Quick Start for Confluent Platform (Docker)

Use this quick start to get up and running with Confluent Platform and its main components, using Docker containers. This quick start uses Confluent Control Center included in Confluent Platform for topic management and event stream processing using ksqlDB.

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 Control Center to monitor and analyze the event streaming queries.

See also

You can also run an automated version of this quick start designed for Confluent Platform local installs.

Prerequisites:

• Docker
  • Docker version 1.11 or later is installed and running.
  • Docker Compose is installed. Docker Compose is installed by default with Docker for Mac.
  • Docker memory is allocated minimally at 6 GB. When using Docker Desktop for Mac, the default Docker memory allocation is 2 GB. You can change the default allocation to 6 GB in Docker. Navigate to Preferences > Resources > Advanced.
• Internet connectivity
• Operating System currently supported by Confluent Platform
• Networking and Kafka on Docker
  • Configure your hosts and ports to allow both internal and external components to the Docker network to communicate.

Step 1: Download and Start Confluent Platform Using Docker

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

   curl --silent --output docker-compose.yml \
     https://raw.githubusercontent.com/confluentinc/cp-all-in-one/6.2.15-post/cp-all-in-one/docker-compose.yml
   

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

   docker-compose up -d
   

   The above command starts Confluent Platform with a separate container for each Confluent Platform component. Your output should resemble the following:

   Creating network "cp-all-in-one_default" with the default driver
   Creating zookeeper ... done
   Creating broker    ... done
   Creating schema-registry ... done
   Creating rest-proxy      ... done
   Creating connect         ... done
   Creating ksql-datagen    ... done
   Creating ksqldb-server   ... done
   Creating control-center  ... done
   Creating ksqldb-cli      ... done
   

3. To verify that the services are up and running, run the following command:

   docker-compose ps
   

   Your output should resemble the following:

        Name                    Command               State                Ports
   ------------------------------------------------------------------------------------------
   broker            /etc/confluent/docker/run        Up      0.0.0.0:29092->29092/tcp,
                                                              0.0.0.0:9092->9092/tcp
   connect           /etc/confluent/docker/run        Up      0.0.0.0:8083->8083/tcp,
                                                              9092/tcp
   control-center    /etc/confluent/docker/run        Up      0.0.0.0:9021->9021/tcp
   ksqldb-cli        /bin/sh                          Up
   ksql-datagen      bash -c echo Waiting for K ...   Up
   ksqldb-server     /etc/confluent/docker/run        Up      0.0.0.0:8088->8088/tcp
   rest-proxy        /etc/confluent/docker/run        Up      0.0.0.0:8082->8082/tcp
   schema-registry   /etc/confluent/docker/run        Up      0.0.0.0:8081->8081/tcp
   zookeeper         /etc/confluent/docker/run        Up      0.0.0.0:2181->2181/tcp,
                                                              2888/tcp, 3888/tcp
   

   If the state is not Up, rerun the docker-compose up -d command.

Step 2: Create Kafka Topics

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

1. Navigate to the Control Center web interface at http://localhost:9021.

   If you installed Confluent Platform on a different host, replace localhost with the host name in the address.

   It may take a minute or two for Control Center to come online.

   Note

   Control Center won’t connect to ksqlDB if Control Center isn’t open and running in a localhost browser session.

2. Click the controlcenter.cluster tile.

   

3. In the navigation bar, click Topics to open the topics list, and then click Add a topic.

   

4. In the Topic name field, specify pageviews and click Create with defaults.

   Note that topic names are case-sensitive.

   

5. In the navigation bar, click Topics to open the topics list, and then click Add a topic.

6. In the Topic name field, specify users and click Create with defaults.

Step 3: Install a Kafka Connector and Generate Sample Data

In this step, you use Kafka Connect to run a demo source connector called kafka-connect-datagen that creates sample data for the Kafka topics pageviews and users.

Tip

The Kafka Connect Datagen connector was installed automatically when you started Docker Compose in Step 1: Download and Start Confluent Platform Using Docker. If you encounter issues locating the Datagen Connector, refer to the Issue: Cannot locate the Datagen connector in the Troubleshooting section.

1. Run the first instance of the Kafka Connect Datagen connector to produce Kafka data to the pageviews topic in AVRO format.

   1. In the navigation bar, click Connect.

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

   3. Click Add connector.

   4. Select the DatagenConnector tile.

      Tip

      To narrow displayed connectors, click Filter by category and click Sources.

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

   6. Enter the following configuration values:

      • Key converter class: org.apache.kafka.connect.storage.StringConverter.
      • kafka.topic: pageviews.
      • max.interval: 100.
      • quickstart: pageviews.

   7. Click Next.

   8. Review the connector configuration and click Launch.

      

2. Run the second instance of the Kafka Connect Datagen connector to produce Kafka data to the users topic in AVRO format.

