[1.5]以二次排序算法的实现为例体验spark高级排序

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场景

完成文件/home/pengyucheng/resource/hellospark.txt中数据的二次升序排序（即第一个数字相同，则比较第二个数字的大小，并以此排序） -

源数据：
1，1，spark
1，3，zookeeper
1，2，akka
3，1，hadoop
3，8，zookeeper
2，1，flink

排序后的数据：1,1,spark 1,2,akka 1,3,zookeeper 2,1,flink 3,1,hadoop 3,8,zookeeper
利用spark内置排序函数sortByKey，分别以java与scala实现上叙二次排序算法。

分析

spark core内置的sorkBykey实现方式：

def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length)      : RDD[(K, V)] = self.withScope  {    val part = new RangePartitioner(numPartitions, self, ascending)    new ShuffledRDD[K, V, V](self, part)      .setKeyOrdering(if (ascending) ordering else ordering.reverse)  }

即默认按照 K 值升序一次排序，完序后产生一个ShuffledRDD。
那么二次排序，三次排序，n次排序如何实现呢？下面代码解说二次排序的实现！

实验

java版

java bean

package cool.pengych.spark.core;import java.io.Serializable;import scala.math.Ordered;/** * 定义二次排序 bean * @author pengyucheng */public class SecondarySort implements Ordered<SecondarySort>, Serializable{    /*     * 排序Key     */    private int first;    private int second;    public SecondarySort(int first, int second) {        super();        this.first = first;        this.second = second;    }    @Override    public int hashCode() {        final int prime = 31;        int result = 1;        result = prime * result + first;        result = prime * result + second;        return result;    }    @Override    public boolean equals(Object obj) {        if (this == obj)            return true;        if (obj == null)            return false;        if (getClass() != obj.getClass())            return false;        SecondarySort other = (SecondarySort) obj;        if (first != other.first)            return false;        if (second != other.second)            return false;        return true;    }    @Override    public boolean $greater(SecondarySort other)     {        if(this.first>other.getFirst())         {            return true;        }        else if(this.first==other.getFirst()&&this.second>other.second)        {            return true;        }        return false;    }    @Override    public boolean $greater$eq(SecondarySort other) {        if(this.$greater(other)){            return true;        }        else if(this.first==other.getFirst() && this.second == other.getSecond()) return true;        return false;    }    @Override    public boolean $less(SecondarySort other) {        if(this.first<other.getFirst()){            return true;        }else if(this.first==other.getFirst() && this.second < other.getSecond()) return true;        return false;    }    @Override    public boolean $less$eq(SecondarySort other) {        if(this.$less(other)){            return true;        }        else if(this.first==other.getFirst() && this.second == other.getSecond()) return true;        return false;    }    @Override    public int compare(SecondarySort other) {        if(this.first  - other.getFirst() !=0){            return this.first - other.getFirst();        }else {            return this.second - other.getSecond();        }    }    @Override    public int compareTo(SecondarySort other) {        if(this.first  - other.getFirst() !=0){            return this.first - other.getFirst();        }else {            return this.second - other.getSecond();        }    }    public int getFirst() {        return first;    }    public int getSecond() {        return second;    }    public void setFirst(int first) {        this.first = first;    }    public void setSecond(int second) {        this.second = second;    }}

实现

package cool.pengych.spark.core;import org.apache.spark.SparkConf;import org.apache.spark.api.java.JavaPairRDD;import org.apache.spark.api.java.JavaRDD;import org.apache.spark.api.java.JavaSparkContext;import org.apache.spark.api.java.function.Function;import org.apache.spark.api.java.function.PairFunction;import org.apache.spark.api.java.function.VoidFunction;import scala.Tuple2;/** * 二次排序，具体实现步骤 * 1、按照Ordered与Serializable接口实现自定义排序的Key * 2、将要进行二次排序的文件加载进来生成<K,V>类型的RDD * 3、使用sortByKey基于自定义的K进行二次排序 * 4、去除排序的key，只保存排序的结果 * @author pengyucheng */public class SecondarySortApp {    public static void main(String[] args) {        SparkConf conf = new SparkConf().setAppName("Spark SecondarySort of java version").setMaster("local[*]");        JavaSparkContext sc = new JavaSparkContext(conf);        JavaRDD<String> lines = sc.textFile("/home/pengyucheng/resource/hellospark.txt");        JavaPairRDD<SecondarySort, String> pairs = lines.mapToPair(new PairFunction<String, SecondarySort, String>() {            private static final long serialVersionUID = 1L;            @Override            public Tuple2<SecondarySort, String> call(String line) throws Exception {                String[]  splited = line.split(",");                SecondarySort ss = new SecondarySort(Integer.valueOf(splited[0]), Integer.valueOf(splited[1]));                return new Tuple2<SecondarySort, String>(ss, line);            }        });        JavaPairRDD<SecondarySort, String> sorted = pairs.sortByKey();        JavaRDD<String> results = sorted.map(new Function<Tuple2<SecondarySort,String>, String>() {            private static final long serialVersionUID = 1L;            @Override            public String call(Tuple2<SecondarySort, String> v1) throws Exception {                return v1._2;            }        });        results.foreach(new VoidFunction<String>() {            private static final long serialVersionUID = 1L;            @Override            public void call(String t) throws Exception {                System.out.println(t);            }        });    }}

scala版

case class

package cool.pengych.spark.core/**  * Created by pengyucheng on 16-6-13.  */case class SecondarySortKey(val first:Int,val second:Int) extends Ordered[SecondarySortKey] with Serializable{  override def compare(that: SecondarySortKey): Int =    if(this.first!=that.first) this.first-that.first else this.second-that.second}

object

package cool.pengych.spark.coreimport org.apache.spark.{SparkConf, SparkContext}/**  * Created by pengyucheng on 16-6-13.  * 二次排序 scala 版本  */object SecondarySortKeyTest {  def main(args: Array[String]) {    val sc = new SparkContext(new SparkConf().setMaster("local[*]").setAppName("Secondary Sort ALG"))    sc.textFile("/home/pengyucheng/resource/hellospark.txt").map(line => {      val splited = line.split(",")     (SecondarySortKey(splited(0).toInt,splited(1).toInt),line)    }).sortByKey().map(pair => pair._2).collect().foreach(println)  }}

执行结果

16/06/13 13:34:15 INFO DAGScheduler: Job 1 finished: collect at SecondarySortKeyTest.scala:17, took 0.606013 s1,1,spark  1,2,akka1,3,zookeeper2,1,flink3,1,hadoop3,8,zookeeper16/06/13 13:34:15 INFO SparkContext: Invoking stop() from shutdown hook

总结

scala版比java版本代码量少了50%以上，相当简洁-这得意于强大的scala编译器！我们用反编译命令来看看scala编译器为我们做了什么：
反编译命令：$ javap -c -p -classpath . SecondarySortKey.class
结果：`public class SecondarySortKey implements SecondarySortKey>, scala.Serializable {

private final int first;
private final int second;

public boolean $less(java.lang.Object);
Code:
0: aload_0
1: aload_1
2: invokestatic #23 // Method scala/math/Ordered$class.$less:(Lscala/math/Ordered;Ljava/lang/Object;)Z
5: ireturn

public boolean $greater(java.lang.Object);
Code:
0: aload_0
1: aload_1
2: invokestatic #30 // Method scala/math/Ordered$class.$greater:(Lscala/math/Ordered;Ljava/lang/Object;)Z
5: ireturn

public boolean $less$eq(java.lang.Object);
Code:
0: aload_0
1: aload_1
2: invokestatic #33 // Method scala/math/Ordered$class.$less$eq:(Lscala/math/Ordered;Ljava/lang/Object;)Z
5: ireturn

public boolean greatereq(java.lang.Object);
Code:
0: aload_0
1: aload_1
2: invokestatic #36 // Method scala/math/Ordered$class.$greater$eq:(Lscala/math/Ordered;Ljava/lang/Object;)Z
5: ireturn

public int compareTo(java.lang.Object);
Code:
0: aload_0
1: aload_1
2: invokestatic #41 // Method scala/math/Ordered$class.compareTo:(Lscala/math/Ordered;Ljava/lang/Object;)I
5: ireturn

public int first();
Code:
0: aload_0
1: getfield #44 // Field first:I
4: ireturn

public int second();
Code:
0: aload_0
1: getfield #46 // Field second:I
4: ireturn

public int compare(cool.pengych.spark.core.SecondarySortKey);
Code:
0: aload_0
1: invokevirtual #50 // Method first:()I
4: aload_1
5: invokevirtual #50 // Method first:()I
8: if_icmpeq 23
11: aload_0
12: invokevirtual #50 // Method first:()I
15: aload_1
16: invokevirtual #50 // Method first:()I
19: isub
20: goto 32
23: aload_0
24: invokevirtual #52 // Method second:()I
27: aload_1
28: invokevirtual #52 // Method second:()I
31: isub
32: ireturn

public int compare(java.lang.Object);
Code:
0: aload_0
1: aload_1
2: checkcast #2 // class cool/pengych/spark/core/SecondarySortKey
5: invokevirtual #54 // Method compare:(Lcool/pengych/spark/core/SecondarySortKey;)I
8: ireturn

public cool.pengych.spark.core.SecondarySortKey(int, int);
Code:
0: aload_0
1: iload_1
2: putfield #44 // Field first:I
5: aload_0
6: iload_2
7: putfield #46 // Field second:I
10: aload_0
11: invokespecial #59 // Method java/lang/Object.””:()V
14: aload_0
15: invokestatic #63 // Method scala/math/Orderedclass.init$:(Lscala/math/Ordered;)V
18: return
}
`
you see that ! you are never coding alone, compiler is alway there, giving you a hand .

参考

王家林DT大数据梦工厂
spark1.6.0源码

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