Scala/Akka NullPointerException on master actor - java

I am learning akka framework for parallel processing in scala, and I was trying to migrating a java project to scala so I can learn both akka and scala at the same time. I am get a NullPointerException on master actor when trying to receive mutable object from the worker actor after some computation in the worker. All code is below...
import akka.actor._
import java.math.BigInteger
import akka.routing.ActorRefRoutee
import akka.routing.Router
import akka.routing.RoundRobinRoutingLogic
object Main extends App {
val system = ActorSystem("CalcSystem")
val masterActor = system.actorOf(Props[Master], "master")
masterActor.tell(new Calculate, ActorRef.noSender)
}
class Master extends Actor {
private val messages: Int = 10;
var resultList: Seq[String] = _
//val workerRouter = this.context.actorOf(Props[Worker].withRouter(new RoundRobinRouter(2)), "worker")
var router = {
val routees = Vector.fill(5) {
val r = context.actorOf(Props[Worker])
context watch r
ActorRefRoutee(r)
}
Router(RoundRobinRoutingLogic(), routees)
}
def receive() = {
case msg: Calculate =>
processMessages()
case msg: Result =>
resultList :+ msg.getFactorial().toString
println(msg.getFactorial())
if (resultList.length == messages) {
end
}
}
private def processMessages() {
var i: Int = 0
for (i <- 1 to messages) {
// workerRouter.tell(new Work, self)
router.route(new Work, self)
}
}
private def end() {
println("List = " + resultList)
this.context.system.shutdown()
}
}
import akka.actor._
import java.math.BigInteger
class Worker extends Actor {
private val calculator = new Calculator
def receive() = {
case msg: Work =>
println("Called calculator.calculateFactorial: " + context.self.toString())
val result = new Result(calculator.calculateFactorial)
sender.tell(result, this.context.parent)
case _ =>
println("I don't know what to do with this...")
}
}
import java.math.BigInteger
class Result(bigInt: BigInteger) {
def getFactorial(): BigInteger = bigInt
}
import java.math.BigInteger
class Calculator {
def calculateFactorial(): BigInteger = {
var result: BigInteger = BigInteger.valueOf(1)
var i = 0
for(i <- 1 to 4) {
result = result.multiply(BigInteger.valueOf(i))
}
println("result: " + result)
result
}
}


You initialize the resultList with null and then try to append something.


Does your calculation ever stop? In line
resultList :+ msg.getFactorial().toString
you're creating a copy of sequence with an element appended. But there is no assignment to var resultList
This line will work as you want.
resultList = resultList :+ msg.getFactorial().toString
I recommend you to avoid mutable variables in actor and use context.become
https://github.com/alexandru/scala-best-practices/blob/master/sections/5-actors.md#52-should-mutate-state-in-actors-only-with-contextbecome

Related

Scala udf UnsupportedOperationException

I have a dataframe a2 written in scala :
val a3 = a2.select(printme.apply(col(“PlayerReference”)))
the column PlayerReference contains a string.
that calls an udf function :
val printme = udf({
st: String =>
val x = new JustPrint(st)
x.printMe();
})
this udf function calls a java class :
public class JustPrint {
private String ss = null;
public JustPrint(String ss) {
this.ss = ss;
}
public void printMe() {
System.out.println("Value : " + this.ss);
}
}
but i have this error for the udf :
java.lang.UnsupportedOperationException: Schema for type Unit is not supported
The goal of this exercise is to validate the chain of calls.
What should I do to solve this problem ?

The reason you're getting this error is that your UDF doesn't return anything, which, in terms of spark is called Unit.
What you should do depends on what you actually want, but, assuming you just want to track values coming through your UDF you should either change printMe so it returns String, or the UDF.
Like this:
public String printMe() {
System.out.println("Value : " + this.ss);
return this.ss;
}
or like this:
val printme = udf({
st: String =>
val x = new JustPrint(st)
x.printMe();
x
})

