dataframe的进行json数据的压平、增加一列的id自增列

{"name":"Michael", "age":25,"myScore":[{"score1":19,"score2":23},{"score1":58,"score2":50}]}
{"name":"Andy", "age":30,"myScore":[{"score1":29,"score2":33},{"score1":38,"score2":52},{"score1":88,"score2":71}]}
{"name":"Justin", "age":19,"myScore":[{"score1":39,"score2":43},{"score1":28,"score2":53}]}
{"name":"Michael", "age":25,"myScore":[{"score1":19,"score2":23},{"score1":58,"score2":50}]}

object explodeTest {
  def main(args: Array[String]): Unit = {

    val sparks = SparkSession.builder.master("local[4]").appName("test1").getOrCreate
    val sc = sparks.sparkContext

    val df=  sparks.read.json("file:///C:\Users\imp\Desktop\bo-kong\data\josn")

    df.show()
    //spark  读取json 数据
    /**+---+--------------------+-------+
|age|             myScore|   name|
+---+--------------------+-------+
| 25|  [[19,23], [58,50]]|Michael|
| 30|[[29,33], [38,52]...|   Andy|
| 19|  [[39,43], [28,53]]| Justin|
| 25|  [[19,23], [58,50]]|Michael|
| 30|[[29,33], [38,52]...|   Andy|
| 19|  [[39,43], [28,53]]| Justin|
| 25|  [[19,23], [58,50]]|Michael|
| 30|[[29,33], [38,52]...|   Andy|
| 19|  [[39,43], [28,53]]| Justin|
+---+--------------------+-------+
      *
      *
      *
      */

    //使用spark.sql.functions._ explode函数进行压平操作  行转列
    import org.apache.spark.sql.functions._
    val dfScore = df.select(df("name"),explode(df("myScore"))).toDF("name","myScore")
    val dfMyScore = dfScore.select("name","myScore.score1", "myScore.score2")
    dfScore.show()
   df.createOrReplaceTempView("df")
    //u.answer, ''
    /**
      *
      *
      *
      * +-------+-------+
      * |   name|myScore|
      * +-------+-------+
      * |Michael|[19,23]|
      * |Michael|[58,50]|
      * |   Andy|[29,33]|
      * |   Andy|[38,52]|
      * |   Andy|[88,71]|
      * | Justin|[39,43]|
      * | Justin|[28,53]|
      * |Michael|[19,23]|
      * |Michael|[58,50]|
      * |   Andy|[29,33]|
      * |   Andy|[38,52]|
      * |   Andy|[88,71]|
      * | Justin|[39,43]|
      * | Justin|[28,53]|
      * |Michael|[19,23]|
      * |Michael|[58,50]|
      * |   Andy|[29,33]|
      * |   Andy|[38,52]|
      * |   Andy|[88,71]|
      * | Justin|[39,43]|
      * +-------+-------+
      * only showing top 20 rows
      */



  }
}

数据
aa
bb
cc
dd
ee
ff

dataframe增加index主键列

 case  class Log(map:scala.collection.mutable.Map[String,String],ID: Long)
    import sparks.implicits._
  val data2 =  sc.parallelize(Seq((Map("uuid"->"sxexx","ip"->"192.168")),Map("uuid"->"man","ip"->"192.168.10.1"))).zipWithIndex()
    .map(i=>(i._1,i._2))
    data2.collect().foreach(print(_))
    /**
      * 先创造一个Rdd[map] 使用zipWithIndex 看看效果  第二个元素为id主键
      * 
      * 
      * (Map(uuid -> sxexx, ip -> 192.168),0)
      * (Map(uuid -> man, ip -> 192.168.10.1),1)
      */




val data=  sc.textFile("file:///C:\Users\imp\Desktop\bo-kong\data\data")
      .zipWithIndex().toDF("id","value")
    data.show()

    /**
      * 使用上面的数据的得出结果
      * +---+-----+
      * | id|value|
      * +---+-----+
      * | aa|    0|
      * | bb|    1|
      * | cc|    2|
      * | dd|    3|
      * | ee|    4|
      * | ff|    5|
      * +---+-----+
      */