Reading files fast with multi-threading in Python

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In this tutorial, we will explore the concept of multi-threading in Python and how to utilize it to read files concurrently. Multi-threading allows us to perform multiple tasks simultaneously, making it a powerful technique to improve performance when handling I/O-bound operations, such as reading files. You can supercharge your data pipelines by utilizing all the cores in your machine. By the end of this tutorial, you will be able to leverage multi-threading to efficiently read files and make your ingestion data pipelines fast.

Multi-Threaded File Reader

Let us just dive right into the nitty-gritty of things and get our hands dirty,

1. Install and import the threading library

To begin, ensure you have Python installed, and let’s import the necessary modules:

import threading

2. Defining the function to read a file

We’ll create a function that reads the content of a file. For simplicity, we’ll use a text file, but this method applies to other file formats as well.

def read_file(file_path):
    with open(file_path, 'r') as file:
        content = file.read()
    return content

3. Create a multi-threaded function to read files

Now, we’ll create a function that uses multi-threading to read multiple files concurrently.

def multi_threaded_file_reader(file_paths):
    threads = []
    results = {}

    # Define the worker function
    def read_file_thread(file_path):
        result = read_file(file_path)
        results[file_path] = result

    # Create and start threads
    for file_path in file_paths:
        thread = threading.Thread(target=read_file_thread, args=(file_path,))
        threads.append(thread)
        thread.start()

    # Wait for all threads to finish
    for thread in threads:
        thread.join()

    return results

4. Test the Multi-Threaded File Reader

Now that we have our multi-threaded file reader function, let’s test it with a sample list of file paths.

if __name__ == "__main__":
    file_paths = ["file1.txt", "file2.txt", "file3.txt", "file4.txt"]
    results = multi_threaded_file_reader(file_paths)
    separator_size = 50
    for file_path, content in results.items():
        print(f"Reading {file_path}:")
        print(content)
        print("-" * separator_size)

And there we go, that’s all there is to it.

Conclusion

Congratulations! You have successfully learned how to use multi-threading to read files concurrently in Python. This technique can significantly improve the performance of file reading operations, especially when dealing with large datasets or I/O-bound tasks. Always remember to handle thread synchronization if multiple threads are writing to shared resources.