/*
 * Copyright 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.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KTable;

import java.util.Properties;

/**
 * Demonstrates how to use `reduce` to sum numbers. See {@code SumLambdaIntegrationTest} for an
 * end-to-end example.
 * <p>
 * Note: This example uses lambda expressions and thus works with Java 8+ only.
 * <p>
 * <br>
 * HOW TO RUN THIS EXAMPLE
 * <p>
 * 1) Start Zookeeper and Kafka. Please refer to <a href='http://docs.confluent.io/current/quickstart.html#quickstart'>QuickStart</a>.
 * <p>
 * 2) Create the input and output topics used by this example.
 * <pre>
 * {@code
 * $ bin/kafka-topics --create --topic numbers-topic \
 *                    --zookeeper localhost:2181 --partitions 1 --replication-factor 1
 * $ bin/kafka-topics --create --topic sum-of-odd-numbers-topic \
 *                    --zookeeper localhost:2181 --partitions 1 --replication-factor 1
 * }</pre>
 * Note: The above commands are for the Confluent Platform. For Apache Kafka it should be {@code `bin/kafka-topics.sh ...}.
 * <p>
 * 3) Start this example application either in your IDE or on the command line.
 * <p>
 * If via the command line please refer to <a href='https://github.com/confluentinc/kafka-streams-examples#packaging-and-running'>Packaging</a>.
 * Once packaged you can then run:
 * <pre>
 * {@code
 * $ java -cp target/kafka-streams-examples-8.3.0-0-standalone.jar io.confluent.examples.streams.SumLambdaExample
 * }
 * </pre>
 * 4) Write some input data to the source topic (e.g. via {@link SumLambdaExampleDriver}). The
 * already running example application (step 3) will automatically process this input data and write
 * the results to the output topic.
 * <pre>
 * {@code
 * # Here: Write input data using the example driver. Once the driver has stopped generating data,
 * # you can terminate it via `Ctrl-C`.
 * $ java -cp target/kafka-streams-examples-8.3.0-0-standalone.jar io.confluent.examples.streams.SumLambdaExampleDriver
 * }
 * </pre>
 * 5) Inspect the resulting data in the output topics, e.g. via {@code kafka-console-consumer}.
 * <pre>
 * {@code
 * $ bin/kafka-console-consumer --topic sum-of-odd-numbers-topic --from-beginning \
 *                              --bootstrap-server localhost:9092 \
 *                              --property value.deserializer=org.apache.kafka.common.serialization.IntegerDeserializer
 * }</pre>
 * You should see output data similar to:
 * <pre>
 * {@code
 * 2500
 * }</pre>
 * 6) Once you're done with your experiments, you can stop this example via {@code Ctrl-C}. If needed,
 * also stop the Kafka broker ({@code Ctrl-C}), and only then stop the ZooKeeper instance ({@code Ctrl-C}).
 */
public class SumLambdaExample {

  static final String SUM_OF_ODD_NUMBERS_TOPIC = "sum-of-odd-numbers-topic";
  static final String NUMBERS_TOPIC = "numbers-topic";

  public static void main(final String[] args) {
    final String bootstrapServers = args.length > 0 ? args[0] : "localhost:9092";
    final Properties streamsConfiguration = new Properties();
    // Give the Streams application a unique name.  The name must be unique in the Kafka cluster
    // against which the application is run.
    streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, "sum-lambda-example");
    streamsConfiguration.put(StreamsConfig.CLIENT_ID_CONFIG, "sum-lambda-example-client");
    // Where to find Kafka broker(s).
    streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
    // Specify default (de)serializers for record keys and for record values.
    streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.Integer().getClass().getName());
    streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.Integer().getClass().getName());
    streamsConfiguration.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
    streamsConfiguration.put(StreamsConfig.STATE_DIR_CONFIG, "/tmp/kafka-streams");
    // Records should be flushed every 10 seconds. This is less than the default
    // in order to keep this example interactive.
    streamsConfiguration.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 10 * 1000);

    final Topology topology = getTopology();
    final KafkaStreams streams = new KafkaStreams(topology, streamsConfiguration);
    // Always (and unconditionally) clean local state prior to starting the processing topology.
    // We opt for this unconditional call here because this will make it easier for you to play around with the example
    // when resetting the application for doing a re-run (via the Application Reset Tool,
    // https://docs.confluent.io/platform/current/streams/developer-guide/app-reset-tool.html).
    //
    // The drawback of cleaning up local state prior is that your app must rebuilt its local state from scratch, which
    // will take time and will require reading all the state-relevant data from the Kafka cluster over the network.
    // Thus in a production scenario you typically do not want to clean up always as we do here but rather only when it
    // is truly needed, i.e., only under certain conditions (e.g., the presence of a command line flag for your app).
    // See `ApplicationResetExample.java` for a production-like example.
    streams.cleanUp();
    streams.start();

    // Add shutdown hook to respond to SIGTERM and gracefully close Kafka Streams
    Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
  }

  static Topology getTopology() {
    final StreamsBuilder builder = new StreamsBuilder();
    // We assume the input topic contains records where the values are Integers.
    // We don't really care about the keys of the input records;  for simplicity, we assume them
    // to be Integers, too, because we will re-key the stream later on, and the new key will be
    // of type Integer.
    final KStream<Void, Integer> input = builder.stream(NUMBERS_TOPIC);

    final KTable<Integer, Integer> sumOfOddNumbers = input
      // We are only interested in odd numbers.
      .filter((k, v) -> v % 2 != 0)
      // We want to compute the total sum across ALL numbers, so we must re-key all records to the
      // same key.  This re-keying is required because in Kafka Streams a data record is always a
      // key-value pair, and KStream aggregations such as `reduce` operate on a per-key basis.
      // The actual new key (here: `1`) we pick here doesn't matter as long it is the same across
      // all records.
      .selectKey((k, v) -> 1)
      // no need to specify explicit serdes because the resulting key and value types match our default serde settings
      .groupByKey()
      // Add the numbers to compute the sum.
      .reduce((v1, v2) -> v1 + v2);

    sumOfOddNumbers.toStream().to(SUM_OF_ODD_NUMBERS_TOPIC);

    return builder.build();
  }

}
