- BEAUTIFUL SOUP GITHUB WEBSCRAPER FULL
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time () download_stories ( story_urls ) t1 = time. map ( download_url, story_urls ) def main ( story_urls ): t0 = time. ThreadPoolExecutor ( max_workers = threads ) as executor : executor. sleep ( 0.25 ) def download_stories ( story_urls ): threads = min ( MAX_THREADS, len ( story_urls )) with concurrent. isalpha ()) + "html" with open ( title, "wb" ) as fh : fh. Import concurrent.futures MAX_THREADS = 30 def download_url ( url ): print ( url ) resp = requests. We can take advantage of multithreading by making a tiny change to our scraper. While it’s slightly more complicated to understand, multithreading with concurrent.futures can give us a significant boost here.
BEAUTIFUL SOUP GITHUB WEBSCRAPER CODE
You can wrap Python code around blazing fast CUDA code (to take advantage of the GPU) that isn’t bound by the GIL! This is how data science libraries like cuDF and CuPy can be so fast. You can release the GIL in your own library code, too. Oh, and it’s not just I/O that can release the GIL. This means I/O tasks can be executed concurrently across multiple threads in the same process, and that these tasks can happen while other Python bytecode is being interpreted. In Python, I/O functionality releases the Global Interpreter Lock (GIL). If I were to use multiprocessing on my 2015 Macbook Air, it would at best make my web scraping task just less than 2x faster on my machine (two physical cores, minus the overhead of multiprocessing). The benefits of multiprocessing are basically capped by the number of cores in the machine, and multiple Python processes come with more overhead than simply using multiple threads. This isn’t surprising, as multiprocessing is easy to understand conceptually. Unfortunately, the top results are primarily about speeding up web scraping in Python using the built-in multiprocessing library. So, what do we do next? Google “fast web scraping in python”, probably. At this point, we’re definitely screwed if we need to scale up and we don’t change our approach.
BEAUTIFUL SOUP GITHUB WEBSCRAPER FULL
On the full 289 files, this scraper took 319.86 seconds.
BEAUTIFUL SOUP GITHUB WEBSCRAPER DOWNLOAD
Even though we’re only making 10 requests, it’s good to be nice.ģ19.86593675613403 seconds to download 289 stories on the pageĪs expected, this scales pretty poorly. Let’s also make sure to sleep for a bit between calls, to be nice to the Hacker News server. requests and BeautifulSoup make extracting the URLs easy. Since there are 30 per page, we only need a few pages to demonstrate the power of multithreading. I’ll walk through a quick example below.įirst, we need get the URLs of all the posts. Let’s say you wanted to download the HTML for a bunch of stories submitted to Hacker News. This is really just about highlighting how you can do faster web scraping with almost no changes. I’ll briefly touch on how multithreading is possible here and why it’s better than multiprocessing, but won’t go into detail. In this post, I’ll use concurrent.futures to make a simple web scraping task 20x faster on my 2015 Macbook Air. I wasn’t as well versed in concurrency and asynchronous programming back in 2016, so this didn’t even enter my mind. In light of that, I recently took a look at some of my old web scraping code across various projects and realized I could have gotten results much faster if I had just made a small change and used Python’s built-in concurrent.futures library. You might even say I think about it all the time. Working on GPU-accelerated data science libraries at NVIDIA, I think about accelerating code through parallelism and concurrency pretty frequently.
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