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Speed Up Web Scraping Using Concurrency and Parallelism

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Want to make web scraping faster? Read on to learn about two methods: Concurrency and Parallelism. They both reduce the code execution time, but their approach differs entirely. This article covers both methods with examples in Python. Concurrency and parallelism improve performance differently: The following table illustrates how exactly concurrency and parallelism differ. Concurrency Parallelism You can use concurrency and parallelism in any programming language. Here, you will read how to make web scraping faster in Python. Python has several modules for implementing concurrency and parallelism to make web scraping faster. This tutorial shows the example using two modules: Here, you will see a code that scrapes the details of 100 companies. This code takes 1 minute to execute on Colab. However, it takes only 14 seconds if you use the ThreadPoolExecuter. Here is how you import ThreadPoolExecutor. from concurrent.futures import ThreadPoolExecutor, as_completed You only have to modify

Table of contents Concurrency vs. Parallelism How to Make Web Scraping Faster Using Concurrency and Parallelism Which Option is Better for Making Web Scraping Faster? Challenges in Making Web Scraping Faster Are There Other Tips for Fast Web Scraping? Conclusion Want to make web scraping faster? Read on to learn about two methods: Concurrency and Parallelism. They both reduce the code execution time, but their approach differs entirely. This article covers both methods with examples in Python. Concurrency vs. Parallelism Concurrency and parallelism improve performance differently: Concurrency

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