Overview

docs
tests
package

A reliable distributed scheduler with pluggable storage backends for Async Python.

• Free software: MIT license

Installation

Minimal installation (just SQLite persistence):

pip install pyncette

Full installation (all the backends and Prometheus metrics exporter):

pip install pyncette[all]

You can also install the in-development version with:

pip install https://github.com/tibordp/pyncette/archive/master.zip

Documentation

https://pyncette.readthedocs.io

Usage example

Simple in-memory scheduler (does not persist state)

from pyncette import Pyncette, Context

app = Pyncette()

@app.task(schedule='* * * * *')
async def foo(context: Context):
    print('This will run every minute')

if __name__ == '__main__':
    app.main()

Persistent distributed cron using Redis (coordinates execution with parallel instances and survives restarts)

from pyncette import Pyncette, Context
from pyncette.redis import redis_repository

app = Pyncette(repository_factory=redis_repository, redis_url='redis://localhost')

@app.task(schedule='* * * * * */10')
async def foo(context: Context):
    print('This will run every 10 seconds')

if __name__ == '__main__':
    app.main()

See the examples directory for more examples of usage.

Use cases

Pyncette is designed for reliable (at-least-once or at-most-once) execution of recurring tasks (think cronjobs) whose lifecycles are managed dynamically, but can work effectively for non-reccuring tasks too.

Example use cases:

• You want to perform a database backup every day at noon
• You want a report to be generated daily for your 10M users at the time of their choosing
• You want currency conversion rates to be refreshed every 10 seconds
• You want to allow your users to schedule non-recurring emails to be sent at an arbitrary time in the future

Pyncette might not be a good fit if:

• You want your tasks to be scheduled to run (ideally) once as soon as possible. It is doable, but you will be better served by a general purpose reliable queue like RabbitMQ or Amazon SQS.
• You need tasks to execute at sub-second intervals with low jitter. Pyncette coordinates execution on a per task-instance basis and this corrdination can add overhead and jitter.

Supported backends

Pyncette comes with an implementation for the following backends (used for persistence and coordination) out-of-the-box:

• SQLite (included)
• Redis (pip install pyncette[redis])
• PostgreSQL (pip install pyncette[postgres])
• MySQL 8.0+ (pip install pyncette[mysql])
• Amazon DynamoDB (pip install pyncette[dynamodb])

Pyncette imposes few requirements on the underlying datastores, so it can be extended to support other databases or custom storage formats / integrations with existing systems. For best results, the backend needs to provide:

• Some sort of serialization mechanism, e.g. traditional transactions, atomic stored procedures or compare-and-swap
• Efficient range queries over a secondary index, which can be eventually consistent

Development

To run integration tests you will need Redis, PostgreSQL, MySQL and Localstack (for DynamoDB) running locally.

To run the all tests run:

tox

Alternatively, there is a Docker Compose environment that will set up all the backends so that integration tests can run seamlessly:

docker-compose up -d
docker-compose run --rm shell
tox

To run just the unit tests (excluding integration tests):

tox -e py310  # or your Python version of choice

Note, to combine the coverage data from all the tox environments run:

Windows	set PYTEST_ADDOPTS=--cov-append tox
Other	PYTEST_ADDOPTS=--cov-append tox