How to compute the minimum or maximum value of a field with Kafka Streams

An aggregation in Kafka Streams is a stateful operation used to perform a "clustering" or "grouping" of values with the same key. An aggregation in Kafka Streams may return a different type than the input value. In this example the input value is a MovieTicketSales object but the result is a YearlyMovieFigures object used to keep track of the minimum and maximum total ticket sales by release year. You can also use windowing with aggregations to get discrete results per segment of time.

       builder.stream(INPUT_TOPIC, Consumed.with(Serdes.String(), movieSalesSerde))
              .groupBy((k, v) -> v.releaseYear(),
                      Grouped.with(Serdes.Integer(), movieSalesSerde))
              .aggregate(() -> new YearlyMovieFigures(0, Integer.MAX_VALUE, Integer.MIN_VALUE),
                      ((key, value, aggregate) ->
                              new YearlyMovieFigures(key,
                                      Math.min(value.totalSales(), aggregate.minTotalSales()),
                                      Math.max(value.totalSales(), aggregate.maxTotalSales()))),
                      Materialized.with(Serdes.Integer(), yearlySalesSerde))
              .toStream()
              .peek((key, value) -> LOG.info("Aggregation min-max results key[{}] value[{}]", key, value))
              .to(OUTPUT_TOPIC, Produced.with(Serdes.Integer(), yearlySalesSerde));

Let's review the key points in this example

   .groupBy((k, v) -> v.releaseYear(),

Aggregations must group records by key. Since the stream source topic doesn't define any, the code has a groupByKey operation on the releaseYear field of the MovieTicketSales value object.

        .groupBy((k, v) -> v.releaseYear(), Grouped.with(Serdes.Integer(), movieSalesSerde) 

Since you've changed the key, under the covers Kafka Streams performs a repartition immediately before it performs the grouping.
Repartitioning is simply producing records to an internal topic and consuming them back into the application. By producing the records the updated keys land on the correct partition. Additionally, since the key-value types have changed you need to provide updated Serde objects, via the Grouped configuration object to Kafka Streams for the (de)serialization process for the repartitioning.

.aggregate(() -> new YearlyMovieFigures(0, Integer.MAX_VALUE, Integer.MIN_VALUE),
                      ((key, value, aggregate) ->
                              new YearlyMovieFigures(key,
                                      Math.min(value.totalSales(), aggregate.minTotalSales()),
                                      Math.max(value.totalSales(), aggregate.maxTotalSales()))),
                      Materialized.with(Serdes.Integer(), yearlySalesSerde))

This aggregation performs a running average of movie ratings. To enable this, it keeps the running sum and count of the ratings. The aggregate operator takes 3 parameters (there are overloads that accept 2 and 4 parameters):

1. An initializer for the default value in this case a new instance of the YearlyMovieFigures object which is a Java POJO containing current min and max sales.
2. An Aggregator instance which performs the aggregation action. Here the code uses a Java lambda expression instead of a concrete object instance.
3. A Materialized object describing how the underlying StateStore is materialized.

 .toStream()
 .to(OUTPUT_TOPIC, Produced.with(Serdes.Integer(), yearlySalesSerde));

Aggregations in Kafka Streams return a KTable instance, so it's converted to a KStream. Then results are produced to an output topic via the to DSL operator.

The following steps use Confluent Cloud. To run the tutorial locally with Docker, skip to the Docker instructions section at the bottom.

Prerequisites

• A Confluent Cloud account
• The Confluent CLI installed on your machine
• Apache Kafka or Confluent Platform (both include the Kafka Streams application reset tool)
• Clone the confluentinc/tutorials repository and navigate into its top-level directory:

  git clone git@github.com:confluentinc/tutorials.git
  cd tutorials

Create Confluent Cloud resources

Login to your Confluent Cloud account:

confluent login --prompt --save

Install a CLI plugin that will streamline the creation of resources in Confluent Cloud:

confluent plugin install confluent-quickstart

Run the plugin from the top-level directory of the tutorials repository to create the Confluent Cloud resources needed for this tutorial. Note that you may specify a different cloud provider (gcp or azure) or region. You can find supported regions in a given cloud provider by running confluent kafka region list --cloud <CLOUD>.

confluent quickstart \
  --environment-name kafka-streams-aggregating-minmax-env \
  --kafka-cluster-name kafka-streams-aggregating-minmax-cluster \
  --create-kafka-key \
  --kafka-java-properties-file ./aggregating-minmax/kstreams/src/main/resources/cloud.properties

The plugin should complete in under a minute.

Create topics

Create the input and output topics for the application:

confluent kafka topic create min-max-input
confluent kafka topic create min-max-output

Start a console producer:

confluent kafka topic produce min-max-input

Enter a few JSON-formatted movie sales:

{"title":"Guardians of the Galaxy", "releaseYear":2020, "totalSales":300000000}
{"title":"Spiderman", "releaseYear":2020, "totalSales":200000000}
{"title":"The Poseidon Adventure", "releaseYear":1972, "totalSales":42000000}
{"title":"Cabaret", "releaseYear":1972, "totalSales":22000000}

Enter Ctrl+C to exit the console producer.

Compile and run the application

Compile the application from the top-level tutorials repository directory:

./gradlew aggregating-minmax:kstreams:shadowJar

Navigate into the application's home directory:

cd aggregating-minmax/kstreams

Run the application, passing the Kafka client configuration file generated when you created Confluent Cloud resources:

java -cp ./build/libs/aggregating-minmax-standalone.jar \
    io.confluent.developer.AggregatingMinMax \
    ./src/main/resources/cloud.properties

Validate that you see the correct min and max ticket sales per year in the min-max-output topic.

confluent kafka topic consume min-max-output -b

You should see:

{"releaseYear":2020,"minTotalSales":200000000,"maxTotalSales":300000000}
{"releaseYear":1972,"minTotalSales":22000000,"maxTotalSales":42000000}

Clean up

When you are finished, delete the kafka-streams-aggregating-minmax-env environment by first getting the environment ID of the form env-123456 corresponding to it:

confluent environment list

Delete the environment, including all resources created for this tutorial:

confluent environment delete <ENVIRONMENT ID>

docker compose -f ./docker/docker-compose-kafka.yml up -d

docker exec -it broker /bin/bash

kafka-topics --bootstrap-server localhost:9092 --create --topic min-max-input
kafka-topics --bootstrap-server localhost:9092 --create --topic min-max-output

kafka-console-producer --bootstrap-server localhost:9092 --topic min-max-input

{"title":"Guardians of the Galaxy", "releaseYear":2020, "totalSales":300000000}
{"title":"Spiderman", "releaseYear":2020, "totalSales":200000000}
{"title":"The Poseidon Adventure", "releaseYear":1972, "totalSales":42000000}
{"title":"Cabaret", "releaseYear":1972, "totalSales":22000000}

./gradlew aggregating-minmax:kstreams:shadowJar

cd aggregating-minmax/kstreams

java -cp ./build/libs/aggregating-minmax-standalone.jar \
    io.confluent.developer.AggregatingMinMax \
    ./src/main/resources/local.properties

kafka-console-consumer --bootstrap-server localhost:9092 --topic min-max-output --from-beginning

{"releaseYear":2020,"minTotalSales":200000000,"maxTotalSales":300000000}
{"releaseYear":1972,"minTotalSales":22000000,"maxTotalSales":42000000}

docker compose -f ./docker/docker-compose-kafka.yml down