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How to join two tables in ksqlDB

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How to join two tables in ksqlDB

Consider that you have two tables of reference data in Kafka topics, and you want to join them on a common key.

Setup

For this example, let's say you have data about movies in one table, and you want to add additional information like who was the lead actor.

First, here's your table containing movie information:

CREATE TABLE movies (
        title VARCHAR PRIMARY KEY,
        id INT,
        release_year INT
   ) WITH (
       KAFKA_TOPIC='movies',
       PARTITIONS=1,
       VALUE_FORMAT='JSON'
   );

And here's a table containing additional movie data with information on actors:

CREATE TABLE lead_actors (
        title VARCHAR PRIMARY KEY,
        actor_name VARCHAR
    ) WITH (
         KAFKA_TOPIC='lead_actors',
         PARTITIONS=1,
         VALUE_FORMAT='JSON'
    );

For the join between these tables, you create another table containing your desired information:

CREATE TABLE movies_enriched AS
    SELECT m.id, m.title, m.release_year, l.actor_name
    FROM movies m
    INNER JOIN lead_actors l
    ON m.title = l.title;

Running the example

You can run the example backing this tutorial in one of two ways: locally with the ksql CLI against Kafka and ksqlDB running in Docker, or with Confluent Cloud.

Local With Docker

Prerequisites

Run the commands

Clone the confluentinc/tutorials GitHub repository (if you haven't already) and navigate to the tutorials directory:

git clone git@github.com:confluentinc/tutorials.git
cd tutorials

Start ksqlDB and Kafka:

docker compose -f ./docker/docker-compose-ksqldb.yml up -d

Next, open the ksqlDB CLI:

docker exec -it ksqldb-cli ksql http://ksqldb-server:8088

Run the following SQL statements to create the movies and lead_actors tables backed by Kafka running in Docker and populate them with test data.

CREATE TABLE movies (
        title VARCHAR PRIMARY KEY,
        id INT,
        release_year INT
   ) WITH (
       KAFKA_TOPIC='movies',
       PARTITIONS=1,
       VALUE_FORMAT='JSON'
   );
CREATE TABLE lead_actors (
        title VARCHAR PRIMARY KEY,
        actor_name VARCHAR
    ) WITH (
         KAFKA_TOPIC='lead_actors',
         PARTITIONS=1,
         VALUE_FORMAT='JSON'
    );
INSERT INTO lead_actors (TITLE, ACTOR_NAME) VALUES ('Twisters', 'Glen Powell');
INSERT INTO lead_actors (TITLE, ACTOR_NAME) VALUES ('Barbie', 'Ryan Gosling');
INSERT INTO lead_actors (TITLE, ACTOR_NAME) VALUES ('Oppenheimer', 'Cillian Murphy');
INSERT INTO lead_actors (TITLE, ACTOR_NAME) VALUES ('Blink Twice', 'Channing Tatum');

INSERT INTO movies (ID, TITLE, RELEASE_YEAR) VALUES (48, 'Twisters', 2024);
INSERT INTO movies (ID, TITLE, RELEASE_YEAR) VALUES (294, 'Barbie', 2023);
INSERT INTO movies (ID, TITLE, RELEASE_YEAR) VALUES (128, 'Oppenheimer', 2024);
INSERT INTO movies (ID, TITLE, RELEASE_YEAR) VALUES (42, 'Blink Twice', 2024);

Finally, run the table-table join query and land the results in a new movies_enriched table.

CREATE TABLE movies_enriched AS
    SELECT m.id, m.title, m.release_year, l.actor_name
    FROM movies m
    INNER JOIN lead_actors l
    ON m.title = l.title;

Query the new table:

SELECT * FROM movies_enriched;

The query output should look like this:

+-------------------+-------------------+-------------------+-------------------+
|M_TITLE            |ID                 |RELEASE_YEAR       |ACTOR_NAME         |
+-------------------+-------------------+-------------------+-------------------+
|Barbie             |294                |2023               |Ryan Gosling       |
|Blink Twice        |42                 |2024               |Channing Tatum     |
|Oppenheimer        |128                |2024               |Cillian Murphy     |
|Twisters           |48                 |2024               |Glen Powell        |
+-------------------+-------------------+-------------------+-------------------+

