[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-oltp::en":3,"gloss-cluster-oltp::en":23,"gloss-next-oltp::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"oltp","data-infra","OLTP (Online Transaction Processing)","OLTP describes the database workload behind your live application: many short, concurrent transactions — signups, orders, profile edits, likes. Each one reads or writes a handful of rows and must complete in milliseconds. Correctness and concurrency matter more than crunching huge datasets, so OLTP databases lean on ACID guarantees, indexes for fast point lookups, and row-based storage.\n\nMost SaaS products run on an OLTP database: Postgres, MySQL, or a managed equivalent. It's optimized for exactly the traffic your users generate — frequent small operations with strict consistency.\n\nThe key thing to understand is the contrast with OLAP (analytics). OLTP is bad at scanning and aggregating millions of rows; OLAP is bad at high-frequency single-row writes. Mixing them on one database is a classic scaling mistake: a heavy report can lock tables and stall checkout. As you grow, keep OLTP for the app and offload analytics to a separate warehouse via replication or CDC. Design indexes around your actual query patterns and keep transactions short to avoid lock contention.","OLTP is the workload behind your live app: many short concurrent transactions — signups, orders, edits — each touching a few rows and finishing in milliseconds.",null,[11,14,17,20],{"slug":12,"name":13},"acid","ACID",{"slug":15,"name":16},"database-migration","Database Migration",{"slug":18,"name":19},"olap","OLAP (Online Analytical Processing)",{"slug":21,"name":22},"postgresql","PostgreSQL",[24,26,29,32,35,39,42,45,48,51,55,58],{"slug":12,"category":5,"name":13,"updated_at":25},"2026-08-24T02:46:37+00:00",{"slug":27,"category":5,"name":28,"updated_at":25},"ann-search","ANN Search",{"slug":30,"category":5,"name":31,"updated_at":25},"backpressure","Backpressure",{"slug":33,"category":5,"name":34,"updated_at":25},"batch-processing","Batch Processing",{"slug":36,"category":5,"name":37,"updated_at":38},"bm25","BM25","2026-08-24T02:46:38+00:00",{"slug":40,"category":5,"name":41,"updated_at":25},"cache","Cache",{"slug":43,"category":5,"name":44,"updated_at":25},"cap-theorem","CAP Theorem",{"slug":46,"category":5,"name":47,"updated_at":25},"change-data-capture","Change Data Capture (CDC)",{"slug":49,"category":5,"name":50,"updated_at":25},"chroma","Chroma",{"slug":52,"category":5,"name":53,"updated_at":54},"chunk-overlap","Chunk Overlap","2026-08-24T03:30:02+00:00",{"slug":56,"category":5,"name":57,"updated_at":25},"columnar-storage","Columnar Storage",{"slug":59,"category":5,"name":60,"updated_at":25},"connection-pooling","Connection Pooling"]