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Add a key to data ingested through Kafka Connect

Question:

How can you stream data from a source system (such as a database) into Kafka using Kafka Connect, and add a key to the data as part of the ingestion?

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Example use case:

Kafka Connect is the integration API for Apache Kafka. It enables you to stream data from source systems (such as databases, message queues, SaaS platforms, and flat files) into Kafka, and from Kafka to target systems. When you stream data into Kafka, you often need to set the key correctly for partitioning and application logic reasons. In this example, we have a database containing data about cities, and we want to key the resulting Kafka messages by the city_id field. This tutorial will show you different ways of setting the key correctly. It will also cover how to declare the schema and use Kafka Streams to process the data using SpecificAvro.

Hands-on code example:

ksqlDB Kafka Streams Basic Kafka

Run it

Prerequisites

1

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

Initialize the project

2

To get started, make a new directory anywhere you’d like for this project:

mkdir connect-add-key-to-source && cd connect-add-key-to-source

Get Confluent Platform

3

Create a Dockerfile called Dockerfile-connect that builds a custom container for Kafka Connect bundled with the free and open source JDBC connector, installed from Confluent Hub.

FROM confluentinc/cp-kafka-connect-base:7.3.0

ENV CONNECT_PLUGIN_PATH="/usr/share/java,/usr/share/confluent-hub-components"

RUN confluent-hub install --no-prompt confluentinc/kafka-connect-jdbc:10.0.2

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
  connect:
    image: localimage/kafka-connect-jdbc:latest
    build:
      context: .
      dockerfile: Dockerfile-connect
    hostname: connect
    container_name: connect
    depends_on:
    - schema-registry
    ports:
    - 8083:8083
    environment:
      CONNECT_BOOTSTRAP_SERVERS: broker:9092
      CONNECT_REST_ADVERTISED_HOST_NAME: connect
      CONNECT_GROUP_ID: compose-connect-group
      CONNECT_CONFIG_STORAGE_TOPIC: docker-connect-configs
      CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR: 1
      CONNECT_OFFSET_FLUSH_INTERVAL_MS: 10000
      CONNECT_OFFSET_STORAGE_TOPIC: docker-connect-offsets
      CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR: 1
      CONNECT_STATUS_STORAGE_TOPIC: docker-connect-status
      CONNECT_STATUS_STORAGE_REPLICATION_FACTOR: 1
      CONNECT_KEY_CONVERTER: org.apache.kafka.connect.storage.StringConverter
      CONNECT_VALUE_CONVERTER: io.confluent.connect.avro.AvroConverter
      CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_URL: http://schema-registry:8081
      CONNECT_INTERNAL_KEY_CONVERTER: org.apache.kafka.connect.json.JsonConverter
      CONNECT_INTERNAL_VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverter
      CONNECT_PLUGIN_PATH: /usr/share/java,/usr/share/confluent-hub-components
      CONNECT_LOG4J_LOGGERS: org.apache.zookeeper=ERROR,org.I0Itec.zkclient=ERROR,org.reflections=ERROR

Now launch Confluent Platform by running the following command. Note the --build argument which automatically builds the Docker image for Kafka Connect and the bundled kafka-connect-jdbc connector.

docker compose up -d --build

Configure the project

4

Create the following Gradle build file, named build.gradle for the project:

import java.beans.EventSetDescriptor

buildscript {
  repositories {
    mavenCentral()
  }
}

plugins {
  id "java"
  id "application"
  id "com.github.johnrengelman.shadow" version "6.1.0"
  id "com.github.davidmc24.gradle.plugin.avro" version "1.7.0"
}

sourceCompatibility = JavaVersion.VERSION_17
targetCompatibility = JavaVersion.VERSION_17
version = "0.0.1"

repositories {
  mavenCentral()

  maven {
    url "https://packages.confluent.io/maven"
  }
}

apply plugin: "com.github.johnrengelman.shadow"

