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How to sum a stream of events

Question:

How can you calculate the sum of one or more fields from all records in a Kafka topic?

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Example use case:

Suppose you have a topic with events that represent ticket sales for movies. Each event contains the movie that the ticket was purchased for, as well as its price. In this tutorial, we'll write a program that calculates the sum of all ticket sales per movie.

Hands-on code example:

Confluent Cloud ksqlDB Kafka Streams

Run it

Prerequisites

1

This tutorial installs Confluent Platform using Docker. Before proceeding:

  • • Install Docker Desktop (version 4.0.0 or later) or Docker Engine (version 19.03.0 or later) if you don’t already have it

  • • Install the Docker Compose plugin if you don’t already have it. This isn’t necessary if you have Docker Desktop since it includes Docker Compose.

  • • Start Docker if it’s not already running, either by starting Docker Desktop or, if you manage Docker Engine with systemd, via systemctl

  • • Verify that Docker is set up properly by ensuring no errors are output when you run docker info and docker compose version on the command line

Initialize the project

2

To get started, make a new directory anywhere you’d like for this project:

mkdir aggregate-sum && cd aggregate-sum

Then make the following directories to set up its structure:

mkdir src test

Get Confluent Platform

3

Next, create the following docker-compose.yml file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud):

version: '2'
services:
  broker:
    image: confluentinc/cp-kafka:7.4.1
    hostname: broker
    container_name: broker
    ports:
    - 29092:29092
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT,CONTROLLER:PLAINTEXT
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_NODE_ID: 1
      KAFKA_CONTROLLER_QUORUM_VOTERS: 1@broker:29093
      KAFKA_LISTENERS: PLAINTEXT://broker:9092,CONTROLLER://broker:29093,PLAINTEXT_HOST://0.0.0.0:29092
      KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
      KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
      KAFKA_LOG_DIRS: /tmp/kraft-combined-logs
      CLUSTER_ID: MkU3OEVBNTcwNTJENDM2Qk
  schema-registry:
    image: confluentinc/cp-schema-registry:7.3.0
    hostname: schema-registry
    container_name: schema-registry
    depends_on:
    - broker
    ports:
    - 8081:8081
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: broker:9092
  ksqldb-server:
    image: confluentinc/ksqldb-server:0.28.2
    hostname: ksqldb-server
    container_name: ksqldb-server
    depends_on:
    - broker
    - schema-registry
    ports:
    - 8088:8088
    environment:
      KSQL_CONFIG_DIR: /etc/ksqldb
      KSQL_LOG4J_OPTS: -Dlog4j.configuration=file:/etc/ksqldb/log4j.properties
      KSQL_BOOTSTRAP_SERVERS: broker:9092
      KSQL_HOST_NAME: ksqldb-server
      KSQL_LISTENERS: http://0.0.0.0:8088
      KSQL_CACHE_MAX_BYTES_BUFFERING: 0
      KSQL_KSQL_SCHEMA_REGISTRY_URL: http://schema-registry:8081
  ksqldb-cli:
    image: confluentinc/ksqldb-cli:0.28.2
    container_name: ksqldb-cli
    depends_on:
    - broker
    - ksqldb-server
    entrypoint: /bin/sh
    environment:
      KSQL_CONFIG_DIR: /etc/ksqldb
    tty: true
    volumes:
    - ./src:/opt/app/src
    - ./test:/opt/app/test

And launch it by running:

docker compose up -d

Write the program interactively using the CLI

4

To begin developing interactively, open up the ksqlDB CLI:

docker exec -it ksqldb-cli ksql http://ksqldb-server:8088

First, you’ll need to create a Kafka topic and stream to represent the ticket sales. The statement below creates both at the same time. This stream contains the name of the movie and the price of the ticket to watch it. We model the price as an integer to make the example simple.

Another important characteristic of the data is the timestamp column, sale_ts. Every message in Kafka is timestamped, and unless you specify otherwise, ksqlDB will use that existing timestamp for any time-related processing. In this example, we’re telling it to use a field in the message for the timestamp. This is called the event time rather than the ingestion time.

