Get Started Free
Tutorial

How to aggregate over tumbling windows with ksqlDB

Flink SQLksqlDBKafka Streams
‹ Back to Tutorials

How to aggregate over tumbling windows with ksqlDB

If you have time series events in a Kafka topic, tumbling windows let you group and aggregate them in fixed-size, non-overlapping, contiguous time intervals.

For example, in this tutorial we start with a stream of movie ratings and calculate the number of ratings per movie over 6-hour tumbling windows.

Setup

First we need to create a stream of ticket sales. This line of ksqlDB DDL creates a stream and its underlying Kafka topic to represent a stream of movie ratings. If the topic already exists, then ksqlDB simply registers it as the source of data underlying the new stream. The stream has three fields: title, the name of the movie; release_year, the year the movie was released; rating, the rating a viewer gave it; and timestamp, the time at which the rating was made.

CREATE STREAM ratings (title VARCHAR, release_year INT, rating DOUBLE, timestamp VARCHAR)
    WITH (KAFKA_TOPIC='ratings',
          TIMESTAMP='timestamp',
          TIMESTAMP_FORMAT='yyyy-MM-dd HH:mm:ss',
          PARTITIONS=1,
          VALUE_FORMAT='AVRO');

Compute aggregation over tumbling windows

Given the stream of movie ratings, compute the count of ratings per title over 6-hour tumbling windows as follows:

SELECT title,
       COUNT(*) AS rating_count,
       WINDOWSTART AS window_start,
       WINDOWEND AS window_end
FROM ratings
WINDOW TUMBLING (SIZE 6 HOURS)
GROUP BY title
EMIT CHANGES;

Running the example

You can run the example backing this tutorial in one of two ways: locally with the ksql CLI against Kafka and ksqlDB running in Docker, or with Confluent Cloud.

Local With Docker

Prerequisites

Run the commands

Clone the confluentinc/tutorials GitHub repository (if you haven't already) and navigate to the tutorials directory:

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

Start ksqlDB and Kafka:

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

Next, open the ksqlDB CLI:

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

Run the following SQL statements to create the ratings stream backed by Kafka running in Docker and populate it with test data.

CREATE STREAM ratings (title VARCHAR, release_year INT, rating DOUBLE, timestamp VARCHAR)
  WITH (KAFKA_TOPIC='ratings',
        TIMESTAMP='timestamp',
        TIMESTAMP_FORMAT='yyyy-MM-dd HH:mm:ss',
        PARTITIONS=1,
        VALUE_FORMAT='AVRO');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Twisters', 2024, 8.2, '2024-09-24 01:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Twisters', 2024, 4.5, '2024-09-24 05:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Twisters', 2024, 5.1, '2024-09-24 07:00:00');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Unfrosted', 2024, 4.9, '2024-09-24 09:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Unfrosted', 2024, 5.6, '2024-09-24 08:00:00');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Family Switch', 2023, 3.6, '2024-09-24 12:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Family Switch', 2023, 6.0, '2024-09-24 15:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Family Switch', 2023, 4.6, '2024-09-24 22:00:00');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Oppenheimer', 2023, 9.9, '2024-09-24 05:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Oppenheimer', 2023, 4.2, '2024-09-24 02:00:00');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Inside Out 2', 2024, 3.5, '2024-09-24 18:00:00');

Next, run the tumbling window query to generate a table of ratings per title over 6-hour tumbling windows. Note that we first tell ksqlDB to consume from the beginning of the stream, and we also configure the query to use caching so that we only get one row per tumbling window.

SET 'auto.offset.reset'='earliest';
SET 'ksql.streams.cache.max.bytes.buffering' = '10000000';

SELECT title,
       COUNT(*) AS rating_count,
       WINDOWSTART AS window_start,
       WINDOWEND AS window_end
FROM ratings
WINDOW TUMBLING (SIZE 6 HOURS)
GROUP BY title
EMIT CHANGES;

The query output should look like this:

+-------------------+-------------------+-------------------+-------------------+
|TITLE              |RATING_COUNT       |WINDOW_START       |WINDOW_END         |
+-------------------+-------------------+-------------------+-------------------+
|Twisters           |2                  |1727136000000      |1727157600000      |
|Twisters           |1                  |1727157600000      |1727179200000      |
|Unfrosted          |2                  |1727157600000      |1727179200000      |
|Family Switch      |2                  |1727179200000      |1727200800000      |
|Family Switch      |1                  |1727200800000      |1727222400000      |
|Oppenheimer        |2                  |1727136000000      |1727157600000      |
|Inside Out 2       |1                  |1727200800000      |1727222400000      |
+-------------------+-------------------+-------------------+-------------------+

