GET STARTED FREE

How to convert a stream's serialization format

Question:

If you have a Kafka topic with the data serialized in a particular format, how can you change that format?

Edit this page

Example use case:

Consider a topic with events that represent movie releases. The events in the topic are formatted with Avro. In this tutorial, we'll write a program that creates a new topic with the same events, but formatted with Protobuf.

Hands-on code example:

ksqlDB Kafka Streams

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 ksql-serialization && cd ksql-serialization

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

The first thing we’ll need is to create a Kafka topic and stream to represent the movie data. We declare the VALUE_FORMAT of the stream to be avro to denote the format of the events.

CREATE STREAM movies_avro (MOVIE_ID BIGINT KEY, title VARCHAR, release_year INT)
    WITH (KAFKA_TOPIC='avro-movies',
          PARTITIONS=1,
          VALUE_FORMAT='avro');

Then produce the following events to the stream. This will automatically format the data that goes onto the topic in Avro since the stream’s value format is declared as such.

INSERT INTO movies_avro (MOVIE_ID, title, release_year) VALUES (1, 'Lethal Weapon', 1992);
INSERT INTO movies_avro (MOVIE_ID, title, release_year) VALUES (2, 'Die Hard', 1988);
INSERT INTO movies_avro (MOVIE_ID, title, release_year) VALUES (3, 'Predator', 1997);

Now that you have a stream of Avro events, let’s convert them to Protobuf. Set the following properties to ensure that you’re reading from the beginning of the stream:

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

To convert the events to Protobuf, we’re going to create a derived stream. All that is needed is to specify the VALUE_FORMAT as protobuf, and the conversion will happen automatically. You can also optionally specify the topic name as we’ve done here. Omitting this parameter will cause the underlying topic to be named the same as the stream name.

CREATE STREAM movies_proto
    WITH (KAFKA_TOPIC='proto-movies', VALUE_FORMAT='protobuf') AS
    SELECT * FROM movies_avro;

Because this is a continuous query, any new records arriving on the source in Avro (avro-movies) will be automatically converted to Protobuf on the derived topic (proto-movies).

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

PRINT 'proto-movies' FROM BEGINNING LIMIT 3;

Note: the topic name needs to be quoted as it contains invalid characters, namely the '-'.

This should yield the following output:

Key format: KAFKA_BIGINT or KAFKA_DOUBLE or KAFKA_STRING
Value format: PROTOBUF
rowtime: 4/30/20 4:34:10 PM UTC, key: 1, value: TITLE: "Lethal Weapon" RELEASE_YEAR: 1992, partition: 0
rowtime: 4/30/20 4:34:10 PM UTC, key: 2, value: TITLE: "Die Hard" RELEASE_YEAR: 1988, partition: 0
rowtime: 4/30/20 4:34:11 PM UTC, key: 3, value: TITLE: "Predator" RELEASE_YEAR: 1997, partition: 0
Topic printing ceased

Notice the 'Value format' is reported as PROTOBUF.

Congrats! You’ve taken a topic formatted with Avro and created a continuously updating copy on a new topic in Protobuf.

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 movies_avro (MOVIE_ID BIGINT KEY, title VARCHAR, release_year INT)
    WITH (KAFKA_TOPIC='avro-movies',
          PARTITIONS=1,
          VALUE_FORMAT='avro');

CREATE STREAM movies_proto
    WITH (KAFKA_TOPIC='proto-movies',
          PARTITIONS=1,
          VALUE_FORMAT='protobuf') AS
    SELECT * FROM movies_avro;

Test it

Create the test data

1

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

{
  "inputs": [
    {
      "topic": "avro-movies",
      "key": 1,
      "value": {
        "TITLE": "Lethal Weapon",
        "release_year": 1992
      }
    },
    {
      "topic": "avro-movies",
      "key": 2,
      "value": {
        "TITLE": "Die Hard",
        "release_year": 1988
      }
    },
    {
      "topic": "avro-movies",
      "key": 3,
      "value": {
        "TITLE": "Predator",
        "release_year": 1997
      }
    }
  ]
}

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

{
  "outputs": [
    {
      "topic": "proto-movies",
      "key": 1,
      "value": {
        "TITLE": "Lethal Weapon",
        "RELEASE_YEAR": 1992
      }
    },
    {
      "topic": "proto-movies",
      "key": 2,
      "value": {
        "TITLE": "Die Hard",
        "RELEASE_YEAR": 1988
      }
    },
    {
      "topic": "proto-movies",
      "key": 3,
      "value": {
        "TITLE": "Predator",
        "RELEASE_YEAR": 1997
      }
    }
  ]
}

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.