Full-text search

Searching by words with stemming, typo tolerance and relevance ranking. SQL LIKE queries cannot do this at scale; a search engine kept in sync from the primary database can.

Where it shows up in real systems

  • Search engine

    Documents become searchable after the next refresh, so search is eventually consistent with your primary database. Queries are CPU-heavy; add nodes and replicas for throughput, and keep the source of truth elsewhere.

    In production: Elasticsearch, OpenSearch, Apache Solr, Algolia, Meilisearch

  • Vector database

    Powers semantic search, recommendations and retrieval for AI assistants. Approximate nearest-neighbour indexes such as HNSW trade a little recall for huge speed; they live mostly in memory, so size nodes by the number of vectors.

    In production: pgvector, Pinecone, Weaviate, Milvus, Qdrant, OpenSearch k-NN

See it break, then fix it

Build an architecture, send it a day of traffic and outages, and learn why it holds or fails. Free to start.

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