Get Started Free
Tutorial

How to implement event-time semantics in ksqlDB

ksqlDB
‹ Back to Tutorials

How to implement event-time semantics in ksqlDB

By default, time-based aggregations in ksqlDB (tumbling windows, hopping windows, etc.) operate on the timestamp in the record metadata, which could be either 'CreateTime' (the producer system time) or 'LogAppendTime' (the broker system time), depending on the message.timestamp.type topic configuration value. 'CreateTime' may help with event-time semantics, but in some use cases, the desired event time is a timestamp embedded inside the record payload itself.

For example, consider a topic of temperature sensor readings that contains the temperature and timestamp of the reading. In order to achieve event-time semantics, we specify a field in the record payload as the TIMESTAMP attribute when defining the stream:

CREATE STREAM temperature_event_time (temp DOUBLE, event_time BIGINT)
  WITH (KAFKA_TOPIC='temperature-event-time',
        PARTITIONS=1,
        VALUE_FORMAT='AVRO',
        TIMESTAMP='event_time');

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 two streams: one that uses log-time semantics, and one that uses event-time semantics:

CREATE STREAM temperature_log_time (temp DOUBLE, event_time BIGINT)
WITH (KAFKA_TOPIC='temperature-log-time',
      PARTITIONS=1,
      VALUE_FORMAT='AVRO');
CREATE STREAM temperature_event_time (temp DOUBLE, event_time BIGINT)
    WITH (KAFKA_TOPIC='temperature-event-time',
          PARTITIONS=1,
          VALUE_FORMAT='AVRO',
          TIMESTAMP='event_time');

Next, insert the same event into each stream:

INSERT INTO temperature_log_time   (temp, event_time) VALUES (100.98, 1673560175029);
INSERT INTO temperature_event_time (temp, event_time) VALUES (100.98, 1673560175029);

Now if you query the temperature_log_time stream and include the system column ROWTIME that gets used for time-based aggregations, you'll see that the ROWTIME is the current time and not the time in the event payload. Note that we first tell ksqlDB to consume from the beginning of the stream.

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

SELECT *, ROWTIME
FROM temperature_log_time
EMIT CHANGES;

The query output will show different timestamps, e.g.:

+-----------------------------+-----------------------------+-----------------------------+
|TEMP                         |EVENT_TIME                   |ROWTIME                      |
+-----------------------------+-----------------------------+-----------------------------+
|100.98                       |1673560175029                |1727450542650                |
+-----------------------------+-----------------------------+-----------------------------+

Now query the temperature_event_time stream and include the system column ROWTIME:

SELECT *, ROWTIME
FROM temperature_event_time
EMIT CHANGES;

The system column ROWTIME matches the timestamp in the event:

+-----------------------------+-----------------------------+-----------------------------+
|TEMP                         |EVENT_TIME                   |ROWTIME                      |
+-----------------------------+-----------------------------+-----------------------------+
|100.98                       |1673560175029                |1673560175029                |
+-----------------------------+-----------------------------+-----------------------------+

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 streams we create.

Enter the following statements in the editor and click Run query. This creates two streams: one that uses log-time semantics, and one that uses event-time semantics:

CREATE STREAM temperature_log_time (temp DOUBLE, event_time BIGINT)
WITH (KAFKA_TOPIC='temperature-log-time',
      PARTITIONS=1,
      VALUE_FORMAT='AVRO');

CREATE STREAM temperature_event_time (temp DOUBLE, event_time BIGINT)
    WITH (KAFKA_TOPIC='temperature-event-time',
          PARTITIONS=1,
          VALUE_FORMAT='AVRO',
          TIMESTAMP='event_time');

Next, insert the same event into each stream:

INSERT INTO temperature_log_time   (temp, event_time) VALUES (100.98, 1673560175029);
INSERT INTO temperature_event_time (temp, event_time) VALUES (100.98, 1673560175029);

Now if you query the temperature_log_time stream and include the system column ROWTIME that gets used for time-based aggregations, you'll see that the ROWTIME is the current time and not the time in the event payload.

SELECT *, ROWTIME
FROM temperature_log_time
EMIT CHANGES;

The query output will show different timestamps, e.g.:

+-----------------------------+-----------------------------+-----------------------------+
|TEMP                         |EVENT_TIME                   |ROWTIME                      |
+-----------------------------+-----------------------------+-----------------------------+
|100.98                       |1673560175029                |1727450542650                |
+-----------------------------+-----------------------------+-----------------------------+

Now query the temperature_event_time stream and include the system column ROWTIME:

SELECT *, ROWTIME
FROM temperature_event_time
EMIT CHANGES;

The system column ROWTIME matches the timestamp in the event:

+-----------------------------+-----------------------------+-----------------------------+
|TEMP                         |EVENT_TIME                   |ROWTIME                      |
+-----------------------------+-----------------------------+-----------------------------+
|100.98                       |1673560175029                |1673560175029                |
+-----------------------------+-----------------------------+-----------------------------+

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>