Sample Dataset

In the previous steps you used only a few movies, let’s now import:

• More movies to discover more queries.
• Theaters to discover the geospatial capabilities.
• Users to do some aggregations.

Dataset Description

Movies

The file sample-app/redisearch-docker/dataset/import_movies.redis is a script that creates 922 Hashes.

The movie hashes contain the following fields.

• movie:id : The unique ID of the movie, internal to this database (used as the key of the hash)
• title : The title of the movie.
• plot : A summary of the movie.
• genre : The genre of the movie, for now a movie will only have a single genre.
• release_year : The year the movie was released as a numerical value.
• rating : A numeric value representing the public’s rating for this movie.
• votes : Number of votes.
• poster : Link to the movie poster.
• imdb_id : id of the movie in the IMDB database.

Theaters

The file sample-app/redisearch-docker/dataset/import_theaters.redis is a script that creates 117 Hashes (used for Geospatial queries). This dataset is a list of New York Theaters, and not movie theaters, but it is not that critical for this project ;).

The theater hashes contain the following fields.

• theater:id : The unique ID of the theater, internal to this database (used as the key of the hash)
• name : The name of the theater
• address : The street address
• city : The city, in this sample dataset all the theaters are in New York
• zip : The zip code
• phone : The phone number
• url : The URL of the theater
• location : Contains the longitude,latitude used to create the Geo-indexed field

Users

The file sample-app/redisearch-docker/dataset/import_users.redis is a script that creates 5996 Hashes.

The user hashes contain the following fields.

• user:id : The unique ID of the user.
• first_name : The first name of the user.
• last_name : The last name of the user.
• email : The email of the user.
• gender : The gender of the user (female/male).
• country : The country name of the user.
• country_code : The country code of the user.
• city : The city of the user.
• longitude : The longitude of the user.
• latitude : The latitude of the user.
• last_login : The last login time for the user, as EPOC time.
• ip_address : The IP address of the user.

Importing the Movies, Theaters and Users

Before importing the data, flush the database:

> FLUSHALL

The easiest way to import the file is to use the redis-cli, using the following terminal command:

$ redis-cli -h localhost -p 6379 < ./sample-app/redisearch-docker/dataset/import_movies.redis
$ redis-cli -h localhost -p 6379 < ./sample-app/redisearch-docker/dataset/import_theaters.redis
$ redis-cli -h localhost -p 6379 < ./sample-app/redisearch-docker/dataset/import_users.redis

Using Redis Insight or the redis-cli you can look at the dataset:

> HMGET "movie:343" title release_year genre
1) "Spider-Man"
2) "2002"
3) "Action"
>  HMGET "theater:20" name location
1) "Broadway Theatre"
2) "-73.98335054631019,40.763270202723625"
> HMGET "user:343" first_name last_name last_login
1) "Umeko"
2) "Castagno"
3) "1574769122"

You can also use the DBSIZE command to see how many keys you have in your database.

Create Indexes

Create the idx:movie index:

> 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
"OK"

The movies have now been indexed, you can run the FT.INFO "idx:movie" command and look at the num_docs returned value. (should be 922).

Create the idx:theater index:

This index will mostly be used to show the geospatial capabilties of RediSearch.

In the previous examples we have created indexes with 3 types:

• Text
• Numeric
• Tag

You will now discover a new type of field: Geo.

The theater hashes contains a field location with the longitude and latitude, that will be used in the index as follows:

> FT.CREATE idx:theater ON hash PREFIX 1 "theater:" SCHEMA name TEXT SORTABLE location GEO
"OK"

The theaters have been indexed, you can run the FT.INFO "idx:theater" command and look at the num_docs returned value. (should be 117).

Create the idx:user index:

> FT.CREATE idx:user ON hash PREFIX 1 "user:" SCHEMA gender TAG country TAG SORTABLE last_login NUMERIC SORTABLE location GEO
"OK"

Next: Querying the movie database