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Input file:

___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

Required output:

2018-11-16T06:3937,hortonworks, 2 users

2018-11-16T06:4037,cloudera, 28 users

I'm trying to get my hands on Spark with Scala.
Trying to parse this input file with Spark 2.3.1 and scala 2.11.6 . Here's my code.

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)

}

If run this code from Intellij, I get below output.

((),(),())

((),(),())

((),(),())

If I run from spark-shell, I get below error:

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

What am I missing?

After changing the __DATA__ to pipe "|" , the below code snippet produces the required output. Note that I have used Windows platform, so I'm replacing "r". Please check out

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

Output:

2018-11-16T06:3937,hortonworks, 2 users

2018-11-16T06:4037,cloudera, 28 users

Update1:

With regex matching:

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)

I believe the problem is that you are defining those filters objects (date_pattern) outside of the RDD, so Spark has to send the entire parse_stats object to all of the executors, which it cannot do because it cannot serialize that entire object. This doesn't happen when you run it in local mode because it doesn't need to send any objects to other executors.

Check out the great answer here: Task not serializable: java.io.NotSerializableException when calling function outside closure only on classes not objects

This gist has some quick and simple ways to avoid serialization: https://gist.github.com/kmader/1d64e64621e63d566f67