The speaker introduces web crawlers, their synonyms, and basic functions.

• Web crawlers are also known as spiders, bots, or simply crawlers.
• A web crawler is a framework, tool, or software used to collect web pages from the Internet for easy access and indexing.
• The crawler saves the collected web pages and recursively finds URLs within those pages to continue the process.

Different types of web crawlers and their applications are discussed.

• There are various web crawlers with different applications, and individuals can build custom crawlers for specific purposes.
• Common use cases include search engines, copyright violation detection, keyword-based content finding, web malware detection, and web analytics.
• These functionalities cater to different industry needs such as providing search results, detecting unauthorized content use, monitoring market trends, and collecting data for machine learning models.

The speaker outlines the essential features to support in the web crawler.

• Features include politeness (avoiding server overload), DNS queries, distributed crawling for scalability, priority crawling, and duplicate detection.
• Politeness and crawl prioritization are crucial for respecting website traffic and fetching relevant content efficiently.
• Duplicate detection is vital for avoiding unnecessary crawling of content already stored, saving resources and time.

Estimations for scaling the crawler and storage requirements are provided.

• An estimation of 900 million registered websites is considered, with an assumption that 60% are functional, leading to roughly 500 million websites to crawl.
• On average, each website might contain about 100 pages, resulting in 50 billion pages to crawl and store.
• The average size of a webpage and the storage capacity required to save the crawled pages are calculated, considering only necessary content is downloaded, not the entire media.

A detailed system design diagram for the crawler is explained.

• The diagram includes seed URLs to initiate the crawl, a URL frontier queue for managing crawl order, and fetchers and renderers for retrieving and processing web content.
• The system uses distributed threads or processes to fetch and render web pages concurrently and can scale by adding more machines.
• The URL frontier ensures that politeness and priority features are maintained, while the DNS resolver optimizes the domain name resolution process.

The speaker elaborates on the URL frontier and the implementation of politeness policy.

• The URL frontier consists of front queues and back queues, prioritizers, and back queue selectors to manage URL processing.
• Each back queue corresponds to a specific host to ensure only one connection to a host at a time, maintaining politeness by not overwhelming servers.
• The system uses heaps to keep track of which URL to crawl next based on priority and delay between requests to the same host.

Methods for detecting updates and handling duplicates are discussed.

• Head requests are used to check if a page has been updated by comparing the last modified time without downloading the entire content.
• Duplicate detection is crucial to save resources and is conducted using hashing, signature calculations, and algorithms such as Simhash for near-duplicate detection.
• The Simhash algorithm provides a way to identify near-duplicate documents even when a certain percentage of content is different.

Various storage solutions for the crawled data are suggested.

• Storage solutions like Amazon S3, MinIO, Bigtable, GFS, HDFS, and their alternatives like HDFS Federation are considered for storing crawled web pages.
• The choice of storage solution depends on the size of the data, with considerations for efficient retrieval and optimal resource usage.
• Sharding and consistent hashing are recommended for managing large amounts of data across distributed file systems.