Exploration ·
Follow a query from the box you type it into to the ten blue links: how text becomes terms, how an inverted index stores and compresses them, how BM25 scores a match, how we know a ranking is any good, and how the whole thing is split across thousands of machines — with the tools running live between the paragraphs.
Tool ·
Practise big-tech style system design interviews: a paced 45- or 60-minute framework with a mock interviewer, random questions and a self-scoring rubric, a back-of-the-envelope estimator, 38 reference architectures (likes, web search, webhooks, group chat, Kafka- and Dynamo-style stores, maps, a matching engine and more) as clickable diagrams with model deep-dive answers and interview cards, a live simulation of each one you can overload and break, a searchable question bank with a compare view, runnable building blocks (rate limiters, consistent hashing, Snowflake IDs, Bloom filters, fencing tokens, the outbox pattern and more) with Python, Go and Java versions, and trade-off cards.
Tool ·
Inside an inverted index: build one from documents and look at docIDs, frequencies and positions; compress the postings with VByte, Elias γ/δ, Golomb/Rice, Simple-9/16, FOR and PForDelta blocks of 128, Lucene's PForUtil and Roaring bitmaps (bits per posting, bit by bit); intersect with skips and galloping; and step through MaxScore, WAND and Block-Max WAND against exhaustive BM25.
Tool ·
Build an Elasticsearch analyzer from char filters, a tokenizer and token filters and watch the token stream change stage by stage — terms, positions, offsets, types and position lengths, drawn as a token graph — then index documents to see the postings, field-length norms and phrase/slop matching, and copy the _analyze request and index settings.