# Quick Start for Confluent Platform Confluent Platform is a data-streaming platform that completes Apache Kafka® with advanced capabilities designed to help accelerate application development and connectivity for enterprise use cases. This quick start will help you get up and running locally with Confluent Platform and its main components using either Docker containers or ZIP/TAR archives. For production installation methods, see [Install Confluent Platform On-Premises](../installation/overview.md#installation). In this quick start, you create Apache Kafka® topics, use Kafka Connect to generate mock data to those topics, and use Confluent Control Center to view your data. ## Prerequisites Prerequisites vary based on your installation method. Choose your preferred method below. ### Docker To run this quick start using Docker, you need Git, Docker, and Docker Compose installed on a computer with a supported [Operating System](../installation/versions-interoperability.md#operating-systems). Make sure Docker is running. ### **Detailed prerequisites** - A connection to the internet. - [Operating System](../installation/versions-interoperability.md#operating-systems) 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](https://www.confluent.io/blog/set-up-and-run-kafka-on-windows-and-wsl-2/). - Docker version 1.11 or later is [installed and running](https://docs.docker.com/engine/installation/). - **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**. ### ZIP/TAR To run this quick start using ZIP/TAR archives, you need the following: - A connection to the internet. - [Operating System](../installation/versions-interoperability.md#operating-systems) currently supported by Confluent Platform. - Java 25, Java 21, or Java 17 installed. Starting with Confluent Platform 8.3.x, Java 25 is the recommended version. For more information, see [Java](../installation/versions-interoperability.md#java-sys-req). #### NOTE The `confluent local` commands are intended for single-node development environments and are not suitable for production. For production deployments, see [Install and Upgrade Confluent Platform](../installation/index.md#installation-overview). ## Step 1: Download and start Confluent Platform Choose your preferred installation method using the tabs below. ### Docker In this step, you start by cloning a GitHub repository. This repository contains a Docker compose file and some required configuration files. 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](../installation/docker/config-reference.md#config-reference). 1. Clone the [Confluent Platform all-in-one example repository](https://github.com/confluentinc/cp-all-in-one/tree/latest/cp-all-in-one), for example: ```bash git clone https://github.com/confluentinc/cp-all-in-one.git ``` 2. Change to the cloned repository’s root directory: ```bash cd cp-all-in-one ``` 3. The default branch may not be the latest. Check out the branch for the version you want to run, for example, 8.3.1-post: ```bash git checkout 8.3.1-post ``` 4. The `docker-compose.yml` file is located in a nested directory. Navigate into the following directory: ```bash cd cp-all-in-one ``` 5. Start the Confluent Platform stack with the `-d` option to run in detached mode: ```bash docker compose up -d ``` #### NOTE If you are using Docker Compose V1, you need to use a dash in the `docker compose` commands. For example: ```bash docker-compose up -d ``` To learn more, see [Migrate to Compose V2](https://docs.docker.com/compose/releases/migrate/). 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. ```bash ✔ Network cp-all-in-one_default Created 0.0s ✔ Container broker Started 2.1s ✔ Container flink-jobmanager Started 2.1s ✔ Container prometheus Started 2.2s ✔ Container schema-registry Started 0.6s ✔ Container flink-taskmanager Started 0.5s ✔ Container flink-sql-client Started 0.6s ✔ Container alertmanager Started 0.6s ✔ Container rest-proxy Started 0.6s ✔ Container connect Started 0.6s ✔ Container ksqldb-server Started 0.6s ✔ Container control-center Started 0.7s ``` 6. Verify that the services are up and running: ```bash docker compose ps ``` Your output should resemble: ```none NAME IMAGE COMMAND SERVICE CREATED STATUS PORTS alertmanager confluentinc/cp-enterprise-alertmanager:2.5.0 "alertmanager-start" alertmanager 2 minutes ago Up 2 minutes 0.0.0.0:9093->9093/tcp, [::]:9093->9093/tcp broker confluentinc/cp-server:8.3.0 "/etc/confluent/dock…" broker 2 minutes ago Up 2 minutes 0.0.0.0:9092->9092/tcp, [::]:9092->9092/tcp, 0.0.0.0:9101->9101/tcp, [::]:9101->9101/tcp connect cnfldemos/cp-server-connect-datagen:0.6.4-7.6.0 "/etc/confluent/dock…" connect 2 minutes ago Up 2 minutes 0.0.0.0:8083->8083/tcp, [::]:8083->8083/tcp control-center confluentinc/cp-enterprise-control-center-next-gen:2.5.0 "/etc/confluent/dock…" control-center 2 minutes ago Up 2 minutes 0.0.0.0:9021->9021/tcp, [::]:9021->9021/tcp flink-jobmanager cnfldemos/flink-kafka:1.19.1-scala_2.12-java17 "/docker-entrypoint.…" flink-jobmanager 2 minutes ago Up 2 minutes 0.0.0.0:9081->9081/tcp, [::]:9081->9081/tcp flink-sql-client cnfldemos/flink-sql-client-kafka:1.19.1-scala_2.12-java17 "/docker-entrypoint.…" flink-sql-client 2 minutes ago Up 2 minutes 6123/tcp, 8081/tcp flink-taskmanager cnfldemos/flink-kafka:1.19.1-scala_2.12-java17 "/docker-entrypoint.…" flink-taskmanager 2 minutes ago Up 2 minutes 6123/tcp, 8081/tcp ksqldb-server confluentinc/cp-ksqldb-server:8.3.0 "/etc/confluent/dock…" ksqldb-server 2 minutes ago Up 2 minutes 0.0.0.0:8088->8088/tcp, [::]:8088->8088/tcp prometheus confluentinc/cp-enterprise-prometheus:2.5.0 "prometheus-start" prometheus 2 minutes ago Up 2 minutes 0.0.0.0:9090->9090/tcp, [::]:9090->9090/tcp rest-proxy confluentinc/cp-kafka-rest:8.3.0 "/etc/confluent/dock…" rest-proxy 2 minutes ago Up 2 minutes 0.0.0.0:8082->8082/tcp, [::]:8082->8082/tcp schema-registry confluentinc/cp-schema-registry:8.3.0 "/etc/confluent/dock…" schema-registry 2 minutes ago Up 2 minutes 0.0.0.0:8081->8081/tcp, [::]:8081->8081/tcp ``` 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 `, for example: ```bash docker compose restart control-center ``` ### ZIP/TAR In the next few steps, download and extract Confluent Platform, then use the Confluent CLI to start Confluent Platform services locally in KRaft mode. This method starts the core Confluent Platform components including a KRaft controller, broker, Schema Registry, REST Proxy, Connect, and ksqlDB. Download and start Control Center separately. #### IMPORTANT The Confluent CLI [confluent local](https://docs.confluent.io/confluent-cli/current/command-reference/local/index.html) commands are intended for a single-node development environment and are not suitable for a production environment. The data that are produced are transient and are intended to be temporary. For production-ready workflows, see [Install and Upgrade Confluent Platform](../installation/index.md#installation-overview). 1. Download the Confluent Platform TAR archive and extract it: ```bash curl -O https://packages.confluent.io/archive/8.3/confluent-8.3.1.tar.gz ``` ```bash tar -xvf confluent-8.3.1.tar.gz ``` ```bash cd confluent-8.3.1 ``` ```bash export CONFLUENT_HOME=`pwd` ``` 2. Add the Confluent Platform `bin` directory to your PATH: ```bash export PATH=$PATH:$CONFLUENT_HOME/bin ``` 3. Install the Connect Datagen source connector using the Confluent Marketplace client. This connector generates mock data for demonstration purposes and is not suitable for production. For more connectors, see [Confluent Hub](https://www.confluent.io/hub). ```bash confluent-hub install --no-prompt confluentinc/kafka-connect-datagen:latest ``` 4. Start all Confluent Platform services: ```bash confluent local services start ``` Every service starts in order, printing a message with its status: ```bash Starting KRaft Controller KRaft Controller is [UP] Starting Kafka Kafka is [UP] Starting Schema Registry Schema Registry is [UP] Starting Kafka REST Kafka REST is [UP] Starting Connect Connect is [UP] Starting ksqlDB Server ksqlDB Server is [UP] ``` 5. Verify that the services are running: ```bash confluent local services status ``` Now, install and configure Control Center to work with your local Confluent Platform installation. As of Confluent Platform 8.0, Confluent Control Center is installed and runs separately from Confluent Platform. Follow these steps to install and configure Control Center to work with your local Confluent Platform installation. Installing Control Center requires three additional terminal windows for Prometheus, Alert Manager, and Control Center. 