Multithreaded Pipeline Manager in Python

A reusable, beginner-friendly code template for creating generic multithreaded data processing pipelines in Python. This repository demonstrates how to build robust pipelines that can handle pre-processing, model inference (simulated), and post-processing stages concurrently, with support for parallelizing CPU-bound sub-tasks within a stage.

Table of Contents

• Motivation
• Core Design Concepts
  • Threads vs. Processes
  • Queues for Inter-Thread Communication
  • Sentinel Values for Graceful Shutdown
  • Stopping Events for Error Handling
  • ThreadPoolExecutor for Parallel Sub-tasks
  • Deadlock and Race Condition Prevention
  • Modularity and Reusability
• Repository Tour
  • File Structure
• Quick Start
• How It Works
  • High-Level Diagram
  • Stage-by-Stage Explanation
  • Code Walkthrough (Key Components)
• Extending to Real Workloads
• Troubleshooting Cheatsheet
• License
• Contributing

Motivation

Many data processing and machine learning tasks involve a sequence of steps (a pipeline). Running these steps sequentially can be slow, especially if some steps are I/O-bound or CPU/GPU-bound. Multithreading allows different stages of the pipeline to run concurrently, improving throughput and reducing overall processing time.

This repository helps you:

• Understand the fundamentals of multithreaded pipelines.
• Implement robust pipelines with proper error handling.
• Parallelize CPU-bound sub-tasks using ThreadPoolExecutor.
• Adapt a generic structure for real-world projects.

Core Design Concepts

Threads vs. Processes

• Threads share memory and are lightweight—ideal for I/O-bound or GIL-releasing tasks.
• Processes are isolated—better for CPU-bound tasks in native Python.
• This template uses threads, assuming I/O and C-based libraries release the GIL.

Queues for Inter-Thread Communication

• queue.Queue is used for safe, thread-safe communication.
• Follows a producer-consumer pattern.
• Supports backpressure using maxsize.

Sentinel Values for Graceful Shutdown

• A unique object like SENTINEL = object() signals the end of data.
• Workers propagate SENTINEL downstream and exit cleanly.

Stopping Events for Error Handling

• A shared threading.Event (stop_event) lets workers shut down gracefully on error.

ThreadPoolExecutor for Parallel Sub-tasks

• Used within a stage (e.g. post-processing) to run multiple sub-tasks in parallel.

Deadlock and Race Condition Prevention

• Clear data flow with Queue.
• robust_put() handles blocking puts with timeout and stop signal.
• Shared state should use threading.Lock.

Modularity and Reusability

• Create new stages by subclassing BaseWorker.
• PipelineManager handles orchestration.

Repository Tour

File Structure

multithreaded-pipeline-manager-python/
├── pipeline\_core/
│   ├── **init**.py
│   ├── pipeline\_manager.py
│   └── utils.py
├── demo.py
└── README.md

• pipeline_core/pipeline_manager.py: PipelineManager and BaseWorker classes.
• pipeline_core/utils.py: Utility functions (robust_put, SENTINEL, logger).
• demo.py: Demonstrates building and running a pipeline.

Quick Start

1. Clone or copy files:

# git clone https://github.com/yourusername/multithreaded-pipeline-manager-python.git
# cd multithreaded-pipeline-manager-python

2. Ensure Python 3.8+ is installed.

3. Run the demo:

python demo.py

How It Works

High-Level Diagram

Input Data --> Queue 1 --> PreProcessingWorker --> Queue 2 --> ModelInferenceWorker --> Queue 3 --> PostProcessingWorker --> Final Output
                                                (Simulated)                            (with ThreadPoolExecutor for sub-tasks)

Stage-by-Stage Explanation

• Data Producer: Feeds items into the first queue.
• PreProcessingWorker: Simulates data prep.
• ModelInferenceWorker: Simulates model inference, may raise errors.
• PostProcessingWorker: Submits sub-tasks to a thread pool, buffers and orders results.

Code Walkthrough (Key Components)

PipelineManager

• Manages stop_event, progress bar, workers, and queues.
• add_worker(), start(), wait_for_completion().

BaseWorker

• Implements _run() and requires process_item(item) to be defined.
• Handles queue communication and SENTINEL passing.

robust_put and SENTINEL

• Handles blocked puts safely with timeouts and stop checks.

Parallel Post-Processing

• PostProcessingWorker uses a thread pool for parallel sub-tasks.
• Buffers and re-orders results for consistent output.

Extending to Real Workloads

• Subclass BaseWorker to define custom stages.
• Use PipelineManager to build your pipeline.
• Handle exceptions inside process_item.
• Use robust_put to safely queue items.
• Tune performance via queue sizes, worker count, etc.

Troubleshooting Cheatsheet

Symptom	Cause	Fix
Pipeline hangs	Blocked producer or consumer died	Use robust_put, ensure SENTINEL is passed, avoid circular waits
Data not processed	SENTINEL not propagated or workers exit early	Ensure each stage sends and reacts to SENTINEL properly
Race conditions	Shared state accessed without lock	Use threading.Lock
High idle CPU	Busy-waiting threads	Use queue.get(timeout=...)
Errors in sub-tasks	Exceptions in executor workers	Catch and handle exceptions in sub-task functions
Progress bar not updating	Missing pbar.update() or wrong num_total_items	Ensure correct configuration

License

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

Contributing

Feel free to fork and submit pull requests to improve functionality or documentation. Suggestions and issues are welcome.