Apart from being written in Rust, MeiliSearch (https://github.com/meilisearch/meilisearch) differs mostly on the use of a bucket sort to rank the documents retrieved within the index.
Both MeiliSearch and Typesense use a reverse index with a Levenshtein automaton to handle typos, but when it comes to sorting document:
- Typesense use a default_sorting_field on each document, it means that before indexing your documents you need to compute a relevancy score for typesense to be able to sort them based on your needs (https://typesense.org/docs/0.11.1/guide/#ranking-relevance)
- On the other hand MeiliSearch, uses a bucket sort which means that there is a default relevancy algorithm based on the proximity of words in the documents, the fields in which the words are found and the number of typos (https://docs.meilisearch.com/guides/advanced_guides/ranking....). And you can still add you own custom rules if you want to alter the default search behavior.
> Typesense use a default_sorting_field on each document, it means that before indexing your documents you need to compute a relevancy score for typesense to be able to sort them based on your needs
This is not entirely true. You can just use a field with a constant value (say 100) as the default sorting field and Typesense will just use the text based relevancy. Please update your comment.
The reason why Typesense does insist on one, though is that it's always a good idea to have a field that indicates popularity of a record (or a proxy to it). It makes search so much better.
Lucene was written for public search engine like Google, or DuckDuckGo (which is actually based on Lucene and Solr).
Lucene and Lucene-like projects (Tantivy or Bleve in Golang) are general-purpose search libraries. They can handle enormous datasets, and you can make very complex queries on them (compute the average age of people named Karl in a certain type of document for example).
These libraries are based on tf-idf (term frequency inverse document frequency) algorithm and manage quite poorly typos for example (unless you make the setup to index your documents differently to parse them correctly).
Toshi is like Elastic for Lucene, it provides sharding and JSON over HTTP api.
You can basically used Lucene and its derivatives for basically any search related project, but you may have to dive into how it works and understand concepts like tokenization or ngrams to tune it according to your needs.
On the other hand, MeiliSearch (and I guess Typesense, but I can not talk for them) focus a subset of what you could build with Lucene or Elastic.
It is a fully functionnal Restful API, made for instant search or search-as-you-type. The algorithms behind MeiliSearch are simply different: a inverse index, with a levensthein automaton to handle typos, then a bucket sort you can tune for the ranking of the returned documents.
The aim is to provide a easyer go-to solution to implement for customer-facing search.
You won't be able to make super complex queries on terabytes of data. We just make super fast and ultra relevant search for end-user.
TypeSense and MeiliSearch focus on the same usage, we choose Rust for performance, security and the modern ecosystem that will allow easier maintenance :D
@fulmicoton and @tpayet, I'm thinking then if I want both full text search, and also faceted search, then Tantivy can do that, but at this time, MeiliSearch (and Typesense) don't do that?
@tpayet, for a database like MeiliSearch, is faceted search typically always off-topic? Or you're thinking about adding faceted search, later on?
(My use case is 1) full text search, 2) typo friendly, and with 3) e.g. "begin" matching also "began", "begun", and "run" also matching "running", and 4) in all lanugages, and 5) faceted-search restricted to tags and categories and user groups.)
MeiliSearch does not offer faceted search yet. It is one of the key feature that we are still missing but we plan to work on it in the coming weeks.
For your use case today, I suggest you use Typesense if it fits your needs ( they handle faceted search already ) or Tantivy or Toshi.
To manage different languages, you should make one index per language. MeiliSearch and Tantivy handles kanjis!
We will add faceted search in the coming weeks if you are not in a hurry :D
As of now, my project uses ElasticSearch — it works fine, but it wants lots of RAM which I find slightly annoying, ... and the new Java v 9, 10, 11 etc feels a bit worrying. — So, as of now I'm just staying up-to-date with new Rust based search engines.
The fat tail queries go against PostgreSQL and the long tail queries go against Solr. For shorter queries PostgreSQL takes precedence. Long tail fills in Instance Answers where nothing else catches.
Yes :) Valerian Saliou, the maintainer of Sonic is a friend of us. He built Sonic mainly for his company (crisp.chat) and compared to MeiliSearch there is no relevancy ranking.
Sonic is "just" a inverted index with a levenshtein automaton, it will returns only the documents ids in which there is your requests words and then you will have to retrieve the full documents in your main database and only then can you apply some relevancy ranking by yourself.
