问为什么在reduceByKey之后所有的数据都在一个分区中呢？

EN

val l = List((30002,30000), (50006,50000), (80006,80000), 
             (4,0), (60012,60000), (70006,70000), 
             (40006,40000), (30012,30000), (30000,30000),
             (60018,60000), (30020,30000), (20010,20000), 
             (20014,20000), (90008,90000), (14,0), (90012,90000),
             (50010,50000), (100008,100000), (80012,80000),
             (20000,20000), (30010,30000), (20012,20000), 
             (90016,90000), (18,0), (12,0), (70016,70000), 
             (20,0), (80020,80000), (100016,100000), (70014,70000),
             (60002,60000), (40000,40000), (60006,60000), 
             (80000,80000), (50008,50000), (60008,60000), 
             (10002,10000), (30014,30000), (70002,70000),
             (40010,40000), (100010,100000), (40002,40000),
             (20004,20000), 
             (10018,10000), (50018,50000), (70004,70000),
             (90004,90000), (100004,100000), (20016,20000))

val l_rdd = sc.parallelize(l, 2)

// print each item and index of the partition it belongs to
l_rdd.mapPartitionsWithIndex((index, iter) => {
   iter.toList.map(x => (index, x)).iterator
}).collect.foreach(println)

// reduce on the second element of the list.
// alternatively you can use aggregateByKey  
val l_reduced = l_rdd.map(x => {
                    (x._2, List(x._1))
                  }).reduceByKey((a, b) => {b ::: a})

// print the reduced results along with its partition index
l_reduced.mapPartitionsWithIndex((index, iter) => {
      iter.toList.map(x => (index, x._1, x._2.size)).iterator
}).collect.foreach(println)