prrao87/lancedb-study
Comparing LanceDB and Elasticsearch for full-text search and vector search performance
Benchmarks both async Python clients and FastAPI REST endpoints across full-text and vector search, using the ModernBERT embedding model (256-dim) on a standardized Wine Reviews dataset. Executes fixed query suites (1000 queries per type, 3 trials) measuring QPS and P50/P95/P99 latencies to isolate search-engine performance from API overhead. Includes reproducible workflows for data ingestion, index building (IVF_PQ for LanceDB, standard for Elasticsearch), and containerized setup via Docker Compose for Elasticsearch.
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29
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6
Language
Python
License
MIT
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Last pushed
Feb 08, 2026
Commits (30d)
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