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Couchbase0 Hot1 comments16 min read
Benchmarking Billion-Scale Vector Indexes
Benchmarking a vector index at billion-scale means tracking P99/P99.9 latency, Recall@K, QPS at a fixed recall target, index build time, RSS memory, and CPU usage, since averages hide failures. The team used three datasets for different jobs: SIFT-1B (128-d) to test raw scale, LAION-100M (768-d CLIP floats) to test real float behavior, and a self-built BookCorpus-10M (4096-d, via Llama 2) to approximate LLM-scale embeddings. Building BookCorpus-10M took 16 hours on five GPUs at about 173 vectors/sec, producing 164 GB that restores via cbbackupmgr in 2-3 minutes. Partition elimination, tagging vectors with a key like category_id, let a 10-partition layout skip 90% of the work per query, tightening P99 tail latency and raising throughput per core.
Source: https://www.couchbase.com/blog/benchmarking-billion-scale-vector-indexes. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
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