[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-graph-database::en":3,"gloss-cluster-graph-database::en":26,"gloss-next-graph-database::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"graph-database","data-infra","Graph Database","A graph database stores data as nodes (entities) and edges (the relationships between them), making connections first-class rather than something you reconstruct with JOINs. Where a relational query to find 'friends of friends who like X' means several expensive joins, a graph database walks the edges directly, and its performance barely degrades as the relationships deepen. Neo4j, and Postgres extensions, are common entry points.\n\nIt shines whenever the relationships are the point: social graphs, recommendation engines, fraud rings, permission hierarchies, and knowledge graphs that ground LLM answers (GraphRAG).\n\nFor SaaS builders, reach for a graph database when your queries are mostly about how entities connect, not about aggregating columns — an access-control system asking 'can this user reach this resource through any path' is a perfect fit. Practical note: it's a specialized tool, not a default. Most apps are served fine by a relational database until relationship-traversal queries become the bottleneck.","A graph database stores nodes and edges so relationships are first-class — traversing \"friends of friends who like X\" instead of paying for several expensive JOINs.",null,[11,14,17,20,23],{"slug":12,"name":13},"index-database","Index (Database)",{"slug":15,"name":16},"nosql","NoSQL",{"slug":18,"name":19},"postgresql","PostgreSQL",{"slug":21,"name":22},"retrieval-augmented-generation","Retrieval-Augmented Generation (RAG)",{"slug":24,"name":25},"vector-store","Vector Store",[27,31,34,37,40,44,47,50,53,56,60,63],{"slug":28,"category":5,"name":29,"updated_at":30},"acid","ACID","2026-08-24T02:46:37+00:00",{"slug":32,"category":5,"name":33,"updated_at":30},"ann-search","ANN Search",{"slug":35,"category":5,"name":36,"updated_at":30},"backpressure","Backpressure",{"slug":38,"category":5,"name":39,"updated_at":30},"batch-processing","Batch Processing",{"slug":41,"category":5,"name":42,"updated_at":43},"bm25","BM25","2026-08-24T02:46:38+00:00",{"slug":45,"category":5,"name":46,"updated_at":30},"cache","Cache",{"slug":48,"category":5,"name":49,"updated_at":30},"cap-theorem","CAP Theorem",{"slug":51,"category":5,"name":52,"updated_at":30},"change-data-capture","Change Data Capture (CDC)",{"slug":54,"category":5,"name":55,"updated_at":30},"chroma","Chroma",{"slug":57,"category":5,"name":58,"updated_at":59},"chunk-overlap","Chunk Overlap","2026-08-24T03:30:02+00:00",{"slug":61,"category":5,"name":62,"updated_at":30},"columnar-storage","Columnar Storage",{"slug":64,"category":5,"name":65,"updated_at":30},"connection-pooling","Connection Pooling"]