1

我有以下数据：

+-----------+-----------+-----------+-----+-----------+
| Env1_date | Env2_date | Env3_date | Pid | orderDate |
+-----------+-----------+-----------+-----+-----------+
| Null      | Null      | 1/9/2020  | abc | 10/6/2020 |
| Null      | 1/9/2020  | 1/8/2020  | pqr | 10/4/2020 |
| 1/9/2020  | Null      | Null      | xyz | 10/2/2020 |
| 1/8/2020  | 1/7/2020  | Null      | uvw | 10/1/2020 |
+-----------+-----------+-----------+-----+-----------+

我正在尝试创建 3 个新列，它们基本上说明是否Pid对 env1、env2 和 env3 有效。为此，我首先orderDate按降序对列上的记录进行排序（已在上表中排序）。

  1. 如果对于Env1_date, Env2_date, Env3_date, 最高记录是Null, 它们被认为是有效的。在Null记录之后，如果日期小于特定日期（在此示例中1/9/2020），则认为其有效。任何其他记录都被标记为无效。

  2. 如果顶部记录不是NULL，需要检查日期是否等于1/9/2020。如果是这样，它们也被标记为有效

我的输出应如下所示：

+-----------+-----------+-----------+-----+-----------+-----------+-----------+-----------+
| Env1_date | Env2_date | Env3_date | Pid | orderDate | Env1_Flag | Env2_Flag | Env3_Flag |
+-----------+-----------+-----------+-----+-----------+-----------+-----------+-----------+
| Null      | Null      | 1/9/2020  | abc | 10/6/2020 | Valid     | Valid     | Valid     |
| Null      | 1/9/2020  | 1/8/2020  | pqr | 10/4/2020 | Valid     | Valid     | Invalid   |
| 1/9/2020  | Null      | Null      | xyz | 10/2/2020 | Valid     | Invalid   | Invalid   |
| 1/8/2020  | 1/7/2020  | Null      | uvw | 10/1/2020 | Invalid   | Invalid   | Invalid   |
+-----------+-----------+-----------+-----+-----------+-----------+-----------+-----------+

我正在尝试使用Spark 1.5and来实现这一点scala。

我尝试使用lag功能。但无法包括所有场景。不知道如何解决这个问题。

有人可以在这里帮助我吗？

spark注意：Windows 函数、toDf()、createDataFrame() 函数在我使用的中不起作用。它是一个自定义的火花环境，几乎没有限制

4

2 回答 2

-1 
import spark.implicits._

case class Source(
                 Env1_date: Option[String],
                 Env2_date: Option[String],
                 Env3_date: Option[String],
                 Pid: String,
                 orderDate: String
               )
case class Source1(
                   Env1_date: Option[String],
                   Env2_date: Option[String],
                   Env3_date: Option[String],
                   Pid: String,
                   orderDate: String,
                   Env1_Flag: String,
                    Env2_Flag: String,
                    Env3_Flag: String
                 )

val source = Seq(
  Source(None, None, Some("1/9/2020"), "abc", "10/6/2020"),
  Source(None, Some("1/9/2020"), Some("1/8/2020"), "pqr", "10/4/2020"),
  Source(Some("1/9/2020"), None, None, "xyz", "10/2/2020"),
  Source(Some("1/8/2020"), Some("1/7/2020"), None, "abc", "10/6/2020")
).toDF().as[Source].collect()

var env1NextRowInvalid = false
var env2NextRowInvalid = false
var env3NextRowInvalid = false
val source1 = source.map(i => {
  val env1Flag = if (env1NextRowInvalid == false && (i.Env1_date.getOrElse("") == """1/9/2020""" || i.Env1_date.getOrElse("") == "")) "valid" else "invalid"
  env1NextRowInvalid = if(env1NextRowInvalid == false) (i.Env1_date == "1/9/2020") else true
  val env2Flag = if (env2NextRowInvalid == false && (i.Env2_date.getOrElse("") == """1/9/2020""" || i.Env2_date.getOrElse("") == "")) "valid" else "invalid"
  env2NextRowInvalid = if(env2NextRowInvalid == false) (i.Env2_date.getOrElse("") == "1/9/2020") else true
  val env3Flag = if (env3NextRowInvalid == false && (i.Env3_date.getOrElse("") == """1/9/2020""" || i.Env3_date.getOrElse("") == "")) "valid" else "invalid"
  env3NextRowInvalid = if(env3NextRowInvalid == false) (i.Env3_date.getOrElse("") == "1/9/2020") else true
  Source1(i.Env1_date, i.Env2_date, i.Env3_date, i.Pid, i.orderDate, env1Flag, env2Flag, env3Flag)
})