6. Start a new terminal window, then download and extract Control Center. ```bash curl -O https://packages.confluent.io/confluent-control-center-next-gen/archive/confluent-control-center-next-gen-2.3.1.tar.gz ``` ```bash tar -xvf confluent-control-center-next-gen-2.3.1.tar.gz ``` ```bash cd confluent-control-center-next-gen-2.3.1 ``` 7. Set the `CONTROL_CENTER_HOME` environment variable: ```bash export CONTROL_CENTER_HOME=`pwd` ``` 8. Configure Prometheus to connect to Alert Manager on a non-default port. By default, Alert Manager and the KRaft controller (started in step 3) both use port 9093. To avoid a conflict, you must configure Alert Manager to use port 9098. Edit `etc/confluent-control-center/prometheus-generated.yml`, locate the `alertmanagers` section, and change the target port from 9093 to 9098. ```yaml alerting: alertmanagers: - static_configs: - targets: - localhost:9098 ``` 9. In the same terminal window or a new one, start Prometheus. If Prometheus starts successfully, you will not see any output. - On Linux or Windows: ```bash bin/prometheus-start ``` - On macOS: ```bash bash bin/prometheus-start ``` 10. In a new terminal window, navigate to the Control Center directory and start Alert Manager. If Alert Manager starts successfully, you will not see any output. 1. Navigate to the directory and set the home variable: ```bash cd confluent-control-center-next-gen-2.3.1 ``` ```bash export CONTROL_CENTER_HOME=`pwd` ``` 2. Start Alert Manager using the `ALERTMANAGER_PORT` environment variable to avoid the port conflict: - On Linux or Windows: ```bash export ALERTMANAGER_PORT=9098 bin/alertmanager-start ``` - On macOS: ```bash export ALERTMANAGER_PORT=9098 bash bin/alertmanager-start ``` 11. In another new terminal window, start Control Center: 1. Navigate to the Control Center directory: ```bash cd confluent-control-center-next-gen-2.3.1 ``` 2. Set the home variable: ```bash export CONTROL_CENTER_HOME=`pwd` ``` 3. Configure Control Center to connect to Alert Manager on the new port. Edit `$CONTROL_CENTER_HOME/etc/confluent-control-center/control-center-dev.properties` and uncomment or add the following line: ```properties confluent.controlcenter.alertmanager.url=http://localhost:9098 ``` 4. Run the start script with your updated properties file: ```bash ./bin/control-center-start $CONTROL_CENTER_HOME/etc/confluent-control-center/control-center-dev.properties ``` 12. Open Control Center in your browser at [http://localhost:9021](http://localhost:9021). It may take a few minutes for Control Center to start and load. For more details on Control Center installation, see [Control Center Installation](/control-center/current/installation/overview.html#archive). ## Step 2: Create Kafka topics for storing your data In Confluent Platform, real-time streaming events are stored in a [topic](../_glossary.md#term-topic), which is an append-only log, and the fundamental unit of organization for Kafka. To learn more about Kafka basics, see [Kafka Introduction](/kafka/introduction.html). In this step, you create two topics by using [Control Center for Confluent Platform](https://docs.confluent.io/control-center/current/overview.html). Control Center provides the features for building and monitoring production data pipelines and [event streaming](../_glossary.md#term-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](http://localhost:9021). It takes a few minutes for Control Center to start and load. If needed, refresh your browser until it loads. 2. Click the **controlcenter.cluster** tile.  3. In the navigation menu, click **Topics** to open the topics list. Click **+ Add topic** to start creating the `pageviews` topic.  4. In the **Topic name** field, enter `pageviews` and click **Create with defaults**. Topic names *are* case-sensitive.  ### 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.  ## Step 3: Generate mock data In Confluent Platform, you get [events](../_glossary.md#term-event) from an external source by using [Kafka Connect](../connect/index.md#connect-concepts). Connectors enable you to stream large volumes of data to and from your [Kafka cluster](../_glossary.md#term-Kafka-cluster). Confluent publishes many connectors for integrating with external systems, like MongoDB and Elasticsearch. For more information, see the [Kafka Connect Overview](../connect/index.md#kafka-connect) page. In this step, you run the [Datagen Source Connector](https://www.confluent.io/hub/confluentinc/kafka-connect-datagen) 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 for Confluent Platform](../connect/install.md#connect-install-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. 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**.  Run a second instance of the [Datagen Source connector](https://www.confluent.io/hub/confluentinc/kafka-connect-datagen) 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.  ### 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](/platform/current/schema-registry/index.html). 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:  ## Step 4: Uninstall and clean up When you’re done working with Confluent Platform, clean up using the method that matches your installation. ### Docker Stop and remove the Docker containers and images. 1. Run the following command to stop the Docker containers for Confluent: ```bash docker compose stop ``` 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: ```bash docker system prune -a --volumes --filter "label=io.confluent.docker" ``` For more information, refer to the official [Docker](https://docs.docker.com/) documentation. ### ZIP/TAR Stop all services and clean up the temporary data. 1. If you installed Control Center, stop it first by pressing `Ctrl+C` in the terminal windows running Control Center, Alert Manager, and Prometheus. 2. In the terminal window running Confluent services, stop all Confluent Platform services and delete the temporary data: ```bash confluent local services stop ``` Your output should resemble: ```bash Stopping ksqlDB Server ksqlDB Server is [DOWN] Stopping Connect Connect is [DOWN] Stopping Kafka REST Kafka REST is [DOWN] Stopping Schema Registry Schema Registry is [DOWN] Stopping Kafka Kafka is [DOWN] Stopping KRaft Controller KRaft Controller is [DOWN] ``` 3. To stop all services and delete all temporary data created during this quick start: ```bash confluent local destroy ``` This removes all data stored in the temporary directory used by `confluent local`. ## Related content - To download an automated version of this quick start, see [the Quick Start on GitHub](https://github.com/confluentinc/examples/tree/latest/cp-quickstart/README.md) - To configure and run a multi-broker cluster without Docker, see [Tutorial: Set Up a Multi-Broker Kafka Cluster](tutorial-multi-broker.md#basics-multi-broker-setup) - For alternative installation methods, you can use a [TAR or ZIP archive](../installation/installing_cp/zip-tar.md#prod-kafka-cli-install), or package managers like `systemd`, [Confluent for Kubernetes](https://docs.confluent.io/operator/current/overview.html), and [Ansible](https://docs.confluent.io/ansible/current/overview.html) - To learn how to develop with Confluent Platform, see [Confluent Developer](https://developer.confluent.io/learn-kafka) - To get started with Confluent Platform for Apache Flink in Confluent Control Center, see [Use Confluent Control Center with Confluent Manager for Apache Flink](/control-center/current/cmf.html) - For training and certification guidance, including resources and access to hands-on training and certification exams, see [Confluent Education](https://www.confluent.io/training) - To try out basic Kafka, Kafka Streams, and ksqlDB tutorials with step-by-step instructions, see [Kafka Tutorials](https://developer.confluent.io/tutorials/) - To learn how to build stream processing applications in Java or Scala, see [Kafka Streams documentation](../streams/overview.md#kafka-streams) - To learn how to read and write data to and from Kafka using programming languages such as Go, Python, .NET, C/C++, see [Kafka Clients documentation](../clients/overview.md#kafka-clients)