Question

我的数据框如下：

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.Column
import org.apache.spark.sql.functions._
import spark.implicits._

// some data...
val df = Seq(
  (1, "AA", "BB", ("AA", "BB")),
  (2, "AA", "BB", ("AA", "BB")),
  (3, "AB", "BB", ("AB", "BB"))
).toDF("id","name", "surname", "array")

df.show()

我正在计算连续行中“ array”列之间的编辑距离。作为示例，我想计算第1列中的“数组”实体（“ AA”，“ BB”）和第2列中“ array”实体（“ AA”，“ BB”）之间的编辑距离。这是我正在使用的编辑距离功能：

def editDist2[A](a: Iterable[A], b: Iterable[A]): Int = {
  val startRow = (0 to b.size).toList
  a.foldLeft(startRow) { (prevRow, aElem) =>
    (prevRow.zip(prevRow.tail).zip(b)).scanLeft(prevRow.head + 1) {
      case (left, ((diag, up), bElem)) => {
        val aGapScore = up + 1
        val bGapScore = left + 1
        val matchScore = diag + (if (aElem == bElem) 0 else 1)
        List(aGapScore, bGapScore, matchScore).min
      }
    }
  }.last
}

我知道我需要为此功能创建一个UDF，但似乎无法。如果我按原样使用该功能并使用Spark Windowing获得上一行：

// creating window - ordered by ID
val window = Window.orderBy("id")

// using the window with lag function to compare to previous value in each column
df.withColumn("edit-d", editDist2(($"array"), lag("array", 1).over(window))).show()

我收到以下错误：

<console>:245: error: type mismatch;
 found   : org.apache.spark.sql.ColumnName
 required: Iterable[?]
       df.withColumn("edit-d", editDist2(($"array"), lag("array", 1).over(window))).show()

Answer 1

我发现您可以为此使用Spark自己的levenshtein函数。此函数接受两个字符串进行比较，因此不能与数组一起使用。

// creating window - ordered by ID
val window = Window.orderBy("id")

// using the window with lag function to compare to previous value in each column
df.withColumn("edit-d", levenshtein(($"name"), lag("name", 1).over(window)) + levenshtein(($"surname"), lag("surname", 1).over(window))).show()

提供所需的输出：

+---+----+-------+--------+------+
| id|name|surname|   array|edit-d|
+---+----+-------+--------+------+
|  1|  AA|     BB|[AA, BB]|  null|
|  2|  AA|     BB|[AA, BB]|     0|
|  3|  AB|     BB|[AB, BB]|     1|
+---+----+-------+--------+------+

在“火花数据帧”

1 个答案: