Design a large-scale web crawler (e.g., Googlebot).

Let's design a large-scale web crawler like Googlebot. This is a complex system, and we'll focus on the key components and considerations.

I. Core Components:

Crawler Controller:
- URL Frontier: A prioritized queue of URLs to be crawled. Prioritization can be based on factors like page rank, update frequency, and link depth. Distributed queue systems (like Kafka or a custom solution using a distributed key-value store) are necessary for scale.
- Scheduler: Decides which URLs to crawl next, taking into account politeness policies (robots.txt), crawl rate limits, and other constraints.
- Fetcher: Fetches web pages from the URLs in the frontier. Uses HTTP requests and handles various HTTP responses (200 OK, 404 Not Found, redirects, etc.).
- Parser: Parses the fetched web pages to extract links, content, and other relevant information. Libraries like Beautiful Soup or specialized HTML parsing tools can be used.
- Duplicate Detection: Identifies and avoids crawling the same page multiple times. Hashing and Bloom filters can be used for efficient duplicate detection at scale.
Downloader:
- HTTP Client Pool: Manages a pool of HTTP clients to fetch web pages concurrently. Handles connection management, timeouts, and retries.
- DNS Resolver: Resolves domain names to IP addresses. Caching DNS responses is essential for performance.
- Robots.txt Handler: Respects the robots.txt rules of each website to avoid crawling disallowed pages.
Parser:
- HTML Parser: Parses HTML content to extract links, text, metadata, and other information.
- Link Extractor: Extracts all the links from a web page.
- Content Extractor: Extracts the main content of a web page, removing boilerplate and irrelevant information.
Data Store:
- Web Graph: Stores the relationships between web pages (which pages link to which other pages). Graph databases or distributed key-value stores can be used.
- Page Content: Stores the content of the crawled web pages. Object storage systems (like Amazon S3 or Google Cloud Storage) are suitable for this.
- Index: Builds an index of keywords and their associated web pages for search functionality. Distributed search indexes (like Elasticsearch or Solr) are used at scale.
Indexer:
- Index Builder: Processes the parsed web pages and builds the search index.
- Index Updater: Updates the index as new pages are crawled and existing pages are modified.
Frontier Manager:
- URL Prioritization: Implements algorithms to prioritize URLs for crawling.
- Queue Management: Manages the distributed URL frontier.

II. Key Considerations:

Scalability: The system must be able to crawl billions of pages. Distributed architectures and horizontal scaling are essential.
Performance: Crawling should be fast and efficient. Concurrent fetching, optimized parsing, and efficient data storage are important.
Politeness: Respecting robots.txt rules and avoiding overloading web servers is crucial.
Robustness: The system should be fault-tolerant and able to handle network errors, server downtime, and other issues.
Data Quality: The crawled data should be accurate and consistent. Duplicate detection, content extraction, and data validation are important.
Freshness: The crawler should regularly recrawl pages to keep the index up-to-date.

III. High-Level Architecture:

                                   +-----------------+
                                   | Crawler Controller |
                                   | (Scheduler,     |
                                   |  Fetcher, Parser,|
                                   |  Duplicate Det.)|
                                   +--------+---------+
                                            |
                         +------------------+------------------+
                         |                  |                  |
             +----------v----------+  +----------v----------+
             |    Downloader      |  |     Parser       |
             | (HTTP Client Pool,|  | (HTML Parser,   |
             |   DNS Resolver)   |  |  Link Extractor)|
             +----------+----------+  +----------+----------+
                         |                  |
                         |                  |
            +-----------v-----------+  +-----------v-----------+
            |    Data Store       |  |     Indexer        |
            | (Web Graph, Page   |  | (Index Builder,  |
            |  Content, Index)   |  |  Index Updater)  |
            +-----------------------+  +-----------------------+
                         |
            +-----------v-----------+
            |  Frontier Manager   |
            | (URL Prioritization,|
            |  Queue Management)  |
            +-----------------------+

IV. Data Flow (Example: Crawling a Page):

Frontier Manager: Selects a URL from the URL frontier.
Crawler Controller: Schedules the URL for crawling.
Downloader: Fetches the web page from the URL.
Parser: Parses the web page, extracts links and content.
Duplicate Detection: Checks if the page has already been crawled.
Data Store: Stores the page content and updates the web graph.
Indexer: Processes the page content and updates the search index.
Frontier Manager: Adds new links to the URL frontier.

V. Scaling Considerations:

Crawler Controller: Distributed scheduler, sharded URL frontier.
Downloader: Large pool of HTTP clients, distributed DNS resolution.
Parser: Parallel parsing of web pages.
Data Store: Distributed storage systems, sharded databases, distributed search indexes.
Indexer: Distributed index building and updating.

VI. Advanced Topics:

Focused Crawling: Crawling only pages relevant to a specific topic.
Incremental Crawling: Crawling only changed pages to improve freshness.
Near-Duplicate Detection: Identifying pages with very similar content.
Machine Learning for Crawling: Using machine learning to improve crawl efficiency and data quality.

This design provides a high-level overview of a large-scale web crawler. Each component can be further broken down and discussed in more detail. Remember to consider the trade-offs between different design choices and prioritize the key requirements of the system.