of thousands or millions of cloud tasks per a run. In one case, we were processing serial blocks of a large image where each block depended on the previous block's completion. Each block's run required the generation and upload of millions of tasks and the use of thousands of workers. The workers would rapidly drain the task queue and it was important to ensure that it could be fed fast enough to prevent starvation of this enormous cluster.

There are a few strategies for accomplishing this. One way might be to use a fully featured DAG supporting engine which could generate the next task on demand. However, we were experienced with SQS and had designed our architecture around it. Furthermore, it was, in our experience, robust to thousands of machines knocking on it. This does not discount that there could be better methods out there, but this was convenient for us.

The two major ways to populate the SQS queue at scale would be a task generating task so a single processor could could enlist hundreds or thousands of others or we could just make our task generating client fast and memory efficient and use a handful of cores for multiprocessing. Keeping things simple and local allows for greater operational flexibility and the addition of a drop-in mutiprocessing execution engine allows for the omission of cloud services for small jobs. Importantly, improved small scale performance doesn't preclude the later development of metageneration facilities.

By default, the Python task queue libraries are single threaded and blocking, resulting in upload rates of at most tens of tasks per second. It is possible to do much better by using threads, multiple processes, and by batching requests. TaskQueue has achivied upload rates of over 3000 tasks per second single core, and around 10,000 per second multicore on a single machine. This is sufficient to keep our cluster fed and allows for programmer flexibility as they can populate queues from their local machine using simple scripts.

How to Achieve High Performance

Attaining the quoted upload rates is simple but takes a few tricks to tune the queue. By default, TaskQueue will upload hundreds of tasks per second using its threading model. We'll show via progressive examples how to tune your upload script to get many thousands of tasks per second with near zero latency and memory usage. Note that the examples below use sqs:// , but apply to fq:// as well. These examples also use the old school style of task instantiation, but you can substitute the new style without consequence.

This first example shows how you might use the queue in the most naive fashion. The tasks list takes a long time to compute, uses a lot of memory, and then inserts a single task at a time, failing to exploit the threading model in TaskQueue. Note that this behavior has changed from previous versions where we endorsed the "with" statement where this form was faster, though still problematic.

The listing above allows you to use ordinary iterative programming techniques to achieve an upload rate of hundreds per a second without much configuration, a marked improvement over simply using boto nakedly. However, the initial generation of a list of tasks uses a lot of memory and introduces a delay while the list is generated.

This form also takes advantage of SQS batch upload which allows for submitting 10 tasks at once. As the overhead for submitting a task lies mainly in HTTP/1.1 TCP/IP connection overhead, batching 10 requests results in nearly a 10x improvement in performance. However, in this case we've created all the tasks up front again in order to batch them correctly which results in the same memory and latency issues as in Listing 1.

In Listing 3, we've started using generators instead of lists. Generators are essentially lazy-lists that compute the next list element on demand. Defining a generator is fast and takes constant time, so we are able to begin production of new elements nearly instantly. The elements are produced on demand and consumed instantly, resulting in a small constant memory overhead that can be typically measured in kilobytes to megabytes.

As generators do not support the len operator, we manually pass in the number of items to display a progress bar.

In Listing 4, we use the green=True argument to use cooperative threads. Under the hood, TaskQueue relies on Python kernel threads to achieve concurrent IO. However, on systems with mutliple cores, especially those in a virutalized or NUMA context, the OS will tend to distribute the threads fairly evenly between cores leading to high context-switching overhead. Ironically, a more powerful multicore system can lead to lower performance. To remedy this issue, we introduce a user-space cooperative threading model (green threads) using gevent (which depending on your system is uses either libev or libuv for an event loop).

This can result in a substantial performance increase on some systems. Typically a single core will be fully utilized with extremely low overhead. However, using cooperative threading with networked IO in Python requires monkey patching the standard library (!!). Refusing to patch the standard library will result in single threaded performance. Thus, using GreenTaskQueue can introduce problems into many larger applications (we've seen problems with multiprocessing and ipython). However, often the task upload script can be isolated from the rest of the system and this allows monkey patching to be safely performed. To give users more control over when they wish to accept the risk of monkey patching, it is not performed automatically and a warning will appear with instructions for amending your program.

In Listing 5, we finally move to multiprocessing to attain the highest speeds. There are three critical pieces of this construction to note.

First, we do not use the usual multiprocessing package and instead use concurrent.futures.ProcessPoolExecutor . If a child process dies in multiprocessing , the parent process will simply hang (this is by design unfortunately...). Using this alternative package, at least an exception will be thrown.

Second, we pass parameters for task generation to the child proceses, not tasks. It is not possible to pass generators from parent to child processes in CPython [1]. It is also inefficient to pass tasks directly as it requires first generating them (as in Listing 1) and then invisibly pickling and unpickling them as they are passed to the child processes. Therefore, we pass only a small number of small picklable objects that are used for constructing a task generator on the other side.

Third, as described in the narrative for Listing 5, the GreenTaskQueue has less context-switching overhead than ordinary multithreaded TaskQueue. Using GreenTaskQueue will cause each core to efficiently run independently of the others. At this point, your main bottlenecks will probably be OS/network card related (let us know if they aren't!). Multiprocessing does scale task production, but it's sub-linear in the number of processes. The task upload rate per a process will fall with each additional core added, but each core still adds additional throughput up to some inflection point.

If you insist on wanting to pass generators to your subprocesses, you can use iterators instead. The construction above allows us to write the generator call up front, pass only a few primatives through the pickling process, and transparently call the generator on the other side. We can even support the len() function which is not available for generators.

If you design your iterators such that the slice operator works, TaskQueue can automatically resection the iterator such that it can be fed to multiple processes. Notably, we don't return PrintTaskIterator(self.start+slc.start, self.start+slc.stop) because it triggers an endless recursion during pickling. However, the runtime copy implementation above sidesteps this issue. Internally, PrintTaskIterator(0,200) will be turned into [ PrintTaskIterator(0,100), PrintTaskIterator(100,200) ] . We also perform tracking of exceptions raised by child processes in a queue. gevent.monkey.patch_all(thread=False) was necessary to avoid multiprocess hanging.

[1] You can't pass generators in CPython but you can pass iterators . You can pass generators if you use Pypy or Stackless Python.

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python task queue example

Home » Python Concurrency » Python asyncio.create_task()

Python asyncio.create_task()

Summary : in this tutorial, you’ll learn how to use asyncio.create_task() function to run multiple tasks concurrently.

Simulating a long-running operation

To simulate a long-running operation, you can use the sleep() coroutine of the asyncio package. The sleep() function delays a specified number of the second:

Because sleep() is a coroutine, you need to use the await keyword. For example, the following uses the sleep() coroutine to simulate an API call:

The call_api() is a coroutine. It displays a message, pauses a specified number of seconds (default to three seconds), and returns a result.

The following program uses the call_api() twice and measures the time it takes to complete:

How it works (focusing on the main() coroutine):

First, start a timer to measure the time using the perf_counter() function of the time module:

Second, call the call_api() coroutine and display the result:

Third, call the call_api() a second time:

Finally, show the time the program takes to complete:

Because each call_api() takes three seconds, and calling it twice takes six seconds.

In this example, we call a coroutine directly and don’t put it on the event loop to run. Instead, we get a coroutine object and use the await keyword to execute it and get a result.

The following picture illustrates the execution flow of the program:

In other words, we use async and await to write asynchronous code but can’t run it concurrently. To run multiple operations concurrently, we’ll need to use something called tasks.

Introduction to Python tasks

  • A task is a wrapper of a coroutine that schedules the coroutine to run on the event loop as soon as possible.

The scheduling and execution occur in a non-blocking manner. In other words, you can create a task and execute other code instantly while the task is running.

Notice that the task is different from the await keyword that blocks the entire coroutine until the operation completes with a result.

It’s important that you can create multiple tasks and schedule them to run instantly on the event loop at the same time.

To create a task, you pass a coroutine to the create_task() function of the asyncio package. The create_task() function returns a Task object.

The following program illustrates how to create two tasks that schedule and execute the call_api() coroutine:

How it works.

First, start a timer:

Next, create a task and schedule it to run on the event loop immediately:

Then, create another task and schedule it to run on the event loop immediately:

After that, wait for the tasks to be completed:

It’s important to use the await keyword to wait for the tasks at some point in the program.

If we did not use the await keyword, Python would schedule the task to run but stopped it when the asyncio.run() shutdown the event loop.

Finally, show the time it takes to complete the main() function:

By using the create_task() function, the program is much faster. The more tasks you run, the faster it is.

Running other tasks while waiting

When the call_api is running, you can run other tasks. For example, the following program displays a message every second while waiting for the call_api tasks:

The following picture illustrates the execution flow:

  • Use the create_task() function of the asyncio library to create a task.
  • Use the await keyword with the task at some point in the program so that the task can be completed before the event loop is closed by the asyncio.run() function.

Asyncio.Queue.task_done is not bound to particular object or `Queue.get` call. It can pass queue.join() with unprocessed objects in queue

I have a question about asyncio.Queue . In documentation there is an example with multiple workers. And they use the task_done call to inform queue about the finished task.

I’ve played a little bit with this example and found the task_done function ambiguous. It is not related to particular object or Queue.get call. And it can be called even without queue.get() . And, what is important, multiple calls of it can break queue.join() barrier. Even if the queue still has objects to process.

Here is my modified code:

Here you can find function bad_worker which doesn’t take any task from queue . But it decrements tasks count by queue.task_done() . And after 20 task_done calls in summary from good workers and bad worker we pass through queue.join() .

Is it intended behavior?

I don’t like 2 moments here. Maybe I just don’t understand all the circumstances.

Queue.task_done() is not related to a particular object or Queue.get call. So if somewhere in code I need to notify queue with .task_done() I cannot be sure that I notify about an object which I got with Queue.get . Some other functions may have already notified queue when current function was awaiting.

Queue.join function does not care about the number of items in the queue. In code you can see that I call Queue.qsize() after await queue.join() . And it returns non-zero value. I guess here should be a check of the qsize . Why does Queue class watch at the internal counter of task_done calls instead of actual number of items in queue? So if somewhere in your code task_done will be called by mistake you will anyway pass through queue.join() with unprocessed items in queue. I would not like this behaviour. It would be nice to have at least some warning in such cases.

Yeah, that’s how it’s designed. You just need to have some discipline in your code. If that’s not what you want, you can always wrap the queue in a different mechanism that you design yourself.

Ok, I accept the need for discipline from the developer side. But is it that bad to have a warning in queue.join() about non-zero value of items in the queue? It could help in debugging a lot.

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What's a Task Queue?

Task queues are used as a mechanism to distribute work across threads or machines.

A task queue's input is a unit of work, called a task, dedicated worker processes then constantly monitor the queue for new work to perform.

Celery communicates via messages, usually using a broker to mediate between clients and workers. To initiate a task a client puts a message on the queue, the broker then delivers the message to a worker.

A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling.

Celery is written in Python, but the protocol can be implemented in any language. In addition to Python there's node-celery for Node.js, a PHP client , gocelery , gopher-celery for Go, and rusty-celery for Rust.

Language interoperability can also be achieved by using webhooks in such a way that the client enqueues an URL to be requested by a worker.

What do I need?

Celery version 5.3.5 runs on:

  • Python (3.8, 3.9, 3.10, 3.11, 3.12)
  • PyPy3.9+ (v7.3.12+)

This is the version of celery which will support Python 3.8 or newer.

If you're running an older version of Python, you need to be running an older version of Celery:

  • Python 3.7: Celery 5.2 or earlier.
  • Python 3.6: Celery 5.1 or earlier.
  • Python 2.7: Celery 4.x series.
  • Python 2.6: Celery series 3.1 or earlier.
  • Python 2.5: Celery series 3.0 or earlier.
  • Python 2.4: Celery series 2.2 or earlier.

Celery is a project with minimal funding, so we don't support Microsoft Windows but it should be working. Please don't open any issues related to that platform.

Celery is usually used with a message broker to send and receive messages. The RabbitMQ, Redis transports are feature complete, but there's also experimental support for a myriad of other solutions, including using SQLite for local development.

Celery can run on a single machine, on multiple machines, or even across datacenters.

Get Started

If this is the first time you're trying to use Celery, or you're new to Celery v5.4.x coming from previous versions then you should read our getting started tutorials:

First steps with Celery

Tutorial teaching you the bare minimum needed to get started with Celery.
A more complete overview, showing more features.
You can also get started with Celery by using a hosted broker transport CloudAMQP. The largest hosting provider of RabbitMQ is a proud sponsor of Celery.

Celery is...

Celery is easy to use and maintain, and does not need configuration files . It has an active, friendly community you can talk to for support, like at our mailing-list , or the IRC channel. Here's one of the simplest applications you can make: from celery import Celery app = Celery ( 'hello' , broker = 'amqp://guest@localhost//' ) @ app . task def hello (): return 'hello world'

Highly Available

Workers and clients will automatically retry in the event of connection loss or failure, and some brokers support HA in way of Primary/Primary or Primary/Replica replication.
A single Celery process can process millions of tasks a minute, with sub-millisecond round-trip latency (using RabbitMQ, py-librabbitmq, and optimized settings).
Almost every part of Celery can be extended or used on its own, Custom pool implementations, serializers, compression schemes, logging, schedulers, consumers, producers, broker transports, and much more.

It supports...

Message Transports RabbitMQ , Redis , Amazon SQS

Concurrency

Prefork, Eventlet , gevent , single threaded ( solo )

Result Stores

AMQP, Redis memcached SQLAlchemy, Django ORM Apache Cassandra, IronCache, Elasticsearch

Serialization

pickle , json , yaml , msgpack . zlib , bzip2 compression. Cryptographic message signing.

Framework Integration

Celery is easy to integrate with web frameworks, some of which even have integration packages:

Django not needed Pyramid pyramid_celery Pylons celery-pylons Flask not needed web2py web2py-celery Tornado tornado-celery FastAPI not needed

The integration packages aren't strictly necessary, but they can make development easier, and sometimes they add important hooks like closing database connections at fork .

Documentation

The latest documentation is hosted at Read The Docs, containing user guides, tutorials, and an API reference.

最新的中文文档托管在 https://www.celerycn.io/ 中,包含用户指南、教程、API接口等。

Installation

You can install Celery either via the Python Package Index (PyPI) or from source.

To install using pip :

Celery also defines a group of bundles that can be used to install Celery and the dependencies for a given feature.

You can specify these in your requirements or on the pip command-line by using brackets. Multiple bundles can be specified by separating them by commas.

The following bundles are available:

Serializers

for using the auth security serializer.

for using the msgpack serializer.

for using the yaml serializer.

for using the eventlet pool.

for using the gevent pool.

Transports and Backends

for using the RabbitMQ amqp python library.

for using Redis as a message transport or as a result backend.

for using Amazon SQS as a message transport.

for using the task_remote_tracebacks feature.

for using Memcached as a result backend (using pylibmc )

for using Memcached as a result backend (pure-Python implementation).

for using Apache Cassandra/Astra DB as a result backend with the DataStax driver.

for using Azure Storage as a result backend (using azure-storage )

for using S3 Storage as a result backend.

for using Google Cloud Storage as a result backend.

for using Couchbase as a result backend.

for using ArangoDB as a result backend.

for using Elasticsearch as a result backend.

for using Riak as a result backend.

for using Azure Cosmos DB as a result backend (using pydocumentdb )

for using Zookeeper as a message transport.

for using SQLAlchemy as a result backend ( supported ).

for using the Pyro4 message transport ( experimental ).

for using the SoftLayer Message Queue transport ( experimental ).

for using the Consul.io Key/Value store as a message transport or result backend ( experimental ).

specifies the lowest version possible for Django support.

You should probably not use this in your requirements, it's here for informational purposes only.

Downloading and installing from source

Download the latest version of Celery from PyPI:

You can install it by doing the following:

The last command must be executed as a privileged user if you aren't currently using a virtualenv.

Using the development version

The Celery development version also requires the development versions of kombu , amqp , billiard , and vine .

You can install the latest snapshot of these using the following pip commands:

Please see the Contributing section.

Getting Help

Mailing list.

For discussions about the usage, development, and future of Celery, please join the celery-users mailing list.

Come chat with us on IRC. The #celery channel is located at the Libera Chat network.

Bug tracker

If you have any suggestions, bug reports, or annoyances please report them to our issue tracker at https://github.com/celery/celery/issues/

https://github.com/celery/celery/wiki

Contributors

This project exists thanks to all the people who contribute. Development of celery happens at GitHub: https://github.com/celery/celery

You're highly encouraged to participate in the development of celery . If you don't like GitHub (for some reason) you're welcome to send regular patches.

Be sure to also read the Contributing to Celery section in the documentation.

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  • Python 98.5%

How To Schedule Periodic Tasks Using Celery Beat

asynchronous messaging , Celery , Celery beat , periodic tasks

May 14, 2024 | 6 Minute Read

How To Schedule Periodic Tasks Using Celery Beat

By: Qais Qadri, Senior Software Engineer

Get Your Free Copy

Table of contents.

Developers use Cron and Crontabs to schedule repetitive, asynchronous tasks that have to be carried out at specific intervals.

A Cron allows developers to schedule jobs in Unix-like operating systems to run shell scripts or commands periodically at fixed intervals, dates, or times. This automation of tasks is beneficial when you need to conduct periodical system maintenance, download files from the internet, perform repetitive admin tasks, and other such use cases.

Developers face challenges when reconfiguring Crons. First, they have to log into the system running the service. Then, they can access the system configuration file (crontabs) to update Crons. This process may require them to restart services. Additionally, there is no observability, monitoring, logging, and visibility on top of those Cron Jobs.

If you want to run very small commands, Crons perform and transact well because they retain that simplicity. But when you’re using them on a higher level, in complex projects where you need to monitor and visualize their activities, Crons don’t quite meet the expectations.

In that case, developers should move to something more advanced like Celery and Celery Beat to schedule their repetitive or periodic asynchronous tasks.

What Is Celery And How Does It Work?

Celery is an asynchronous distributed task queue system that allows developers to efficiently use compute resources.

Let’s assume you’re calling an API. Now, the connection is open until this API returns some data. As a result, the CPU resources must process something like database queries or wait for some third-party APIs to return data. But that would mean it is wasting CPU cycles.

The CPU keeps programs running continuously. Celery optimizes resource use by running tasks asynchronously, where you run the job, pass it to a queue, and then let the CPU do other tasks, such as responding to other requests.

And one of its key advantages is dynamic task scheduling. In other words, it allows developers to manage tasks programmatically at runtime rather than limiting them to making direct edits only in Crontab files.

What is Celery Beat?

Celery Beat is a periodic task scheduler that belongs to the Celery distributed task queue system. As the name suggests, just like how heartbeats are continuous, Celery Beat has the beat internally, which keeps checking internally (ticking): Are there any schedules I need to run?   While Celery and Celery Beat are Python-specific tools, they are highly flexible. They are built on an architecture that appreciates diversity and enables them to work with workers written in other languages. All you have to do is configure the Celery package accordingly so it ensures seamless interoperability with the workers.

Why Aren’t Crons A Viable Solution?

If you're using it in a local environment, you can always edit this crontab file. But imagine this situation where you have a production system deployed on multiple machines and multiple VMs right now.  Assume you want to add a Cron to clean some data from your database. Before proceeding, you may have to answer a couple of questions. Which machine would you choose to do this? If you’re using an auto-scaling system, you have to know and decide which type of machine will always be available. How do you know that your resources aren’t misused? You have multiple instances running, and Crons will also be part of these instances. Now, for example, in the first instance where a cron ran, it could’ve replicated itself into the second one also unnecessarily, and it reruns the same thing. Now, that's a clear case of misuse of resources. Also, there can be situations where you intentionally want Crons to run across multiple services. However, that control is lost in the case of Cron jobs. While you don't get that control while using Crons, you get the same when you use Celery Beat.

Celery Beat’s Real-Life Use Cases

Use case 1: generating periodic reports.

Developers can define a scheduled task within Celery by specifying the time, and it will automatically run, ensuring all those failsafe mechanisms are met and will generate a report. After generating the report, whether you want to publish it to your users, push it to centralized storage, or pass it to some model is part of your application's use case and how you have to handle that. However, Celery itself automates task scheduling; no manual intervention is required. You define the task once, and then Celery will take care of it. Now, how do you schedule the tasks? For example, you can run it every five hours, run it every day at 5 pm, or run it every alternate day at 6 pm. All these nuances in terms of scheduling are also taken care of by using the Crontab mechanisms, such as how Crons are defined. The Crons are defined as an asterisk. You can use five asterisks to define the time as follows:

The first asterisk denotes the minute  The second asterisk denotes the hour The third asterisk denotes the day  The fourth asterisk denotes the month  The fifth asterisk denotes the year  This is how you define when a job should run and at what intervals. Using this syntax, you can now define your Crontab within Celery, and Celery Beat automatically runs the task that needs to be run. All you have to do is set up the Celery workers. How the workers fetch the tasks and then run them is Celery's job. It’s like delegating everything to Celery: define the repetitive tasks and assign corresponding workers. Now, Celery will handle everything on its own.

Use Case 2: Dynamic Addition Of Periodic Tasks In Run-Time

Processing employee payslips is another repetitive task that occurs every month. But imagine the case where a new employee joins and is onboarded to an HR tool.

Now, you need to generate that person’s payslip and the leave allowances that should be newly added to this user. If the system does not support dynamically adding a new entry into schedules, you may have to stop the service to schedule the jobs and then restart your service.

Since Celery Beat supports dynamic editing, when you create and submit a new employee profile in the HR tool, a new entry is added to Celery Beat’s scheduling system. The job is now scheduled, and the new employee is processed every month.

Scheduling Periodic Tasks With Celery Beat

# First, install celery $ pip install celery # Next, have the required dependencies up, like in this case, we will use Redis as our ‘broker’ and ‘result backend.’ # To start a Redis server from docker  docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest ****** celery_app.py ******import os from celery import Celery # define workers who run the tasks in the background app = Celery( "Worker", backend=f"redis://{os.environ.get('REDIS_HOST', 'localhost')}:{os.environ.get('REDIS_PORT', '6379')}/0", broker=f"redis://{os.environ.get('REDIS_HOST', 'localhost')}:{os.environ.get('REDIS_PORT', '6379')}/0", ) # define tasks @app.task def send_welcome_email(user_id):     # Fetch user details from the database     user = User.objects.get(id=user_id)          # Construct the email subject and body     subject = f"Welcome to Our Platform, {user.username}!"     message = f"Dear {user.username},\n\nWelcome to our platform We're glad to have you.\n\nBest,\nThe Team"          # Send the email     send_mail(subject, message, settings.DEFAULT_FROM_EMAIL, [user.email]) @app.task(queue="<queue-name>", soft_time_limit=300, ignore_result=True, acks_late=True) def periodic_task_to_do() -> None:     # business logic here e.g., generating hourly report     # Load data from your data source (e.g., database, CSV files)     data = load_data()  # Placeholder function to load data          # Process and aggregate data     processed_data = process_data(data)  # Placeholder function to process data          # Generate report     report = generate_report(processed_data)  # Placeholder function to generate report          # Save the report to a file or send it via email     save_or_send_report(report)  # Placeholder function to save or send report ****** periodic_beats.py ******

# define celery beat schedules from celery.schedules import crontab from celery_app import app, periodic_task_to_do app.conf.timezone = "UTC" # Another way  app.conf.beat_schedule = {     'add-every-minute': {         'task': 'periodic_task_to_do',         'schedule': 10.0,   # This runs the task every minute     }, } @app.on_after_finalize.connect def setup_periodic_tasks(sender, **kwargs):     # Schedule process periodic_task_to_domarkets settlement to run every hour 0th minute     sender.add_periodic_task(         crontab(minute="0", hour="*"),  # 0th minute of every hour         periodic_task_to_do.s(),         name="process-periodic-task-every-hour",     ) bg_executor.autodiscover_tasks() # to discover tasks

# To run: # First, run the workers which listen to the queue for tasks python -m celery --app=celery_app worker --queues=main-queue --concurrency=10 --loglevel=info # and then run celery beat python -m celery -A periodic_beats beat --loglevel=info

You can also use custom schedulers with Celery Beat.

