Quick Start for Confluent Platform

Use this quick start to get up and running locally with Confluent Platform and its main components using Docker containers.

In this quick start, you create Apache Kafka® topics, use Kafka Connect to generate mock data to those topics, and create ksqlDB streaming queries on those topics. You then go to Confluent Control Center to monitor and analyze the event streaming queries. When you finish, you’ll have a real-time app that consumes and processes data streams by using familiar SQL statements.

Tip

Prerequisites

To run this quick start, you will need Docker and Docker Compose installed on a computer with a supported Operating System. Make sure Docker is running. For full prerequisites, expand the Detailed prerequisites section that follows.

Detailed prerequisites

  • A connection to the internet.

  • Operating System currently supported by Confluent Platform.

    Note

    You can run the Confluent Platform Quick Start on Windows if you are running Docker Desktop for Windows on WSL 2. For more information, see How to Run Confluent on Windows in Minutes.

  • Docker version 1.11 or later is installed and running.

  • On Mac: Docker memory is allocated minimally at 6 GB (Mac). When using Docker Desktop for Mac, the default Docker memory allocation is 2 GB. Change the default allocation to 6 GB in the Docker Desktop app by navigating to Preferences > Resources > Advanced.

  • Networking and Kafka on Docker: Configure your hosts and ports to allow both internal and external components to the Docker network to communicate. For more information, see Kafka Listeners Explained.

Step 1: Download and start Confluent Platform

In this step, you start by downloading a Docker compose file. The docker-compose.yml file sets ports and Docker environment variables such as the replication factor and listener properties for Confluent Platform and its components. To learn more about the settings in this file, see Docker Image Configuration Reference for Confluent Platform. In addition, the Confluent Platform image specified in the file uses the new Kafka-based KRaft metadata service, which has many benefits. As of Confluent Platform 7.5, ZooKeeper is deprecated for new deployments. For more information, see KRaft Overview for Confluent Platform.

  1. Download or copy the contents of the Confluent Platform KRaft all-in-one Docker Compose file, for example:

    wget https://raw.githubusercontent.com/confluentinc/cp-all-in-one/7.9.0-post/cp-all-in-one-kraft/docker-compose.yml
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    Note

    To use Apache ZooKeeper as the metadata service for your Kafka cluster, download the Confluent Platform ZooKeeper all-in-one Docker Compose file.

  2. Start the Confluent Platform stack with the -d option to run in detached mode:

    docker compose up -d
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    Note

    If you using an Docker Compose V1, you need to use a dash in the docker compose commands. For example:

    docker-compose up -d
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    To learn more, see Migrate to Compose V2.

    Each Confluent Platform component starts in a separate container. Your output should resemble the following. Your output may vary slightly from these examples depending on your operating system.

    Creating broker ... done
Creating schema-registry ... done
Creating rest-proxy      ... done
Creating connect         ... done
Creating ksqldb-server   ... done
Creating control-center  ... done
Creating ksql-datagen    ... done
Creating ksqldb-cli      ... done
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  3. Verify that the services are up and running:

    docker compose ps
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    Your output should resemble:

        NAME              IMAGE                                             COMMAND                  SERVICE           CREATED       STATUS         PORTS
broker            confluentinc/cp-kafka:7.8.0                       "/etc/confluent/dock…"   broker            4 weeks ago   Up 4 minutes   0.0.0.0:9092->9092/tcp, 0.0.0.0:9101->9101/tcp
connect           cnfldemos/cp-server-connect-datagen:0.6.4-7.6.0   "/etc/confluent/dock…"   connect           4 weeks ago   Up 4 minutes   0.0.0.0:8083->8083/tcp, 9092/tcp
control-center    confluentinc/cp-enterprise-control-center:7.8.0   "/etc/confluent/dock…"   control-center    4 weeks ago   Up 4 minutes   0.0.0.0:9021->9021/tcp
ksqldb-cli        confluentinc/cp-ksqldb-cli:7.8.0                  "/bin/sh"                ksqldb-cli        4 weeks ago   Up 4 minutes
ksqldb-server     confluentinc/cp-ksqldb-server:7.8.0               "/etc/confluent/dock…"   ksqldb-server     4 weeks ago   Up 4 minutes   0.0.0.0:8088->8088/tcp
rest-proxy        confluentinc/cp-kafka-rest:7.8.0                  "/etc/confluent/dock…"   rest-proxy        4 weeks ago   Up 4 minutes   0.0.0.0:8082->8082/tcp
schema-registry   confluentinc/cp-schema-registry:7.8.0             "/etc/confluent/dock…"   schema-registry   4 weeks ago   Up 4 minutes   0.0.0.0:8081->8081/tcp
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    After a few minutes, if the state of any component isn’t Up, run the docker-compose up -d command again, or try docker-compose restart <image-name>, for example:

    docker compose restart control-center
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Step 2: Create Kafka topics for storing your data

In Confluent Platform, real-time streaming events are stored in a Kafka topic, which is an append-only log, and the fundamental unit of organization for Kafka. To learn more about Kafka basics, see Kafka Introduction.

