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How to merge two streams into one with Flink SQL

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How to merge two streams into one with Flink SQL

Suppose that you have two Kafka topics containing songs of different genres and you wish to merge them into one. In this tutorial, we'll use Flink SQL to accomplish this task. We'll have an input topic containing rock songs, another input topic containing classical songs, and we'll populate an output topic containing all songs.

Setup

Let's assume the following DDL for our base rock_songs and classical_songs tables:

CREATE TABLE rock_songs (
    artist STRING,
    title STRING
);

CREATE TABLE classical_songs (
    artist STRING,
    title STRING
);

Merge streams

Given the table definition above, we can create a merged table, and add a genre column, as follows:

CREATE TABLE all_songs (
    artist STRING,
    title STRING,
    genre STRING
);

INSERT INTO all_songs
    SELECT
        artist,
        title,
        'rock' AS genre
    FROM rock_songs;

INSERT INTO all_songs
    SELECT
        artist,
        title,
        'classical' AS genre
    FROM classical_songs;

Running the example

You can run the example backing this tutorial in one of three ways: a Flink Table API-based JUnit test, locally with the Flink SQL Client against Flink and Kafka running in Docker, or with Confluent Cloud.

Flink Table API-based test

Prerequisites

Run the test

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

Run the following command to execute FlinkSqlMergeTablesTest#testMerge:

./gradlew clean :merging:flinksql:test

The test starts Kafka and Schema Registry with Testcontainers, runs the Flink SQL commands above against a local Flink StreamExecutionEnvironment, and ensures that the routed results are what we expect.

Flink SQL Client CLI

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 Flink and Kafka:

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

Next, open the Flink SQL Client CLI:

docker exec -it flink-sql-client sql-client.sh

Finally, run following SQL statements to create the rock_songs and classical_songs tables backed by Kafka running in Docker, populate them with test data, and then create and populate a merged table containing all songs.

CREATE TABLE rock_songs (
    artist STRING,
    title STRING
) WITH (
    'connector' = 'kafka',
    'topic' = 'rock-songs',
    'properties.bootstrap.servers' = 'broker:9092',
    'scan.startup.mode' = 'earliest-offset',
    'key.format' = 'avro-confluent',
    'key.avro-confluent.url' = 'http://schema-registry:8081',
    'key.fields' = 'artist;title',
    'value.format' = 'avro-confluent',
    'value.avro-confluent.url' = 'http://schema-registry:8081',
    'value.fields-include' = 'ALL'
);
INSERT INTO rock_songs VALUES
    ('Metallica', 'Fade to Black'),
    ('Smashing Pumpkins', 'Today'),
    ('Pink Floyd', 'Another Brick in the Wall'),
    ('Van Halen', 'Jump'),
    ('Led Zeppelin', 'Kashmir');
CREATE TABLE classical_songs (
    artist STRING,
    title STRING
) WITH (
    'connector' = 'kafka',
    'topic' = 'classical-songs',
    'properties.bootstrap.servers' = 'broker:9092',
    'scan.startup.mode' = 'earliest-offset',
    'key.format' = 'avro-confluent',
    'key.avro-confluent.url' = 'http://schema-registry:8081',
    'key.fields' = 'artist;title',
    'value.format' = 'avro-confluent',
    'value.avro-confluent.url' = 'http://schema-registry:8081',
    'value.fields-include' = 'ALL'
);
INSERT INTO classical_songs VALUES
    ('Wolfgang Amadeus Mozart', 'The Magic Flute'),
    ('Johann Pachelbel', 'Canon'),
    ('Ludwig van Beethoven', 'Symphony No. 5'),
    ('Edward Elgar', 'Pomp and Circumstance');
CREATE TABLE all_songs (
    artist STRING,
    title STRING,
    genre STRING
) WITH (
    'connector' = 'kafka',
    'topic' = 'all-songs',
    'properties.bootstrap.servers' = 'broker:9092',
    'scan.startup.mode' = 'earliest-offset',
    'key.format' = 'avro-confluent',
    'key.avro-confluent.url' = 'http://schema-registry:8081',
    'key.fields' = 'artist;title',
    'value.format' = 'avro-confluent',
    'value.avro-confluent.url' = 'http://schema-registry:8081',
    'value.fields-include' = 'ALL'
);
INSERT INTO all_songs
    SELECT
        artist,
        title,
        'rock' AS genre
    FROM rock_songs;
INSERT INTO all_songs
    SELECT
        artist,
        title,
        'classical' AS genre
    FROM classical_songs;
SELECT * FROM all_songs;

The query output should look like this:

                       artist                          title                          genre
                    Metallica                  Fade to Black                           rock
            Smashing Pumpkins                          Today                           rock
                   Pink Floyd      Another Brick in the Wall                           rock
                    Van Halen                           Jump                           rock
                 Led Zeppelin                        Kashmir                           rock
      Wolfgang Amadeus Mozart                The Magic Flute                      classical
             Johann Pachelbel                          Canon                      classical
         Ludwig van Beethoven                 Symphony No. 5                      classical
                 Edward Elgar          Pomp and Circumstance                      classical

When you are finished, clean up the containers used for this tutorial by running:

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

Prerequisites

  • A Confluent Cloud account
  • A Flink compute pool created in Confluent Cloud. Follow this quick start to create one.

Run the commands

In the Confluent Cloud Console, navigate to your environment and then click the Open SQL Workspace button for the compute pool that you have created.

Select the default catalog (Confluent Cloud environment) and database (Kafka cluster) to use with the dropdowns at the top right.

Finally, run following SQL statements to create the rock_songs and classical_songs tables, populate them with test data, and then create and populate a merged table containing all songs.

CREATE TABLE rock_songs (
    artist STRING,
    title STRING
);
INSERT INTO rock_songs VALUES
    ('Metallica', 'Fade to Black'),
    ('Smashing Pumpkins', 'Today'),
    ('Pink Floyd', 'Another Brick in the Wall'),
    ('Van Halen', 'Jump'),
    ('Led Zeppelin', 'Kashmir');
CREATE TABLE classical_songs (
    artist STRING,
    title STRING
);
INSERT INTO classical_songs VALUES
    ('Wolfgang Amadeus Mozart', 'The Magic Flute'),
    ('Johann Pachelbel', 'Canon'),
    ('Ludwig van Beethoven', 'Symphony No. 5'),
    ('Edward Elgar', 'Pomp and Circumstance');
CREATE TABLE all_songs (
    artist STRING,
    title STRING,
    genre STRING
);
INSERT INTO all_songs
    SELECT
        artist,
        title,
        'rock' AS genre
    FROM rock_songs;
INSERT INTO all_songs
    SELECT
        artist,
        title,
        'classical' AS genre
    FROM classical_songs;

Now query the merged table:

SELECT * FROM all_songs;

The scrollable query output should start like this:

Query output

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.