Mastering Multiprocessing and Multithreading in Python for Real-Time Data Streaming

Learn how to efficiently combine `multiprocessing` and `multithreading` in Python to handle real-time data streaming and processing.
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Mastering Multiprocessing and Multithreading in Python for Real-Time Data Streaming

In the world of programming, handling asynchronous data streams can be challenging, especially when the processing time exceeds the incoming data frequency. For example, consider streaming Bitcoin Kline (candlestick) data from an API every two seconds. If your data processing takes longer than two seconds, you'll face delays and potential data loss. In this guide, we will provide a clear solution using a combination of multithreading and multiprocessing in Python to effectively manage this situation.

Understanding the Problem

You want to stream Bitcoin Kline data, process it, and ensure that you can keep up with the incoming data rate. Typical issues faced in this approach include:

Long Processing Times: If your data calculation takes longer than two seconds, new incoming data may not be processed in time.

Concurrency Management: Handling multiple threads and processes can be complex for newcomers in programming.

Proposed Solution: Combining Multithreading and Multiprocessing

To tackle these challenges, we will use threads to handle the data stream and processes for processing the data. Here's the general strategy we'll employ:

Stream Data with Threads: Use a thread to continuously receive data from the WebSocket API.

Process Data with Separate Processes: Start a new process for data calculations to ensure that intensive computations do not hinder the streaming of new data.

Step-by-Step Implementation

Let's go through the implementation, starting with a basic structure and issues we can encounter, leading to an enhanced working model.

Initial Code Structure

We'll start with a simplified version of the code that resembles the original structure you provided:

[[See Video to Reveal this Text or Code Snippet]]

Important Modifications

Static Method Decoration: By defining get_kline_combined() as a staticmethod, we ensure that it can be used with multiprocessing without issues related to pickling.

Pooling Processes: Instead of creating a new process each time, we use a multiprocessing.Pool to manage a set of processes. This enhances efficiency and resource management.

Processing Results: In a real-world application, you should consider getting and managing the results from the output of the pool.

Removing Finished Tasks: After processing, it’s beneficial to periodically check and clean up completed tasks to prevent memory leaks. You may even consider another thread to handle this cleanup.

Conclusion

By efficiently combining multithreading and multiprocessing, we can handle real-time data streams in Python without delays or bottlenecks. This approach allows you to tackle complex data processing tasks more effectively while ensuring that your application remains responsive and up-to-date with incoming data.

Whether you are building a financial application or any data-streaming solution, mastering these techniques will prove invaluable. Happy coding!