  
Hello, have you met Paul?
----------------------------

Today and for the third time, we’re very happy to share another one of our Meilistars interviews! The Meilistars are some of our top contributors, they all have a plethora of experience they kindly agree to share with us.

The Meilistar we’d like to shine our spotlight on in this article is Paul Sanders, or [sanders41](https://github.com/sanders41), as you may know him on GitHub! Paul has worked in various positions throughout his tech career, for instance he was a data manager as well as a software engineer.

Aside from that, Paul also enjoys spending his free time working on open-source software, both as a maintainer and contributor, which is how he got involved with Meilisearch.  
  
We were curious to know whether someone had mentioned the project to him or if he discovered us by himself:

> “I stumbled upon it. I believe it was Twitter where I saw it mentioned and it sounded like an interesting project. Looking through the repo on GitHub I saw some open issues that I knew how to fix and have been contributing ever since.”

We’re grateful he found his way to Meilisearch! We then wondered on the reasons that motivated him to keep on contributing all this time:  

> “The way Meilisearch does open source makes it a great project to contribute to, regardless of the repo. With a lot of other open source projects it's not unusual to have PRs sit for weeks before getting any review, then there are several weeks between each review. I've even had PRs sit for almost two years before getting reviewed on some projects. With Meilisearch the team is good about getting reviews done and seems to really appreciate the help. After contributing for so long the team there know me and I know them which also makes new contributions easier.”  

Upon first discovering us, Paul felt like Meilisearch and Elasticsearch were quite similar but that Meilisearch was easier to set up and maintain, which was interesting to him then.  
  
When asked about how Meilisearch today holds up in comparison to Meilisearch at the time, Paul shares:

> “The biggest difference I see from when I first found Meilisearch to now is all the work that has gone into indexing. Work done on indexing speed and batching of jobs have been big improvements. One difference that I haven't used myself, but I see a lot of talk about and seems to be a popular addition is the addition of geo search.”

We were delighted to hear his positive feedback, and we wondered if there was anything he’d noticed could be improved upon:

> “One thing that would be nice is some kind of configuration file for the settings. This is especially true for development when you are frequently starting fresh. What I have done for this so far is created a startup script that runs and updates the settings, but it would be nice to have a configuration file to do this.”

We’re very grateful for his feedback since Paul is such an experienced contributor.

A career in tech
----------------

Indeed, on top of contributing to Meilisearch on the regular, he worked, up until recently, for his former’s company’s open source [Fides projects](https://github.com/ethyca/fides). Fides is an open-source privacy engineering platform made to manage the fulfillment of data privacy requests and the enforcement of privacy regulations in code. And that’s without mentioning his personal projects:

> “ From my own projects the one that Meilisearch people may find interesting is Meilisearch [Python Async.](https://github.com/sanders41/meilisearch-python-async) As the name implies it is an SDK like Meilisearch Python, but for code bases using asyncio.”

  
During his time contributing to Meilisearch, Paul mentions he hasn’t had the chance to talk much with his fellow contributors outside of PRs. However, he would be keen to change that! Especially for anyone looking for a mentor:

> “Most of my time these days is spent in Python so if there is anyone that wants to get started with Meilisearch using Python, or contributing to the Python SDK and has any questions they can reach out to me and I'd be happy to help.”

With his extensive experience, we are sure he can provide valuable insights and guidance, so don't hesitate to reach out to him.  
  
Knowing that Paul works in tech, we wondered whether or not he’s had the chance to use Meilisearch in a professional environment:

> “I was building a system at a previous job where I was using Meilisearch, but ended up moving to a new job at a different company before it was finished. The idea in that system was in clinical trials there is a lot of free text data, audit trails and communications being a few examples. Because this was free text there was traditionally no good way to search this data (typos, misspellings, etc.) so people just had to know that the data existed and where to find it. With typo tolerance in Meilisearch I finally had a way to do it.”

  
And on his free time?
------------------------

Aside from his work, Paul also tells us a bit more about the way he uses Meilisearch for a pet project of his.

> “I am currently working on a personal project where I plan to use Meilisearch. My sister is a school teacher and she has been having me help her with some tracking data the school systems have to do on students. They are currently using Google Sheets to do this, and there are multiple issues with this in my view. For one it just wasn't made to do what they are trying to do, and for two each student is his/her own file so there is no way to search across students. How far this one goes depends on how interested the school system is in solving their pain point.”

As a final thought, Paul also shares with us an alternate way to look at Meilisearch:

>   
> “ I feel like a lot of times Meilisearch only gets looked at for it's ability to search through millions of records because that is impressive and has the "wow factor". I think there is a potential to bring a lot of value in using Meilisearch for much smaller datasets also. Manually searching through even hundreds of records is not something people are going to do, and with Meilisearch being so easy to setup I think this is a missed opportunity for people in these situations. ”

  
We’d like to extend a warm thank you to Paul for his time and candor during this interview. Don’t hesitate to check out more of our Meilistars interviews and the future ones as well!

Paul Sanders: an open-source veteran among the Meilistars

We’re thrilled to announce the release of Meilisearch v1.2. This minor version comprises new features for filters and engine improvements across the board. Let’s dig into the most noteworthy changes!

New feature: enhanced document management with filters
------------------------------------------------------

We’re excited to improve Meilisearch developers' experience by introducing filters for fetching and deleting documents. 🥳

<Callout>
  As always with filters, document fields must first be set as [filterable attributes](https://www.meilisearch.com/docs/learn/core_concepts/indexes#filterable-attributes).
</Callout>

### Retrieving documents

Filters bring new capabilities to fetching documents. For example, we could check how many documents match some filters before performing a deletion.

So we’re introducing a new endpoint to retrieve documents:

`**POST** /indexes/{indexUid}/documents/fetch`

Let’s consider an index that contains movies. We will write a request to retrieve 10 movies from the _Horror_ or _Mystery_ genre. Using the [array syntax](https://www.meilisearch.com/docs/learn/advanced/filtering#creating-filter-expressions-with-arrays), we can express the filters in the request body as following:

```json
{
  "filter": [
    "genre = Horror",
    "genre = Mystery"
  ],
  "limit": 10
}
```

This new endpoint saves you from having to make a search request to explore your data. Read more about fetching documents using filters in the [dedicated docs](http://meilisearch.com/docs/reference/api/documents#get-documents-with-post).

