我正在尝试根据一个(或多个)列的值拆分数据帧,并独立于其余列旋转每个结果数据帧.即,给定输入数据帧：

val inputDF = Seq(("tom","20","a","street a","germany"),("jimmy","30","b","street b","germany"),
                  ("lola","50","c","street c","argentina"), ("maria","60","d","street d","argentina"), ("joe","70","e","street e","argentina")
                  .toDF("name","age","company","address","country")

//+-----+---+-------+--------+---------+
//| name|age|company| address|  country|
//+-----+---+-------+--------+---------+
//|  tom| 20|      a|street a|  germany|
//|jimmy| 30|      b|street b|  germany|
//| lola| 40|      c|street c|argentina|
//|maria| 50|      d|street d|argentina|
//|  joe| 60|      e|street e|argentina|
//+-----+---+-------+--------+---------+

我需要按“国家/地区”列的不同值拆分记录.对于输入数据帧,拆分应该产生：

//+-----+---+-------+--------+---------+
//| name|age|company| address|  country|
//+-----+---+-------+--------+---------+
//|  tom| 20|      a|street a|  germany|
//|jimmy| 30|      b|street b|  germany|
//+-----+---+-------+--------+---------+

//+-----+---+-------+--------+---------+
//| name|age|company| address|  country|
//+-----+---+-------+--------+---------+
//| lola| 40|      c|street c|argentina|
//|maria| 50|      d|street d|argentina|
//|  joe| 60|      e|street e|argentina|
//+-----+---+-------+--------+---------+

而且我还必须在每个数据框下旋转“名称”和“年龄”列,以便每个人拥有不同的公司和地址,同时保持其余列的完整性.所需的输出数据框如下所示：

//+-----+---+-------+--------+---------+
//| name|age|company| address|  country|
//+-----+---+-------+--------+---------+
//|jimmy| 30|      a|street a|  germany|
//|  tom| 20|      b|street b|  germany|
//|  joe| 60|      c|street c|argentina|
//| lola| 40|      d|street d|argentina|
//|maria| 50|      e|street e|argentina|
//+-----+---+-------+--------+---------+

最后的行顺序无关紧要

我的第一次尝试(在Spark-shell上运行)

我尝试为每一行分配一个唯一的id,然后将所需的列(名称和年龄)洗牌,并使用辅助id值将重新排序的数据帧与其余的数据帧连接起来.这里的主要问题是使用collect(),这对于大数据帧可能是危险的,并且重新分区(1)几乎违反分布式计算和Spark(它用于在使用不同数量的分区压缩rdds时避免异常) .

import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.functions.{col, monotonically_increasing_id, rand}
import org.apache.spark.sql.types.LongType

// column(s) names to split the input dataframe
val colToSplit = Seq("country")
val splitCols = colToSplit.map(col)

// list of columns names to be rotated (together)
val colsToRotate = Seq("name", "age")
val rotateCols = colsToRotate.map(col) :+ col(auxCol)

// add an auxiliar column for joining the dataframe in the final step
val auxCol = "aux"
val dfWithID = inputDF.withColumn(auxCol, monotonically_increasing_id())

val splitValuesSchema = dfWithID.select(splitCols: _*).schema

// create one dataframe for each value of the splitting column
val splitValuesDFs = dfWithID.select(splitCols: _*).distinct().collect()
  .map(row => spark.sparkContext.makeRDD(List(row)))
  .map(rdd => spark.createDataFrame(rdd, splitValuesSchema))

val rotateIDCols = Array(auxCol) ++ colsToRotate

// join the split values with their records (DFs with id + colsToRotate)
val splittedDFs = splitValuesDFs
  .map(df => df.join(dfWithID, colToSplit).selectExpr(rotateIDCols: _*))

// random reorder the auxiliar id column (DFs with random ids)
val randIdDFs = splittedDFs
  .map(df => df.select(auxCol).orderBy(rand()).toDF())

// get rdds with random ids
val randIdRdds = randIdDFs
  .map(df => df.select(auxCol).rdd.map(row => row(0)))

