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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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.org.cn/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://meilisearch.org.cn/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#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#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/#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://meilisearch.org.cn/pricing#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#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#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#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/whats-new-in-v1-1) 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 😉

A recruiter's advice on how to answer interview questions about values

[Meilisearch v1.1](/blog/whats-new-in-v1-1/#federated-search&utm_source=blog&utm_medium=introducing-multi-search) introduces the `/multi-search` [endpoint](https://docs.meilisearch.com/reference/api/multi_search.html#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/#federated-search&utm_source=blog&utm_medium=introducing-multi-search)

<Callout>
  [Federated search](/blog/what-is-federated-search/#federated-search&utm_source=blog&utm_medium=introducing-multi-search) is available in [Meilisearch 1.10](/blog/release-1-10/?https://blog.meilisearch.org.cn/what-is-federated-search/#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#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#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/#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#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#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/#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.ac.cn/), 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#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://elastic.ac.cn/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://elastic.ac.cn/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://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#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#comparisons&utm_source=blog&utm_medium=vs-elasticsearch), [faceted search](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#comparisons&utm_source=blog&utm_medium=vs-elasticsearch&utm_content=faceted-search#features), [geo search](https://docs.meilisearch.com/learn/advanced/geosearch.html#release-post&utm_id=v1-announcement&utm_content=geosearch), and [multi-tenancy](https://meilisearch.org.cn/docs/learn/security/tenant_tokens#comparisons&utm_source=blog&utm_medium=vs-elasticsearch). Its [first major version was released](/blog/v1-enterprise-ready-stable/#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://meilisearch.org.cn/pricing#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#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#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/#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://meilisearch.org.cn/docs/learn/getting_started/cloud_quick_start#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!

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