Exception in thread "streaming-job-executor-11" java.lang.ClassFormatError

I am working with kafka (scala) and spark streaming (scala) to insert data from several CSVs to Cassandra tables, and I made a producer and a consumer, here are their respective codes
Producer:
import java.sql.Timestamp
import java.util.Properties
import java.io._
import java.io.File
import java.nio.file.{Files, Paths, Path, SimpleFileVisitor,
FileVisitResult}
import scala.io.Source
import akka.actor.{Actor, ActorSystem, Props}
import com.typesafe.config.ConfigFactory
import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
class produceMessages(brokers: String, topic: String) extends Actor {
// All helpers needed to send messages
def filecontent(namefile: String){
for (line <- Source.fromFile(namefile).getLines) {
println(line)
}
}
def getListOfFiles(dir: String):List[File] = {
val d = new File(dir)
if (d.exists && d.isDirectory) {
d.listFiles.filter(_.isFile).toList
} else {
List[File]()
}
}
def between(value: String, a:String, b: String):String = {
// Return a substring between the two strings.
val posA = value.indexOf(a)
val posB = value.lastIndexOf(b)
val adjustedPosA = posA + a.length()
val res = value.substring(adjustedPosA, posB)
return res
}
def getTableName(filePath: String):String = {
//return table name from filePath
val fileName = filePath.toString.split("\\\\").last
val tableName = between(fileName,"100001_","_2017")
return tableName
}
// end of helpers
object kafka {
val producer = {
val props = new Properties()
props.put("metadata.broker.list", brokers)
//props.put(" max.request.size","5242880")
props.put("serializer.class", "kafka.serializer.StringEncoder")
val config = new ProducerConfig(props)
new Producer[String, String](config)
}
}
def receive = {
case "send" => {
val listeFichiers = getListOfFiles("C:\\Users\\acer\\Desktop\\csvs")
for (i <- 0 until listeFichiers.length)yield{
val chemin = listeFichiers(i).toString
val nomTable = getTableName(chemin)
println(nomTable)
val lines = Source.fromFile(chemin).getLines.toArray
val headerLine = lines(0)
println(headerLine)
val data = lines.slice(1,lines.length)
val messages = for (j <- 0 until data.length) yield{
val str = s"${data(j).toString}"
println(str)
new KeyedMessage[String, String](topic, str)
}
//sending the messages
val numberOfLinesInTable = new KeyedMessage[String, String](topic, data.length.toString)
val table = new KeyedMessage[String, String](topic, nomTable)
val header = new KeyedMessage[String, String](topic, headerLine)
kafka.producer.send(numberOfLinesInTable)
kafka.producer.send(table)
kafka.producer.send(header)
kafka.producer.send(messages: _*)
}
}
/*case "delete" =>{
val listeFichiers = getListOfFiles("C:\\Users\\acer\\Desktop\\csvs")
for (file <- listeFichiers){
if (file.isDirectory)
Option(file.listFiles).map(_.toList).getOrElse(Nil).foreach(Files.delete(_))
file.delete
}
}*/
case _ => println("Not a valid message!")
}
}
// Produces some random words between 1 and 100.
object KafkaStreamProducer extends App {
/*
* Get runtime properties from application.conf
*/
val systemConfig = ConfigFactory.load()
val kafkaHost = systemConfig.getString("KafkaStreamProducer.kafkaHost")
println(s"kafkaHost $kafkaHost")
val kafkaTopic = systemConfig.getString("KafkaStreamProducer.kafkaTopic")
println(s"kafkaTopic $kafkaTopic")
val numRecords = systemConfig.getLong("KafkaStreamProducer.numRecords")
println(s"numRecords $numRecords")
val waitMillis = systemConfig.getLong("KafkaStreamProducer.waitMillis")
println(s"waitMillis $waitMillis")
/*
* Set up the Akka Actor
*/
val system = ActorSystem("KafkaStreamProducer")
val messageActor = system.actorOf(Props(new produceMessages(kafkaHost, kafkaTopic)), name="genMessages")
/*
* Message Loop
*/
var numRecsWritten = 0
while(numRecsWritten < numRecords) {
messageActor ! "send"
numRecsWritten += numRecsWritten
println(s"${numRecsWritten} records written.")
//messageActor ! "delete"
Thread sleep waitMillis
}
}
And here is the consumer:
package com.datastax.demo
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{SQLContext, SaveMode, Row, SparkSession}
import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