### Deleting documents

You can now delete documents matching a list of filters. This feature comes with a dedicated endpoint:

`**POST** /indexes/{indexUid}/documents/delete`

Taking the example of an index that contains movies, let’s write a request to delete all _Horror_ or _Comedy_ movies, as well as movies directed by _James Cameron_. This can expressed with the following request body:

```json
{
  "filter": [
    [ "genre = Horror", "genre = Comedy" ],
    "director = \"James Cameron\""
  ]
}
```

This requests launches a task to delete matching documents. Meilisearch will respond with a summarized task object:

```json
{
  "taskUid": 42,
  "indexUid": "movies",
  "status": "enqueued",
  "type": "documentDeletion",
  "enqueuedAt": "2023-06-05T11:01:58.721841Z"
}
```

This task represents the asynchronous deletion operation. Don’t forget to [check the task status](https://www.meilisearch.com/docs/reference/api/tasks#get-one-task) to ensure that documents have been deleted before proceeding. Read more about deleting documents in the [dedicated docs](http://meilisearch.com/docs/reference/api/documents#delete-documents-by-filter).

New feature: `IS EMPTY` and `IS NULL` filter operators
------------------------------------------------------

This release gives new powers to filters. So we thought, how can we go further? Maybe if we could filter in even more ways. I mean, don’t you want some more filters with your filtering?

So, we’re introducing two new filter operators: `IS EMPTY` and `IS NULL`. `IS EMPTY` matches existing attributes with empty value while `IS NULL`  matches fields with a null value.

Here’s an example considering the following documents:

```json
[
  {
    "id": 0,
    "color": []
  },
  {
    "id": 1,
    "color": null
  },
  {
    "id": 2,
  }
]
```

The new filter operators work like this:

*   `color IS EMPTY`: matches document `0`
*   `color IS NULL`: matches document `1`

Note that neither `IS EMPTY` nor `IS NULL` matches documents missing the `color` field. Both operators can also be used in conjunction with `NOT`. Read more in the [related documentation](https://www.meilisearch.com/docs/learn/advanced/filtering#filter-operators).

Experimental feature: reducing memory usage
-------------------------------------------

We’re introducing a new experimental flag to decrease the RAM usage of Meilisearch. Try it by launching Meilisearch with the `--experimental-reduce-indexing-memory-usage` CLI flag or the `MEILI_EXPERIMENTAL_REDUCE_INDEXING_MEMORY_USAGE` environment variable.

You can give feedback on this [Github discussion](https://github.com/meilisearch/product/discussions/652).

Improvement: relevancy
----------------------

We’ve enhanced [typo tolerance](https://www.meilisearch.com/docs/learn/configuration/typo_tolerance). Split words are now considered a typo, meaning `whit ehorse` may now match documents containing `white horse`. However, [n-grams](https://en.wikipedia.org/wiki/N-gram) and split words will now rank lower. This means that `sun flower` will now return a document containing `Sun Flower` before one with `Sunflower`.

Improvement: automated task deletion
------------------------------------

Meilisearch handles operations like adding documents to an index [asynchronously](https://www.meilisearch.com/docs/learn/advanced/asynchronous_operations). This means they are not handled immediately. Instead, Meilisearch places them in a tasks queue and processes them in the order they were received. Starting with v1.2, the maximum capacity of the task queue is 1 million.

Once the queue is full, Meilisearch will try to delete the oldest 100,000 tasks. Meilisearch will not remove tasks that are _not_ finished (enqueued or processing). It will simply delete as many finished tasks as possible among the 100,000 oldest.

<Callout>
  When automatic deletion fails for a task, Meilisearch will issue logs with \`warning_\`_ levels.
</Callout>

Other changes
-------------

*   Updated how Meilisearch handles ranking rules to improve search consistency and latency
*   [Geosearch](https://www.meilisearch.com/docs/learn/advanced/geosearch) sorting is now more performant for both descending and ascending sort
*   Enhanced language support for Latin languages and Arabic thanks to [charabia](https://github.com/meilisearch/charabia/releases/tag/v0.7.2) update

Conclusion
----------

This wraps up the biggest changes in Meilisearch v1.2. For an exhaustive review, including security updates and bug fixes, consult the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.2.0).

We’re already preparing version 1.3 of Meilisearch. We already have three prototypes ready for you to play with: [search for facet values](https://github.com/meilisearch/product/discussions/515#discussioncomment-5806759), [sorting facet values](https://github.com/meilisearch/product/discussions/519#discussioncomment-6030480), and the  [CONTAINS,  STARTS WITH, and ENDS WITH filter operators](https://github.com/meilisearch/product/discussions/544#discussioncomment-5918077). We’re eager to hear from you to nail the finishing touches.

This release wouldn’t be possible without the contributions of [@cymruu](https://github.com/cymruu), [@GregoryConrad](https://github.com/GregoryConrad), [@inductor](https://github.com/inductor), [@jirutka](https://github.com/jirutka), [@jlucktay](https://github.com/jlucktay), [@roy9495](https://github.com/roy9495), and [@Sufflope](https://github.com/Sufflope) to [meilisearch](https://github.com/meilisearch/meilisearch). This also includes the work of [@akeamc](https://github.com/akeamc), [@DrAliRagab](https://github.com/DrAliRagab), [@goodhoko](https://github.com/goodhoko), and [@mosuka](https://github.com/mosuka) who contributed to the last release of [charabia](https://github.com/meilisearch/charabia) to keep improving language support. Massive thanks to our awesome contributors. 🫶

You can stay in the loop by subscribing to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/) and come participate in our [Product Discussions](https://github.com/meilisearch/product/discussions).

**Join the discussion on [/r/rust](https://www.reddit.com/r/rust/comments/1418k4v/opensource_search_engine_meilisearch_releases_v12/).**

For anything else, join our developers community on [Discord](https://discord.gg/meilisearch).

I’ll see you there.

What's new in v1.2?

[Minipouce.fr](https://www.minipouce.fr/) is an online platform that enables future parents to create a baby registry list, which is a list of items that parents-to-be would like to receive as gifts. With the help of a multi-brand catalog featuring over 150,000 products, Minipouce.fr aims to make navigation intuitive and incredibly smooth for its users, ensuring everything is easy to find.

**By opting for Meilisearch as the search backbone of its marketplace, Minipouce.fr is able to maintain its commitment to delivering the best possible user experience.**

![](https://unable-actionable-car.media.strapiapp.com/gif_minipouce_gif_bf2cb4bae1.jpg)

<Callout>
  __"As a user, it felt like magic compared to other solutions I've tried. I'm not sure whether it's related to how typo tolerance works or to the default configuration. Still, right after setting up Meilisearch with no particular tweaks, I got fantastically relevant results." - Anthony Catel, Co-founder of__ [__Minipouce.__](https://www.minipouce.fr/)
</Callout>

### Challenge

As parents themselves, the cofounders of Minipouce.fr were very familiar with the struggles faced by new parents. It's crucial to create a multi-brand baby registry checklist to enable friends and relatives to shop from various distributors in a single location, rather than being confined to one sole provider.

