Large Scale Web Scraping with Python
Web scraping is an opportunity to capture exceptional information and resources to use for business decisions, project research, or a variety of other tasks. The more information you have, the more opportunity you have to make better decisions. With large scale web scraping Python strategies, you can capture significantly more data to use in a variety of ways.
Table of Contents
Large scale Python projects require a different setup to make them efficient. In this guide, we will provide you with the steps you need to create a scalable framework using specific tools and libraries that make the process easier. Big data web scraping can be a very effective way to gather exceptional information that can be used efficiently. Here’s how to do it.
What Are Large Scale Python Projects?
Large scale web scraping Python projects could be anything you need. In most situations, web scraping enables you to capture a significant amount of information and resources to use as you desire, but as your project size grows and demands increase, it’s essential to make a few changes to how you are scraping data so that it is efficient and still beneficial to use.
To do this, you need to build an automatic process that will crawl the locations you desire quickly and efficiently to capture the information you need and then move that data where you can use it.
There are two specific routes you may wish to take. The first is to build a web scraper that will pull dozens (or thousands) of pages of content from a single website. For example, you may want to capture dozens of listings from AliExpress, and building a web scraper that can do that quickly is essential. You can, for example, access Wayfair’s price history with a scraping bot.
Alternatively, you may want to target a specific element and capture that from numerous websites. You may want to capture all mentions of your company’s name, for example.
How to Build Big Data Web Scraping
Large scale web scraping Python processes are a bit more complex than what you would typically apply with a traditional simple web scraper. However, much of the process will remain the same. If you have not done so yet, check out our web crawling with Python tutorial. It is the best starting point if you are new to web scraping and need to just get started with the basics. As with many of the projects you may tackle with Python, Scrapy tends to be the best all-around tool to help you get started.
If you are brand new to the process, you can download Python a to get started. We also encourage you to get the Scrapy library in place as well. Scrapy is beneficial for dozens of reasons, but at the heart of it is its ability to be expanded. As a scalable tool, you can easily use it as a first time project – for even the smallest tasks – or scale up to big data web scraping.
With the basics in place and understood, consider the strategies you need to create large scale web scraping Python projects with ease. Here are some of the differences you will need to focus on.
Incorporating Asyncio: The next tool you’ll need is aysncio, a library that allows yout o write concurrent code using the async/await syntax. That is, it is a type of asynchronous web scraping, sometimes called non-blocking. As an asynchronous tool, it allows you to handle lengthy tasks while still tackling other projects and needs, without having to wait for that long task to wrap up before you move forward.
Asyncio for asynchronous programming uses the asyncio module to thread, where the code controls the context switching. This process reduces the complexity of creating code for these types of projects while also reducing the risk of errors. This method is usually ideal for web scraping projects.
To work, you will need to use the aiohttp library for web scraping in Python. To get that, use the following in a command line:
python3 -m pip install aiohttp
Then, you need to import asyncio and the aiohttp modules:
import aiohttp
import asyncio
You then need to use the get_response() function to change to a coroutine. The following code makes that possible:
async def get_response(session, url):
async with session.get(url) as resp:
text = await resp.text()
exp = r'(<title>).*(<\/title>)’
return re.search(exp, text, flags=re.DOTALL).group(0)
Multiprocessing: Another way to speed up your project is to use multiprocessing, a tool that utilizes more than one processor core. It is not common to find a single-core CPU, but you can write code that uses all of the cores in a multiprocessing module. For example, you can write code that will split the numerous cores into various components so that a different part of the CPU is focused on one task than the other portion.
To do this, you need to import Pool and cpu_count from the multiprocessing module. Use this code:
from multiprocessing import Pool, cpu_count
Utilizing the following code, along with the requests library, you can create a Pool, which allows you to choose which CPU cores to focus on for the operation. This code will help you do that:
def get_response(url):
resp = requests.get(url)
print(‘.’, end=”, flush=True)
text = resp.text
exp = r'(<title>).*(<\/title>)’
return re.search(exp, text, flags=re.DOTALL).group(0)
def main():
start_time = time.time()
links = get_links()
coresNr = cpu_count()
with Pool(coresNr) as p:
results = p.map(get_response, links)
for result in results:
print(result)
print(f”{(time.time() – start_time):.2f} seconds”)
if __name__ == ‘__main__’:
main()
The Importance of Optimizing Resources with Large Scale Python Projects
Large-scale web scraping Python projects can be highly effective and designed to provide you with excellent data quickly. However, to get the best results, we recommend a few steps that can ultimately use your resources more effectively.
Using proxies: One of the most important steps to take is to use rotating proxies as a component of your web scraping. These proxies will allow you to mask your personal IP address, allowing you to scrape without letting anyone know where you are located or who you are. As a rotating proxy, it also changes the IP address frequently. This means that there is no real risk of being blocked in your big data web scraping process because it looks like you are a different person each time you visit the site.
If you have not done so yet, read our guide on what proxies are and why they are so important to web scraping. You can also learn how proxy pools work, which can help with this process.
Managing rate limits: The next step to maximizing resources for large scale Python projects is to manage rate limits. This process incorporates a short delay in the web scraping process. A delay doesn’t sound like a good thing, but it can be a critical resource because it provides an opportunity for the system or network to catch up instead of overwhelming it to the point where it cannot function as it should. Most importantly, many websites use tools that block large-scale web scraping Python tasks like this using rate limiting – when it sees a big pull on resources, it stops you from capturing that information.
By building a delay into your code, you do not really add any time to the process that is significant or could slow down your large scale Python projects. However, it does reduce the risk of sending too many requests in a short period of time and alerting the target site of what you are doing.
Dynamically handle errors: Another resource-saving solution is to handle errors dynamically. To be effective, your big data web scraping project’s code needs to have a way to manage errors that is efficient and automated.
Data storage solutions: Next, you need to know where you are going to put all of that data so that you can use it. For this, we recommend data storage options that are in robust formats including databases or cloud services. This ensures that the large dataset is properly managed and accessible throughout the process. Advanced data storage systems allow you to capture all of that raw data seamlessly.
The Utilization of Large Scale Python Projects
Big data web scraping is an effective way to capture more information for bigger and even bolder decisions. This method for large scale web scraping Python projects allows you to execute large data sets with ease. Why bother with all of these steps? Doing so ensures efficiency and reliability in your processes while also ensuring you are compliant as applicable.
At Scraping Robot, we have the tools you need to get started. You can get around all blocks, captchas, and other limitations with our system. With the plug-and-play style of our API, it has never been easier to start scraping big data for your project. Contact us to learn more.
The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.
