Free Mermaid diagrams covering search indexing, query processing, autocomplete, ranking algorithms, sharding and more.
Search infrastructure is one of the most technically demanding areas of backend engineering. A production search system must ingest and index billions of documents, answer queries in under 100 milliseconds, and continuously improve its results based on user behavior — all at the same time. This collection of 10 free Mermaid diagrams maps the core components and data flows you'll encounter when building or operating search at scale.
Start with the data path: the Search Indexing Pipeline shows how raw content moves from source through parsing, tokenization, and enrichment into an inverted index distributed across shards. The Search Sharding Architecture shows how that index is partitioned for horizontal scale. At query time, the Search Query Processing diagram traces how a raw string is parsed, rewritten, and executed across shards before results are merged and returned.
User experience features — Autocomplete Engine and Search Suggestion Engine — explain how prefix tries and behavioral signals produce fast, relevant completions. Ranking Algorithm Pipeline breaks down how BM25 scores, freshness signals, and personalization factors are combined into a final result order. Closing feedback loops is covered in Search Relevance Feedback. Operational concerns — Search Result Caching, Search Analytics Pipeline, and Search Log Processing — round out the collection. Every diagram is free to edit and export in Graphlet.
