Java在groupedRDD上触发带有key1的groupByKey并使用key2做aggregateByKey

Question

I am trying to do one simple java spark application which does the following

Input Data csv format : key1,key2,data1,data2

Basically what I am trying to do here is,

First I am mapping each line by key1 and then doing a groupByKey operation on that rdd.

JavaRDD<String> viewRdd = sc.textFile("testfile.csv", 1);
JavaPairRDD<String, String> customerIdToRecordRDD = viewRdd
    .mapToPair(w -> new Tuple2<String, String>(w.split(",")[0], w));
JavaPairRDD<String, Iterable<String>> groupedByKey1RDD = customerIdToRecordRDD.groupByKey();
System.out.println(customerIdToRecordGropedRDD.count());

Now my problem is, I need to do an aggregateByKey with key2 on each group from groupedByKey1RDD. Is there any way to convert Iterable into an RDD ?? or am I missing something here. I am new to this, any help will be

Example input and expected output :

id_1,time0,10,10

id_2,time1,0,10

id_1,time1,11,10

id_1,time0,1,10

id_2,time1,10,10

Output is grouped by 1st column and then aggregated by 2nd column (aggregate logic is to simply add column3 and column4):

id_1 : time0 : { sum1 : 11, sum2 : 20} ,
       time1 : { sum1 : 11, sum2 : 10}

id_2 : time1 : { sum1 : 10, sum2 : 20} 

Answer 1

Here is the solution using Spark 2.0 and Dataframe. Please let me know if you still want to use RDD.

public class SparkGroupBySample {
    public static void main(String[] args) {
    //SparkSession
    SparkSession spark = SparkSession
            .builder()
            .appName("SparkGroupBySample")
            .master("local")
            .getOrCreate();     
    //Schema
    StructType schema = new StructType(new StructField[] { 
            new StructField("key1", DataTypes.StringType, true, Metadata.empty()),
            new StructField("key2", DataTypes.StringType, true, Metadata.empty()),
            new StructField("data1", DataTypes.IntegerType, true, Metadata.empty()),
            new StructField("data2", DataTypes.IntegerType, true, Metadata.empty())});
    //Read csv
    Dataset<Row> dataSet = spark.read().format("csv").schema(schema).option("header", "true").option("delimiter", ",").load("c:\\temp\\sample.csv");
    dataSet.show();     
    //groupBy and aggregate
    Dataset<Row> dataSet1 = dataSet.groupBy("key1","key2").sum("data1","data2").toDF("key1","key2","sum1","sum2");
    dataSet1.show();
    //stop
    spark.stop();
   }
}

Here is the output.

+----+-----+----+----+
|key1| key2|sum1|sum2|
+----+-----+----+----+
|id_1|time1|  11|  10|
|id_2|time1|  10|  20|
|id_1|time0|  11|  20|
+----+-----+----+----+