How to transform a stream of events using ksqlDB

Run it

Prerequisites

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

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

mkdir transform-stream && cd transform-stream

Then make the following directories to set up its structure:

mkdir src test

Get Confluent Platform

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

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 publications. The following creates both in one shot:

CREATE STREAM raw_movies (ID INT KEY, title VARCHAR, genre VARCHAR)
    WITH (kafka_topic='movies', partitions=1, value_format = 'avro');

Then produce the following events to the stream:

INSERT INTO raw_movies (id, title, genre) VALUES (294, 'Die Hard::1988', 'action');
INSERT INTO raw_movies (id, title, genre) VALUES (354, 'Tree of Life::2011', 'drama');
INSERT INTO raw_movies (id, title, genre) VALUES (782, 'A Walk in the Clouds::1995', 'romance');
INSERT INTO raw_movies (id, title, genre) VALUES (128, 'The Big Lebowski::1998', '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 break apart the title field and extract the year that the movie was published into its own column. Issue the following transient push query. This will block and continue to return results until its limit is reached or you tell it to stop.

SELECT id, split(title, '::')[1] as title, split(title, '::')[2] AS year, genre FROM raw_movies EMIT CHANGES LIMIT 4;

This should yield the following output:

+---------------------+---------------------+---------------------+---------------------+
|ID                   |TITLE                |YEAR                 |GENRE                |
+---------------------+---------------------+---------------------+---------------------+
|294                  |Die Hard             |1988                 |action               |
|354                  |Tree of Life         |2011                 |drama                |
|782                  |A Walk in the Clouds |1995                 |romance              |
|128                  |The Big Lebowski     |1998                 |comedy               |
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 stream that is continuously populated by its query:

CREATE STREAM movies WITH (kafka_topic = 'parsed_movies', partitions = 1) AS
    SELECT id,
           split(title, '::')[1] as title,
           CAST(split(title, '::')[2] AS INT) AS year,
           genre
    FROM raw_movies;

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

PRINT parsed_movies FROM BEGINNING LIMIT 4;

This should yield the following output:

Key format: KAFKA_INT
Value format: AVRO
rowtime: 2020/05/04 22:09:54.713 Z, key: 294, value: {"TITLE": "Die Hard", "YEAR": 1988, "GENRE": "action"}, partition: 0
rowtime: 2020/05/04 22:09:55.012 Z, key: 354, value: {"TITLE": "Tree of Life", "YEAR": 2011, "GENRE": "drama"}, partition: 0
rowtime: 2020/05/04 22:09:55.217 Z, key: 782, value: {"TITLE": "A Walk in the Clouds", "YEAR": 1995, "GENRE": "romance"}, partition: 0
rowtime: 2020/05/04 22:09:55.379 Z, key: 128, value: {"TITLE": "The Big Lebowski", "YEAR": 1998, "GENRE": "comedy"}, partition: 0
Topic printing ceased

Write your statements to a file

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 raw_movies (ID INT KEY, title VARCHAR, genre VARCHAR)
    WITH (kafka_topic='movies', partitions=1, value_format = 'avro');

CREATE STREAM movies WITH (kafka_topic = 'parsed_movies', partitions = 1) AS
    SELECT id,
           split(title, '::')[1] as title,
           CAST(split(title, '::')[2] AS INT) AS year,
           genre
    FROM raw_movies;

Test it

Create the test data

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

{
  "inputs": [
    {
      "topic": "movies",
      "key": 294,
      "value": {
        "title": "Die Hard::1988",
        "genre": "action"
      }
    },
    {
      "topic": "movies",
      "key": 354,
      "value": {
        "title": "Tree of Life::2011",
        "genre": "drama"
      }
    },
    {
      "topic": "movies",
      "key": 782,
      "value": {
        "title": "A Walk in the Clouds::1995",
        "genre": "romance"
      }
    },
    {
      "topic": "movies",
      "key": 128,
      "value": {
        "title": "The Big Lebowski::1998",
        "genre": "comedy"
      }
    }
  ]
}

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

{
  "outputs": [
    {
      "topic": "parsed_movies",
      "key": 294,
      "value": {
        "TITLE": "Die Hard",
        "YEAR": 1988,
        "GENRE": "action"
      }
    },
    {
      "topic": "parsed_movies",
      "key": 354,
      "value": {
        "TITLE": "Tree of Life",
        "YEAR": 2011,
        "GENRE": "drama"
      }
    },
    {
      "topic": "parsed_movies",
      "key": 782,
      "value": {
        "TITLE": "A Walk in the Clouds",
        "YEAR": 1995,
        "GENRE": "romance"
      }
    },
    {
      "topic": "parsed_movies",
      "key": 128,
      "value": {
        "TITLE": "The Big Lebowski",
        "YEAR": 1998,
        "GENRE": "comedy"
      }
    }
  ]
}

Invoke the tests

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

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

Sign up for Confluent Cloud, a fully managed Apache Kafka service.
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.
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.
Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.

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.

How to transform a stream of events

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