TNTSearch VS meilisearch-js

FWIW an alternative might be to have your endpoint perform the full text search server-side with something like https://github.com/teamtnt/tntsearch. No external search service required.

While we're at it, would it be possible to store the index in the existing database instead of SQLite ? Wouldn't that allow for index syncing reliability (I hear there are file corruption problems ) ? And reducing the number of processes (no index queue) ?

As you saw from the tutorial, adding a full-text search to your project is pretty straightforward. We covered only the tip of the iceberg here. TNTSearch is a powerful engine that can do a lot of stuff - even classification if that's something you might need. For more info, you can check out the GitHub documentation.

Thanks! I removed the URLs and now the searchable attributes are only title, description and some author fields!

> Just a detail, if you're making a `du -sh` on your computer, the size on the disk will stay unchanged because we are doing soft deletion ;). Don't worry. It will be physically deleted after a while if you need it in the future.

Ah I was just wildy undershooting the size I gave the PVC! I just gave it much more and it's fine -- right now it's resting around 19Gi of usage, which is actually a bit of a problem considering the data set was only like 4GB or something like that originally. That said, disk is really not an issue so I'll just throw more at it, maybe leave it at 32GB and call it a day (it's around 1.6MM documents out of ~2MM), so shouldn't be too much more.

> If you kept the default configuration of Meilisearch, the maximum size of the HTTP payload is 100Mb (for security). You change it here -> https://docs.meilisearch.com/learn/configuration/instance_op...

Thanks for this, I'll keep this in mind -- so I could actually pass off HUGE chunks to Meilisearch.

It seems like the larger the chunk the more efficient? There didn't seem to be much of a change in how much time it took to work through a chunk of documents, more just that having lots of smaller chunks would go slower. I started off with 10k in a batch, then went to 1k then back to 5k, maybe I should go to 100k docs in a batch and see the performance.

There's a blog post waiting to be written in here...

> addDocumentsInBatches() is just an helper to send your big json array into multiple parts, not absolutely sure you'll need it. (Code -> https://github.com/meilisearch/meilisearch-js/blob/807a6d827...)

Thanks! Was this something someone requested? Is there a tangible benefit (were there some customers that didn't want to split up the payloads themselves)? Because it seems like unnecessary cruft in the API otherwise.

sunspot: looks active, but I would question the idea of using Solr as your search engine. Have you considered https://www.meilisearch.com it a bit like Algolia but open source.

You could use Meilisearch: https://www.meilisearch.com/

COMPANY: Meilisearch, here is our website and Github repository.

Meilisearch - Provides search for the main BookStack static site/docs/blog.

Meilisearch is an open source search engine that adds highly performant search engines to any website or app, including ecommerce stores.

Medusa allows you to integrate any search engine of your choice into the platform. It already integrates with search systems like Meilisearch or Algolia to provide an intuitive search experience for the customers.

I would return a question to you. How much of a database do you need? Wouldn't it be more efficient to implement something like Elastic or https://www.meilisearch.com to index your metadata and quickly return to the users?

Not so long ago I was working on a system based on Keystone.js CMS. But there it was used much more sophisticated way than just as basic headless CMS. I was easily able to extend it with search engine (Rust-based Meilisearch) and connect to external APIs.

Only semi-related, but I've recently started using https://www.meilisearch.com/. It's relatively limited, but works great for small use cases. It's also pretty easy to operate. I'm hoping as it continues to grow it will support more features and use cases. I don't think the creators intend to address the same depth of complex features in ElasticSearch (and the like), but that's a desirable attribute in my opinion.