   1. Click Add connector.

   2. Select the DatagenConnector tile.

      Tip

      To narrow displayed connectors, click Filter by category and click Sources.

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

   4. Enter the following configuration values:

      • Key converter class: org.apache.kafka.connect.storage.StringConverter
      • kafka.topic: users
      • max.interval: 1000
      • quickstart: users

   5. Click Next.

   6. Review the connector configuration and click Launch.

Step 4: Create and Write to a Stream and Table using ksqlDB

Tip

You can also run these commands using the ksqlDB CLI from your Docker container with this command: docker-compose exec ksqldb-cli ksql http://ksqldb-server:8088.

Create Streams and Tables

In this step, you use ksqlDB to create a stream for the pageviews topic and a table for the users topic.

1. In the navigation bar, click ksqlDB.

2. Select the ksqlDB application.

3. Copy the following code into the editor window and click Run query to create the pageviews stream. Stream names are not case-sensitive.

   CREATE STREAM pageviews WITH (KAFKA_TOPIC='pageviews', VALUE_FORMAT='AVRO');
   

4. Copy the following code into the editor window and click Run query to create the users table. Table names are not case-sensitive.

   CREATE TABLE users (id VARCHAR PRIMARY KEY)
     WITH (KAFKA_TOPIC='users', VALUE_FORMAT='AVRO');
   

Write Queries

In this step, you create ksqlDB queries against the stream and the table you created above.

1. In the Editor tab, click Add query properties to add a custom query property.

2. Set the auto.offset.reset parameter to Earliest.

   The setting instructs ksqlDB queries to read all available topic data from the beginning. This configuration is used for each subsequent query. For more information, see the ksqlDB Configuration Parameter Reference.

3. Create the following queries.

   1. Click Stop to stop the current running query.

   2. Create a non-persistent query that returns data from a stream with the results limited to a maximum of three rows:

      Enter the following query in the editor:

      SELECT pageid FROM pageviews EMIT CHANGES LIMIT 3;
      

   3. Click Run query. Your output should resemble:

      

      Click the Card view or Table view icon to change the output layout.

   4. Create a persistent query (as a stream) that filters the PAGEVIEWS stream for female users. The results from this query are written to the Kafka PAGEVIEWS_FEMALE topic:

      Enter the following query in the editor:

      CREATE STREAM pageviews_female
        AS SELECT users.id AS userid, pageid, regionid
        FROM pageviews LEFT JOIN users ON pageviews.userid = users.id
        WHERE gender = 'FEMALE'
        EMIT CHANGES;
      

   5. Click Run query. Your output should resemble:

      

   6. Create a persistent query where REGIONID ends with 8 or 9. Results from this query are written to the Kafka topic named pageviews_enriched_r8_r9 as explicitly specified in the query:

      Enter the following query in the editor:

      CREATE STREAM pageviews_female_like_89
        WITH (KAFKA_TOPIC='pageviews_enriched_r8_r9', VALUE_FORMAT='AVRO')
        AS SELECT * FROM pageviews_female
        WHERE regionid LIKE '%_8' OR regionid LIKE '%_9'
        EMIT CHANGES;
      

   7. Click Run query. Your output should resemble:

      

   8. Create a persistent query that counts the PAGEVIEWS for each REGION and GENDER combination in a tumbling window of 30 seconds when the count is greater than 1. Because the procedure is grouping and counting, the result is now a table, rather than a stream. Results from this query are written to a Kafka topic called PAGEVIEWS_REGIONS:

      Enter the following query in the editor:

      CREATE TABLE pageviews_regions WITH (KEY_FORMAT='JSON')
        AS SELECT gender, regionid, COUNT(*) AS numusers
        FROM pageviews LEFT JOIN users ON pageviews.userid = users.id
        WINDOW TUMBLING (SIZE 30 SECOND)
        GROUP BY gender, regionid
        HAVING COUNT(*) > 1
        EMIT CHANGES;
      

   9. Click Run query. Your output should resemble:

      

   10. Click the Persistent queries tab. You should see the following persisted queries:

       • PAGEVIEWS_FEMALE
       • PAGEVIEWS_FEMALE_LIKE_89
       • PAGEVIEWS_REGIONS

   11. Click the Editor tab. The All available streams and tables pane shows all of the streams and tables that you can access.

       

   12. In the All available streams and tables section, click KSQL_PROCESSING_LOG to view the stream’s schema, including nested data structures.

Run Queries

In this step, you run the ksqlDB queries you save as streams and tables above in the previous section.

1. In the Streams tab, select the PAGEVIEWS_FEMALE stream.

2. Click Query stream.

   The editor opens, and streaming output of the query displays.

3. Click Stop to stop the output generation.

4. In the Tables tab, select PAGEVIEWS_REGIONS table.

5. Click Query table.

   The editor opens, and streaming output of the query displays.

6. Click Stop to stop the output generation.

Step 5: Monitor Consumer Lag

1. In the navigation bar, click Consumers to view the consumers created by ksqlDB.

2. Click the consumer group ID to view details for the _confluent-ksql-default_query_CSAS_PAGEVIEWS_FEMALE_5 consumer group.

   From the page, you can see the consumer lag and consumption values for your streaming query.

For more information, see the Control Center Consumers documentation.

Step 6: Stop Confluent Containers and Clean Up

When you are done working with Docker, you can stop and remove Docker containers and images.

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

   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:

   docker system prune -a --volumes --filter "label=io.confluent.docker"
   

For more information, refer to the official Docker documentation.

Next Steps

Learn more about the components shown in this quick start:

• ksqlDB documentation Learn about processing your data with ksqlDB for use cases such as streaming ETL, real-time monitoring, and anomaly detection. You can also learn how to use ksqlDB with this collection of scripted demos.
• Kafka Tutorials Try out basic Kafka, Kafka Streams, and ksqlDB tutorials with step-by-step instructions.
• Kafka Streams documentation Learn how to build stream processing applications in Java or Scala.
• Kafka Connect documentation Learn how to integrate Kafka with other systems and download ready-to-use connectors to easily ingest data in and out of Kafka in real-time.
• Kafka Clients documentation Learn how to read and write data to and from Kafka using programming languages such as Go, Python, .NET, C/C++.

Troubleshooting

If you encountered any issues while going through the quickstart workflow, review the following resolutions before trying the steps again.

Issue: Cannot locate the Datagen connector

For details, see Step 1: Download and Start Confluent Platform Using Docker.

Resolution: Run the build command just for connect if the connect container was not built successfully.

docker-compose build --no-cache connect

Your output should resemble:

Building connect
...
Completed
Removing intermediate container cdb0af3550c8
---> 36d00047d29b
Successfully built 36d00047d29b
Successfully tagged confluentinc/kafka-connect-datagen:latest

If the connect container was already built successfully, you will see an output similar to this:

connect uses an image, skipping

Resolution: Check the Connect log for Datagen.

docker-compose logs connect | grep -i Datagen

Your output should resemble:

connect  | [2019-04-17 20:03:26,137] INFO Loading plugin from: /usr/share/confluent-hub-components/confluentinc-kafka-connect-datagen (org.apache.kafka.connect.runtime.isolation.DelegatingClassLoader)
connect  | [2019-04-17 20:03:26,206] INFO Registered loader: PluginClassLoader{pluginLocation=file:/usr/share/confluent-hub-components/confluentinc-kafka-connect-datagen/} (org.apache.kafka.connect.runtime.isolation.DelegatingClassLoader)
connect  | [2019-04-17 20:03:26,206] INFO Added plugin 'io.confluent.kafka.connect.datagen.DatagenConnector' (org.apache.kafka.connect.runtime.isolation.DelegatingClassLoader)
connect  | [2019-04-17 20:03:28,102] INFO Added aliases 'DatagenConnector' and 'Datagen' to plugin 'io.confluent.kafka.connect.datagen.DatagenConnector' (org.apache.kafka.connect.runtime.isolation.DelegatingClassLoader)

Resolution: Check the Connect log for a warning and reminder to run the docker-compose up -d command properly.

docker-compose logs connect | grep -i Datagen

Resolution: Verify the .jar file for kafka-connect-datagen has been added and is present in the lib subfolder.

docker-compose exec connect ls /usr/share/confluent-hub-components/confluentinc-kafka-connect-datagen/lib/

Your output should resemble:

...
kafka-connect-datagen-0.1.0.jar
...

Resolution: Verify the plugin exists in the connector path.

docker-compose exec connect bash -c 'echo $CONNECT_PLUGIN_PATH'

Your output should resemble:

/usr/share/java,/usr/share/confluent-hub-components

Confirm its contents are present:

docker-compose exec connect ls /usr/share/confluent-hub-components/confluentinc-kafka-connect-datagen

Your output should resemble:

assets   doc  etc  lib  manifest.json

Issue: Stream-Stream joins error

An error states Stream-Stream joins must have a WITHIN clause specified. This error can occur if you created both pageviews and users as streams by mistake.

Resolution: Ensure that you created a stream for pageviews, and a table for users in Step 4: Create and Write to a Stream and Table using ksqlDB.

Issue: Unable to successfully complete ksqlDB query steps

Java errors or other severe errors were encountered.

Resolution: Ensure you are on an Operating System currently supported by Confluent Platform.

Resolution: Ensure that the Docker memory was increased to 8 MB. Go to Docker > Preferences > Advanced. If Docker memory is insufficient, other unpredictable issues could occur.

Issue: Demo times out, some or all components do not start

You must allocate a minimum of 6 GB of Docker memory resource. The default memory allocation on Docker Desktop for Mac is 2 GB and must be changed. Confluent Platform demos and examples running on Docker may fail to work properly if Docker memory allocation does not meet this minimum requirement.

Memory settings on Docker preferences for resources