For more details, please refer to Celery Beat’s documentation here .

Handling Failures With Celery Beat

Task retries.

Celery Beat automatically retries a task when it fails due to an exception or error. Instead of marking it as failed immediately, Celery retries executing the task based on a predefined policy.

This predefined policy specifies parameters such as delay between retry attempts, maximum number of retry attempts, and back-off strategy.

logger = logging.getLogger(__name__) @app.task(bind=True, autoretry_for=(Exception,), max_retries=3, retry_backoff=True) def send_welcome_email(self, user_id):     try:         # Fetch user details from the database         user = User.objects.get(id=user_id)                  # Construct the email subject and body         subject = f"Welcome to Our Platform, {user.username}!"         message = f"Dear {user.username},\n\nWelcome to our platform We're glad to have you.\n\nBest,\nThe Team"                  # Send the email         send_mail(subject, message, settings.DEFAULT_FROM_EMAIL, [user.email])     except Exception as ex:         logger.exception("Failed to send welcome email")         self.retry(countdown=3**self.request.retries)

@app.task: This decorator defines a Celery task within a module other than the main module of your Celery application. It's similar to shared_task but is used when you want to define tasks within a specific application or feature module. bind=True: This argument allows the task to access the self-keyword argument, which is necessary for using the retry method within the task. Without bind=True, the task would not be able to call self.retry. autoretry_for=(Exception,): This specifies that the task should automatically retry on any exception. If you want to retry only on certain types of errors, you can replace 'Exception' with specific exceptions. max_retries=3: This sets the maximum number of retries for the task. If the task fails after being retried this many times, it will stop retrying and raise the last exception. retry_backoff=True: This enables exponential backoff for retries, meaning the delay between retries increases exponentially, reducing the load on the system and giving transient issues time to resolve themselves.

self.retry(countdown=3**self.request.retries): This line attempts to retry the task. The countdown parameter specifies the initial delay before the first retry. It uses an exponential backoff strategy, where the delay increases exponentially with each retry attempt.

Qais Qadri, Senior Software Engineer

Qais Qadri, Senior Software Engineer

Qais enjoys exploring places, going on long drives, and hanging out with close ones. He likes to read books about life and self-improvement, as well as blogs about technology. He also likes to play around with code. Qais lives by the values of Gratitude, Patience, and Positivity.

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Hanush Kumar, Marketing Associate

Hanush finds joy in YouTube content on automobiles and smartphones, prefers watching thrillers, and enjoys movie directors' interviews where they give out book recommendations. His essential life values? Positivity, continuous learning, self-respect, and integrity.

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  • Python »
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  • 1. Whetting Your Appetite
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1. Whetting Your Appetite ¶

If you do much work on computers, eventually you find that there’s some task you’d like to automate. For example, you may wish to perform a search-and-replace over a large number of text files, or rename and rearrange a bunch of photo files in a complicated way. Perhaps you’d like to write a small custom database, or a specialized GUI application, or a simple game.

If you’re a professional software developer, you may have to work with several C/C++/Java libraries but find the usual write/compile/test/re-compile cycle is too slow. Perhaps you’re writing a test suite for such a library and find writing the testing code a tedious task. Or maybe you’ve written a program that could use an extension language, and you don’t want to design and implement a whole new language for your application.

Python is just the language for you.

You could write a Unix shell script or Windows batch files for some of these tasks, but shell scripts are best at moving around files and changing text data, not well-suited for GUI applications or games. You could write a C/C++/Java program, but it can take a lot of development time to get even a first-draft program. Python is simpler to use, available on Windows, macOS, and Unix operating systems, and will help you get the job done more quickly.

Python is simple to use, but it is a real programming language, offering much more structure and support for large programs than shell scripts or batch files can offer. On the other hand, Python also offers much more error checking than C, and, being a very-high-level language , it has high-level data types built in, such as flexible arrays and dictionaries. Because of its more general data types Python is applicable to a much larger problem domain than Awk or even Perl, yet many things are at least as easy in Python as in those languages.

Python allows you to split your program into modules that can be reused in other Python programs. It comes with a large collection of standard modules that you can use as the basis of your programs — or as examples to start learning to program in Python. Some of these modules provide things like file I/O, system calls, sockets, and even interfaces to graphical user interface toolkits like Tk.

Python is an interpreted language, which can save you considerable time during program development because no compilation and linking is necessary. The interpreter can be used interactively, which makes it easy to experiment with features of the language, to write throw-away programs, or to test functions during bottom-up program development. It is also a handy desk calculator.