dependencies {
  implementation "org.apache.avro:avro:1.11.1"
  implementation "org.slf4j:slf4j-simple:2.0.7"
  implementation 'org.apache.kafka:kafka-streams:3.4.0'
    implementation ('org.apache.kafka:kafka-clients') {
       version {
           strictly '3.4.0'
        }
      }
  implementation "io.confluent:kafka-streams-avro-serde:7.3.0"
  implementation 'com.google.code.gson:gson:2.10.1'

  testImplementation "org.apache.kafka:kafka-streams-test-utils:3.4.0"
  testImplementation 'junit:junit:4.13.2'
}

test {
  testLogging {
    outputs.upToDateWhen { false }
    showStandardStreams = true
    exceptionFormat = "full"
  }
}

jar {
  manifest {
    attributes(
        "Class-Path": configurations.compileClasspath.collect { it.getName() }.join(" "),
        "Main-Class": "io.confluent.developer.connect.jdbc.specificavro.StreamsIngest"
    )
  }
}

shadowJar {
  archiveBaseName = "connect-add-key-to-source-standalone"
  archiveClassifier = ''
}

// Define the main class for the application
mainClassName = 'io.confluent.developer.connect.jdbc.specificavro.StreamsIngest'

And be sure to run the following command to obtain the Gradle wrapper:

gradle wrapper

Next, create a directory for configuration data:

mkdir configuration

Then create a development file at configuration/dev.properties:

application.id=cities_ingestion
bootstrap.servers=127.0.0.1:29092
schema.registry.url=http://127.0.0.1:8081

input.topic.name=cities
input.topic.partitions=1
input.topic.replication.factor=1

output.topic.name=cities_keyed
output.topic.partitions=1
output.topic.replication.factor=1

Create a schema for the events

5

This tutorial uses one stream of cities. Let’s create the schemas for it.

Create a directory for the schema that represents each city in the stream:

mkdir -p src/main/avro

Then create the following Avro schema file at src/main/avro/city.avsc for the cities lookup table:

{
  "namespace": "io.confluent.developer.avro",
  "type": "record",
  "name": "City",
  "fields": [
    {
      "name": "city_id",
      "type": "long"
    },
    {
      "name": "name",
      "type": "string"
    },
    {
      "name": "state",
      "type": "string"
    }
  ]
}

Because we will use this Avro schema in our Java code, we’ll need to compile it. The Gradle Avro plugin is a part of the build, so it will see your new Avro file, generate Java code for them, and compile those and all other Java sources. Run this command to get it all done:

./gradlew build

Create the source database

6

Create a file cities.sql with commands to prepopulate the database table with city information:

DROP TABLE IF EXISTS cities;
CREATE TABLE cities (city_id INTEGER KEY NOT NULL, name VARCHAR(255), state VARCHAR(255));
INSERT INTO cities (city_id, name, state) VALUES (1, 'Raleigh', 'NC');
INSERT INTO cities (city_id, name, state) VALUES (2, 'Mountain View', 'CA');
INSERT INTO cities (city_id, name, state) VALUES (3, 'Knoxville', 'TN');
INSERT INTO cities (city_id, name, state) VALUES (4, 'Houston', 'TX');
INSERT INTO cities (city_id, name, state) VALUES (5, 'Olympia', 'WA');
INSERT INTO cities (city_id, name, state) VALUES (6, 'Bismarck', 'ND');

Then create a Dockerfile to build the sqlite container. Notice that it pulls in the cities.sql file created in the previous step.

FROM alpine:3.4
RUN apk add --update sqlite
RUN mkdir /db
WORKDIR /db
ADD cities.sql /db
RUN sqlite3 geos.db < /db/cities.sql

ENTRYPOINT ["sqlite3"]

Create the source database, running this from the directory with the Dockerfile and the cities.sql file:

docker build -t sqlitekt . && docker run -d -i --name sqlitekt sqlitekt

View the data in the source table:

echo 'select * from cities;' | docker exec -i sqlitekt sqlite3 geos.db

Run the connector

7

Copy the sqlite database from the sqlitekt docker image to the connect docker image:

docker cp sqlitekt:/db/geos.db /tmp/geos.db && docker cp /tmp/geos.db connect:/tmp/geos.db

Make a configuration file called jdbc_source.config for the JDBC source connector to pull data from the cities table. Notice that it uses a single message transformation (SMT) called SetSchemaMetadata to set the schema name to the City class name.