CREATE STREAM MOVIE_TICKET_SALES (title VARCHAR, sale_ts VARCHAR, ticket_total_value INT)
    WITH (KAFKA_TOPIC='movie-ticket-sales',
          PARTITIONS=1,
          VALUE_FORMAT='avro',
          TIMESTAMP='sale_ts',
          TIMESTAMP_FORMAT='yyyy-MM-dd''T''HH:mm:ssX');

With the stream in place, we can now produce the following events to it:

INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('Aliens',           '2019-07-18T10:00:00Z', 10);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('Die Hard',         '2019-07-18T10:00:00Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('Die Hard',         '2019-07-18T10:01:00Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Godfather',    '2019-07-18T10:01:31Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('Die Hard',         '2019-07-18T10:01:36Z', 24);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Godfather',    '2019-07-18T10:02:00Z', 18);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Big Lebowski', '2019-07-18T11:03:21Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Big Lebowski', '2019-07-18T11:03:50Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Godfather',    '2019-07-18T11:40:00Z', 36);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Godfather',    '2019-07-18T11:40:09Z', 18);

Before we continue, let’s make sure that each ksqlDB query we execute will begin its processing from the beginning of the stream:

SET 'auto.offset.reset' = 'earliest';

For the purposes of this example only, we are also going to configure ksqlDB to buffer the aggregates as it builds them. This makes the query feel like it responds more slowly, but means that you get just one row per movie from which it is simpler to understand the concept:

SET 'ksql.streams.cache.max.bytes.buffering' = '10000000';

Let’s calculate the total sales per movie using a SUM aggregation on the TICKET_TOTAL_VALUE field. This will block and continue to return results until it’s limit is reached or you tell it to stop.

SELECT TITLE,
       SUM(TICKET_TOTAL_VALUE) AS TOTAL_VALUE
FROM MOVIE_TICKET_SALES
GROUP BY TITLE
EMIT CHANGES
LIMIT 3;

This should yield the following output:

+--------------------+--------------------+
|TITLE               |TOTAL_VALUE         |
+--------------------+--------------------+
|Aliens              |10                  |
|Die Hard            |48                  |
|The Big Lebowski    |24                  |
Limit Reached
Query terminated

Since the output looks right, the next step is to make the query persistent. We do this with the CREATE TABLE AS statement. This statement creates a stream processor that runs continuously, always consuming events from the source stream (MOVIE_TICKET_SALES) and creating and updating entries in the resulting table (MOVIE_REVENUE).

It should not escape your notice that we are turning a stream into a table. A table is always the result of using the GROUP BY clause on a stream. As we noted in the previous step, we are also computing an aggregate over the grouped values with SUM(TICKET_TOTAL_VALUE). This function creates a new column in the resulting table, which we give a readable name using the AS TOTAL_VALUE clause.

Issue the following to create the new table:

CREATE TABLE MOVIE_REVENUE AS
    SELECT TITLE,
           SUM(TICKET_TOTAL_VALUE) AS TOTAL_VALUE
    FROM MOVIE_TICKET_SALES
    GROUP BY TITLE;

To check that it’s working, print out the contents of the output stream’s underlying topic:

PRINT MOVIE_REVENUE FROM BEGINNING LIMIT 3;

This should yield the following output:

Key format: KAFKA_STRING
Value format: AVRO
rowtime: 2019/07/18 10:00:00.000 Z, key: Aliens, value: {"TOTAL_VALUE": 10}, partition: 0
rowtime: 2019/07/18 10:01:36.000 Z, key: Die Hard, value: {"TOTAL_VALUE": 48}, partition: 0
rowtime: 2019/07/18 11:03:50.000 Z, key: The Big Lebowski, value: {"TOTAL_VALUE": 24}, partition: 0
Topic printing ceased

Notice that ksqlDB is storing the TITLE in the key of the Kafka message. It does this because TITLE is the primary key of the MOVIE_REVENUE table. If needed, a copy of TITLE can also be stored in the value by adding AsValue(TITLE) in the projection.

Write your statements to a file

5

Now that you have a series of statements that’s doing the right thing, the last step is to put them into a file so that they can be used outside the CLI session. Create a file at src/statements.sql with the following content:

CREATE STREAM MOVIE_TICKET_SALES (title VARCHAR, sale_ts VARCHAR, ticket_total_value INT)
    WITH (KAFKA_TOPIC='movie-ticket-sales',
          PARTITIONS=1,
          VALUE_FORMAT='avro',
          TIMESTAMP='sale_ts',
          TIMESTAMP_FORMAT='yyyy-MM-dd''T''HH:mm:ssX');

CREATE TABLE MOVIE_REVENUE AS
    SELECT TITLE,
           SUM(TICKET_TOTAL_VALUE) AS TOTAL_VALUE
    FROM MOVIE_TICKET_SALES
    GROUP BY TITLE;

