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.
Let's start with a stream representing a user click events:
CREATE STREAM CLICKS (IP_ADDRESS VARCHAR, URL VARCHAR)
WITH (KAFKA_TOPIC = 'CLICKS',
FORMAT = 'JSON',
PARTITIONS=1);
Then create a table to collect the click events:
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;
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');
Then create another stream that will de-duplicate the events. The stream will set the key of the DISTINCT_CLICKS stream to just the IP address using the PARTITION BY statement. The WHERE clause is where we filter out duplicates by specifying to only retrieve IP addresses with a IP_COUNT of 1.
CREATE STREAM DISTINCT_CLICKS AS
SELECT
IP_ADDRESS,
URL,
TIMESTAMP
FROM RAW_VALUES_CLICKS
WHERE IP_COUNT = 1
PARTITION BY IP_ADDRESS;