In this blog post, I will share how I significantly improved the efficiency of a code snippet that retrieves data from an FTP server. By implementing multithreading, I achieved parallel processing, resulting in a remarkable 92% reduction in the time required for data retrieval. This improvement not only benefited the FTP retrieval process but also holds value for other data science tasks that involve downloading data from servers. In this post, I will highlight specific examples and provide code snippets to demonstrate the versatility and effectiveness of multithreading.

Identifying the Performance Bottleneck

The original code relied on sequential processing, which proved to be a major hindrance in terms of time-consuming data retrieval, particularly when dealing with a large number of accessions. Recognizing the need for optimization, I thoroughly analyzed the code and identified the sequential iteration over accessions as the primary performance bottleneck. This led me to focus on finding an effective solution to this problem.

Harnessing the Power of Multithreading in Python

Multithreading is a powerful technique in Python that enables concurrent execution of tasks, leading to significant performance improvements in data processing tasks. By utilizing the concurrent.futures module, I was able to introduce multithreading and harness its capabilities to expedite the data retrieval process.

Code Snippet: FTP Data Retrieval with Multithreading

from ftplib import FTP
import os
import tarfile
import tqdm
import concurrent.futures

def get_xml(accession):
    # Code for retrieving and processing XML data

if __name__ == '__main__':
    # Code for creating directory

    with open('high_t.txt', 'r') as acc_file:
        accessions = acc_file.read().splitlines()

    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        futures = []
        for accession in accessions:
            futures.append(executor.submit(get_xml, accession))
        for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
            pass

In the original code, the ftplib library was used to connect to an FTP server, retrieve data files, extract them, and store them locally. However, due to the sequential nature of the code, the data retrieval process proved to be time-consuming, especially when dealing with a large number of accessions.

To overcome this limitation, I introduced multithreading using the concurrent.futures module. By utilizing the ThreadPoolExecutor, the get_xml function can be executed concurrently, allowing for multiple FTP requests to be processed simultaneously. This parallel processing significantly reduces the overall execution time for data retrieval.

Expanding the Scope to Other Areas

The gains achieved through multithreading in FTP data retrieval can be extended to various other data science tasks. Let's explore a few examples where multithreading can enhance efficiency:

1. Web Scraping: Whether extracting data from multiple websites or scraping large volumes of data from a single website, multithreading can accelerate the process by fetching and processing data concurrently.

2. API Calls: When making multiple API calls to retrieve data, employing multithreading can significantly reduce the time required to obtain responses by executing requests in parallel.

3. Data Downloading: Downloading large datasets from servers can be time-consuming. By leveraging multithreading, data scientists can enhance download speeds by fetching multiple chunks of data simultaneously.

By embracing multithreading, data scientists can unlock substantial performance improvements in various data retrieval and processing tasks, empowering them to accomplish their goals faster and more efficiently.