Launching a new marketplace is a complex task: you need to generate enough traffic to convince distributors to list their products in the first place. On the other hand, you need a diverse and extensive catalog in order to attract customers and drive that traffic.

For Anthony Catel, the co-founder and head of engineering, it was clear that Minipouce.fr needed to offer a competitive and high-quality user experience right from the beginning. It is important to enhance the search functionality for a platform like Minipouce.fr: more relevant search results leads to increased user purchases, which in turn allow Minipouce.fr to collect an affiliate percentage fee from every product purchase originating from its platform. To achieve this, the technology behind the platform needed to be top-notch and enable quicker and smoother product selection by incorporating layers of meaning to accommodate various search terms users might use.

To add to the complexity, each retailer tends to use different formats for its product catalog, which results in mismatched and missing information that Minipouce.fr then needs to address. Synonyms also play a crucial role as one product can have multiple names, and customers may only be familiar with one of them. Without the search accounting for all potential synonyms, users may not find the right products.

All such intricacies needed to be addressed with the help of the most appropriate search solution.

### Why Minipouce.fr chose Meilisearch  

1.  **Higher out-of-the-box relevancy**

At the very beginning, Minipouce.fr considered using Algolia but couldn't find an appropriate plan that would accommodate a young, rapidly growing platform. While searching for an alternative, Anthony discovered Typesense and conducted extensive testing on it. After an initial set of tests, he decided to continue searching for another solution that could offer higher relevance in search results compared to Typesense. Eventually, Minipouce.fr came across Meilisearch and found that its default configuration demonstrated **higher relevancy in search results** than Typesense during the testing phase, without requiring any additional tweaks.

2\. **Lower maintenance**

Anthony chose not to consider ElasticSearch or similar solutions due to his desire to avoid managing numerous Java dependencies. Furthermore, he was drawn to Meilisearch's open-source platform, which operates on **Rust, a modern language in comparison to the C++** predominantly used by its competitors. This choice resulted in less time spent on debugging and infrastructure maintenance, a crucial factor for a smaller-sized team.

3\. **Meilisearch’s support and services bolster its open-source offering**

With high business stakes and the quality of search results being central, Minipouce.fr sought a solid, well-maintained solution. With Meilisearch’s open source engine, the organized handling of tickets and commits hinted at an established business backing it.

### Implementation

Today, Minipouce.fr is using Meilisearch to fully power the entire website. The migration of the marketplace from Typesense to Meilisearch's offering was completed within half a day, making it a quick and efficient process. Anthony particularly appreciated the availability of different SDKs in multiple programming languages, including a Python SDK. This feature made Meilisearch a natural addition to his tech stack, as it facilitated the setup of the platform without requiring additional resources.

To further optimize the search experience, additional tweaks are implemented using Meilisearch. This includes sorting functions that prioritize certain items in the catalog based on popularity. Updates are pushed in real-time, ensuring the search results are always up to date.

### Results

[Minipouce.fr](https://www.minipouce.fr/) was delighted with the **seamless integration and low maintenance requirements of Meilisearch**, thanks to its default configuration working effectively from the start. The relevancy of the search results was impressive, surpassing the performance of other tested alternative solutions. On top of that, since the implementation of Meilisearch, Minipouce.fr has observed a 5x growth in search volume across its platform.  

Looking ahead, Minipouce.fr envisions even greater possibilities, such as the implementation of vector search, which would enable intelligent connections between searches, further enhancing search experience. Considering the future, Minipouce sees the potential benefit of migrating to Meilisearch's Cloud offering to outsource the infrastructure maintenance, allowing for greater efficiency and scalability.

Minipouce.fr provides a top-grade customer experience with Meilisearch's open-source offering

Full text search refers to matching some or all of a text query with documents stored in a database. Compared to traditional database queries, full text search provides results even in case of partial matches. It allows building more flexible search interfaces for users, thus enabling them to find accurate results more quickly.

From simple in-app search to browsing huge ecommerce catalogs, full text search use cases are numerous. It is so common that Postgres and other relational databases include dedicated APIs for full text search. Unfortunately, Postgres falls short of search-focused databases in multiple areas.

1\. Complex setup
-----------------

To provide relevant results, full text search should tolerate typos, allow synonyms, and allow partial matches. Additionally, results ranking needs to be highly customizable to adapt to a business’s specific needs. Configuring full text search on Postgres comes at the cost of [comprehensive configuration](https://rachbelaid.com/postgres-full-text-search-is-good-enough/) and often requires extensions that can’t be used when using managed Cloud services.

Creating database indexes, writing queries, and ranking algorithms quickly overstep domain knowledge and demand expertise in searching, indexing, and linguistics. Optimizing for performance gets even harder when working around constraints from mixed-and-matched extensions aimed at coping with Postgres full text search limitations.

Conversely, a search-focused database comes with [state-of-the-art features](https://www.meilisearch.com/docs/learn/what_is_meilisearch/overview#features) like typo tolerance, prefix search, fuzzy matching, synonyms, and customizable rankings out of the box.

2\. Faceted search
------------------

[Faceted search](https://www.meilisearch.com/docs/learn/fine_tuning_results/faceted_search) allows users to refine search results by broad categories. It’s often used in ecommerce applications. For example, a clothing shop can implement filtering by facets such as brand, size, or rating range.

[![Faceted search on an ecommerce website](https://unable-actionable-car.media.strapiapp.com/ecommerce_demo_4_final_1_48b9d742ac.gif)](https://ecommerce.meilisearch.com/)

Faceted search on an ecommerce website ([see demo](https://ecommerce.meilisearch.com/))

Implementing filtering is tricky enough for a single facet. But facets can take many shapes: category tags, price ranges, or minimum rating. Implementing filtering for all types is very challenging. By all means, the trickiest query to implement is aggregating results to build facet counts. This becomes very resource intensive on large datasets.

The complexity of implementing faceted search with Postgres grows exponentially with the number of facets. Faceted search alone makes a strong selling point for search engines like [Elasticsearch or Meilisearch](/blog/why-should-you-use-meilisearch-over-elasticsearch/)hey come with optimized, first-class APIs to handle facets filtering and counting.

3\. Typo tolerance
------------------

By default, Postgres full text search cannot handle misspellings. Users commonly install the `pg_trgm` extension to work around this limitation. (Again, this solution is not always available in managed Postgres.) This extension notably introduces new operators to compare similarity between strings along with search-optimized GIN and GIST indexes.

