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Top 23 Python Scheduler Projects
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Scout Monitoring
Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
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couler
Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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django-q2
A multiprocessing distributed task queue for Django. Django Q2 is a fork of Django Q. Big thanks to Ilan Steemers for starting this project. Unfortunately, development has stalled since June 2021. Django Q2 is the new updated version of Django Q, with dependencies updates, docs updates and several bug fixes. Original repository: https://github.com/Koed00/django-q
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AnnA_Anki_neuronal_Appendix
Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity
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regta
📅 Production-ready scheduler with async, multithreading and multiprocessing support for Python
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Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities.
Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities.
author seems to be describing the kind of patterns you might make with https://argoproj.github.io/argo-workflows/ . or see for example https://github.com/couler-proj/couler , which is an sdk for describing tasks that may be submitted to different workflow engines on the backend.
it's a little confusing to me that the author seems to object to "pipelines" and then equate them with messaging-queues. for me at least, "pipeline" vs "workflow-engine" vs "scheduler" are all basically synonyms in this context. those things may or may not be implemented with a message-queue for persistence, but the persistence layer itself is usually below the level of abstraction that $current_problem is really concerned with. like the author says, eventually you have to track state/timestamps/logs, but you get that from the beginning if you start with a workflow engine.
i agree with author that message-queues should not be a knee-jerk response to most problems because the LoE for edge-cases/observability/monitoring is huge. (maybe reach for a queue only if you may actually overwhelm whatever the "scheduler" can handle.) but don't build the scheduler from scratch either.. use argowf, kubeflow, or a more opinionated framework like airflow, mlflow, databricks, aws lamda or step-functions. all/any of these should have config or api that's robust enough to express rate-limit/retry stuff. almost any of these choices has better observability out-of-the-box than you can easily get from a queue. but most importantly.. they provide idioms for handling failure that data-science folks and junior devs can work with. the right way to structure code is just much more clear and things like structuring messages/events, subclassing workers, repeating/retrying tasks, is just harder to mess up.
Project mention: [Automation] Scheduling Python Programs: Pushing Notifications, Executing SQLs, etc... | dev.to | 2023-09-24If your automation needs are focused on Python exclusively, Regta emerges as a compelling option, offering a wealth of Python-specific optimizations. Give it a try, and feel free to share your thoughts in the comments 🙌
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
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A note from our sponsor - SaaSHub
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Index
What are some of the best open-source Scheduler projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Airflow | 34,877 |
| 2 | dagster | 10,468 |
| 3 | rocketry | 3,194 |
| 4 | rq-scheduler | 1,396 |
| 5 | flask-apscheduler | 1,096 |
| 6 | couler | 891 |
| 7 | OpenCue | 812 |
| 8 | scheduler-component | 592 |
| 9 | girok | 455 |
| 10 | TerrariumPI | 397 |
| 11 | django-q2 | 287 |
| 12 | Flask-RQ2 | 225 |
| 13 | deck-chores | 191 |
| 14 | diffusion-for-beginners | 145 |
| 15 | beatserver | 142 |
| 16 | plugsched | 78 |
| 17 | walnats | 64 |
| 18 | AnnA_Anki_neuronal_Appendix | 57 |
| 19 | WSPRBeacon | 42 |
| 20 | regta | 20 |
| 21 | JDR | 3 |
| 22 | pipelines | 3 |
| 23 | NiceHash-Mining-Scheduler | 2 |
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