Vector Databases
Index families, filtered search, freshness and deletes, sharding, and the operational reality of billion-scale ANN.
5concepts
62flashcards
35minutes of reading
- 01 Operating a Vector Store in Production The operational facts that decide whether a vector search system works, why memory is the binding constraint, and the migration nobody plans for.
- 02 Choosing an Index Family The four index families and the workload property that selects each, why the recall-latency-memory triangle admits no universal answer, and how to read a published benchmark.
- 03 Filtered Vector Search Why combining a metadata filter with a nearest-neighbour search is harder than either alone, the two naive strategies and how each fails, and what a native implementation does instead.
- 04 Freshness, Deletes and Index Maintenance Why approximate indexes are built for static data, what insertion and deletion do to their structure over time, and the segment architecture that reconciles freshness with query performance.
- 05 Sharding and Distributed Vector Search The two ways to partition a vector collection across machines, why one preserves recall and the other preserves latency, and the fan-out arithmetic that decides tail latency.