Scala

In this tutorial, you will run a Scala 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

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 Scala.

    cd clients/cloud/scala/
    
  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
      

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.

Consume Records

  1. Build the client examples:

    sbt clean compile
    
  2. Run the consumer:

    sbt “runMain io.confluent.examples.clients.scala.Consumer $HOME/.confluent/java.config test1”
    

    You should see:

    <snipped>
    
    Polling
    ....
    <snipped>
    
  3. View the consumer code.

Kafka Streams

  1. In a new window, run the Streams app:

    cd examples/clients/cloud/scala
    
  2. Build the client examples:

    sbt clean compile
    
  3. Run the streams app:

    sbt "runMain io.confluent.examples.clients.scala.Streams $HOME/.confluent/java.config  test1"
    
  4. View the Kafka Streams code.

Produce Records

  1. In new a window, run the Kafka producer application to write records to the Kafka cluster:

    sbt "runMain io.confluent.examples.clients.scala.Producer $HOME/.confluent/java.config test1"
    

    You see the following output:

    <snipped>
    Produced record at test1-0@120
    Produced record at test1-0@121
    Produced record at test1-0@122
    Produced record at test1-0@123
    Produced record at test1-0@124
    Produced record at test1-0@125
    Produced record at test1-0@126
    Produced record at test1-0@127
    Produced record at test1-0@128
    Produced record at test1-0@129
    Wrote ten records to test1
    [success] Total time: 6 s, completed 10-Dec-2018 16:50:13
    
  2. In the consumer window, verify you see the following output:

    <snipped>
    Polling
    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
    Consumed record with key alice and value {"count":10}, and updated total count to 55
    Polling
    
  3. In the streams app, verify you see the following output:

    [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
    [Consumed record]: alice, 10
    [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
    [Running count]: alice, 55
    
  4. When you are done, press CTRL-C in both windows to stop the Consumer and Streams.

  5. View the producer code.