zincsearch VS db-benchmarks

Please give the benefit of the doubt on HN.

This company created ZincSearch:

https://github.com/zincsearch/zincsearch

Prabhat is one of the core contributors/maintainers:

https://github.com/zincsearch/zincsearch/graphs/contributors

https://github.com/prabhatsharma

Also the negative insinuation of using “cheap” labor out of India to build the product is unnecessary. If you’re concerned about code quality, look at the code.

Assuming everyone working with devs in India is doing so cynically is not charitable.

I dont know why the headquarters was set as india versus SF but does it actually even matter?

See also this lightweight alternative to ES: https://github.com/zinclabs/zinc

I would pay $5 to have every one of these projects stop saying "alternative to ElasticSearch" unless they implement the ES API (as https://github.com/zinclabs/zinc at least claims) because if one just wanted some schemaless full text searching wizardry, there are about 10 of those projects. If one is trying to replace kibana or the damn near infinite log gathering tools that target ES, Sonic and Melisearch and and and are not going to get it done

q.v. https://github.com/zinclabs/zinc/blob/v0.3.6/docs/swagger.ya...

ZincSearch – lightweight alternative to Elasticsearch written in Go\ (0 comments)

> 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: