com.twitterfeed.consumer.KafkaSparkConsumer.java Source code

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Here is the source code for com.twitterfeed.consumer.KafkaSparkConsumer.java

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/*
 * 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 com.twitterfeed.consumer;

import com.google.common.collect.Lists;
import java.io.IOException;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.concurrent.atomic.AtomicReference;
import java.util.regex.Pattern;
import kafka.serializer.StringDecoder;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.function.*;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.*;
import org.apache.spark.streaming.kafka.*;
import scala.Tuple2;

/**
 * Consumes messages from one or more topics in Kafka and does wordcount.
 * Usage: DirectKafkaWordCount <brokers> <topics>
 *   <brokers> is a list of one or more Kafka brokers
 *   <topics> is a list of one or more Kafka topics to consume from
 *
 * Example:
 *    $ bin/run-example streaming.KafkaWordCount broker1-host:port,broker2-host:port topic1,topic2
 */

public final class KafkaSparkConsumer {
    private static final Pattern SPACE = Pattern.compile(" ");

    public static void main(String[] args) {
        String brokers = "localhost:9092";
        String topics = "test";

        // Create context with 2 second batch interval
        SparkConf sparkConf = new SparkConf().setAppName("TwitterAnalysis");
        JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2));

        HashSet<String> topicsSet = new HashSet<String>(Arrays.asList(topics.split(",")));
        HashMap<String, String> kafkaParams = new HashMap<String, String>();
        kafkaParams.put("metadata.broker.list", brokers);

        // Create direct kafka stream with brokers and topics
        JavaPairInputDStream<String, String> messages = KafkaUtils.createDirectStream(jssc, String.class,
                String.class, StringDecoder.class, StringDecoder.class, kafkaParams, topicsSet);

        // Get the lines, split them into words, count the words and print
        JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
            @Override
            public String call(Tuple2<String, String> tuple2) {
                return tuple2._2();
            }
        });
        JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public Iterable<String> call(String x) {
                return Lists.newArrayList(SPACE.split(x));
            }
        });

        JavaPairDStream<String, Integer> wordCounts = words.mapToPair(new PairFunction<String, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(String s) {
                return new Tuple2<String, Integer>(s, 1);
            }
        }).reduceByKey(new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer i1, Integer i2) {
                return i1 + i2;
            }
        });

        wordCounts.print();

        // Start the computation
        jssc.start();
        jssc.awaitTermination();
    }
}