zip函数用于将两个RDD组合成Key/Value形式的RDD,这里默认两个RDD的partition数量以及元素数量都相同，否则会抛出异常。
scala> val aa=sc.makeRDD(1 to 10)
aa: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[86] at makeRDD at <console>:26
scala> val cc=sc.makeRDD(21 to 30)
cc: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[88] at makeRDD at <console>:26
scala> aa.zip(bb).collect
res62: Array[(Int, Int)] = Array((1,21), (2,22), (3,23), (4,24), (5,25), (6,26), (7,27), (8,28), (9,29), (10,30))
zipPartitions函数将多个RDD按照partition组合成为新的RDD，该函数需要组合的RDD具有相同的分区数，但对于每个分区内的元素数量没有要求。
def zipPartitions[B, C, D, V](rdd2: org.apache.spark.rdd.RDD[B],rdd3: org.apache.spark.rdd.RDD[C],rdd4: org.apache.spark.rdd.RDD[D],preservesPartitioning: Boolean)(f: (Iterator[Int], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V])(implicit evidence$21: scala.reflect.ClassTag[B],implicit evidence$22: scala.reflect.ClassTag[C],implicit evidence$23: scala.reflect.ClassTag[D],implicit evidence$24: scala.reflect.ClassTag[V]): org.apache.spark.rdd.RDD[V]
def zipPartitions[B, C, V](rdd2: org.apache.spark.rdd.RDD[B],rdd3: org.apache.spark.rdd.RDD[C],preservesPartitioning: Boolean)(f: (Iterator[Int], Iterator[B], Iterator[C]) => Iterator[V])(implicit evidence$15: scala.reflect.ClassTag[B],implicit evidence$16: scala.reflect.ClassTag[C],implicit evidence$17: scala.reflect.ClassTag[V]): org.apache.spark.rdd.RDD[V]
def zipPartitions[B, C, D, V](rdd2: org.apache.spark.rdd.RDD[B],rdd3: org.apache.spark.rdd.RDD[C],rdd4: org.apache.spark.rdd.RDD[D])(f: (Iterator[Int], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V])(implicit evidence$25: scala.reflect.ClassTag[B],implicit evidence$26: scala.reflect.ClassTag[C],implicit evidence$27: scala.reflect.ClassTag[D],implicit evidence$28: scala.reflect.ClassTag[V]): org.apache.spark.rdd.RDD[V]
def zipPartitions[B, V](rdd2: org.apache.spark.rdd.RDD[B],preservesPartitioning: Boolean)(f: (Iterator[Int], Iterator[B]) => Iterator[V])(implicit evidence$11: scala.reflect.ClassTag[B],implicit evidence$12: scala.reflect.ClassTag[V]): org.apache.spark.rdd.RDD[V]
def zipPartitions[B, V](rdd2: org.apache.spark.rdd.RDD[B])(f: (Iterator[Int], Iterator[B]) => Iterator[V])(implicit evidence$13: scala.reflect.ClassTag[B],implicit evidence$14: scala.reflect.ClassTag[V]): org.apache.spark.rdd.RDD[V]
def zipPartitions[B, C, V](rdd2: org.apache.spark.rdd.RDD[B],rdd3: org.apache.spark.rdd.RDD[C])(f: (Iterator[Int], Iterator[B], Iterator[C]) => Iterator[V])(implicit evidence$18: scala.reflect.ClassTag[B],implicit evidence$19: scala.reflect.ClassTag[C],implicit evidence$20: scala.reflect.ClassTag[V]): org.apache.spark.rdd.RDD[V]
举例如下：
scala> val aa=sc.makeRDD(1 to 10)
aa: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[86] at makeRDD at <console>:26
scala> val bb=sc.makeRDD(11 to 15)
bb: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[87] at makeRDD at <console>:26
scala> val cc=sc.makeRDD(21 to 35)
cc: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[88] at makeRDD at <console>:26
scala> aa.zipPartitions(aa,bb){(aaiter,bbiter,cciter)=>{var result=List[(Int,Int,Int)]();while(aaiter.hasNext&&bbiter.hasNext&&cciter.hasNext){result::=(aaiter.next,bbiter.next,cciter.next)};result.toIterator}}.collect
res56: Array[(Int, Int, Int)] = Array((1,1,11), (3,3,12), (6,6,13), (9,9,15), (8,8,14))
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def zipWithIndex(): org.apache.spark.rdd.RDD[(Int, Long)]
zipWithIndex将RDD中的元素和这个元素在RDD中的ID（索引号）组合成键/值对
scala> bb.zipWithIndex().collect
res64: Array[(Int, Long)] = Array((21,0), (22,1), (23,2), (24,3), (25,4), (26,5), (27,6), (28,7), (29,8), (30,9))
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def zipWithUniqueId(): org.apache.spark.rdd.RDD[(Int, Long)]
zipWithUniqueId将RDD中的元素和一个唯一的ID组成键/值对
这个唯一ID生成算法如下：
每个分区中第一个元素的唯一ID值为：该分区索引号;
每个分区中第N个元素的唯一ID值为：(前一个元素的唯一ID值) + (该RDD总的分区数)
scala> bb.zipWithUniqueId().collect
res67: Array[(Int, Long)] = Array((21,0), (22,4), (23,1), (24,5), (25,9), (26,2), (27,6), (28,3), (29,7), (30,11))