0

输入文件：

___DATE___

2018-11-16T06:3937
Linux hortonworks 3.10.0-514.26.2.el7.x86_64 #1 SMP Fri Jun 30 05:26:04 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux
 06:39:37 up 100 days,  1:04, 2 users,  load average: 9.01, 8.30, 8.48
06:30:01 AM     all      6.08      0.00      2.83      0.04      0.00     91.06

___DATE___

2018-11-16T06:4037
Linux cloudera 3.10.0-514.26.2.el7.x86_64 #1 SMP Fri Jun 30 05:26:04 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux
 06:40:37 up 100 days,  1:05, 28 users,  load average: 8.39, 8.26, 8.45
06:40:01 AM     all      6.92      1.11      1.88      0.04      0.00     90.05

所需输出：

2018-11-16T06:3937,hortonworks, 2 users
2018-11-16T06:4037,cloudera, 28 users

我正在尝试使用 Scala 来使用 Spark。尝试使用 Spark 2.3.1 和 scala 2.11.6 解析此输入文件。这是我的代码。

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
import org.apache.hadoop.io.Text
import org.apache.hadoop.io.LongWritable
import org.apache.spark.{SparkConf, SparkContext}

object parse_stats extends App {

  case class LoadSchema(date:String)

  val conf = new SparkConf().setAppName("ParseStats").setMaster("local[*]")
  val sc = new SparkContext(conf)

  val hadoopConf = new Configuration(sc.hadoopConfiguration)
  hadoopConf.set("textinputformat.record.delimiter","___DATE___")

  val input = sc.newAPIHadoopFile("C:\\Users\\rohit\\Documents\\dataset\\sys_stats.log",classOf[TextInputFormat],classOf[LongWritable],classOf[Text],hadoopConf).map(line=>line._2.toString)

  lazy val date_pattern="[0-9]+[-][0-9]+[-][0-9]+[T][0-9]+[:][0-9]+".r
  lazy val uname_pattern="[Linux][0-9a-zA-z-#() . : _ /]+[GNU/Linux]".r
  lazy val cpu_regex="[ 0-9]+[:][0-9]+[:][0-9]+[0-9a-zA-Z, : .]+[load average][:][0-9 . ,]+".r

  val transformRDD = input.map{eachline=>((if(date_pattern.pattern.matcher(eachline).matches()) eachline), //collects date
    (if(uname_pattern.pattern.matcher(eachline).matches()) eachline.split("\\s+")(1).trim() ), //collects hostname
    (if (cpu_regex.pattern.matcher(eachline).matches()) eachline.split(",")(2).trim()) //collects cpu users
  )
  }

  transformRDD.collect().foreach(println)
}

如果从 Intellij 运行此代码，我会得到以下输出。

((),(),())
((),(),())
((),(),())

如果我从 spark-shell 运行，我会收到以下错误：

scala> import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.conf.Configuration

scala> import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat

scala> import org.apache.hadoop.io.Text
import org.apache.hadoop.io.Text

scala> import org.apache.hadoop.io.LongWritable
import org.apache.hadoop.io.LongWritable

scala> import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.{SparkConf, SparkContext}

scala>   val hadoopConf = new Configuration(sc.hadoopConfiguration)
hadoopConf: org.apache.hadoop.conf.Configuration = Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml, hdfs-site.xml, __spark_hadoop_conf__.xml

scala>   hadoopConf.set("textinputformat.record.delimiter","___DATE___")

scala>   val input = sc.newAPIHadoopFile("C:\\Users\\rnimmal1\\Documents\\dataset\\sys_stats.log",classOf[TextInputFormat],classOf[LongWritable],classOf[Text],hadoopConf).map(line=>line._2.toString)
input: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[16] at map at <console>:37

scala>

scala>   lazy val date_pattern="[0-9]+[-][0-9]+[-][0-9]+[T][0-9]+[:][0-9]+".r
date_pattern: scala.util.matching.Regex = <lazy>

scala>   lazy val uname_pattern="[Linux][0-9a-zA-z-#() . : _ /]+[GNU/Linux]".r
uname_pattern: scala.util.matching.Regex = <lazy>

scala>   lazy val cpu_regex="[ 0-9]+[:][0-9]+[:][0-9]+[0-9a-zA-Z, : .]+[load average][:][0-9 . ,]+".r
cpu_regex: scala.util.matching.Regex = <lazy>

scala>

scala>   val transformRDD = input.map{eachline=>((if(date_pattern.pattern.matcher(eachline).matches()) eachline), //collects date
     |     (if(uname_pattern.pattern.matcher(eachline).matches()) eachline.split("\\s+")(1).trim() ), //collects hostname
     |     (if (cpu_regex.pattern.matcher(eachline).matches()) eachline.split(",")(2).trim()) //collects cpu users
     |   )
     |   }
org.apache.spark.SparkException: Task not serializable
  at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:345)
  at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:335)
  at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159)
  at org.apache.spark.SparkContext.clean(SparkContext.scala:2299)
  at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:371)
  at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:370)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
  at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
  at org.apache.spark.rdd.RDD.map(RDD.scala:370)
  ... 54 elided
Caused by: java.io.NotSerializableException: org.apache.hadoop.conf.Configuration
Serialization stack:
        - object not serializable (class: org.apache.hadoop.conf.Configuration, value: Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml, hdfs-site.xml, __spark_hadoop_conf__.xml)
        - field (class: $iw, name: hadoopConf, type: class org.apache.hadoop.conf.Configuration)
        - object (class $iw, $iw@63fa0b9)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@3f4b52fa)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@338f9bb5)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@3d63becf)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@3aca7082)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@4ccfd904)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@6e4e7a62)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@5aaab2b0)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@5c51a7eb)
        - field (class: $line36.$read, name: $iw, type: class $iw)
        - object (class $line36.$read, $line36.$read@2ba3b4a6)
        - field (class: $iw, name: $line36$read, type: class $line36.$read)
        - object (class $iw, $iw@6559f04e)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@8f7cbcc)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@465b16bb)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@373efaa2)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@5f2896fa)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@f777d41)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@43ec41d7)
        - field (class: $iw, name: $iw, type: class $iw)
        - object (class $iw, $iw@61c0a61)
        - field (class: $line38.$read, name: $iw, type: class $iw)
        - object (class $line38.$read, $line38.$read@10d1f6da)
        - field (class: $iw, name: $line38$read, type: class $line38.$read)
        - object (class $iw, $iw@2095e085)
        - field (class: $iw, name: $outer, type: class $iw)
        - object (class $iw, $iw@380cb7e3)
        - field (class: $anonfun$1, name: $outer, type: class $iw)
        - object (class $anonfun$1, <function1>)
  at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)
  at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:46)
  at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
  at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:342)
  ... 63 more

我错过了什么？

4

2 回答 2

1

更改__DATA__管道“|”后 ，下面的代码片段会产生所需的输出。请注意，我使用的是 Windows 平台，因此我将替换“\r”。请查看

val spark = SparkSession.builder().appName("Spark_test").master("local[*]").getOrCreate()

import spark.implicits._

val file1 = spark.sparkContext.textFile("./in/machine_logs.txt")

spark.sparkContext.hadoopConfiguration.set("textinputformat.record.delimiter","|")

val file2 = file1.filter( line => { val x = line.split("""\n"""); x.length > 5 } )
                    .map( line => { val x = line.split("""\n""")
                      val p = x(2).replaceAll("\\r","") // not needed if Unix platform
                      val q = x(3).split(" ")(1)
                      val r = x(4).split(",")(2)
                      (p + "," + q + "," + r)
                    } )

file2.collect.foreach(println)
//file2.saveAsTextFile("./in/machine_logs.out") --> comment above line and uncomment this line to save in file

输出：

2018-11-16T06:3937,hortonworks, 2 users
2018-11-16T06:4037,cloudera, 28 users

更新1：

使用正则表达式匹配：

val date_pattern="[0-9]+[-][0-9]+[-][0-9]+[T][0-9]+[:][0-9]+".r
val uname_pattern="(Linux) (.*?) [0-9a-zA-z-#() . : _ /]+(GNU/Linux)".r
val cpu_regex="""(.+),(.*?),\s+(load average)[:][0-9 . ,]+""".r
val file2 = file1.filter( line => { val x = line.split("""\n"""); x.length > 5 } )
  .map( line => {
          var q = ""; var r = "";
          val p = date_pattern.findFirstIn(line).mkString
          uname_pattern.findAllIn(line).matchData.foreach(m=> {q = m.group(2).mkString} )
          cpu_regex.findAllIn(line).matchData.foreach(m=> {r = m.group(2).mkString} )
          (p + "," + q + "," + r)
  } )
file2.collect.foreach(println)
于 2018-11-20T14:49:03.180 回答
1

我认为问题在于您date_pattern在 RDD 之外定义了那些过滤器parse_stats对象（当您在本地模式下运行它时不会发生这种情况，因为它不需要将任何对象发送给其他执行程序。

在此处查看最佳答案：Task not serializable: java.io.NotSerializableException when call function outsideclosure only on classes not objects

这个要点有一些快速简单的方法来避免序列化：https ://gist.github.com/kmader/1d64e64621e63d566f67

于 2018-11-20T16:01:51.370 回答