vespa VS db-benchmarks

Vespa(4.3k ⭐) → A fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time.

If ES doesn't work for you, I recommend Vespa. https://github.com/vespa-engine/vespa

Others have made other suggestions, but Vespa has two unique features. First it is battle tested at a large scale, second it supports combining the keyword and vector scores in several ways. The latter is something that other hybrid systems don't do very well in my experience including ES/Solr.

After looking at various alternatives, I'm thinking of trying out https://vespa.ai/ [0]

[0] https://github.com/vespa-engine/vespa

> I decided to experiment with this setup and the NY Taxi Dataset. The initial goal was to populate ElasticSearch with ~14 million rows, loading data from a compressed parquet file of ~350 MB.

> I tried multiple times, but the operation failed continuously, due to JVM memory constraints

Here's a script https://github.com/db-benchmarks/db-benchmarks/blob/main/tes... which loads 1.7B NYC taxi ride documents into Elasticsearch.

In general, you are correct about another missing key: since we added an exception for MySQL by including two keys, we should perhaps optimize it to the maximum and add the rest as well. However, it would be better to make this more visible directly within the results then. I've created a task about it https://github.com/db-benchmarks/db-benchmarks/issues/30 . Thank you for pointing this out. If you see more issues, feel free to file them on github.

> It's interesting to note that Elasticsearch and Opensearch are general purpose search engine, Solr as well. They are all powered by Lucene, the popular and performant search engine library.

Another search engine which can be considered general, is not based on Lucene and is not less powerful than Elasticsearch/Solr is Manticore Search [1]

[1]: https://github.com/manticoresoftware/manticoresearch

> I would love to see some benchmarks by category :)

I'd love too. We started this work on db-benchmarks [2] , hopefully we'll have resources to continue it. Contributions are very welcome. It's 100% opensource [3]

[2]: https://db-benchmarks.com/

Comparing full-text search engines on queries that aren't full-text search are of course slow, these tests should be adapted to the proper usage of the tested DBs and not just benchmarked across the board..

Example: https://db-benchmarks.com/?cache=fast_avg&engines=elasticsea...

Clickhouse: no tuning , just CREATE TABLE ... ENGINE = MergeTree() ORDER BY id and standard clickhouse-server docker image.

MySQL and Percona Server for MySQL: no tuning , just CREATE TABLE ..., FULLTEXT(story_text,story_author,comment_text,comment_author))and standard mysql docker image .

https://db-benchmarks.com is a platform and a framework for making the most fair, transparent and open source database and search engines benchmarks. No more benchmarketing, because: