Full-Text: Exploring the Netflix Catalog

Georg Traar

September 29, 2023

4 min read

In this tutorial, we will explore how to manage a dataset of Netflix titles, making use of CrateDB Cloud’s full-text search capabilities. Each entry in our imaginary dataset will have the following attributes:

show_id:

A unique identifier for each show or movie.

type:

Specifies whether the title is a movie, TV show, or another format.

title:

The title of the movie or show.

director:

The name of the director.

cast:

An array listing the cast members.

country:

The country where the title was produced.

date_added:

A timestamp indicating when the title was added to the catalog.

release_year:

The year the title was released.

rating:

The content rating (e.g., PG, R, etc.).

duration:

The duration of the title in minutes or seasons.

listed_in:

An array containing genres that the title falls under.

description:

A textual description of the title, indexed using full-text search.

To begin, let’s create the schema for this dataset.

1

Create the table

The fastest and easiest way to get started with CrateDB is by deploying a free tier (CRFREE) cluster on CrateDB Cloud. Otherwise, see the Installation section to run CrateDB yourself.

CrateDB uses SQL, the most popular query language for database management. To store the data, create a table with columns tailored to the dataset using the CREATE TABLE command.

Importantly, you will also take advantage of CrateDB’s full-text search capabilities by setting up a full-text index on the description column. This will enable you to perform complex textual queries later on.

CREATE TABLE "netflix_catalog" (
   "show_id" TEXT PRIMARY KEY,
   "type" TEXT,
   "title" TEXT,
   "director" TEXT,
   "cast" ARRAY(TEXT),
   "country" TEXT,
   "date_added" TIMESTAMP,
   "release_year" TEXT,
   "rating" TEXT,
   "duration" TEXT,
   "listed_in"  ARRAY(TEXT),
   "description" TEXT INDEX using fulltext
);

Run the above SQL command in CrateDB to set up your table. With the table ready, you’re now set to insert the dataset.

2

Insert data

Now, insert data into the table you just created, by using the COPY FROM SQL statement.

COPY netflix_catalog
FROM 'https://cdn.crate.io/downloads/datasets/cratedb-datasets/cloud-tutorials/data_netflix.json.gz'
WITH (format = 'json', compression='gzip');

Run the above SQL command in CrateDB to import the dataset. After this command finishes, you are now ready to start querying the dataset.

3

Use full-text search

Start with a basic SELECT statement on all columns, and limit the output to display only 10 records, in order to quickly explore a few samples worth of data.

SELECT *
FROM netflix_catalog
LIMIT 10;

CrateDB Cloud’s full-text search can be leveraged to find specific entries based on text matching. In this query, you are using the MATCH function on the description field to find all movies or TV shows that contain the word “love”. The results can be sorted by relevance score by using the synthetic _score column.

SELECT title, description
FROM netflix_catalog
WHERE MATCH(description, 'love')
ORDER BY _score DESC
LIMIT 10;

While full-text search is incredibly powerful, you can still perform more traditional types of queries. For example, to find all titles directed by “Kirsten Johnson”, and sort them by release year, you can use:

SELECT title, release_year
FROM netflix_catalog
WHERE director = 'Kirsten Johnson'
ORDER BY release_year DESC;

This query uses the conventional WHERE clause to find movies directed by Kirsten Johnson, and the ORDER BY clause to sort them by their release year in descending order.

Through these examples, you can see that CrateDB Cloud offers you a wide array of querying possibilities, from basic SQL queries to advanced full-text searches, making it a versatile choice for managing and querying your datasets.