How to find distinct values in a stream of events using Flink SQL

Run it

Prerequisites

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

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

mkdir finding-distinct && cd finding-distinct

Get Confluent Platform and Apache Flink®

Next, create the following docker-compose.yml file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud) and Apache Flink®. The Docker Compose file will start three Flink® containers that have Kafka connector dependencies preinstalled: an interactive Flink SQL client (flink-sql-client) that sends streaming SQL jobs to the Flink Job Manager (flink-job-manager), which in turn assigns tasks to the Flink Task Manager (flink-task-manager) in the Flink cluster.

---
version: '2'

services:
  broker:
    image: confluentinc/cp-kafka:7.4.1
    hostname: broker
    container_name: broker
    ports:
      - 9092:9092
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: 'CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT'
      KAFKA_ADVERTISED_LISTENERS: 'PLAINTEXT://broker:29092,PLAINTEXT_HOST://localhost:9092'
      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:29092,CONTROLLER://broker:29093,PLAINTEXT_HOST://0.0.0.0:9092'
      KAFKA_INTER_BROKER_LISTENER_NAME: 'PLAINTEXT'
      KAFKA_CONTROLLER_LISTENER_NAMES: 'CONTROLLER'
      KAFKA_LOG_DIRS: '/tmp/kraft-combined-logs'
      # This is done for running a single broker in combined mode for local development only
      # For multi-node deployments you should generate using the following
      # Replace CLUSTER_ID with a unique base64 UUID using "bin/kafka-storage.sh random-uuid"
      # See https://docs.confluent.io/kafka/operations-tools/kafka-tools.html#kafka-storage-sh
      CLUSTER_ID: 'MkU3OEVBNTcwNTJENDM2Qk'

  flink-sql-client:
    image: cnfldemos/flink-sql-client-kafka:1.16.0-scala_2.12-java11
    hostname: flink-sql-client
    container_name: flink-sql-client
    depends_on:
      - flink-jobmanager
    environment:
      FLINK_JOBMANAGER_HOST: flink-jobmanager
    volumes:
      - ./settings/:/settings

  flink-jobmanager:
    image: cnfldemos/flink-kafka:1.16.0-scala_2.12-java11
    hostname: flink-jobmanager
    container_name: flink-jobmanager
    ports:
      - "9081:9081"
    command: jobmanager
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: flink-jobmanager
        rest.bind-port: 9081

  flink-taskmanager:
    image: cnfldemos/flink-kafka:1.16.0-scala_2.12-java11
    hostname: flink-taskmanager
    container_name: flink-taskmanager
    depends_on:
      - flink-jobmanager
    command: taskmanager
    scale: 1
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: flink-jobmanager
        taskmanager.numberOfTaskSlots: 10

And launch it by running:

docker compose up -d

Write the program interactively using the CLI

The best way to interact with Flink SQL when you’re learning how things work is with the Flink SQL CLI. Fire it up as follows:

docker exec -it flink-sql-client sql-client.sh

First, you’ll need to create a table to represent click events from users. Notice that we’re intentionally adding some duplicate entries that we’ll eventually need to deduplicate.

CREATE TABLE clicks (
    ip_address VARCHAR,
    url VARCHAR,
    click_ts_raw BIGINT
) WITH (
    'connector' = 'kafka',
    'topic' = 'clicks',
    'properties.bootstrap.servers' = 'broker:29092',
    'scan.startup.mode' = 'earliest-offset',
    'key.format' = 'raw',
    'key.fields' = 'ip_address',
    'value.format' = 'json',
    'value.fields-include' = 'EXCEPT_KEY'
);

Note that we are using the field clicks_ts_raw as the record’s timestamp. This is going to be important later on when we write a query to deduplicate these events and knowing when each event occurred is important.

Confluent Cloud manages several options for you when using Flink SQL. So, if you run this tutorial on Confluent Cloud, you can copy just the CREATE TABLE statements without the WITH clause when creating tables. Consult the Flink SQL WITH documentation for the full list supported options when creating a table.

Now let’s insert the following click events:

INSERT INTO clicks
VALUES ( '10.0.0.1',  'https://acme.com/index.html',    1692812175),
       ( '10.0.0.12', 'https://amazon.com/index.html',  1692826575),
       ( '10.0.0.13', 'https://confluent/index.html',   1692826575),
       ( '10.0.0.1',  'https://acme.com/index.html',    1692812175),
       ( '10.0.0.12', 'https://amazon.com/index.html',  1692819375),
       ( '10.0.0.13', 'https://confluent/index.html',   1692826575);

Let’s now deduplicate these click events. Execute the following query and study its output.

SELECT ip_address, url, FROM_UNIXTIME(click_ts_raw) as click_timestamp
FROM (
       SELECT *,
       ROW_NUMBER() OVER ( PARTITION BY ip_address ORDER BY TO_TIMESTAMP(FROM_UNIXTIME(click_ts_raw)) ) as rownum FROM clicks
      )
WHERE rownum = 1;

This should yield the following output:

                    ip_address                            url                click_timestamp
                      10.0.0.1    https://acme.com/index.html              2023-08-23 17:36:15
                      10.0.0.13   https://confluent/index.html             2023-08-23 21:36:15
                      10.0.0.12   https://amazon.com/index.html            2023-08-23 19:36:15

What you’ve done here is to select the desired columns from a sub-query. The sub-query orders events by time and assigns a unique number to each row. This process makes it possible to eliminate duplicate records where the row number is greater than one. Let’s discuss the critical parts of the sub-query:

ROW_NUMBER() starting at one, this assigns a unique, sequential number to each row
PARTITION BY specifies how to partition the data for deduplication. This should be the column(s) which will only have one row per value after deduplication. In our case here it’s the key of the table, ip_address
ORDER BY orders by the provided column and it’s required to be a time attribute. The time attribute column can be processing time (system time of the machine running the Flink job) or event time. By default ORDER BY puts rows in ascending (ASC) order. By using ASC order you’ll keep the first row. Should you want to keep the last row you should use ORDER BY <time_attribute> DESC

Since the output of our transient query looks right, the next step is to make the query persistent. This looks exactly like the transient query, except we first create a new table and then execute an INSERT INTO statement to populate the table. The INSERT INTO statement returns to the CLI prompt right away, having created a persistent stream processing program running in the Flink cluster, continuously processing input records and updating the resulting deduplicated_clicks table.

Now go ahead and run the following two commands in your Flink SQL session. First this command to create the table:

CREATE TABLE deduplicated_clicks (
    ip_address VARCHAR,
    url VARCHAR,
    click_ts VARCHAR,
    PRIMARY KEY (ip_address) NOT ENFORCED
) WITH (
    'connector' = 'upsert-kafka',
    'topic' = 'deduplicated_clicks',
    'properties.bootstrap.servers' = 'broker:29092',
    'key.format' = 'raw',
    'value.format' = 'json',
    'value.fields-include' = 'EXCEPT_KEY'
);

Then run this command that will populate the table:

INSERT INTO deduplicated_clicks
    SELECT ip_address, url, FROM_UNIXTIME(click_ts_raw) as click_timestamp
    FROM (
          SELECT *,
          ROW_NUMBER() OVER ( PARTITION BY ip_address ORDER BY TO_TIMESTAMP(FROM_UNIXTIME(click_ts_raw)) ) as rownum FROM clicks
         )
    WHERE rownum = 1;

Validate output

Seeing is believing, so let’s query the persistent deduplicated_clicks table.

SELECT * FROM deduplicated_clicks;

This will yield the same output that the transient query did (perhaps in a different order)

                    ip_address                            url                click_timestamp
                      10.0.0.1    https://acme.com/index.html             2023-08-23 17:36:15
                      10.0.0.13   https://confluent/index.html            2023-08-23 21:36:15
                      10.0.0.12   https://amazon.com/index.html           2023-08-23 19:36:15

We could also query the underlying topic directly using kafka-console-consumer. Open a new terminal window and run the following command:

docker exec -it broker /usr/bin/kafka-console-consumer\
 --topic deduplicated_clicks\
 --bootstrap-server broker:9092 \
 --from-beginning \
 --max-messages 3

This will yield the following results:

{"url":"https://acme.com/index.html","click_ts":"2023-08-23 17:36:15"}
{"url":"https://amazon.com/index.html","click_ts":"2023-08-23 21:36:15"}
{"url":"https://confluent/index.html","click_ts":"2023-08-23 21:36:15"}
Processed a total of 3 messages

Test it

Decide what testing tools to use

Now that you have manually developed and tested your Flink SQL application, how might you create an automated test for it so that it’s easier to maintain and upgrade over time? Imagine how painful it would be to have to manually test every change or software dependency upgrade of your application, and then imagine having to do this for many applications. The benefits of automated testing are clear, but how do we get there?

First, what do we want in an automated integration test? For starters:

Real running services that our application depends on
Small resource footprint so that developers can run the test locally
Low enough latency so that development iterations aren’t hampered — not as low latency as is required for a unit test, but test duration should be on the order of seconds
Isolation so that many tests can run concurrently on the same machine when this test gets run on a build automation server, e.g., no hard-coded ports

Luckily, tools are at our disposal to solve these problems. We’ll use Testcontainers to run containerized Kafka and Schema Registry servers on dynamic ports, Flink’s support for local execution environments so that we don’t need to spin up a Flink cluster, and Flink’s Table API in order to execute the Flink SQL statements that comprise our application.

Create the test skeleton

The primary choices for programming language in which to write our tests are Java and Python given the need for Flink’s Table API. We’ll write ours in Java.

To start our test project, make new directories for test source code and resources within the same finding-distinct folder that you created earlier:

mkdir -p src/test/java/io/confluent/developer
mkdir src/test/resources

Create the following Gradle build file, named build.gradle, in the finding-distinct directory.

buildscript {
    repositories {
        mavenCentral()
    }
}

plugins {
    id "java"
    id "idea"
}

sourceCompatibility = JavaVersion.VERSION_11
targetCompatibility = JavaVersion.VERSION_11
version = "0.0.1"

repositories {
    mavenCentral()
}

dependencies {
    testImplementation "com.google.guava:guava:31.1-jre"
    testImplementation "junit:junit:4.13.2"
    testImplementation 'org.testcontainers:testcontainers:1.17.6'
    testImplementation 'org.testcontainers:kafka:1.17.6'
    testImplementation "org.apache.flink:flink-sql-connector-kafka:1.17.1"
    testImplementation "org.apache.flink:flink-sql-avro-confluent-registry:1.17.1"
    testImplementation "org.apache.flink:flink-test-utils:1.17.1"
    testImplementation "org.apache.flink:flink-test-utils-junit:1.17.1"
    testImplementation 'org.apache.flink:flink-json:1.17.1'
    testImplementation "org.apache.flink:flink-table-api-java-bridge:1.17.0"
    testImplementation "org.apache.flink:flink-table-planner_2.12:1.17.1"
    testImplementation "org.apache.flink:flink-table-planner_2.12:1.17.1:tests"
    testImplementation "org.apache.flink:flink-statebackend-rocksdb:1.17.1"
}

There are a couple of important points to note in the Gradle build file:

Java sourceCompatibility and targetCompatibility are set to Java 11. Flink supports Java 8 (deprecated) and 11 as of the writing of this tutorial
The dependencies section declares test dependencies for Testcontainers and Flink. Among the handful of Flink dependencies are ones providing local execution (e.g., flink-statebackend-rocksdb), the Table API (flink-table-api-java-bridge), and Kafka connectors (flink-sql-connector-kafka).

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

gradle wrapper

Create SQL resources

Create the following file at src/test/resources/create-clicks-table.sql.template. Note the KAFKA_PORT placeholder.

CREATE TABLE clicks (
    ip_address VARCHAR,
    url VARCHAR,
    click_ts_raw BIGINT
) WITH (
    'connector' = 'kafka',
    'topic' = 'clicks',
    'properties.bootstrap.servers' = 'localhost:KAFKA_PORT',
    'scan.startup.mode' = 'earliest-offset',
    'key.format' = 'raw',
    'key.fields' = 'ip_address',
    'value.format' = 'json',
    'value.fields-include' = 'EXCEPT_KEY'
);

Create the following file at src/test/resources/populate-clicks.sql:

INSERT INTO clicks
VALUES ( '10.0.0.1',  'https://acme.com/index.html',    1692812175),
       ( '10.0.0.12', 'https://amazon.com/index.html',  1692826575),
       ( '10.0.0.13', 'https://confluent/index.html',   1692826575),
       ( '10.0.0.1',  'https://acme.com/index.html',    1692812175),
       ( '10.0.0.12', 'https://amazon.com/index.html',  1692819375),
       ( '10.0.0.13', 'https://confluent/index.html',   1692826575);

Next, create the table for deduplicated results with the following file at src/test/resources/create-deduplicated-clicks-table.sql.template:

CREATE TABLE deduplicated_clicks (
    ip_address VARCHAR,
    url VARCHAR,
    click_ts VARCHAR,
    PRIMARY KEY (ip_address) NOT ENFORCED
) WITH (
    'connector' = 'upsert-kafka',
    'topic' = 'deduplicated_clicks',
    'properties.bootstrap.servers' = 'localhost:KAFKA_PORT',
    'key.format' = 'raw',
    'value.format' = 'json',
    'value.fields-include' = 'EXCEPT_KEY'
);

Note again we specify the bootstrap servers configuration port with KAFKA_PORT which our test code will replace with the actual port.

Now create the SQL file that will populate the deduped table: src/test/resources/populate-deduplicated-clicks-table.sql.

INSERT INTO deduplicated_clicks
    SELECT ip_address, url, FROM_UNIXTIME(click_ts_raw) as click_timestamp
    FROM (
          SELECT *,
          ROW_NUMBER() OVER ( PARTITION BY ip_address ORDER BY TO_TIMESTAMP(FROM_UNIXTIME(click_ts_raw)) ) as rownum FROM clicks
         )
    WHERE rownum = 1;

Next, create the following file at src/test/resources/query-deduplicated-clicks.sql to select all results from the deduplicated table:

SELECT * FROM deduplicated_clicks;

Finally, create the following file at src/test/resources/expected-deduplicated-clicks.txt that contains our test’s expected output:

+----+--------------------------------+--------------------------------+--------------------------------+
| op |                     ip_address |                            url |                       click_ts |
+----+--------------------------------+--------------------------------+--------------------------------+
| +I |                       10.0.0.1 |    https://acme.com/index.html |            2023-08-23 17:36:15 |
| +I |                      10.0.0.12 |  https://amazon.com/index.html |            2023-08-23 21:36:15 |
| +I |                      10.0.0.13 |   https://confluent/index.html |            2023-08-23 21:36:15 |
| -U |                      10.0.0.12 |  https://amazon.com/index.html |            2023-08-23 21:36:15 |
| +U |                      10.0.0.12 |  https://amazon.com/index.html |            2023-08-23 19:36:15 |

Write a test

Create the following abstract test class at src/test/java/io/confluent/developer/AbstractFlinkKafkaTest.java:

package io.confluent.developer;


import com.google.common.io.Resources;
import org.apache.commons.lang3.exception.ExceptionUtils;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend;
import org.apache.flink.runtime.client.JobCancellationException;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.TableResult;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.assertj.core.util.Sets;
import org.junit.BeforeClass;
import org.testcontainers.containers.GenericContainer;
import org.testcontainers.containers.KafkaContainer;
import org.testcontainers.containers.Network;
import org.testcontainers.utility.DockerImageName;

import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.PrintStream;
import java.net.URL;
import java.nio.charset.StandardCharsets;
import java.time.ZoneId;
import java.util.Arrays;
import java.util.List;
import java.util.Optional;
import java.util.Set;
import java.util.stream.Collectors;

import static org.testcontainers.containers.KafkaContainer.KAFKA_PORT;

/**
 * Base class for Flink SQL integration tests that use Flink's Kafka connectors. Encapsulates
 * Kafka broker and Schema Registry Testcontainer management and includes utility methods for
 * dynamically configuring Flink SQL Kafka connectors and processing Table API results.
 */
public class AbstractFlinkKafkaTest {

  protected static StreamTableEnvironment streamTableEnv;
  protected static Integer schemaRegistryPort, kafkaPort;

  @BeforeClass
  public static void setup() {
    // create Flink table environment that test subclasses will use to execute SQL statements
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setParallelism(4);
    env.getConfig().setRestartStrategy(RestartStrategies.noRestart());
    env.setStateBackend(new EmbeddedRocksDBStateBackend());
    Configuration configuration = new Configuration();
    configuration.setString("table.local-time-zone", "UTC");
    streamTableEnv = StreamTableEnvironment.create(env, EnvironmentSettings.newInstance().inStreamingMode().withConfiguration(configuration).build());

    // Start Kafka and Schema Registry Testcontainers. Set the exposed ports that test subclasses
    // can use to dynamically configure Kafka connectors. Schema Registry enables connectors to
    // be configured with 'value.format' = 'avro-confluent'
    Network network = Network.newNetwork();

    KafkaContainer kafka = new KafkaContainer(DockerImageName.parse("confluentinc/cp-kafka:7.3.2"))
        .withEnv("KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR", "1")
        .withEnv("KAFKA_TRANSACTION_STATE_LOG_MIN_ISR", "1")
        .withEnv("KAFKA_TRANSACTION_STATE_LOG_NUM_PARTITIONS", "1")
        .withEnv("KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS", "500")
        .withEnv("KAFKA_AUTO_CREATE_TOPICS_ENABLE", "true")
        .withReuse(true)
        .withNetwork(network);
    kafka.start();
    kafkaPort = kafka.getMappedPort(KAFKA_PORT);

    GenericContainer schemaRegistry = new GenericContainer(DockerImageName.parse("confluentinc/cp-schema-registry:7.3.2"))
        .withExposedPorts(8081)
        .withNetwork(kafka.getNetwork())
        .withEnv("SCHEMA_REGISTRY_HOST_NAME", "localhost")
        .withEnv("SCHEMA_REGISTRY_LISTENERS", "http://0.0.0.0:8081")
        .withEnv("SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS", "PLAINTEXT://" + kafka.getNetworkAliases().get(0) + ":9092");
    schemaRegistry.start();
    schemaRegistryPort = schemaRegistry.getMappedPort(8081);
  }

  /**
   * Given a resource filename and optional Kafka / Schema Registry ports, return the resource
   * file contents as a String with ports substituted for KAFKA_PORT and SCHEMA_REGISTRY_PORT
   * placeholders.
   *
   * @param resourceFileName    the resource file name
   * @param kafkaPort           the port that Kafka broker exposes
   * @param schemaRegistryPort  the port that Schema Registry exposes
   * @return resource file contents with port values substituted for placeholders
   * @throws IOException if resource file can't be read
   */
  protected static String getResourceFileContents(
      String resourceFileName,
      Optional<Integer> kafkaPort,
      Optional<Integer> schemaRegistryPort
  ) throws IOException {
    URL url = Resources.getResource(resourceFileName);
    String contents = Resources.toString(url, StandardCharsets.UTF_8);
    if (kafkaPort.isPresent()) {
      contents = contents.replaceAll("KAFKA_PORT", kafkaPort.get().toString());
    }
    if (schemaRegistryPort.isPresent()) {
      contents = contents.replaceAll("SCHEMA_REGISTRY_PORT", schemaRegistryPort.get().toString());
    }
    return contents;
  }

  /**
   * Given a resource filename, return the resource file contents as a String.
   *
   * @param resourceFileName    the resource file name
   * @return resource file contents
   * @throws IOException if resource file can't be read
   */
  protected static String getResourceFileContents(
      String resourceFileName
  ) throws IOException {
    // no Kafka / Schema Registry ports
    return getResourceFileContents(resourceFileName, Optional.empty(), Optional.empty());
  }

  /**
   * Utility method to convert a String containing multiple lines into a set of Strings where
   * each String is one line. This is useful for creating Flink SQL integration tests based on
   * the tableau results printed via the Table API where the order of results is nondeterministic.
   *
   * @param s multiline String
   * @return set of Strings where each member is one line
   */
  protected static Set<String> stringToLineSet(String s) {
    return s.lines().map(line -> line.replaceAll("\\s", ""))
            .collect(Collectors.toSet());
  }

  /**
   * Given a Flink Table API `TableResult` representing a SELECT statement result,
   * capture and return the statement's tableau results.
   *
   * @param tableResult Flink Table API `TableResult` representing a SELECT statement result
   * @return the SELECT statement's tableau results
   */
  protected static String tableauResults(TableResult tableResult) {
    // capture tableau results printed to stdout in a String
    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    System.setOut(new PrintStream(baos));

    // The given table result may come from a table backed by the Kafka or Upsert Kafka connector,
    // both of which perform unbounded (neverending) scans. So, in order to prevent tests from blocking
    // on calls to this method, we kick off a thread to kill the underlying job once output has
    // been printed.
    //
    // Note: as of Flink 1.17.0, the Kafka connector will support bounded scanning, which would obviate
    // the need to do this. However, the Upsert Kafka connector will still be unbounded.
    new Thread(() -> {
      while (0 == baos.size()) {
        try {
          Thread.sleep(500);
        } catch (InterruptedException e) {
          // do nothing; keep waiting
        }
      }
      tableResult.getJobClient().get().cancel();
    }).start();

    try {
      tableResult.print();
    } catch (RuntimeException rte) {
      if (ExceptionUtils.indexOfThrowable(rte, JobCancellationException.class) != -1) {
        // a JobCancellationException in the exception stack is expected due to delayed
        // job cancellation in separate thread; do nothing
      } else {
        rte.printStackTrace();
        System.exit(1);
      }
    }
    System.setOut(System.out);
    return baos.toString();
  }

}

Take a look at this class. It contains the functionality and utility methods that any Flink SQL test would use. Namely, it encapsulates Kafka broker Testcontainer management and includes utility methods for dynamically configuring Flink SQL Kafka connectors and processing Table API results.

Next, create the test implementation at src/test/java/io/confluent/developer/FlinkSqlFindingDistinctTest.java:

package io.confluent.developer;


import org.apache.flink.table.api.TableResult;
import org.junit.Test;

import java.util.Optional;

import static org.junit.Assert.*;

public class FlinkSqlFindingDistinctTest extends AbstractFlinkKafkaTest {

  @Test
  public void simpleSelect() throws Exception {
    // create base movie sales table and aggregation table, and populate with test data
    streamTableEnv.executeSql(getResourceFileContents("create-clicks-table.sql.template",
        Optional.of(kafkaPort),Optional.of(schemaRegistryPort))).await();
    streamTableEnv.executeSql(getResourceFileContents("create-deduplicated-clicks-table.sql.template",
        Optional.of(kafkaPort),Optional.of(schemaRegistryPort))).await();
    streamTableEnv.executeSql(getResourceFileContents("populate-clicks.sql"));

    // In Flink 17 and later
    // by setting 'scan.bounded.mode' = 'latest-offset' the CREATE TABLE statement, will
    // cause this INSERT to terminate once the latest offset is reached.
    streamTableEnv.executeSql(getResourceFileContents("populate-deduplicated-clicks-table.sql"));

    // execute query on result table that should have joined shipments with orders
    TableResult tableResult = streamTableEnv.executeSql(getResourceFileContents("query-deduplicated-clicks.sql"));

    // Compare actual and expected results. Convert result output to line sets to compare so that order
    // doesn't matter
    String actualTableauResults = tableauResults(tableResult);
    String expectedTableauResults = getResourceFileContents("expected-deduplicated-clicks.txt");
    assertEquals(stringToLineSet(actualTableauResults), stringToLineSet(expectedTableauResults));
  }

}

The test itself is straightforward to follow. It executes the SQL from our resource files, then runs a select statement against the final output TABLE of our application and compares the results to what’s expected.

Invoke the test

Now run the test, which is as simple as:

./gradlew test

Deploy on Confluent Cloud

Run your app with Confluent Cloud

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.

How to find distinct values in a stream of events

Question:

Example use case:

Hands-on code example: