UserCountsPerRegionLambdaIntegrationTest.java

/*
 * Copyright 2016 Confluent Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 * in compliance with the License. You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software distributed under the License
 * is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 * or implied. See the License for the specific language governing permissions and limitations under
 * the License.
 */

package io.confluent.examples.streams;

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.common.serialization.LongDeserializer;
import org.apache.kafka.common.serialization.Serde;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.apache.kafka.common.serialization.StringSerializer;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStreamBuilder;
import org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.test.TestUtils;
import org.junit.BeforeClass;
import org.junit.ClassRule;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;
import java.util.Properties;

import io.confluent.examples.streams.kafka.EmbeddedSingleNodeKafkaCluster;

import static org.assertj.core.api.Assertions.assertThat;

/**
 * End-to-end integration test that demonstrates how aggregations on a KTable produce the expected
 * results even though new data is continuously arriving in the KTable's input topic in Kafka.
 *
 * The use case we implement is to continuously compute user counts per region based on location
 * updates that are sent to a Kafka topic.  What we want to achieve is to always have the
 * latest and correct user counts per region even as users keep moving between regions while our
 * stream processing application is running (imagine, for example, that we are tracking passengers
 * on air planes).  More concretely,  whenever a new messages arrives in the Kafka input topic that
 * indicates a user moved to a new region, we want the effect of 1) reducing the user's previous
 * region by 1 count and 2) increasing the user's new region by 1 count.
 *
 * You could use the code below, for example, to create a real-time heat map of the world where
 * colors denote the current number of users in each area of the world.
 *
 * This example is related but not equivalent to {@link UserRegionLambdaExample}.
 *
 * Note: This example uses lambda expressions and thus works with Java 8+ only.
 */
public class UserCountsPerRegionLambdaIntegrationTest {

  @ClassRule
  public static final EmbeddedSingleNodeKafkaCluster CLUSTER = new EmbeddedSingleNodeKafkaCluster();

  private static final String inputTopic = "input-topic";
  private static final String outputTopic = "output-topic";

  @BeforeClass
  public static void startKafkaCluster() throws Exception {
    CLUSTER.createTopic(inputTopic);
    CLUSTER.createTopic(outputTopic);
  }

  @Test
  public void shouldCountUsersPerRegion() throws Exception {
    // Input: Region per user (multiple records allowed per user).
    List<KeyValue<String, String>> userRegionRecords = Arrays.asList(
        // This first record for Alice tells us that she is currently in Asia.
        new KeyValue<>("alice", "asia"),
        // First record for Bob.
        new KeyValue<>("bob", "europe"),
        // This second record for Alice tells us that her latest location is Europe.  Combining the
        // information in this record with the previous record for Alice, we know that she has moved
        // from Asia to Europe;  in other words, it's a location update for Alice.
        new KeyValue<>("alice", "europe"),
        // Second record for Bob, who moved from Europe to Asia (i.e. the opposite direction of Alice).
        new KeyValue<>("bob", "asia")
    );

    List<KeyValue<String, Long>> expectedUsersPerRegion = Arrays.asList(
        new KeyValue<>("europe", 1L), // in the end, Alice is in europe
        new KeyValue<>("asia", 1L)    // in the end, Bob is in asia
    );

    //
    // Step 1: Configure and start the processor topology.
    //
    final Serde<String> stringSerde = Serdes.String();
    final Serde<Long> longSerde = Serdes.Long();

    Properties streamsConfiguration = new Properties();
    streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, "user-regions-lambda-integration-test");
    streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, CLUSTER.bootstrapServers());
    streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
    streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
    // The commit interval for flushing records to state stores and downstream must be lower than
    // this integration test's timeout (30 secs) to ensure we observe the expected processing results.
    streamsConfiguration.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 10 * 1000);
    streamsConfiguration.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
    // Use a temporary directory for storing state, which will be automatically removed after the test.
    streamsConfiguration.put(StreamsConfig.STATE_DIR_CONFIG, TestUtils.tempDirectory().getAbsolutePath());

    KStreamBuilder builder = new KStreamBuilder();

    KTable<String, String> userRegionsTable = builder.table(stringSerde, stringSerde, inputTopic, "UserRegionsStore");

    KTable<String, Long> usersPerRegionTable = userRegionsTable
        // no need to specify explicit serdes because the resulting key and value types match our default serde settings
        .groupBy((userId, region) -> KeyValue.pair(region, region))
        .count("UserCountsByRegion");

    usersPerRegionTable.to(stringSerde, longSerde, outputTopic);

    KafkaStreams streams = new KafkaStreams(builder, streamsConfiguration);
    streams.start();

    //
    // Step 2: Publish user-region information.
    //
    Properties userRegionsProducerConfig = new Properties();
    userRegionsProducerConfig.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, CLUSTER.bootstrapServers());
    userRegionsProducerConfig.put(ProducerConfig.ACKS_CONFIG, "all");
    userRegionsProducerConfig.put(ProducerConfig.RETRIES_CONFIG, 0);
    userRegionsProducerConfig.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
    userRegionsProducerConfig.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
    IntegrationTestUtils.produceKeyValuesSynchronously(inputTopic, userRegionRecords, userRegionsProducerConfig);

    //
    // Step 3: Verify the application's output data.
    //
    Properties consumerConfig = new Properties();
    consumerConfig.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, CLUSTER.bootstrapServers());
    consumerConfig.put(ConsumerConfig.GROUP_ID_CONFIG, "user-regions-lambda-integration-test-standard-consumer");
    consumerConfig.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
    consumerConfig.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    consumerConfig.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, LongDeserializer.class);
    List<KeyValue<String, Long>> actualClicksPerRegion = IntegrationTestUtils.waitUntilMinKeyValueRecordsReceived(consumerConfig,
        outputTopic, expectedUsersPerRegion.size());
    streams.close();
    assertThat(actualClicksPerRegion).containsExactlyElementsOf(expectedUsersPerRegion);
  }

}