I encountered the following when I tried to do a spark submit:
Exception in thread "main" java.lang.NullPointerException
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:328)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Is this a known problem?
Thanks and regards,
The following program is able to reproduce the above error message:
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
class anomaly_model(val inputfile: String, val clusterNum: Int, val maxIterations: Int, val epsilon: Double, val scenarioNum: Int, val outputfile: String){
val conf = new SparkConf().setAppName("Anomaly Model")
val sc = new SparkContext(conf)
val data = sc.textFile(inputfile)
def main(args: Array[String]) {
val inputfile = "sqlexpt.txt"
val clusterNum = 5
val maxIterations = 1000
val epsilon = 0.001
val scenarioNum = 10
val outputfile = "output.csv"
val am = new anomaly_model(inputfile, clusterNum, maxIterations, epsilon, scenarioNum, outputfile)
}
}
Probably it was my mistake to define the main method inside the class... I should have defined it inside a companion object instead (I come from a Java background!).
If you want to put main class in anomaly_model class, you may need to change "class" to "object". And yes, if you want to have anomaly_model a class, you may need to put main method in another object.
In anomaly_model class:
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
class anomaly_model(val inputfile: String, val clusterNum: Int, val maxIterations: Int, val epsilon: Double, val scenarioNum: Int, val outputfile: String){
val conf = new SparkConf().setAppName("Anomaly Model")
val sc = new SparkContext(conf)
val data = sc.textFile(inputfile)
}
And in Main.scala:
import anomaly_model
object Main {
def main(args: Array[String]) : Unit{
val inputfile = "sqlexpt.txt"
val clusterNum = 5
val maxIterations = 1000
val epsilon = 0.001
val scenarioNum = 10
val outputfile = "output.csv"
val am = new anomaly_model(inputfile, clusterNum, maxIterations, epsilon, scenarioNum, outputfile)
}
}
You have a null value in one of your variables, as the message says.
I had the same problem and had made the same mistake as you. anomaly_model should be an object and not a class
Related
I am trying to create a Dataset with only one column from Case Class.
Below is the code:
case class vectorData(value: Array[String], vectors: Vector)
def main(args: Array[String]) {
val spark = SparkSession.builder
.appName("Hello world!")
.master("local[*]")
.getOrCreate()
import spark.implicits._
//blah blah and read data etc.
val word2vec = new Word2Vec()
.setInputCol("value").setOutputCol("vectors")
.setVectorSize(5).setMinCount(0).setWindowSize(5)
val dataset = spark.createDataset(data)
val model = word2vec.fit(dataset)
val encoder = org.apache.spark.sql.Encoders.product[vectorData]
val result = model.transform(dataset).as(encoder)
//val output: Dataset[Vector] = ???
}
As shown in last line of the code, I want the output to be only the 2nd column which has Vector type with vectors data.
I tried with:
val output = result.map(o => o.vectors)
But this line highlighted error No implicit arguments of type: Encoder[Vector]
How to resolve this?
I think line:
implicit val vectorEncoder: Encoder[Vector] = org.apache.spark.sql.Encoders.product[Vector]
should make
val output = result.map(o => o.vectors)
correct
I am trying out frameless library for Scala and getting an "No implicits found for parameters i0: TypedColumn.Exists". If you can help me resolve it - that would be awesome....
I am using spark 2.4.0 and frameless 0.8.0.
Following is my code
import org.apache.spark.sql.SparkSession
import frameless.TypedDataset
object TestSpark {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.master("local[*]")
.appName("Spark Test")
.getOrCreate
import spark.implicits._
val empDS = spark.read
.option("header",true)
.option("delimiter",",")
.csv("emp.csv")
.as[Emp]
val empTyDS = TypedDataset.create(empDS)
import frameless.syntax._
empTyDS.show(10,false).run
val deptCol = empTyDS('dept) //Get the error here.`
}
}
case class for the code is
case class Emp (
name: String,
dept: String,
manager: String,
salary: String
)
Is it possible to create broadcast variables with the sparkContext provided by SparkSession ? I keep getting an error under sc.broadcast , however in a different project when using the SparkContext from org.apache.spark.SparkContext I have no problems.
import org.apache.spark.sql.SparkSession
object MyApp {
def main(args: Array[String]){
val spark = SparkSession.builder()
.appName("My App")
.master("local[*]")
.getOrCreate()
val sc = spark.sparkContext
.setLogLevel("ERROR")
val path = "C:\\Boxes\\github-archive\\2015-03-01-0.json"
val ghLog = spark.read.json(path)
val pushes = ghLog.filter("type = 'PushEvent'")
pushes.printSchema()
println("All events: "+ ghLog.count)
println("Only pushes: "+pushes.count)
pushes.show(5)
val grouped = pushes.groupBy("actor.login").count()
grouped.show(5)
val ordered = grouped.orderBy(grouped("count").desc)
ordered.show(5)
import scala.io.Source.fromFile
val fileName= "ghEmployees.txt"
val employees = Set() ++ (
for {
line <- fromFile(fileName).getLines()
} yield line.trim
)
val bcEmployees = sc.broadcast(employees)
}
}
Or is it a problem of using a Set () instead of a Seq object ?
