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Groovy

In this tutorial, you will run a Groovy client application that produces messages to and consumes messages from an Apache Kafka® cluster.

After you run the tutorial, view the provided source code and use it as a reference to develop your own Kafka client application.

Prerequisites

Client

• Java 1.8 or higher to run the demo application.

Kafka Cluster

• You can use this tutorial with a Kafka cluster in any environment:
  • In Confluent Cloud
  • On your local host
  • Any remote Kafka cluster
• If you are running on Confluent Cloud, you must have access to a Confluent Cloud cluster
  • The first 20 users to sign up for Confluent Cloud and use promo code C50INTEG will receive an additional $50 free usage (details)

Setup

1. Clone the confluentinc/examples GitHub repository and check out the 5.5.15-post branch.

   git clone https://github.com/confluentinc/examples
   cd examples
   git checkout 5.5.15-post
   

2. Change directory to the example for Groovy.

   cd clients/cloud/groovy/
   

3. Create a local file (for example, at $HOME/.confluent/java.config) with configuration parameters to connect to your Kafka cluster. Starting with one of the templates below, customize the file with connection information to your cluster. Substitute your values for {{ BROKER_ENDPOINT }}, {{CLUSTER_API_KEY }}, and {{ CLUSTER_API_SECRET }} (see Connecting Clients to Confluent Cloud for instructions on how to create or find those values).

   • Template configuration file for Confluent Cloud

     # Kafka
     bootstrap.servers={{ BROKER_ENDPOINT }}
     security.protocol=SASL_SSL
     sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username="{{ CLUSTER_API_KEY }}" password="{{ CLUSTER_API_SECRET }}";
     ssl.endpoint.identification.algorithm=https
     sasl.mechanism=PLAIN
     

   • Template configuration file for local host

     # Kafka
     bootstrap.servers=localhost:9092
     

Basic Producer and Consumer

In this example, the producer application writes Kafka data to a topic in your Kafka cluster. If the topic does not already exist in your Kafka cluster, the producer application will use the Kafka Admin Client API to create the topic. Each record written to Kafka has a key representing a username (for example, alice) and a value of a count, formatted as json (for example, {"count": 0}). The consumer application reads the same Kafka topic and keeps a rolling sum of the count as it processes each record.

Produce Records

1. Build the client examples:

   ./gradlew clean build
   

2. Run the producer, passing in arguments for:

   • the local file with configuration parameters to connect to your Kafka cluster
   • the topic name

   ./gradlew runApp -PmainClass="io.confluent.examples.clients.cloud.ProducerExample" \
      -PconfigPath="$HOME/.confluent/java.config" \
      -Ptopic="test1"
   

3. Verify the producer sent all the messages. You should see:

   ...
   Producing record: alice        {"count":0}
   Producing record: alice        {"count":1}
   Producing record: alice        {"count":2}
   Producing record: alice        {"count":3}
   Producing record: alice        {"count":4}
   Producing record: alice        {"count":5}
   Producing record: alice        {"count":6}
   Producing record: alice        {"count":7}
   Producing record: alice        {"count":8}
   Producing record: alice        {"count":9}
   Produced record to topic test1 partition [0] @ offset 0
   Produced record to topic test1 partition [0] @ offset 1
   Produced record to topic test1 partition [0] @ offset 2
   Produced record to topic test1 partition [0] @ offset 3
   Produced record to topic test1 partition [0] @ offset 4
   Produced record to topic test1 partition [0] @ offset 5
   Produced record to topic test1 partition [0] @ offset 6
   Produced record to topic test1 partition [0] @ offset 7
   Produced record to topic test1 partition [0] @ offset 8
   Produced record to topic test1 partition [0] @ offset 9
   10 messages were produced to topic test1
   ...
   

4. View the producer code.

Consume Records

1. Run the consumer, passing in arguments for:

   • the local file with configuration parameters to connect to your Kafka cluster
   • the same topic name you used earlier.

   ./gradlew runApp -PmainClass="io.confluent.examples.clients.cloud.ConsumerExample"\
        -PconfigPath="$HOME/.confluent/java.config"\
        -Ptopic="test1"
   

2. Verify the consumer received all the messages. You should see:

   ...
   Consumed record with key alice and value {"count":0}, and updated total count to 0
   Consumed record with key alice and value {"count":1}, and updated total count to 1
   Consumed record with key alice and value {"count":2}, and updated total count to 3
   Consumed record with key alice and value {"count":3}, and updated total count to 6
   Consumed record with key alice and value {"count":4}, and updated total count to 10
   Consumed record with key alice and value {"count":5}, and updated total count to 15
   Consumed record with key alice and value {"count":6}, and updated total count to 21
   Consumed record with key alice and value {"count":7}, and updated total count to 28
   Consumed record with key alice and value {"count":8}, and updated total count to 36
   Consumed record with key alice and value {"count":9}, and updated total count to 45
   

3. When you are done, press CTRL-C.

4. View the consumer code.

Kafka Streams

1. Run the Kafka Streams application, passing in arguments for:

   • the local file with configuration parameters to connect to your Kafka cluster
   • the same topic name you used earlier

   ./gradlew runApp -PmainClass="io.confluent.examples.clients.cloud.StreamsExample" \
        -PconfigPath="$HOME/.confluent/java.config" \
        -Ptopic="test1"
   

2. Verify the consumer received all the messages. You should see:

   ...
   [Consumed record]: alice, 0
   [Consumed record]: alice, 1
   [Consumed record]: alice, 2
   [Consumed record]: alice, 3
   [Consumed record]: alice, 4
   [Consumed record]: alice, 5
   [Consumed record]: alice, 6
   [Consumed record]: alice, 7
   [Consumed record]: alice, 8
   [Consumed record]: alice, 9
   ...
   [Running count]: alice, 0
   [Running count]: alice, 1
   [Running count]: alice, 3
   [Running count]: alice, 6
   [Running count]: alice, 10
   [Running count]: alice, 15
   [Running count]: alice, 21
   [Running count]: alice, 28
   [Running count]: alice, 36
   [Running count]: alice, 45
   ...
   

3. When you are done, press CTRL-C.

4. View the Kafka Streams code.