System Design: Web Crawler
Learn about the web crawler service.
We'll cover the following...
Introduction
A web crawler is an internet bot that systematically
The core functionality of a web crawler involves fetching web pages, parsing their content and metadata, and extracting new URLs or lists of URLs for further crawling. This is the first step performed by search engines. The output of the crawling process serves as input for subsequent stages such as:
Data cleaning
Indexing
Relevance scoring using algorithms like PageRank
URL frontier management
Analytics
This specific design problem is focused on web crawlers’ System Design and excludes explanations of the later stages of indexing, ranking in search engines, etc. To learn about some of these subsequent stages, refer to our chapter on distributed search.
Benefits of a Web Crawler
Challenges of a Web Crawler System Design
While designing a web crawler, several challenges arise:
Crawler traps: Infinite loops caused by dynamic links or calendar pages.
Duplicate content: Crawling the same web pages repeatedly wastes resources.
Rate limiting: Fetching too many pages from a single domain can lead to server overload. We need load balancing to balance the loads on web servers or application servers.
DNS lookup latency: Frequent domain name system (DNS) lookups increase latency.
Scalability: Handling large-scale crawling is challenging and demands a distributed system that can process millions of seed URLs and distribute load across multiple web servers.
Designing a web crawler is a common System Design interview question to test candidates’ understanding of components like HTML fetcher, extractor, scheduler, etc. The interviewer can ask the following interesting questions:
How would you design a web crawler system that can handle large datasets, and how would you incorporate Redis for caching and Amazon web services (AWS) for scalability?
How would you handle request timeouts and manage rate limits set by websites?
What optimization strategies would you use for components like parser, fetcher, etc., for large-scale use cases like those at FAANG?
How metrics like response time, cache hit rate, etc., help evaluate web crawlers’ performance to crawl large datasets for aggregation.
Let’s now discuss how we will design a web crawler system.