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How to change the number of partitions and replicas of a Kafka topic

Question:

How can you change the number of partitions or replicas of a Kafka topic?

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Example use case:

If you want to change the number of partitions or replicas of your Kafka topic, you can use a streaming transformation to automatically stream all of the messages from the original topic into a new Kafka topic that has the desired number of partitions or replicas.

Hands-on code example:

ksqlDB

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 change-topic-partitions-replicas && cd change-topic-partitions-replicas

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: '3'
services:
  controller:
    image: confluentinc/cp-kafka:7.5.1
    container_name: controller
    hostname: controller
    ports:
     - "9091:9091"
    environment:
      KAFKA_NODE_ID: 1
      KAFKA_PROCESS_ROLES: 'controller'
      KAFKA_CONTROLLER_QUORUM_VOTERS: '1@controller:9091'
      KAFKA_INTER_BROKER_LISTENER_NAME: 'PLAINTEXT'
      KAFKA_CONTROLLER_LISTENER_NAMES: 'CONTROLLER'
      KAFKA_LISTENERS: 'CONTROLLER://controller:9091'
      CLUSTER_ID: '4L6g3nShT-eMCtK--X86sw'
  broker:
    image: confluentinc/cp-kafka:7.5.1
    container_name: broker
    hostname: broker
    ports:
      - "9092:9092"
    environment:
      KAFKA_NODE_ID: 2
      KAFKA_PROCESS_ROLES: 'broker'
      KAFKA_CONTROLLER_QUORUM_VOTERS: '1@controller:9091'
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: 'PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT,CONTROLLER:PLAINTEXT'
      KAFKA_LISTENERS: 'PLAINTEXT://broker:29092,PLAINTEXT_HOST://0.0.0.0:9092'
      KAFKA_ADVERTISED_LISTENERS: 'PLAINTEXT://broker:29092,PLAINTEXT_HOST://localhost:9092'
      KAFKA_CONTROLLER_LISTENER_NAMES: 'CONTROLLER'
      KAFKA_INTER_BROKER_LISTENER_NAME: 'PLAINTEXT'
      CLUSTER_ID: '4L6g3nShT-eMCtK--X86sw'
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 2
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 2
    depends_on:
    - controller

  broker2:
    image: confluentinc/cp-kafka:7.5.1
    container_name: broker2
    hostname: broker2
    ports:
      - "9093:9093"
    environment:
      KAFKA_NODE_ID: 3
      KAFKA_PROCESS_ROLES: 'broker'
      KAFKA_CONTROLLER_QUORUM_VOTERS: '1@controller:9091'
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: 'PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT,CONTROLLER:PLAINTEXT'
      KAFKA_LISTENERS: 'PLAINTEXT://broker2:29093,PLAINTEXT_HOST://0.0.0.0:9093'
      KAFKA_ADVERTISED_LISTENERS: 'PLAINTEXT://broker2:29093,PLAINTEXT_HOST://localhost:9093'
      KAFKA_CONTROLLER_LISTENER_NAMES: 'CONTROLLER'
      KAFKA_INTER_BROKER_LISTENER_NAME: 'PLAINTEXT'
      CLUSTER_ID: '4L6g3nShT-eMCtK--X86sw'
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 2
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 2
    depends_on:
    - controller

  schema-registry:
    image: confluentinc/cp-schema-registry:7.5.1
    hostname: schema-registry
    container_name: schema-registry
    depends_on:
    - broker
    - broker2
    ports:
    - 8081:8081
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: broker:29092
  ksqldb-server:
    image: confluentinc/ksqldb-server:0.28.2
    hostname: ksqldb-server
    container_name: ksqldb-server
    depends_on:
    - broker
    - broker2
    - 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:29092
      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
    - broker2
    - 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

Create the original topic

4

Your first step is to create the original Kafka topic. Use the following command to create the topic topic1 with 1 partition and 1 replica:

docker exec -it broker kafka-topics --bootstrap-server broker:29092 --topic topic1 --create --replication-factor 1 --partitions 1

Describe the original topic

5

Describe the properties of the topic that you just created.

docker exec -t broker kafka-topics --bootstrap-server broker:29092 --topic topic1 --describe

The output should be the following. Notice that the topic has 1 partition numbered 0, and 1 replica on a broker with an id of 101 (or 102).

Topic: topic1	TopicId: MtGWXVWVSM2aiFLL3Lvwug	PartitionCount: 1	ReplicationFactor: 1	Configs:
	Topic: topic1	Partition: 0	Leader: 1	Replicas: 1	Isr: 1

Write the program interactively using the CLI

6

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 ksqlDB stream for the original topic topic1—let’s call the stream s1. The statement below specifies the message value serialization format is JSON but in your environment, VALUE_FORMAT should be set to match the serialization format of your original topic.

CREATE STREAM S1 (COLUMN0 VARCHAR KEY, COLUMN1 VARCHAR) WITH (KAFKA_TOPIC = 'topic1', VALUE_FORMAT = 'JSON');

Next, create a new ksqlDB stream—let’s call it s2—that will be backed by a new target Kafka topic topic2 with the desired number of partitions and replicas. Using the WITH clause, you can specify the partitions and replicas of the underlying Kafka topic.

The result of SELECT * FROM S1 causes every record from Kafka topic topic1 (with 1 partition and 1 replica) to be produced to Kafka topic topic2 (with 2 partitions and 2 replicas).

CREATE STREAM S2 WITH (KAFKA_TOPIC = 'topic2', VALUE_FORMAT = 'JSON', PARTITIONS = 2, REPLICAS = 2) AS SELECT * FROM S1;

Exit ksqlDB by typing exit;

Describe the new topic

7

Describe the properties of the new topic, topic2, underlying the ksqlDB stream you just created.

docker exec -t broker kafka-topics --bootstrap-server broker:29092 --topic topic2 --describe

The output should be the following. Notice that the topic has 2 partitions, numbered 0 and 1, and 2 replicas on brokers with ids of 101 and 102.

Topic: topic2	TopicId: FebuvQBIQHqNorJoWbkCkA	PartitionCount: 2	ReplicationFactor: 2	Configs: cleanup.policy=delete
	Topic: topic2	Partition: 0	Leader: 2	Replicas: 2,1	Isr: 2,1
	Topic: topic2	Partition: 1	Leader: 1	Replicas: 1,2	Isr: 1,2

Write your statements to a file

8

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 S1 (COLUMN0 VARCHAR KEY, COLUMN1 VARCHAR) WITH (KAFKA_TOPIC = 'topic1', VALUE_FORMAT = 'JSON');

CREATE STREAM S2 WITH (KAFKA_TOPIC = 'topic2', VALUE_FORMAT = 'JSON', PARTITIONS = 2, REPLICAS = 2) AS SELECT * FROM S1;

Test it

Run the console Kafka producer

1

To produce data into the original Kafka topic topic1, open another terminal window and run the following command to open a second shell on the broker container:

docker exec -i broker kafka-console-producer \
  --bootstrap-server broker:29092 \
  --topic topic1 \
  --property parse.key=true \
  --property key.separator=,

The producer will start and wait for you to enter input in the next step.

Produce data to the original Kafka topic

2

The following text represents records to be written to the original topic topic1. Each line has the format <key>,<value>, whereby the , is the special delimiter character that separates the record key from the record value. Copy these records and paste them into the kafka-console-producer prompt that you started in the previous step.

a,{"column1": "1"}
b,{"column1": "1"}
c,{"column1": "1"}
d,{"column1": "1"}
a,{"column1": "2"}
b,{"column1": "2"}
c,{"column1": "2"}
d,{"column1": "2"}
a,{"column1": "3"}
b,{"column1": "3"}
c,{"column1": "3"}
d,{"column1": "3"}

Stop the producer with Ctrl-C.

View the data in the original topic (partition 0)

3

Consume data from the original Kafka topic, specifying only to read from partition 0. Notice that all the data is read because all the data resides in the topic’s single partition.

docker exec -t broker kafka-console-consumer \
  --bootstrap-server broker:29092 \
  --topic topic1 \
  --property print.key=true \
  --property key.separator=, \
  --partition 0 \
  --from-beginning

You should see all the records in this partition.

a,{"column1": "1"}
b,{"column1": "1"}
c,{"column1": "1"}
d,{"column1": "1"}
a,{"column1": "2"}
b,{"column1": "2"}
c,{"column1": "2"}
d,{"column1": "2"}
a,{"column1": "3"}
b,{"column1": "3"}
c,{"column1": "3"}
d,{"column1": "3"}
Processed a total of 12 messages

Close the consumer with Ctrl-C.

View the data in the new topic (partition 0)

4

Now consume data from the new Kafka topic topic2. First look at the data in partition 0.

docker exec -t broker kafka-console-consumer \
  --bootstrap-server broker:29092 \
  --topic topic2 \
  --property print.key=true \
  --property key.separator=, \
  --partition 0 \
  --from-beginning

You should see some of the records in this partition. In this example, the partitioner put all records with a key value of a, b, or c into partition 0.

a,{"COLUMN1":"1"}
b,{"COLUMN1":"1"}
c,{"COLUMN1":"1"}
a,{"COLUMN1":"2"}
b,{"COLUMN1":"2"}
c,{"COLUMN1":"2"}
a,{"COLUMN1":"3"}
b,{"COLUMN1":"3"}
c,{"COLUMN1":"3"}
Processed a total of 9 messages

Notice that the ordering of the data is still maintained per key.

Close the consumer with Ctrl-C.

View the data in the new topic (partition 1)

5

Next look at the data in partition 1.

docker exec -t broker kafka-console-consumer \
  --bootstrap-server broker:29092 \
  --topic topic2 \
  --property print.key=true \
  --property key.separator=, \
  --partition 1 \
  --from-beginning

You should see the rest of the records in this partition. In this example, the partitioner put all records with a key value of d into partition 1.

d,{"COLUMN1":"1"}
d,{"COLUMN1":"2"}
d,{"COLUMN1":"3"}
Processed a total of 3 messages

Close the consumer with Ctrl-C.

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.