Engineering Atlasby Abhishek Goyal

Tag

Vector search

3 diagram-led guides on Vector search by Abhishek Goyal.

  1. Deep diveNewMembers

    Data Structures for System Design: B-Tree, LSM Tree, Bloom Filter, Inverted Index, Geo and Vector Indexes

    The data structures behind real databases and search engines, explained for system design interviews: B-Tree vs LSM tree, Bloom filters, tries and FSTs for autocomplete, the inverted index, Roaring bitmaps, geohash, quadtree, S2 and H3 for location search, and HNSW for vector search, with when to pick each.

    ~12 min read · Beginner → Senior · 8 diagrams

  2. Deep diveNewMembers

    Types of Search in System Design: Autocomplete, Fuzzy, Phrase, Geo, Faceted, Vector and Hybrid

    The seven kinds of search behind one search box, for system design interviews: autocomplete with edge n-grams and FSTs, fuzzy search with Levenshtein automata, phrase search with positions, geo search with cells and BKD trees, faceted search and why counts are approximate across shards, vector search with HNSW, and hybrid search merged with Reciprocal Rank Fusion.

    ~16 min read · Beginner → Senior · 5 diagrams

  3. Deep diveNewMembers

    Search System Design Case Studies: Discord, GitHub, LinkedIn, Uber, Pinterest and More

    Real-world search system design case studies for HLD interviews: how Discord, GitHub, LinkedIn, Uber, DoorDash, Yelp, Instacart, Pinterest, Twitter, Slack, eBay and others scaled Elasticsearch, built their own Lucene engines, added vector and hybrid search, and ranked results, with the patterns that repeat.

    ~18 min read · Beginner → Senior · 7 diagrams

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