val resDF = source1.toSeq.toDF()
resDF.show(false)
//  +---------+---------+---------+---+---------+---------+---------+---------+
//  |Env1_date|Env2_date|Env3_date|Pid|orderDate|Env1_Flag|Env2_Flag|Env3_Flag|
//  +---------+---------+---------+---+---------+---------+---------+---------+
//  |null     |null     |1/9/2020 |abc|10/6/2020|valid    |valid    |valid    |
//  |null     |1/9/2020 |1/8/2020 |pqr|10/4/2020|valid    |valid    |invalid  |
//  |1/9/2020 |null     |null     |xyz|10/2/2020|valid    |invalid  |invalid  |
//  |1/8/2020 |1/7/2020 |null     |abc|10/6/2020|invalid  |invalid  |invalid  |
//  +---------+---------+---------+---+---------+---------+---------+---------+
于 2020-10-06T22:08:40.737 回答
-2

您可以做到这一点的一种方法是将所有数据收集到驱动程序并将其作为常规数组处理，然后再次将其转换为 DF。但请注意，数据应该适合驱动程序。

我编写了可以处理您提供的数据的代码。如果你稍微调整一下（尤其是数据比较部分），你应该得到你所期望的。

  // This is how your data is going to look like when you collect it with df.collect
  val arrayData = Array(
    Array("null", "null", "1/9/2020", "abc", "10/6/2020"),
    Array("null", "1/9/2020", "1/8/2020", "pqr", "10/4/2020"),
    Array("1/9/2020", "null", "null", "xyz", "10/2/2020"),
    Array("1/8/2020", "1/7/2020", "null", "uvw", "10/1/2020"),
  )

  // just printing
  arrayData.foreach(arr => println(arr.mkString(" \t| ")))
  println("-".repeat(30))

  // rotates the array, so column become rows and vice verse
  def shiftArray(arr: Array[Array[String]])
    = for(i <- arr(0).indices.toArray) yield arr.map(arr => arr(i))

  // the function that does the validation part
  val someDate = "1/9/2020"
  def processColumn(arr: Array[String]) = {
    val (startingNulls, rest) = arr.span(_ == "null")
    val startingNullsValidated: Array[String] = startingNulls.map(_ => "Valid")
    val restValidated: Array[String] = rest.map(date => if (date == someDate) "Valid" else "Invalid") // implement custom date comparison
    startingNullsValidated ++ restValidated
  }

  val shiftedArray: Array[Array[String]] = shiftArray(arrayData)

  // you need to validate only first 3 columns, so i used take/slice
  val validatedArray = {
    val columnsToProcess = shiftedArray.take(3)
    val otherColumns = shiftedArray.slice(3, shiftedArray.length)
    val processedColumns = for (arr <- columnsToProcess) yield processColumn(arr)
    processedColumns ++ otherColumns
  }

  // rotate array back
  val shiftBackValidatedArray = shiftArray(validatedArray)

  // just printing the final result
  shiftBackValidatedArray.foreach(arr => println(arr.mkString(" \t| ")))

这是上面打印线的输出

null    | null  | 1/9/2020  | abc   | 10/6/2020
null    | 1/9/2020  | 1/8/2020  | pqr   | 10/4/2020
1/9/2020    | null  | null  | xyz   | 10/2/2020
1/8/2020    | 1/7/2020  | null  | uvw   | 10/1/2020
------------------------------
Valid   | Valid     | Valid     | abc   | 10/6/2020
Valid   | Valid     | Invalid   | pqr   | 10/4/2020
Valid   | Invalid   | Invalid   | xyz   | 10/2/2020
Invalid     | Invalid   | Invalid   | uvw   | 10/1/2020
于 2020-10-06T21:45:13.927 回答