How to join a table and a table

Question:

If you have two tables of reference data in Kafka topics, how can you join the tables on a common key?

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Example use case:

Suppose you have a set of data about movies and want to add further details, such as who the lead actor was. In this tutorial, we'll write a program that joins each movie to another set of data about movies' lead actors.

Hands-on code example:

Run it

Prerequisites

1

This tutorial installs Confluent Platform using Docker. Before proceeding:

  • • Install Docker Desktop (version 4.0.0 or later) or Docker Engine (version 19.03.0 or later) if you don’t already have it

  • • Install the Docker Compose plugin if you don’t already have it. This isn’t necessary if you have Docker Desktop since it includes Docker Compose.

  • • Start Docker if it’s not already running, either by starting Docker Desktop or, if you manage Docker Engine with systemd, via systemctl

  • • Verify that Docker is set up properly by ensuring no errors are output when you run docker info and docker compose version on the command line

Initialize the project

2

To get started, make a new directory anywhere you’d like for this project:

mkdir join-table-and-table && cd join-table-and-table

Then make the following directories to set up its structure:

mkdir src test

Get Confluent Platform

3

Next, create the following docker-compose.yml file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud):

version: '2'
services:
  broker:
    image: confluentinc/cp-kafka:7.4.1
    hostname: broker
    container_name: broker
    ports:
    - 29092:29092
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT,CONTROLLER:PLAINTEXT
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_NODE_ID: 1
      KAFKA_CONTROLLER_QUORUM_VOTERS: 1@broker:29093
      KAFKA_LISTENERS: PLAINTEXT://broker:9092,CONTROLLER://broker:29093,PLAINTEXT_HOST://0.0.0.0:29092
      KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
      KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
      KAFKA_LOG_DIRS: /tmp/kraft-combined-logs
      CLUSTER_ID: MkU3OEVBNTcwNTJENDM2Qk
  schema-registry:
    image: confluentinc/cp-schema-registry:7.3.0
    hostname: schema-registry
    container_name: schema-registry
    depends_on:
    - broker
    ports:
    - 8081:8081
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: broker:9092
  ksqldb-server:
    image: confluentinc/ksqldb-server:0.28.2
    hostname: ksqldb-server
    container_name: ksqldb-server
    depends_on:
    - broker
    - schema-registry
    ports:
    - 8088:8088
    environment:
      KSQL_CONFIG_DIR: /etc/ksqldb
      KSQL_LOG4J_OPTS: -Dlog4j.configuration=file:/etc/ksqldb/log4j.properties
      KSQL_BOOTSTRAP_SERVERS: broker:9092
      KSQL_HOST_NAME: ksqldb-server
      KSQL_LISTENERS: http://0.0.0.0:8088
      KSQL_CACHE_MAX_BYTES_BUFFERING: 0
      KSQL_KSQL_SCHEMA_REGISTRY_URL: http://schema-registry:8081
  ksqldb-cli:
    image: confluentinc/ksqldb-cli:0.28.2
    container_name: ksqldb-cli
    depends_on:
    - broker
    - ksqldb-server
    entrypoint: /bin/sh
    tty: true
    environment:
      KSQL_CONFIG_DIR: /etc/ksqldb
    volumes:
    - ./src:/opt/app/src
    - ./test:/opt/app/test

And launch it by running:

docker compose up -d

Write the program interactively using the CLI

4

To begin developing interactively, open up the ksqlDB CLI:

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

First, you’ll need to create a Kafka topic and table to represent the movie reference data. The following creates both in one shot:

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

Likewise, you’ll need a Kafka topic and a second table to represent the additional movie data about leading actor:

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

Then insert the following movies:

INSERT INTO MOVIES (ID, TITLE, RELEASE_YEAR) VALUES (48, 'Aliens', 1986);
INSERT INTO MOVIES (ID, TITLE, RELEASE_YEAR) VALUES (294, 'Die Hard', 1998);
INSERT INTO MOVIES (ID, TITLE, RELEASE_YEAR) VALUES (128, 'The Big Lebowski', 1998);
INSERT INTO MOVIES (ID, TITLE, RELEASE_YEAR) VALUES (42, 'The Godfather', 1998);

