[Meilisearch v1.1](/blog/whats-new-in-v1-1/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search) introduces the `/multi-search` [endpoint](https://docs.meilisearch.com/reference/api/multi_search.html?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search#multi-search). With this endpoint, users can now send multiple search queries on one or more indexes by bundling them into a single HTTP request. Essentially, the `/multi-search` endpoint paves the way for [federated search](/blog/what-is-federated-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search)

<Callout>
  [Federated search](/blog/what-is-federated-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search) is available in [Meilisearch 1.10](/blog/release-1-10/?https://blog.meilisearch.com/what-is-federated-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search) and highger.
</Callout>

Multi-index search with Meilisearch
-----------------------------------

With 277 votes, federated search was one of the [most requested features](https://roadmap.meilisearch.com/c/74-multi-index-search?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search). We are glad to finally announce the first stage of the feature.

We have introduced a `/multi-search` route that allows searching in multiple indexes with a single HTTP request. Suppose we have two indexes: `movies` and `actors`. Using the `/multi-search` endpoint, we can search for the name of an actress and retrieve both her biography and movies in the search results.

Take, for instance, the following multi-search request:

```bash
curl \
-X POST 'http://localhost:7700/multi-search' \
-H 'Content-Type: application/json' \
--data-binary '{
  "queries": [
     {
       "indexUid": "movies",
       "q": "Kate Winslet",
       "limit": 1
     },
    {
       "indexUid": "actors",
       "q": "Kate Winslet",
       "limit": 1
    }
  ]
}'
```

The response will include a set of search results for each index searched:

```JSON
{
   "results": [
     {
       "indexUid": "movies",
       "hits": [
         {
           "title": "The Reader",
           "overview": "The story of Michael Berg, a German lawyer who, as a teenager in the late 1950s, had an affair with an older woman, Hanna, who then disappeared only to resurface years later as one of the defendants in a war crimes trial stemming from her actions as a concentration camp guard late in the war. He alone realizes that Hanna is illiterate and may be concealing that fact at the expense of her freedom.",
           "crew": [...],
           "cast": [
		...
             {
               "character": "Hanna Schmitz",
               "name": "Kate Winslet",
               "profile_path": "/e3tdop3WhseRnn8KwMVLAV25Ybv.jpg"
             },
		...
           ]
         }
       ],
// other search results fields: processingTimeMs, limit, ...
     },
     {
       "indexUid": "actors",
       "hits": [
     {
       "name": "Kate Winslet",
       "known_for": [
           "Titanic",
           "Eternal Sunshine of the Spotless Mind",
           "The Reader"
       ],
       "birthday": "1975-10-05",
       "deathday": null,
       "biography": "Kate Elizabeth Winslet (born 5 October 1975) is an English actress. Known for her work in independent films..."
     }
       ],
	// other search results fields: processingTimeMs, limit, ...
     }
   ]
 }

```

As you can see, in the results array, the indexes are sorted in the same order as in the query. The response contains the [usual fields](https://docs.meilisearch.com/reference/api/search?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search#response) that a conventional search returns along with the index UIDs. Note that, we restricted the number of documents returned to just one using the `limit` parameter in this example.

Multi-index search demo
-----------------------

![Searching for 'Kate Winslet' returns both her biography and movies in which she starred, while searching for 'Titanic' returns the movie itself as well as actors who starred in it.](https://unable-actionable-car.media.strapiapp.com/ezgif_com_optimize_1_da861cfac7.gif)

[Try it](https://federated-search.meilisearch.com/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search) yourself!

This demo uses two datasets: movies and actors. Each dataset is stored in its own index, has its own settings, and, as you can see in the example above, its own schema.

Since the two indexes have different schemas, they have different [searchable attributes](https://docs.meilisearch.com/learn/configuration/displayed_searchable_attributes?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search#the-searchableattributes-list). In the movies index, you can search for a movie by its title, characters, actors, producers, directors, or movie overview.

```JSON
//movies index 
searchableAttributes: [
   'title',
   'crew.name',
   'cast.name',
   'cast.character',
   'overview',
 ]
```

The actors index allows you to search for actors by name, as well as the movies or shows they are most known for, and by keywords found in their biographies.

```JSON
//actors index 
searchableAttributes: ['name', 'known_for', 'biography']
```

The full list of settings, dataset, and code are all available on [GitHub](https://github.com/meilisearch/demos/tree/main/src/federated-search/), so feel free to explore them on your own. Don't hesitate to play around with the settings to customize the behavior to your needs. If you have ideas for improving the demo, like adding facets, don't hesitate to submit a pull request. All contributions are welcome!

Conclusion
==========

We are excited to deliver this first iteration of federated search, and we’ll be working hard to push toward aggregated search results. We couldn't have achieved this without the valuable feedback and insights provided by our developer community. Your feedback has been instrumental in helping us prioritize and shape the feature to meet your needs.

If you're interested in keeping up with our progress, we encourage you to check out our [product roadmap](https://roadmap.meilisearch.com?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search), join our [discussions](https://github.com/meilisearch/product/discussions) on GitHub, or connect with us on [Discord](https://discord.meilisearch.com/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search).

As always, we are committed to providing our users with the best possible search experience and appreciate your help in achieving this goal.

Introducing multi-index search

Let's take a look at some of the most significant changes in Meilisearch's latest update. We’ll go over the main changes in this article, but you can also view the [full changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.1.0) on GitHub.

New feature: multi-search API endpoint
--------------------------------------

With the introduction of the `/multi-search` API endpoint, Meilisearch allows you to send multiple search queries using a single HTTP request. This will allow you to accomplish a number of different things, like federated search and multi-select facets!

### Federated search

Suppose you have two distinct datasets: “brands” and “products”. Wouldn’t it be convenient if you could search for both at once while keeping them separate in their respective indexes? You can perform a federated search, also known as multi-index search, by querying multiple indexes:

```bash
curl \
-X POST 'http://localhost:7700/multi-search' \
-H 'Content-Type: application/json' \
--data-binary '{
  "queries": [
     {
       "indexUid": "products",
       "q": "Nike",
       "limit": 1
     },
    {
       "indexUid": "brands",
       "q": "Nike",
       "limit": 1
    }
  ]
}'

```

The response is a `results` array containing one object for each queried index:

```JSON
{
 "results": [
   {
     "indexUid": "products",
     "hits": […],
     "query": "Nike",
     "processingTimeMs": 1,
     "limit": 1,
     "offset": 0,
     "estimatedTotalHits": 17
   },
   {
     "indexUid": "brands",
     "hits": […],
     "query": "Nike",
     "processingTimeMs": 0,
     "limit": 1,
     "offset": 0,
     "estimatedTotalHits": 7
   }
 ]
}

```

### Multi-select facets

The multi-search requests API is particularly useful when it comes to implementing multi-select facets behavior in a faceted search interface.

