[英]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操作无法正确处理数据的所有元素或FileNotFound异常。 我已阅读所有现有问题和以下两个帖子:
https://www.nicolaferraro.me/2016/02/18/exception-handling-in-apache-spark
我在行attributes => mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble
尝试了一个Try语句
所以它读取attributes => Try(mHealthUser(attributes(0).toDouble, attributes(1).toDouble, attributes(2).toDouble)
但这不会编译; 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。
这是代码:
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")
}
}
所有建议都赞赏。 谢谢!
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,在调用者方面,处理它像:
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。
要么让异常被抛出createDataFrame
方法(并在外面处理它),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: $unknown")
None
}
}
}
编辑:在createDataFrame的调用者端有几种模式。 如果您正在处理多个文件名,您可以例如:
val dfs : Seq[DataFrame] = Seq("file1","file2","file3").map(createDataFrame).flatten
如果您正在使用单个文件名,则可以执行以下操作:
createDataFrame("file1.csv") match {
case Some(df) => {
// proceed with your pipeline
val df2 = df.filter($"activityLabel" > 0).withColumn("binaryLabel", when($"activityLabel".between(1, 3), 0).otherwise(1))
}
case None => println("could not create dataframe")
}
在dataframe列上应用try和catch块:
(try{$"credit.amount"} catch{case e:Exception=> lit(0)}).as("credit_amount")
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.