Spark的join与cogroup简单示例

1.join

join就是把两个集合根据key,进行内容聚合;

元组集合A:(1,"Spark"),(2,"Tachyon"),(3,"Hadoop")

元组集合B:(1,100),(2,95),(3,65)



A join B的结果:(1,("Spark",100)),(3,("hadoop",65)),(2,("Tachyon",95))

2.cogroup

cogroup就是:
有两个元组Tuple的集合A与B,先对A组集合中key相同的value进行聚合,

然后对B组集合中key相同的value进行聚合,之后对A组与B组进行"join"操作;

示例代码:

public class CoGroup {

public static void main(String[] args) {
SparkConf conf=new SparkConf().setAppName("spark WordCount!").setMaster("local");
JavaSparkContext sContext=new JavaSparkContext(conf);
List<Tuple2<Integer,String>> namesList=Arrays.asList(
new Tuple2<Integer, String>(1,"Spark"),
new Tuple2<Integer, String>(3,"Tachyon"),
new Tuple2<Integer, String>(4,"Sqoop"),
new Tuple2<Integer, String>(2,"Hadoop"),
new Tuple2<Integer, String>(2,"Hadoop2")
);

List<Tuple2<Integer,Integer>> scoresList=Arrays.asList(
new Tuple2<Integer, Integer>(1,100),
new Tuple2<Integer, Integer>(3,70),
new Tuple2<Integer, Integer>(3,77),
new Tuple2<Integer, Integer>(2,90),
new Tuple2<Integer, Integer>(2,80)
);
JavaPairRDD<Integer, String> names=sContext.parallelizePairs(namesList);
JavaPairRDD<Integer, Integer> scores=sContext.parallelizePairs(scoresList);
/**
* <Integer> JavaPairRDD<Integer, Tuple2<Iterable<String>, Iterable<Integer>>>
* org.apache.spark.api.java.JavaPairRDD.cogroup(JavaPairRDD<Integer, Integer> other)
*/
JavaPairRDD<Integer, Tuple2<Iterable<String>, Iterable<Integer>>> nameScores=names.cogroup(scores);

nameScores.foreach(new VoidFunction<Tuple2<Integer, Tuple2<Iterable<String>, Iterable<Integer>>>>() {
private static final long serialVersionUID = 1L;
int i=1;
@Override
public void call(
Tuple2<Integer, Tuple2<Iterable<String>, Iterable<Integer>>> t)
throws Exception {
String string="ID:"+t._1+" , "+"Name:"+t._2._1+" , "+"Score:"+t._2._2;
string+="     count:"+i;
System.out.println(string);
i++;
}
});

sContext.close();
}
}

示例结果:

ID:4 , Name:[Sqoop] , Score:[]     count:1
ID:1 , Name:[Spark] , Score:[100]     count:2
ID:3 , Name:[Tachyon] , Score:[70, 77]     count:3
ID:2 , Name:[Hadoop, Hadoop2] , Score:[90, 80]     count:4