Multi-select facets, also called disjunctive facets, allow users to view items that meet multiple criteria at the same time. This type of interface also shows the number of items available for each option, even if these items have not been selected. This allows for fast and efficient exploration of items that meet your users’ specific criteria, ultimately saving time and effort.

![](https://lh5.googleusercontent.com/HMSHnrtzKaXnDTE1Wh2AH3KupbhGTEM7-xIEid8X7oKjgHF30bQfb07gkpMBc9vz0Bb07NfanDCUHWfBhq37sxP_L_KMPem-sUKuhZ2ZNfJQ-z_vmXJPIuVEDB6978G0kYTy76Vvcrqvd6Xp4tvW560)

Since multi-select facets operate according to the inclusive `OR` logic, computing them requires sending several queries to Meilisearch. Sending these queries separately can lead to reduced performance and potentially higher hosting service costs.

Using the `/multi-search` endpoint, you can send multiple queries to the same index in a single request, thereby optimizing performance and reducing hosting costs.

<Callout>
  Prior to v1.1, achieving disjunctive facets behavior with [Instant Meilisearch](https://github.com/meilisearch/meilisearch-js-plugins/tree/main/packages/instant-meilisearch) was possible but required making multiple requests to the API. This workaround is no longer necessary since Instant Meilisearch now uses the `/multi-search` endpoint.
</Callout>

Check our documentation to learn more about [multi-search requests](https://docs.meilisearch.com/reference/api/multi_search) and [faceted search](https://docs.meilisearch.com/learn/advanced/faceted_search).

New feature: \_geoBoundingBox
-----------------------------

A bounding box is a rectangular box that completely encloses an area. With the new `_geoBoundingBox` filter expression, you can search for results contained within a rectangular geographic area.

![](https://lh4.googleusercontent.com/YuchesiNXoIqJBYTcBTjVRUkuNiwTTn9xUG0ac7nvq8KDVIJH9sMmsBexIzyfbqfxjxUewijKk5XXIuUjEQPi7uozvu48lrF8XpYg_CC8VR7TU5qzdT2e8S3o06ahx05_THxQR4d0307Qevq9RQLoIQ)

It takes two parameters corresponding to the coordinates of the top right (northeast) and bottom left (southwest) corners of the desired area: `_geoBoundingBox([lat, lng], [lat, lng])`

```bash
curl \
-X POST 'http://localhost:7700/indexes/restaurants/search' \
-H 'Content-type:application/json' \
--data-binary '{ "filter": "_geoBoundingBox([45.472735, 9.184019],[45.473711, 9.185613] )" }'
```

Check our [guide](https://docs.meilisearch.com/learn/advanced/geosearch#filtering-results-with-georadius-and-geoboundingbox) on filtering results based on geographic coordinates.

New feature: introducing facetStats
-----------------------------------

When using the `facets` parameter in a search query, the search results object will now include a `facetStats` object that provides minimum and maximum values for each numerical facet. Any facets without numerical values will be omitted.

Given an index of products, suppose that you want to know the highest and the lowest price to implement a range slider component:

```bash
curl \
 -X POST 'http://localhost:7700/indexes/products/search' \
 -H 'Content-Type: application/json' \
 --data-binary '{
 "facets": ["price"]
 }'

```

You would get the following response with all necessary information ready to use:

```JSON
{
   "hits": […],
   "facetDistribution": {
     "price": {…}
   },
   "facetStats": {
     "price": {
       "min": 1.99,
       "max": 1898.28
     }
   }
 }

```

Experimental feature: systems monitoring with Prometheus
--------------------------------------------------------

This release introduces a new `/metrics` endpoint that provides data compatible with [Prometheus](https://prometheus.io/), an open-source systems monitoring and alerting toolkit. This experimental endpoint gives valuable insights into the behavior and performance of Meilisearch.

To enable it, use the `--experimental-enable-metrics` flag when launching Meilisearch.

Are you planning to use this feature? Tell us about your experience in the [GitHub discussion](https://github.com/meilisearch/product/discussions/625).

Improvement: removing limits on index size and count
----------------------------------------------------

Meilisearch has lifted restrictions on the number and size of indexes per instance, so you can create as many indexes as you need 🥳

The only limit now is the memory address space allocated to a single process by your operating system. Read more about this in the [documentation](https://docs.meilisearch.com/learn/advanced/known_limitations#maximum-number-of-indexes-in-an-instance).

Other improvements
------------------

*   You can now use wildcards (`*`) at the end of index names when managing API keys and tenant tokens. For example, `"medical*"` will match the `medical_records` and `medical_equipement` indexes
*   You can override the default CSV delimiter–a single comma (`,`)– using the `csvDelimiter` parameter of the [add or update documents](https://docs.meilisearch.com/reference/api/documents#add-or-update-documents) or [add or replace documents](https://docs.meilisearch.com/reference/api/documents#add-or-replace-documents) endpoints
*   Interleaved document addition and deletion tasks are now automatically batched together, speeding up the indexing process
*   Our fantastic community has helped us enhance support for Greek and Arabic languages
*   Language detection now happens at indexing time, reducing erroneous language recognition during search and thus improving relevancy

We are really grateful for all the community members who participated in this release. We would like to thank: [@akhildevelops](https://github.com/akhildevelops), [@AymanHamdoun](https://github.com/AymanHamdoun), [@cymruu](https://github.com/cymruu), [@FrancisMurillo](https://github.com/FrancisMurillo) [@GregoryConrad](https://github.com/GregoryConrad), [@gregsadetsky](https://github.com/gregsadetsky), [@james-2001](https://github.com/james-2001), [@MixusMinimax](https://github.com/MixusMinimax), [@waveywaves](https://github.com/waveywaves), and [@ztkmkoo](https://github.com/ztkmkoo) for their help with [Meilisearch](https://github.com/meilisearch/meilisearch).

We also want to send a special shout-out to [@choznerol](https://github.com/choznerol), [@cymruu](https://github.com/cymruu), and [@james-2001](https://github.com/james-2001) for their work on [Charabia](https://github.com/meilisearch/charabia?ref=meilisearch-blog).

And that’s it for v1.1! Remember to check the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.1.0) for the full release notes. If you have any questions or would like to stay updated, you can join us on [Discord](https://discord.gg/meilisearch) or subscribe to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e). You can also check out our [roadmap](https://roadmap.meilisearch.com/) and participate in our [Product Discussions](https://github.com/meilisearch/product/discussions) to help us improve.

What’s new in v1.1

Are you wondering if Meilisearch is the right choice for you? Well, don't worry! We're here to help make that decision a bit easier for you. In this article, we'll break down the main strengths and weaknesses of both search engines.

Why should I consider Meilisearch over Elasticsearch?
-----------------------------------------------------

If you are looking for a search engine that attempts to do everything from app search to log analytics, and you don’t mind spending the time and effort necessary to make it work for your search needs, then Elasticsearch might be suitable for you.

