Spark是否支持melt和dcast

Question

我们使用melt和dcast将数据从宽->长和长->宽格式转换。有关更多详细信息，请参阅http://seananderson.ca/2013/10/19/reshape.html。

scala或SparkR都可以。

我已经经历了blog、scala functions和R API。我看不到有类似功能的函数。

Spark中有没有等价的函数？如果没有，在Spark中有没有其他的方法呢？

Answer 1

Reshaping Data with Pivot in Spark支持使用pivot进行整形。我知道melt大致与pivot相反，也被称为unpivot。我对Spark比较陌生。根据我的知识，我尝试实现了熔化操作。

    def melt(df: DataFrame, columns: List[String]): DataFrame ={

    val restOfTheColumns =  df.columns.filterNot(columns.contains(_))
    val baseDF = df.select(columns.head, columns.tail: _*)
    val newStructure =StructType(baseDF.schema.fields ++ List(StructField("variable", StringType, true), StructField("value", StringType, true)))
    var newdf  = sqlContext.createDataFrame(sqlContext.sparkContext.emptyRDD[Row], newStructure)

    for(variableCol <- restOfTheColumns){
      val colValues = df.select(variableCol).map(r=> r(0).toString)
      val colRdd=baseDF.rdd.zip(colValues).map(tuple => Row.fromSeq(tuple._1.toSeq.:+(variableCol).:+(tuple._2.toString)))
      var colDF =sqlContext.createDataFrame(colRdd, newStructure)
      newdf =newdf.unionAll(colDF)
    }
    newdf
  }

它起作用了。但我对效率不是很确定。

+-----+---+---+----------+------+
| name|sex|age|    street|weight|
+-----+---+---+----------+------+
|Alice|  f| 34| somewhere|    70|
|  Bob|  m| 63|   nowhere|   -70|
|Alice|  f|612|nextstreet|    23|
|  Bob|  m|612|      moon|     8|
+-----+---+---+----------+------+

可用作

melt(df, List("name", "sex"))

结果如下：

+-----+---+--------+----------+
| name|sex|variable|     value|
+-----+---+--------+----------+
|Alice|  f|     age|        34|
|  Bob|  m|     age|        63|
|Alice|  f|     age|       612|
|  Bob|  m|     age|       612|
|Alice|  f|  street| somewhere|
|  Bob|  m|  street|   nowhere|
|Alice|  f|  street|nextstreet|
|  Bob|  m|  street|      moon|
|Alice|  f|  weight|        70|
|  Bob|  m|  weight|       -70|
|Alice|  f|  weight|        23|
|  Bob|  m|  weight|         8|
+-----+---+--------+----------+

我希望它是有用的，并感谢您的意见，如果有改进的空间。

Answer 2

这是一个只使用数据集操作的spark.ml.Transformer (没有RDD的东西)

case class Melt(meltColumns: String*) extends Transformer{

  override def transform(in: Dataset[_]): DataFrame = {
    val nonMeltColumns =  in.columns.filterNot{ meltColumns.contains }
    val newDS = in
      .select(nonMeltColumns.head,meltColumns:_*)
      .withColumn("variable", functions.lit(nonMeltColumns.head))
      .withColumnRenamed(nonMeltColumns.head,"value")

    nonMeltColumns.tail
      .foldLeft(newDS){ case (acc,col) =>
        in
          .select(col,meltColumns:_*)
          .withColumn("variable", functions.lit(col))
          .withColumnRenamed(col,"value")
          .union(acc)
      }
      .select(meltColumns.head,meltColumns.tail ++ List("variable","value") : _*)
  }

  override def copy(extra: ParamMap): Transformer = defaultCopy(extra)

  @DeveloperApi
  override def transformSchema(schema: StructType): StructType = ???

  override val uid: String = Identifiable.randomUID("Melt")
}

下面是一个使用它的测试

"spark" should "melt a dataset" in {
    import spark.implicits._
    val schema = StructType(
      List(StructField("Melt1",StringType),StructField("Melt2",StringType)) ++
      Range(3,10).map{ i => StructField("name_"+i,DoubleType)}.toList)

    val ds = Range(1,11)
      .map{ i => Row("a" :: "b" :: Range(3,10).map{ j => Math.random() }.toList :_ *)}
      .|>{ rows => spark.sparkContext.parallelize(rows) }
      .|>{ rdd => spark.createDataFrame(rdd,schema) }

    val newDF = ds.transform{ df =>
      Melt("Melt1","Melt2").transform(df) }

    assert(newDF.count() === 70)
  }

.|>是scalaZ管道运算符

Answer 3

Spark DataFrame具有提供R melt功能的explode方法。在Spark 1.6.1中工作的示例：

// input df has columns (anyDim, n1, n2)
case class MNV(measureName: String, measureValue: Integer);
val dfExploded = df.explode(col("n1"), col("n2")) {
  case Row(n1: Int, n2: Int) =>
  Array(MNV("n1", n1), MNV("n2", n2))
}
// dfExploded has columns (anyDim, n1, n2, measureName, measureValue)