SparkStreamingOnKafkaDirected

package com.bjsxt.sparksql;

import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;

import kafka.serializer.DefaultEncoder;
import kafka.serializer.StringDecoder;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;

import scala.Tuple2;



/**
 * 并行度:
 * 1、linesDStram里面封装到的是RDD， RDD里面有partition与读取topic的parititon数是一致的。
 * 2、从kafka中读来的数据封装一个DStram里面，可以对这个DStream重分区 reaprtitions(numpartition)
 * 
 * @author root
 *
 */
public class SparkStreamingOnKafkaDirected {

	public static void main(String[] args) {
		
		SparkConf conf = new SparkConf().setMaster("local").setAppName("SparkStreamingOnKafkaDirected");
//		conf.set("spark.streaming.backpressure.enabled", "false");
//		conf.set("spark.streaming.kafka.maxRatePerPartition	", "100");
		JavaStreamingContext jsc = new JavaStreamingContext(conf, Durations.seconds(5));
		/**
		 * 可以不设置checkpoint 不设置不保存offset,offset默认在内存中有一份，如果设置checkpoint在checkpoint也有一份offset， 一般要设置。
		 */
//		jsc.checkpoint("./checkpoint");
		Map<String, String> kafkaParameters = new HashMap<String, String>();
		kafkaParameters.put("metadata.broker.list", "node1:9092,node2:9092,node3:9092");
//		kafkaParameters.put("auto.offset.reset", "smallest");
	
		HashSet<String> topics = new HashSet<String>();
		topics.add("t0426");
		JavaPairInputDStream<String,String> lines = KafkaUtils.createDirectStream(jsc,
				String.class,  
				String.class,
				StringDecoder.class,
				StringDecoder.class,
				kafkaParameters,
				topics);
		
		JavaDStream<String> words = lines.flatMap(new FlatMapFunction<Tuple2<String,String>, String>() { //如果是Scala，由于SAM转换，所以可以写成val words = lines.flatMap { line => line.split(" ")}
			private static final long serialVersionUID = 1L;

			public Iterable<String> call(Tuple2<String,String> tuple) throws Exception {
				return Arrays.asList(tuple._2.split("\t"));
			}
		});
		
		JavaPairDStream<String, Integer> pairs = words.mapToPair(new PairFunction<String, String, Integer>() {
		
			private static final long serialVersionUID = 1L;

			public Tuple2<String, Integer> call(String word) throws Exception {
				return new Tuple2<String, Integer>(word, 1);
			}
		});
		JavaPairDStream<String, Integer> wordsCount = pairs.reduceByKey(new Function2<Integer, Integer, Integer>() { //对相同的Key，进行Value的累计（包括Local和Reducer级别同时Reduce）
			private static final long serialVersionUID = 1L;

			public Integer call(Integer v1, Integer v2) throws Exception {
				return v1 + v2;
			}
		});
		wordsCount.print();
		jsc.start();
		jsc.awaitTermination();
		jsc.close();
	}

}