[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-table-partitioning::en":3,"gloss-cluster-table-partitioning::en":26,"gloss-next-table-partitioning::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"table-partitioning","data-infra","Table Partitioning","Table partitioning splits one logically large table into many smaller physical pieces — partitions — usually by a key like date, tenant ID, or region. Queries and writes still address the single logical table; the database routes each row to the right partition underneath. It's distinct from sharding: partitioning divides data within one server, while sharding spreads it across many.\n\nThe payoff is that the engine can skip entire partitions it knows are irrelevant — a query for last week's rows never touches last year's — which keeps scans fast as the table grows into billions of rows. Dropping old data becomes an instant partition drop instead of a slow, lock-heavy DELETE.\n\nFor SaaS builders, time-based partitioning pairs naturally with data-retention policies, and tenant-based partitioning can isolate your largest customers. Practical note: choose a partition key that most queries filter on, or you lose the pruning benefit and just add overhead.","Table partitioning splits one logical table into smaller physical pieces by date, tenant, or region — queries still hit one table, and old partitions drop cheaply.",null,[11,14,17,20,23],{"slug":12,"name":13},"columnar-storage","Columnar Storage",{"slug":15,"name":16},"data-retention-policy","Data Retention Policy",{"slug":18,"name":19},"index-database","Index (Database)",{"slug":21,"name":22},"postgresql","PostgreSQL",{"slug":24,"name":25},"sharding","Sharding",[27,31,34,37,40,44,47,50,53,56,60,61],{"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":12,"category":5,"name":13,"updated_at":30},{"slug":62,"category":5,"name":63,"updated_at":30},"connection-pooling","Connection Pooling"]