The new indexes allow more configuration for full text search, but choosing between GIN and GIST indexes is not always trivial. Moreover, the new operators do not take into account word proximity, space separators, or word size. This, in particular, makes it hard to achieve real fuzzy matching with Postgres.

Ideally, a search-focused database should allow configuring different rules for single versus multiple words queries. It is the case of Meilisearch, which allows disabling typos entirely for specific fields. This enables users to search via unique identifiers like a book’s _International Standard Book Number_ (_ISBN_).

[![Example of typo tolerance](https://unable-actionable-car.media.strapiapp.com/typo_tolerance_2_gif_4409419422.gif)](https://typo-tolerance.meilisearch.com/)

Typo tolerance is disabled for the ISBN field ([see demo](https://typo-tolerance.meilisearch.com/))

4\. Language support
--------------------

Linguistic specificities vary a lot between languages using the Latin alphabet and others like Arabic or Chinese. As of Postgres 15, full text search dictionaries are unavailable for Simplified and Traditional Chinese, Korean, and Japanese, among others. This means resorting to specific implementations for different languages.

<Callout>
  To see the [list of languages supported by Postgres full text search](https://gist.github.com/Strift/3155b9238bac6495d4cd57b83d78415f), you can run the \`\\dFd\` command in Postgres.
</Callout>

Language support constraints are amplified in managed environments like Amazon RDS where users are unable to access the filesystem. This restricted access prevents them from implementing custom dictionaries, stemmers, synonyms, and more.

Meilisearch comes with [optimized language support](https://www.meilisearch.com/docs/learn/what_is_meilisearch/language) for Chinese, Japanese, Korean, Hebrew and more, on top of all languages using whitespace to separate words.

<Callout>
  Thanks to its vibrant open-source community, Meilisearch benefits from native speakers' contributions to [improve language-specific features](/blog/improving-meilisearchs-language-support/)
</Callout>

5\. Paying the backend price
----------------------------

Postgres is a database intended to communicate with server-side languages. When building a public-facing client application, it means building an API on top of the database to communicate with clients. On top of additional development time, creating such a proxy entails further problems.

First comes the issue of latency: making requests to an API that queries the database before returning results is bound to take some time. This does not affect dedicated search engines, as they come with a public API intended to deliver data to end users.

Now comes the second issue: security. Search engines APIs are designed, from the start, for public consumption. And security’s baked in for that use case. By default, API keys restrict search requests, while advanced features like [tenant tokens enable multi-tenancy](https://www.meilisearch.com/docs/learn/security/tenant_tokens#tenant-token-payload-reference).

[![Example of tenant tokens usage](https://unable-actionable-car.media.strapiapp.com/meili_create_token_c6fb7330a6.gif)](https://tenant-token.meilisearch.com/)

Using tenant tokens to restrict access to documents ([see demo](https://tenant-token.meilisearch.com/))

6\. Scaling limitations
-----------------------

There is a legitimate motive for wanting to keep all data in a single database. But having search-related data in the main database comes with great technical consequences. Full text search queries on large dataset become costly with Postgres, especially when ranking results and computing facet counts.

Monolithic databases often become the bottleneck of applications that need to scale. Don’t add unnecessary search-related costs to this resource when you can avoid it. These costs are only multiplied when building user-facing applications with high traffic.

Unlike relational databases, full text search engines like [Meilisearch use inverted indexes](/blog/best-practices-for-faster-indexing/)his data structure creates information redundancy to allow quicker information retrieval. It is designed to perform search operations, and will naturally outperform relational databases on large datasets. And, when search usage spikes, there is a single service that needs scaling.

7\. Relevancy
-------------

As we mentioned earlier, relevant search requires typo tolerance, custom rankings, and synonyms. In modern applications, users expect results to update on each keystroke, which requires [prefix searching](https://www.meilisearch.com/docs/learn/advanced/prefix). But Postgres full text search `ts_rank` function only allows attribute weighting. When using the `pg_trgm` extension, developers are left to implement their own sorting based on similarity.

In search-focused databases, the concepts of result ranking, attribute priority, numbers of word matched, and exactness of the query are first-class concepts. They match high-level APIs that allow explicit fine-tuning of search behavior. This makes it easier to make these concepts available to non-tech, business stakeholders. This was cited as a key reason for [why Bookshop chose Meilisearch](/blog/bookshop-increases-search-based-purchases/)or their ecommerce search.

8\. Missing out on InstantSearch libraries
------------------------------------------

When it comes to search experience, websites and applications often implement the same user interface patterns: a textual search bar, checkbox lists of facets, range sliders, sorting menu, page navigation, etc. The open-source InstantSearch library comes with implementations of all these features in the form of widgets available via SDKs in JavaScript, iOS, and Android.

When time to market is paramount, it’s hard to pass on such niceties. Supported by Algolia, the InstantSearch libraries enjoy wide adoption and several search engine databases come with [InstantSearch-compatible APIs](https://github.com/meilisearch/meilisearch-js-plugins/tree/main/packages/instant-meilisearch). Read our Nuxt guide for ecommerce search to learn [how to implement InstantSearch widgets with Vue](/blog/nuxt-ecommerce-search-guide/)Callout>
  Prefer React? Read our tutorial on [using InstantSearch with React](/blog/instant-meilisearch/)/Callout>

9\. Limited Cloud support
-------------------------

Outsourcing the provisioning, maintenance, and scaling of servers is a common strategy in the Cloud era. Instead of managing servers, teams can focus on delivering value to their users. Postgres, like other databases, is available in a wide range of Cloud offerings for managed services. Unfortunately, managed services often come with limitations.

In the case of Postgres, implementing state-of-the-art full text search requires installing extensions. Moreover, fine-tuning language dictionaries and more configuration require access to the file system. Unfortunately, this means that many features are unavailable in Cloud environments.

To enable delegation of infrastructure, search engines often come with [dedicated cloud services](https://meilisearch.com/cloud). Those bespoke platforms do not compromise and allow using the full palette of search features. Moreover, customers can benefit from premium SLAs, support, and other enterprise services tailored to their searching use cases.

* * *

Postgres is a great, flexible database that allows the implementation of many custom, all-in-one solutions. Its full text search features might be sufficient for basic search, but it falls short when it comes to real-time search with relevance concerns. These limitations become worse on large datasets. It’s only natural, because Postgres is a database, not a search engine.

Meilisearch is an open-source search engine to build fast and relevant search experiences. It aims at providing state of the art experiences for end users while providing a simple and intuitive developer experience. You can try it by [running Meilisearch locally](https://www.meilisearch.com/docs/learn/getting_started/quick_start) or creating an account [for free on Meilisearch Cloud](https://cloud.meilisearch.com/login).

