Scala Spark-将转义分隔符的字符串列拆分为一部分

我有一个包含两个字符串列（术语、代码）的CSV文件。代码列具有特殊格式

[num]-[2个字母]-[text]

，其中

文本也可以包含破折号
-
。我想使用Spark将此文件读入正好有四列（term、num、两个字母、text）的数据框中

如果
code
列的
text
部分中没有破折号，我可以将
code
列拆分为三列，但如何实现解决所有情况的解决方案（例如将恰好两个破折号后的所有文本合并为一列）

将一列拆分为三列的代码在答案中得到了很好的说明
这里有一个带有
regexp\u extract的选项
：

val df = Seq(("term01", "12-AB-some text"), ("term02", "130-CD-some-other-text")).toDF("term", "code")

// define the pattern that matches the string column
val p = "([0-9]+)-([a-zA-Z]{2})-(.*)"
// p: String = ([0-9]+)-([a-zA-Z]{2})-(.*)

// define the map from new column names to the group index in the pattern
val cols = Map("num" -> 1, "letters" -> 2, "text" -> 3)
// cols: scala.collection.immutable.Map[String,Int] = Map(num -> 1, letters -> 2, text -> 3)

// create the new columns on data frame
cols.foldLeft(df){ 
    case (df, (colName, groupIdx)) => df.withColumn(colName, regexp_extract($"code", p, groupIdx)) 
}.drop("code").show

+------+---+-------+---------------+
|  term|num|letters|           text|
+------+---+-------+---------------+
|term01| 12|     AB|      some text|
|term02|130|     CD|some-other-text|
+------+---+-------+---------------+

这里有一个带有
regexp\u extract
的选项：

val df = Seq(("term01", "12-AB-some text"), ("term02", "130-CD-some-other-text")).toDF("term", "code")

// define the pattern that matches the string column
val p = "([0-9]+)-([a-zA-Z]{2})-(.*)"
// p: String = ([0-9]+)-([a-zA-Z]{2})-(.*)

// define the map from new column names to the group index in the pattern
val cols = Map("num" -> 1, "letters" -> 2, "text" -> 3)
// cols: scala.collection.immutable.Map[String,Int] = Map(num -> 1, letters -> 2, text -> 3)

// create the new columns on data frame
cols.foldLeft(df){ 
    case (df, (colName, groupIdx)) => df.withColumn(colName, regexp_extract($"code", p, groupIdx)) 
}.drop("code").show

+------+---+-------+---------------+
|  term|num|letters|           text|
+------+---+-------+---------------+
|term01| 12|     AB|      some text|
|term02|130|     CD|some-other-text|
+------+---+-------+---------------+

我将通过利用UDF来实现这一点：

case class MyData(num: Int, letters: String, text: String)
def udfSplit = udf(
  (input: String) => { 
    val res = input.split("-", 3) // limit=3 => pattern applied at most n - 1 times
    MyData(res(0).toInt, res(1), res(2))
  }
)

val df = spark.createDataFrame(
    Seq(
      ("term01", "12-AB-some text"), 
      ("term02", "130-CD-some-other-text")
    )
).toDF("term", "code")
df.show(false)
+------+----------------------+
|term  |code                  |
+------+----------------------+
|term01|12-AB-some text       |
|term02|130-CD-some-other-text|
+------+----------------------+

val res = df.withColumn("code", udfSplit($"code"))
res.show(false)
+------+------------------------+
|term  |code                    |
+------+------------------------+
|term01|[12,AB,some text]       |
|term02|[130,CD,some-other-text]|
+------+------------------------+

res.printSchema
root
 |-- term: string (nullable = true)
 |-- code: struct (nullable = true)
 |    |-- num: integer (nullable = false)
 |    |-- letters: string (nullable = true)
 |    |-- text: string (nullable = true)

res.select("term", "code.*").show(false)
+------+---+-------+---------------+
|term  |num|letters|text           |
+------+---+-------+---------------+
|term01|12 |AB     |some text      |
|term02|130|CD     |some-other-text|
+------+---+-------+---------------+

我将通过利用UDF来实现这一点：

case class MyData(num: Int, letters: String, text: String)
def udfSplit = udf(
  (input: String) => { 
    val res = input.split("-", 3) // limit=3 => pattern applied at most n - 1 times
    MyData(res(0).toInt, res(1), res(2))
  }
)

val df = spark.createDataFrame(
    Seq(
      ("term01", "12-AB-some text"), 
      ("term02", "130-CD-some-other-text")
    )
).toDF("term", "code")
df.show(false)
+------+----------------------+
|term  |code                  |
+------+----------------------+
|term01|12-AB-some text       |
|term02|130-CD-some-other-text|
+------+----------------------+

val res = df.withColumn("code", udfSplit($"code"))
res.show(false)
+------+------------------------+
|term  |code                    |
+------+------------------------+
|term01|[12,AB,some text]       |
|term02|[130,CD,some-other-text]|
+------+------------------------+

res.printSchema
root
 |-- term: string (nullable = true)
 |-- code: struct (nullable = true)
 |    |-- num: integer (nullable = false)
 |    |-- letters: string (nullable = true)
 |    |-- text: string (nullable = true)

res.select("term", "code.*").show(false)
+------+---+-------+---------------+
|term  |num|letters|text           |
+------+---+-------+---------------+
|term01|12 |AB     |some text      |
|term02|130|CD     |some-other-text|
+------+---+-------+---------------+

感谢您提供了非常清晰的解决方案。它也解决了这个问题。谢谢你提供了非常清晰的解决方案。它也解决了这个问题。