agronholm/apscheduler
Apscheduler
APScheduler is a Python task scheduler designed to execute functions at specific times or recurring intervals. It functions as an asynchronous background scheduler and distributed job dispatcher, allowing tasks to run concurrently with application lifecycles and web server request handling.
The system distinguishes itself through a persistent job store that saves schedules and task states in external databases, ensuring continuity across process restarts. It separates task scheduling from execution by dispatching jobs to distributed workers in separate processes to prevent execution bottlenecks.
The library covers background job orchestration and recurring job scheduling using a variety of trigger logics. Its architecture includes pluggable execution backends and job store abstractions to decouple schedule storage from the execution engine.
Features
- Job Schedulers - Provides a comprehensive system for automating repetitive workflows through recurring job scheduling.
- Job State Persistence - Persists job metadata and schedules in external storage to maintain continuity across process restarts.
- Time-Based Triggers - Provides mechanisms for scheduling tasks based on cron expressions, fixed intervals, or specific dates.
- Task Schedulers - Registers and executes persistent Python functions at scheduled intervals or specific times.
- Background Job Processing - Offloads and manages asynchronous tasks in the background to keep the main request-response cycle responsive.
- Distributed Job Execution - Dispatches tasks from a central coordinator to distributed workers in separate processes to prevent bottlenecks.
- Job Scheduling - Manages the persistent scheduling and execution of background processing jobs.
- Thread Pools - Uses worker thread pools to execute asynchronous jobs without blocking the main scheduling loop.
- Scheduling Loops - Implements a continuous internal cycle that checks the job store and triggers pending tasks based on time.
- Background Job Dispatchers - Functions as a dispatcher that separates task scheduling from execution by queuing jobs for workers.
- Execution Backends - Allows swapping execution backends to run jobs either locally or on distributed worker processes.
- Storage Abstractions - Decouples schedule storage from the execution engine via interchangeable backends like memory or relational databases.
- Asynchronous Schedulers - Implements a scheduler that maps computational tasks to thread pools for non-blocking background execution.
- Job Schedulers - In-process task scheduler.
