How can you filter out duplicate events from a Kafka topic based on a field in the event, producing a new stream of unique events per time window?
This tutorial installs Confluent Platform using Docker. Before proceeding:
• Install Docker Desktop (version 4.0.0
or later) or Docker Engine (version 19.03.0
or later) if you don’t already have it
• Install the Docker Compose plugin if you don’t already have it. This isn’t necessary if you have Docker Desktop since it includes Docker Compose.
• Start Docker if it’s not already running, either by starting Docker Desktop or, if you manage Docker Engine with systemd
, via systemctl
• Verify that Docker is set up properly by ensuring no errors are output when you run docker info
and docker compose version
on the command line
To get started, make a new directory anywhere you’d like for this project:
mkdir finding-distinct && cd finding-distinct
Then make the following directories to set up its structure:
mkdir src test
Next, create the following docker-compose.yml
file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud):
---
version: '2'
services:
zookeeper:
image: confluentinc/cp-zookeeper:7.3.0
hostname: zookeeper
container_name: zookeeper
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
broker:
image: confluentinc/cp-kafka:7.3.0
hostname: broker
container_name: broker
depends_on:
- zookeeper
ports:
- "29092:29092"
environment:
KAFKA_BROKER_ID: 1
KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
KAFKA_LOG_RETENTION_MS: -1
KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
schema-registry:
image: confluentinc/cp-schema-registry:7.3.0
hostname: schema-registry
container_name: schema-registry
depends_on:
- broker
ports:
- "8081:8081"
environment:
SCHEMA_REGISTRY_HOST_NAME: schema-registry
SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: 'broker:9092'
ksql-server:
image: confluentinc/ksqldb-server:0.28.2
hostname: ksql-server
container_name: ksql-server
depends_on:
- broker
- schema-registry
ports:
- "8088:8088"
environment:
KSQL_CONFIG_DIR: "/etc/ksqldb"
KSQL_LOG4J_OPTS: "-Dlog4j.configuration=file:/etc/ksqldb/log4j.properties"
KSQL_BOOTSTRAP_SERVERS: "broker:9092"
KSQL_HOST_NAME: ksql-server
KSQL_LISTENERS: "http://0.0.0.0:8088"
KSQL_KSQL_SCHEMA_REGISTRY_URL: "http://schema-registry:8081"
KSQL_KSQL_STREAMS_AUTO_OFFSET_RESET: "earliest"
KSQL_KSQL_STREAMS_CACHE_MAX_BYTES_BUFFERING: 0
ksql-cli:
image: confluentinc/ksqldb-cli:0.28.2
container_name: ksql-cli
depends_on:
- broker
- ksql-server
entrypoint: /bin/sh
environment:
KSQL_CONFIG_DIR: "/etc/ksqldb"
tty: true
volumes:
- ./src:/opt/app/src
- ./test:/opt/app/test
And launch it by running:
docker compose up -d
To begin developing interactively, open up the KSQL CLI:
docker exec -it ksql-cli ksql http://ksql-server:8088
To start off the implementation of this scenario, we will create a stream that represents the clicks from the users. Since we will be handling time, it is important that each click contains a timestamp indicating when that click was done. The field TIMESTAMP
will be used for this purpose.
CREATE STREAM CLICKS (IP_ADDRESS VARCHAR, URL VARCHAR, TIMESTAMP VARCHAR)
WITH (KAFKA_TOPIC = 'CLICKS',
FORMAT = 'JSON',
TIMESTAMP = 'TIMESTAMP',
TIMESTAMP_FORMAT = 'yyyy-MM-dd''T''HH:mm:ssXXX',
PARTITIONS = 1);
Now let’s produce some events that represent user clicks. Note that we are going to purposely produce duplicate events, in which each IP address will have clicked twice in the same URL.
INSERT INTO CLICKS (IP_ADDRESS, URL, TIMESTAMP) VALUES ('10.0.0.1', 'https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html', '2021-01-17T14:50:43+00:00');
INSERT INTO CLICKS (IP_ADDRESS, URL, TIMESTAMP) VALUES ('10.0.0.12', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen', '2021-01-17T14:53:44+00:01');
INSERT INTO CLICKS (IP_ADDRESS, URL, TIMESTAMP) VALUES ('10.0.0.13', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen', '2021-01-17T14:56:45+00:03');
INSERT INTO CLICKS (IP_ADDRESS, URL, TIMESTAMP) VALUES ('10.0.0.1', 'https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html', '2021-01-17T14:50:43+00:00');
INSERT INTO CLICKS (IP_ADDRESS, URL, TIMESTAMP) VALUES ('10.0.0.12', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen', '2021-01-17T14:53:44+00:01');
INSERT INTO CLICKS (IP_ADDRESS, URL, TIMESTAMP) VALUES ('10.0.0.13', 'https://www.confluent.io/hub/confluentinc/kafka-connect-datagen', '2021-01-17T14:56:45+00:03');
Now that you have a stream with some events in it, let’s start to leverage them. The first thing to do is set the following properties to ensure that you’re reading from the beginning of the stream:
SET 'auto.offset.reset' = 'earliest';
Next, set cache.max.bytes.buffering
to configure the frequency of output for tables. The value of 0
instructs ksqlDB to emit each matching record as soon as it is processed. Without this configuration, the queries below could appear to "miss" some records due to the default batching behavior.
SET 'cache.max.bytes.buffering' = '0';
Let’s experiment with these events.
First, let’s create a query to select the information we need and count each occurrence of an IP address.
SELECT
IP_ADDRESS,
COUNT(IP_ADDRESS) as IP_COUNT,
URL,
TIMESTAMP
FROM CLICKS WINDOW TUMBLING (SIZE 2 MINUTES)
GROUP BY IP_ADDRESS, URL, TIMESTAMP
EMIT CHANGES
LIMIT 6;
And it should produce the following output:
+-----------------------------------------+-----------------------------------------+-----------------------------------------+-----------------------------------------+
|IP_ADDRESS |IP_COUNT |URL |TIMESTAMP |
+-----------------------------------------+-----------------------------------------+-----------------------------------------+-----------------------------------------+
|10.0.0.1 |1 |https://docs.confluent.io/current/tutoria|2021-01-17T14:50:43+00:00 |
| | |ls/examples/kubernetes/gke-base/docs/inde| |
| | |x.html | |
|10.0.0.12 |1 |https://www.confluent.io/hub/confluentinc|2021-01-17T14:53:44+00:01 |
| | |/kafka-connect-datagen | |
|10.0.0.13 |1 |https://www.confluent.io/hub/confluentinc|2021-01-17T14:56:45+00:03 |
| | |/kafka-connect-datagen | |
|10.0.0.1 |2 |https://docs.confluent.io/current/tutoria|2021-01-17T14:50:43+00:00 |
| | |ls/examples/kubernetes/gke-base/docs/inde| |
| | |x.html | |
|10.0.0.12 |2 |https://www.confluent.io/hub/confluentinc|2021-01-17T14:53:44+00:01 |
| | |/kafka-connect-datagen | |
|10.0.0.13 |2 |https://www.confluent.io/hub/confluentinc|2021-01-17T14:56:45+00:03 |
| | |/kafka-connect-datagen | |
Limit Reached
Query terminated
Notice that the duplicate IP addresses have an IP_COUNT
value greater than one. So our next step is to filter out the duplicates by only selecting records where the IP_COUNT
column equals 1
.
Now let’s create some continuous queries to implement this scenario.
CREATE TABLE DETECTED_CLICKS AS
SELECT
IP_ADDRESS AS KEY1,
URL AS KEY2,
TIMESTAMP AS KEY3,
AS_VALUE(IP_ADDRESS) AS IP_ADDRESS,
COUNT(IP_ADDRESS) as IP_COUNT,
AS_VALUE(URL) AS URL,
AS_VALUE(TIMESTAMP) AS TIMESTAMP
FROM CLICKS WINDOW TUMBLING (SIZE 2 MINUTES, RETENTION 1000 DAYS)
GROUP BY IP_ADDRESS, URL, TIMESTAMP;
CREATE STREAM RAW_VALUES_CLICKS (IP_ADDRESS VARCHAR, IP_COUNT BIGINT, URL VARCHAR, TIMESTAMP VARCHAR)
WITH (KAFKA_TOPIC = 'DETECTED_CLICKS',
PARTITIONS = 1,
FORMAT = 'JSON');
CREATE STREAM DISTINCT_CLICKS AS
SELECT
IP_ADDRESS,
URL,
TIMESTAMP
FROM RAW_VALUES_CLICKS
WHERE IP_COUNT = 1
PARTITION BY IP_ADDRESS;
In the first statement above, we created the query that finds click events, naming it DETECTED_CLICKS
. We modeled it as a table since the query performs aggregations.
