<ul data-eligibleForWebStory="true"><li>The author found Celery and Cron to be inadequate for task scheduling in Python.</li><li>They developed a basic, non-blocking task runner using asyncio.</li><li>The approach allowed mixing async with blocking logic without freezing the main event loop.</li><li>The main thread can run the task runner or be spun off into its own daemon thread.</li><li>They utilized a pattern to handle transient failures like network blips or file locks.</li><li>The system provided predictable teardown behavior when running as a service.</li><li>Every job now runs with full visibility, eliminating the need to grep logs.</li><li>The architecture supported running over 10k jobs per week in production with minimal issues.</li><li>The project was rewarding and educational, offering insights into Python's concurrency.</li><li>It emphasized leveraging Python's core primitives to build powerful systems tailored to specific use cases.</li><li>The author encouraged others to try this approach for scheduling background tasks, starting small and growing iteratively.</li><li>Automation is highlighted as a means of both saving time and reclaiming control.</li></ul>

Mastering Background Tasks in Python: My Journey Building a Task Scheduler From Scratch

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