In a similar manner, populate the lead actor information:

INSERT INTO LEAD_ACTOR (TITLE, ACTOR_NAME) VALUES ('Aliens','Sigourney Weaver');
INSERT INTO LEAD_ACTOR (TITLE, ACTOR_NAME) VALUES ('Die Hard','Bruce Willis');
INSERT INTO LEAD_ACTOR (TITLE, ACTOR_NAME) VALUES ('The Big Lebowski','Jeff Bridges');
INSERT INTO LEAD_ACTOR (TITLE, ACTOR_NAME) VALUES ('The Godfather','Al Pacino');

Now that you have events in both tables, let’s join them up to obtain an enriched table of movie information. The first thing to do is set the following properties to ensure that you’re reading from the beginning of the stream:

SET 'auto.offset.reset' = 'earliest';

Let’s enrich the movie data with more information about who the lead actor in the movie is. The following query does a left join between the movie table and the lead actor table. This will block and continue to return results until it’s limit is reached or you tell it to stop.

SELECT M.ID, M.TITLE, M.RELEASE_YEAR, L.ACTOR_NAME
    FROM MOVIES M
    INNER JOIN LEAD_ACTOR L
    ON M.TITLE = L.TITLE
    EMIT CHANGES
    LIMIT 3;

This should yield the following output:

+--------------------+--------------------+--------------------+--------------------+
|ID                  |M_TITLE             |RELEASE_YEAR        |ACTOR_NAME          |
+--------------------+--------------------+--------------------+--------------------+
|48                  |Aliens              |1986                |Sigourney Weaver    |
|294                 |Die Hard            |1998                |Bruce Willis        |
|128                 |The Big Lebowski    |1998                |Jeff Bridges        |
Limit Reached
Query terminated

Since the output looks right, the next step is to make the query continuous. Issue the following to create a new table that is continuously populated by its query:

CREATE TABLE MOVIES_ENRICHED AS
    SELECT M.ID, M.TITLE, M.RELEASE_YEAR, L.ACTOR_NAME
    FROM MOVIES M
    INNER JOIN LEAD_ACTOR L
    ON M.TITLE = L.TITLE
    EMIT CHANGES;

To check that it’s working, print out the contents of the output stream’s underlying topic:

PRINT MOVIES_ENRICHED FROM BEGINNING LIMIT 3;

This should yield the following output:

Key format: KAFKA_STRING
Value format: AVRO
rowtime: 2020/05/04 22:03:47.754 Z, key: Aliens, value: {"ID": 48, "RELEASE_YEAR": 1986, "ACTOR_NAME": "Sigourney Weaver"}, partition: 0
rowtime: 2020/05/04 22:03:47.879 Z, key: Die Hard, value: {"ID": 294, "RELEASE_YEAR": 1998, "ACTOR_NAME": "Bruce Willis"}, partition: 0
rowtime: 2020/05/04 22:03:47.997 Z, key: The Big Lebowski, value: {"ID": 128, "RELEASE_YEAR": 1998, "ACTOR_NAME": "Jeff Bridges"}, partition: 0
Topic printing ceased

Type 'exit' and hit enter to exit the ksqlDB CLI.

