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How to aggregate over hopping windows with ksqlDB

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How to aggregate over hopping windows with ksqlDB

A hopping window is a fixed-size window with an advance that is smaller than the window size. Due to that fact that the advance is smaller than the window size, hopping windows contains overlapping results (i.e., the same event can be included in multiple consecutive hopping windows).

Setup

Imagine you have a topic of temperature readings. The first step is to create a stream over this topic:

CREATE STREAM temperature_readings (id VARCHAR KEY, timestamp VARCHAR, reading BIGINT)
    WITH (KAFKA_TOPIC='temperature_readings',
          VALUE_FORMAT='JSON',
          TIMESTAMP='TIMESTAMP',
          TIMESTAMP_FORMAT='yyyy-MM-dd HH:mm:ss',
          PARTITIONS=1);

Now you want to generate an average temperature reading every 5 minutes over the last 10 minutes of data:

CREATE TABLE average_temps AS
    SELECT
        id AS key,
        AS_VALUE(id) AS id,
        SUM(reading) / COUNT(reading) AS avg_reading
    FROM temperature_readings
    WINDOW HOPPING (SIZE 10 MINUTES, ADVANCE BY 5 MINUTES)
    GROUP BY id;

KsqlDB automatically includes the window start (WINDOWSTART) and end (WINDOWEND) timestamps as columns in the result. If you wanted to change to format you could use the TIMESTAMPTOSTRING scalar function in the SELECT expression, e.g.:

TIMESTAMPTOSTRING(WINDOWSTART, 'HH:mm:ss', 'UTC') AS start_period,
TIMESTAMPTOSTRING(WINDOWEND, 'HH:mm:ss', 'UTC') AS end_period,

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 temperature_readings stream backed by Kafka running in Docker and populate it with test data.

CREATE STREAM temperature_readings (id VARCHAR KEY, timestamp VARCHAR, reading BIGINT)
    WITH (KAFKA_TOPIC='temperature_readings',
          VALUE_FORMAT='JSON',
          TIMESTAMP='TIMESTAMP',
          TIMESTAMP_FORMAT='yyyy-MM-dd HH:mm:ss',
          PARTITIONS=1);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:15:30', 55);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:20:30', 50);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:25:30', 45);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:30:30', 40);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:35:30', 45);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:40:30', 50);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:45:30', 55);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:50:30', 60);

Next, run the hopping window query to generate a table of average temperature readings every 5 minutes over the last 10 minutes of data. Note that we first tell ksqlDB to consume from the beginning of the temperature_readings stream.

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

CREATE TABLE average_temps AS
    SELECT
        id AS key,
        AS_VALUE(id) AS id,
        SUM(reading) / COUNT(reading) AS avg_reading
    FROM temperature_readings
    WINDOW HOPPING (SIZE 10 MINUTES, ADVANCE BY 5 MINUTES)
    GROUP BY id;

Query the table of average temperatures:

SELECT * FROM average_temps;

The query output should look like this:

+---------------------+---------------------+---------------------+---------------------+---------------------+
|KEY                  |WINDOWSTART          |WINDOWEND            |ID                   |AVG_READING          |
+---------------------+---------------------+---------------------+---------------------+---------------------+
|1                    |1579054200000        |1579054800000        |1                    |55                   |
|1                    |1579054500000        |1579055100000        |1                    |52                   |
|1                    |1579054800000        |1579055400000        |1                    |47                   |
|1                    |1579055100000        |1579055700000        |1                    |42                   |
|1                    |1579055400000        |1579056000000        |1                    |42                   |
|1                    |1579055700000        |1579056300000        |1                    |47                   |
|1                    |1579056000000        |1579056600000        |1                    |52                   |
|1                    |1579056300000        |1579056900000        |1                    |57                   |
|1                    |1579056600000        |1579057200000        |1                    |60                   |
+---------------------+---------------------+---------------------+---------------------+---------------------+

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.

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

CREATE STREAM temperature_readings (id VARCHAR KEY, timestamp VARCHAR, reading BIGINT)
    WITH (KAFKA_TOPIC='temperature_readings',
          VALUE_FORMAT='JSON',
          TIMESTAMP='TIMESTAMP',
          TIMESTAMP_FORMAT='yyyy-MM-dd HH:mm:ss',
          PARTITIONS=1);

INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:15:30', 55);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:20:30', 50);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:25:30', 45);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:30:30', 40);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:35:30', 45);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:40:30', 50);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:45:30', 55);
INSERT INTO temperature_readings (id, timestamp, reading) VALUES ('1', '2020-01-15 02:50:30', 60);

Next, paste the hopping window query in the query edit and click Run query. This will to generate a table of average temperature readings every 5 minutes over the last 10 minutes of data.

CREATE TABLE average_temps AS
    SELECT
        id AS key,
        AS_VALUE(id) AS id,
        SUM(reading) / COUNT(reading) AS avg_reading
    FROM temperature_readings
    WINDOW HOPPING (SIZE 10 MINUTES, ADVANCE BY 5 MINUTES)
    GROUP BY id;

Query the table of average temperatures:

SELECT * FROM average_temps;

The query output should look like this:

+---------------------+---------------------+---------------------+---------------------+---------------------+
|KEY                  |WINDOWSTART          |WINDOWEND            |ID                   |AVG_READING          |
+---------------------+---------------------+---------------------+---------------------+---------------------+
|1                    |1579054200000        |1579054800000        |1                    |55                   |
|1                    |1579054500000        |1579055100000        |1                    |52                   |
|1                    |1579054800000        |1579055400000        |1                    |47                   |
|1                    |1579055100000        |1579055700000        |1                    |42                   |
|1                    |1579055400000        |1579056000000        |1                    |42                   |
|1                    |1579055700000        |1579056300000        |1                    |47                   |
|1                    |1579056000000        |1579056600000        |1                    |52                   |
|1                    |1579056300000        |1579056900000        |1                    |57                   |
|1                    |1579056600000        |1579057200000        |1                    |60                   |
+---------------------+---------------------+---------------------+---------------------+---------------------+

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>