But if ease of use, performance, and relevancy are important to you, Meilisearch was made for you. Exhausted from scouring documentation pages to configure an extensive list of parameters just to deliver a decent search experience? We understand your frustration.

Let’s be honest, even if you have the most flexible and powerful tool in the world, it doesn’t count for much if it’s a pain to use and understand. That's why we made Meilisearch: to give developers the tool they need to create remarkable search experiences effortlessly.

Before making your decision, you need to understand the differences between the two engines and what you plan to use them for.

Understanding the key differences and their importance
------------------------------------------------------

Since its creation in 2010, Elasticsearch has quickly become a popular option in the search engine market, finding use in all kinds of applications and datasets. However, while it is undeniably a powerful tool for heavy full-text search, the need for extensive configuration before use means that all that power comes with a high resource cost.

Elasticsearch's open-source nature has played a significant role in its popularity and adoption. And, because historically there haven't been many open-source alternatives, developers continue to go for Elasticsearch as the default solution and wind up losing a disproportionate amount of time on setup and training.

This is where Meilisearch comes in. It’s an open-source, easy-to-use, fast, relevant, and typo-tolerant search engine that fits effortlessly into any app, website, and workflow. It works out-of-the-box with default settings that meet the needs of most projects, while also being highly customizable to meet individual needs.

What is Elasticsearch made for?
-------------------------------

Shay Bannon created Elasticsearch, a distributed search and analytics engine based on the [Apache Lucene library](https://lucene.apache.org/core/). Elasticsearch is now developed alongside other tools for data collection, analysis, and visualization. These products are designed to be used as an integrated solution called the "Elastic Stack".

### Key strengths

Elasticsearch’s main strengths lie in its flexibility and scalability, as well as its capabilities for aggregation and analysis.

**Flexibility**

Elasticsearch provides search and analytics for all types of data. It has extensive use cases spanning everything from simple site search, management and analysis of log data to endpoint security.

**Scalability**

Elasticsearch’s distributed architecture enables scalability by adding or removing nodes from the cluster, making it a suitable choice for managing large volumes of data.

**Aggregation and analysis**

Elasticsearch provides advanced aggregation and analytics capabilities that enable users to extract insights from their data and perform sophisticated analysis operations.

### Challenges

Elasticsearch is a very powerful tool, but it’s also a very complex one. It can be arduous to set up and maintain. Since there are no defaults for implementing a basic site or app search, it needs to be configured. While Elasticsearch has extensive documentation; there is a lot to learn before jumping into implementation, which can be daunting, especially for new users.

Before you can perform your first search, there are several parameters you'll need to set up, including relevancy or typo tolerance, a must-have in a modern app or site search. It can take some time to become familiar with the process.

Let's look at an example. Given [a dataset of news headlines](https://www.kaggle.com/datasets/rmisra/news-category-dataset), the request below asks for results containing “Khloe Kardashian Kendall Jenner” in the headline:

```JSON
GET news_headlines/_search
{
 "query": {
   "match": {
   "headline": {
       "query": "Khloe Kardashian Kendall Jenner"
   }
   }
 }
}
```

Here is Elasticsearch’s default response:

```JSON
{
   "_index": "news_headlines",
   "_type": "_doc",
   "_id": "E9CoJ3cBwsjPafpAr39g",
   "_score": 17.15928,
   "_source": {
       "_date": "2015-05-16",
       "short_description": "",
       "@timestamp": "2015-05-16T00:00:00.000-06:00",
       "link": "https://www.huffingtonpost.com/entry/kendall-jenner-kourtney-kardashian-height-difference_n_7297968.html",
       "category": "ENTERTAINMENT",
       "headline": "Kendall Jenner And Kourtney Kardashian Are Not Quite Twins In Hilarious Instagram Photo",
       "authors": "Lily Karlin"
   }
}
```

Contrary to what one might expect, the first result returned by Elasticsearch is not the most relevant; it only contains some of the search terms. As you can see in the \`headline\` field: it contains “Kendall Jenner”, which is expected, but instead of “Khloe Kardashian”, we find “Kourtney Kardashian”.

When a query contains multiple terms, users typically expect the search engine to prioritize results that contain all the terms rather than just some of them. In other words, users generally expect an "AND" operation rather than an "OR" when searching for multiple terms.

Elasticsearch’s match query uses the "OR" logic by default. If a document contains one of the search terms, Elasticsearch will consider that document as a hit. If we want all the query terms to be present, we have to add the [“AND” operator](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-match-query.html#query-dsl-match-query-boolean) to the query, but we’ll get far fewer results.

The other option Elasticsearch provides to tweak the relevancy is to specify the minimum number of terms a document should have to be included in the search results with the \`minimum\_should\_match\` parameter.

That’s a lot of effort for something that should be quick and painless. And we are only talking about the very basics of search.

Moreover, querying for “Kardasian”—without the \`h\`— returns no results. This is because Elasticsearch doesn’t allow any typos unless we set an [extra parameter](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-match-query.html#query-dsl-match-query-fuzziness) called \`fuzziness\`.

We sent the same query—with a typo—to Meilisearch:

```JSON
POST /indexes/news_headlines/search
{ 
   "q": "Khloe Kardasian Kendall Jenner" 
}
```

With zero configuration, the first result returned by Meilisearch was:

```JSON
{
   "link": "https://www.huffingtonpost.com/entry/khloe-kardashian-licks-kendall-jenner-abs_us_559d7e27e4b05b1d028f98cf",
   "headline": "Khloe Kardashian Licks Kendall Jenner's 'Irresistible' Abs In New Instagram Photo",
   "category": "ENTERTAINMENT",
   "short_description": "Khloe and Kendall shared quite the intimate moment at the gym!",
   "authors": "Julia Brucculieri",
   "date": "2015-07-08",
   "id": 94869
}
```

As we saw earlier, achieving this level of relevancy with Elastic required adjusting several search parameters. So, just because you can do anything with Elasticsearch, doesn’t mean you should. In many cases, Elasticsearch might just be overkill.

What is Meilisearch made for?
-----------------------------

Meilisearch is an open-source, user-focused search engine written in Rust. It was created in 2018 to provide an accessible search solution designed to meet a vast majority of needs, with zero configuration required and a high degree of customization.

Meilisearch delivers an instant search experience, including powerful built-in capabilities such as [search-as-you-type](https://www.meilisearch.com/docs/learn/what_is_meilisearch/overview?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch&utm_content=search-as-you-type#features), [typo tolerance](https://docs.meilisearch.com/learn/configuration/typo_tolerance.html?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch), [faceted search](https://www.meilisearch.com/docs/learn/what_is_meilisearch/overview?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch&utm_content=faceted-search#features), [geo search](https://docs.meilisearch.com/learn/advanced/geosearch.html?utm_source=release-post&utm_id=v1-announcement&utm_content=geosearch), and [multi-tenancy](https://www.meilisearch.com/docs/learn/security/tenant_tokens?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch). Its [first major version was released](/blog/v1-enterprise-ready-stable/?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch)n early 2023, making it completely stable, forward-compatible, and enterprise-ready.