{
  "name": "jdbc-cities",
  "config": {
    "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
    "connection.url": "jdbc:sqlite:/tmp/geos.db",
    "mode": "incrementing",
    "incrementing.column.name": "city_id",
    "topic.prefix": "",
    "table.whitelist": "cities",
    "value.converter": "io.confluent.connect.avro.AvroConverter",
    "value.converter.schema.registry.url": "http://schema-registry:8081",
    "value.converter.schemas.enable": "true",
    "transforms": "SetValueSchema",
    "transforms.SetValueSchema.type": "org.apache.kafka.connect.transforms.SetSchemaMetadata$Value",
    "transforms.SetValueSchema.schema.name": "io.confluent.developer.avro.City",
    "tasks.max": "1"
  }
}

Run the JDBC source connector:

curl -X POST -H Accept:application/json -H Content-Type:application/json http://localhost:8083/connectors/ -d @jdbc_source.config

Create the Kafka Streams topology

8

Create a directory for the Java files in this project:

mkdir -p src/main/java/io/confluent/developer/connect/jdbc/specificavro

Then create the following file at src/main/java/io/confluent/developer/connect/jdbc/specificavro/StreamsIngest.java. Let’s take a close look at the buildTopology() method, which uses the Kafka Streams DSL.

The first thing the method does is build a stream from the input topic. It uses the SpecificAvroSerde<> to create a KStream called citiesNoKey that has no message key and a message value of type City.

Next, using the map method, it extracts a field in the message value that corresponds to the city ID, and assigns it as the message key. It results in a KStream called citiesKeyed that has a message key of type Long and a message value of type City.

Finally this is written to the output topic using the to method.

package io.confluent.developer.connect.jdbc.specificavro;

import org.apache.kafka.clients.admin.AdminClient;
import org.apache.kafka.clients.admin.NewTopic;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.kstream.Consumed;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.Produced;

import java.io.FileInputStream;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.CountDownLatch;

import io.confluent.developer.avro.City;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;

import static io.confluent.kafka.serializers.AbstractKafkaSchemaSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG;

public class StreamsIngest {

  public Properties buildStreamsProperties(Properties envProps) {
    Properties props = new Properties();

    //props.put(StreamsConfig.APPLICATION_ID_CONFIG, envProps.getProperty("application.id"));
    props.put(StreamsConfig.APPLICATION_ID_CONFIG, "foo2");
    props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
    props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, envProps.getProperty("bootstrap.servers"));
    props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
    props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
    props.put(SCHEMA_REGISTRY_URL_CONFIG, envProps.getProperty("schema.registry.url"));
    props.put(StreamsConfig.STATESTORE_CACHE_MAX_BYTES_CONFIG, 0);

    return props;
  }

  private SpecificAvroSerde<City> citySerde(final Properties envProps) {
    final SpecificAvroSerde<City> serde = new SpecificAvroSerde<>();
    Map<String, String> config = new HashMap<>();
    config.put(SCHEMA_REGISTRY_URL_CONFIG, envProps.getProperty("schema.registry.url"));
    serde.configure(config, false);
    return serde;
  }

  public Topology buildTopology(Properties envProps,
                                final SpecificAvroSerde<City> citySerde) {
    final StreamsBuilder builder = new StreamsBuilder();

    final String inputTopic = envProps.getProperty("input.topic.name");
    final String outputTopic = envProps.getProperty("output.topic.name");

    KStream<String, City> citiesNoKey = builder.stream(inputTopic, Consumed.with(Serdes.String(), citySerde));
    final KStream<Long, City> citiesKeyed = citiesNoKey.map((k, v) -> new KeyValue<>(v.getCityId(), v));
    citiesKeyed.to(outputTopic, Produced.with(Serdes.Long(), citySerde));

    return builder.build();
  }

  public void createTopics(Properties envProps) {
    Map<String, Object> config = new HashMap<>();
    config.put("bootstrap.servers", envProps.getProperty("bootstrap.servers"));
    AdminClient client = AdminClient.create(config);

    List<NewTopic> topics = new ArrayList<>();
    topics.add(new NewTopic(
        envProps.getProperty("input.topic.name"),
        Integer.parseInt(envProps.getProperty("input.topic.partitions")),
        Short.parseShort(envProps.getProperty("input.topic.replication.factor"))));
    topics.add(new NewTopic(
        envProps.getProperty("output.topic.name"),
        Integer.parseInt(envProps.getProperty("output.topic.partitions")),
        Short.parseShort(envProps.getProperty("output.topic.replication.factor"))));

    client.createTopics(topics);
    client.close();
  }

  public Properties loadEnvProperties(String fileName) throws IOException {
    Properties envProps = new Properties();
    FileInputStream input = new FileInputStream(fileName);
    envProps.load(input);
    input.close();

    return envProps;
  }

  public static void main(String[] args) throws IOException {
    if (args.length < 1) {
      throw new IllegalArgumentException(
          "This program takes one argument: the path to an environment configuration file.");
    }

    new StreamsIngest().runRecipe(args[0]);
  }

  private void runRecipe(final String configPath) throws IOException {
    Properties envProps = this.loadEnvProperties(configPath);
    Properties streamProps = this.buildStreamsProperties(envProps);

    Topology topology = this.buildTopology(envProps, this.citySerde(envProps));
    this.createTopics(envProps);

    final KafkaStreams streams = new KafkaStreams(topology, streamProps);
    final CountDownLatch latch = new CountDownLatch(1);

    // Attach shutdown handler to catch Control-C.
    Runtime.getRuntime().addShutdownHook(new Thread("streams-shutdown-hook") {
      @Override
      public void run() {
        streams.close();
        latch.countDown();
      }
    });

    try {
      streams.cleanUp();
      streams.start();
      latch.await();
    } catch (Throwable e) {
      System.exit(1);
    }
    System.exit(0);
  }
}

Compile and run the Kafka Streams program

9

In your terminal, run:

./gradlew shadowJar

Now that you have an uberjar for the Kafka Streams application, you can launch it locally. When you run the following, the prompt won’t return, because the application will run until you exit it. There is always another message to process, so streaming applications don’t exit until you force them.

java -jar build/libs/connect-add-key-to-source-standalone-0.0.1.jar configuration/dev.properties

Consume events from the input topic and output topic

10

Run the Avro console consumer to view the messages in the topic cities. This is the topic that the JDBC source connector writes to, and note that the keys are null:

docker exec -it schema-registry /usr/bin/kafka-avro-console-consumer --topic cities --bootstrap-server broker:9092 --from-beginning --property schema.registry.url=http://localhost:8081 --property print.key=true --property key.deserializer=org.apache.kafka.common.serialization.LongDeserializer --timeout-ms 20000

Next, run the Avro console consumer to view the messages in the topic cities_keyed. This is the output topic that the Kafka Streams application creates from the input topic cities, and note that it has added the keys:

docker exec -it schema-registry /usr/bin/kafka-avro-console-consumer --topic cities_keyed --bootstrap-server broker:9092 --from-beginning --property schema.registry.url=http://localhost:8081 --property print.key=true --property key.deserializer=org.apache.kafka.common.serialization.LongDeserializer --timeout-ms 20000

Test it

Create a test configuration file

1

First, create a test file at configuration/test.properties:

application.id=cities_ingestion
bootstrap.servers=127.0.0.1:9092
schema.registry.url=mock://cities_ingestion:8081

input.topic.name=cities
input.topic.partitions=1
input.topic.replication.factor=1

output.topic.name=cities_keyed
output.topic.partitions=1
output.topic.replication.factor=1

Write a test

2

Then, create a directory for the tests to live in:

mkdir -p src/test/java/io/confluent/developer/connect/jdbc/specificavro

Create the following test file at src/test/java/io/confluent/developer/connect/jdbc/specificavro/StreamsIngestTest.java:

package io.confluent.developer.connect.jdbc.specificavro;

import org.apache.kafka.common.serialization.Deserializer;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.serialization.Serializer;
import org.apache.kafka.streams.TestInputTopic;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.TopologyTestDriver;
import org.junit.Assert;
import org.junit.Test;

import java.io.IOException;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.stream.Collectors;

import io.confluent.developer.avro.City;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;

import static java.util.Arrays.asList;

public class StreamsIngestTest {

  private final static String TEST_CONFIG_FILE = "configuration/test.properties";

  private SpecificAvroSerde<City> makeSerializer(Properties envProps) {
    SpecificAvroSerde<City> serde = new SpecificAvroSerde<>();

    Map<String, String> config = new HashMap<>();
    config.put("schema.registry.url", envProps.getProperty("schema.registry.url"));
    serde.configure(config, false);

    return serde;
  }

  @Test
  public void shouldCreateKeyedStream() throws IOException {
    StreamsIngest si = new StreamsIngest();
    Properties envProps = si.loadEnvProperties(TEST_CONFIG_FILE);
    Properties streamProps = si.buildStreamsProperties(envProps);

    String inputTopic = envProps.getProperty("input.topic.name");
    String outputTopic = envProps.getProperty("output.topic.name");

    final SpecificAvroSerde<City> citySpecificAvroSerde = makeSerializer(envProps);

    Topology topology = si.buildTopology(envProps, citySpecificAvroSerde);
    final List<Long> expectedOutput;
    List<Long> actualOutput;
    try (TopologyTestDriver testDriver = new TopologyTestDriver(topology, streamProps)) {

      Serializer<String> keySerializer = Serdes.String().serializer();
      Deserializer<Long> keyDeserializer = Serdes.Long().deserializer();

      final TestInputTopic<String, City>
          driverInputTopic =
          testDriver.createInputTopic(inputTopic, keySerializer, citySpecificAvroSerde.serializer());

      // Fixture
      City c1 = new City(1L, "Raleigh", "NC");
      City c2 = new City(2L, "Mountain View", "CA");
      City c3 = new City(3L, "Knoxville", "TN");
      City c4 = new City(4L, "Houston", "TX");
      City c5 = new City(5L, "Olympia", "WA");
      City c6 = new City(6L, "Bismarck", "ND");
      // end Fixture

      final List<City>
          input = asList(c1, c2, c3, c4, c5, c6);

      expectedOutput = asList(1L, 2L, 3L, 4L, 5L, 6L);

      for (City city : input) {
        driverInputTopic.pipeInput(null, city);
      }

      actualOutput = testDriver.createOutputTopic(outputTopic, keyDeserializer, citySpecificAvroSerde.deserializer())
          .readKeyValuesToList()
          .stream()
          .filter(longCityKeyValue -> longCityKeyValue.key != null)
          .map(record -> record.key)
          .collect(Collectors.toList());

      Assert.assertEquals(expectedOutput, actualOutput);
    }
  }

}

Invoke the tests

3

Now run the test, which is as simple as:

./gradlew test

Deploy on Confluent Cloud

Run your app with Confluent Cloud

1

Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully managed Apache Kafka service.

  1. Sign up for Confluent Cloud, a fully managed Apache Kafka service.

  2. 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.

  3. 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.

  4. Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.

Confluent Cloud

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. In the case of this tutorial, add the following properties to the client application’s input properties file, substituting all curly braces with your Confluent Cloud values.

# Required connection configs for Kafka producer, consumer, and admin
bootstrap.servers={{ BROKER_ENDPOINT }}
security.protocol=SASL_SSL
sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username='{{ CLUSTER_API_KEY }}' password='{{ CLUSTER_API_SECRET }}';
sasl.mechanism=PLAIN
# Required for correctness in Apache Kafka clients prior to 2.6
client.dns.lookup=use_all_dns_ips

# Best practice for Kafka producer to prevent data loss
acks=all

# Required connection configs for Confluent Cloud Schema Registry
schema.registry.url=https://{{ SR_ENDPOINT }}
basic.auth.credentials.source=USER_INFO
schema.registry.basic.auth.user.info={{ SR_API_KEY }}:{{ SR_API_SECRET }}

Now you’re all set to run your streaming application locally, backed by a Kafka cluster fully managed by Confluent Cloud.