pynenc 0.1.0

A task management system for complex distributed orchestration

Pynenc

A task management system for complex distributed orchestration

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Documentation: https://docs.pynenc.org

Source Code: https://github.com/pynenc/pynenc

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Pynenc addresses the complex challenges of task management in distributed environments, offering a robust solution for developers looking to efficiently orchestrate asynchronous tasks across multiple systems. By combining intuitive configuration with advanced features like automatic task prioritization, Pynenc empowers developers to build scalable and reliable distributed applications with ease.

🆕 What's New in v0.1.0

• Plugin Architecture: Modular backend system with Redis, MongoDB, and RabbitMQ as separate plugins
• Invocation State Machine: Declarative, type-safe status management with ownership tracking and automatic recovery
• Runner Heartbeat & Recovery: Automatic detection of stuck invocations and recovery from inactive runners
• Enhanced Monitoring (Pynmon): SVG-based timeline visualization, runner monitoring, and workflow tracking
• Fluent Builder API: Extensible PynencBuilder with plugin-provided methods for seamless configuration

See the Changelog for the complete list of changes.

Key Features

• Modular Plugin Architecture: Pynenc is built with modularity at its core, supporting various backend implementations through a plugin system:

  • Memory Backend: Built-in development/testing mode for local execution (single-host only)
  • SQLite Backend: Built-in backend for testing on a single host (compatible with any runner sharing the same database file)
  • Redis Plugin (pynenc-redis): Production-ready distributed task management
  • MongoDB Plugin (pynenc-mongodb): Document-based storage with full feature support
  • RabbitMQ Plugin (pynenc-rabbitmq): Message queue-based broker for distributed task orchestration

  The plugin system allows easy extension with additional databases, message queues, and services, enabling customization for different operational needs and environments.

• Intuitive Orchestration: Simplifies the setup and management of tasks in distributed systems, focusing on usability and practicality.

• Flexible Configuration with PynencBuilder: A fluent builder interface allows users to configure apps programmatically with method chaining. Backend plugins automatically extend the builder with their own methods:

  from pynenc.builder import PynencBuilder
  
  # Production setup with Redis (requires pynenc-redis plugin)
  app = (
      PynencBuilder()
      .app_id("my_app")
      .redis(url="redis://localhost:6379")  # Plugin-provided method
      .multi_thread_runner(min_threads=2, max_threads=8)
      .logging_level("info")
      .build()
  )
  
  # Development/testing setup (no plugins required)
  app = PynencBuilder().app_id("my_app").memory().dev_mode().build()
  

• Invocation Status State Machine: Type-safe, declarative status management with:

  • Ownership tracking for invocations across distributed runners
  • Automatic recovery of stuck PENDING and RUNNING invocations
  • Runner heartbeat monitoring to detect inactive runners
  • Comprehensive state transitions with validation

• Configurable Concurrency Management: Pynenc offers versatile concurrency control mechanisms at various levels:

  • Task-Level Concurrency: Ensures only one instance of a specific task is in a running state at any given time.
  • Argument-Level Concurrency: Limits concurrent execution based on the arguments of the task, allowing only one task with a unique set of arguments to be running or pending.
  • Key Argument-Level Concurrency: Further refines control by focusing on key arguments, ensuring uniqueness in task execution based on specified key arguments.

  This structured approach to concurrency management in Pynenc allows for precise control over task execution, ensuring efficient handling of tasks without overloading the system and adhering to specified constraints.

• Real-Time Monitoring with Pynmon: Built-in web-based monitoring interface featuring:

  • SVG-based timeline visualization of invocations and state transitions
  • Runner health monitoring with heartbeat tracking
  • Workflow visualization with parent-child relationships
  • Task details with execution history and context
  • HTMX-powered real-time updates

• Comprehensive Trigger System: Enables declarative task scheduling and event-driven workflows:

  • Diverse Trigger Conditions: Schedule tasks using cron expressions, react to events, task status changes, results, or exceptions.
  • Flexible Argument Handling:
    • ArgumentProvider: Dynamically generate arguments for triggered tasks from the context of the condition (static values or using custom functions).
    • ArgumentFilter: Filter task execution based on original task arguments (exact match dictionary or custom validation function).
    • ResultFilter: Conditionally trigger tasks based on specific result values of the preceding task.
    • Event Payload Filtering: Selectively process events based on payload content.
  • Composable Conditions: Combine multiple conditions with AND/OR logic for complex triggering rules.

• Advanced Workflow System: Sophisticated task orchestration with deterministic execution and state management:

  • Deterministic Execution: All non-deterministic operations (random numbers, UUIDs, timestamps) are made deterministic for perfect replay.
  • Workflow Identity: Unique workflow contexts with parent-child relationships and inheritance.
  • State Persistence: Automatic key-value storage for workflow data with failure recovery capabilities.
  • Task Integration: Integration with existing Pynenc tasks using force_new_workflow decorator option.
  • Failure Recovery: Workflows can resume from exact points of failure with identical replay behavior.

• Automatic Task Prioritization: The broker prioritizes tasks by counting how many other tasks depend on them. The task blocking the most others is selected first.

• Automatic Task Pausing: Tasks waiting for dependencies are paused, freeing their runner slots. Higher-priority tasks (those with more dependents waiting) run instead, preventing thread-pool exhaustion and deadlocks.

• Direct Task Execution: Use @app.direct_task for tasks that return results directly instead of invocations.

