# TaskShed
A high-performance, asynchronous, ready for production job scheduling framework.
TaskShed provides a simple API to schedule your Python coroutines for later execution. You can run tasks just once or on a recurring interval. The scheduler is dynamic, allowing you to add, update or remove tasks on the fly. Furthermore, by connecting to a persistent datastore, TaskShed ensures your tasks survive restarts and automatically catches up on any executions that were missed while the system was offline.
The key features are:
* **Fast**: TaskShed has a [very low latency, overhead and can execute several thousands tasks a second](https://chase-labs.github.io/taskshed/benchmarks/).
* **Distributed**: TaskShed has the capacity to spawn several workers and schedules across many machines, while also providing optimisation for monolinth architectures.
* **Persistant**: Tasks are stored in database, meaning that they won't get dropped on shutdown. TaskShed currently supports Redis and MySQL.
* **Easy**: TaskShed's modular architecture is straightforward and easy to set-up, and works in any asynchronous environement.
# Installation 🔧
Install the core package using pip:
```sh
pip install taskshed
```
TaskShed has no extra dependencies beyond its core framework. However, if you want **persistent task storage**, you’ll need to install one of the optional backends. TaskShed currently supports [Redis](https://redis.io/) and [MySQL](https://www.mysql.com/). You can install the appropriate driver using:
```shs
pip install "taskshed[redis]"
```
or
```sh
pip install "taskshed[mysql]"
```
# Quick Start 🏁
Here's a simple example of scheduling a task to run in 5 seconds.
```py
from datetime import datetime, timedelta
from taskshed.datastores import InMemoryDataStore
from taskshed.schedulers import AsyncScheduler
from taskshed.workers import EventDrivenWorker
async def say_hello(name: str):
print(f"Hello, {name}!")
datastore = InMemoryDataStore()
worker = EventDrivenWorker(callback_map={"say_hello": say_hello}, datastore=datastore)
scheduler = AsyncScheduler(datastore=datastore, worker=worker)
async def main():
await scheduler.start()
await worker.start()
await scheduler.add_task(
callback="say_hello",
run_at=datetime.now() + timedelta(seconds=3),
kwargs={"name": "World"},
)
if __name__ == "__main__":
import asyncio
loop = asyncio.new_event_loop()
loop.create_task(main())
loop.run_forever()
```
# Documentation 📚
https://chase-labs.github.io/taskshed/
# Contributing 🤝
Contributions are welcome! Please feel free to submit a pull request or open an issue.
# License 📜
This project is licensed under the MIT License.
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"description": "# TaskShed\n\nA high-performance, asynchronous, ready for production job scheduling framework.\n\nTaskShed provides a simple API to schedule your Python coroutines for later execution. You can run tasks just once or on a recurring interval. The scheduler is dynamic, allowing you to add, update or remove tasks on the fly. Furthermore, by connecting to a persistent datastore, TaskShed ensures your tasks survive restarts and automatically catches up on any executions that were missed while the system was offline.\n\nThe key features are:\n\n* **Fast**: TaskShed has a [very low latency, overhead and can execute several thousands tasks a second](https://chase-labs.github.io/taskshed/benchmarks/).\n* **Distributed**: TaskShed has the capacity to spawn several workers and schedules across many machines, while also providing optimisation for monolinth architectures.\n* **Persistant**: Tasks are stored in database, meaning that they won't get dropped on shutdown. TaskShed currently supports Redis and MySQL.\n* **Easy**: TaskShed's modular architecture is straightforward and easy to set-up, and works in any asynchronous environement.\n\n\n# Installation \ud83d\udd27\n\nInstall the core package using pip:\n\n```sh\npip install taskshed\n```\n\nTaskShed has no extra dependencies beyond its core framework. However, if you want **persistent task storage**, you\u2019ll need to install one of the optional backends. TaskShed currently supports [Redis](https://redis.io/) and [MySQL](https://www.mysql.com/). You can install the appropriate driver using:\n\n```shs\npip install \"taskshed[redis]\"\n```\n\nor\n\n```sh\npip install \"taskshed[mysql]\"\n```\n\n# Quick Start \ud83c\udfc1\n\nHere's a simple example of scheduling a task to run in 5 seconds.\n\n```py\nfrom datetime import datetime, timedelta\nfrom taskshed.datastores import InMemoryDataStore\nfrom taskshed.schedulers import AsyncScheduler\nfrom taskshed.workers import EventDrivenWorker\n\n\nasync def say_hello(name: str):\n print(f\"Hello, {name}!\")\n\n\ndatastore = InMemoryDataStore()\nworker = EventDrivenWorker(callback_map={\"say_hello\": say_hello}, datastore=datastore)\nscheduler = AsyncScheduler(datastore=datastore, worker=worker)\n\n\nasync def main():\n await scheduler.start()\n await worker.start()\n await scheduler.add_task(\n callback=\"say_hello\",\n run_at=datetime.now() + timedelta(seconds=3),\n kwargs={\"name\": \"World\"},\n )\n\n\nif __name__ == \"__main__\":\n import asyncio\n\n loop = asyncio.new_event_loop()\n loop.create_task(main())\n loop.run_forever()\n```\n\n# Documentation \ud83d\udcda\n\nhttps://chase-labs.github.io/taskshed/\n\n# Contributing \ud83e\udd1d\n\nContributions are welcome! Please feel free to submit a pull request or open an issue.\n\n# License \ud83d\udcdc\n\nThis project is licensed under the MIT License.\n",
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