When you are finished, exit the ksqlDB CLI by entering CTRL-D and clean up the containers used for this tutorial by running:

docker compose -f ./docker/docker-compose-ksqldb.yml down
Confluent Cloud

Prerequisites

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-cloud_kickstart

Run the following command to create a Confluent Cloud environment and Kafka cluster. This will create resources in AWS region us-west-2 by default, but you may override these choices by passing the --cloud argument with a value of aws, gcp, or azure, and the --region argument that is one of the cloud provider's supported regions, which you can list by running confluent kafka region list --cloud <CLOUD PROVIDER>

confluent cloud-kickstart --name ksqldb-tutorial \
  --environment-name ksqldb-tutorial \
  --output-format stdout

Now, create a ksqlDB cluster by first getting your user ID of the form u-123456 when you run this command:

confluent iam user list

And then create a ksqlDB cluster called ksqldb-tutorial with access linked to your user account:

confluent ksql cluster create ksqldb-tutorial \
  --credential-identity <USER ID>

Run the commands

Login to the Confluent Cloud Console. Select Environments in the lefthand navigation, and then click the ksqldb-tutorial environment tile. Click the ksqldb-tutorial Kafka cluster tile, and then select ksqlDB in the lefthand navigation.

The cluster may take a few minutes to be provisioned. Once its status is Up, click the cluster name and scroll down to the editor.

In the query properties section at the bottom, change the value for auto.offset.reset to Earliest so that ksqlDB will consume from the beginning of the stream we create. Then click Add another field and add a property cache.max.bytes.buffering with value 10000000. This configures the tumbling window query to use caching so that we only get one row per tumbling window.

Enter the following statements in the editor and click Run query. This creates the ratings stream and populates it with test data.

CREATE STREAM ratings (title VARCHAR, release_year INT, rating DOUBLE, timestamp VARCHAR)
  WITH (KAFKA_TOPIC='ratings',
        TIMESTAMP='timestamp',
        TIMESTAMP_FORMAT='yyyy-MM-dd HH:mm:ss',
        PARTITIONS=1,
        VALUE_FORMAT='AVRO');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Twisters', 2024, 8.2, '2024-09-24 01:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Twisters', 2024, 4.5, '2024-09-24 05:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Twisters', 2024, 5.1, '2024-09-24 07:00:00');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Unfrosted', 2024, 4.9, '2024-09-24 09:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Unfrosted', 2024, 5.6, '2024-09-24 08:00:00');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Family Switch', 2023, 3.6, '2024-09-24 12:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Family Switch', 2023, 6.0, '2024-09-24 15:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Family Switch', 2023, 4.6, '2024-09-24 22:00:00');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Oppenheimer', 2023, 9.9, '2024-09-24 05:00:00');
INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Oppenheimer', 2023, 4.2, '2024-09-24 02:00:00');

INSERT INTO ratings (title, release_year, rating, timestamp) VALUES ('Inside Out 2', 2024, 3.5, '2024-09-24 18:00:00');

Next, paste the tumbling window query in the query edit and click Run query. This will to generate a table of ratings per title over 6-hour tumbling windows

SELECT title,
       COUNT(*) AS rating_count,
       WINDOWSTART AS window_start,
       WINDOWEND AS window_end
FROM ratings
WINDOW TUMBLING (SIZE 6 HOURS)
GROUP BY title
EMIT CHANGES;

The query output should look like this:

+-------------------+-------------------+-------------------+-------------------+
|TITLE              |RATING_COUNT       |WINDOW_START       |WINDOW_END         |
+-------------------+-------------------+-------------------+-------------------+
|Twisters           |2                  |1727136000000      |1727157600000      |
|Twisters           |1                  |1727157600000      |1727179200000      |
|Unfrosted          |2                  |1727157600000      |1727179200000      |
|Family Switch      |2                  |1727179200000      |1727200800000      |
|Family Switch      |1                  |1727200800000      |1727222400000      |
|Oppenheimer        |2                  |1727136000000      |1727157600000      |
|Inside Out 2       |1                  |1727200800000      |1727222400000      |
+-------------------+-------------------+-------------------+-------------------+

Clean up

When you are finished, delete the ksqldb-tutorial 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>