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Celery is an implementation of the task queue concept, one that significantly enhances the end user's experience. It also benefits developers by saving them time, so that they can focus on what really matters — their ideas and innovations. That is why it remains one of the most popular background job managers in the Python world.

 

If you are a junior developer, Celery implementation can be one of the more challenging parts of a Python web application stack to grasp initially. However, this asynchronous application is well-worth the learning investment. Celery will allow you to better plan in terms of your overall work progress and become more efficient. That way, you can continue working on more important tasks while Celery tasks work their magic in the background.

Related: Celery vs. IronWorker — Which One Should You Choose?

 

Looking for a Celery Alternative?

Learn how to easily process tasks with IronWorker without all Phython code.

In this post, we will give an introduction to Celery as well as:

Introducing Message Queues and Workers

When developing data-intensive applications, users often experience longer load times in response to data-heavy tasks; and in today's highly technological world, they expect more. To ensure a faster, more streamlined experience, you'll need to offload some of the work.

 

This is achieved through asynchronicity and works as follows:

  • While a web server is busy loading the next page, a secondary server does the computations in the background. These are known as workers, which allow you to actively process numerous tasks without the user's awareness.
  • For example, these worker servers can send emails, make changes in the database, process files, add items to the cache, and much more! During this time, the main web server is able to respond to the user and their commands, significantly improving their immediate experience.
  • These workers know what to do because of message queues. These queues ensure that all tasks are started in the order that they're added. So, once a worker becomes available, it will begin to process the next task. This is where Celery comes into play. Celery is a task queue implementation for Python web applications. Meaning, it allows Python applications to rapidly implement task queues for many workers. It essentially does the hard work in that it receives tasks and then assigns them to workers as needed.

 

Bottom line: Celery is a framework that decreases performance load through postponed tasks, as it processes asynchronous and scheduled jobs. This can be achieved on the same server (as other tasks), or on a separate server. This Python-based task queue also saves designers a significant amount of time.

Celery is…

  • Simple — Once you understand what Celery is and how it's used, it's fairly easy to to implement and maintain. This application does not require configuration files.
  • Fast — A single process in Celery will process millions of tasks per minute with the support of applications such as RabbitMQ and IronMQ.
  • Flexible — Nearly every part of Celery can be extended or used on its own, offering greater flexibility and customization in terms of logging, broker transports, schedulers, compression schemes, etc.
  • It supports everything from Redis and Amazon SQS (brokers) to Apache Cassandra and Django ORM (result stores), as well as yaml, pickle, JSON, etc. (serialization).

 

It's important to note that although Celery is written in Python, it can be implemented in any language.

 

As a developer, you can use Celery is accomplish numerous goals, including your ability to:

  • Define tasks that workers can do as a Python function
  • Listen to a broker message (i.e. IronMQ) to receive new task requests)
  • Assign new requests to workers
  • Monitor the ongoing progress of tasks and workers

 

Please note: Celery is useful and most practical when aiming to execute tasks that will cost you in terms of resource utilization and performance. A great example is within the handler of an HTTP request.

Iron

IronWorker is the Celery Alternative

Learn how to easily process background tasks with IronWorker without all Phython code.

What Are the Main Features of Python Celery?

To get started, you'll need to create a Celery instance and then use that instance to mark Python functions as tasks. As you become more familiar with Celery, you can utilize a range of beneficial features.

Celery offers numerous features, including:

  • Monitoring — Events produced by workers are used by external and built-in tools in real-time to tell you what your cluster is doing. Learn more here.
  • Scheduling — Easily specify the time you want a task to run in seconds or datetime. You can use recurring events or periodic tasks, as well as Crontab expressions (i.e. minute, hour, day of the week, day of the month, and month of the year). Learn more here.
  • Work-flows — Both complex and simple workflows can be created using a set of primitives known as the canvas. These primitives include chains, groups, chunks, chords, etc. Learn more here.
  • Time and rate limits — Control how many tasks are executed per second, minute, or hour, as well as how long each task runs. This is highly customizable in terms of specific workers and individual task types. Learn more here.
  • Autoscaling — Celery has the ability to dynamically resize the worker pool to enforce a specific service quality.
  • High availability and horizontal scaling — Celery is compatible with multiple brokers and workers. It can also run on a single machine, one numerous machines, or across data centers.

Overall, some of the main pros associated with Celery include:

  • It is a free, open-source software.
  • It is easy to install. In most cases, you can install Celery from the terminal (simply use the command pip install -U Celery.
  • It supports multiple message brokers.
  • It integrates with multiple web frameworks.

Celery Best Practices

When you follow best practices, your Celery-enabled applications will perform better, be more resilient, and allow for improved monitoring. Here are some of the best practices to consider:

  • Give your tasks a name
  • Always define queue
  • Apply priority to a single queue
  • Become comfortable with chunking
  • Choose the correct result back end
  • Don't underestimate testing
  • Share common behavior among tasks
  • Write large tasks as classes

If you're programming with Celery, you will learn best practices as you go. The more experience you have, the easier these best practices will be.

Continue Learning about Celery...

Python Celery: Pros, Cons and Reviews

Before you set up Celery, check out IronWorker

Learn how IronWorker can manage and process your background tasks without having to write a ton of Phython code.

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