Python enables programs to be written compactly and readably. Programs written in Python are typically much shorter than equivalent C, C++, or Java programs, for several reasons:

the high-level data types allow you to express complex operations in a single statement;

statement grouping is done by indentation instead of beginning and ending brackets;

no variable or argument declarations are necessary.

Python is extensible : if you know how to program in C it is easy to add a new built-in function or module to the interpreter, either to perform critical operations at maximum speed, or to link Python programs to libraries that may only be available in binary form (such as a vendor-specific graphics library). Once you are really hooked, you can link the Python interpreter into an application written in C and use it as an extension or command language for that application.

By the way, the language is named after the BBC show “Monty Python’s Flying Circus” and has nothing to do with reptiles. Making references to Monty Python skits in documentation is not only allowed, it is encouraged!

Now that you are all excited about Python, you’ll want to examine it in some more detail. Since the best way to learn a language is to use it, the tutorial invites you to play with the Python interpreter as you read.

In the next chapter, the mechanics of using the interpreter are explained. This is rather mundane information, but essential for trying out the examples shown later.

The rest of the tutorial introduces various features of the Python language and system through examples, beginning with simple expressions, statements and data types, through functions and modules, and finally touching upon advanced concepts like exceptions and user-defined classes.

Previous topic

The Python Tutorial

2. Using the Python Interpreter

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  1. Task Queues

    The Celery distributed task queue is the most commonly used Python library for handling asynchronous tasks and scheduling. The RQ (Redis Queue) is a simple Python library for queueing jobs and processing them in the background with workers. RQ is backed by Redis and is designed to have a low barrier to entry. Taskmaster is a lightweight simple ...

  2. Developing an Asynchronous Task Queue in Python

    Download and install Redis if you do not already have it installed. Then, install the Python interface: (env)$ pip install redis==4 .5.5. We'll break the logic up into four files: redis_queue.py creates new queues and tasks via the SimpleQueue and SimpleTask classes, respectively.

  3. Flask by Example

    Part Four: Implement a Redis task queue to handle the text processing. (current) Part Five: Set up Angular on the front-end to continuously poll the back-end to see if the request is done processing. Part Six: Push to the staging server on Heroku - setting up Redis and detailing how to run two processes (web and worker) on a single Dyno.

  4. queue

    The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer thread calls task_done() to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero, join() unblocks. Example of how to wait for enqueued tasks to be completed:

  5. Python Stacks, Queues, and Priority Queues in Practice

    It's a basic priority queue implementation, which defines a heap of elements using a Python list and two methods that manipulate it. The .enqueue_with_priority() method takes two arguments, a priority and a corresponding value, which it then wraps in a tuple and pushes onto the heap using the heapq module.

  6. GitHub

    Taskiq is an asynchronous distributed task queue for python. This project takes inspiration from big projects such as Celery and Dramatiq . But taskiq can send and run both the sync and async functions, has integration with popular async frameworks, such as FastAPI and AioHTTP. Also, we use PEP-612 to provide the best autosuggestions possible.

  7. Python Celery Tutorial

    Order Queue is a task queue in Celery. A Task Queue is queue of tasks to be executed by workers. NOTE : Celery uses a Message Broker and it's Messaging Queue for it's operations.

  8. GitHub

    a task queue; written in python; clean and simple API; redis, sqlite, file-system, or in-memory storage; example code. read the documentation. huey supports: multi-process, multi-thread or greenlet task execution models; schedule tasks to execute at a given time, or after a given delay; schedule recurring tasks, like a crontab; automatically ...

  9. Using Python RQ for Task Queues in Python

    Install python-rq: Create the task which will be actioned by our workers, in our case it will just be a simple function that adds all the numbers from a given string to a list, then adds them up and return the total value. This is however a very basic task, but its just for demonstration. Our tasks.py: return total.

  10. Task Queues

    The task queues are not all compatible with Python but ones that work with it are tagged with the "Python" keyword. Why Task Queues is a presentation for what task queues are and why they are needed. Flask by Example Implementing a Redis Task Queue provides a detailed walkthrough of setting up workers to use RQ with Redis.

  11. Huey: A little task queue for python

    huey is: a task queue ( 2019-04-01: version 2.0 released) written in python (2.7+, 3.4+) clean and simple API. redis, sqlite, or in-memory storage. example code. huey supports: multi-process, multi-thread or greenlet task execution models. schedule tasks to execute at a given time, or after a given delay.

  12. Working with Queues in Python

    Use Cases for Queues in Python. Some examples of where queues are commonly used in Python: - Asynchronous task queues — Use a queue to store tasks to be processed asynchronously by workers - Job schedulers — A queue can store jobs to be executed by multiple threads/processes

  13. RQ: Simple job queues for Python

    RQ ( Redis Queue) is a simple Python library for queueing jobs and processing them in the background with workers. It is backed by Redis and it is designed to have a low barrier to entry. It can be integrated in your web stack easily. RQ requires Redis >= 3.0.0.

