Web Crawling vs Web Scraping – How Do They Compare?

Web crawling and web scraping are two of the most powerful techniques used today to extract massive amounts of data from the internet. But how exactly do they work, and what‘s the difference between crawling and scraping?

In this comprehensive guide, we‘ll examine the key distinctions between these two data collection processes to gain a deeper understanding of their inner workings and use cases. Whether you‘re looking to harness their capabilities yourself or simply want to learn more about how the web works, read on for an in-depth look at web crawling versus web scraping.

How Web Crawlers Work

Web crawlers, also called spiders or bots, are automated programs that browse the World Wide Web in a methodical, algorithmic fashion. Their primary purpose is to index the contents of the internet for search engines like Google, Bing, and Yandex.

But before we dig into what exactly crawlers do, let‘s break down the key components that allow them to work:

• URL Frontier – This queue data structure holds the list of URLs that remain to be crawled. It gets initialized with seed URLs to start with and expanded as new links are extracted.

• Fetcher – The fetching module takes URLs from the frontier, downloads the webpage content, and passes it to the parser.

• Parser – This component analyzes the HTML content of fetched pages to extract information like text, titles, links, images, and metadata.

• Data Store – Extracted information gets stored in a database or search indexes for later use.

• URL Filters – These determine which URLs should be added to the frontier versus ignored. Filters prevent crawling unimportant or problematic pages.

Now, let‘s walk through the high-level workflow of a typical web crawler:

1. Initialize the URL frontier with seed pages
2. Take the next URL from the frontier
3. Use the fetcher to download the page content
4. Parse the page to extract information
5. Filter and add child URLs to the frontier
6. Rinse and repeat!

Of course, this is a simplified algorithm – real web crawlers employ complex logic to crawl efficiently at massive scale. Features like politeness policies, parallel request handling, and incrementality enable huge sites to be crawled politely and efficiently.

But the core crawler functionality remains similar to the process above. By repeating these steps continuously, crawlers can index hundreds of billions of webpages over time.

How Search Engines Use Crawlers

The largest web crawlers are operated by search engines to power their indexing and make the internet searchable. Let‘s look at how the Googlebot crawler works specifically:

• Initializes its frontier with a large seed list of websites and sitemaps
• Respects robots.txt to only crawl permitted parts of sites
• Analyzes page content and extracts key facts, text, titles, links, media, etc.
• Parses structured data, alt text, metadata to understand page semantics
• Crawls billions of URLs per day to constantly update Google‘s indexes
• Supplements crawling with Sitemaps, RSS feeds, API data, and submissions

Google is estimated to crawl over 20 billion pages per day using multiple specialized bots! All this data enables Google to understand website content, rank pages, and return high-quality search results.

Other search engines like Bing, Yandex, Baidu and DuckDuckGo similarly rely on large-scale web crawlers to power their search results. Crawlers allow building rich indexes of the web that support search, recommendations, and data analytics.

How Web Scrapers Work

Now that we understand how web crawlers catalog the web, let‘s examine web scrapers and how they extract specific data.

The primary goal of a web scraper is to extract target information from websites in an automated fashion. For example:

• Product details like prices, images, and descriptions from ecommerce stores
• Business listings, addresses, and phone numbers from directories
• Article text, headlines, authors, and dates from news sites
• User profiles, friends, and posts from social media pages

Unlike general-purpose crawlers, web scrapers are tailored to parse the DOM structure of websites and extract only the data points needed. Here‘s how they work:

1. Identify the websites and URLs to scrape
2. Locate the target information in the DOM of each page
3. Write scraping rules to extract the desired data
4. Iterate through URLs, applying rules to parse target data
5. Output scraped data structured as JSON, CSV, etc.

For example, consider a scraper extracting laptop prices from an electronics store:

1. Find the laptop category page URL to scrape from
2. Inspect to see prices stored in <div class="price"> elements
3. Write a rule to grab <div class="price"> text for each product
4. Loop through pagination, applying the price extraction rule
5. Output laptop names matched to their prices

Scrapers can use libraries like Beautiful Soup in Python or Cheerio in Node.js to parse HTML and apply extraction rules. The data is typically structured as JSON objects or lines in a CSV file.

For large scrapers, distributed scraping helps parallelize requests across proxies to efficiently collect large datasets without getting blocked.

Scraping Best Practices

When building web scrapers, it‘s important to follow best practices that allow effective scraping while avoiding harm:

• Respect robots.txt – Exclude pages that site owners don‘t want scraped.

• Limit scrape rate – Crawling too fast can overwhelm servers. Apply delays.

• Distribute requests – Spread scrapes across proxies and IP addresses.

• Use randomized user agents – Don‘t fake other scrapers or spam same UA.

• Cache locally when possible – Avoid re-hitting sites unnecessarily.

• Confirm IP not blocked – Rotate IPs if you get access denied.

• Identify scrape friendly sites – Some sites prohibit scraping in ToS.

Adhering to these principles allows you to extract data at scale without getting sites blacklisted or banned.

Key Differences Between Crawlers and Scrapers

Now that we‘ve explored how web crawlers and scrapers operate independently, let‘s directly compare them across several dimensions:

	Web Crawlers	Web Scrapers
Purpose	Index website content for search engines	Extract specific datasets from sites
Scope	Broad, comprehensive coverage	Narrow, focused on target data
Output	Search indexes and graphs	Structured datasets like JSON
Page Treatment	Crawl and catalog all permitted pages	Only visit pages with needed data
Server Impact	Light, following politeness policies	Can be heavy if not throttled
Speed	Billions of pages per day	Hundreds to millions of pages per day
Primary Users	Search engines	Businesses, researchers, aggregators

As this comparison shows, crawlers take a broad approach while scrapers are highly targeted. But they can work together – scrapers may use crawling techniques internally to navigate sites.

Scrapers Rely on Crawlers for Discovery

One area where scraping and crawling intersect is discovery of URLs to scrape. While scrapers focus on extracting specific data points, they still need to locate the pages that contain the target data.

For example, a scraper may need to visit the product page for every laptop sold on an electronics store to gather laptop specs and prices. To find these URLs, the scraper can mimic crawler behavior:

1. Start from the site homepage
2. Recursively follow internal links to discover pages
3. Check each page‘s content for data to scrape
4. If not target data, follow links to continue discovery

This demonstrates how scrapers can utilize crawling concepts like link graphs and frontier queues internally as part of the scraping process. So crawlers support scrapers by providing the navigation logic needed for URL discovery.

Ethical Considerations for Crawlers and Scrapers

Both web crawlers and scrapers gain their power from accessing publicly available information on websites. However, just because data is public does not mean accessing it is necessarily ethical or legal. Crawler and scraper operators should adhere to some basic principles:

• Respect robots.txt – This file indicates if site owners permit crawling or scraping.

• Consider bandwidth costs – Crawling uses site‘s bandwidth which costs money, especially at scale.

• Don‘t over-request – Crawling/scraping too aggressively can take down sites.

• Understand legal context – Some scraping may violate CFAA or website terms.

• De-identify data – Mask personal information if aggregating public profiles.

• Credit sources – When re-publishing scraped data, properly cite the origins.

• Add value – Re-sharing scraped data as-is is not very useful without additional analysis or processing.

Ultimately there are gray areas around what constitutes fair and ethical use of public data. Open communication with site owners is recommended when possible.

Crawler and Scraper Use Cases

Now that we‘ve explored how crawlers and scrapers work independently and compared their capabilities, let‘s look some common use cases taking advantage of their strengths:

Web Crawler Use Cases

• Search engines – Crawlers are crucial for indexing websites to power search results for queries.

• Archiving – Non-profits like Internet Archive crawl sites historically to preserve records.

• Marketing analytics – Understand SEO landscape, get competitor intel, find link opportunities.

• Academic research – Analyze language usage, meme spread, network effects across web.

• Testing – Crawl sites to validate internal and external links are working.

Web Scraper Use Cases

• Price monitoring – Track prices for travel, ecommerce, real estate, stocks over time.

• Lead generation – Build business contact lists from directories.

• News monitoring – Get alerts on developments relevant to key topics or keywords.

• Market research – Analyze consumer sentiment on brands/products from forums.

• Content aggregation – Assemble articles, videos, images on given subjects from around the web.

• Data enrichment – Enhance business databases with additional details scraped from websites.

There are nearly endless possibilities for harnessing web data at scale thanks to the combined capabilities of crawlers and scrapers!

Should You Build or Buy Your Crawler/Scraper?

For many use cases, trying to build your own web crawler or scraper from scratch is unnecessary. There are excellent third-party services available:

• Web scraper APIs – Abstract away complexities so you can scrape via simple API requests.

• Search engines – Leverage search APIs for organic discovery vs. crawling yourself.

• Crawler-as-a-service – Let a vendor handle large-scale crawling infrastructure.

• Managed scraping – Scraping experts handle the work as a service.

Going with an existing solution gets you crawling or scraping data quickly without operational overhead. But for advanced needs, custom builds provide control to tailor systems exactly for your domain.

Powerful Tools for Web Data Extraction

We‘ve covered a lot of ground exploring the world of web crawlers versus web scrapers. The key takeaway is that while their capabilities overlap, they serve very different primary functions:

• Web crawlers aim to catalog the entire web for broad indexing.

• Web scrapers extract narrowly defined datasets from specific sites.

Both techniques allow gathering web data at scale to power various applications and services. When used ethically, they open up a world of possibilities for harnessing the vast richness of information on the internet.