As we’re grouping by ip-address, url and timestamp, 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, url, timestamp columns, and as the table is windowed, the window start time. Wouldn’t it be nice if the key was just the IP address?
You’ll take care of that as well as finding distinct IP addresses with the next two queries.
The second statement declares a stream on top of the DETECTED_CLICKS
table, defining only the value columns we’re interested in.
In the third statement you 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
.
To verify everything is working as expected, run the following query:
SELECT
IP_ADDRESS,
URL,
TIMESTAMP
FROM DISTINCT_CLICKS
EMIT CHANGES
LIMIT 3;
The output should look similar to:
+-----------------------------------------+-----------------------------------------+-----------------------------------------+
|IP_ADDRESS |URL |TIMESTAMP |
+-----------------------------------------+-----------------------------------------+-----------------------------------------+
|10.0.0.1 |https://docs.confluent.io/current/tutoria|2021-01-17T14:50:43+00:00 |
| |ls/examples/kubernetes/gke-base/docs/inde| |
| |x.html | |
|10.0.0.12 |https://www.confluent.io/hub/confluentinc|2021-01-17T14:53:44+00:01 |
| |/kafka-connect-datagen | |
|10.0.0.13 |https://www.confluent.io/hub/confluentinc|2021-01-17T14:56:45+00:03 |
| |/kafka-connect-datagen | |
Limit Reached
Query terminated
Finally, let’s see what’s available on the underlying Kafka topic for the table. We can print that out easily.
PRINT DISTINCT_CLICKS FROM BEGINNING LIMIT 3;
The output should look similar to:
Key format: JSON or HOPPING(KAFKA_STRING) or TUMBLING(KAFKA_STRING) or KAFKA_STRING
Value format: JSON or KAFKA_STRING
rowtime: 2021/01/17 14:50:43.000 Z, key: "10.0.0.1", value: {"URL":"https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html","TIMESTAMP":"2021-01-17T14:50:43+00:00"}, partition: 0
rowtime: 2021/01/17 14:52:44.000 Z, key: "10.0.0.12", value: {"URL":"https://www.confluent.io/hub/confluentinc/kafka-connect-datagen","TIMESTAMP":"2021-01-17T14:53:44+00:01"}, partition: 0
rowtime: 2021/01/17 14:53:45.000 Z, key: "10.0.0.13", value: {"URL":"https://www.confluent.io/hub/confluentinc/kafka-connect-datagen","TIMESTAMP":"2021-01-17T14:56:45+00:03"}, partition: 0
Topic printing ceased
Now that you have a series of statements that’s doing the right thing, the last step is to put them into a file so that they can be used outside the CLI session. Create a file at src/statements.sql
with the following content:
CREATE STREAM CLICKS (IP_ADDRESS STRING, URL STRING, TIMESTAMP STRING)
WITH (KAFKA_TOPIC = 'CLICKS',
FORMAT = 'JSON',
TIMESTAMP = 'TIMESTAMP',
TIMESTAMP_FORMAT = 'yyyy-MM-dd''T''HH:mm:ssXXX',
PARTITIONS = 1);
CREATE TABLE DETECTED_CLICKS AS
SELECT
IP_ADDRESS AS KEY1,
URL AS KEY2,
TIMESTAMP AS KEY3,
AS_VALUE(IP_ADDRESS) AS IP_ADDRESS,
COUNT(IP_ADDRESS) as IP_COUNT,
AS_VALUE(URL) AS URL,
AS_VALUE(TIMESTAMP) AS TIMESTAMP
FROM CLICKS WINDOW TUMBLING (SIZE 2 MINUTES, RETENTION 1000 DAYS)
GROUP BY IP_ADDRESS, URL, TIMESTAMP;
CREATE STREAM RAW_VALUES_CLICKS (IP_ADDRESS STRING, IP_COUNT BIGINT, URL STRING, TIMESTAMP STRING)
WITH (KAFKA_TOPIC = 'DETECTED_CLICKS',
PARTITIONS = 1,
FORMAT = 'JSON');
CREATE STREAM DISTINCT_CLICKS AS
SELECT
IP_ADDRESS,
URL,
TIMESTAMP
FROM RAW_VALUES_CLICKS
WHERE IP_COUNT = 1
PARTITION BY IP_ADDRESS;
Create a file at test/input.json
with the inputs for testing:
{
"inputs": [
{
"topic": "CLICKS",
"value": {
"IP_ADDRESS": "10.0.0.1",
"URL": "https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html",
"TIMESTAMP": "2021-01-17T14:50:43+00:00"