### Key strengths

Meilisearch focuses on simplicity, relevancy, and performance.

**Simplicity**

Meilisearch aims for a simple and intuitive experience for developers. It works out of the box with no configuration needed. But you can easily tailor it to your needs in just a few minutes, so you don't have to be a search expert to get the most out of Meilisearch.

**Relevancy**

Meilisearch provides a pre-configured set of ranking rules, delivering high relevancy right out of the box. These default settings were developed iteratively by working directly with the community and are suitable for most use cases. These rules can be customized to suit individual requirements.

**Performance**

Meilisearch aims to provide a search-as-you-type experience, returning results within a maximum of 50 milliseconds.

Challenges
----------

While it can handle a considerable amount of data, the open-source version of Meilisearch (self-hosted) may not be the best fit for applications requiring a distributed architecture to handle massive amounts of data across multiple nodes or clusters.

Meilisearch Cloud, on the other hand, already provides scalability and high availability with its [Pro and Enterprise plans](https://www.meilisearch.com/pricing?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch).

However, our team is currently [gathering feedback](https://github.com/meilisearch/product/discussions/617) to implement a distributed open-source Meilisearch in the near future.

Meilisearch is primarily designed to deliver performant instant-search experiences aimed at end-users and does not provide comprehensive data analysis features. While Meilisearch can help you search through your data and retrieve relevant results instantly, it has yet to offer advanced data analysis capabilities such as data visualization or statistical analysis. Meilisearch Cloud plans to provide similar features down the line, as you can see in its [roadmap](https://roadmap.meilisearch.com/c/129-search-analytics).

Meilisearch or Elasticsearch?
-----------------------------

Hopefully, it’s clear by now that one search engine is not better than the other. The most important factor lies in the purpose you are using it for. Both search engines have their strengths and weaknesses, and it is essential to understand them to make the right choice for your particular use case.

Meilisearch is not made to search through billions of large text files or parse complex queries. This kind of searching power would require a higher degree of complexity and lead to slower search experiences, which runs against our instant search philosophy. For those purposes, look no further than Elasticsearch; it’s an excellent solution for companies with the necessary resources, whether that be the financial means to hire consultants or the time and money required to implement it themselves.

Meilisearch is a perfect choice if you need a developer-friendly tool to effortlessly deploy a typo-tolerant search, intuitive for users and return results instantly with excellent relevance out of the box. It's ideal for on-site and in-app search, including [e-commerce](/blog/minipouce-top-grade-customer-experience/) [documentation](/blog/symfonys-solution-for-documentation-search/) and [content search](/blog/hugging-face-democratizes-ai/) * *

Learn more about what Meilisearch can bring to your business

[Talk to a sales expert](https://dub.sh/meili-sales?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch)

* * *

Conclusion
----------

To recap, we have addressed both search engines' main strengths and weaknesses, but we have only scratched the surface. The capabilities of both search engines go far beyond what this article has covered. For a comprehensive comparison of Meilisearch with Elasticsearch and other search engines, please refer to the [dedicated section](https://docs.meilisearch.com/learn/what_is_meilisearch/comparison_to_alternatives.html?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch#comparison-table) in our documentation.

Do you want to see Meilisearch in action? Try our [demo](https://where2watch.meilisearch.com/?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch). You can also run it and start searching through your data in less than five minutes by following our [quick start guide](https://www.meilisearch.com/docs/learn/getting_started/cloud_quick_start?utm_campaign=comparisons&utm_source=blog&utm_medium=vs-elasticsearch).

We continuously collaborate with the open-source community to develop new features and provide the best developer and search experience. See what's next on our [public roadmap](https://roadmap.meilisearch.com/)!

If you have any feedback or suggestions, please join our [Discord server](https://discord.gg/meilisearch) and have a chat with us!

Meilisearch vs Elasticsearch

A step by step guide to adding site search to your Nuxt ecommerce

This year, we’ve launched Meilisearch's very first contributors program: the Meilistars. The aim of a contributors’ program is to gather top community contributors and foster our relationship, highlight the fantastic work they produce, and maybe even help create connections. We won’t go much in-depth on this topic at the moment as we’re hoping to share more in a dedicated blog post, so watch this space!  
  
As an initiative to bring the spotlight to our amazing community members, we’ve asked if they would be interested in participating in a series of interviews so that we can get to know them better.  
  
And we’re kick-starting our interviews with Minoru Osuka. You may have already met him on GitHub as [Mosuka](https://github.com/mosuka) or on Twitter as [@minoru\_osuka](https://twitter.com/minoru_osuka).

Let’s hear all together a bit more from him!

Meet Minoru
===========

First things first, we’ve asked him to introduce himself in his own words!

> “I'm Minoru Osuka. I work as a software engineer and tech lead for a company that provides a job search engine in Japan. I'm mainly involved in search platform development. My hobby is software development, and I've released some of the software I developed as OSS on GitHub.”

So not only does Minoru work in tech, but he also considers it his hobby! We were very curious to know how he got into the tech field.

Minoru explains that he started in a technical school as a programming instructor, but he wished to increase his practical experience and decided to join a software development company.

> “This was the start of my career as a software developer. As I used search engines in my work, I became interested in how they work, so I moved to an Internet portal site and have worked in the search engine field ever since.”

Minoru + Meiliseach: it was meant to be
=======================================

Hearing that Minoru actually worked with search engines, we couldn’t help but ask when he heard about Meilisearch and how he started using it.

> “It was about May 2022 when Meilisearch started supporting the Japanese language. Meilisearch used the Japanese morphological analyzer [Lindera](https://github.com/lindera-morphology/lindera) as their Japanese tokenizer. I maintain Lindera, so it was a very fortunate event.”

Minoru is very grateful to [Kination](https://github.com/kination) and [Miiton](https://github.com/miiton), who respectively created the [first pull request](https://github.com/meilisearch/charabia/pull/70) to add Japanese language support in Meilisearch and implemented it. It is thanks to their combined work that the Japanese support of Meilisearch has become what it is today.

> “I have yet to actually use Meilisearch in my work, but [voluntas](https://github.com/voluntas) introduced [a case study using Meilisearch](https://zenn.dev/voluntas/scraps/77ae77d06deadc) for their Japanese document search service, which attracted a lot of attention.”

Open source to the bone
=======================

Minoru's frequent references to other members of the Meilisearch community during the interview were too significant to overlook. He expressed great satisfaction at having had the opportunity to connect with fellow community members.

