The built-in thread pool from the previous versions has been replaced with a pluggable executor system. With some frameworks, the integration can even provide a custom default executor (more on those in the next section) that runs the jobs in a built-in thread pool or similar. This avoids the use of a dedicated thread for the scheduler. The integration involves, at a minimum, the use of the event loop's built-in delayed execution mechanism. Integration with asynchronous event loopsĪPScheduler now integrates with several widely used asynchronous application frameworks. Individual jobs can also be scheduled with different timezones if necessary. The scheduler has a set timezone which defaults to the local timezone, but can easily set to, say, UTC instead. ![]() Now, in 3.0, all datetimes are timezone aware. Many users would've preferred it to always use the UTC timezone instead. One of the most frequent complaints about APScheduler was that it always operated in the host's local time. This will greatly help reduce the memory footprints of applications that need to handle large numbers of jobs. So starting with 3.0, persistent job stores no longer keep the jobs in memory, but instead rely on backend specific mechanisms (such as indexes) to efficiently fetch due jobs. And that would've been fine with a small number of jobs, but when use cases started popping up that required thousands upon thousands of jobs, it became a severe problem. This was to eliminate the overhead of fetching them from the backend (file or database). In previous versions, all the job stores cached all their jobs in memory. ![]() Probably the most important change in 3.0 is about the job stores. A guide is also provided for 2.x users for smoother migration to 3.0. Virtually all of the feature requests from 2.x have been fulfilled. The 3.0 update brings many new features and enhancements, albeit at the cost of a backward-incompatible API. I haven't personally used Celery, so I can't comment on that. Users tell me that APScheduler is easier to set up. If one were to compare APScheduler with Celery, the difference could be summarized like this: Celery is a distributed task queue with basic scheduling capabilities, while APScheduler is a full featured scheduler with basic task queuing capabilities. And of course it runs Python code instead of shell commands. Thinking of it as a cron/at daemon running inside your application is not far off, but APScheduler also provides management and monitoring of jobs, and much more. ![]() The first final version of APScheduler's 3.0 branch has been released.įor the uninitiated, APScheduler is a task scheduling and management system written in Python.
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