Advanced Python Scheduler (APScheduler) is a Python library that lets you schedule your Python codeto be executed later, either just once or periodically. You can add new jobs or remove old ones onthe fly as you please. If you store your jobs in a database, they will also survive schedulerrestarts and maintain their state. When the scheduler is restarted, it will then run all the jobsit should have run while it was offline 1.
Among other things, APScheduler can be used as a cross-platform, application specific replacementto platform specific schedulers, such as the cron daemon or the Windows task scheduler. Pleasenote, however, that APScheduler is not a daemon or service itself, nor does it come with anycommand line tools. It is primarily meant to be run inside existing applications. That said,APScheduler does provide some building blocks for you to build a scheduler service or to run adedicated scheduler process.
APScheduler has three built-in scheduling systems you can use:
- For every task created by the producer an entry is made in the database with a flag NotStarted, Running, Completed, Failed, and so on. At any point task workers (say a never-ending python program) will query this DB and look for incomplete tasks and start running it. It is a simple implementation but it has its own disadvantages.
- Since plain python is used to define your tasks the python debugger pdb is available. Doit command allows you to list and obtain help/documentation for tasks. Build tool & Pipelines. Simple task runners simply do not scale-up. Doit as other build-tools can be much more efficient at repeateadly running tasks. Cache task results.
To create one, go to the Tasks page, and look near the bottom - you'll see a table that looks like this: To set up an always-on task, you just need to enter the full command to run it into the input field - for example, - and click 'Create'. It will appear in the table below, with a state of 'Starting'. You can refresh the table using the.
Cron-style scheduling (with optional start/end times)
Interval-based execution (runs jobs on even intervals, with optional start/end times)
One-off delayed execution (runs jobs once, on a set date/time)
Python Task Runner
You can mix and match scheduling systems and the backends where the jobs are stored any way youlike. Supported backends for storing jobs include:
Python Task Runner Interview
Memory
SQLAlchemy (any RDBMS supported by SQLAlchemy works)
APScheduler also integrates with several common Python frameworks, like:
asyncio (PEP 3156)
Qt (using eitherPyQt ,PySide2 orPySide)
There are third party solutions for integrating APScheduler with other frameworks:
The cutoff period for this is also configurable.