  14. GitHub

    A TaskTiger object keeps track of TaskTiger's settings and is used to decorate and queue tasks. The constructor takes the following arguments: connection. Redis connection object. The connection should be initialized with decode_responses=True to avoid encoding problems on Python 3. config.

  15. How to Run Your First Task with RQ, Redis, and Python

    Start up the virtual environment again with the command source venv/bin/activate. Then type python app.py to run the project. Go back to the tab that is running rq worker --with-scheduler. Wait 5 more seconds after the first task is executed to see the next task.

  16. Async IO in Python: A Complete Walkthrough

    Using a Queue. The asyncio package provides queue classes that are designed to be similar to classes of the queue module. In our examples so far, we haven't really had a need for a queue structure. In chained.py, each task (future) is composed of a set of coroutines that explicitly await each other and pass through a single input per chain.

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    The async with statement will wait for all tasks in the group to finish. While waiting, new tasks may still be added to the group (for example, by passing tg into one of the coroutines and calling tg.create_task() in that coroutine). Once the last task has finished and the async with block is exited, no new tasks may be added to the group.. The first time any of the tasks belonging to the ...

  18. Python Thread Queue

    For example, the following creates a queue that can store up to 10 items: queue = Queue(maxsize= 10) Code language: Python (python) ... time.sleep(2) queue.task_done() Code language: Python (python) The queue.task_done() indicates that the function has processed the item on the queue.

  19. Asyncio Queue in Python

    What is an Asyncio Queue. The asyncio.Queue provides a FIFO queue for use with coroutines.. Before we dive into the details of the asyncio.Queue, let's take a quick look at queues more generally in Python.. Queue. A queue is a data structure on which items can be added by a call to put() and from which items can be retrieved by a call to get().. Python provides a thread-safe queue via the ...

  20. task-queue · PyPI

    python-task-queue. A client and system for generating, uploading, leasing, and executing dependency free tasks both locally and in the cloud using AWS SQS or on a single machine or cluster with a common file system using file based queues. ... This first example shows how you might use the queue in the most naive fashion. The tasks list takes a ...

  21. Queues

    A first in, first out (FIFO) queue. If maxsize is less than or equal to zero, the queue size is infinite. If it is an integer greater than 0, then await put() blocks when the queue reaches maxsize until an item is removed by get(). Unlike the standard library threading queue, the size of the queue is always known and can be returned by calling ...

  22. Python asyncio.create_task(): Run Multiple Tasks Concurrently

    To create a task, you pass a coroutine to the create_task() function of the asyncio package. The create_task() function returns a Task object. The following program illustrates how to create two tasks that schedule and execute the call_api() coroutine: import time. async def call_api(message, result=1000, delay=3):

  23. Asyncio.Queue.task_done is not bound to particular object or `Queue.get

    I have a question about asyncio.Queue. In documentation there is an example with multiple workers. And they use the task_done call to inform queue about the finished task. I've played a little bit with this example and found the task_done function ambiguous. It is not related to particular object or Queue.get call.

  24. Python: Distributed task queue for different specific workers

    I am looking for a python library / framework that manages task distribution (e.g. a task queue). However, tasks will require specialized workers: Worker A can only handle tasks of type a, workers B and C only of type b etc. Also, these workers will run on different computers and cannot share the same codebase (since, like in a fabrication line, each task is bound to controlling specific ...

  25. GitHub

    A task queue's input is a unit of work, called a task, dedicated worker processes then constantly monitor the queue for new work to perform. Celery communicates via messages, usually using a broker to mediate between clients and workers. To initiate a task a client puts a message on the queue, the broker then delivers the message to a worker.

  26. How To Schedule Periodic Tasks Using Celery Beat

    Celery is an asynchronous distributed task queue system that allows developers to efficiently use compute resources. ... Now, for example, in the first instance where a cron ran, it could've replicated itself into the second one also unnecessarily, and it reruns the same thing. ... run the workers which listen to the queue for tasks python -m ...

  27. heapq

    heapq. — Heap queue algorithm. ¶. Source code: Lib/heapq.py. This module provides an implementation of the heap queue algorithm, also known as the priority queue algorithm. Heaps are binary trees for which every parent node has a value less than or equal to any of its children. We refer to this condition as the heap invariant.

  28. 1. Whetting Your Appetite

    Whetting Your Appetite — Python 3.14.0a0 documentation. 1. Whetting Your Appetite ¶. If you do much work on computers, eventually you find that there's some task you'd like to automate. For example, you may wish to perform a search-and-replace over a large number of text files, or rename and rearrange a bunch of photo files in a ...

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  30. Welcome to Claude

    Visit claude.ai! Claude is a family of large language models developed by Anthropic and designed to revolutionize the way you interact with AI. Claude excels at a wide variety of tasks involving language, reasoning, analysis, coding, and more. Our models are highly capable, easy to use, and can be customized to suit your needs.