> “Ever since Meilisearch adopted Lindera, my Twitter followers have increased. I am happy to have met them. I'm very grateful to Meilisearch.”

It's truly remarkable how deeply involved Minoru is in the open-source community. In addition to contributing to Meilisearch and maintaining Lindera, he has also built his own [distributed search server](https://github.com/mosuka/bayard).

> “I had been using Elasticsearch and Solr for a long time, but using them was not enough for me, so I decided to build a distributed search server while also learning Rust. It was tough, but I learned a lot.”

Oddly, it was this project that led him to maintain Lindera:

> “I started working on Lindera because I developed a distributed search server on my own. \[...\] My friend, who is developing a full-text search library, also developed a Japanese morphological analyzer, but it was not registered on [crates.io](https://crates.io/). When I contacted him to see if he would register it on crates.io, he gave me a surprising answer: ‘I want you to take over this project.’ I was also interested in morphological analyzers, so I decided to take over the development.”

Minoru also thanked [fulmicoton](https://github.com/fulmicoton), the developer of [kuromoji-rs](https://github.com/fulmicoton/kuromoji-rs), the original software that eventually evolved into Lindera, describing it as “a wonderful OSS.”

A vision for the future
=======================

Given his extensive contributions, it's no surprise that Minoru knows Meilisearch inside out. Its immediate usability is what Minoru values most about it. In fact, he particularly values a specific feature that contributes to this accessibility.

> “Meilisearch's automatic detection of what language the indexed documents are written in is great. This is a very helpful feature for users unfamiliar with search engines.”

During our conversation with Minoru, we couldn't pass up the chance to ask him about any improvements he'd like to see in Meilisearch's near future. Unsurprisingly, his suggestions focused on language support. Specifically, he suggested implementing a mechanism to normalize characters **before** they are tokenized.

For those unfamiliar with the process, it currently happens the other way around. A text is tokenized—segmented into words—and then, each word is normalized based on language particularities. For a romance language like French, this process includes lowercasing and removing diacritical marks such as accents or anything that doesn’t impact the meaning of the text. To those interested in the subject, you can join the discussion on [GitHub](https://github.com/meilisearch/product/discussions/532#discussioncomment-3812425) or [read more](/blog/improving-meilisearchs-language-support/)bout how we handle language support.

Minoru's suggestions for language support included customizing the normalizer for each field. Suppose there is a document with an address field, and he would like to be able to instruct Meilisearch to convert the Kanji numbers in the address field into Arabic numbers. In his words:

> "Right now, Meilisearch does not have a normalizer for Japanese, but it would be nice to be able to customize it for each field \[…\] I would also like to contribute to Japanese normalizers."

We look forward to working on improving our language support with Minoru, and any language aficionados that would want to support our efforts!

It was a real pleasure talking with Minoru, getting to know him better, and understanding his insight on Meilisearch, his usage, and the people he managed to meet through it.

As a reminder, you can find [Minoru on GitHub](https://github.com/mosuka) or contribute to [Lindera](https://github.com/lindera-morphology/lindera).  
We hope you found this interview as interesting as we did and look forward to meeting all of our incredible Meilistars.

Minoru Osuka: POV of a main language contributor

<Callout>
  Due to the recent documentation site migration, our documentation doesn't currently use Vale. You can still view our style guide on [GitHub](https://github.com/meilisearch/documentation/tree/b6837449f09587f9d6df92e1eea0c44cc394f398/.vale/styles/Meilisearch).
</Callout>

Writing is a time-consuming process. Depending on the writer and the review process, it can take a while to get content ready for publication. Whether you’re a solo writer or a big team, prose linters can help ensure a consistent tone and style.  

Similar to code linters, prose linters automatically check your text for errors. Unlike grammar checkers, which highlight violations of grammatical rules, prose linters focus on how you can make your text better by addressing common usage problems like extra spaces, repeated words, excessive use of jargon, sexist language, and incorrect capitalization.  

Prose linters can also provide a framework for creating and enforcing an [editorial style guide](https://www.writethedocs.org/guide/writing/style-guides/). This helps with the review process as you can now focus on reviewing the content itself instead of pointing out typos and preferred usage patterns. This is particularly important when working in open-source projects like Meilisearch, where you have many contributors unfamiliar with your style guide.  

What is Vale?
-------------

[Vale](https://vale.sh/docs/vale-cli/overview/) is an open-source, highly customizable, syntax-aware prose linter. It supports documents written in many different formats such as Markdown, HTML, reStructuredText, AsciiDoc, DITA, and XML.  

Vale isn’t your only option when it comes to prose linting. There are many other open-source tools available, including [proselint](http://proselint.com/), [textlint](https://textlint.github.io/), and [alex](https://alexjs.com/).  

At Meilisearch, we decided to go with Vale because it’s fast, easy to set up, flexible, and comes with existing rules to help you get started.

Where do I start?
-----------------

Though it may seem daunting, setting up Vale can be fairly straightforward if you start small and keep things simple. In this post, we’ll go over how to use Vale in a project much like [Meilisearch’s documentation](https://github.com/meilisearch/documentation).

### Step 1: Style guide

The first step is to create a style guide. A style guide ensures a consistent tone and style regardless of how big your team gets. It establishes standard practices when people may have different opinions, such as whether or not to use the oxford comma.  

If you don’t have an in-house style guide, you can check out [Google’s](https://developers.google.com/style) or [Microsoft’s](https://learn.microsoft.com/en-us/style-guide/welcome/) to help you get started. Over time you will memorize most of the rules, but it’s possible to overlook mistakes and sometimes forget the rules altogether. We’re only human.  

And that is where Vale comes in. It allows you to “codify” a style guide and checks your text against all the rules in that style guide. If it detects any issues, it displays suggestions, warnings, or errors on the console.

![](https://lh4.googleusercontent.com/jqa7GUDjATPh8UaNybvMrcuCURAdHu1mMrrm_M6T9MdKq_M3CPf0i8Gs60_yEt08zZGrCd43ZxsrdRDQ3nP8EBEXitjEeqFdDHYlD6HZ19CcGhkvpHHlR6NwZxbjkOMl6IpzUR1RCJ9P6myZoEHIOhs)

### Step 2: Install Vale

I’m using macOS and ran `brew install vale` in my console to install Vale.

If you’re using a different operating system, check out Vale’s documentation for [installation instructions](https://vale.sh/docs/vale-cli/installation/).  

Verify the installation by typing  `vale -v` in your console. If this command returns Vale’s version number, the installation was successful.  

Finally, create the following files and folders:

```json
├── .vale.ini
│   ├──  styles
│   │	    ├── Style guide
│   │       └── Vocab
```

### Step 3: Configuring Vale - vale.ini

Create a `vale.ini` file at the root of your repository. This is the Vale configuration file where you define what you want Vale to do and what files to lint. Let’s start with a basic setup—you can always add to it later based on your project’s needs.

```json
StylesPath = .vale/styles
MinAlertLevel = suggestion

Vocab = word_list

[*.md]
BasedOnStyles = Meilisearch
```

*   `StylesPath` is where Vale looks for your style guide (more on that in the next step). The path can be relative or absolute to the location of the `vale.ini` file.  
    
*   `MinAlertLevel` specifies the minimum alert level that Vale will report. By default, it’s set to `warning`. The other options are `error`  and `suggestion`.  
    An error indicates you did something wrong, like using extra spaces or making a typo. A warning isn’t as severe as an error, but indicates something you should avoid, like making sure your sentences don’t become too long. A suggestion is a recommendation to do something that is usually—but not always—a good idea, like breaking your sentence into two instead of using a semicolon.  
    If a rule is set to `suggestion`, you will see suggestions, warnings, and errors. If it is set to `warning`, Vale will only show errors and warnings, no suggestions.  
    
*   `Vocab`: This is where you create a directory containing the `accept.txt` and `reject.txt` files. Both files accept words, phrases, and [regular expressions](https://ubuntu.com/blog/regex-basics). If your text contains words that do not exist in the dictionary (e.g. “Meilisearch”), you can add them to `accept.txt` and Vale won’t angrily scream at you for making a “typo”. Vale will flag all occurrences listed in `reject.txt` as errors. This can be useful when you want writers to avoid a specific word—if you are writing about search engines and databases, for example, using “indexation” can be confusing.  
    
*   `[*.md]` tells Vale to only lint markdown files. If you want to lint plain text files, use `[*.txt]`.  
    
*   `BasedOnStyles` specifies the style guide Vale should use for linting.  
    

You can specify other settings, including what tokens and HTML tags to ignore and what Vale should consider an individual word. You can read more about the `vale. ini` file in [Vale’s documentation](https://vale.sh/docs/topics/config/).

### Step 4: Rules and the styles folder

As I mentioned earlier, you need a style guide to use Vale. You then convert this style guide into something Vale can understand: rules.

Rules use different [extension points](https://vale.sh/docs/topics/styles) to perform specific tasks. For example, the `existence` extension point looks for the existence of a particular token, `repetition` looks for repeated tokens, `spelling` implements spell checking, and so on. In Vale, each rule is a YAML file. The `styles` folder contains the individual rules that make up the style guide.  

If you don’t want to create your own style guide or need a starting point to build from, Vale comes with ready-to-use style guides that you can apply to your docs and start linting. Here are some style guides that helped us get started:

*   [Google](https://github.com/errata-ai/Google)
*   [GitLab](https://gitlab.com/gitlab-org/gitlab/-/tree/master/doc/.vale/gitlab)
*   [RedHat](https://github.com/redhat-documentation/vale-at-red-hat/tree/main/.vale/styles/RedHat)

You can find more on [Vale’s GitHub](https://github.com/errata-ai/packages) repository.  

Let’s start with a rule for sentence length. Your style would say something like “Ensure sentences don’t exceed 40 words”. This is what the rule looks like as a YAML file:

```json
# Warning: Meilisearch.SentenceLength

# Counts words in a sentence and alerts if a sentence exceeds 40 words.

extends: occurrence
message: 'Shorter sentences improve readability (max 40 words).'
scope: sentence
link: https://docs.gitlab.com/ee/development/documentation/styleguide/index.html#language
level: warning
max: 40
token: \b(\w+)\b
```

This rule counts the words in a sentence and throws a warning if it exceeds 40 words. The `scope` is set to `sentence`: this ensures that Vale does not apply this rule to other parts of the text, like headings or tables.  

The `level` is set to `warning`. Written text is complicated, and Vale will find false positives. There are no sure-fire ways of deciding when a rule should be a suggestion, warning, or an error. You will need to make decisions and learn as you go.  

I suggest reviewing your rules over time to update and, in some cases, delete outdated rules. Initially, the Meilisearch docs didn’t have a rule on sentence length. When we added it, the maximum length of a sentence was 45. Now it’s 40, and we plan on bringing it down to 35.  

You can also enable or disable specific rules within a style guide by adding them to `vale.ini`:  

```json
Meilisearch.Headings = NO
Meilisearch.Spelling = NO
Meilisearch.Semicolons = NO
```

The above lines disable the Heading, Spelling, and Semicolons rules from the [Meilisearch style guide](https://github.com/meilisearch/documentation/tree/main/.vale/styles).  

### Step 5: Run Vale

Now, when you use the following command on your console to lint your whole project:  

```json
vale .
```

Vale will check all your files against the rules stored in `BasedOnStyles`. If Vale detects any issues, it will display suggestions, warnings, and errors on the console.  

You can also lint individual files using:  

```json
vale {file_path} 
```

### Step 6: Automate Vale checks

All the checks we’ve discussed so far are for your local files. Once you’re confident the rules work as intended, you can automate these checks using the [Vale GitHub action](https://github.com/errata-ai/vale-action)! At the Meilisearch documentation repository, we configured it to run for every pull request. As mentioned before, Vale may find false positives. Since you don’t want a PR blocked because Vale isn’t working as it should, I recommend starting with a few rules, and slowly tweaking them to avoid failing checks.  

### Conclusion

That is all, folks! I hope I was able to help you get started with Vale (and a style guide). This was a quick overview to introduce you to Vale’s features. Tweaking it to your needs takes time and many, _many_ iterations.  

![](https://lh4.googleusercontent.com/zp5NecwV9Bc8pMsSf2QeV8yOMMGWoJR8bENrOuQNNFWat7J0KtzDq1n9oBrEW1lKgJO8utxivJS9UqXuMutCmft2o8x3LIE3bUIQFuqcCTkgQ6H5y6hzVAEXgEPYkGHlwnTZnaJ3r3rEq4LyWGFYOVU)

Once configured, Vale can automate parts of the review process and allow you to focus on the parts of a text that computers are not very good at. At least not yet.  

Oh, and if you’re curious, check out [our style guide on GitHub](https://github.com/meilisearch/documentation/tree/main/.vale/styles) to see how we use Vale!

Prose linting with Vale

A few weeks ago we spoke to [Guillaume Mourier](/blog/author/gmourier/ about [the product manager’s role in the release of Meilisearch v1.0](/blog/planning-v1-and-beyond/ But what is a product manager, actually? What is his role in Meilisearch? We decided to go after him and shed some light on this mysterious (but extremely important) job.

![](https://media.tenor.com/WqrGt08ogFwAAAAC/scooby-doo-mystery-machine.gif)

What is a product manager?
==========================

Guillaume says the product manager is a key role in software development and can be divided into three main tasks.

First, product managers need to understand how people use a product: what makes their lives easier, what makes their lives harder, what they want to do but can’t because the product lacks a specific feature.

