background-workflows 0.3.0

Background Workflows for task processing

Background Workflows

A pluggable framework for managing background tasks in Python. Supports both local task execution with threading and distributed task execution using Celery.

Features

• Task orchestration with support for Azure Table Storage or SQLite for task persistence.
• Queue management for local in-memory queues or Celery for distributed task processing.
• Dynamic task creation and registration for extensible workflow management.

Additional Documentation

For more detailed documentation, please refer to the following sections:

• Summary: summary.md – A brief summary of the framework and its key components.
• Controller Documentation: controller.md – Detailed information about the controller components and how they work.
• Storage Documentation: storage.md – Information on task storage options, including Azure and SQLite.
• Task Documentation: tasks.md – Overview of task management, including how to define and run tasks.
• Utils Documentation: utils.md – A guide to the utility functions and classes used in the framework.
• Overall Framework Diagram: overal_diagram.md – A high-level diagram illustrating the components and their relationships.

Installation

You can install the background_workflows library via pip:

pip install background_workflows

Usage

1. Initialize Task Store and Queue Backend

from background_workflows.storage.tables.task_store_factory import TaskStoreFactory
from background_workflows.storage.queue.local_queue_backend import LocalQueueBackend

# Initialize task store and queue backend
factory = TaskStoreFactory(store_mode="sqlite")
task_store = factory.get_task_store()
queue_backend = LocalQueueBackend()

2. Start a Background Activity

from background_workflows.utils.workflow_client import WorkflowClient

workflow_client = WorkflowClient(task_store, queue_backend)
workflow_client.start_activity("MY_CUSTOM_TASK", resource_id="1234", payload={"data": "value"})

3. Monitor Task Status

status = workflow_client.get_status(row_key="unique-row-id", resource_id="1234")
print(f"Task Status: {status}")

Configuration

Environment Variables

To configure the library, set the following environment variables:

• STORE_MODE: Choose between "azure" or "sqlite" for task storage.
• AZURE_STORAGE_CONNECTION_STRING: Required for Azure storage mode.
• SQLITE_DB_PATH: Path to the SQLite database (default is local_tasks.db).
• CELERY_BROKER_URL: The URL for the Celery message broker (e.g., Redis).
• CELERY_BACKEND_URL: The URL for the Celery result backend (e.g., Redis).

Example:

export STORE_MODE=azure
export AZURE_STORAGE_CONNECTION_STRING="your_connection_string_here"

Controllers

The framework provides two key controllers for managing task execution:

1. MainController

• Purpose: Polls queues and executes tasks in a local thread pool.
• Configuration: Can be configured to use a specific number of threads and CPU usage threshold.
• Usage:

from background_workflows.controller.main.main_controller import MainController

controller = MainController(task_store, queue_backend)
controller.run()  # Starts the continuous polling and task execution loop

1. CeleryController

• Purpose: Delegates task processing to Celery workers, removing the need for local polling.
• Configuration: Requires a Celery setup and message broker.
• Usage:

from background_workflows.controller.celery.celery_controller import CeleryController

controller = CeleryController(task_store, celery_queue_backend)
controller.run_once()  # Executes a single pass of task handling

Contributing

We welcome contributions to the background_workflows library! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes.
4. Run tests to ensure everything works.
5. Submit a pull request with a description of the changes you’ve made.

Running Tests

To run the tests, you can use pytest:

pytest tests/

License

This project is licensed under the MIT License - see the LICENSE file for details.