* * *

Learn more about what Meilisearch can bring to your business

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

* * *

For more things Meilisearch, you can join the community on [Discord](https://discord.meilisearch.com/) or subscribe to the [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e). You can learn more about the product by checking the [roadmap](https://roadmap.meilisearch.com/) and participating in [product discussions](https://github.com/meilisearch/product/discussions).

When does Postgres stop being good enough for full text search?

Meet Markus
-----------

To kick things off, we asked Markus to introduce himself.

> “I'm Markus, a software developer at [devjob.at](https://devjobs.at/). I enjoy experimenting with new technologies and packages, prototyping ideas, and improving the code base. My main area of interest lies in backend and infrastructure topics. During my free time, I read a lot of tech articles and follow Tech Twitter to see what's new in our ever-changing dev world.”

You can find him on Twitter as [@m4ch4tschek](https://twitter.com/m4ch4tschek)

> “Apart from the tech stuff, I also enjoy spending time in nature. Since owning a dog, I got interested in dog training. I dedicate a significant amount of time to it and consider it time well spent.”

Curious minds want to know! We couldn’t help but ask for account recommendations. If you are dying to know too, don’t worry, here’s the list of Markus’ top Twitter accounts:

*   [@marcelpociot](https://twitter.com/marcelpociot): Markus praised Marcel Pociot for creating the best developer tools around Laravel and other projects, and for quickly providing working solutions to developers' issues.
*   [@JackEllis](https://twitter.com/jackellis): He finds Jack Ellis' journey as a CTO challenging Google's analytics business to be very inspiring.
*   [@mathemagic1an](https://twitter.com/mathemagic1an): This account covers all things AI, and he recommends it to anyone interested in the field.
*   [@mattpocockuk](https://twitter.com/mattpocockuk): Markus believes this account owner has "TypeScript superpowers"
*   [@tobias\_petry](https://twitter.com/tobias_petry): For SQL content, he advises following Tobias
*   [@mjackson](https://twitter.com/mjackson) and [@kentcdodds](https://twitter.com/kentcdodds) were recommended for their work with Remix

And, of course, he also suggests following [@meilisearch](https://twitter.com/meilisearch) 😎

Love at first sight
-------------------

Markus discovered Meilisearch in late 2020. While he couldn't recall exactly how he first heard of Meilisearch, he did remember being drawn to its simplicity:

> “Finally, something fast and configurable, that I can easily understand and deploy without a PhD.”

He ended up implementing it into two projects, with one of them, [DEVWorkplaces](https://en.devworkplaces.com/), being highlighted in our [newsletter](https://mailchi.mp/meilisearch/newsletter-6025733?e=[UNIQID]). DEVWorkplaces focuses on job search and dev workplace discovery in the DACH region. It was already using Algolia in production, but it didn’t fully meet the product’s needs.

When asked what Meilisearch brought to the table that Algolia couldn't, Markus listed a few key advantages. First, **Meilisearch offers a self-hosting option**, which was a big plus for his team. Second, he appreciated the **local development environment** and the fact that Meilisearch is open-source. Finally, Markus found that Meilisearch was **easy to use and understand for team members**, making it a more accessible tool for everyone involved.

We were curious to know Markus’ take on how Meilisearch has evolved from 2020 to 2023. Has Meilisearch gone from good to great, or has it gotten worse? Thankfully, Meilisearch seems to have evolved in the right direction 😀

> “Seeing the community grow is the biggest point. Increased adoption of framework/language integrations has been wild. Also, there are a lot more team members now. Everything is way more professional (in a good way), from the releases, product decisions to communication, blog posts, and community interaction.”

As a developer advocate and one of the earliest team members, I love to hear that! The truth is that everyone at Meilisearch is very proud of how far we have come and very excited about the future!  We are moved to see that there are people who have been supporting us from the beginning 🥰

From user to contributor
------------------------

We're always intrigued by how individuals take the leap to contribute to a project. In Markus’ case, it was a matter of necessity. When he encountered an issue, he saw no other option but to fix it himself. As he became more involved with open-source projects, Markus began contributing to [Meilisearch](https://github.com/meilisearch/meilisearch) and [KeystoneJS](https://github.com/keystonejs/keystone), two projects that he uses in his work.

> “As open-source projects are mostly dependent on user contributions, this was the way to go. Also, contributing to an open-source project (no matter which size) is something I enjoy, even though I would like to contribute more.”

Markus’ contributions have evolved over time. Although he still contributes to code fixes, he has become more involved in non-coding tasks. Markus is particularly interested in [Laravel Scout](https://github.com/laravel/scout)\-related issues or questions and tries to provide helpful answers whenever possible. He’s been also participating actively in product discussions.

> “I've subscribed to topics that make my daily usage of Meilisearch easier. [Guillaume Mourier](https://github.com/gmourier/) (Meilisearch's Product Manager) makes a big effort to try to collect as much information as possible about new features or changes. This is great as we (contributors and users) can add input to any product decision before it gets implemented.”

It's feedback like this that has helped us continue to improve Meilisearch over time, and we are grateful for Markus’ and our community's contributions. We are excited to continue improving our product with his and our community's input. That's why we asked him about the features he would like to see in the near future, and he did not disappoint! He’s got plenty of ideas:

> “I think it would be great if Meilisearch would be [upgradable without the need of creating dumps and re-importing them](https://github.com/meilisearch/product/discussions/213). Just shutting down Meilisearch and starting it with a newer version. Currently, if we want to implement a zero downtime deployment (having the old Meilisearch instance and a new one and syncing the changes to both instances until everything is up to date), it leads to extended downtime on the search endpoint and overhead on the implementation side.”

> “Furthermore, for my current project at work, we would appreciate [multi-geopoint](https://github.com/meilisearch/product/discussions/508) support. As this is missing, we need to use the distinct attribute feature, which unfortunately is extremely slow on search requests with a lot of hits.”

It was great chatting with Markus and learning about his experience with Meilisearch. It's always a pleasure to hear from our community members and learn about the diverse ways in which they are using Meilisearch. We are grateful to Markus for taking the time to share his thoughts and experience with us, and we look forward to continuing to work with him and our community to create fast, powerful, and reliable search experiences.

If you are interested in learning more about how Meilisearch is being used by other developers and organizations, check out our [previous Meilistar interview](/blog/meilistar-interview-minoru-osuka/ with Minoru Osuka, one of our main language contributors.