Second, they need to define a strategic vision of the product’s future so development is coherent and efficient. In Meilisearch’s case, this fundamental step means Guillaume works very closely with Quentin, our CEO.

Third, based on the product’s strategic vision and user feedback, product managers decide what features to implement with the resources they have at hand.

Guillaume tells us it’s a difficult balancing act. Sometimes our choices do not please everyone. Sometimes we don’t prioritize popular requests because we believe they do not align with our strategic vision.

The good news is this is done hand in hand with our community. Guillaume first analyzes topics discussed on [GitHub](https://github.com/meilisearch), as well as issues raised with our support team in our [Discord server.](https://discord.gg/meilisearch) Then, he comes up with solutions to the problems the users described, and plans when they should be done. As you can imagine, this is made much easier by the fact Meilisearch is open source.

How did you become Meilisearch’s Product Manager?
=================================================

![](https://media.tenor.com/fiBaWJK3vQQAAAAC/how-did-this-happen-david-rose.gif)

None of this is enough to scare Guillaume. Though he had experience as a backend developer, his transition into product management was, in a way, rather natural.

First of all, he tells us, he was interested not only in writing code and shipping new features, but knowing how his work impacted others. Did users actually adopt the feature he spent weeks carefully implementing and debugging? What did they like about it? What did they hate about it? He had no way of answering these questions. For him, it was like marching forward with his eyes closed—and without a map.

Rather than seeing his former job as a different career he closed the door on, Guillaume is very grateful for his technical knowledge. It gives him valuable insight in the technical challenges developers might have when implementing a feature, as well as some measure of empathy with Meilisearch’s users. He knows first-hand how good it feels to use a well designed product.

Does the product manager work alone?
====================================

One thing Guillaume is particularly proud of is having integrated product squads in Meilisearch’s development process. These working groups include one member from each team and meet periodically to discuss a feature, bringing to the table insights, needs, and challenges a proposed change might entail for their department.

Prior to the creation of the squads, Guillaume observed that a single team would make unilateral development decisions and begin implementation almost immediately. However, as other teams caught up, they would point out problems with the initial decision. Fixing these often required reworking fundamental aspects of the implementation very late in the process, which was risky, inefficient, and sometimes frustrating for everyone involved.

Product squads made it easier to establish smooth communication between teams and resulted in clearer processes leading to better and solid solutions.

What are the most and least appealing aspects of your job?
==========================================================

![](https://media.tenor.com/2iXnRerU6XkAAAAC/i-wanted-to-be-clear-on-the-pros-and-cons-to-be-real.gif)

One of the best experiences for Guillaume is seeing how new features improve the lives of Meilisearch’s users, making their jobs easier and more pleasant.  
  
He confesses that being Product Manager isn’t your usual 9 to 5 that you can simply forget about when you clock out. He has spent more than a few evenings thinking about a particular insightful piece of negative user feedback or features being worked on at that moment. One must be truly passionate to be a product manager!

What features are you the happiest about?
=========================================

Out-of-the-box support for nested fields is one feature Guillaume is particularly satisfied with, especially because of the incredible amount of positive feedback. Sorting, which Meilisearch only implemented in 2021, had a similarly powerful impact for many users.

Multi-tenancy using tenant tokens is another personal favorite. He thinks the implementation is still not there and will certainly be polished in the near future, but it has nevertheless been a huge help for users working with complex applications and sensitive data.

What about the evolution of the product manager’s role?
=======================================================

Guillaume doesn’t truly belong to only one team within Meilisearch. This autonomy allows him to be involved with other teams and build bridges between projects and initiatives.

That said, he spends a considerable amount of time with the engine team. Right now this is good and necessary, but he would like to eventually take a step away from the search engine and look at the broader context of Meilisearch. For example, how can he help the many users of our SDKs and plugins? It’s a difference not so much in the things a product manager does, but in their scope and the potential reach of his actions.

* * *

That was an interesting peek into all the planning that goes inside Meilisearch, wasn’t it? A big thanks to Guillaume for his time. Stay tuned for more interviews with our lovely colleagues. Want to know more about a specific task, team, or internal process? Tell us all about it on our public [Discord](https://discord.gg/meilisearch) server!

Balancing business, technology, and user experience

Winter was foretold. Now winter has come. In the Northern Hemisphere, it’s currently cold, gray, and the world news is as wintry as it gets.

“But at least,” said one of our team members, “thanks to remote work, I have _no_ dry skin!” At which point they proceeded to sympathize with all those poor people who had to work from the office, wash their hands with subpar soap 10 times a day, and end up with mummified hands, micro-cuts, and a snubbed cat that didn’t like being pet by such coarse hands.

Now, this is one silver lining to remote work that I had never considered before. It could very well be made into a viral parody video, but instead, I immediately added it to the long list of why we love remote work at Meilisearch.

**Make your own work environment**
==================================

Like many companies, we occasionally dabbled in remote work—and had to embrace it on a permanent basis during the Covid-19 lockdowns. Offering full remote work to all our team members is now an integral part of Meilisearch. We believe it's the best chance workers have to grind the daily grind to the ground.

Remote work doesn’t necessarily mean working from home, though. Some of us love it, but others like the energy of a shared place or enjoy a change of scene. (At least that's what they say. Personally, I'm convinced they enjoy the proximity of restaurants with fantastic takeaway for their lunch break.)

We still have an office in Paris, France. We have a small squad of 10 people close enough to come regularly. The rest of us can count on a 500 EUR monthly stipend dedicated to coworking spaces, cafés, or other shared places.

This flexibility suits us as it allows each person to work in their preferred environment. We never have heated disputes with our colleagues about whether or not a draft means a soar throat or a pleasant breeze. It’s possible to work in a very calm or very animated environment, fidget all day or listen to music without headphones. Flexible remote work makes for the most inclusive work environment; everyone can personalize their experience based on their needs.

Of course, we need our team to have a good setup. We follow a basic rule: each team member must have a desk, a chair, an external screen, and headphones. If they're already equipped or have any stipend left, they can select any other items! Team members receive their computers at their own addresses before their contract starts.

We haven't found a solution for people who can’t work without seeing their peers physically yet—and probably never will! Working with a team of remote people is a different experience. We try to be as transparent as possible during the hiring process so that people have a good idea of what to expect and prevent any disappointment.

**The infamous work-life balance**
==================================

Remote work is also a great equalizer of work-life balance. Weather doesn't have to be weathered, and traffic or crowded undergrounds don't have to be endured. As it gives more flexibility, our team members can save time and stress.

If a 5-10 minute pause is about turning on the washing machine, then all the better! It doesn’t take anything from us and can lift some mental load. We try to facilitate access to sports and [mental health support](/blog/our-first-steps-to-support-the-mental-health-of-our-team-members/)ome of our team members are running uphill first thing in the morning without us lifting a finger to support them though, so we can't begin to take the credit for all their activities!

An inherent risk of remote work is putting too much pressure on employees. As a French company, we are particularly careful about protecting their right to disconnect. We implemented [the 4-day workweek](/blog/how-we-implemented-the-4-day-work-week/)o emphasize this point. We are still looking into ways to help our team members create a good space for themselves. We are currently looking into child-care benefits for the parents in our midst. There may be other benefits that haven’t occurred to us yet—we are always on the lookout!

When speaking with companies opposed to remote work, we are sometimes asked how we ensure our team is _actually_ working. Well, we believe it's considerably easier to fake working in an office than remotely. At the end of the week, if the work is not done, no amount of "but I was online" is going to help. If managers in your company need to physically see someone in an office to be sure they're working, it puts the value on [presence](https://blog.dropbox.com/topics/work-culture/job-performance-isn-t-about-showing-up-anymore-and-that-s-a-good) rather than productivity. We believe people can be trusted until proven otherwise, rather than the contrary.

**The world is your oyster**
============================

We started by eliminating the constraint of only hiring people that live in the same city. As an open-source company, we’ve always been very proud to work with contributors from many different countries, and it only seemed logical to continue doing so with our team members. This meant we had to [adapt our salary grid to be more attractive worldwide](/blog/our-approach-to-salaries/)nd we continue to observe the market.

It also means our team members are not tied to a city and can move permanently to the countryside or a more attractive town with fewer job opportunities. Some of us use remote work to travel—working during the day and visiting the rest of the time.

We are not [an asynchronous team](https://about.gitlab.com/company/culture/all-remote/non-linear-workday/) yet. While we don’t need to have perfect overlap between team members, we need at least a half-day in which we can hold meetings or discuss issues. This still deprives us of applicants from opposite time zones, but this may change as we grow.

It’s important to us to maintain a strong team spirit among all coworkers. We've written before about how [we handle informal discussions in the workplace](/blog/how-we-create-space-for-casual-conversation-within-our-remote-team/)he future of our office is in question: though it’s a perfect go-to space for people in the area, it does create a different experience depending on where our employees are based. We may decide to close it entirely in the future!

As our offsite gets closer, we're excited to meet and chat IRL with our colleagues. This doesn’t mean, in our opinion, that it’s a bad thing to be working remotely—rather that our system is working really well if people are eager to come to these outings (for some of us, this means a 15 hours journey!).

* * *

We still have a lot of exciting adjustments to make: launch a complete company survey on the changes we could make, refine our asynchronous processes, and keep on our toes regarding our team-building activities. The list goes on!

I have to be honest with you: though I work from home, I still need to use hand cream in the winter. So maybe remote work doesn’t magic away _all_ the problems of the world… but the door it opens leads to a new spring for work. Remote work is a significant social progress for workers when implemented well, and we believe it is the future.

Remote work is the future of work

The release day of Meilisearch v1 has come and gone. [We presented the major changes in our latest version](/blog/whats-new-in-v1-0/)nd [we spoke at length about all the features you get when you use Meilisearch](/blog/v1-enterprise-ready-stable/)oday we give the spotlight to [Guillaume Mourier](/blog/author/gmourier/)ur Product Manager.

Guillaume’s role is strategic in nature, and that comes with the privilege of having a very good idea about Meilisearch’s future, and how we plan to reach our destination.

Planning Meilisearch’s first stable release
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The process for v1 was a bit different compared to the past. This time, Guillaume tells us, the focus was not on including new features or even fixing major issues, but rather on the stability of the product and its API. He believes developers and businesses must have a predictable and stable product—they need to know they can depend on Meilisearch for their daily work. Our ever growing user-base also pointed towards this need. More and more people have grown to rely on our product, and it is our responsibility to not disappoint them.

Because of that, Guillaume planned 2022 as a year heavily focused on stabilization and performance optimization. To give our users the reliability they needed, we had to stop making changes that required programmers altering their workflows or reworking parts of their application. The first step was revising the API so it remained easy to use while serving as a solid and flexible base for new features. The second was adding more fundamental functionality. And the last, but certainly not the least important, was to find ways of improving performance and stabilizing the command-line interface.

Now these strong foundations have been implemented, Guillaume is very excited to start planning and implementing new features. It is time to move into a new era.

![](https://unable-actionable-car.media.strapiapp.com/balanced_rocks_a98f5966b7.jpg)

It’s the little things that matter
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Now v1 is online, Guillaume wants to iron out a longer-term roadmap—there are quite a lot of ideas already, but in Meilisearch’s new development phase we need to be more careful. Users will certainly be more vigilant and demanding as they follow our next steps. 

In terms of new features, Guillaume has his sights on a few quality of life improvements.

First of all, he wants to focus on bringing multi-index search to Meilisearch. This much-requested feature would make it easier to create unified search experiences in applications pooling data from many different indexes. This is also sort of a pet project of his: multi-index search has been in the works for some time and it was with a heavy heart he deprioritized it in 2022. 

Multi-index search will be released in incremental iterations. A first iteration will make it possible to send multiple search queries in a single HTTP call. This lays down the foundation for the following step: to release a new iteration that natively aggregates search results from different indexes into a single list.

Another area ripe for incremental improvements is Meilisearch's faceted search, which could be much simpler and more intuitive. For example, it is not possible today to sort facet values by the number of matched documents or their name. It is also impossible to search among the facet values themselves. Guillaume is enthusiastic about finding solutions to these problems—he assures us both things will soon be possible with Meilisearch.

![](/blog/content/images/2023/02/gestion-planning-cabinet-expertise-comptable.webp

Planning and pacing
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As the Product Manager, Guillaume needs to think not only about new features, but about Meilisearch’s development process. He believes our current product development cycle is too short, which means we tend to be trapped in a constant rush, running from release to release without enough time to properly think everything through. 

He’s very excited with the chance releasing v1 gives him: being able to plan things better, trial more features with users for a longer period of time, and reflect more on the valuable feedback our community gives us. Taking the time for all that enables us to start working more efficiently and prepare things much earlier.

What about semantic and AI-powered search?
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Recently, there has been widespread hype in the field of semantic search and natural language processing. Guillaume says it's an area we're following closely. Though these ideas might be very exciting, it is definitely challenging to provide contextualized and personalized search to end users while maintaining excellent performance and keeping Meilisearch a tool developers love to use. 

In the future, Meilisearch could evolve to a hybrid search service, uniting the best of both worlds. For now, however, we are focused on continuing to build upon the solid foundation we have created with v1.0.

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We hope that piqued your interest—if so, stay tuned for more information on the future of search! 

  
Remember, you can get started with Meilisearch v1 in just a few minutes—everything works out of the box, no configuration required. Want more? We also have a handy [Quick Start guide on our documentation website](https://docs.meilisearch.com/learn/getting_started/quick_start.html).

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