Get Started Free
Tutorial

How to filter duplicate events per time window from a Kafka topic with ksqlDB

Flink SQLksqlDBKafka Streams
‹ Back to Tutorials

How to filter duplicate events per time window from a Kafka topic with ksqlDB

Consider a topic with events that represent clicks on a website. Each event contains an IP address, a URL, and a timestamp. In this tutorial, we'll use ksqlDB to deduplicate these click events.

Setup

Let's start with a stream representing user click events:

CREATE STREAM clicks (ip_address VARCHAR, url VARCHAR)
    WITH (KAFKA_TOPIC='clicks',
          PARTITIONS=1,
          FORMAT='JSON');

Deduplicate events per time window

The first step toward deduplication is to create a table to collect the click events by time window:

CREATE TABLE detected_clicks AS
    SELECT
        ip_address AS key1,
        url AS key2,
        AS_VALUE(ip_address) AS ip_address,
        COUNT(ip_address) as ip_count,
        AS_VALUE(url) AS url,
        FORMAT_timestamp(FROM_UNIXTIME(EARLIEST_BY_OFFSET(ROWTIME)), 'yyyy-MM-dd HH:mm:ss.SSS') AS timestamp
    FROM clicks WINDOW TUMBLING (SIZE 2 MINUTES, RETENTION 1000 DAYS)
    GROUP BY ip_address, url
    EMIT CHANGES;

As we’re grouping by IP address and URL, these columns will become part of the primary key of the table. Primary key columns are stored in the Kafka message’s key. As we’ll need them in the value later, we use AS_VALUE to copy the columns into the value and set their name. To avoid the value column names clashing with the key columns, we add aliases to rename the key columns.

As it stands, the key of the detected_clicks table contains the IP address and URL columns, and as the table is windowed, the window start and end timestamps. Create another stream that will only contain ip_address, ip_count, url, and timestamp from the topic backing the detected_clicks table:

CREATE STREAM raw_values_clicks (ip_address STRING, ip_count BIGINT, url STRING, timestamp STRING)
    WITH (KAFKA_TOPIC='DETECTED_CLICKS',
          PARTITIONS=1,
          FORMAT='JSON');

Now we can filter out duplicates by only retrieving records with a ip_count of 1:

SELECT
    ip_address,
    url,
    timestamp
FROM raw_values_clicks
WHERE ip_count = 1
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 clicks stream backed by Kafka running in Docker and populate it with test data.

CREATE STREAM clicks (ip_address VARCHAR, url VARCHAR)
    WITH (KAFKA_TOPIC='clicks',
          PARTITIONS=1,
          FORMAT='JSON');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.1', 'https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.12', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.13', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen');

INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.1', 'https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.12', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.13', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen');

Next, create a table to collect the click events by time window. Note that we first tell ksqlDB to consume from the beginning of the stream.

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

CREATE TABLE detected_clicks AS
    SELECT
        ip_address AS key1,
        url AS key2,
        AS_VALUE(ip_address) AS ip_address,
        COUNT(ip_address) as ip_count,
        AS_VALUE(url) AS url,
        FORMAT_timestamp(FROM_UNIXTIME(EARLIEST_BY_OFFSET(ROWTIME)), 'yyyy-MM-dd HH:mm:ss.SSS') AS timestamp
    FROM clicks WINDOW TUMBLING (SIZE 2 MINUTES, RETENTION 1000 DAYS)
    GROUP BY ip_address, url
    EMIT CHANGES;

Now create another stream that will only contain ip_address, ip_count, url, and timestamp from the topic backing the detected_clicks table:

CREATE STREAM raw_values_clicks (ip_address STRING, ip_count BIGINT, url STRING, timestamp STRING)
    WITH (KAFKA_TOPIC='DETECTED_CLICKS',
          PARTITIONS=1,
          FORMAT='JSON');

Finally, filter out duplicates by only retrieving records with a ip_count of 1:

SELECT
    ip_address,
    url,
    timestamp
FROM raw_values_clicks
WHERE ip_count = 1
EMIT CHANGES;

The query output should look like this:

+-----------+----------------------------------------------------------------------------------------+-------------------------+
|IP_ADDRESS |URL                                                                                     |TIMESTAMP                |
+-----------+----------------------------------------------------------------------------------------+-------------------------+
|10.0.0.1   |https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html|2024-09-24 17:39:48.650  |
|10.0.0.12  |https://www.confluent.io/hub/confluentinc/kafka-connect-datagen                         |2024-09-24 17:39:48.688  |
|10.0.0.13  |https://www.confluent.io/hub/confluentinc/kafka-connect-datagen                         |2024-09-24 17:39:48.724  |
+-----------+----------------------------------------------------------------------------------------+-------------------------+

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 the clicks stream and populates it with test data.

CREATE STREAM clicks (ip_address VARCHAR, url VARCHAR)
    WITH (KAFKA_TOPIC='clicks',
          PARTITIONS=1,
          FORMAT='JSON');

INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.1', 'https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.12', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.13', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen');

INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.1', 'https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.12', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen');
INSERT INTO clicks (ip_address, url) VALUES ('10.0.0.13', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen');

Next, create a table to collect the click events by time window.

CREATE TABLE detected_clicks AS
    SELECT
        ip_address AS key1,
        url AS key2,
        AS_VALUE(ip_address) AS ip_address,
        COUNT(ip_address) as ip_count,
        AS_VALUE(url) AS url,
        FORMAT_timestamp(FROM_UNIXTIME(EARLIEST_BY_OFFSET(ROWTIME)), 'yyyy-MM-dd HH:mm:ss.SSS') AS timestamp
    FROM clicks WINDOW TUMBLING (SIZE 2 MINUTES, RETENTION 1000 DAYS)
    GROUP BY ip_address, url
    EMIT CHANGES;

Now create another stream that will only contain ip_address, ip_count, url, and timestamp from the topic backing the detected_clicks table:

CREATE STREAM raw_values_clicks (ip_address STRING, ip_count BIGINT, url STRING, timestamp STRING)
    WITH (KAFKA_TOPIC='DETECTED_CLICKS',
          PARTITIONS=1,
          FORMAT='JSON');

Finally, filter out duplicates by only retrieving records with a ip_count of 1:

SELECT
    ip_address,
    url,
    timestamp
FROM raw_values_clicks
WHERE ip_count = 1
EMIT CHANGES;

The query output should look like this:

+-----------+----------------------------------------------------------------------------------------+-------------------------+
|IP_ADDRESS |URL                                                                                     |TIMESTAMP                |
+-----------+----------------------------------------------------------------------------------------+-------------------------+
|10.0.0.1   |https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html|2024-09-24 17:39:48.650  |
|10.0.0.12  |https://www.confluent.io/hub/confluentinc/kafka-connect-datagen                         |2024-09-24 17:39:48.688  |
|10.0.0.13  |https://www.confluent.io/hub/confluentinc/kafka-connect-datagen                         |2024-09-24 17:39:48.724  |
+-----------+----------------------------------------------------------------------------------------+-------------------------+

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>
Do you have questions or comments? Join us in the #confluent-developer community Slack channel to engage in discussions with the creators of this content.