VectorDBBench VS lucene-grep

We conducted benchmark tests on Elastic's queries per second (QPS) performance using datasets of 500,000 and 1 million vectors. Result was Zilliz is 13x and 22x faster, per number of vectors respectively. https://zilliz.com/blog/elasticsearch-cloud-vs-zilliz

Feel free to explore our open-source benchmarking tool, which allows you to examine our methodology and even compare it with your vector database. https://github.com/zilliztech/VectorDBBench

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the link is: https://github.com/zilliztech/VectorDBBench/issues/200#issue...

Interesting graphic, bland and unvoiced conclusion

You're also missing a lot of details. For example, Milvus and Zilliz are actually a little different, check this out for more details: https://github.com/zilliztech/VectorDBBench (of course run it on your own stuff, don't blindly trust companies just because their product is open source)

Also if you want to throw some more comparisons in their checkout elastic search

- Graal native images are real. These boast a far lower startup overhead and much lower steady state memory usage for simpler applications.

Probably my counterexample of choice is this: https://github.com/dainiusjocas/lucene-grep - it uses Lucene, probably the best search library (core of Elasticsearch, Solr, most websites), which is notoriously not simple code to implement grep-like functionality. In simple cases, they demonstrate a 30ms whole process runtime with no more than 32MB of RAM used (which looks suspiciously like a default).

The JVM is fast becoming a bit like Postgres... one of those 'second best at everything' pieces of tech.

Here goes: https://github.com/dainiusjocas/lucene-grep/issues/84

I realize some relatively obscure Finnish stemmer and Lucene with GraalVM aren't exactly a common use case. I did some testing and provided my use case. I certainly have much English language content to search with using lucene-grep. So, thank you for making it!