Today we are releasing the first release candidate (RC) of Meilisearch v0.21. This is the initial step towards the next version of Meilisearch, and a pretty important one given that it will be our first major release since March.

A big part of v0.21 will be relatively invisible to most end-users: our core team has been hard at work refactoring Meilisearch’s engine, something that is as time-consuming as it is crucial for the long-term health of our codebase. In this post, I want to go over why we decided to refactor Meilisearch, how that impacted our product, and where we’ll go from here.

We won’t be discussing everything that’s changed between v0.20 and v0.21—that’s for another blog post—but we will touch on several of the new features we are most excited about.

Why was refactoring needed?
===========================

We first realized that something was wrong when it became increasingly difficult for our core team to add new features without impacting performance. We could see our code was becoming more difficult to maintain, which is something particularly concerning for an open-source project: our community is our greatest ally and keeping code clean, readable, and pleasant-to-use is crucial if we want to keep our allies happy. We were clearly reaching the limits of what the previous codebase could offer us in terms of performance and ease-of-use, so we decided it was time for a change.

How did we do?
==============

We have a lot of good news—but, as with any major software release, there were some goals we weren’t able to attain.

### Search relevancy

The v0.21.0 will provide new ways to make your search more relevant:

*   You can now use numeric attributes as facets—think things like the price of a product or the release date of a book
*   This was actually requested by our users: the equivalent of Algolia’s `words` criterion [is now available](https://www.meilisearch.com/docs/learn/core_concepts/relevancy#1-words) in Meilisearch!
*   Phrase search: users can now force the engine to only return exact matches by enclosing query terms in double quotes (`"`)
*   Highlighting is now available for array and object values

### Search performance

Searching through huge datasets with Meilisearch has never been so fast! With a 1GB dataset containing around 115 million documents:

*   Queries that return many results are more than ten times faster: searching for a single letter takes less than 50ms in v0.21, compared with 2 seconds in v0.20
*   Similar speed gains when filtering on numbers: most searches take less than 50ms in v0.21

### Indexation

TLDR; indexation is now faster in most cases, but there are exceptions. Lots of room for improvement!

First, the good bits: indexation is globally faster with the refactored search engine.

Unfortunately, we noticed a loss of performance when parsing datasets with lengthy description values (think a list of blog posts or scientific articles with excerpts and abstracts) in v0.21. This is not a complete surprise: the new search engine requires more calculations, which means indexation might take more time. This is not ideal, but we would rather release something that’s not completely optimized than delay v0.21 and all the new features it will introduce. Rest assured, we are doing our best to improve indexation performance.

Another bit of bad news: while we have made significant steps in the right direction and reduced memory consumption across the board, the issue of Meilisearch sometimes crashing due to excessive memory usage has still not been fixed. This is another priority for us going forward.

### **Are there any breaking changes**?

There are indeed! We did our best to avoid breaking changes, but that’s not always possible when we want to keep improving things and making our API easier to use. The two biggest changes related to v.021’s refactoring are:

*   The parameters `filters` and `facetFilter` have been replaced by `filter`. Having two parameters doing almost the same thing added unnecessary complexity when filtering query results
*   Several naming changes that we believe make using Meilisearch more intuitive. None of these have any impact on the end-user experience. A full list will be available when we officially release v0.21

What’s next?
============

Once v0.21 is officially released some time within the next three months, we will return to our monthly release cycle. We are particularly looking forward to working on `sortBy` and geographical search—two features that are highly requested by our users. We hear you loud and clear!

If you are curious about Meilisearch’s upcoming version, feel free to test it and give us as much feedback as possible—we depend on you to catch as many remaining bugs as possible. You can [download the release candidate on GitHub](https://github.com/meilisearch/MeiliSearch/releases/tag/v0.21.0rc0).

Also, a word of advice: we do not recommend using this RC in production. We are confident v0.21 RC1 is looking pretty good, but there are still issues that we need to work out before it is ready for the spotlight.

Thank you again, and see you soon for the official v0.21.0 release!

Refactoring Meilisearch: v0.21 has entered the chat

Happy New Year, Everyone! 2021 brings [a new version](https://github.com/meilisearch/MeiliSearch/releases/tag/v0.18.0?ref=blog.meilisearch.com) of Meilisearch. How good is that?

In this release, we have fixed some bugs and raised the default payload size when you add documents to Meilisearch. The star of the show, however, is the **new tokenizer**.

Our main goal is to offer the **best search experience**, and a good tokenizer is critical to having quality search results. We are very proud of our core team and our contributors—they worked hard, and they delivered! Let’s take a closer look at it.

