With over 5 billion internet users generating massive amounts of data every day, websites represent an invaluable data source for gaining powerful insights. However, extracting useful data from the web is not straightforward. This is where web crawling and web scraping come in – two related but distinct techniques for collecting website data programmatically.
In this comprehensive guide, we‘ll dive deep into web crawling versus web scraping, arming you with the knowledge to determine which approach works best for your needs. We‘ll explore the key differences, technical inner workings, use cases, challenges, and best practices for each method. Let‘s get started!
Defining Web Crawling vs Web Scraping
What is Web Crawling?
Web crawling refers to systematically browsing and cataloging all pages of a website by following links between pages. The aim is to index every bit of content – pages, images, documents, videos, etc. – on a site for search purposes.
Crawlers, also called spiders or bots, are programs that start with a seed list of URLs to visit. As they encounter links on each page, those URLs get added to the queue for the crawler to visit later. This recursive process allows crawlers to explore entire websites and build a comprehensive index.
What is Web Scraping?
Web scraping involves extracting specific pieces of data from websites in an automated fashion using scraping software. The goal is to copy relevant content from web pages into a structured format like a spreadsheet or database to analyze or monitor the information.
Scraping targets particular data points of interest rather than attempting to index all page contents. For example, an e-commerce site might be scraped for product descriptions, prices, reviews and stock levels.
How Web Scraping and Crawling Work
Web Scraping Step-by-Step Process
Here is the typical web scraping workflow:
- Identify the website(s) and pages to scrape
- Inspect pages to locate the data to extract (e.g. via browser developer tools)
- Use proxies and anti-scraping evasion tactics if needed
- Write scraping rules to pinpoint the data
- Run the scraper to visit pages and extract data
- Save scraped data structured format (JSON, CSV, etc)
- Clean and transform data as needed for analysis
Scraping individual pages is manageable manually, but automating the extraction across thousands of pages requires a web scraper tool. There are many software options providing point-and-click GUIs, code-based customization and headless browser emulation.
Web Crawler Step-by-Step Process
The workflow behind a basic web crawler looks like this:
- Discover seed pages to crawl (e.g. site home page)
- Fetch and parse page content
- Extract links to queue other pages for crawling
- Respect politeness constraints and robots.txt rules
- Archive indexed page data
- Revisit updated pages to keep index current
Following links recursively allows crawlers to map website structure and catalog all available pages. Crawlers must take care to control request rates and avoid traps that lead to scraping unwanted content.
Key Use Cases and Applications
Web Crawler Use Cases
The most common web crawler application is powering search engine indexing. Google, Bing and other search providers use crawlers to explore the internet and catalog pages to enable users to search relevant content.
Beyond external search engines, companies deploy internal crawlers to index content within intranets, document management systems, wikis and other internal sites to allow employees to search internal resources.
Web crawlers also have uses in web archiving to take snapshots of websites over time. The Internet Archive‘s Wayback Machine uses crawling to archive billions of web pages.
Web Scraping Use Cases
Web scraping has a multitude of business use cases, including:
- Price monitoring – Track competitors‘ pricing data on e-commerce sites to adjust your own prices
- Lead generation – Build lists of prospects from directories and contact data on sites
- Market research – Gather intel about trends, products and sentiment from discussions
- Supply chain analysis – Scrape shipment data to identify issues and optimize logistics
- Financial modeling – Ingest company financials, economic indicators and more into models
- News aggregation – Automatically pull news articles from different sources
These examples illustrate common scenarios where web scraping excels in extracting highly targeted data at scale for analytics and monitoring.
Challenges Facing Both Methods
While extremely useful, web crawling and scraping pose technical hurdles, including:
Spider Traps
Some sites deliberately create crawler traps to catch and block scrapers and bots. Traps trick software into getting stuck scraping endless irrelevant pages.
Exceeding Politeness Limits
Overloading sites with too many rapid requests can overwhelm servers. Crawling and scraping software must employ safeguards like delays and limits.
robots.txt Restrictions
This file gives sites a way to communicate which pages can/cannot be accessed. Non-compliant software risks being banned entirely.
IP Blocking
Making many scraping requests from the same IP address often triggers defensive blocking. Proxy rotation is required to mask scraper traffic.
These issues make proper web crawling and scraping non-trivial. Next we‘ll explore some best practices for overcoming the challenges.
Expert Best Practices and Tactics
Here are my top tips for successful, sustainable web data extraction based on years of hands-on experience:
Utilize Proxy Rotation
Proxy servers act as intermediaries that sit between your scraper and target sites. Using proxies from different IP addresses is crucial for distributing requests and avoiding blocks.
Vary User Agents
Changing the browser user agent string with each request helps avoid patterns that sites may use to identify scrapers versus real users.
Take Things Slowly
Limit request frequency by building in delays, throttles, timeouts and other politeness safeguards to avoid overloading servers.
Check robots.txt
Review and follow each site‘s specific crawling guidelines listed in this file to avoid locks or bans.
Additionally, general software best practices like exception handling and logging apply to keep scrapers running smoothly 24/7.
For much more detail on web scraping and crawling tactics, refer to my guides covering web scraping best practices and leveraging proxies.
Putting the Pieces Together
Now that we‘ve broken down web crawling versus web scraping, how do you decide which approach to use? Here is a recap of the key differences:
- Crawling indexes entire website contents for search purposes
- Scraping extracts specific data points for analytics/monitoring use cases
Often the methods work better together than separately. For example, web crawlers can discover pages and content to feed into scrapers extracting pertinent information from sites.
Understanding the capabilities of each technique allows matching the right solution to your web data extraction needs to deliver actionable intelligence.
