# Benchmark with Elasticsearch

> Here we present a comprehensive benchmark comparing our implementation of VectorChord-BM25 with ElasticSearch, focusing on two key metrics: QPS (Queries Per Sec

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LLMS index: [llms.txt](/llms.txt)

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Here we present a comprehensive benchmark comparing our implementation of VectorChord-BM25 with ElasticSearch, focusing on two key metrics: QPS (Queries Per Second) and NDCG@10 (Normalized Discounted Cumulative Gain at rank 10). If you are interested in the details of our implementation, please refer to our blog [VectorChord-BM25: Revolutionize PostgreSQL Search with BM25 Ranking — 3x Faster Than ElasticSearch](/blog/post/vectorchord-bm25-revolutionize-postgresql-search-with-bm25-ranking-3x-faster-than-elasticsearch/).

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## RPS

<img src="/docs/images/es-vchord-bm25-rps.png" alt="RPS" style="width: 100%; height: auto; margin: 0 auto; display: block;" />

We compare the Top1000 Queries Per Second (QPS)—a metric that measures how many queries a system can process per second while retrieving the top 1000 results for each query—between our implementation and ElasticSearch across various datasets from [bm25-benchmarks](https://github.com/xhluca/bm25-benchmarks). On average, our implementation achieves **3 times higher QPS** compared to ElasticSearch across the tested datasets, showcasing its efficiency and scalability.

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## NDCG@10

<img src="/docs/images/es-vchord-bm25-ndcg10.png" alt="NDCG@10" style="width: 100%; height: auto; margin: 0 auto; display: block;" />

We have dedicated substantial effort to align VectorChord-BM25 with ElasticSearch’s behavior, ensuring a fair and precise comparison. As demonstrated in the table, our implementation achieves NDCG@10 scores that are comparable across most datasets, with certain cases even surpassing ElasticSearch (e.g., trec-covid and scifact).