content: /*
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements. See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You 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 kafka.examples;
  
  import java.util.List;
  import java.util.Optional;
  import java.util.concurrent.CountDownLatch;
  import java.util.concurrent.TimeUnit;
  import java.util.stream.Collectors;
  import java.util.stream.IntStream;
  
  /**
   * This example can be decomposed into the following stages:
   * <p>
   * 1. Clean any topics left from previous runs.
   * 2. Set up a producer thread to pre-populate a set of records with even number keys into the input topic.
   *    The demo will block for the record generation to finish, so the producer is synchronous.
   * 3. Set up the transactional instances in separate threads, each one executing a read-process-write loop
   *    (See {@link ExactlyOnceMessageProcessor}). Each EOS instance will drain all records from either given
   *    partitions or auto assigned partitions by actively comparing log end offset with committed offset.
   *    Each record will be processed exactly-once with strong partition level ordering guarantee.
   *    The demo will block until all records are processed and written to the output topic.
   * 4. Create a read_committed consumer thread to verify we have all records in the output topic,
   *    and record ordering at the partition level is maintained.
   *    The demo will block for the consumption of all committed records, with transactional guarantee.
   * <p>
   * Broker version must be >= 2.5.0 in order to run, otherwise the example will throw
   * {@link org.apache.kafka.common.errors.UnsupportedVersionException}.
   * <p>
   * If you are using IntelliJ IDEA, the above arguments should be put in `Modify Run Configuration - Program Arguments`.
   * You can also set an output log file in `Modify Run Configuration - Modify options - Save console output to file` to
   * record all the log output together.
   */
  public class KafkaExactlyOnceDemo {
      private static final String INPUT_TOPIC = "input-topic";
      private static final String OUTPUT_TOPIC = "output-topic";
      public static final String GROUP_NAME = "check-group";
  
      public static void main(String[] args) {
          try {
              if (args.length != 3) {
                  Utils.printHelp("This example takes 3 parameters (i.e. 6 3 10000):%n" +
                      "- partition: number of partitions for input and output topics (required)%n" +
                      "- instances: number of application instances (required)%n" +
                      "- records: total number of records (required)");
                  return;
              }
  
              int numPartitions = Integer.parseInt(args[0]);
              int numInstances = Integer.parseInt(args[1]);
              int numRecords = Integer.parseInt(args[2]);
  
              // stage 1: clean any topics left from previous runs
              Utils.recreateTopics(KafkaProperties.BOOTSTRAP_SERVERS, numPartitions, INPUT_TOPIC, OUTPUT_TOPIC);
  
              // stage 2: send demo records to the input-topic
              CountDownLatch producerLatch = new CountDownLatch(1);
              Producer producerThread = new Producer(
                      "producer",
                      KafkaProperties.BOOTSTRAP_SERVERS,
                      INPUT_TOPIC,
                      false,
                      null,
                      true,
                      numRecords,
                      -1,
                      producerLatch);
              producerThread.start();
              if (!producerLatch.await(2, TimeUnit.MINUTES)) {
                  Utils.printErr("Timeout after 2 minutes waiting for data load");
                  producerThread.shutdown();
                  return;
              }
  
              // stage 3: read from input-topic, process once and write to the output-topic
              CountDownLatch processorsLatch = new CountDownLatch(numInstances);
              List<ExactlyOnceMessageProcessor> processors = IntStream.range(0, numInstances)
                  .mapToObj(id -> new ExactlyOnceMessageProcessor(
                          "processor-" + id,
                          KafkaProperties.BOOTSTRAP_SERVERS,
                          INPUT_TOPIC,
                          OUTPUT_TOPIC,
                          processorsLatch))
                  .collect(Collectors.toList());
              processors.forEach(ExactlyOnceMessageProcessor::start);
              if (!processorsLatch.await(2, TimeUnit.MINUTES)) {
                  Utils.printErr("Timeout after 2 minutes waiting for record copy");
                  processors.forEach(ExactlyOnceMessageProcessor::shutdown);
                  return;
              }
  
              // stage 4: check consuming records from the output-topic
              CountDownLatch consumerLatch = new CountDownLatch(1);
              Consumer consumerThread = new Consumer(
                      "consumer",
                      KafkaProperties.BOOTSTRAP_SERVERS,
                      OUTPUT_TOPIC,
                      GROUP_NAME,
                      Optional.empty(),
                      true,
                      numRecords,
                      consumerLatch);
              consumerThread.start();
              if (!consumerLatch.await(2, TimeUnit.MINUTES)) {
                  Utils.printErr("Timeout after 2 minutes waiting for output read");
                  consumerThread.shutdown();
              }
          } catch (Throwable e) {
              e.printStackTrace();
          }
      }
  }
  