Write your statements to a file

5

Now that you have a series of statements that’s doing the right thing, the last step is to put them into a file so that they can be used outside the CLI session. Create a file at src/statements.sql with the following content:

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

CREATE TABLE lead_actor (TITLE VARCHAR PRIMARY KEY, actor_name VARCHAR)
             WITH (KAFKA_TOPIC='lead_actors',
                   PARTITIONS=1,
                   VALUE_FORMAT='avro');

CREATE TABLE MOVIES_ENRICHED AS
  SELECT M.ID, M.TITLE, M.RELEASE_YEAR, L.ACTOR_NAME
  FROM MOVIES M
  INNER JOIN LEAD_ACTOR L
  ON M.TITLE = L.TITLE;

Test it

Create the test data

1

Create a file at test/input.json with the inputs for testing:

{
  "inputs": [
    {
      "topic": "movies",
      "key": "Aliens",
      "value": {
        "ID": 48,
        "RELEASE_YEAR": 1986
      }
    },
    {
      "topic": "movies",
      "key": "Die Hard",
      "value": {
        "ID": 294,
        "RELEASE_YEAR": 1998
      }
    },
    {
      "topic": "movies",
      "key": "The Big Lebowski",
      "value": {
        "ID": 128,
        "RELEASE_YEAR": 1998
      }
    },
    {
      "topic": "movies",
      "key": "The Godfather",
      "value": {
        "ID": 128,
        "RELEASE_YEAR": 1998
      }
    },
    {
      "topic": "lead_actors",
      "key": "Aliens",
      "value": {
        "ACTOR_NAME": "Sigourney Weaver"
      }
    },
    {
      "topic": "lead_actors",
      "key": "Die Hard",
      "value": {
        "ACTOR_NAME": "Bruce Willis"
      }
    },
    {
      "topic": "lead_actors",
      "key": "The Big Lebowski",
      "value": {
        "ACTOR_NAME": "Jeff Bridges"
      }
    },
    {
      "topic": "lead_actors",
      "key": "The Godfather",
      "value": {
        "ACTOR_NAME": "Al Pacino"
      }
    }
  ]
}

Similarly, create a file at test/output.json with the expected outputs:

{
  "outputs": [
    {
      "topic": "MOVIES_ENRICHED",
      "key": "Aliens",
      "value": {
        "ID": 48,
        "RELEASE_YEAR": 1986,
        "ACTOR_NAME": "Sigourney Weaver"
      }
    },
    {
      "topic": "MOVIES_ENRICHED",
      "key": "Die Hard",
      "value": {
        "ID": 294,
        "RELEASE_YEAR": 1998,
        "ACTOR_NAME": "Bruce Willis"
      }
    },
    {
      "topic": "MOVIES_ENRICHED",
      "key": "The Big Lebowski",
      "value": {
        "ID": 128,
        "RELEASE_YEAR": 1998,
        "ACTOR_NAME": "Jeff Bridges"
      }
    },
    {
      "topic": "MOVIES_ENRICHED",
      "key": "The Godfather",
      "value": {
        "ID": 128,
        "RELEASE_YEAR": 1998,
        "ACTOR_NAME": "Al Pacino"
      }
    }
  ]
}

Invoke the tests

2

Lastly, invoke the tests using the test runner and the statements file that you created earlier:

docker exec ksqldb-cli ksql-test-runner -i /opt/app/test/input.json -s /opt/app/src/statements.sql -o /opt/app/test/output.json

Which should pass:

	 >>> Test passed!

Deploy on Confluent Cloud

Run your app with Confluent Cloud

1

Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully managed Apache Kafka service.

  1. Sign up for Confluent Cloud, a fully managed Apache Kafka service.

  2. After you log in to Confluent Cloud Console, click Environments in the lefthand navigation, click on Add cloud environment, and name the environment learn-kafka. Using a new environment keeps your learning resources separate from your other Confluent Cloud resources.

  3. From the Billing & payment section in the menu, apply the promo code CC100KTS to receive an additional $100 free usage on Confluent Cloud (details).

  4. Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.

Confluent Cloud

Next, from the Confluent Cloud Console, click on Clients to get the cluster-specific configurations, e.g., Kafka cluster bootstrap servers and credentials, Confluent Cloud Schema Registry and credentials, etc., and set the appropriate parameters in your client application.

Now you’re all set to run your streaming application locally, backed by a Kafka cluster fully managed by Confluent Cloud.