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Quick Start for Apache Kafka using Confluent Platform (Docker)

This quick start shows you how to get up and running with Confluent Platform and its main components using Docker containers. This quick start demonstrates both the basic and most powerful capabilities of Confluent Platform, including using Control Center 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 8 GB. When using Docker Desktop for Mac, the default Docker memory allocation is 2 GB. You can change the default allocation to 8 GB in Docker > Preferences > Advanced.
  • Git.
  • Internet connectivity.
  • Ensure you are on an 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. For more details, see this article.

Step 1: Download and Start Confluent Platform Using Docker

  1. Clone the confluentinc/cp-all-in-one GitHub repository and check out the 5.5.15-post branch.

    git clone https://github.com/confluentinc/cp-all-in-one
cd cp-all-in-one
git checkout 5.5.15-post

  2. Navigate to /cp-all-in-one/cp-all-in-one directory.

    cd cp-all-in-one/

  3. Start Confluent Platform specifying the -d option to run in detached mode.

    Important

    You must allocate a minimum of 8 GB of Docker memory resource. The default memory allocation on Docker Desktop for Mac is 2 GB and must be changed.

    docker-compose up -d

    This starts Confluent Platform with separate containers for all Confluent Platform components. 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

  4. Optional: Run this command to verify that the services are up and running.

    docker-compose ps

    You should see 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        ksql http://localhost:8088       Up
ksqldb-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 by 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/ and select your cluster.

    Important

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

    ../_images/c3-landing-page.png

  2. Select Topics from the cluster submenu and click Add a topic.

    ../_images/c3-create-topic-docker.png

  3. Create a topic named pageviews and click Create with defaults.

    ../_images/c3-create-topic-name.png

  4. Repeat the previous steps and create a topic named 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 one instance of the Kafka Connect Datagen connector to produce Kafka data to the pageviews topic in AVRO format.

    1. From your cluster, click Connect.

    2. Select the connect-default cluster and click Add connector.

    3. Find the DatagenConnector tile and click Connect.

      Tip

      To narrow displayed connectors, click Filter by type -> Sources.

      ../_images/connect-page-new-source.png

    4. Name the connector datagen-pageviews. After naming the connector, new fields appear. Scroll down and specify the following configuration values:

      • In the Key converter class field, type org.apache.kafka.connect.storage.StringConverter.
      • In the kafka.topic field, type pageviews.
      • In the max.interval field, type 100.
      • In the quickstart field, type pageviews.
      ../_images/connect-configure-pageviews.png

    5. Click Continue.

    6. Review the connector configuration and click Launch.

      ../_images/connect-review-pageviews.png

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

    1. Select the connect_default cluster and click Add connector.
    2. Find the DatagenConnector tile and click Connect.
    3. Name the connector datagen-users. After naming the connector, new fields appear. Scroll down and specify the following configuration values:
      • In the Key converter class field, type org.apache.kafka.connect.storage.StringConverter.
      • In the kafka.topic field, type users.
      • In the max.interval field, type 1000.
      • In the quickstart field, type users.
    4. Click Continue.
    5. Review the connector configuration and click Launch.

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

In this step, SQL queries are run on the pageviews and users topics that were created in the previous step. The ksqlDB commands are run using the ksqlDB tab in Control Center.

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, ksqlDB is used to create a stream for the pageviews topic, and a table for the users topic.

