Spark Streaming的样本demo统计详解大数据

package com.demo; 
import java.util.List; 
import java.util.regex.Pattern; 
import org.apache.log4j.Level; 
import org.apache.log4j.Logger; 
import org.apache.spark.SparkConf; 
import org.apache.spark.api.java.StorageLevels; 
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.JavaReceiverInputDStream; 
import org.apache.spark.streaming.api.java.JavaStreamingContext; 
import com.google.common.base.Optional; 
import com.google.common.collect.Lists; 
import scala.Tuple2; 
public class NetWorkWordCount { 
private static final Pattern SPACE = Pattern.compile(" "); 
public static void main(String[] args) { 
//屏蔽日志 
Logger.getLogger("org.apache.spark").setLevel(Level.OFF); 
// Create the context with a 1 second batch size 
SparkConf sparkConf = new SparkConf().setAppName("NetworkWordCount").setMaster("local[2]"); 
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1)); 
// Create a JavaReceiverInputDStream on target ip:port and count the 
// words in input stream of /n delimited text (eg. generated by 'nc') 
// Note that no duplication in storage level only for running locally. 
// Replication necessary in distributed scenario for fault tolerance. 
JavaReceiverInputDStream<String> lines = ssc.socketTextStream("192.168.49.151",9999, StorageLevels.MEMORY_AND_DISK_SER); 
//增加checkpoint 
ssc.checkpoint("/home/dinpay/stream/checkpoint"); 
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); 
} 
}); 
//无状态统计计算 
JavaPairDStream<String, Integer> nostat =  wordCounts.reduceByKey(new Function2<Integer, Integer, Integer>() { 
@Override 
public Integer call(Integer i1, Integer i2) { 
return i1 + i2; 
} 
}); 
//有状态统计计算 
JavaPairDStream<String, Integer> stat =  wordCounts.updateStateByKey(new Function2<List<Integer>, Optional<Integer>,  
Optional<Integer>>() { 
@Override 
public Optional<Integer> call(List<Integer> values, Optional<Integer> state){ 
Integer updateValue = 0; 
if(state.isPresent()){ 
updateValue = state.get(); 
} 
for (Integer value : values) { 
updateValue += value; 
} 
return Optional.of(updateValue); 
} 
}); 
//窗口计算 滑动10秒 统计窗口长度是15秒 
JavaPairDStream<String, Integer> windowstat = wordCounts 
.reduceByKeyAndWindow(new Function2<Integer, Integer, Integer>() { 
@Override  
public Integer call(Integer i1, Integer i2) { 
return i1 + i2; 
} 
}, Durations.seconds(15), Durations.seconds(30)); 
//nostat.print(); 
//stat.print(); 
        windowstat.print(); 
ssc.start(); 
ssc.awaitTermination(); 
ssc.close(); 
} 
}