Markus Machatschek: POV of a long-time Meilisearch contributor

Squeezing millions of documents in 128 TB of virtual memory

Meilisearch, one of the fastest-growing open-source search engines on GitHub, has reached general availability with its cloud-based Search-as-a-Service product, [Meilisearch Cloud](https://cloud.meilisearch.com/register?utm_campaign=cloud-ga&utm_source=blog).

What is Meilisearch Cloud?
--------------------------

Meilisearch Cloud is a **cloud-based SaaS platform** that enables developers to implement search functionality quickly and easily. It is built on top of the popular [Meilisearch open-source search engine](https://github.com/meilisearch/meilisearch), which has gained a reputation for being lightning-fast, easy to use, and incredibly flexible. Meilisearch Cloud takes these attributes to the next level, offering a managed search service that is scalable, reliable, and designed to meet the needs of all companies.

With Meilisearch Cloud, we handle **all server management**, including

*   monitoring server health
*   installing and updating Meilisearch
*   server upgrades and patches

Our service is designed to **scale automatically** as your usage increases, so you don't have to worry about any sudden surge in traffic affecting your search performance.

Additionally, our costs scale with your usage, so there are no hidden fees to worry about. You only **pay for what you use**, which makes our platform an affordable and efficient solution for businesses of all sizes.

### Save time and resources

Meilisearch Cloud simplifies the process of implementing search, saving time and resources. It is made to be so user-friendly that a front-end developer with no prior search experience can build a world-class search experience in no time. Our intuitive interface streamlines the whole search process — no server management or DevOps experience is required!

We handle the grunt work. Once your Meilisearch project is up and running, we’ll ensure it stays that way so **you can focus on building your products**.

### Meilisearch Cloud has a great track-record

We’ve spent the last year gradually rolling out Meilisearch Cloud for selected users.

> _______Initial value came from the fact that Bookshop started using the Meilisearch open source solution, hosting the infrastructure locally. However, working with it in staging environments and test environments was a harder task for a small team like ours that can't focus on search year-round. We did eventually switch to Meilisearch Cloud because it was better for us to outsource._______

__\- Mason Stewart, CTO at Bookshop.org__

Our platform's commitment to customer satisfaction is reflected in our top-of-the-line support, which is always available to help customers with any issues they encounter. Overall, Meilisearch Cloud's customer-focused approach makes it a top choice for businesses looking to enhance their search capabilities.

Why businesses love Meilisearch Cloud?
--------------------------------------

**1\. Lightning-fast relevant search results**  
Meilisearch Cloud is designed from the ground up for performance and delivers search results in milliseconds. It does not only improve the user experience, but it also leads to increased engagement and conversion rates.

![](https://unable-actionable-car.media.strapiapp.com/where2watch_f91f072ead.gif)

[See Meilisearch Cloud in action here](https://where2watch.meilisearch.com/?utm_campaign=cloud-ga&utm_source=blog)

**2\. Reliable and secure**  
Meilisearch Cloud is built on top of a highly secure and reliable infrastructure, leveraging the industry's best practices to ensure your search solution is always available and secure. You can focus on building the best possible search experience for your users without worrying about servers going down in the middle of the night.

**3\. Easy to use**  
Meilisearch Cloud is designed to be easy to use, even for developers without extensive experience working with search engines. Its intuitive interface makes creating and managing search indexes easy, allowing developers to quickly create highly relevant search experiences tailored to their specific needs.

![](https://unable-actionable-car.media.strapiapp.com/attributes_1_eb989f4eec.png)

![](https://unable-actionable-car.media.strapiapp.com/ranking_rules_3035b01c74.png)

Meilisearch Cloud upcoming interface

**4\. Highly scalable**  
Meilisearch Cloud is designed to be highly scalable, making it ideal for businesses of all sizes. Whether you are a small startup or a large enterprise, Meilisearch Cloud can easily scale to meet your needs.

### Meilisearch Cloud will adapt to your business

Meilisearch provides a range of plans to meet the needs of your business. The _Pro plan_ includes enough search units to accommodate most large businesses. It becomes the most cost-effective plan available, starting from 5 million searches per month. But until you reach 10M searches, it won't cost another penny. Many online merchants will be able to handle flash sales: even if the number of requests double, the price won't.

### Usage-based pricing

Meilisearch Cloud scales with your success. You will never be surprised by [our pricing](https://www.meilisearch.com/pricing?utm_campaign=cloud-ga&utm_source=blog): **we’ll only bill when your users search**. And when they do, they find more and make your business more successful.

This usage-based pricing works for any project: from a developer's personal project to a super-scaled company. And our free tier plan includes enough credits to accommodate most individual or small projects, so you’ll only start paying when it makes sense.

### Bookshop.org, an e-commerce platform, sees a +43% surge in search-based sales

[For the Bookshop.org team](/blog/bookshop-increases-search-based-purchases?utm_campaign=cloud-ga&utm_source=blog the advantage of Meilisearch lies in its simplicity and ease of use, making it possible for decision-makers and non-developers to be very close to the ranking rules. It was necessary to accommodate the ever-changing book search criteria during the busy holiday period and keep the search results relevant at all times.

> ______The fact that we can iterate back and forth quickly and have speedy re-indexing times when we change our rules was a new way of working with search. Previously, only our engineers had the expertise to tweak the rules for book search.______

__\- Mason Stewart, CTO at Bookshop.org__

Future of Meilisearch Cloud
---------------------------

### Upcoming Meilisearch features

In upcoming versions of Meilisearch, a range of new features will cater specifically to the e-commerce industry:

*   Searching for facet values and sorting them
*   Defining searchable fields at search time
*   Defining ranking rules at search time

Additionally, we will introduce scalability improvements enabling search engines to handle more queries and data more efficiently.

### Enterprise-ready improvements

The Cloud development team will soon release new features for production purposes:

*   geo-replication to provide fast search results to end users
*   specific e-commerce analytics
*   an even simpler interface

![](https://unable-actionable-car.media.strapiapp.com/Cloud_UI_1_7e3ac5c3c0.png)

![](https://unable-actionable-car.media.strapiapp.com/Cloud_UI_2_ae1bc3536b.png)

![](https://unable-actionable-car.media.strapiapp.com/Cloud_UI_3_90c8c9fa3c.png)

The upcoming interface of Meilisearch Cloud

We are currently in the process of obtaining a SOC 2 security certification, an auditing procedure that ensures we are securely managing sensitive and private data.

Conclusion
----------

With Meilisearch Cloud, you can be confident that your search is in safe hands. We offer a comprehensive and reliable search engine solution that can help you improve the performance and functionality of your search.

[Register for a Meilisearch Cloud account](https://cloud.meilisearch.com/register?utm_campaign=cloud-ga&utm_source=blog) today.

Search-as-a-service Meilisearch Cloud is open to the public

[Bookshop.org](https://bookshop.org/) is an online bookstore based in Brooklyn, New York, with a mission to support local, independent bookstores financially. By design, Bookshop.org gives away over 80% of its profit margin to stores and affiliates of stores: publications, authors, and others who make up the thriving, inspirational culture around books. As more and more people buy their books online, Bookshop.org creates an easy, convenient way for customers to get books and support local bookstores at the same time.

**By partnering with Meilisearch Cloud, the Bookshop.org team can enjoy improved search relevancy, reduce maintenance and operational costs and have its engineering team focus on its core business.**

[![](https://unable-actionable-car.media.strapiapp.com/bookshop_meilisearch_ded1e7997a.gif)](https://bookshop.org/)

The search experience on Bookshop.org

<Callout>
  _"With our prior search engine (Elasticsearch), **14% of searches resulted in a purchase, and with Meilisearch, 20% of searches currently result in a purchase**. That's a huge improvement without a lot of development time: **1 out of 5 customers found the book they were looking for and purchased it.**"_  
\- _Andy Hunter, CEO at _Bookshop.org__
</Callout>

Challenge
---------

Bookshop.org carries an impressive inventory of over **six million books**. It’s essential for the customer experience that anyone searching for a specific book on the website can find it as fast as possible. A considerable number of books available in the inventory means a proportionally high amount of items that need to be **indexed and ranked in the search** by the internal team.

> You only get one shot at it. We have a lot of visitors that care enough about their communities and ethical consumption - those buyers are specifically choosing to use Bookshop, and **we have to make sure that they can find a book as easily as they could find it in other major bookstores or else we're gonna lose them**.

_\- Mason Stewart, CTO at Bookshop.org_

Prior to switching to Meilisearch, Bookshop.org was utilizing a search engine solution provided by [Elasticsearch](/blog/why-should-you-use-meilisearch-over-elasticsearch?utm_campaign=bookshop_story&utm_source=blog However, with a small team of eight engineers and many unique customers, Bookshop.org needed **a solution that would require less maintenance and fine-tuning**. Additionally, a more streamlined solution would enable workers across the company to contribute to the ranking rules and configurations, improving the search relevancy on a rolling basis.

Why Bookshop.org chose Meilisearch
----------------------------------

**1\. A streamlined process that keeps the decision-makers involved**

For the Bookshop.org team, the advantage of Meilisearch lies in its simplicity and ease of use, making it possible **for decision-makers and non-developers to be very close to the ranking rules**. This was necessary to accommodate the ever-changing book search criteria during the busy holiday period and keep the search results relevant at all times.

> ___The fact that we can iterate back and forth quickly and have speedy re-indexing times when we change our rules was a new way of working with search. Previously, only our engineers had the expertise to tweak the rules for book search.___

___\-_ Mason Stewart, CTO at Bookshop.org__

**2\. Quick iterative product cycle**

The Bookshop.org team also highlighted the quick iterative cycle that has enabled them to solve issues faster. Bookshop's team can directly interact with the Meilisearch team whenever they need to resolve issues or introduce quick fixes. According to CTO Mason Stewart, the company values the combined agility of a compact search engine backed up by a highly responsive team.  
  
**3\. Ability to outsource hosting and infrastructure maintenance to the Meilisearch team**

Ensuring stability in search is essential for the success of the Bookshop.org business. After meeting and interacting with the Meilisearch team, the CTO felt confident in **entrusting them to manage their Meilisearch servers** through the Cloud offer.

> ___Initial value came from the fact that Bookshop started using the Meilisearch open source solution, hosting the infrastructure locally. However, working with it in staging environments and test environments was a harder task for a small team like ours that can't focus on search year-round. We did eventually switch to Meilisearch Cloud because it was better for us to outsource.___

_\- Mason Stewart, CTO at Bookshop.org_

By transitioning from the open-source version of Meilisearch to Meilisearch Cloud, the Bookshop.org team can now concentrate on its primary operations while delegating the management of the search infrastructure to experts.

**4\. Ability to support the scale and business model fit**

The Bookshop.org’s extensive inventory, coupled with a high amount of monthly users, made some pricing models that exist on the market untenable for the bookstore’s distinct business model. For a business with low margins, very high volume searches, very high volume users, and a high number of documents, Meilisearch proved to be a perfect fit.

Implementation
--------------

After reassessing its main challenges with book search, the Bookshop.org team conducted research and evaluated alternative solutions to address their need for a more user-friendly search solution that requires less maintenance. They came across Meilisearch, which was recommended on a popular tech news discussion platform. A quick test environment was set up out of the box, and the team found that Meilisearch was a good fit as the test continued. The CTO and engineers presented the working prototype to the management, and it was decided to implement Meilisearch before the holiday season, which typically starts at the end of the year. **The timing was crucia**l, as the end-of-year seasonal influx in sales brings a lot of increased consumer activity and a significant portion of profits for a bookseller as a result of it.

The initial demo included connecting Meilisearch to a book catalog and was quick and easy. Then followed the process of balancing and ranking the data that should inform the quality of a result internally, weighing different factors such as how recently the book was sold or how long ago it was published. The team acknowledged that the transition to Meilisearch would have been seamless if a clean data catalog had existed beforehand.

The process of changing the ranking rules to keep the book search relevant for shoppers is very streamlined. Suppose they want to rank a book's description higher or lower in the ranking rules. All they need to do is move it up or down, reindexing the entire catalog and making any change easy. Having global ranking rules that apply to the entire database proved healthy for a small team that needs to understand how one change can impact search across the repository.

> ___After a few weeks of tuning the rules, we've introduced almost no changes. It's been just about perfect.___

___\-_ Mason Stewart, CTO at Bookshop.org__

To add to the vision of the future with Meilisearch, the Bookshop.org team mentioned that having a multi-index search would be the next most important thing to improve the search experience of the website visitors. Say, a website visitor searches for a specific _keyword_ in a search bar. Multi-index search allows the engine to perform concurrent searches across multiple sources. To that extent, Meilisearch is happy to announce the release of the [federated search](/blog/introducing-federated-search?utm_campaign=bookshop_story&utm_source=blog in v1.1 as an additional step in boosting user experience and increasing relevancy for Bookshop.org clients.

Results
-------

With an initial Meilisearch deployment, the team could compare the search performance to the metrics they were getting with a previous Elasticsearch configuration.

By bringing Meilisearch to the table, the team introduced a straightforward configuration process, removing the need to increase the team’s bandwidth or expertise. **With Elasticsearch, 14% of searches resulted in a purchase. With Meilisearch, that number jumped to 20%,  increasing overall conversion by 43%**.

