spark 行转列

StructType

注意这种方案解决的是形如下面myScore这样的扩展
数据是json格式

      /*
      root
       |-- age: long (nullable = true)
       |-- myScore: array (nullable = true)
       |    |-- element: struct (containsNull = true)
       |    |    |-- score1: long (nullable = true)
       |    |    |-- score2: long (nullable = true)
       |-- name: string (nullable = true)
       参考样例：{"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}]}
       */
      def explodeDataFrameStruct(df: DataFrame, explodeCol: String, nextLevelCols: Array[String]) = {
            var dfScore = df.withColumn(explodeCol, explode(df(explodeCol)))
            for (str <- nextLevelCols) {
                  dfScore = dfScore.withColumn(str, dfScore(explodeCol + "." + str))
            }
            println(dfScore.show(2))
            dfScore

      }

String

对string类型的数据进行正则表达式匹配，然后利用explode函数进行对应操作

 def explodeDataFrameString(df: DataFrame, explodeCol: String,splitRule:Array[String],nextLevelCols: Array[String])={
            /*
            数据形如：
            val dft = Seq((1, "scene_id1,scene_name1;scene_id2,scene_name2","michal"),
                           (2, "scene_id1,scene_name1;scene_id2,scene_name2;scene_id3,scene_name3","john"),
                           (3, "scene_id4,scene_name4;scene_id2,scene_name2","mary"),
                           (4, "scene_id6,scene_name6;scene_id5,scene_name5","lily")
                            ).toDF("id", "int_id","name");
             */
            var dt=df.withColumn(explodeCol, explode(split(col(explodeCol), splitRule(0))))
            //var ind=0
            for(i<-0 until nextLevelCols.length) {
                   dt = dt.withColumn(nextLevelCols(i), split(col(explodeCol), splitRule(1))(i))

            }
            dt
      }