GET STARTED FREE

How to split a stream of events into substreams

Question:

How do you split events in a Kafka topic so that the events are placed into subtopics?

Edit this page

Example use case:

Suppose that you have a Kafka topic representing appearances of an actor or actress in a film, with each event denoting the genre. In this tutorial, we'll write a program that splits the stream into substreams based on the genre. We'll have a topic for drama films, a topic for fantasy films, and a topic for everything else. Related pattern: Event Router.

Hands-on code example:

Confluent Cloud ksqlDB Flink SQL Kafka Streams Basic Kafka

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 split-stream && cd split-stream

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
    environment:
      KSQL_CONFIG_DIR: /etc/ksqldb
    tty: true
    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 stream to represent the actors. The following creates both in one shot:

CREATE STREAM actingevents (name VARCHAR, title VARCHAR, genre VARCHAR)
    WITH (KAFKA_TOPIC = 'acting-events', PARTITIONS = 1, VALUE_FORMAT = 'AVRO');

Then produce the following events to the stream:

INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Bill Murray', 'Ghostbusters', 'fantasy');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Christian Bale', 'The Dark Knight', 'crime');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Diane Keaton', 'The Godfather: Part II', 'crime');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Jennifer Aniston', 'Office Space', 'comedy');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Judy Garland', 'The Wizard of Oz', 'fantasy');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Keanu Reeves', 'The Matrix', 'fantasy');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Laura Dern', 'Jurassic Park', 'fantasy');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Matt Damon', 'The Martian', 'drama');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Meryl Streep', 'The Iron Lady', 'drama');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Russell Crowe', 'Gladiator', 'drama');
INSERT INTO ACTINGEVENTS (name, title,genre) VALUES ('Will Smith', 'Men in Black', 'comedy');

Now that you have stream with some events in it, let’s read them out. 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 find all of the drama films. Issue the following transient push query. This will block and continue to return results until it’s limit is reached or you tell it to stop.

SELECT NAME, TITLE FROM ACTINGEVENTS WHERE GENRE='drama' EMIT CHANGES LIMIT 3;

This should yield the following output:

+------------------------------+------------------------------+
|NAME                          |TITLE                         |
+------------------------------+------------------------------+
|Matt Damon                    |The Martian                   |
|Meryl Streep                  |The Iron Lady                 |
|Russell Crowe                 |Gladiator                     |
Limit Reached
Query terminated

You can also use negative matches, that is, messages that don’t match the condition. Run this query to get a list of all films that aren’t drama or fantasy.

SELECT NAME, TITLE, GENRE FROM ACTINGEVENTS WHERE GENRE != 'drama' AND GENRE != 'fantasy' EMIT CHANGES LIMIT 4;

This should yield the following output:

+------------------------------+------------------------------+------------------------------+
|NAME                          |TITLE                         |GENRE                         |
+------------------------------+------------------------------+------------------------------+
|Christian Bale                |The Dark Knight               |crime                         |
|Diane Keaton                  |The Godfather: Part II        |crime                         |
|Jennifer Aniston              |Office Space                  |comedy                        |
|Will Smith                    |Men in Black                  |comedy                        |
Limit Reached
Query terminated

Since the output looks right, the next step is to make the queries continuous. Issue the following to create three new streams that are continuously populated by the queries:

CREATE STREAM actingevents_drama AS
    SELECT NAME, TITLE
      FROM ACTINGEVENTS
     WHERE GENRE='drama';

CREATE STREAM actingevents_fantasy AS
    SELECT NAME, TITLE
      FROM ACTINGEVENTS
     WHERE GENRE='fantasy';

CREATE STREAM actingevents_other AS
    SELECT NAME, TITLE, GENRE
      FROM ACTINGEVENTS
     WHERE GENRE != 'drama'
       AND GENRE != 'fantasy';

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

PRINT ACTINGEVENTS_FANTASY FROM BEGINNING LIMIT 4;

This should yield the following output:

Key format: ¯\_(ツ)_/¯ - no data processed
Value format: AVRO or KAFKA_STRING
rowtime: 2020/05/04 23:40:52.078 Z, key: <null>, value: {"NAME": "Bill Murray", "TITLE": "Ghostbusters"}, partition: 0
rowtime: 2020/05/04 23:40:52.454 Z, key: <null>, value: {"NAME": "Judy Garland", "TITLE": "The Wizard of Oz"}, partition: 0
rowtime: 2020/05/04 23:40:52.537 Z, key: <null>, value: {"NAME": "Keanu Reeves", "TITLE": "The Matrix"}, partition: 0
rowtime: 2020/05/04 23:40:52.607 Z, key: <null>, value: {"NAME": "Laura Dern", "TITLE": "Jurassic Park"}, partition: 0
Topic printing ceased

Try dropping the LIMIT from the print command so that it runs indefinitely. To see how any new message on the source stream is automatically routed to the correct target stream, open a new CLI session and insert a record like we did above.

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 STREAM actingevents (name VARCHAR, title VARCHAR, genre VARCHAR)
    WITH (KAFKA_TOPIC = 'acting-events', PARTITIONS = 1, VALUE_FORMAT = 'AVRO');

CREATE STREAM actingevents_drama AS
    SELECT NAME, TITLE
      FROM ACTINGEVENTS
     WHERE GENRE='drama';

CREATE STREAM actingevents_fantasy AS
    SELECT NAME, TITLE
      FROM ACTINGEVENTS
     WHERE GENRE='fantasy';

CREATE STREAM actingevents_other AS
    SELECT NAME, TITLE, GENRE
      FROM ACTINGEVENTS
     WHERE GENRE != 'drama'
       AND GENRE != 'fantasy';

Test it

Create the test data

1

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

{
  "inputs": [
    {
      "topic": "acting-events",
      "value": {
        "name": "Bill Murray",
        "title": "Ghostbusters",
        "genre": "fantasy"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Christian Bale",
        "title": "The Dark Knight",
        "genre": "crime"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Diane Keaton",
        "title": "The Godfather: Part II",
        "genre": "crime"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Jennifer Aniston",
        "title": "Office Space",
        "genre": "comedy"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Judy Garland",
        "title": "The Wizard of Oz",
        "genre": "fantasy"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Keanu Reeves",
        "title": "The Matrix",
        "genre": "fantasy"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Laura Dern",
        "title": "Jurassic Park",
        "genre": "fantasy"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Matt Damon",
        "title": "The Martian",
        "genre": "drama"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Meryl Streep",
        "title": "The Iron Lady",
        "genre": "drama"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Russell Crowe",
        "title": "Gladiator",
        "genre": "drama"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Will Smith",
        "title": "Men in Black",
        "genre": "comedy"
      }
    },
    {
      "topic": "acting-events",
      "value": {
        "name": "Barret Oliver",
        "title": "The NeverEnding Story",
        "genre": "fantasy"
      }
    }
  ]
}

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

{
  "outputs": [
    {
      "topic": "ACTINGEVENTS_FANTASY",
      "value": {
        "NAME": "Bill Murray",
        "TITLE": "Ghostbusters"
      }
    },
    {
      "topic": "ACTINGEVENTS_OTHER",
      "value": {
        "NAME": "Christian Bale",
        "TITLE": "The Dark Knight",
        "GENRE": "crime"
      }
    },
    {
      "topic": "ACTINGEVENTS_OTHER",
      "value": {
        "NAME": "Diane Keaton",
        "TITLE": "The Godfather: Part II",
        "GENRE": "crime"
      }
    },
    {
      "topic": "ACTINGEVENTS_OTHER",
      "value": {
        "NAME": "Jennifer Aniston",
        "TITLE": "Office Space",
        "GENRE": "comedy"
      }
    },
    {
      "topic": "ACTINGEVENTS_FANTASY",
      "value": {
        "NAME": "Judy Garland",
        "TITLE": "The Wizard of Oz"
      }
    },
    {
      "topic": "ACTINGEVENTS_FANTASY",
      "value": {
        "NAME": "Keanu Reeves",
        "TITLE": "The Matrix"
      }
    },
    {
      "topic": "ACTINGEVENTS_FANTASY",
      "value": {
        "NAME": "Laura Dern",
        "TITLE": "Jurassic Park"
      }
    },
    {
      "topic": "ACTINGEVENTS_DRAMA",
      "value": {
        "NAME": "Matt Damon",
        "TITLE": "The Martian"
      }
    },
    {
      "topic": "ACTINGEVENTS_DRAMA",
      "value": {
        "NAME": "Meryl Streep",
        "TITLE": "The Iron Lady"
      }
    },
    {
      "topic": "ACTINGEVENTS_DRAMA",
      "value": {
        "NAME": "Russell Crowe",
        "TITLE": "Gladiator"
      }
    },
    {
      "topic": "ACTINGEVENTS_OTHER",
      "value": {
        "NAME": "Will Smith",
        "TITLE": "Men in Black",
        "GENRE": "comedy"
      }
    },
    {
      "topic": "ACTINGEVENTS_FANTASY",
      "value": {
        "NAME": "Barret Oliver",
        "TITLE": "The NeverEnding Story"
      }
    }
  ]
}

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). To avoid having to enter a credit card, add an additional promo code CONFLUENTDEV1. With this promo code, you will not have to enter a credit card for 30 days or until your credits run out.

  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.