Installation

Installing Pynenc is a simple process. The core package provides the framework, and you'll need to install backend plugins separately:

Core (supports Python 3.11+)

pip install pynenc

Backend Plugins

Choose the backend that fits your needs:

Redis Backend (recommended for production):

pip install pynenc-redis

MongoDB Backend:

pip install pynenc-mongodb

RabbitMQ Backend:

pip install pynenc-rabbitmq

Optional Features

Include the monitoring web app:

pip install pynenc[monitor]

Complete Installation Examples

For a Redis-based setup with monitoring:

pip install pynenc pynenc-redis pynenc[monitor]

For a MongoDB-based setup:

pip install pynenc pynenc-mongodb

For a RabbitMQ-based setup:

pip install pynenc pynenc-rabbitmq

For development/testing (memory or SQLite backend only):

pip install pynenc

This modular approach allows you to install only the components you need, keeping your dependencies minimal and focused.

For more detailed instructions and advanced installation options, please refer to the Pynenc Documentation.

Quick Start Example

To get started with Pynenc, here's a simple example that demonstrates the creation of a distributed task for adding two numbers. Follow these steps to quickly set up a basic task and execute it.

1. Define a Task: Create a file named tasks.py and define a simple addition task:

   from pynenc import Pynenc
   
   app = Pynenc()
   
   @app.task
   def add(x: int, y: int) -> int:
       add.logger.info(f"{add.task_id=} Adding {x} + {y}")
       return x + y
   
   @app.direct_task
   def direct_add(x: int, y: int) -> int:
       return x + y
   

2. Start Your Runner or Run Synchronously:

   Before executing the task, decide if you want to run it asynchronously with a runner or synchronously for testing or development purposes.

   • Asynchronously: Start a runner in a separate terminal or script:

     pynenc --app=tasks.app runner start
     

     Check for the basic_redis_example (requires pynenc-redis plugin)

   • Synchronously: For test or local demonstration, to try synchronous execution, you can set the environment variable PYNENC__DEV_MODE_FORCE_SYNC_TASKS=True to force tasks to run in the same thread.

3. Execute the Task:

    # Standard task (returns invocation)
    result = add(1, 2).result  # 3
   
    # Direct task (returns result directly)
    direct_result = direct_add(1, 2)  # 3
   

Using the Trigger System

Here's an example of creating and using triggers:

from pynenc import Pynenc
from datetime import datetime

app = Pynenc()

@app.task
def process_data(data: dict) -> dict:
    return {"processed": data, "timestamp": datetime.now().isoformat()}

@app.task
def notify_admin(result: dict, urgency: str = "normal") -> None:
    print(f"Admin notification ({urgency}): {result}")

# Create a trigger that runs when process_data completes successfully
trigger = app.trigger.on_success(process_data).run(notify_admin)

# Create a trigger with argument filtering - only trigger when data contains 'urgent'
trigger_urgent = (app.trigger
    .on_success(process_data)
    .with_argument_filter(lambda args: args.get('data', {}).get('priority') == 'urgent')
    .run(notify_admin, argument_provider=lambda ctx: [ctx.result, "high"])
)

# Create a cron-based scheduled task
scheduled_task = (app.trigger
    .on_cron("*/30 * * * *")  # Every 30 minutes
    .run(process_data, argument_provider={"data": {"source": "scheduled"}})
)

For a complete guide on how to set up and run pynenc, visit our samples library.

Monitoring with Pynmon

Pynenc includes Pynmon, a built-in web-based monitoring interface that provides real-time visibility into your distributed task execution. Pynmon offers:

• Dashboard Overview: Quick summary of system health, active runners, and task statistics
• Timeline Visualization: SVG-based timeline showing invocations and state transitions across all runners
• Runner Monitoring: Track active runners with heartbeat status, hostname, PID, and uptime
• Task Browser: Explore all registered tasks with execution history and statistics
• Invocation Details: Drill down into individual task executions with full context and status history
• Workflow Tracking: Visualize workflow hierarchies with parent-child relationships

Starting the Monitor

The monitor requires a Pynenc app defined in your codebase:

pynenc --app your_app_module monitor --host 127.0.0.1 --port 8000

Then open http://127.0.0.1:8000 in your browser to access the dashboard.

Installing the Monitor

The monitoring web app is an optional feature:

pip install pynenc[monitor]

Requirements

Pynenc supports multiple backend options through its plugin system:

Backend Options

• Memory Backend: Built-in, no additional requirements (for development/testing, single-host only, not suitable for distributed systems; only compatible with ThreadRunner for memory save)
• SQLite Backend: Built-in, no additional requirements (for testing on a single host, not suitable for distributed systems; compatible with any runner that shares the same database file)
• Redis Backend: Requires pynenc-redis plugin and a Redis server
• MongoDB Backend: Requires pynenc-mongodb plugin and a MongoDB server
• RabbitMQ Backend: Requires pynenc-rabbitmq plugin and a RabbitMQ server

Production Deployment

For distributed systems, choose either:

• Redis: Install pynenc-redis and ensure Redis server is running
• MongoDB: Install pynenc-mongodb and ensure MongoDB server is running
• RabbitMQ: Install pynenc-rabbitmq and ensure RabbitMQ server is running

The plugin architecture allows you to switch between backends or add new ones without changing your application code.

Future Updates:

• Pynenc is being developed to support additional databases and message queues. This will expand its compatibility and usability in various distributed systems.

Contact or Support

If you need help with Pynenc or want to discuss any aspects of its usage, feel free to reach out through the following channels:

• GitHub Issues: For bug reports, feature requests, or other technical queries, please use our GitHub Issues page. You can create a new issue or contribute to existing discussions.

• GitHub Discussions: For more general questions, ideas exchange, or discussions about Pynenc, consider using GitHub Discussions on our repository. It's a great place to connect with other users and the development team.

Remember, your feedback and contributions are essential in helping Pynenc grow and improve!

License

Pynenc is released under the MIT License.