Search Engine Architecture: Crawling, Inverted Indexes, & Ranking
Architect large-scale distributed search engines: Web crawling frontiers and politeness policies, Inverted Index data structures (Finite State Transducers, Postings Lists), Okapi BM25 relevance ranking, Lucene immutable segments, and Elasticsearch distributed sharding.
01.1. Distributed Web Crawling: The Crawler Frontier & Politeness
A search engine starts by harvesting unstructured text across billions of web pages:
Key Crawler Components:
- URL Frontier: A multi-queue priority scheduling system. To prevent Denial of Service (DoS) attacks on external websites, the frontier enforces Politeness Policies:
- One queue per target domain host.
- A strict inter-request delay (e.g.,
500ms - 1,000ms) per host. - Strict adherence to
robots.txtexclusion rules.
- Duplicate Detection via Bloom Filters & SimHash:
- URL Deduplication: A distributed Bloom Filter checks if a URL has already been visited with zero disk I/O and
< 0.1\%false positive probability. - Near-Duplicate Content Detection: 64-bit SimHash generates document fingerprints where similar web pages (e.g., identical articles with different sidebar ads) produce Hamming distances
≤ 3, allowing crawlers to discard duplicate pages instantly.
- URL Deduplication: A distributed Bloom Filter checks if a URL has already been visited with zero disk I/O and
Search Engine Inverted Index & Query Execution Architecture 🔍
Search Engine Inverted Index & Query Execution Architecture 🔍
From web crawler ingestion and text tokenization to Lucene FST term dictionaries, postings list intersection, and Okapi BM25 relevance scoring.
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