What does crawling data mean?

Crawling data refers to the process of systematically browsing the web to index pages for search engines. Search engines like Google use crawlers, also known as spiders or bots, to discover new web pages and content to add to their search results. The crawler visits web pages, extracts information, and follows links to crawl through the entire web.

How does data crawling work?

Crawling begins with a list of seed URLs to visit. As the crawler visits these pages, it parses the content and adds any links it finds to its queue of pages to crawl. This creates a crawl graph as more links are discovered and added to the queue.

Key aspects of the crawling process include:

• URL frontier – The list of URLs queued to crawl
• Page fetching – Downloading the HTML content of each page
• Content extraction – Parsing the HTML and extracting information to index
• URL discovery – Finding and adding new URLs to the frontier
• URL canonicalization – Cleaning and preparing URLs for consistent indexing
• Politeness policies – Crawling ethically by limiting requests and bandwidth usage
• Duplicate detection – Avoiding reindexing the same content multiple times

As the crawler visits each page, it stores the content and metadata to be indexed. The indexed data is then used to respond to search queries. Large search engines like Google have complex distributed crawlers running on thousands of machines to index billions of web pages.

Why is crawling important for search engines?

Crawling is the first step that powers search engines. It enables them to build their web index that serves users’ search queries. Without crawling the web, a search engine would have no data about web pages and would be unable to return relevant results.

Here are some key reasons crawling is important:

• Discover new and updated content – Crawlers continually scan the web to find new pages and content to index.
• Keep indexes fresh – Pages change frequently. Crawling allows search engines to update their indexes to reflect new content.
• Crawl the deep web – Beyond public web pages, crawlers can access “deep web” content that’s not linked for indexing.
• Understand web page structure – Crawlers analyze page content and architecture to improve search relevancy.
• Follow links and patterns – Crawlers recursively follow links across the web to discover publicly accessible content.

Without crawling, search engine results would be limited to a small subset of pages and quickly become outdated and irrelevant. Broad crawling enables robust, high quality search at scale.

What are the stages of a web crawler?

Crawlers follow a general sequence of stages as they browse and index the web:

1. Seeds – Starting URLs are added to the frontier as “seeds”.
2. DNS resolution – Domains are resolved to IP addresses for page fetching.
3. Fetch – The page content is downloaded for parsing.
4. Parse – Extract text, links, and data to index from the page.
5. Index – Store the relevant page data into the search index database.
6. Rank URLs – Prioritize URLs in the frontier based on importance.
7. Recurse links – Add new links from the page to the frontier and repeat the process.

This is a continuous cycle as the crawler revisits URLs to check for updates and finds new links to follow across the web. Some steps may be skipped based on the crawler’s policies and settings.

How are large websites crawled efficiently?

Crawling huge sites with millions of pages introduces challenges of scale, bandwidth, and computing resources. Large websites are crawled efficiently by:

• Running parallel crawlers across multiple servers
• Distributing URLs across crawler instances
• Caching DNS lookups and common requests
• Optimizing frontier data structures for efficiency
• Crawling incrementally instead of recrawling all pages
• Generating politeness policies to limit crawl rate
• Using sitemaps and feeds to discover new URLs
• Partitioning indexes by website domains
• Sampling pages intelligently when crawled comprehensively is infeasible

Leveraging distribution, politeness, caching, and sampling mechanisms allows crawlers to efficiently process even the largest websites with billions of objects.

What are some commonly crawled data types?

In addition to plain HTML pages, crawlers may also parse and index other common data formats found throughout the web, such as:

• Images – Extract alt-text and other metadata.
• Videos – Index metadata like titles, descriptions, and captions.
• PDFs – Crawl PDF content through text extraction.
• XML Sitemaps – Discover additional URLs to crawl.
• Product feeds – Index product catalogs and pricing.
• JavaScript – Execute JS to index dynamic page content.
• APIs – Access API metadata and schema.
• Social media – Profile information, posts, and comments.

By indexing diverse data types beyond HTML, search engines can provide richer results tailored to different media and document formats.

How are web crawlers used apart from search engines?

In addition to powering search engine indexes, web crawlers have other applications as well:

• Archiving – Preserve websites by crawling and storing web page history over time.
• Web mining – Analyze crawled content to uncover trends, statistics, and insights.
• Market research – Track product info and pricing from ecommerce sites.
• News aggregation – Crawl news sites and blogs to collect current events.
• Spam detection – Identify spam pages based on crawled content and metadata.
• Vulnerability assessment – Crawl sites to discover security flaws and vulnerabilities.

Focused web crawlers can automate data collection for business intelligence, research, digital preservation, and security applications.

What are some best practices for large-scale crawling?

Best practices that crawler engineers should follow when designing and operating large-scale crawlers include:

• Distribute crawling across multiple servers and IPs.
• Limit request rates to avoid overwhelming websites.
• Correctly handle robots.txt policies and meta tags.
• Retry failed requests and handle errors gracefully.
• Detect duplicate content to avoid re-crawling.
• Prioritize important pages to crawl more frequently.
• Cache DNS lookups, HTTP requests, and page hashes.
• Validate HTML and handle malformed content.
• Respect website owners’ wishes and crawl ethically.

Adhering to good practices allows large crawlers to efficiently and courteously gather web data at scale.

What are bots and how are they different from crawlers?

Bots and crawlers refer to automated scripts that browse the web, but with different purposes:

• Crawlers – Index web pages to build search engine indexes and archives.
• Bots – Carry out simple repetitive tasks on websites like checking prices.

Crawlers focus on comprehensively gathering data from across the web. Bots typically perform focused individual tasks on websites. Other differences include:

However, the terms are sometimes used interchangeably. Common types of bots include scrapers, chatbots, and web spiders focused on specific sites.

How can websites be crawled politely?

It’s important for crawlers to crawl websites politely to avoid overloading servers. Politeness policies that crawlers should follow include:

• Honoring robots.txt rules
• Crawling at reasonable speeds
• Limiting concurrent connections
• Not overloading pages with parameters
• Using site maps instead of scraping
• Obeying noindex metadata
• Respecting crawling preferences
• Avoiding denial of service
• Reasonable cache times
• Having crawl contacts available

Beyond basic manners of limiting requests, crawlers can further improve politeness through courtesy wait times, user-agent strings, and communicating with site owners. Overall, crawlers should gather data without harming site performance or availability.

Conclusion

Crawling is foundational to the operation of search engines on the web. Without comprehensive crawling, search engines would have limited indexes and struggle to provide relevant results. While complex at scale, crawling follows a basic workflow of discovering, fetching, extracting, indexing, and recursively processing pages. By handling politeness, caching, distribution, and other optimizations, crawlers can efficiently gather web data to power various applications.