Bookshop.org increases search-based purchases by 43% with Meilisearch

Hi 👋 I'm Ingrid. Not that long ago, I was in your position. I was searching for a new job and had to navigate these waters. Job interviews are a difficult reef to get through!

Now I’m a Talent Acquisition Manager at Meilisearch. Though I’m on the other side of the process now, it was my experience as a candidate for Meilisearch that made me want to join the company. During my first interview, it became clear that Meilisearch strives for transparency, openness, and authenticity. As a candidate, I could already imagine what it could be like to work there.

An important part of my role today at Meilisearch is to be the safekeeper of the recruitment process. Our goal is to hire people who are the best possible match for us, in a reasonable amount of time, while providing candidates with a great experience.

I have spent time designing our recruitment process. For us, what you value in work is as important as your technical skills, if not more so. This is why I am iterating more specifically on steps that will help us assess if candidates share our core values.

So how do we assess this crucial, but apparently very subjective characteristic? What does “value-fit” even mean? In this blog post, I will explain this specific aspect of the interview process, how we assess the value-fit generally, and tips on how you can prepare yourself as a candidate.

What does value-fit mean?
-------------------------

You’re probably like me: when you apply for a job, you want your values to be shared by the company. Since joining Meilisearch in October 2022, I have observed that value-fit is mainly about ways of collaborating. When assessing value-fit, we focus on how your work style aligns with our values and culture. Additionally to questions about your experience and technical skills, we ask questions about your communication style, work preferences, and [interpersonal skills](https://www.coursera.org/articles/interpersonal-skills). This will help us clarify your compatibility with the future team and our values.

We defined [our values](https://www.welcometothejungle.com/en/companies/meilisearch/values) in 2020. We chose to put forward characteristics that represent us individually and collectively. These values might mean completely different things in other companies, and we have tried our best to describe our work values at Meilisearch.

Our values serve as the basis for our strategic decision-making and impact our company’s culture and individual behaviors. We use them in our career review process, in the objectives that we set for ourselves, and, as you may have understood, in our recruitment processes.

The term “value-fit” itself can be subject to misconceptions, especially the fact that value-fit in people's minds means recruiting people who look like us or share the same interests. For us, it is rather the opposite. We are looking to hire _outside_ of groups of people who are similar to us.

We acknowledge the importance of inclusivity, and we believe that hiring for value-fit supports diversity. [Studies have shown](https://hbr.org/2019/09/hiring-for-culture-fit-doesnt-have-to-undermine-diversity) that people with similar values and work styles are more likely to collaborate effectively, leading to improve teamwork, communication, and problem-solving.

It doesn't matter if we don't have the same background if we're trying to hit the same goal.

How do we assess value-fit?
---------------------------

As a candidate interviewing for a full-time position, you should expect 4 to 5 steps: meeting with a recruiter, the hiring manager, a skills assessment, a team-fit interview, and reference checks.

Evaluating value-fit is not a question of intuition or feeling, which is why we have integrated value-based questions into every step of the recruiting process.

Value-based questions are behavioral questions, that showcase how you handled different work situations in the past. As a recruiter, when I ask a value-based question, I’m looking to get a better understanding of you as a team member.

Depending on the role you are applying for, we identify different skills and abilities stemming from our values.

Here are a few examples of value-based questions:

*   ___Please tell us about a time when someone took a decision you didn’t agree with, in your previous job. What did you do? Looking back, what do you think about this decision now?___

The goal of this question is to gather a specific example of how you handle disagreement with colleagues. At Meilisearch we value the [disagree and commit](https://handbook.meilisearch.com/guide/values.html#our-values:~:text=We%20disagree%20and%20commit) principle. For example, if you join our engineering team you will see that people thrive on input from everybody and openly share their opinions and objections, without letting this difference become an obstacle and stop the team from moving.

*   _Please tell us about a time when you didn’t have the knowledge/experience to work on a task, what did you do?_

Team members at Meilisearch [find solutions together](https://handbook.meilisearch.com/guide/values.html#our-values:~:text=support%20we%20bring.-,We%20find%20solutions%20together,-One%20thing%20we), and are not afraid to ask for help when they don’t know. With this question, we try to assess your ability to collaborate and share knowledge.

Some preparation is necessary when getting ready for a value-fit interview. We want to give candidates all the information they need to succeed in their interviews. Below are some guidelines to help you.

How can you prepare to answer value-based questions?
----------------------------------------------------

Figuring out how to showcase your soft skills before an interview can be challenging but should not be underestimated. When you’re clear about what you value in work, you can’t lose; this will help you find a company where you’re happy to work, and showing you’ve prepared for these questions can make a big difference in the eyes of interviewers.

Here are a few tips:

*   Before the interview, **research our** [**company's values**](https://www.welcometothejungle.com/en/companies/meilisearch/values); this will help you understand our company’s culture and what we stand for.
*   **Reflect on your values**: take some time to reflect on your own values and what is important to you. This will help you articulate your values and how they align with ours.
*   ******Practice answering value-based questions:** prepare concrete and meaningful examples of your past experiences, such as successful work collaboration or communication with your manager or team, how you received feedback, and what you learned from your mistakes.****
*   To answer behavioral questions, **use the** [**STAR method**](https://capd.mit.edu/resources/the-star-method-for-behavioral-interviews/); find a concrete example, and practice describing it in the following order: Situation, Task, Action, Result.
*   During the interview, **ask questions** about the team, our values, and how they are lived out in the workplace. This will help you understand our company's culture and values better.
*   Keep in mind we are not looking for a 100% match with our values. No one at Meillisearch matches our values 100%. In recruitment, unless we spot really big red flags, what we are ultimately looking for is a good balance between what you value in work, and what you know how to do.

To make sure we get a clear view of what those are, we will do our best to make you feel as comfortable as possible and to give you all the information you need to succeed in your interviews. We truly believe that candidates who are well prepared and in excellent interviewing conditions will better show who they are, which is ultimately what we’re _really_ interested in.

Conclusion
----------

As a recruiter, I can tell you that value-fit is critical in the recruitment process, and it goes beyond technical skills and experience. From my personal experience as a candidate, I know how important it is to assess how your work style aligns with a company's values and culture. That's why, at Meilisearch, we are committed to hiring people who share our core values while promoting inclusivity and diversity.

Interviewing at Meilisearch is a two-way process, it is not only meant for us to get to know you but also for you to understand if our work environment would suit you and a place for you to grow.

Have a look at our [open positions](https://jobs.lever.co/meili)! I’m looking forward to discussing all of this with you in person 😉

Meilisearch latest news and company updates