Test it

Create the test data

1

Create a file at test/input.json with the inputs for testing:

{
  "inputs": [
    {"topic": "movie-ticket-sales", "value": {"TITLE": "Aliens", "SALE_TS": "2019-07-18T10:00:00Z", "TICKET_TOTAL_VALUE": 10}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "Die Hard", "SALE_TS": "2019-07-18T10:00:00Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "Die Hard", "SALE_TS": "2019-07-18T10:01:00Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "The Godfather", "SALE_TS": "2019-07-18T10:01:31Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "Die Hard", "SALE_TS": "2019-07-18T10:01:36Z", "TICKET_TOTAL_VALUE": 24}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "The Godfather", "SALE_TS": "2019-07-18T10:02:00Z", "TICKET_TOTAL_VALUE": 18}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "The Big Lebowski", "SALE_TS": "2019-07-18T11:03:21Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "The Big Lebowski", "SALE_TS": "2019-07-18T11:03:50Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "The Godfather", "SALE_TS": "2019-07-18T11:40:00Z", "TICKET_TOTAL_VALUE": 36}},
    {"topic": "movie-ticket-sales", "value": {"TITLE": "The Godfather", "SALE_TS": "2019-07-18T11:40:09Z", "TICKET_TOTAL_VALUE": 18}}
  ]
}

Similarly, create a file at test/output.json with the expected outputs:

{
  "outputs": [
    {"topic": "MOVIE_REVENUE", "key": "Aliens", "value": {"TOTAL_VALUE": 10}, "timestamp": 1563444000000},
    {"topic": "MOVIE_REVENUE", "key": "Die Hard", "value": {"TOTAL_VALUE": 12}, "timestamp": 1563444000000},
    {"topic": "MOVIE_REVENUE", "key": "Die Hard", "value": {"TOTAL_VALUE": 24}, "timestamp": 1563444060000},
    {"topic": "MOVIE_REVENUE", "key": "The Godfather", "value": {"TOTAL_VALUE": 12}, "timestamp": 1563444091000},
    {"topic": "MOVIE_REVENUE", "key": "Die Hard", "value": {"TOTAL_VALUE": 48}, "timestamp": 1563444096000},
    {"topic": "MOVIE_REVENUE", "key": "The Godfather", "value": {"TOTAL_VALUE": 30}, "timestamp": 1563444120000},
    {"topic": "MOVIE_REVENUE", "key": "The Big Lebowski", "value": {"TOTAL_VALUE": 12}, "timestamp": 1563447801000},
    {"topic": "MOVIE_REVENUE", "key": "The Big Lebowski", "value": {"TOTAL_VALUE": 24}, "timestamp": 1563447830000},
    {"topic": "MOVIE_REVENUE", "key": "The Godfather", "value": {"TOTAL_VALUE": 66}, "timestamp": 1563450000000},
    {"topic": "MOVIE_REVENUE", "key": "The Godfather", "value": {"TOTAL_VALUE": 84}, "timestamp": 1563450009000}
  ]
}

Invoke the tests

2

Lastly, invoke the tests using the test runner and the statements file that you created earlier:

docker exec ksqldb-cli ksql-test-runner -i /opt/app/test/input.json -s /opt/app/src/statements.sql -o /opt/app/test/output.json

Which should pass:

	 >>> Test passed!

Deploy on Confluent Cloud

Run your app with Confluent Cloud

1

Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully managed Apache Kafka service.

  1. Sign up for Confluent Cloud, a fully managed Apache Kafka service.

  2. After you log in to Confluent Cloud Console, click Environments in the lefthand navigation, click on Add cloud environment, and name the environment learn-kafka. Using a new environment keeps your learning resources separate from your other Confluent Cloud resources.

  3. From the Billing & payment section in the menu, apply the promo code CC100KTS to receive an additional $100 free usage on Confluent Cloud (details). To avoid having to enter a credit card, add an additional promo code CONFLUENTDEV1. With this promo code, you will not have to enter a credit card for 30 days or until your credits run out.

  4. Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.

Confluent Cloud

Next, from the Confluent Cloud Console, click on Clients to get the cluster-specific configurations, e.g., Kafka cluster bootstrap servers and credentials, Confluent Cloud Schema Registry and credentials, etc., and set the appropriate parameters in your client application.

Now you’re all set to run your streaming application locally, backed by a Kafka cluster fully managed by Confluent Cloud.