manticoresearch VS db-benchmarks

Note, you need to do it in the root folder of a clone from https://github.com/manticoresoftware/manticoresearch

Interesting, how does it compare to Mantico search?

Where Manticore Search? https://manticoresearch.com/

It's not open source since 2017. The open source fork is https://github.com/manticoresoftware/manticoresearch

I haven't looked at the code, but I've seen https://github.com/manticoresoftware/manticoresearch popping up in a few places. Might be worth looking at. It would be great to have a faster drop-in replacement for Elasticsearch that's not written in Java. They blogged about it here: https://manticoresearch.com/blog/manticore-alternative-to-elasticsearch/

I don't see anywhere where they claim that it's faster simply because it's written in C++. They do mention that they make use of C++ to add low level optimizations that make queries faster and the memory imprint smaller, but any claims about performance in the readme are linked to benchmarks to back up their claims

https://github.com/manticoresoftware/manticoresearch/

https://db-benchmarks.com/test-taxi/#manticore-search-vs-ela...

contributors

> 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: