Asynchronous web scraping is a programming technique that allows for running multiple scrape tasks in effective parallel. This approach can significantly enhance the efficiency and speed of data collection processes by allowing a program to execute other tasks while waiting for HTTP requests to return data. In the context of web scraping, leveraging an API for web scraping can further streamline this process. Such APIs provide a more sophisticated, efficient way to handle multiple requests simultaneously, reducing the complexity of coding asynchronous scrapers and offering a powerful solution for scaling web scraping operations.
Asynchronous programming is especially important in web scraping as web scraping programs have a lot of waiting time. In other words, every time a web scraper requests a web page, it has to wait for the response. This waiting time can be relatively long, especially when scraping large amounts of web pages.
For example, let’s take a look at this synchronous scraping example in Python:
import httpx
from time import time
_start = time()
pages = [
"https://httpbin.dev/delay/2",
"https://httpbin.dev/delay/2",
"https://httpbin.dev/delay/2",
"https://httpbin.dev/delay/2",
"https://httpbin.dev/delay/2",
]
for page in pages:
httpx.get(page)
print(f"finished scraping {len(pages)} pages in {time() - _start:.2f} seconds")
"finished scraping 5 pages in 15.46 seconds"
Here we have a list of 5 web pages that load in 2 seconds each. If we run this code, we’ll see that it completes in ~15 seconds every time.
This is because our code waits for each page to fully complete scraping before moving on even if the program itself does nothing but wait for the server to respond.
In contrast, asynchronous web scraping allows for running multiple scrape tasks in effective parallel:
import httpx
import asyncio
from time import time
async def run():
_start = time()
async with httpx.AsyncClient() as client:
pages = [
"https://httpbin.dev/delay/2",
"https://httpbin.dev/delay/2",
"https://httpbin.dev/delay/2",
"https://httpbin.dev/delay/2",
"https://httpbin.dev/delay/2",
]
# run all requests concurrently using asyncio.gather
await asyncio.gather(*[client.get(page) for page in pages])
print(f"finished scraping {len(pages)} pages in {time() - _start:.2f} seconds")
asyncio.run(run())
"finished scraping 5 pages in 2.93 seconds"
This Python example uses httpx.AsyncClient and asyncio to eliminate the waiting time by running all requests in parallel. As a result, the code completes in 2-3 seconds every time.
Asynchronous programming is an ideal fit for web scraping and one of the easiest ways to speed up web scraping. For more see:
