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Learn about web scraping, what are the main challenges, and find out how to overcome them.
Web scraping has become an important technique to master when it comes to building large sets of data for a complex machine learning project, or simply for data analysis. However, it isn’t so simple to do. There are many challenges that comes with web scraping that need to be carefully looked into, to make sure you build an effective scraper, in an ethical manner.
So, let’s take a look at the challenges that you will face with web scraping, and explore ways to overcome them!
But first, let’s talk about web scraping.
At its core, web scraping is simply the process of going through websites on the internet and downloading the data into your app server. This involves a series of steps like:
After you’ve scraped the DOM and have gotten all the data that you need, you can choose to store it in your own database as a CSV or a JSON file.
But, you might be wondering, isn’t this a lot of work to do?
Well, you’re right. Doing it all on your own would be time-consuming. That’s why there’s scraping solutions that readily that lets you scrape webpages with little to no effort. In fact, there are more web scrapers such as custom scripts, cloud-based services and even browser extensions that help automate scraping.
Well, you might wonder next – why would I do this?
The simple answer is – analysis. Data is a cruicial asset to any business. The information that you obtained by analyzing the raw data becomes the lifeline of domains such as market research, price monitoring and comparison, lead generation, customer sentiment management, and academic research.
For instance, researchers may gather data for trend analysis, whereas e-commerce companies use web scraping to track brand competitor pricing. Thus, especially in businesses, it is an essential technique for staying a step ahead of the competition.
But, scraping isn’t as straight forward as it looks. There are challenges that you will encounter while building your scraping solution. So, let’s take a look at them in detail.
Understanding Legal Boundaries
The legal landscape of web scraping is complex and often varies by jurisdiction, but not without several potential gray areas.
Web scraping practices and tools themselves are not explicitly considered illegal. Yet, since web scraping involves data extraction from a multitude of websites owned by different parties, it is one of the major challenges that could cause significant repercussions if ignored or violated.
Such legal issues include:
Furthermore, several laws have been established to regulate such illegal practices, including the CFAA (Computer Fraud and Abuse Act) in the US and the GDPR (General Data Protection Regulation) in Europe.
It is always a best practice to be aware of these data protection laws since they have been central in previous cases, such as the lawsuit of hiQ Labs vs LinkedIn Corp. In case you are unsure about the legality of the scraping activities you employ, discuss a solution with an expert third party company providing web scraping services or seek legal advice from a professional.
Ethical Scraping PracticesClosely aligned with abiding by the legalities related to web scraping, following ethical data extraction practices is vital to maintain a good reputation and prevent potential backlash later on.
Such ethical practices comprise of:
User-agent: * Disallow: /private/ Disallow: /temp/ Disallow: /admin/ User-agent: Googlebot Allow: /public/ User-agent: Bingbot Disallow: /no-bing/
CAPTCHA
Completely Automated Public Turing test to tell Computers and Humans Apart, or simply CAPTCHA, is a common anti-scraping mechanism specifically designed to distinguish humans from bots by presenting challenges that are too difficult for automated bots to solve. Thus, it prevents bots from accessing web sources, generally to prevent spamming, fake registrations, and scraping.
To overcome CAPTCHA challenges, which include identifying visuals such as text, images, and sometimes audio, developers can use CAPTCHA-solving services like Anti-Captcha, and CAPTCHA Solver or employ machine learning models trained to recognize and resolve CAPTCHA.
IP Blocking
Websites use IP blocking when receiving excessive requests from a single IP address as a common tactic to prevent automated scraping and cyber attacks. A complete IP ban or a temporary restriction over the website’s resources can break down and halt the scraping process, triggered by multiple requests or based on the geographical location the traffic is coming from.
In such cases, you can use rotating proxies or residential proxies, as well as services like ProxyMesh or ScraperAPI, to bypass IP blocking. Rotating proxies ensures that requests are made from different IP addresses, rendering it difficult for the website to detect and block your scraper.
User-Agent Blocking
Websites often look for proper user-agent strings and block traffic from those associated with commonly used scraping bots. They are more likely to block requests where the user-agent string is unusual, for instance, where the browser is unknown or does not provide a user-agent string at all.An example of a user-agent string:
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36
To overcome this, developers can use a set of real browser user-agent strings and rotate them to mimic regular users. In addition, mimicking human-like behavior in scraping scripts, such as randomizing the time intervals between requests, can further reduce the risk of being detected and blocked. However, it is important to make sure that your activities all fall within the legal and ethical landscape.
JavaScript-Rendered Content
Scraping JavaScript-rendered content can be a huge obstacle when automated scrapers are designed and developed to scrape static HTML content. As JavaScript dynamically modifies the web page’s DOM after the initial loading, it is often challenging for traditional scrapers to follow up, making them miss out on the dynamically loaded content.
In cases where user interaction is required to load dynamic content, such as scrolling or clicking, scrapers must be capable of mimicking these behaviors to capture all necessary data. In such instances, tools like Selenium, ZenRows, Playwright, or Puppeteer can help you render JavaScript and scrape dynamic content by stimulating the browser’s behavior.
Infinite Scrolling
As the name suggests, infinite scrolling dynamically loads new content as long as the user scrolls down the page. Social media platforms, including Instagram, Facebook, and some e-commerce platforms, use this technique to keep users engaged. Therefore, the scrapers end up capturing just the content from the initial page load and not the additional content loaded from the infinite scrolling.To tackle this challenge, you can employ a tool like Selenium or Puppeteer to simulate user scrolling, ensuring all content is loaded and captured. Alternatively, it is also possible to intercept network requests to fetch the data more efficiently and directly.
AJAX Calls
AJAX, Asynchronous JavaScript and XML, calls facilitate loading data asynchronously, making it possible to update different parts of a web page without reloading the whole page. It is widely used in websites to improve the loading speed and overall user experience. For instance, infinity scrolls is an instance where AJAX calls are used for loading content from the server. These asynchronous data loads pose challenges for web scrapers, as they need to trigger the necessary AJAX calls to extract the data.
To scrape the data loaded through AJAX calls, one solution you have is to intercept the network requests and identify the AJAX endpoints and parameters. You can do this using the Network tab in the browser’s developer tools. Then select the XHR filter, pick a request after scrolling down the page slowly, and click on the Headers tab to get further information. You can use HTTP libraries like Requests in Python to send direct requests to AJAX endpoints to extract comprehensive data despite the complexities.
Network
Headers
import requests Defining the URL target_url = 'http://example.com/ajax-endpoint' Sending the GET request response = requests.get(target_url) Retrieving the content in response data = response.json() print(data)
Handling Different HTML Structures
Another significant challenge developers usually encounter is dealing with the variability in HTML structures across different websites or even between various pages of the same website. Web designers and developers typically follow their own designs and formats, resulting in a diverse range of structures the scrapers must handle.
Apart from that, periodic design and layout changes, even on a small scale, can significantly impact the functionality of the web scrapers, as they are tailored to fit the initial structure of the website.In cases where there is a considerable variation in the HTML structures, libraries like BeautifulSoup (Python) or Cheerio (Node.js) can aid in parsing HTML and extracting data as they are easy to use, offer powerful parsing capabilities, and handle malformed HTML efficiently. Also, designing flexible and modular scrapers that can successfully and dynamically adapt to different structures without requiring a complete rewrite is a best practice.
Managing Inconsistent Data
What web scraping provides you with is a large volume of raw data. In most cases, data presentation in varying data formats, sometimes with different naming conventions, can cause inconsistencies in the datasets. Imagine a situation where two product prices are given as “$ 19.99” and “USD 19.99” in two sections of the same web page as a simple example. These inconsistencies, redundant, outdated, and incomplete data can lead to hugely affect the overall data quality.
Therefore, it is essential to have robust data validation, error handling, and logging practices in place, along with regular updates to the scraping scripts to handle changes in HTML structure. Machine learning models can also be used to identify and extract specific data elements more intelligently.
Efficient Scraping Techniques
When working with large websites or even multiple sites, efficient scraping techniques are the key to handling the volume of data and ensuring timely extraction. One of the best approaches here is to employ asynchronous programming, which allows the scraper to cut back on idle waiting time and perform multiple tasks concurrently.
You can easily incorporate libraries like asyncio (Python) and use Node.js’s async/await to make multiple asynchronous requests at once and reduce the waiting time for responses. Not only that, but for large-scale projects, distributed scraping frameworks such as Scrapy (Python) and HYPERLINK “https://apify.com/”Apify (JavaScript) are excellent choices for raising efficiency.
async/await
Managing Large Volumes of Data
Even after successfully extracting data, storing and managing large volumes of it could put a strain on the available resources. Depending on the type of data you amass, MongoDB or PostgreSQL are highly efficient and flexible database choices, whereas cloud storage solutions like Azure Blob Storage, Amzon S3, and Google Cloud storage are ideal for managing storage costs and performance. Their pay-as-you-go pricing models and auto-scaling capabilities are ideal in cases where businesses need cost-effective solutions without the painstaking workload of manually scaling resources.
Furthermore, to ensure data integrity, it is essential to perform real-time data processing. Employing data pipeline tools like Apache Kafka or RabbitMQ are capable of handling data streams efficiently, allowing real-time ingestion, processing, and distribution.
Web scraping presents numerous challenges, ranging from legal and ethical considerations, anti-scraping mechanisms, dynamic content, data format and HTML structure variability to scalability issues when scraping large amounts of data from varying sources. For developers, understanding each potential challenge is the key to building and employing robust and adaptable scraping solutions.Thank you for reading!
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