This document describes the current stable version of Celery (5.6). For development docs, go here.

# What’s new in Celery 4.4 (Cliffs)

Author:

  Asif Saif Uddin (`auvipy at gmail.com`)

Celery is a simple, flexible, and reliable distributed programming framework to process vast amounts of messages, while providing operations with the tools required to maintain a distributed system with python.

It’s a task queue with focus on real-time processing, while also supporting task scheduling.

Celery has a large and diverse community of users and contributors, you should come join us on IRC or our mailing-list.

To read more about Celery you should go read the introduction.

While this version is backward compatible with previous versions it’s important that you read the following section.

This version is officially supported on CPython 2.7, 3.5, 3.6, 3.7 & 3.8 and is also supported on PyPy2 & PyPy3.

Table of Contents

Make sure you read the important notes before upgrading to this version.

## Preface

The 4.4.0 release continues to improve our efforts to provide you with the best task execution platform for Python.

This release has been codenamed Cliffs which is one of my favorite tracks.

This release focuses on mostly bug fixes and usability improvement for developers. Many long standing bugs, usability issues, documentation issues & minor enhancement issues were squashed which improve the overall developers experience.

Celery 4.4 is the first release to support Python 3.8 & pypy36-7.2.

As we now begin to work on Celery 5, the next generation of our task execution platform, at least another 4.x is expected before Celery 5 stable release & will get support for at least 1 years depending on community demand and support.

We have also focused on reducing contribution friction and updated the contributing tools.

— Asif Saif Uddin

### Wall of Contributors

Note

This wall was automatically generated from git history, so sadly it doesn’t not include the people who help with more important things like answering mailing-list questions.

## Upgrading from Celery 4.3

Please read the important notes below as there are several breaking changes.

## Important Notes

### Supported Python Versions

The supported Python Versions are:

- CPython 2.7

- CPython 3.5

- CPython 3.6

- CPython 3.7

- CPython 3.8

- PyPy2.7 7.2 (`pypy2`)

- PyPy3.5 7.1 (`pypy3`)

- PyPy3.6 7.2 (`pypy3`)

### Dropped support for Python 3.4

Celery now requires either Python 2.7 or Python 3.5 and above.

Python 3.4 has reached EOL in March 2019. In order to focus our efforts we have dropped support for Python 3.4 in this version.

If you still require to run Celery using Python 3.4 you can still use Celery 4.3. However we encourage you to upgrade to a supported Python version since no further security patches will be applied for Python 3.4.

### Kombu

Starting from this release, the minimum required version is Kombu 4.6.6.

### Billiard

Starting from this release, the minimum required version is Billiard 3.6.1.

### Redis Message Broker

Due to multiple bugs in earlier versions of redis-py that were causing issues for Celery, we were forced to bump the minimum required version to 3.3.0.

### Redis Result Backend

Due to multiple bugs in earlier versions of redis-py that were causing issues for Celery, we were forced to bump the minimum required version to 3.3.0.

### DynamoDB Result Backend

The DynamoDB result backend has gained TTL support. As a result the minimum boto3 version was bumped to 1.9.178 which is the first version to support TTL for DynamoDB.

### S3 Results Backend

To keep up with the current AWS API changes the minimum boto3 version was bumped to 1.9.125.

### SQS Message Broker

To keep up with the current AWS API changes the minimum boto3 version was bumped to 1.9.125.

### Configuration

CELERY_TASK_RESULT_EXPIRES has been replaced with CELERY_RESULT_EXPIRES.

## News

### Task Pools

#### Threaded Tasks Pool

We reintroduced a threaded task pool using concurrent.futures.ThreadPoolExecutor.

The previous threaded task pool was experimental. In addition it was based on the threadpool package which is obsolete.

You can use the new threaded task pool by setting `worker_pool` to ‘threads` or by passing –pool threads to the celery worker command.

### Result Backends

#### ElasticSearch Results Backend

##### HTTP Basic Authentication Support

You can now use HTTP Basic Authentication when using the ElasticSearch result backend by providing the username and the password in the URI.

Previously, they were ignored and only unauthenticated requests were issued.

#### MongoDB Results Backend

##### Support for Authentication Source and Authentication Method

You can now specify the authSource and authMethod for the MongoDB using the URI options. The following URI does just that:

> `mongodb://user:password@example.com/?authSource=the_database&authMechanism=SCRAM-SHA-256`

Refer to the documentation for details about the various options.

### Tasks

#### Task class definitions can now have retry attributes

You can now use autoretry_for, retry_kwargs, retry_backoff, retry_backoff_max and retry_jitter in class-based tasks:

    class BaseTaskWithRetry(Task):
      autoretry_for = (TypeError,)
      retry_kwargs = {'max_retries': 5}
      retry_backoff = True
      retry_backoff_max = 700
      retry_jitter = False

### Canvas

#### Replacing Tasks Eagerly

You can now call self.replace() on tasks which are run eagerly. They will work exactly the same as tasks which are run asynchronously.

#### Chaining Groups

Chaining groups no longer result in a single group.

The following used to join the two groups into one. Now they correctly execute one after another:

    >>> result = group(add.si(1, 2), add.si(1, 2)) | group(tsum.s(), tsum.s()).delay()
    >>> result.get()
    [6, 6]