How can you transform the values of a Kafka topic using a stateless scalar function not already provided by ksqlDB?
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 udf && cd udf
Then make the following directories:
mkdir src extensions
Create the following Gradle build file, named build.gradle
for the project:
buildscript {
repositories {
mavenCentral()
}
}
plugins {
id "java"
}
// Use Java 11 since CP Docker images package Java 11 as of CP 7.3.x
sourceCompatibility = JavaVersion.VERSION_11
targetCompatibility = JavaVersion.VERSION_11
version = "0.0.1"
repositories {
mavenCentral()
maven {
url "https://packages.confluent.io/maven"
}
}
dependencies {
implementation 'io.confluent.ksql:ksql-udf:5.4.11'
testImplementation 'junit:junit:4.13.2'
}
task copyJar(type: Copy) {
from jar
into "extensions/"
}
build.dependsOn copyJar
test {
testLogging {
outputs.upToDateWhen { false }
showStandardStreams = true
exceptionFormat = "full"
}
}
The build.gradle
also contains a copyJar
step to copy the jar file to the extensions/
directory where it will be picked up by KSQL. This is convenient when you are iterating on a function. For example, you might have tested your UDF against your suite of unit tests and you are now ready to test against streams in KSQL. With the jar in the correct place, a restart of KSQL will load your updated jar.
And be sure to run the following command to obtain the Gradle wrapper:
gradle wrapper
Create a directory for the Java files in this project:
mkdir -p src/main/java/io/confluent/developer
Then create the following file at src/main/java/io/confluent/developer/VwapUdf.java
. This file contains the Java logic of your custom function. Read through the code to familiarize yourself.
package io.confluent.developer;
import io.confluent.ksql.function.udf.Udf;
import io.confluent.ksql.function.udf.UdfDescription;
import io.confluent.ksql.function.udf.UdfParameter;
@UdfDescription(name = "vwap", description = "Volume weighted average price")
public class VwapUdf {
@Udf(description = "vwap for market prices as integers, returns double")
public double vwap(
@UdfParameter(value = "bid")
final int bid,
@UdfParameter(value = "bidQty")
final int bidQty,
@UdfParameter(value = "ask")
final int ask,
@UdfParameter(value = "askQty")
final int askQty) {
return ((ask * askQty) + (bid * bidQty)) / (bidQty + askQty);
}
@Udf(description = "vwap for market prices as doubles, returns double")
public double vwap(
@UdfParameter(value = "bid")
final double bid,
@UdfParameter(value = "bidQty")
final int bidQty,
@UdfParameter(value = "ask")
final double ask,
@UdfParameter(value = "askQty")
final int askQty) {
return ((ask * askQty) + (bid * bidQty)) / (bidQty + askQty);
}
}
Here we have created a new Class which defines two functions, both of which are annotated with Udf
, indicating they are ksqlDB UDF function definitions. Both functions take parameters of type double
or int
and produce a single result of type double
, representing the calculated volume-weighted average price of the inputs.
In your terminal, run:
./gradlew build
The copyJar
gradle task will automatically deliver the jar to the extensions/
directory.
Next, create the following docker-compose.yml
file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud):
version: '2'
services:
broker:
image: confluentinc/cp-kafka:7.4.1
hostname: broker
container_name: broker
ports:
- 29092:29092
environment:
KAFKA_BROKER_ID: 1
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT,CONTROLLER:PLAINTEXT
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
KAFKA_PROCESS_ROLES: broker,controller
KAFKA_NODE_ID: 1
KAFKA_CONTROLLER_QUORUM_VOTERS: 1@broker:29093
KAFKA_LISTENERS: PLAINTEXT://broker:9092,CONTROLLER://broker:29093,PLAINTEXT_HOST://0.0.0.0:29092
KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
KAFKA_LOG_DIRS: /tmp/kraft-combined-logs
CLUSTER_ID: MkU3OEVBNTcwNTJENDM2Qk
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
ksqldb-server:
image: confluentinc/ksqldb-server:0.28.2
hostname: ksqldb-server
container_name: ksqldb-server
depends_on:
- broker
- schema-registry
volumes:
- ./extensions:/etc/ksqldb/ext
ports:
- 8088:8088
environment:
KSQL_CONFIG_DIR: /etc/ksqldb
KSQL_KSQL_EXTENSION_DIR: /etc/ksqldb/ext/
KSQL_LOG4J_OPTS: -Dlog4j.configuration=file:/etc/ksqldb/log4j.properties
KSQL_BOOTSTRAP_SERVERS: broker:9092
KSQL_HOST_NAME: ksqldb-server
KSQL_LISTENERS: http://0.0.0.0:8088
KSQL_CACHE_MAX_BYTES_BUFFERING: 0
KSQL_KSQL_SCHEMA_REGISTRY_URL: http://schema-registry:8081
ksqldb-cli:
image: confluentinc/ksqldb-cli:0.28.2
container_name: ksqldb-cli
depends_on:
- broker
- ksqldb-server
entrypoint: /bin/sh
tty: true
environment:
KSQL_CONFIG_DIR: /etc/ksqldb
volumes:
- ./src:/opt/app/src
- ./test:/opt/app/test
Note docker-compose.yml
has configured the ksql-server
container with KSQL_KSQL_EXTENSION_DIR: "/etc/ksql/ext/"
, mapping the local extensions
directory to /etc/ksql/ext
in the container. KSQL is now configured to look in this location for your extensions such as custom functions.
Launch the platform by running:
docker compose up -d
To begin developing interactively, open up the ksqlDB CLI:
docker exec -it ksqldb-cli ksql http://ksqldb-server:8088
Let’s confirm the UDF jar has been loaded correctly. You will see VWAP
in the list of functions.
SHOW FUNCTIONS;
You can see some additional detail about the function with DESCRIBE FUNCTION
.
DESCRIBE FUNCTION VWAP;
The result gives you a description of the function including input parameters and the return type.
Name : VWAP
Overview : Volume weighted average price
Type : SCALAR
Jar : /etc/ksqldb/ext/udf-0.0.1.jar
Variations :
Variation : VWAP(bid DOUBLE, bidQty INT, ask DOUBLE, askQty INT)
Returns : DOUBLE
Description : vwap for market prices as doubles, returns double
Variation : VWAP(bid INT, bidQty INT, ask INT, askQty INT)
Returns : DOUBLE
Description : vwap for market prices as integers, returns double
You’ll need to create a Kafka topic and stream to represent the stock quote stream. The following creates both in one shot:
CREATE STREAM raw_quotes(ticker varchar key, bid int, ask int, bidqty int, askqty int)
WITH (kafka_topic='stockquotes', value_format='avro', partitions=1);
Then produce the following events to the stream:
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZTEST', 15, 25, 100, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZVV', 25, 35, 100, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZVZZT', 35, 45, 100, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZXZZT', 45, 55, 100, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZTEST', 10, 20, 50, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZVV', 30, 40, 100, 50);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZVZZT', 30, 40, 50, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZXZZT', 50, 60, 100, 50);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZTEST', 15, 20, 100, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZVV', 25, 35, 100, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZVZZT', 35, 45, 100, 100);
INSERT INTO raw_quotes (ticker, bid, ask, bidqty, askqty) VALUES ('ZXZZT', 45, 55, 100, 100);
Now that you have stream with some events in it, let’s read them out. 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';
Let’s invoke the vwap
function for every observed raw quote. Pay attention to the parameter ordering of the UDF when invoking from the ksqlDB syntax.
SELECT ticker, vwap(bid, bidqty, ask, askqty) AS vwap FROM raw_quotes EMIT CHANGES LIMIT 12;
This should yield the following output:
+--------------------+--------------------+
|TICKER |VWAP |
+--------------------+--------------------+
|ZTEST |20.0 |
|ZVV |30.0 |
|ZVZZT |40.0 |
|ZXZZT |50.0 |
|ZTEST |16.0 |
|ZVV |33.0 |
|ZVZZT |36.0 |
|ZXZZT |53.0 |
|ZTEST |17.0 |
|ZVV |30.0 |
|ZVZZT |40.0 |
|ZXZZT |50.0 |
Limit Reached
Query terminated
Since the output looks right, the next step is to make the query continuous. Issue the following to create a new stream that is continuously populated by its query:
CREATE STREAM vwap WITH (kafka_topic = 'vwap', partitions = 1) AS
SELECT ticker,
vwap(bid, bidqty, ask, askqty) AS vwap
FROM raw_quotes
EMIT CHANGES;
To check that it’s working, print out the contents of the output stream’s underlying topic:
PRINT vwap FROM BEGINNING LIMIT 12;
This should yield the following output:
Key format: KAFKA_STRING
Value format: AVRO
rowtime: 2020/05/04 23:03:23.467 Z, key: ZTEST, value: {"VWAP": 20.0}, partition: 0
rowtime: 2020/05/04 23:03:23.672 Z, key: ZVV, value: {"VWAP": 30.0}, partition: 0
rowtime: 2020/05/04 23:03:23.801 Z, key: ZVZZT, value: {"VWAP": 40.0}, partition: 0
rowtime: 2020/05/04 23:03:23.967 Z, key: ZXZZT, value: {"VWAP": 50.0}, partition: 0
rowtime: 2020/05/04 23:03:24.100 Z, key: ZTEST, value: {"VWAP": 16.0}, partition: 0
rowtime: 2020/05/04 23:03:24.399 Z, key: ZVV, value: {"VWAP": 33.0}, partition: 0
rowtime: 2020/05/04 23:03:24.551 Z, key: ZVZZT, value: {"VWAP": 36.0}, partition: 0
rowtime: 2020/05/04 23:03:24.705 Z, key: ZXZZT, value: {"VWAP": 53.0}, partition: 0
rowtime: 2020/05/04 23:03:24.844 Z, key: ZTEST, value: {"VWAP": 17.0}, partition: 0
rowtime: 2020/05/04 23:03:24.980 Z, key: ZVV, value: {"VWAP": 30.0}, partition: 0
rowtime: 2020/05/04 23:03:25.096 Z, key: ZVZZT, value: {"VWAP": 40.0}, partition: 0
rowtime: 2020/05/04 23:03:25.400 Z, key: ZXZZT, value: {"VWAP": 50.0}, 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 raw_quotes(ticker varchar key, bid int, ask int, bidqty int, askqty int)
WITH (kafka_topic='stockquotes', value_format='avro', partitions=1);
CREATE STREAM vwap WITH (kafka_topic = 'vwap', partitions = 1) AS
SELECT ticker,
vwap(bid, bidqty, ask, askqty) AS vwap
FROM raw_quotes;
Then, create a directory for the tests to live in:
mkdir -p src/test/java/io/confluent/developer
Create the following test file at src/test/java/io/confluent/developer/VwapUdfTest.java
:
package io.confluent.developer;
import static org.junit.Assert.*;
import org.junit.Test;
public class VwapUdfTest {
@Test
public void testVwapAllInts() {
assertEquals(100D,
new VwapUdf().vwap(95, 100, 105, 100),
0D);
}
@Test
public void testVwap() {
assertEquals(100D,
new VwapUdf().vwap(95D, 100, 105D, 100),
0D);
}
}
Now run the test, which is as simple as:
./gradlew test
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 Environments
in the lefthand navigation, 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). To avoid having to enter a credit card, add an additional promo code CONFLUENTDEV1
. With this promo code, you will not have to enter a credit card for 30 days or until your credits run out.
Click on LEARN and follow the instructions to launch a Kafka cluster and 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.