In this step, you create two topics by using Control Center for Confluent Platform. Control Center provides the features for building and monitoring production data pipelines and event streaming applications.

The topics are named pageviews and users. In later steps, you create connectors that produce data to these topics.

Create the pageviews topic

Confluent Control Center enables creating topics in the UI with a few clicks.

  1. Navigate to Control Center at http://localhost:9021. It may take a minute or two for Control Center to start and load.

  2. Click the controlcenter.cluster tile.

    The Cluster tile in Confluent Control Center

  3. In the navigation menu, click Topics to open the topics list. Click + Add topic to start creating the pageviews topic.

    The Topics page in Confluent Control Center

  4. In the Topic name field, enter pageviews and click Create with defaults. Topic names are case-sensitive.

    Creating a Kafka topic in Confluent Control Center

Create the users topic

Repeat the previous steps to create the users topic.

  1. In the navigation menu, click Topics to open the topics list. Click + Add topic to start creating the users topic.

  2. In the Topic name field, enter users and click Create with defaults.

  3. You can optionally inspect a topic. On the users page, click Configuration to see details about the users topic.

    The Topic Configuration page in Confluent Control Center

Step 3: Generate mock data

In Confluent Platform, you get events from an external source by using Kafka Connect. Connectors enable you to stream large volumes of data to and from your Kafka cluster. Confluent publishes many connectors for integrating with external systems, like MongoDB and Elasticsearch. For more information, see the Kafka Connect Overview page.

In this step, you run the Datagen Source Connector to generate mock data. The mock data is stored in the pageviews and users topics that you created previously. To learn more about installing connectors, see Install Self-Managed Connectors.

  1. In the navigation menu, click Connect.

  2. Click the connect-default cluster in the Connect clusters list.

  3. Click Add connector to start creating a connector for pageviews data.

  4. Select the DatagenConnector tile.

    Tip

    To see source connectors only, click Filter by category and select Sources.

  5. In the Name field, enter datagen-pageviews as the name of the connector.

  6. Enter the following configuration values in the following sections:

    Common section:

    • Key converter class: org.apache.kafka.connect.storage.StringConverter

    General section:

    • kafka.topic: Choose pageviews from the dropdown menu
    • max.interval: 100
    • quickstart: pageviews

  7. Click Next to review the connector configuration. When you’re satisfied with the settings, click Launch.

    Reviewing connector configuration in Confluent Control Center

Run a second instance of the Datagen Source connector connector to produce mock data to the users topic.

  1. In the navigation menu, click Connect.

  2. In the Connect clusters list, click connect-default.

  3. Click Add connector.

  4. Select the DatagenConnector tile.

  5. In the Name field, enter datagen-users as the name of the connector.

  6. Enter the following configuration values:

    Common section:

    • Key converter class: org.apache.kafka.connect.storage.StringConverter

    General section:

    • kafka.topic: Choose users from the dropdown menu
    • max.interval: 1000
    • quickstart: users

  7. Click Next to review the connector configuration. When you’re satisfied with the settings, click Launch.

  8. In the navigation menu, click Topics and in the list, click users.

  9. Click Messages to confirm that the datagen-users connector is producing data to the users topic.

    Incoming messages displayed in the Topics page in Confluent Control Center

Inspect the schema of a topic

By default, the Datagen Source Connector produces data in Avro format, which defines the schemas of pageviews and users messages.

Schema Registry ensures that messages sent to your cluster have the correct schema. For more information, see Schema Registry Documentation.

  1. In the navigation menu, click Topics, and in the topic list, click pageviews.

  2. Click Schema to inspect the Avro schema that applies to pageviews message values.

    Your output should resemble:

    ../_images/qs-schema-ex.png

Step 4: Uninstall and clean up

If you’re done working with Confluent Platform, you can stop and remove the Docker containers and images.

  1. Run the following command to stop the Docker containers for Confluent:

    docker-compose stop
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  2. After stopping the Docker containers, run the following commands to prune the Docker system. Running these commands deletes containers, networks, volumes, and images, freeing up disk space:

    docker system prune -a --volumes --filter "label=io.confluent.docker"
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    For more information, refer to the official Docker documentation.

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