## New Tokenizer
The **tokenizer** is sort of like the brain of Meilisearch: it understands how languages work and adapts the way documents are stored in Meilisearch accordingly. Without a tokenizer, we wouldn't be able to know where a word starts and where it ends. We wouldn’t be able to understand what a user asks for when performing a search.

### A Closer Look
The tokenizer’s role is to **find and retrieve all the words in a string based on the language’s particularities**. Every language requires a unique process. For example:

- In **Latin-based languages**, words are generally separated by spaces.
- In **Chinese**, splitting words is more complicated.

Meilisearch’s new tokenizer goes **field by field**, determining the most likely language for the field and running a different pipeline for each language. Since the tokenizer is **modular**, adding new languages is much easier than before.

The new tokenizer might not feel different for **Latin-based alphabets** such as English, but it should dramatically improve the experience for our **Chinese users**.

## The Evolution of Meilisearch in Chinese

Before, the tokenizer considered **one Hanzi character (Chinese character) as one word**. The new tokenizer:

- Identifies **Chinese words** made of one or more characters.
- Assesses the **distance between matched query terms** more accurately (see: [proximity rule](https://docs.meilisearch.com/learn/core_concepts/relevancy.html#proximity-rule)).
- **Positively improves search relevancy**.
- A **single search query** now gives results in both **traditional and simplified Chinese**.

## Increased Max Payload Size

In previous versions of Meilisearch, the **default limit** of a document payload was **10MB**. We have increased this limit to **100MB**. Of course, you still have the possibility to change this limit.

```bash
$ ./meilisearch --http-payload-size-limit=209715200
# Change limit to 200MB
```


## Bug Fixes

- Meilisearch now **correctly adds attributes** to `searchableAttributes`, even when they have been added to `attributesForFaceting` first.
- Attributes are now added to `searchableAttributes` **in order of appearance**, as intended.
- When no attribute is set to be displayed, Meilisearch **returns an empty object **`` instead of an error.


## Other Changes

- We’ve made some modifications to our **testing suite**.
- **Dump tests can now run in parallel**.
- Most of the **dependencies have been updated**.

For a **specific list of changes and commits**, please check the [release notes](https://github.com/meilisearch/meilisearch/releases).


## Contributions

We are fortunate to have this **amazingly supportive community**.

Special thanks to:

- [**@piaoger**](https://github.com/piaoger) for beta-testing the new tokenizer.
- [**@woshilapin**](https://github.com/woshilapin) for improving the CI.
- [**@sanders41**](https://github.com/sanders41) for increasing the default payload size.

We would also like to thank **all the contributors** who help maintain the toolkits that make Meilisearch easier to use.

Your involvement **means a lot to us**! 💜

---

## 💬 Get Involved!

We are always eager to hear our users’ and contributors’ **suggestions**! Come and talk with us using the method you prefer:

- [GitHub Discussions](https://github.com/meilisearch/meilisearch/discussions)
- [Discord](https://discord.meilisearch.com)
- [Twitter](https://twitter.com/meilisearch)

🚀 Happy coding!


What's new in v0.18.0

Announcing Meilisearch v0.13.0  

=================================

Meilisearch v0.13.0 introduces a bunch of new features and changes. Bugs have  
been fixed and some behavior have changed for more consistency, here what's new in meilisearch:

Placeholder search
------------------

  
We've been asked for a placeholder search feature a lot, and it's landed in  
v0.13.0. It allows you to perform a search without specifying query strings,  
and get "placeholder" search results. The results are sorted according to the  
ranking rules you specified, and it supports faceting and filtering.

Post Route search
-----------------

  
A new route has been added to allow you to perform search with a `POST` route instead of `GET`. This will improve performance for searches that require token  
authentication, by allowing [preflight request](https://developer.mozilla.org/en-US/docs/Glossary/Preflight_request) caching.  
  
This is now the recommended way to perform searches with Meilisearch.

New settings behavior
---------------------

  
We have made a couple of changes to the way settings work in order to make them easier to use and intuitive. `searchableAttributes` and `displayedAttributes` now default to all attributes (`*` ). This means that unless you specify otherwise, all attributes will be searchable by default. This removes the need for the `acceptNewField`, that no longer exists.

Sys-info route removal
----------------------

  
We decided not to support the sys-info route anymore, and remove it. This was motivated by the fact that there already are better tools to do this job, and it was not worth the time maintaining this part of the code.

Check that Meilisearch is launched with the correct database version
--------------------------------------------------------------------

  
We added a check to ensure that Meilisearch is launched with a database that's compatible with the engine version. Meilisearch will refuse to start unless versions match.

Bug fixes
---------

*   `facetDistribution` returning the values in lowercase, instead of the correct case.
*   Bug that caused Meilisearch to add documents with the same document id twice, if they were added in the same batch.

Special thanks
--------------

  
We'd like to give a special thank to our external contributors on this release.  
Their involvement means a lot to us!

*   [@balajisivaraman](https://github.com/balajisivaraman) for his help on making our tests faster and cleaner
*   [@Rio](https://github.com/Rio) for his work on authentication and analytics
*   [@aeriksson](https://github.com/aeriksson) for finding and fixing typos in our code

If you feel like contributing, and don't know where to start, ping us on [slack](https://slack.meilisearch.com/).

What's new in 0.13.0

The v0.11 version of Meilisearch introduces a highly anticipated feature: **faceted search**. Every modern search experience includes faceted search. We were eager to offer this experience to our users, and we are excited to have finally achieved this critical step.

Faceted search
--------------

If you're not familiar: Faceted search lets you search by including or excluding some attributes from categories called facets. You have probably come across faceted search during your online consumerist life. It is often presented as a sidebar with checkboxes. Giving me, as a woman, the possibility to show only 8.5 size shoes and see my possible choices decrease to oblivion.

![](https://unable-actionable-car.media.strapiapp.com/Screenshot_2020_06_03_at_15_00_21_89a6e9099b.png)

In this case the facet is `size`, the attribute is `UK 8 ½`, and the number of products found `2`

Faceted search is not easy to understand. It is often confused with Filters. However, filtering is used to filter the returned results by adding constraints. **Facets**, on the other hand, are used to categorize the data into subsets that will be searched upon: **they reduce the number of documents to process**.

We didn’t see any reason to reinvent the wheel; if you’re familiar with how Algolia does facets, you’ll see we’ve gone with a very similar API. We improved a bit on the usage to reduce furthermore all possible friction in the hope that it is accessible for all users.

To set up faceted search, only two steps are required:

**Add facets in the settings**. For an attribute to be used as a facet it must be added in the `attributesForFaceting` list.

```bash
$ curl \
  -X POST 'http://localhost:7700/indexes/movies/settings' \
  --data '{
      "attributesForFaceting": [
          "director",
          "genres"
      ]
  }'
```

**Declare** director and genres as **facets**.

**Use facets during search**. By using the query parameter `facetFilters` it is possible to add the chosen facets attributes to narrow down the results:

```bash
$ curl --get 'http://localhost:7700/indexes/movies/search' \
    --data-urlencode 'q=Get' \
    --data-urlencode 'facetFilters=["director:Jordan Peele"]'
```

Search for movies by a specific director

This is all explained in our [guide on Faceting](https://docs.meilisearch.com/reference/features/faceted_search.html#faceted-search).

SSL support
-----------

All the configuration options to enable SSL are now available.

[Learn more about it in our configuration guide.](https://docs.meilisearch.com/reference/features/configuration.html#ssl-authentication-path)

Max payload size
----------------

In the early versions of Meilisearch, there was no limit to the size of the body Meilisearch accepted. This version introduces the standard default limit of a **maximum payload of 10 Megabytes** and the possibility of changing the limit.

```bash
$ ./meilisearch --http-payload-size-limit=107374182400
```

Change the upload limit to 100 Megabytes

Improved analytics
------------------

To further understand how our users use Meilisearch and how to improve it, more analytics are added.

[A complete list can be found here.](https://docs.meilisearch.com/reference/features/configuration.html#analytics)

Users can deactivate analytics by using the following command line option:

```json
$ ./meilisearch --no-analytics
```

Conclusion
----------

Those are the main changes in this version. For a more thorough list, please visit the [changelogs of this version](https://github.com/meilisearch/MeiliSearch/blob/master/CHANGELOG.md#v011).

We are eager to hear some feedback. Feel free to come and [talk to us any way you prefer](https://docs.meilisearch.com/learn/what_is_meilisearch/contact.html). We are thrilled by all the support given to our search engine and the never-ending growth in users and stars.

Thanks a lot,

The Meilisearch team and the girl with big feet. 🦶

####

Meilisearch latest news and company updates

Refactoring Meilisearch: v0.21 has entered the chat

What's new in v0.18.0

What's new in 0.13.0

Meilisearch v0.11: What’s new?