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add user dataset
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‎docs/003-create-index.md‎

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@@ -85,7 +85,7 @@ This is where RediSearch module is helping, and why it as been created.
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RediSearch simplifies a lot this by offering a simple and automatic way to create secondary indices on Redis Hashes. (more datastructure will eventually come)
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![Secondary Index](https://raw.githubusercontent.com/RediSearch/redisearch-getting-started/blob/master/docs/images/secondary-index.png)
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![Secondary Index](https://github.com/RediSearch/redisearch-getting-started/blob/master/docs/images/secondary-index.png?raw=true)
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Using RediSearch if you want to query on a field, you must index the fields. Let's start by indexing the following fields in of our movies:
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‎docs/006-import-dataset.md‎

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# Sample Dataset
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In the previous steps you used only few movies, let's now import more movies and some theaters (to discover the geospatial capabilities)
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In the previous steps you used only few movies, let's now import:
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* More movies *to discover more queries*.
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* Theaters *to discover the geospatial capabilities*.
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* Users *to do some aggregations*.
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## Dataset Description
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</table>
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</details>
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## Importing the Movies & Theaters
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**Users**
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The file `sample-app/redisearch-docker/dataset/import_users.redis` is a script that create 5996 Hashes.
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The user hashes contain the following fields.
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* **`user:id`** : The unique ID of the user.
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* **`first_name`** : The first name of the user.
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* **`last_name`** : The last name of the user.
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* **`email`** : The email the user.
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* **`gender`** : The gender the user (`female`/`male`).
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* **`country`** : The country name the user.
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* **`country_code`** : The country code the user.
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* **`city`** : The city the user.
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* **`longitude`** : The longitude the user.
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* **`latitude`** : The latitude the user.
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* **`last_login`** : The last login the user, as EPOC time.
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* **`ip_address`** : The IP address of the user.
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<details>
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<summary>Sample Data: <b>user:3233</b></summary>
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<table>
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<thead>
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<tr>
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<th>Field</th>
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<th>Value</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>first_name</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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Rosetta
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</td>
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</tr>
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<tr>
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<th>last_name</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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Olyff
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</td>
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</tr>
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<tr>
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<th>email</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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[email protected]
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</td>
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</tr>
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<tr>
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<th>gender</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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female
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</td>
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</tr>
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<tr>
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<th>country</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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China
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</td>
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</tr>
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<tr>
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<th>country_code</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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CN
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</td>
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</tr>
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<tr>
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<th>city</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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Huangdao
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</td>
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</tr>
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<tr>
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<th>longitude</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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120.04619
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</td>
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</tr>
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<tr>
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<th>latitude</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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35.872664
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</td>
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</tr>
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<tr>
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<th>last_login</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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1570386621
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</td>
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</tr>
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<tr>
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<th>ip_address</th>
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<td style='font-family:monospace; font-size: 0.875em; "'>
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218.47.90.79
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</td>
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</tr>
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<tbody>
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</table>
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</details>
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---
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## Importing the Movies, Theaters and Users
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Before importing the data, flush the database:
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$ redis-cli -h localhost -p 6379 < ./sample-app/redisearch-docker/dataset/import_theaters.redis
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$ redis-cli -h localhost -p 6379 < ./sample-app/redisearch-docker/dataset/import_users.redis
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```
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> HMGET "theater:20" name location
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1) "Broadway Theatre"
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2) "-73.98335054631019,40.763270202723625"
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> HMGET "user:343" first_name last_name last_login
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1) "Umeko"
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2) "Castagno"
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3) "1574769122"
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```
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You can also use the `DBSIZE` command to see how many keys you have in your database.
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The script create 922 movies and 117 theathers, and not the RediSearch index, this will be done in the next step.
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Next: [Querying the movie database](007-query-movies.md)

‎docs/007-query-movies.md‎

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**Create the `idx:movie` index:**
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```
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> FT.CREATE idx:movie ON hash PREFIX 1 "movie:" SCHEMA title TEXT SORTABLE plot TEXT WEIGHT 0.5 release_year NUMERIC SORTABLE rating NUMERIC SORTABLE genre TAG SORTABLE
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> FT.CREATE idx:movie ON hash PREFIX 1 "movie:" SCHEMA title TEXT SORTABLE plot TEXT WEIGHT 0.5 release_year NUMERIC SORTABLE rating NUMERIC SORTABLE votes NUMERIC SORTABLE genre TAG SORTABLE
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"OK"
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```

‎docs/008-aggregation.md‎

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</details>
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<details>
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<summary>
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<i><b>
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Number of movies by genre, with the total number of votes, and average rating
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</b></i>
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</summary>
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```
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> FT.AGGREGATE idx:movie "*" GROUPBY 1 @genre REDUCE COUNT 0 AS nb_of_movies REDUCE SUM 1 votes AS nb_of_votes REDUCE AVG 1 rating AS avg_rating SORTBY 4 @avg_rating DESC @nb_of_votes DESC
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1) (integer) 26
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2) 1) "genre"
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2) "fantasy"
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3) "nb_of_movies"
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4) "1"
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5) "nb_of_votes"
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6) "1500090"
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7) "avg_rating"
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8) "8.8"
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...
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11) 1) "genre"
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2) "romance"
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3) "nb_of_movies"
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4) "2"
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5) "nb_of_votes"
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6) "746"
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7) "avg_rating"
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8) "6.65"
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```
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---
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</details>
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Next: [Advanced Options](009-advanced-features.md)

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