// finally, zip and append the rdds with the random ids to the dataframes created by
// splitting the main df to obtain the rotated dataframe with all the data
val tuples = (splittedDFs, randIdRdds).zipped
val newRdds = tuples
  .map((df: DataFrame, rdd) => df.rdd.repartition(1).zip(rdd.repartition(1))
  .map(row => Row.fromSeq(row._1.toSeq ++ Seq(row._2))))

val tuples2 = (splittedDFs, newRdds).zipped
val rotatedDF = tuples2.map((df: DataFrame, rdd) => spark
  .createDataFrame(rdd, df.schema.add("rotated_id", LongType)).drop(auxCol))
  .reduce(_ union _).withColumnRenamed("rotated_id", "column2join")

// get the rest of the columns
val noRotateCols = dfWithID.columns.diff(colsToRotate).map(col)
val noRotatedDF = dfWithID.select(noRotateCols: _*)
  .withColumnRenamed(auxCol, "column2join")

// join both dataframes
val outputDF = noRotatedDF.join(rotatedDF, "column2join")
  .select(inputDF.columns.map(col): _*) // to keep the initial columns order

显示输出数据帧会产生与上述预期输出类似的结果(它主要取决于rand()函数顺序)

我想尽可能避免使用收集和重新分区,以获得更实用的解决方案.

欢迎任何评论或想法！

最佳答案 通过尽可能地删除表现不佳的电话(重新分配和一些收集),我一直在努力寻找更好,更清晰,更实用的解决方案.我添加了一个辅助方法来索引数据帧行,以便能够连接不相关的部分(无法通过任何公共列连接的列或dfs).这是我目前的开发,它还删除了rdds和数据帧之间的多个转换,看起来更易读和易懂.

我希望这可以帮助有同样担忧的人.

import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.functions.{col, monotonically_increasing_id, rand}
import org.apache.spark.sql.types.{LongType, StructField, StructType}

// auxiliar method to index row in dataframes
def addRowIndex(df: DataFrame) = spark.createDataFrame(
  df.rdd.zipWithIndex.map { case (row, index) => Row.fromSeq(row.toSeq :+ index) },
  StructType(df.schema.fields :+ StructField("index", LongType, false))
)

// column(s) names to split the input dataframe
val colToSplit = Seq("country")
val splitCols = colToSplit.map(col)

// list of columns names to be rotated (together)
val colsToRotate = Seq("name", "age")

// add an auxiliar column for joining the dataframe in the final step
val auxCol = "aux"
val dfWithID = inputDF.withColumn(auxCol, monotonically_increasing_id())

val rotateIDCols = (Array(auxCol) ++ colsToRotate).map(col)

// get an array of dfs with the different values of the splitter column(s)
// --assuming there will not be too much different values in the splitter column--
val filterValues = dfWithID.select(splitCols: _*).distinct().collect()

// retrieve the different dfs according to the splitter values
val splitDfs = filterValues.map(filterRow => filterRow.getValuesMap(colToSplit)
  .foldLeft(dfWithID) {
    (df, filterField) =>
      df.filter(col(filterField._1) === filterField._2)
        .select(rotateIDCols: _*)
  })

// get and random reorder the aux id column for each dataframe
val randIdDfs = splitDfs.map(_.select(auxCol).orderBy(rand()).toDF())

// remove aux column for each dataframe
val splitWithoutIdDfs = splitDfs.map(_.drop(auxCol))

val dfsTuples = splitWithoutIdDfs.zip(randIdDfs)

// index row of dfs with columns to rotate and dfs with random ids
val indexedDfsTuples = dfsTuples.map {
  case (colsDf, idsDf) => (addRowIndex(colsDf), addRowIndex(idsDf))
}

// join reordered-ids dfs and cols to rotate dataframes by the index
val reorderedDfs = indexedDfsTuples.map {
  case (df1, df2) => df1.join(df2, Seq("index"))
    .drop("index").withColumnRenamed(auxCol, "column2join")
}

// union both dataframes to create the rotated df
reorderedDfs.tail.foldLeft(reorderedDfs.head) { (acc, df) => acc.union(df) }

// get the rest of the columns to get the part of the main df which does not change
val noRotateCols = dfWithID.columns.diff(colsToRotate).map(col)
val noRotatedDF = dfWithID.select(noRotateCols: _*)
  .withColumnRenamed(auxCol, "column2join")

// join the rotated and no rotated dataframes
val outputDF = noRotatedDF.join(rotatedDF, "column2join")
  .select(inputDF.columns.map(col): _*) // to keep the initial columns order