feat: add Apache Pinot vector search client#757
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Adds a complete Apache Pinot client for VectorDBBench. Index types: HNSW (Lucene), IVF_FLAT, IVF_PQ, IVF_ON_DISK Metrics: L2, IP, COSINE Filters: NumGE, StrEqual Optional dep: pip install "vectordb-bench[pinot]" Parallel loading: thread_safe=True — each worker thread maintains its own row buffer and flushes to Pinot via a fresh HTTP session. Since Pinot's ingestFromFile is synchronous (blocks until HNSW index is built, ~6 min per 100K×768D segment), concurrent flushes across threads reduce load time significantly vs sequential flushing. Benchmark results: Small dataset (OpenAI 50K, 768D, L2): HNSW: 798 QPS, recall=1.000 IVF_FLAT: 800 QPS, recall=1.000 IVF_PQ: 795 QPS, recall=1.000 IVF_ON_DISK: 691 QPS, recall=1.000 Large dataset (Cohere 1M, 768D, COSINE): HNSW m=16: 74 QPS, recall=0.982 Filter benchmark (Cohere 1M, COSINE, HNSW m=32): 1% NumGE: 71 QPS, recall=0.977 99% NumGE: 97 QPS, recall=0.649 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Summary
Adds a complete Apache Pinot client for VectorDBBench.
thread_safe=True— each worker thread maintains its own row buffer and flushes to Pinot via a fresh HTTP session. Since Pinot'singestFromFileis synchronous (blocks until the HNSW index is built, ~6 min per 100K×768D segment), running concurrent flushes across threads reduces load time significantly compared to sequential flushing.pip install "vectordb-bench[pinot]"Benchmark Results
Small dataset (OpenAI 50K, 768D, L2)
Large dataset (Cohere 1M, 768D, COSINE)
Filter benchmark (Cohere 1M, COSINE, HNSW m=32)
Low recall on 99% filter is expected: the HNSW graph is built on the full dataset, so filtering to 1% of vectors at query time causes many graph neighbors to be pruned.
Test plan
make lintpasses on all modified filesmake unittestpassesdocker compose(see Pinot quickstart)vectordbbench pinot-hnsw --db-label testagainst a local Pinot cluster🤖 Generated with Claude Code