Measuring Index Build Time
Index build time is the wall-clock time from when vector data is available until the index (e.g. HNSW, IVF-PQ) is ready for queries. It matters for ingestion pipelines, real-time vs. offline indexing, and distributed index building.
Summary
- Index build time: wall-clock time from when vectors are available until the index is committed and queryable. Matters for ingestion, real-time vs. offline, and distributed builds.
- Start timer when build begins; stop when index is queryable. Report total time and optionally time per million vectors. Depends on index type (flat fastest, HNSW/IVF-PQ slower), parameters (e.g.
M,efConstruction), dimension, size, and hardware (CPU, GPU, architecture). - For incremental or streaming, build time can mean time to add a segment or update. Compare in-memory vs. on-disk builders and load time from disk. Pipeline: data ready, build starts, index committed. Practical tip: benchmark with your dimension and scale; tune efConstruction for build vs. recall.
How to measure
Start a timer when the build job begins (e.g. after vectors are written to disk or in memory) and stop when the index is committed and queryable. Report total time and optionally time per million vectors for scalability comparison.
Build time depends on index type (flat is fastest, HNSW and IVF-PQ slower), parameters (e.g. HNSW M, efConstruction), dimensionality, dataset size, and hardware (CPU cores, GPU, CPU architecture). Trade-off: higher efConstruction often improves recall but increases build time.
Incremental and streaming
For incremental or streaming workloads, build time may refer to the time to add a new segment or update the index with new vectors. Comparing in-memory vs. on-disk builders and load time from disk is part of the same evaluation.
For incremental or streaming workloads, “build time” may refer to the time to add a new segment or update the index with new vectors. Comparing in-memory vs. on-disk builders and load time from disk is part of the same evaluation. Faster builds reduce time-to-search after bulk loads and help meet streaming ingestion targets. Pipeline: data ready, build starts, index committed. Practical tip: benchmark with your dimension and scale; tune efConstruction for build vs. recall.
Frequently Asked Questions
What is index build time?
Wall-clock time from when vector data is available until the index (e.g. HNSW, IVF-PQ) is ready for queries. Matters for ingestion pipelines, real-time vs. offline indexing, and distributed index building.
What affects build time?
Index type (flat fastest, HNSW/IVF-PQ slower), parameters (M, efConstruction), dimensionality, dataset size, and hardware (CPU cores, GPU, CPU architecture). Higher efConstruction often improves recall but increases build time.
How do I measure it?
Start a timer when the build begins and stop when the index is committed and queryable. Report total time and optionally time per million vectors. For incremental workloads, measure time to add a segment or update.
How does build time relate to real-time indexing?
Real-time indexing adds vectors as they arrive; segment or incremental build time determines how quickly new data becomes searchable. See real-time vs. offline indexing and streaming ingestion.