  1. From your cluster, click ksqlDB and choose the ksqlDB application.

  2. From the ksqlDB EDITOR page, click the Streams tab and Add Stream.

    ../_images/ksql-interface-create-stream1.png

  3. Select the pageviews topic.

    ../_images/c3-ksql-create-stream-pageview.png

  4. Choose your stream options:

    • In the Encoding field, select AVRO.
    • In the Field(s) you’d like to include in your STREAM field, ensure fields are set as follows:
      • viewtime with type BIGINT
      • userid with type VARCHAR
      • pageid with type VARCHAR
    ../_images/c3-ksql-create-stream-pageview-2.png

  5. Click Save STREAM.

  6. Click the Tables tab -> Add a Table and select the users topic.

    ../_images/c3-ksql-create-stream-users.png

  7. Choose your table options:

    • In the Encoding field, select AVRO.
    • In the Key field, select userid.
    • In the Field(s) you’d like to include in your TABLE field, ensure fields are set as follows:
      • registertime with type BIGINT
      • userid with type VARCHAR
      • regionid with type VARCHAR
      • gender with type VARCHAR
    ../_images/c3-ksql-create-table-users.png

  8. Click Save TABLE.

Write Queries

These examples write queries using the ksqlDB tab in Control Center.

  1. From your cluster, click ksqlDB and choose the Editor page.

  2. From the ksqlDB EDITOR page, click Add query properties to add a custom query property. Set the auto.offset.reset parameter to earliest.

    This 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.

    ../_images/c3-ksql-query-properties.png

  3. Run the following queries.

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

      SELECT pageid FROM pageviews EMIT CHANGES LIMIT 3;

      Your output should resemble:

      ../_images/c3-ksql-query-results-pageid.png

      Tip

      Click the Card view or Tabular view icon to change the layout. Click the expand icon to expand a message.

    2. Create a persistent query that filters for female users. The results from this query are written to the Kafka PAGEVIEWS_FEMALE topic. This query enriches the pageviews STREAM by doing a LEFT JOIN with the users TABLE on the user ID, where a condition (gender = 'FEMALE') is met.

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

      Your output should resemble:

      ../_images/c3-ksql-persist-query-pv-female-results.png

    3. Create a persistent query where a condition (regionid) is met, using LIKE. Results from this query are written to a Kafka topic named pageviews_enriched_r8_r9.

      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';

      Your output should resemble:

      ../_images/c3-ksql-persist-query-pv-female89-results.png

    4. 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.

      CREATE TABLE pageviews_regions AS
 SELECT gender, regionid , COUNT(*) AS numusers
 FROM pageviews_female
   WINDOW TUMBLING (size 30 second)
 GROUP BY gender, regionid
 HAVING COUNT(*) > 1;

      Your output should resemble:

      ../_images/c3-ksql-persist-query-table-results.png

    5. Click Running queries. You should see the following persisted queries:

      ../_images/c3-ksql-persistent-query1.png

    6. Click Editor. On the right side of the page, find the All available streams and tables pane, which shows all of the streams and tables that you can access.

      ../_images/c3-ksql-stream-table-view-1.png

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

      ../_images/c3-ksql-stream-table-view-2.png

Step 5: Monitor Consumer Lag

Navigate to the Consumers tab to view the consumers created by ksqlDB.

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

../_images/ksql-interface-monitor.png

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

../_images/ksql-interface-monitor-cnsmgp1.png

For more information, see the Control Center Consumers documentation.

Step 6: Stop Docker

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

  1. View a list of all Docker container IDs.

    docker container ls -aq

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

    docker container stop $(docker container ls -a -q -f "label=io.confluent.docker")

  3. Run the following commands to stop the containers and prune the Docker system. Running these commands deletes containers, networks, volumes, and images; freeing up disk space:

    docker container stop $(docker container ls -a -q -f "label=io.confluent.docker") && docker system prune -a -f --volumes

    Tip

    Remove the filter label for Confluent Docker (-f "label=io.confluent.docker") to clear all Docker containers from your system.

You can rebuild and restart the containers at any time using the docker-compose up -d command.

For more information, refer to the official Docker documentation.

Troubleshooting

If you encountered any issues, 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.

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

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

Resolution: In Kafka Connect > Setup Connection, scroll down through the list of connectors to locate DatagenConnector; there are multiple connectors in the menu.

Issue: Stream-Stream joins error

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

../_images/c3-ksql-stream-stream-join-error.png

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.

Next Steps

Learn more about the components shown in this quick start: