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Meilisearch | 2 senior roles |Full-time | Hybrid (Paris, France) - Remote EU

Platform Engineer - https://jobs.lever.co/meili/b2d59837-20d6-470d-899b-8ddc081f...

Senior Software Engineer Rust - https://jobs.lever.co/meili/736ec392-9070-49b9-9a63-7db05a1c...

I read Zig's new LinkedIn API, and I was intrigued

Meilisearch keeps all the data on disk. It uses memory-mapping for optimizing performance, by default everything is safe on disk and the OS cache the most needed pages in memory.

So it works on any machine, really. 2GiB is usually enough for most workloads, but the bigger the dataset, the faster it will be if you give it more memory!

Actually, Meilisearch uses the RAM available by design because it uses LMDB (which is memory-mapped) under the hood for key-value storage.

It's a feature, not a bug :D

The cool thing about that is that it is the OS that will get to choose which process to allocate memory, and you can always run it somewhere with less memory available, and it will work the same way

Yup it is, Meilisearch Cloud offers 99.99% SLA :)

We served billions of searches for hundreds of customers monthly

Reach out to the sales or CS team, depending on your workload we could open that region pretty quickly :D

Hi! I am from the Meilisearch team :) Meilisearch has been designed from the ground up for end-user search. We target to answer results in less than 50ms to give the impression of instantaneity, which is what you are looking for when building cmd + k search

Meilisearch is one of the most recent search engines amongst those you cited, which makes for the best developer experience both for deployment/hosting & for development. You can find integrations with the most popular frameworks & languages https://www.meilisearch.com/docs/learn/what_is_meilisearch/s... which allows you to get up to speed in few minutes.

And for relevancy/accuracy, Meilisearch offers full-text search with typo tolerance, synonyms, etc and the latest releases introduced semantic search & hybrid search. This is an API we are very proud of because it gives you a state-of-the-art search with the best DX out there.

Typesense might be a good alternative because it can be more performant for some use cases, but the DX is less smooth, I'd say.

Vespa might be relevant if you are looking for a very specific recommendation system. Still, now that Meilisearch offers semantic search & hybrid search I am not sure today in which context Vespa might be more relevant

I don't know much about Manticore unfortunately.

Let me know if you have any questions about it, but I'd encourage you to spend 10 minutes to see by yourself what's possible or not and join the community on Discord if you have any questions https://discord.meilisearch.com

Congrats on the launch!

Hello! No we don't yet, we are considering it though.

Thank you Markus <3

Thanks :D

That's the point! We don't ambition to compete with Elastic on everything (logs, analytics, etc). We are doing search for front-end users with a strong focus on relevancy, speed & developer experience. You can read a bit more on our documentation https://docs.meilisearch.com/learn/what_is_meilisearch/compa...

Thank you very much! I'll share your comment with the team <3

Feel free to reach out to quentin@meilisearch.com, we'll find a way for you to evaluate the pro plan!

Sorry, it might not be obvious, but you can go over the free tier and pay for the usage at 0.25$ for each 1000 searches/documents :)

Meilisearch | https://jobs.lever.co/meili | Remote (GMT-4 to GMT +5) | 4day workweek

Meilisearch is an open-source search API. We provide developers tools to implement search bars, so their users spend less time searching & more time finding.

We are looking for a few different roles:

- Full-stack software engineer working on the cloud offering (React, Rails, K8s)

- Senior software engineer working on the core engine (Rust)

- Technical community manager

Salaries are location independent; you'll get equity, six weeks of PTO, and three months of paid parental leave.

You can see our repositories on https://github.com/meilisearch & learn more about the company on https://blog.meilisearch.com.

We are building Meilisearch, an open-source search engine API and we are working 4-day a week :) -> https://jobs.lever.co/meili

Thanks kevinsf90 :D Meilisearch is really focus towards `end-user search` or `customer-facing search` so, there is few chances we go in that direction. But we are really aiming to be the easier, simpler and thus more performant alternative to Elasticsearch. Since Meilisearch won't cover every uses cases that Elasticsearch covers, it is much more performant and intuitive on this subset. If you want to build complex queries, with terabytes of data & aggregation queries you should definitely use Elasticsearch. If you want to build the best search experience for your end-user that will be the most relevant and answers in a few milliseconds, then Meilisearch is the obvious choice here :)

to be quite honest, it dit not. We are not there yet and if even get to the size of what elastic is today, we would be extremely glad :D

Thank you nojs! Future of search is exciting

Thank you shuky :D

Hello, MeiliSearch team here :) Please, do not hesitate to leave an issue on the Golang repository so we can improve it! Also, indexing time will be much better with v0.21 planned to be released in a couple of days / weeks. You can test the RC in the meanwhile

Cool project :) How would you compare QuestDB against TimescaleDB?

I am glad to hear there are more and more people that accepts those new kind of open source, not the official definition of the OSI but more like the global idea behind it

You might want to talk to kero (https://github.com/Kerollmops/). He is currently working on it !

We are working on both replication (for high availability and we may use the Raft consensus) and distribution (sharding to scale horizontally and keeping low latency)

Actually Lucene is the library for search that Elastic uses under the hood. Lucene does not provide any HTTP API, which Elastic does. Before using Lucene, you have to build the interface around it.

In this way MeiliSearch is comparable to ES, especially for site search and app search working out of the box as standard with its http api.

MeiliSearch does not offer distribution yet, but it is something the team is working on :)

We are using LMDB as the key/value store, so the documents are memory-mapped (usually on disk, and in memory when needed)

We are currently working with this dataset: https://data.discogs.com/?prefix=data/2020/

It's a dataset of 107M songs, 7.6 Gb of compressed files which represents 250 Gb of disk usage by MeiliSearch. We are indexing the release, song and artists names.

We also work with a dataset of 2M cities that we can index in less than 2 minutes when the db uses 3 shards.

Replication for MeiliSearch is on its way :) The main differentiator is that MeiliSearch algorithms are made for end-user search not for complex queries. MeiliSearch focus on site search or app search, not analytics on hyper large datasets