Python

Speeding Up Web Scraping with Parallelism and Multithreading

Introduction

Web scraping refers to the automated process of acquiring data from websites. This is an enormous task, and if data is extracted one page at a time, it takes a bit longer. An alternative is to web scrape multiple pages simultaneously, which drastically reduces the time taken.

This can be accomplished in two ways:

Multithreading: simultaneous tasks run within an application
Parallel Processing: independent tasks are processed separately

Both of them help save time as well as make the scraping more effective.

1. Faster Scraping with Multithreading

Python offers a very handy tool—concurrent.futures—that enables pages to be scrapped simultaneously rather than page by page.

Example: Simplifying the process to extract information from two web pages simultaneously.

import requests from concurrent.futures import ThreadPoolExecutor def scrape_page(url): response = requests.get(url) return response.text urls = ["https://example.com/page1","https://example.com/page2"] with ThreadPoolExecutor(max_workers=2) as executor: results = executor.map(scrape_page, urls) for result in results: print(result)

This loads two pages at the same time, making it faster.

2. Scraping Faster with Browser – Selenium

Some pages on particular websites will only load from browsers. To deal with such cases, we take help from Selenium to open a browser base and scrape the data.

Example: Working with Two Browsers Simultaneously

from selenium import webdriverimport threadingdef scrape_page(url): driver = webdriver.Chrome() driver.get(url) print(driver.page_source) driver.quit()urls = ["https://example.com/page1", "https://example.com/page2"]threads = []for url in urls: thread = threading.Thread(target=scrape_page, args=(url,)) threads.append(thread) thread.start()for thread in threads: thread.join()

This enables you to use two browsers simultaneously while scraping data.

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