}
},
{
"topic": "CLICKS",
"value": {
"IP_ADDRESS": "10.0.0.12",
"URL": "https://www.confluent.io/hub/confluentinc/kafka-connect-datagen",
"TIMESTAMP": "2021-01-17T14:53:44+00:01"
}
},
{
"topic": "CLICKS",
"value": {
"IP_ADDRESS": "10.0.0.13",
"URL": "https://www.confluent.io/hub/confluentinc/kafka-connect-datagen",
"TIMESTAMP": "2021-01-17T14:56:45+00:03"
}
},
{
"topic": "CLICKS",
"value": {
"IP_ADDRESS": "10.0.0.1",
"URL": "https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html",
"TIMESTAMP": "2021-01-17T14:50:43+00:00"
}
},
{
"topic": "CLICKS",
"value": {
"IP_ADDRESS": "10.0.0.12",
"URL": "https://www.confluent.io/hub/confluentinc/kafka-connect-datagen",
"TIMESTAMP": "2021-01-17T14:53:44+00:01"
}
},
{
"topic": "CLICKS",
"value": {
"IP_ADDRESS": "10.0.0.13",
"URL": "https://www.confluent.io/hub/confluentinc/kafka-connect-datagen",
"TIMESTAMP": "2021-01-17T14:56:45+00:03"
}
}
]
}
Similarly, create a file at test/output.json
with the expected outputs.
{
"outputs": [
{
"topic": "DISTINCT_CLICKS",
"key": "10.0.0.1",
"value": {
"URL": "https://docs.confluent.io/current/tutorials/examples/kubernetes/gke-base/docs/index.html",
"TIMESTAMP": "2021-01-17T14:50:43+00:00"
},
"timestamp": 1610895043000
},
{
"topic": "DISTINCT_CLICKS",
"key": "10.0.0.12",
"value": {
"URL": "https://www.confluent.io/hub/confluentinc/kafka-connect-datagen",
"TIMESTAMP": "2021-01-17T14:53:44+00:01"
},
"timestamp": 1610895164000
},
{
"topic": "DISTINCT_CLICKS",
"key": "10.0.0.13",
"value": {
"URL": "https://www.confluent.io/hub/confluentinc/kafka-connect-datagen",
"TIMESTAMP": "2021-01-17T14:56:45+00:03"
},
"timestamp": 1610895225000
}
]
}
Lastly, invoke the tests using the test runner and the statements file that you created earlier:
docker exec ksql-cli ksql-test-runner -i /opt/app/test/input.json -s /opt/app/src/statements.sql -o /opt/app/test/output.json
Which should pass:
>>> Test passed!
Launch your statements into production by sending them to the REST API with the following command:
tr '\n' ' ' < src/statements.sql | \
sed 's/;/;\'$'\n''/g' | \
while read stmt; do
echo '{"ksql":"'$stmt'", "streamsProperties": {}}' | \
curl -s -X "POST" "http://localhost:8088/ksql" \
-H "Content-Type: application/vnd.ksql.v1+json; charset=utf-8" \
-d @- | \
jq
done
In the steps above, we customized cache.max.bytes.buffering
interactively via the CLI. Since this setting can affect overall throughput, it’s a good idea to assess its impact on disk I/O in production environments. The Kafka Streams documentation details the relevant internal mechanisms.
Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully-managed Apache Kafka service.
Sign up for Confluent Cloud, a fully-managed Apache Kafka service.
After you log in to Confluent Cloud Console, click on Add cloud environment
and name the environment learn-kafka
. Using a new environment keeps your learning resources separate from your other Confluent Cloud resources.
From the Billing & payment
section in the Menu, apply the promo code CC100KTS
to receive an additional $100 free usage on Confluent Cloud (details).
Click on LEARN and follow the instructions to launch a Kafka cluster and to enable Schema Registry.
Next, from the Confluent Cloud Console, click on Clients
to get the cluster-specific configurations, e.g. Kafka cluster bootstrap servers and credentials, Confluent Cloud Schema Registry and credentials, etc., and set the appropriate parameters in your client application.
Now you’re all set to run your streaming application locally, backed by a Kafka cluster fully managed by Confluent Cloud.