我正在尝试处理Spark中的常见异常，例如.map操作无法在数据的所有元素上正常工作或FileNotFound异常。我已经阅读了所有现有问题和以下两篇文章：
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
我已经尝试了attributes => mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble
行内的 Try 语句，因此它显示为attributes => Try(mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble)
但这不会编译;编译器稍后不会识别.toDF()语句。我也尝试过类似 Java 的尝试 { Catch {}} 块，但无法正确确定范围; 然后不返回df。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 FileNotFound 异常，而无需我添加异常。但是，例如，如果输入文件的列数错误，我想生成架构中字段数的错误。
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")
}
}
所有建议都表示赞赏。谢谢！
另一种选择是在scala中使用Try type。
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，在调用方端，像这样处理它：
createDataFrame("file1.csv") match {
case Success(df) => {
// proceed with your pipeline
}
case Failure(ex) => //handle exception
}
这比使用 Option 稍微好一点，因为调用方会知道失败的原因并且可以更好地处理。
要么让 Exception 从createDataFrame方法中抛出(并在外部处理它(，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: $unknown")
None
}
}
}
编辑：在createDataFrame的调用方端有几种模式。如果您正在处理多个文件名，则可以执行以下操作：
val dfs : Seq[DataFrame] = Seq("file1","file2","file3").map(createDataFrame).flatten
如果您正在处理单个文件名，则可以执行以下操作：
createDataFrame("file1.csv") match {
case Some(df) => {
// proceed with your pipeline
val df2 = df.filter($"activityLabel" > 0).withColumn("binaryLabel", when($"activityLabel".between(1, 3), 0).otherwise(1))
}
case None => println("could not create dataframe")
}
在数据帧列上应用 try 和 catch 块：
(try{$"credit.amount"} catch{case e:Exception=> lit(0)}).as("credit_amount")