| Name | celery-mongobeat JSON |
| Version |
0.1.12
JSON |
| download |
| home_page | None |
| Summary | A Celery Beat scheduler that stores the schedule in MongoDB. |
| upload_time | 2025-11-01 02:16:11 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
celery
beat
scheduler
mongodb
mongo
|
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# Celery-MongoBeat
<!--
[](https://pypi.org/project/celery-mongobeat/)
[](https://pypi.org/project/celery-mongobeat)
[](https://opensource.org/licenses/Apache-2.0)
-->
A modern, drop-in replacement for `celerybeat-mongo`. This project provides a Celery Beat scheduler that stores and retrieves task schedules from a MongoDB collection, allowing for dynamic management of periodic tasks without restarting the Celery Beat service.
## Why `celery-mongobeat`?
The original `celerybeat-mongo` library is no longer actively maintained and contains several critical bugs. This project was created to provide a stable, reliable, and modern alternative for the community, ensuring continued support for dynamic, database-backed Celery schedules.
## Features
- **Stable and Reliable**: Fixes critical bugs from `celerybeat-mongo`, such as the issue where disabling one task would prevent all tasks from running.
- **Dynamic Task Management**: Add, modify, and remove periodic tasks on the fly without restarting the beat service.
- **MongoDB Backend**: Leverages MongoDB for a robust and scalable schedule store.
- **Fine-Grained Control**:
- **Run Count Limiting**: Use `max_run_count` to run a task a specific number of times and then automatically disable it.
- **Flexible Configuration**: Full support for advanced `pymongo.MongoClient` options (like SSL) via `mongodb_scheduler_client_kwargs`.
- **Backwards Compatible**: Supports legacy configuration variables from `celerybeat-mongo` for a smoother transition.
- **Modern Tooling**: Built with a modern Python packaging structure (`pyproject.toml`).
- **All Schedule Types**: Natively supports `interval`, `crontab`, and `solar` schedules.
## Installation
Install the package from PyPI:
```bash
pip install celery-mongobeat
```
## Configuration
To use this scheduler, set the `beat_scheduler` option in your Celery configuration.
### Recommended Configuration
```python
# celeryconfig.py
mongodb_scheduler_url = "mongodb://localhost:27017/"
mongodb_scheduler_db = "celery"
mongodb_scheduler_collection = "schedules"
beat_scheduler = "celery_mongobeat.beat:MongoScheduler"
```
## Migrating from `celerybeat-mongo`
`celery-mongobeat` is designed as a near drop-in replacement, but there is one important configuration change you must make when migrating:
* **Update the Scheduler Path**: The import path for the scheduler has been updated to align with modern package structures and Celery best practices.
You must change your `beat_scheduler` setting from:
`'celerybeat_mongo.schedulers.MongoScheduler'` (the old path)
to:
`'celery_mongobeat.beat:MongoScheduler'` (the new path)
```
### Legacy (Backwards-Compatible) Configuration
If you are migrating from `celerybeat-mongo`, this library provides backward compatibility for the uppercase configuration variables. Modern, lowercase settings (e.g., `mongodb_scheduler_url`) will always take precedence.
```python
# celeryconfig.py
# Legacy uppercase individual settings (from celerybeat-mongo)
CELERY_MONGODB_SCHEDULER_URL = "mongodb://localhost:27017/"
CELERY_MONGODB_SCHEDULER_DB = "celery"
CELERY_MONGODB_SCHEDULER_COLLECTION = "schedules"
beat_scheduler = "celery_mongobeat.beat:MongoScheduler"
```
## Usage
Once configured, start Celery Beat as you normally would:
```bash
celery -A your_app beat -l info
```
You can now manage your schedules by adding, updating, or removing documents in the configured MongoDB collection.
## Programmatic Usage Example
For users who prefer a programmatic API over manually inserting documents into MongoDB, `celery-mongobeat` provides a convenient `ScheduleManager` helper class.
This allows you to easily create, update, and disable tasks from within your application code.
### Example Usage
```python
# In your application code (e.g., a management script or view)
from celery import current_app
from celery_mongobeat.helpers import ScheduleManager
# The recommended way to get a manager instance.
# It automatically reads the database configuration from your Celery settings.
app = current_app._get_current_object()
manager = ScheduleManager.from_celery_app(app)
# Example: Create a task to run every 30 seconds
manager.create_interval_task(
name='my-periodic-task',
task='your_project.tasks.some_task',
every=30,
period='seconds',
args=[1, 2, 3]
)
print("Upserted interval task: 'my-periodic-task'")
# Example: Create a task that runs 5 times and then stops
manager.create_interval_task(
name='run-five-times-task',
task='your_project.tasks.some_task',
every=60,
period='seconds',
max_run_count=5
)
print("Upserted limited-run task: 'run-five-times-task'")
# Example: Disable a task
manager.disable_task('my-periodic-task')
print("Disabled task: 'my-periodic-task'")
# Example: Get all enabled interval tasks
enabled_interval_tasks = manager.get_tasks(enabled=True, schedule_type='interval')
print(f"Found {len(enabled_interval_tasks)} enabled interval tasks.")
for task in enabled_interval_tasks:
print(f" - {task['name']}")
```
### Advanced Usage: Subclassing and Direct Database Access
The `ScheduleManager` is designed to be a flexible base. For more complex applications, it is highly recommended to subclass it to create a domain-specific API for your tasks. This encapsulates your application's scheduling logic, making your code cleaner and more maintainable.
