spark zip && zipPartitions && zipWithIndex && zipWithUniqueId

zip  transformation算子，将两个RDD中的元素（KV格式/非KV格式）变成一个KV格式的RDD,两个RDD的每个分区元素个数必须相同。

spark.sparkContext.setLogLevel("error")

 spark.sparkContext.setLogLevel("error")
    val kzc=spark.sparkContext.parallelize(1.to(10),2)
    val bd=spark.sparkContext.parallelize(List("a","b","c","d","e","f","g","h","i","j"),2)
    kzc.zip(bd).collect().foreach(println(_))

zipPartitions

 spark.sparkContext.setLogLevel("error")
    val kzc=spark.sparkContext.parallelize(1.to(10),2)
    val bd=spark.sparkContext.parallelize(List("a","b","c","d","e","f"),2)
    val res=kzc.zipPartitions(bd){
      (iterator1,iterator2)=>{
        val result=new scala.collection.mutable.ListBuffer[String]()
        while(iterator1.hasNext && iterator2.hasNext){
          result.append(iterator1.next()+"|"+iterator2.next())
        }
        result.iterator
      }
    }
    res.collect().foreach(println(_))

zipWithIndex  该函数将RDD中的元素和这个元素在RDD中的索引号（从0开始）组合成（K,V）对。

spark.sparkContext.setLogLevel("error")
    val kzc=spark.sparkContext.parallelize(1.to(10),2)
    val bd=spark.sparkContext.parallelize(List("a","b","c","d","e","f"),2)
    bd.zipWithIndex().collect().foreach(println(_))