spark streaming JavaQueueStream实例改造测试数据流

为了搞清楚Spark Streaming处理数据流，改造了以有的例子来进行测试数据在Spark内部的流向。

 

package org.apache.spark.examples.streaming;

import java.util.LinkedList;
import java.util.List;
import java.util.Queue;

import scala.Tuple2;

import com.google.common.collect.Lists;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
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.JavaStreamingContext;

public final class JavaQueueStream {
  private JavaQueueStream() {
  }

  public static void main(String[] args) throws Exception {

    StreamingExamples.setStreamingLogLevels();
    SparkConf sparkConf = new SparkConf().setAppName("JavaQueueStream");

    // Create the context
    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));

    // Create the queue through which RDDs can be pushed to
    // a QueueInputDStream
    Queue<JavaRDD<Integer>> rddQueue = new LinkedList<JavaRDD<Integer>>();

    // Create and push some RDDs into the queue
    List<Integer> list = Lists.newArrayList();
    for (int i = 0; i < 10; i++) {
      list.add(i);
    }
    
    rddQueue.add(ssc.sparkContext().parallelize(list));

   /* for (int i = 0; i <5; i++) {
      for(int j=0;j<i;j++){
    	  list.remove(j);
      }
      rddQueue.add(ssc.sparkContext().parallelize(list));
    }
    */
    // Create the QueueInputDStream and use it do some processing
    JavaDStream<Integer> inputStream = ssc.queueStream(rddQueue);
    //new PairFunction<Integer, Integer, Integer> 第一个参数为Call中的参数类型，第二个和第三个为Tupe2中的Key Value
    //JavaPairDStream<Integer, Integer> 是Tupe2的返回类型
    JavaPairDStream<Integer, Integer> mappedStream = inputStream.mapToPair(
        new PairFunction<Integer, Integer, Integer>() {
          @Override
          public Tuple2<Integer, Integer> call(Integer i) {
        	  if(i==3 ){
        		  i=5;
        	  }else if(i%2==0){
        		  i=2;
        	  }
            return new Tuple2<Integer, Integer>(i, 1);
          }
        });
    //Function2<T1,T2,R>
    JavaPairDStream<Integer, Integer> reducedStream = mappedStream.reduceByKey(
      new Function2<Integer, Integer, Integer>() {
        @Override
        public Integer call(Integer i1, Integer i2) {
          System.out.println(i1+"====&===="+i2);
          return i1+i2;
        }
    });
    System.out.println("==================================================begin");
    reducedStream.print();
    System.out.println("==================================================end");
    ssc.start();
    ssc.awaitTermination();
  }
}

 测试结果1：

 

==================================================begin
==================================================end
14/08/28 09:58:04 WARN SizeEstimator: Failed to check whether UseCompressedOops is set; assuming yes
1====&====1
2====&====1
1====&====1
3====&====1
4====&====1
-------------------------------------------
Time: 1409245080000 ms
-------------------------------------------
(1,1)
(7,1)
(9,1)
(5,2)
(2,5)

-------------------------------------------
Time: 1409245081000 ms
-------------------------------------------

-------------------------------------------
Time: 1409245082000 ms
-------------------------------------------

 

/*测试结果2( return new Tuple2<Integer, Integer>(i, 1)代码
        改为return new Tuple2<Integer, Integer>(i, 10)) 
        为了测试为什么(1,1)(9,1)等的数据为什么在Reduce里不打印出来*/
==================================================begin
==================================================end
14/08/28 10:05:55 WARN SizeEstimator: Failed to check whether UseCompressedOops is set; assuming yes
10====&====10
20====&====10
10====&====10
30====&====10
40====&====10
-------------------------------------------
Time: 1409245552000 ms
-------------------------------------------
(1,10)
(7,10)
(9,10)
(5,20)
(2,50)

    针对1进行数据流分析：