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Java

In this tutorial, you will run a Java 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.
• Maven to compile 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 Java.

   cd clients/cloud/java/
   

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 and Kafka Streams

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. Compile the Java code.

   mvn clean package
   

2. Run the producer, passing in arguments for:

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

   mvn exec:java -Dexec.mainClass="io.confluent.examples.clients.cloud.ProducerExample" \
   -Dexec.args="$HOME/.confluent/java.config test1"
   

3. Verify that 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 topic name you used earlier

   mvn exec:java -Dexec.mainClass="io.confluent.examples.clients.cloud.ConsumerExample" \
   -Dexec.args="$HOME/.confluent/java.config 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 topic name you used earlier

   mvn exec:java -Dexec.mainClass="io.confluent.examples.clients.cloud.StreamsExample" \
   -Dexec.args="$HOME/.confluent/java.config test1"
   

2. Verify that the Kafka Streams application processed 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.

Avro and Confluent Cloud Schema Registry

This example is similar to the previous example, except the value is formatted as Avro and integrates with the Confluent Cloud Schema Registry. Before using Confluent Cloud Schema Registry, check its availability and limits.

1. As described in the Schema Registry and Confluent Cloud in the Confluent Cloud GUI, enable Confluent Cloud Schema Registry and create an API key and secret to connect to it.

2. Verify that your VPC can connect to the Confluent Cloud Schema Registry public internet endpoint.

3. Update your local configuration file (for example, at $HOME/.confluent/java.config) with parameters to connect to Schema Registry.

   • 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
     
     # Confluent Cloud Schema Registry
     schema.registry.url=https://{{ SR_ENDPOINT }}
     basic.auth.credentials.source=USER_INFO
     schema.registry.basic.auth.user.info={{ SR_API_KEY }}:{{ SR_API_SECRET }}
     

   • Template configuration file for local host

     # Kafka
     bootstrap.servers=localhost:9092
     
     # Confluent Schema Registry
     schema.registry.url=http://localhost:8081
     

4. Verify your Confluent Cloud Schema Registry credentials work from your host. In the following example, substitute your values for {{ SR_API_KEY}}, {{SR_API_SECRET }}, and {{ SR_ENDPOINT }}.

   # View the list of registered subjects
   $ curl -u {{ SR_API_KEY }}:{{ SR_API_SECRET }} https://{{ SR_ENDPOINT }}/subjects
   
   # Same as above, as a single bash command to parse the values out of  $HOME/.confluent/java.config
   $ curl -u $(grep "^schema.registry.basic.auth.user.info"  $HOME/.confluent/java.config | cut -d'=' -f2) $(grep "^schema.registry.url"  $HOME/.confluent/java.config | cut -d'=' -f2)/subjects
   

Produce Avro Records

1. Run the Avro producer, passing in arguments for:

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

   mvn exec:java -Dexec.mainClass="io.confluent.examples.clients.cloud.ProducerAvroExample" \
   -Dexec.args="$HOME/.confluent/java.config test2"
   

2. View the producer Avro code.

Consume Avro Records

1. Run the Avro consumer, passing in arguments for:

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

   mvn exec:java -Dexec.mainClass="io.confluent.examples.clients.cloud.ConsumerAvroExample" \
   -Dexec.args="$HOME/.confluent/java.config test2"
   

2. View the consumer Avro code.

Avro Kafka Streams

1. Run the Avro 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

   mvn exec:java -Dexec.mainClass="io.confluent.examples.clients.cloud.StreamsAvroExample" \
   -Dexec.args="$HOME/.confluent/java.config test2"
   

2. View the Kafka Streams Avro code.

Schema Evolution with Confluent Cloud Schema Registry

1. View the schema subjects registered in Confluent Cloud Schema Registry. In the following output, substitute values for <SR API KEY>, <SR API SECRET>, and <SR ENDPOINT>.

   curl -u <SR API KEY>:<SR API SECRET> https://<SR ENDPOINT>/subjects
   

2. Verify that the subject test2-value exists.

   ["test2-value"]
   

3. View the schema information for subject test2-value. In the following output, substitute values for <SR API KEY>, <SR API SECRET>, and <SR ENDPOINT>.

   curl -u <SR API KEY>:<SR API SECRET> https://<SR ENDPOINT>/subjects/test2-value/versions/1
   

4. Verify the schema information for subject test2-value.

   {"subject":"test2-value","version":1,"id":100001,"schema":"{\"name\":\"io.confluent.examples.clients.cloud.DataRecordAvro\",\"type\":\"record\",\"fields\":[{\"name\":\"count\",\"type\":\"long\"}]}"}
   

5. For schema evolution, you can test schema compatibility between newer schema versions and older schema versions in Confluent Cloud Schema Registry. The pom.xml hardcodes the Schema Registry subject name to test2-value—change this if you didn’t use topic name test2. Then test local schema compatibility for DataRecordAvro2a.avsc, which should fail, and DataRecordAvro2b.avsc, which should pass.

   # DataRecordAvro2a.avsc compatibility test: FAIL
   mvn schema-registry:test-compatibility "-DschemaRegistryUrl=https://{{ SR_ENDPOINT }}" "-DschemaRegistryBasicAuthUserInfo={{ SR_API_KEY }}:{{ SR_API_SECRET }}" "-DschemaLocal=src/main/resources/avro/io/confluent/examples/clients/cloud/DataRecordAvro2a.avsc"
   
   # DataRecordAvro2b.avsc compatibility test: PASS
   mvn schema-registry:test-compatibility "-DschemaRegistryUrl=https://{{ SR_ENDPOINT }}" "-DschemaRegistryBasicAuthUserInfo={{ SR_API_KEY }}:{{ SR_API_SECRET }}" "-DschemaLocal=src/main/resources/avro/io/confluent/examples/clients/cloud/DataRecordAvro2b.avsc"