如何在 Spark 和 Scala 中处理异常

How to handle exceptions in Spark and Scala

我正在尝试处理 Spark 中的常见异常,例如 .map 操作无法对数据的所有元素正常工作或 FileNotFound 异常。我已阅读所有现有问题和以下两个帖子:

https://rcardin.github.io/big-data/apache-spark/scala/programming/2016/09/25/try-again-apache-spark.html

https://www.nicolaferraro.me/2016/02/18/exception-handling-in-apache-spark

我已经尝试在 attributes => mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble
行中使用 Try 语句 所以它显示 attributes => Try(mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble)

但是那不会编译;编译器稍后将无法识别 .toDF() 语句。我也尝试过类似 Java 的 Try { Catch {}} 块,但无法获得正确的范围; df 然后不返回。有谁知道如何正确地做到这一点?我什至需要处理这些异常吗,因为 Spark 框架似乎已经处理了一个 FileNotFound 异常,而我没有添加一个。但是,例如,如果输入文件的列数错误,我想生成模式中字段数的错误。

代码如下:

object DataLoadTest extends SparkSessionWrapper {
/** Helper function to create a DataFrame from a textfile, re-used in        subsequent tests */
def createDataFrame(fileName: String): DataFrame = {

import spark.implicits._

//try {
val df = spark.sparkContext
  .textFile("/path/to/file" + fileName)
  .map(_.split("\t"))
//mHealth user is the case class which defines the data schema
  .map(attributes => mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble,
        attributes(3).toDouble, attributes(4).toDouble,
        attributes(5).toDouble, attributes(6).toDouble, attributes(7).toDouble,
        attributes(8).toDouble, attributes(9).toDouble, attributes(10).toDouble,
        attributes(11).toDouble, attributes(12).toDouble, attributes(13).toDouble,
        attributes(14).toDouble, attributes(15).toDouble, attributes(16).toDouble,
        attributes(17).toDouble, attributes(18).toDouble, attributes(19).toDouble,
        attributes(20).toDouble, attributes(21).toDouble, attributes(22).toDouble,
        attributes(23).toInt))
  .toDF()
  .cache()
df
} catch {
    case ex: FileNotFoundException => println(s"File $fileName not found")
    case unknown: Exception => println(s"Unknown exception: $unknown")

}
}

感谢所有建议。谢谢!

要么让异常从 createDataFrame 方法中抛出(并在外部处理),要么将签名更改为 return Option[DataFrame]:

  def createDataFrame(fileName: String): Option[DataFrame] = {

    import spark.implicits._

    try {
      val df = spark.sparkContext
        .textFile("/path/to/file" + fileName)
        .map(_.split("\t"))
        //mHealth user is the case class which defines the data schema
        .map(attributes => mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble,
        attributes(3).toDouble, attributes(4).toDouble,
        attributes(5).toDouble, attributes(6).toDouble, attributes(7).toDouble,
        attributes(8).toDouble, attributes(9).toDouble, attributes(10).toDouble,
        attributes(11).toDouble, attributes(12).toDouble, attributes(13).toDouble,
        attributes(14).toDouble, attributes(15).toDouble, attributes(16).toDouble,
        attributes(17).toDouble, attributes(18).toDouble, attributes(19).toDouble,
        attributes(20).toDouble, attributes(21).toDouble, attributes(22).toDouble,
        attributes(23).toInt))
        .toDF()
        .cache()

      Some(df)
    } catch {
      case ex: FileNotFoundException => {
        println(s"File $fileName not found")
        None
      }
      case unknown: Exception => {
        println(s"Unknown exception: $unknown")
        None
      }
    }
  }

编辑:在 createDataFrame 的调用方有几种模式。如果您正在处理多个文件名,您可以例如做:

 val dfs : Seq[DataFrame] = Seq("file1","file2","file3").map(createDataFrame).flatten

如果您正在处理单个文件名,您可以这样做:

createDataFrame("file1.csv") match {
  case Some(df) => {
    // proceed with your pipeline
    val df2 = df.filter($"activityLabel" > 0).withColumn("binaryLabel", when($"activityLabel".between(1, 3), 0).otherwise(1))
  }
  case None => println("could not create dataframe")
}

另一种选择是在 scala 中使用 Try 类型。

例如:

def createDataFrame(fileName: String): Try[DataFrame] = {

try {
      //create dataframe df
      Success(df)
    } catch {
      case ex: FileNotFoundException => {
        println(s"File $fileName not found")
        Failure(ex)
      }
      case unknown: Exception => {
        println(s"Unknown exception: $unknown")
        Failure(unknown)
      }
    }
  }

现在,在调用方,处理如下:

createDataFrame("file1.csv") match {
  case Success(df) => {
    // proceed with your pipeline
  }
  case Failure(ex) => //handle exception
}

这比使用 Option 稍微好一些,因为调用者会知道失败的原因并且可以更好地处理。

在数据框列上应用 try 和 catch 块:

(try{$"credit.amount"} catch{case e:Exception=> lit(0)}).as("credit_amount")