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* 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
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package org.apache.spark.examples.streaming;
import java.util.Map;
import java.util.HashMap;
import java.util.regex.Pattern;
import scala.Tuple2;
import com.google.common.collect.Lists;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.examples.streaming.StreamingExamples;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;
/**
* Consumes messages from one or more topics in Kafka and does wordcount.
*
* Usage: JavaKafkaWordCount
* is a list of one or more zookeeper servers that make quorum
* is the name of kafka consumer group
* is a list of one or more kafka topics to consume from
* is the number of threads the kafka consumer should use
*
* To run this example:
* `$ bin/run-example org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \
* zoo03 my-consumer-group topic1,topic2 1`
*/
public final class JavaKafkaWordCount {
private static final Pattern SPACE = Pattern.compile(" ");
private JavaKafkaWordCount() {
}
public static void main(String[] args) {
if (args.length < 4) {
System.err.println("Usage: JavaKafkaWordCount ");
System.exit(1);
}
StreamingExamples.setStreamingLogLevels();
SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
// Create the context with a 1 second batch size
JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
int numThreads = Integer.parseInt(args[3]);
Map topicMap = new HashMap();
String[] topics = args[2].split(",");
for (String topic: topics) {
topicMap.put(topic, numThreads);
}
JavaPairReceiverInputDStream messages =
KafkaUtils.createStream(jssc, args[0], args[1], topicMap);
JavaDStream lines = messages.map(new Function, String>() {
@Override
public String call(Tuple2 tuple2) {
return tuple2._2();
}
});
JavaDStream words = lines.flatMap(new FlatMapFunction() {
@Override
public Iterable call(String x) {
return Lists.newArrayList(SPACE.split(x));
}
});
JavaPairDStream wordCounts = words.mapToPair(
new PairFunction() {
@Override
public Tuple2 call(String s) {
return new Tuple2(s, 1);
}
}).reduceByKey(new Function2() {
@Override
public Integer call(Integer i1, Integer i2) {
return i1 + i2;
}
});
wordCounts.print();
jssc.start();
jssc.awaitTermination();
}
}