**1. Subclassing `ScheduleManager`**
```python
# In your_app/scheduling.py
from celery_mongobeat.helpers import ScheduleManager
class AppScheduleManager(ScheduleManager):
"""A custom manager for our application's specific tasks."""
def create_user_report_task(self, user_id: int):
"""Creates a recurring daily report for a specific user."""
task_name = f"user-report-{user_id}"
super().create_crontab_task(
name=task_name,
task='your_app.tasks.generate_report',
kwargs={'user_id': user_id},
minute='0', # At the start of the hour
hour='3' # At 3 AM
)
print(f"Scheduled daily report for user {user_id}.")
# In your application code, you can now use this custom manager:
# from celery import current_app
# from your_app.scheduling import AppScheduleManager
# app = current_app._get_current_object()
# app_manager = AppScheduleManager.from_celery_app(app)
# app_manager.create_user_report_task(user_id=123)
```
**2. Direct Database Access**
For advanced queries, such as MongoDB aggregation pipelines, you can and should use the `pymongo` collection object that you used to initialize the manager. This gives you the full power of `pymongo` for any use case not directly covered by the helper.
```python
# For example, to find the most common task paths using an aggregation:
pipeline = [
{"$group": {"_id": "$task", "count": {"$sum": 1}}},
{"$sort": {"count": -1}}
]
most_common_tasks = list(schedules_collection.aggregate(pipeline))
print("Most common tasks:", most_common_tasks)
```
Raw data
{
"_id": null,
"home_page": null,
"name": "celery-mongobeat",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "celery, beat, scheduler, mongodb, mongo",
"author": null,
"author_email": "William Thomas <52611686+sockmysox@users.noreply.github.com>",
"download_url": "https://files.pythonhosted.org/packages/d3/63/c3eabae5b465d25e8e91735a4d7abe8b4fe9c492a958b209126c20f26c28/celery_mongobeat-0.1.12.tar.gz",
"platform": null,
"description": "# Celery-MongoBeat\n\n<!--\n[](https://pypi.org/project/celery-mongobeat/)\n[](https://pypi.org/project/celery-mongobeat)\n[](https://opensource.org/licenses/Apache-2.0)\n-->\n\nA modern, drop-in replacement for `celerybeat-mongo`. This project provides a Celery Beat scheduler that stores and retrieves task schedules from a MongoDB collection, allowing for dynamic management of periodic tasks without restarting the Celery Beat service.\n\n## Why `celery-mongobeat`?\n\nThe original `celerybeat-mongo` library is no longer actively maintained and contains several critical bugs. This project was created to provide a stable, reliable, and modern alternative for the community, ensuring continued support for dynamic, database-backed Celery schedules.\n\n## Features\n\n- **Stable and Reliable**: Fixes critical bugs from `celerybeat-mongo`, such as the issue where disabling one task would prevent all tasks from running.\n- **Dynamic Task Management**: Add, modify, and remove periodic tasks on the fly without restarting the beat service.\n- **MongoDB Backend**: Leverages MongoDB for a robust and scalable schedule store.\n- **Fine-Grained Control**:\n - **Run Count Limiting**: Use `max_run_count` to run a task a specific number of times and then automatically disable it.\n- **Flexible Configuration**: Full support for advanced `pymongo.MongoClient` options (like SSL) via `mongodb_scheduler_client_kwargs`.\n- **Backwards Compatible**: Supports legacy configuration variables from `celerybeat-mongo` for a smoother transition.\n- **Modern Tooling**: Built with a modern Python packaging structure (`pyproject.toml`).\n- **All Schedule Types**: Natively supports `interval`, `crontab`, and `solar` schedules.\n\n## Installation\n\nInstall the package from PyPI:\n\n```bash\npip install celery-mongobeat\n```\n\n## Configuration\n\nTo use this scheduler, set the `beat_scheduler` option in your Celery configuration.\n\n### Recommended Configuration\n\n```python\n# celeryconfig.py\n\nmongodb_scheduler_url = \"mongodb://localhost:27017/\"\nmongodb_scheduler_db = \"celery\"\nmongodb_scheduler_collection = \"schedules\"\n\nbeat_scheduler = \"celery_mongobeat.beat:MongoScheduler\"\n```\n\n## Migrating from `celerybeat-mongo`\n\n`celery-mongobeat` is designed as a near drop-in replacement, but there is one important configuration change you must make when migrating:\n\n* **Update the Scheduler Path**: The import path for the scheduler has been updated to align with modern package structures and Celery best practices.