Question

我试图处理Spark中的常见异常，例如.map操作无法正常处理数据的所有元素或FileNotFound异常。我已阅读所有现有问题以及以下两篇文章：

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

我在attributes => mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble行中尝试了一个Try语句所以它显示为attributes => Try(mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble)

但那不会编译;编译器以后不会识别.toDF()语句。我也尝试了类似Java的Try {Catch {}}块但无法正确获取范围;然后不会返回df。有谁知道如何正确地做到这一点？我甚至需要处理这些异常，因为Spark框架似乎已经处理了FileNotFound异常，而我没有添加一个异常。但是，如果输入文件的列数错误，我想生成模式中字段数的错误。

以下是代码：

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")

}
}

所有建议都表示赞赏。谢谢！

Answer 1

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，在来电方面，处理它：

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

这比使用Option略好，因为调用者会知道失败的原因并且可以更好地处理。

Answer 2

要么让Exception被抛出createDataFrame方法（并在外面处理），要么更改签名以返回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的调用者端，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

如果您正在使用单个文件名，则可以执行以下操作：

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")
}

Answer 3

在数据框列上应用try and catch块：

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

如何处理Spark和Scala中的异常

3 个答案: