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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.
Sample Data: movie:343
Field Value
title Spider-Man
plot When bitten by a genetically modified spider a nerdy shy and awkward high school student gains spider-like abilities that he eventually must use to fight evil as a superhero after tragedy befalls his family.
genre Action
release_year 2002
rating 7.3
votes 662219
poster https://m.media-amazon.com/images/M/MV5BZDEyN2NhMjgtMjdhNi00MmNlLWE5YTgtZGE4MzNjMTRlMGEwXkEyXkFqcGdeQXVyNDUyOTg3Njg@._V1_SX300.jpg
imdb_id tt0145487

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
Sample Data: theater:20
Field Value
name Broadway Theatre
address 1681 Broadway
city New York
zip 10019
phone 212 944-3700
url http://www.shubertorganization.com/theatres/broadway.asp
location -73.98335054631019,40.763270202723625

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.
Sample Data: user:3233
Field Value
first_name Rosetta
last_name Olyff
email [email protected]
gender female
country China
country_code CN
city Huangdao
longitude 120.04619
latitude 35.872664
last_login 1570386621
ip_address 218.47.90.79

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