Java Spring Boot

In this tutorial, you will run a Java Spring Boot 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:
  • If you are running on Confluent Cloud, you must have access to a Confluent Cloud cluster with an API key and secret.

Setup

  1. Clone the confluentinc/examples GitHub repository and check out the 6.0.0-post branch.

    git clone https://github.com/confluentinc/examples
    cd examples
    git checkout 6.0.0-post
    
  2. Change directory to the example for Java Spring Boot.

    cd clients/cloud/java-springboot/
    
  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 Configure Confluent Cloud Clients for instructions on how to manually find these values, or use the ccloud-stack Utility for Confluent Cloud to automatically create them).

    • Template configuration file for Confluent Cloud

      # Required connection configs for Kafka producer, consumer, and admin
      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 }}';
      sasl.mechanism=PLAIN
      # Required for correctness in Apache Kafka clients prior to 2.6
      client.dns.lookup=use_all_dns_ips
      
      # Best practice for Kafka producer to prevent data loss 
      acks=all
      
    • Template configuration file for local host

      # Kafka
      bootstrap.servers=localhost:9092
      

Avro and Confluent Cloud Schema Registry

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.

  1. As described in the Quick Start for Schema Management on 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

      # Required connection configs for Kafka producer, consumer, and admin
      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 }}';
      sasl.mechanism=PLAIN
      # Required for correctness in Apache Kafka clients prior to 2.6
      client.dns.lookup=use_all_dns_ips
      
      # Best practice for Kafka producer to prevent data loss 
      acks=all
      
      # Required connection configs for 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 and Consume Records

This Spring Boot application has the following two components: Producer and Consumer that are initialized during the Spring Boot application startup. The producer writes Kafka data to a topic in your Kafka cluster. Each record has a String key representing a username (for example, alice) and a value of a count, formatted with the Avro schema DataRecordAvro.avsc

{"namespace": "io.confluent.examples.clients.cloud",
 "type": "record",
 "name": "DataRecordAvro",
 "fields": [
     {"name": "count", "type": "long"}
 ]
}
  1. Run the producer and consumer with the following command. It builds the jar and executes spring-kafka powered producer and consumer.

    ./startProducerConsumer.sh
    
  2. Verify the producer sent all the messages. You should see:

    ...
    2020-02-13 14:41:57.924  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 20
    2020-02-13 14:41:57.927  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 21
    2020-02-13 14:41:57.927  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 22
    2020-02-13 14:41:57.927  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 23
    2020-02-13 14:41:57.928  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 24
    2020-02-13 14:41:57.928  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 25
    2020-02-13 14:41:57.928  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 26
    2020-02-13 14:41:57.929  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 27
    2020-02-13 14:41:57.929  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 28
    2020-02-13 14:41:57.930  INFO 44191 --- [ad | producer-1] i.c.e.c.c.springboot.ProducerExample     : Produced record to topic test partition 3 @ offset 29
    10 messages were produced to topic test
    ...
    
  3. Verify the consumer received all the messages. You should see:

    ...
    2020-02-13 14:41:58.248  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 0}
    2020-02-13 14:41:58.248  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 1}
    2020-02-13 14:41:58.248  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 2}
    2020-02-13 14:41:58.248  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 3}
    2020-02-13 14:41:58.249  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 4}
    2020-02-13 14:41:58.249  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 5}
    2020-02-13 14:41:58.249  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 6}
    2020-02-13 14:41:58.249  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 7}
    2020-02-13 14:41:58.249  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 8}
    2020-02-13 14:41:58.249  INFO 44191 --- [ntainer#0-0-C-1] i.c.e.c.c.springboot.ConsumerExample     : received alice {"count": 9}
    
  4. When you are done, press CTRL-C.

  5. View the producer code and consumer code.

Kafka Streams

The Kafka Streams API reads from the same topic and does a rolling count and stateful sum aggregation as it processes each record.

  1. Run the Kafka Streams application:

    ./startStreams.sh
    
  2. Verify that you see the output:

    ...
    [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.