\n\nYou must change your `beat_scheduler` setting from:\n`'celerybeat_mongo.schedulers.MongoScheduler'` (the old path)\nto:\n`'celery_mongobeat.beat:MongoScheduler'` (the new path)\n```\n\n### Legacy (Backwards-Compatible) Configuration\n\nIf you are migrating from `celerybeat-mongo`, this library provides backward compatibility for the uppercase configuration variables. Modern, lowercase settings (e.g., `mongodb_scheduler_url`) will always take precedence.\n\n```python\n# celeryconfig.py\n\n# Legacy uppercase individual settings (from celerybeat-mongo)\nCELERY_MONGODB_SCHEDULER_URL = \"mongodb://localhost:27017/\"\nCELERY_MONGODB_SCHEDULER_DB = \"celery\"\nCELERY_MONGODB_SCHEDULER_COLLECTION = \"schedules\"\n\nbeat_scheduler = \"celery_mongobeat.beat:MongoScheduler\"\n```\n\n## Usage\n\nOnce configured, start Celery Beat as you normally would:\n\n```bash\ncelery -A your_app beat -l info\n```\n\nYou can now manage your schedules by adding, updating, or removing documents in the configured MongoDB collection.\n\n## Programmatic Usage Example\n\nFor users who prefer a programmatic API over manually inserting documents into MongoDB, `celery-mongobeat` provides a convenient `ScheduleManager` helper class.\n\nThis allows you to easily create, update, and disable tasks from within your application code.\n\n### Example Usage\n\n```python\n# In your application code (e.g., a management script or view)\nfrom celery import current_app\nfrom celery_mongobeat.helpers import ScheduleManager\n\n# The recommended way to get a manager instance.\n# It automatically reads the database configuration from your Celery settings.\napp = current_app._get_current_object()\nmanager = ScheduleManager.from_celery_app(app)\n\n# Example: Create a task to run every 30 seconds\nmanager.create_interval_task(\n name='my-periodic-task',\n task='your_project.tasks.some_task',\n every=30,\n period='seconds',\n args=[1, 2, 3]\n)\nprint(\"Upserted interval task: 'my-periodic-task'\")\n\n# Example: Create a task that runs 5 times and then stops\nmanager.create_interval_task(\n name='run-five-times-task',\n task='your_project.tasks.some_task',\n every=60,\n period='seconds',\n max_run_count=5\n)\nprint(\"Upserted limited-run task: 'run-five-times-task'\")\n\n# Example: Disable a task\nmanager.disable_task('my-periodic-task')\nprint(\"Disabled task: 'my-periodic-task'\")\n\n# Example: Get all enabled interval tasks\nenabled_interval_tasks = manager.get_tasks(enabled=True, schedule_type='interval')\nprint(f\"Found {len(enabled_interval_tasks)} enabled interval tasks.\")\nfor task in enabled_interval_tasks:\n print(f\" - {task['name']}\")\n\n```\n\n### Advanced Usage: Subclassing and Direct Database Access\n\nThe `ScheduleManager` is designed to be a flexible base. For more complex applications, it is highly recommended to subclass it to create a domain-specific API for your tasks. This encapsulates your application's scheduling logic, making your code cleaner and more maintainable.\n \n**1. Subclassing `ScheduleManager`**\n\n```python\n# In your_app/scheduling.py\n\nfrom celery_mongobeat.helpers import ScheduleManager\n\nclass AppScheduleManager(ScheduleManager):\n \"\"\"A custom manager for our application's specific tasks.\"\"\"\n\n def create_user_report_task(self, user_id: int):\n \"\"\"Creates a recurring daily report for a specific user.\"\"\"\n task_name = f\"user-report-{user_id}\"\n super().create_crontab_task(\n name=task_name,\n task='your_app.tasks.generate_report',\n kwargs={'user_id': user_id},\n minute='0', # At the start of the hour\n hour='3' # At 3 AM\n )\n print(f\"Scheduled daily report for user {user_id}.\")\n\n# In your application code, you can now use this custom manager:\n# from celery import current_app\n# from your_app.scheduling import AppScheduleManager\n\n# app = current_app._get_current_object()\n# app_manager = AppScheduleManager.from_celery_app(app)\n# app_manager.create_user_report_task(user_id=123)\n```\n\n**2. Direct Database Access**\n\nFor advanced queries, such as MongoDB aggregation pipelines, you can and should use the `pymongo` collection object that you used to initialize the manager. This gives you the full power of `pymongo` for any use case not directly covered by the helper.\n\n```python\n# For example, to find the most common task paths using an aggregation:\npipeline = [\n {\"$group\": {\"_id\": \"$task\", \"count\": {\"$sum\": 1}}},\n {\"$sort\": {\"count\": -1}}\n]\nmost_common_tasks = list(schedules_collection.aggregate(pipeline))\nprint(\"Most common tasks:\", most_common_tasks)\n```\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n \n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n \n 2. Grant of Copyright License. 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