/*
 * 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 org.apache.kafka.streams.examples.wordcount;

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.StreamsConfig;
import org.apache.kafka.streams.kstream.KStreamBuilder;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.streams.kstream.KeyValueMapper;
import org.apache.kafka.streams.kstream.ValueMapper;

import java.util.Arrays;
import java.util.Locale;
import java.util.Properties;

/**
 * Demonstrates, using the high-level KStream DSL, how to implement the WordCount program
 * that computes a simple word occurrence histogram from an input text.
 *
 * In this example, the input stream reads from a topic named "streams-file-input", where the values of messages
 * represent lines of text; and the histogram output is written to topic "streams-wordcount-output" where each record
 * is an updated count of a single word.
 *
 * Before running this example you must create the input topic and the output topic (e.g. via
 * bin/kafka-topics.sh --create ...), and write some data to the input topic (e.g. via
 * bin/kafka-console-producer.sh). Otherwise you won't see any data arriving in the output topic.
 */
public class WordCountDemo {

    public static void main(String[] args) throws Exception {
        Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-wordcount");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
        props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());

        // setting offset reset to earliest so that we can re-run the demo code with the same pre-loaded data
        // Note: To re-run the demo, you need to use the offset reset tool:
        // https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Streams+Application+Reset+Tool
        props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");

        KStreamBuilder builder = new KStreamBuilder();

        KStream<String, String> source = builder.stream("streams-file-input");

        KTable<String, Long> counts = source
                .flatMapValues(new ValueMapper<String, Iterable<String>>() {
                    @Override
                    public Iterable<String> apply(String value) {
                        return Arrays.asList(value.toLowerCase(Locale.getDefault()).split(" "));
                    }
                }).map(new KeyValueMapper<String, String, KeyValue<String, String>>() {
                    @Override
                    public KeyValue<String, String> apply(String key, String value) {
                        return new KeyValue<>(value, value);
                    }
                })
                .groupByKey()
                .count("Counts");

        // need to override value serde to Long type
        counts.to(Serdes.String(), Serdes.Long(), "streams-wordcount-output");

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

        // usually the stream application would be running forever,
        // in this example we just let it run for some time and stop since the input data is finite.
        Thread.sleep(5000L);

        streams.close();
    }
}