pisa VS Apache Solr

The EF core algorithm implemented in folly [3] may be a bit faster, and implementing partitioning on top of that is relatively easy.

It would definitely compress much better than roaring bitmaps. In terms of performance, it depends on the access patterns. If very sparse (large jumps) PEF would likely be faster, if dense (visit a large fraction of the bitmap) it'd be slower.

It is possible to squeeze a bit more compression out of PEF by introducing a chunk type for Elias-Fano of the chunk complement (for very dense chunks), but you lose the operation of skipping to a given position, which is however not needed in inverted indexes (you only need to skip past a given id, and that can be supported efficiently). That is not mentioned in the paper because at the time I thought the skip-to-position operation was a non-negotiable.

[1] https://github.com/ot/ds2i/

[2] https://github.com/pisa-engine/pisa

[3] https://github.com/facebook/folly/blob/main/folly/experiment...

There are already many project about search:

- https://www.marginalia.nu/

- https://searchmysite.net/

- https://lucene.apache.org/

- elastic search

- https://presearch.com/

- https://stract.com/

- https://wiby.me/

I think that all project are fun. I would like to see one succeeding at reaching mainstream level of attention.

I have also been gathering links meta data for some time. Maybe I will use them to feed any eventual self hosted search engine, or language model, if I decide to experiment with that.

- domains for seed https://github.com/rumca-js/Internet-Places-Database

- bookmarks seed https://github.com/rumca-js/RSS-Link-Database

- links for year https://github.com/rumca-js/RSS-Link-Database-2024

Elasticsearch is based on Lucene and is used by various companies and developers across the world to build custom search solutions.

elastic search is kinda heavyweight infra for a small project. Its built on top of apache lucene (https://lucene.apache.org), which you can use directly.

Elasticsearch is based on Lucene, which is built in Java. This means that monitoring the Java Virtual Machine (JVM) memory is crucial to understand the current usage of the whole system.

Apache Lucene which seems to have a lot more features than Elasticsearch

Generally with term vectors and a tf-idf index. Lucene is a good starting place to help.

try elasticsearch or solr, behind the scenes they both use https://lucene.apache.org/ if you don't want basically a full nosql database service, but I'd just slap solr up and call it a day.

OpenSearch is an open-source database to ingest, search, visualize, and analyze data. It’s built on top of Apache Lucerce, a FOSS library for indexing and search, which OpenSearch leverages for more advanced analytics capabilities, like anomaly detection, machine learning, full-text search, and more.

Lucene is the thing you think you need. Elastic Search is a nice wrapper for it. But these are Java, so maybe you want Sphinx Search (C++) or MeiliSearch (Rust).