Thanks for any help
Edit:
I keep getting a "cannot resolve symbol broadcast" error msg in intellij
after complying I get an error of:
Error:(47, 28) value broadcast is not a member of Unit
val bcEmployees = sc.broadcast(employees)
^
Your sc variable has type Unit because, according to the docs, setLogLevel has return type Unit. Do this instead:
val sc: SparkContext = spark.sparkContext
sc.setLogLevel("ERROR")
It is important to keep track of the types of your variables to catch errors earlier.
I have been trying to create Spark Dataset using case classes that contain Enums but I'm not able to. I'm using Spark version 1.6.0. The exceptions is complaining about that there are no encoder found for my Enum. Is this not possible in Spark, to have enums in the data?
Code:
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}
object MyEnum extends Enumeration {
type MyEnum = Value
val Hello, World = Value
}
case class MyData(field: String, other: MyEnum.Value)
object EnumTest {
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
val sc = new SparkContext(sparkConf)
val sqlCtx = new SQLContext(sc)
import sqlCtx.implicits._
val df = sc.parallelize(Array(MyData("hello", MyEnum.World))).toDS()
println(s"df: ${df.collect().mkString(",")}}")
}
}
Error:
Exception in thread "main" java.lang.UnsupportedOperationException: No Encoder found for com.company.MyEnum.Value
- field (class: "scala.Enumeration.Value", name: "other")
- root class: "com.company.MyData"
at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$extractorFor(ScalaReflection.scala:597)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$extractorFor$1.apply(ScalaReflection.scala:509)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$extractorFor$1.apply(ScalaReflection.scala:502)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.immutable.List.foreach(List.scala:318)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105)
at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$extractorFor(ScalaReflection.scala:502)
at org.apache.spark.sql.catalyst.ScalaReflection$.extractorsFor(ScalaReflection.scala:394)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:54)
at org.apache.spark.sql.SQLImplicits.newProductEncoder(SQLImplicits.scala:41)
at com.company.EnumTest$.main(EnumTest.scala:22)
at com.company.EnumTest.main(EnumTest.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)
You can create your own encoder:
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}
object MyEnum extends Enumeration {
type MyEnum = Value
val Hello, World = Value
}
case class MyData(field: String, other: MyEnum.Value)
object MyDataEncoders {
implicit def myDataEncoder: org.apache.spark.sql.Encoder[MyData] =
org.apache.spark.sql.Encoders.kryo[MyData]
}
object EnumTest {
import MyDataEncoders._
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
val sc = new SparkContext(sparkConf)
val sqlCtx = new SQLContext(sc)
import sqlCtx.implicits._
val df = sc.parallelize(Array(MyData("hello", MyEnum.World))).toDS()
println(s"df: ${df.collect().mkString(",")}}")
}
}
I am newbie to both scala and spark, and trying some of the tutorials, this one is from Advanced Analytics with Spark. The following code is supposed to work:
import com.cloudera.datascience.common.XmlInputFormat
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.io._
val path = "/home/petr/Downloads/wiki/wiki"
val conf = new Configuration()
conf.set(XmlInputFormat.START_TAG_KEY, "<page>")
conf.set(XmlInputFormat.END_TAG_KEY, "</page>")
val kvs = sc.newAPIHadoopFile(path, classOf[XmlInputFormat],
classOf[LongWritable], classOf[Text], conf)
val rawXmls = kvs.map(p => p._2.toString)
import edu.umd.cloud9.collection.wikipedia.language._
import edu.umd.cloud9.collection.wikipedia._
def wikiXmlToPlainText(xml: String): Option[(String, String)] = {
val page = new EnglishWikipediaPage()
WikipediaPage.readPage(page, xml)
if (page.isEmpty) None
else Some((page.getTitle, page.getContent))
}
val plainText = rawXmls.flatMap(wikiXmlToPlainText)
But it gives
scala> val plainText = rawXmls.flatMap(wikiXmlToPlainText)
org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
at org.apache.spark.SparkContext.clean(SparkContext.scala:1622)
at org.apache.spark.rdd.RDD.flatMap(RDD.scala:295)
...
Running Spark v1.3.0 on a local (and I have loaded only about a 21MB of the wiki articles, just to test it).
All of https://stackoverflow.com/search?q=org.apache.spark.SparkException%3A+Task+not+serializable didn't get me any clue...
Thanks.
try
import com.cloudera.datascience.common.XmlInputFormat
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.io._
val path = "/home/terrapin/Downloads/enwiki-20150304-pages-articles1.xml-p000000010p000010000"
val conf = new Configuration()
conf.set(XmlInputFormat.START_TAG_KEY, "<page>")
conf.set(XmlInputFormat.END_TAG_KEY, "</page>")
val kvs = sc.newAPIHadoopFile(path, classOf[XmlInputFormat],
classOf[LongWritable], classOf[Text], conf)
val rawXmls = kvs.map(p => p._2.toString)
import edu.umd.cloud9.collection.wikipedia.language._
import edu.umd.cloud9.collection.wikipedia._
val plainText = rawXmls.flatMap{line =>
val page = new EnglishWikipediaPage()
WikipediaPage.readPage(page, line)
if (page.isEmpty) None
else Some((page.getTitle, page.getContent))
}
The first guess which comes to mind is that: all your code is wrapped in the object where SparkContext is defined. Spark tries to serialize this object to transfer wikiXmlToPlainText function to nodes. Try to create different object with the only one function wikiXmlToPlainText.