Reorder README parts

README.md

@@ -7,8 +7,12 @@
 ⚡ Ultra relevant and instant full-text search API 🔍
 
 MeiliSearch is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.
+For more [details about those features, go to our documentation](https://docs.meilisearch.com/).
 
-## What MeiliSearch has to offer
+[![crates.io demo gif](misc/crates-io-demo.gif)](https://crates.meilisearch.com)
+> Meili helps the Rust community find crates on [crates.meilisearch.com](https://crates.meilisearch.com)
+
+## Features
 * Search as-you-type experience (answers < 50ms)
 * Full-text search
 * Typo tolerant (understands typos and spelling mistakes)
@@ -19,48 +23,32 @@ MeiliSearch is a powerful, fast, open-source, easy to use, and deploy search eng
 * Highly customizable
 * RESTfull API
 
-For more [details about those features, go to our documentation](https://docs.meilisearch.com/introduction/features.html).
-
-[![crates.io demo gif](misc/crates-io-demo.gif)](https://crates.meilisearch.com)
-
-> Meili helps the Rust community find crates on [crates.meilisearch.com](https://crates.meilisearch.com)
-
-### In-depth features
-
-- Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/criterion/mod.rs#L106-L111) used to [bucket sort](https://en.wikipedia.org/wiki/Bucket_sort) documents
-- Accepts [custom criteria](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/criterion/mod.rs#L20-L29) and can apply them in any custom order
-- Support [ranged queries](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L342), useful for paginating results
-- Can [distinct](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L324-L329) and [filter](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L313-L318) returned documents based on context defined rules
-- Searches for [concatenated](https://github.com/meilisearch/MeiliSearch/pull/164) and [splitted query words](https://github.com/meilisearch/MeiliSearch/pull/232) to improve the search quality.
-- Can store complete documents or only [user schema specified fields](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/datasets/movies/schema.toml)
-- The [default tokenizer](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-tokenizer/src/lib.rs) can index latin and kanji based languages
-- Returns [the matching text areas](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-types/src/lib.rs#L49-L65), useful to highlight matched words in results
-- Accepts query time search config like the [searchable attributes](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L331-L336)
-- Supports [runtime incremental indexing](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/store/mod.rs#L143-L212)
-
-
 ## Quick Start
 
-You can deploy your instant, relevant, and typo-tolerant MeiliSearch search engine by yourself too.
-Something similar to the demo above can be achieved by following these little three steps first.
-You still need to create your front-end to make it pretty, though.
-
 ### Deploy the Server
 
-If you have not yet installed Rust and its package manager `cargo`, go to [the installation page](https://www.rust-lang.org/tools/install).<br/>
-You can deploy the server on your machine; it listens to HTTP requests on the 7700 port by default.
-
 ```bash
+# If you have the Rust toolchain already installed, you can compile from the source
+git clone https://github.com/meilisearch/MeiliSearch.git
+cd MeiliSearch
 cargo run --release
-```
 
-For more logs during the execution, run:
+# You can also use Docker
-```bash
+docker run -it -p 7700:7700 --rm getmeili/MeiliSearch
-RUST_LOG=info cargo run --release
+
+# You can also download the binary
+curl -L https://install.meilisearch.com | sh
+./meilisearch
 ```
 
 ### Create an Index and Upload Some Documents
 
+We provide a movie dataset that you can use for testing purposes.
+
+```bash
+curl -L 'https://bit.ly/33MKvk4' -o movies.json
+```
+
 MeiliSearch can serve multiple indexes, with different kinds of documents,
 therefore, it is required to create the index before sending documents to it.
 
@@ -74,7 +62,7 @@ We provided you a small dataset that is available in the `datasets/` directory.
 ```bash
 curl -i -X POST 'http://127.0.0.1:7700/indexes/movies/documents' \
   --header 'content-type: application/json' \
-  --data @datasets/movies/movies.json
+  --data-binary @movies.json
 ```
 
 ### Search for Documents
@@ -83,34 +71,57 @@ The search engine is now aware of our documents and can serve those via our HTTP
 The [`jq` command-line tool](https://stedolan.github.io/jq/) can significantly help you read the server responses.
 
 ```bash
-curl 'http://127.0.0.1:7700/indexes/movies/search?q=botman'
+curl 'http://127.0.0.1:7700/indexes/movies/search?q=botman+robin&limit=2' | jq
 ```
 
 ```json
 {
   "hits": [
     {
-      "id": "29751",
+      "id": "415",
-      "title": "Batman Unmasked: The Psychology of the Dark Knight",
+      "title": "Batman & Robin",
-      "poster": "https://image.tmdb.org/t/p/w1280/jjHu128XLARc2k4cJrblAvZe0HE.jpg",
+      "poster": "https://image.tmdb.org/t/p/w1280/79AYCcxw3kSKbhGpx1LiqaCAbwo.jpg",
-      "overview": "Delve into the world of Batman and the vigilante justice tha",
+      "overview": "Along with crime-fighting partner Robin and new recruit Batgirl...",
-      "release_date": "2008-07-15"
+      "release_date": "1997-06-20",
     },
     {
-      "id": "471474",
+      "id": "411736",
-      "title": "Batman: Gotham by Gaslight",
+      "title": "Batman: Return of the Caped Crusaders",
-      "poster": "https://image.tmdb.org/t/p/w1280/7souLi5zqQCnpZVghaXv0Wowi0y.jpg",
+      "poster": "https://image.tmdb.org/t/p/w1280/GW3IyMW5Xgl0cgCN8wu96IlNpD.jpg",
-      "overview": "ve Victorian Age Gotham City, Batman begins his war on crime",
+      "overview": "Adam West and Burt Ward returns to their iconic roles of Batman and Robin...",
-      "release_date": "2018-01-12"
+      "release_date": "2016-10-08",
     }
   ],
   "offset": 0,
   "limit": 2,
   "processingTimeMs": 1,
-  "query": "botman"
+  "query": "botman robin"
 }
 ```
 
+### Documentation
+
+Now, that you have a running MeiliSearch, you can learn more and tune your search engine using [the documentation](https://docs.meilisearch.com).
+
+## How it works
+
+MeiliSearch uses [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database) as the internal key-value store. The key-value store allows us to handle updates and queries with small memory and CPU overheads. The whole ranking system is [data oriented](https://github.com/meilisearch/MeiliSearch/issues/82) and provides great performances.
+
+You can [read the deep dive](deep-dive.md) if you want more information on the engine; it describes the whole process of generating updates and handling queries. Also, you can take a look at the [typos and ranking rules](typos-ranking-rules.md) if you want to know the default rules used to sort the documents.
+
+### Technical features
+
+- Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/criterion/mod.rs#L106-L111) used to [bucket sort](https://en.wikipedia.org/wiki/Bucket_sort) documents
+- Accepts [custom criteria](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/criterion/mod.rs#L20-L29) and can apply them in any custom order
+- Support [ranged queries](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L342), useful for paginating results
+- Can [distinct](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L324-L329) and [filter](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L313-L318) returned documents based on context defined rules
+- Searches for [concatenated](https://github.com/meilisearch/MeiliSearch/pull/164) and [splitted query words](https://github.com/meilisearch/MeiliSearch/pull/232) to improve the search quality.
+- Can store complete documents or only [user schema specified fields](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/datasets/movies/schema.toml)
+- The [default tokenizer](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-tokenizer/src/lib.rs) can index latin and kanji based languages
+- Returns [the matching text areas](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-types/src/lib.rs#L49-L65), useful to highlight matched words in results
+- Accepts query time search config like the [searchable attributes](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L331-L336)
+- Supports [runtime incremental indexing](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/store/mod.rs#L143-L212)
+
 ## Performances
 
 With a dataset composed of _100 353_ documents with _352_ attributes each and _3_ of them indexed.
@@ -137,12 +148,6 @@ The resulting database was _16 GB_ and search results were between _30 ms_ and _
 With Rust 1.32 the allocator has been [changed to use the system allocator](https://blog.rust-lang.org/2019/01/17/Rust-1.32.0.html#jemalloc-is-removed-by-default).
 We have seen much better performances when [using jemalloc as the global allocator](https://github.com/alexcrichton/jemallocator#documentation).
 
-## How it works
-
-MeiliSearch uses [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database) as the internal key-value store. The key-value store allows us to handle updates and queries with small memory and CPU overheads. The whole ranking system is [data oriented](https://github.com/meilisearch/MeiliSearch/issues/82) and provides great performances.
-
-You can [read the deep dive](deep-dive.md) if you want more information on the engine; it describes the whole process of generating updates and handling queries. Also, you can take a look at the [typos and ranking rules](typos-ranking-rules.md) if you want to know the default rules used to sort the documents.
-
 ## Contributing
 
 We will be glad if you submit issues and pull requests. You can help to grow this project and start contributing by checking [issues tagged "good-first-issue"](https://github.com/meilisearch/MeiliSearch/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). It is a good start!