[schappim]

We’ve used Meilisearch in production and it is the closest thing to self hosted Algolia you can get, which in itself is pretty amazing.

Unfortunately the performance of indexing (constantly changing records) wasn’t great and Meilisearch would fall behind on indexing records for hours.

Meilisearch has been amazingly great for projects where records don’t change all that much (eg docs, or even a customer database), but if you have for example a fast paced ecommerce system with 50k records constantly changing (eg product inventory), it falls over pretty quick. We had to transition over to Elastic for this aspect of our app.

The other issue we faced is their Rails gems falling out of step with the server, and when fixes came out, the Rails gem was incompatible for a while.

I really really hope 1.0 increases performance to the point where it becomes production ready, because the initial out of the box performance (before getting bogged down with indexing) was pretty amazing. Better than Elastic and on par with Algolia.

I recommend keeping Meilisearch on your radar. It is going to be great.

I wish the best for the Meili team and hope they succeed!

[Kerollmops]

Thank you very much for this amazing feedback, really appreciated.

We did a lot of improvement to the indexing part of the engine and now can auto-batch updates which gaves incredible improvements. We will continue to work on this in 2023. Can I know the version you were using?

[nezirus]

My experience with indexing is similar. Up to lets say 1M docs it works fine, but after that it goes south. Even with auto-batch I had to manually prepare large bulk updates and wait for completion during inserts to not overload MS. (I am using Rust client).

Other than that, it is simply great. Ranking stuff is great, simple, I only need custom weights there, some additional functions (not just asc/desc) and it would be perfect.

[nacs]

I had the same experience.

Pro: Meilisearch search speed and memory use was great compared to others (at the cost of large storage requirements but that's the cheapest thing to upgrade).

Con: Indexing documents (even with recommended batch sizes) was extremely intensive on the system as the document count increased (upwards of 20 million docs to index).

I had to modify the indexing script to completely pause indexing when system load average went too high to prevent the whole server crashing.

Also, this 1.0 upgrade apparently requires a full export and import of data if you're upgrading from the previous release? I hope this isn't the case for >= v1.0 releases because I'm not looking forward to exporting/reimporting 200+GB of Meilisearch data files over and over again.

[message]

Same ran into same version inconsistencies with Java library.