When you are finished, exit the ksqlDB CLI by entering CTRL-D and clean up the containers used for this tutorial by running:

docker compose -f ./docker/docker-compose-ksqldb.yml down
Confluent Cloud

Prerequisites

Create Confluent Cloud resources

Login to your Confluent Cloud account:

confluent login --prompt --save

Install a CLI plugin that will streamline the creation of resources in Confluent Cloud:

confluent plugin install confluent-cloud_kickstart

Run the following command to create a Confluent Cloud environment and Kafka cluster. This will create resources in AWS region us-west-2 by default, but you may override these choices by passing the --cloud argument with a value of aws, gcp, or azure, and the --region argument that is one of the cloud provider's supported regions, which you can list by running confluent kafka region list --cloud <CLOUD PROVIDER>

confluent cloud-kickstart --name ksqldb-tutorial \
  --environment-name ksqldb-tutorial \
  --output-format stdout

Now, create a ksqlDB cluster by first getting your user ID of the form u-123456 when you run this command:

confluent iam user list

And then create a ksqlDB cluster called ksqldb-tutorial with access linked to your user account:

confluent ksql cluster create ksqldb-tutorial \
  --credential-identity <USER ID>

Run the commands

Login to the Confluent Cloud Console. Select Environments in the lefthand navigation, and then click the ksqldb-tutorial environment tile. Click the ksqldb-tutorial Kafka cluster tile, and then select ksqlDB in the lefthand navigation.

The cluster may take a few minutes to be provisioned. Once its status is Up, click the cluster name and scroll down to the editor.

Enter the following statements in the editor and click Run query. This creates the movies and lead_actors tables and populates them with test data.

CREATE TABLE movies (
        title VARCHAR PRIMARY KEY,
        id INT,
        release_year INT
   ) WITH (
       KAFKA_TOPIC='movies',
       PARTITIONS=1,
       VALUE_FORMAT='JSON'
   );

CREATE TABLE lead_actors (
        title VARCHAR PRIMARY KEY,
        actor_name VARCHAR
    ) WITH (
         KAFKA_TOPIC='lead_actors',
         PARTITIONS=1,
         VALUE_FORMAT='JSON'
    );

INSERT INTO lead_actors (TITLE, ACTOR_NAME) VALUES ('Twisters', 'Glen Powell');
INSERT INTO lead_actors (TITLE, ACTOR_NAME) VALUES ('Barbie', 'Ryan Gosling');
INSERT INTO lead_actors (TITLE, ACTOR_NAME) VALUES ('Oppenheimer', 'Cillian Murphy');
INSERT INTO lead_actors (TITLE, ACTOR_NAME) VALUES ('Blink Twice', 'Channing Tatum');

INSERT INTO movies (ID, TITLE, RELEASE_YEAR) VALUES (48, 'Twisters', 2024);
INSERT INTO movies (ID, TITLE, RELEASE_YEAR) VALUES (294, 'Barbie', 2023);
INSERT INTO movies (ID, TITLE, RELEASE_YEAR) VALUES (128, 'Oppenheimer', 2024);
INSERT INTO movies (ID, TITLE, RELEASE_YEAR) VALUES (42, 'Blink Twice', 2024);

Now, paste the table-table join query in the editor and click Run query. This will land the results in a new movies_enriched table.

CREATE TABLE movies_enriched AS
    SELECT m.id, m.title, m.release_year, l.actor_name
    FROM movies m
    INNER JOIN lead_actors l
    ON m.title = l.title;

Query the new table:

SELECT * FROM movies_enriched;

The query output should look like this:

+-------------------+-------------------+-------------------+-------------------+
|M_TITLE            |ID                 |RELEASE_YEAR       |ACTOR_NAME         |
+-------------------+-------------------+-------------------+-------------------+
|Barbie             |294                |2023               |Ryan Gosling       |
|Blink Twice        |42                 |2024               |Channing Tatum     |
|Oppenheimer        |128                |2024               |Cillian Murphy     |
|Twisters           |48                 |2024               |Glen Powell        |
+-------------------+-------------------+-------------------+-------------------+

Clean up

When you are finished, delete the ksqldb-tutorial environment by first getting the environment ID of the form env-123456 corresponding to it:

confluent environment list

Delete the environment, including all resources created for this tutorial:

confluent environment delete <ENVIRONMENT ID>
Do you have questions or comments? Join us in the #confluent-developer community Slack channel to engage in discussions with the creators of this content.