How do you measure recall against an index that is changing? This talk will explore that, using the example of ScyllaDB Vector Search with 100M vectors. We’ll walk through where float32, float16, int8 and 1-bit actually sit on the recall/QPS/RAM frontier, and when oversampling or rescoring is worth its throughput. What filtering costs across a selectivity sweep, on global versus local indexes. How long after a write a vector is really findable, at p50 and at the tail. And what happens to p99 and to recall when search runs concurrently with inserts, and then with deletes.