import org.apache.spark.streaming.{Milliseconds, StreamingContext, Time}
import org.apache.spark.streaming.kafka.KafkaUtils
import com.datastax.spark.connector._
import kafka.serializer.StringDecoder
import org.apache.spark.rdd.RDD
import java.sql.Timestamp
import java.io.File
import scala.io.Source
import scala.reflect.runtime.universe
import scala.tools.reflect.ToolBox
case class cellmodu(collecttime: Double,sbnid: Double,enodebid: Double,cellid: Double,c373515500: Double,c373515501: Double,c373515502: Double,c373515503: Double,c373515504: Double,c373515505: Double,c373515506: Double,c373515507: Double,c373515508: Double,c373515509: Double,c373515510: Double,c373515511: Double,c373515512: Double,c373515513: Double,c373515514: Double,c373515515: Double,c373515516: Double,c373515517: Double,c373515518: Double,c373515519: Double,c373515520: Double,c373515521: Double,c373515522: Double,c373515523: Double,c373515524: Double,c373515525: Double,c373515526: Double,c373515527: Double,c373515528: Double,c373515529: Double,c373515530: Double,c373515531: Double,c373515532: Double,c373515533: Double,c373515534: Double,c373515535: Double,c373515536: Double,c373515537: Double,c373515538: Double,c373515539: Double,c373515540: Double,c373515541: Double,c373515542: Double,c373515543: Double,c373515544: Double,c373515545: Double,c373515546: Double,c373515547: Double,c373515548: Double,c373515549: Double,c373515550: Double,c373515551: Double,c373515552: Double,c373515553: Double,c373515554: Double,c373515555: Double,c373515556: Double,c373515557: Double,c373515558: Double,c373515559: Double,c373515560: Double,c373515561: Double,c373515562: Double,c373515563: Double,c373515564: Double,c373515565: Double,c373515566: Double,c373515567: Double,c373515568: Double,c373515569: Double,c373515570: Double,c373515571: Double,c373515572: Double,c373515573: Double,c373515574: Double,c373515575: Double,c373515576: Double,c373515577: Double,c373515578: Double,c373515589: Double,c373515590: Double,c373515591: Double,c373515592: Double,c373515593: Double,c373515594: Double,c373515595: Double,c373515596: Double,c373515597: Double,c373515598: Double,c373515601: Double,c373515602: Double,c373515608: Double,c373515609: Double,c373515610: Double,c373515611: Double,c373515616: Double,c373515618: Double,c373515619: Double,c373515620: Double,c373515621: Double,c373515622: Double,c373515623: Double,c373515624: Double,c373515625: Double,c373515626: Double,c373515627: Double,c373515628: Double,c373515629: Double,c373515630: Double,c373515631: Double,c373515632: Double,c373515633: Double,c373515634: Double,c373515635: Double,c373515636: Double,c373515637: Double,c373515638: Double,c373515639: Double,c373515640: Double,c373515641: Double,c373515642: Double,c373515643: Double,c373515644: Double,c373515645: Double,c373515646: Double,c373515647: Double,c373515648: Double,c373515649: Double,c373515650: Double,c373515651: Double,c373515652: Double,c373515653: Double,c373515654: Double,c373515655: Double,c373515656: Double,c373515657: Double,c373515658: Double,c373515659: Double,c373515660: Double,c373515661: Double,c373515662: Double,c373515663: Double,c373515664: Double,c373515665: Double,c373515666: Double,c373515667: Double,c373515668: Double,c373515669: Double,c373515670: Double,c373515671: Double,c373515672: Double,c373515673: Double,c373515674: Double,c373515675: Double,c373515676: Double,c373515677: Double,c373515678: Double,c373515679: Double,c373515680: Double,c373515681: Double,c373515682: Double,c373515683: Double,c373515684: Double,c373515685: Double,c373515686: Double,c373515687: Double,c373515688: Double,c373515689: Double,c373515690: Double,c373515691: Double,c373515692: Double,c373515693: Double,c373515694: Double,c373515695: Double,c373515696: Double,c373515697: Double,c373515698: Double,c373515699: Double,c373515700: Double,c373515701: Double,c373515702: Double,c373515703: Double,c373515704: Double,c373515705: Double,c373515706: Double,c373515707: Double,c373515708: Double,c373515709: Double,c373515710: Double,c373515711: Double,c373515712: Double,c373515713: Double,c373515714: Double,c373515715: Double,c373515716: Double,c373515717: Double,c373515718: Double,c373515719: Double,c373515720: Double,c373515721: Double,c373515722: Double,c373515723: Double,c373515724: Double,c373515725: Double,c373515726: Double,c373515727: Double,c373515728: Double,c373515729: Double,c373515730: Double,c373515731: Double,c373515732: Double,c373515733: Double,c373515734: Double,c373515735: Double,c373515736: Double,c373515737: Double,c373515738: Double,c373515739: Double,c373515740: Double,c373515741: Double,c373515742: Double,c373515743: Double,c373515744: Double,c373515745: Double,c373515746: Double,c373515747: Double,c373515748: Double,c373515749: Double,c373515750: Double,c373515751: Double,c373515752: Double,c373515753: Double,c373515754: Double,c373515755: Double,c373515756: Double) {}
object SparkKafkaConsumerCellmodu extends App {
//START OF HELPERS
def isNumeric(str:String): Boolean = str.matches("[-+]?\\d+(\\.\\d+)?")
def printList(args: List[_]): Unit = {args.foreach(println)}
//END OF HELPERS
val appName = "SparkKafkaConsumer"
val conf = new SparkConf()
.set("spark.cores.max", "2")
//.set("spark.executor.memory", "512M")
.set("spark.cassandra.connection.host","localhost")
.setAppName(appName)
val spark: SparkSession = SparkSession.builder.master("local").getOrCreate
val sc = SparkContext.getOrCreate(conf)
val sqlContext = SQLContext.getOrCreate(sc)
import sqlContext.implicits._
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
val ssc = new StreamingContext(sc, Milliseconds(1000))
ssc.checkpoint(appName)
val kafkaTopics = Set("test")
//val kafkaParams = Map[String, String]("metadata.broker.list" -> "localhost:9092")
val kafkaParams = Map(
"bootstrap.servers" -> "localhost:9092",
"fetch.message.max.bytes" -> "5242880")
val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, kafkaTopics)
kafkaStream
.foreachRDD {
(message: RDD[(String, String)]) => {
val rddToArray = message.collect().toList
val msg = rddToArray.map(_._2)
var i = 0
while (i < msg.length){
if(isNumeric(msg(i))){
println("HHHHHHHHHHHHHHHHHHHHHHHHHHHHH")
val numberLines = msg(i).toInt //get number of lines to insert in table
val nameTable = msg(i+1) //get table name
val headerTable = msg(i+2).toLowerCase //get the columns of the table
println(headerTable)
if(msg(i+1)=="CELLMODU"){
val typedCols : Array[String] = headerTable.split(",") // transform headerTable into array to define dataframe dynamically
val listtoinsert:Array[String] = new Array[String](numberLines) // an empty list that will contain the lines to insert in the adequate table
val k = i + 3 //to skip name of table and header
//fill the toinsert array with the lines
for (j <- 0 until numberLines){
listtoinsert(j) = msg(k + j)
println (listtoinsert(j))
}
//convert the array to RDD
val rddtoinsert: RDD[(String)] = sc.parallelize(listtoinsert)
//rddtoinsert.foreach(println)
//convert rdd to dataframe
val df = rddtoinsert.map {
case (v) => v.split(",")
}.map(payload1 => { // instance of dynamic class
cellmodu(payload1(0).toDouble,payload1(1).toDouble,payload1(2).toDouble,payload1(3).toDouble,payload1(4).toDouble,payload1(5).toDouble,payload1(6).toDouble,payload1(7).toDouble,payload1(8).toDouble,payload1(9).toDouble,payload1(10).toDouble,payload1(11).toDouble,payload1(12).toDouble,payload1(13).toDouble,payload1(14).toDouble,payload1(15).toDouble,payload1(16).toDouble,payload1(17).toDouble,payload1(18).toDouble,payload1(19).toDouble,payload1(20).toDouble,payload1(21).toDouble,payload1(22).toDouble,payload1(23).toDouble,payload1(24).toDouble,payload1(25).toDouble,payload1(26).toDouble,payload1(27).toDouble,payload1(28).toDouble,payload1(29).toDouble,payload1(30).toDouble,payload1(31).toDouble,payload1(32).toDouble,payload1(33).toDouble,payload1(34).toDouble,payload1(35).toDouble,payload1(36).toDouble,payload1(37).toDouble,payload1(38).toDouble,payload1(39).toDouble,payload1(40).toDouble,payload1(41).toDouble,payload1(42).toDouble,payload1(43).toDouble,payload1(44).toDouble,payload1(45).toDouble,payload1(46).toDouble,payload1(47).toDouble,payload1(48).toDouble,payload1(49).toDouble,payload1(50).toDouble,payload1(51).toDouble,payload1(52).toDouble,payload1(53).toDouble,payload1(54).toDouble,payload1(55).toDouble,payload1(56).toDouble,payload1(57).toDouble,payload1(58).toDouble,payload1(59).toDouble,payload1(60).toDouble,payload1(61).toDouble,payload1(62).toDouble,payload1(63).toDouble,payload1(64).toDouble,payload1(65).toDouble,payload1(66).toDouble,payload1(67).toDouble,payload1(68).toDouble,payload1(69).toDouble,payload1(70).toDouble,payload1(71).toDouble,payload1(72).toDouble,payload1(73).toDouble,payload1(74).toDouble,payload1(75).toDouble,payload1(76).toDouble,payload1(77).toDouble,payload1(78).toDouble,payload1(79).toDouble,payload1(80).toDouble,payload1(81).toDouble,payload1(82).toDouble,payload1(83).toDouble,payload1(84).toDouble,payload1(85).toDouble,payload1(86).toDouble,payload1(87).toDouble,payload1(88).toDouble,payload1(89).toDouble,payload1(90).toDouble,payload1(91).toDouble,payload1(92).toDouble,payload1(93).toDouble,payload1(94).toDouble,payload1(95).toDouble,payload1(96).toDouble,payload1(97).toDouble,payload1(98).toDouble,payload1(99).toDouble,payload1(100).toDouble,payload1(101).toDouble,payload1(102).toDouble,payload1(103).toDouble,payload1(104).toDouble,payload1(105).toDouble,payload1(106).toDouble,payload1(107).toDouble,payload1(108).toDouble,payload1(109).toDouble,payload1(110).toDouble,payload1(111).toDouble,payload1(112).toDouble,payload1(113).toDouble,payload1(114).toDouble,payload1(115).toDouble,payload1(116).toDouble,payload1(117).toDouble,payload1(118).toDouble,payload1(119).toDouble,payload1(120).toDouble,payload1(121).toDouble,payload1(122).toDouble,payload1(123).toDouble,payload1(124).toDouble,payload1(125).toDouble,payload1(126).toDouble,payload1(127).toDouble,payload1(128).toDouble,payload1(129).toDouble,payload1(130).toDouble,payload1(131).toDouble,payload1(132).toDouble,payload1(133).toDouble,payload1(134).toDouble,payload1(135).toDouble,payload1(136).toDouble,payload1(137).toDouble,payload1(138).toDouble,payload1(139).toDouble,payload1(140).toDouble,payload1(141).toDouble,payload1(142).toDouble,payload1(143).toDouble,payload1(144).toDouble,payload1(145).toDouble,payload1(146).toDouble,payload1(147).toDouble,payload1(148).toDouble,payload1(149).toDouble,payload1(150).toDouble,payload1(151).toDouble,payload1(152).toDouble,payload1(153).toDouble,payload1(154).toDouble,payload1(155).toDouble,payload1(156).toDouble,payload1(157).toDouble,payload1(158).toDouble,payload1(159).toDouble,payload1(160).toDouble,payload1(161).toDouble,payload1(162).toDouble,payload1(163).toDouble,payload1(164).toDouble,payload1(165).toDouble,payload1(166).toDouble,payload1(167).toDouble,payload1(168).toDouble,payload1(169).toDouble,payload1(170).toDouble,payload1(171).toDouble,payload1(172).toDouble,payload1(173).toDouble,payload1(174).toDouble,payload1(175).toDouble,payload1(176).toDouble,payload1(177).toDouble,payload1(178).toDouble,payload1(179).toDouble,payload1(180).toDouble,payload1(181).toDouble,payload1(182).toDouble,payload1(183).toDouble,payload1(184).toDouble,payload1(185).toDouble,payload1(186).toDouble,payload1(187).toDouble,payload1(188).toDouble,payload1(189).toDouble,payload1(190).toDouble,payload1(191).toDouble,payload1(192).toDouble,payload1(193).toDouble,payload1(194).toDouble,payload1(195).toDouble,payload1(196).toDouble,payload1(197).toDouble,payload1(198).toDouble,payload1(199).toDouble,payload1(200).toDouble,payload1(201).toDouble,payload1(202).toDouble,payload1(203).toDouble,payload1(204).toDouble,payload1(205).toDouble,payload1(206).toDouble,payload1(207).toDouble,payload1(208).toDouble,payload1(209).toDouble,payload1(210).toDouble,payload1(211).toDouble,payload1(212).toDouble,payload1(213).toDouble,payload1(214).toDouble,payload1(215).toDouble,payload1(216).toDouble,payload1(217).toDouble,payload1(218).toDouble,payload1(219).toDouble,payload1(220).toDouble,payload1(221).toDouble,payload1(222).toDouble,payload1(223).toDouble,payload1(224).toDouble,payload1(225).toDouble,payload1(226).toDouble,payload1(227).toDouble,payload1(228).toDouble,payload1(229).toDouble,payload1(230).toDouble,payload1(231).toDouble,payload1(232).toDouble,payload1(233).toDouble,payload1(234).toDouble,payload1(235).toDouble,payload1(236).toDouble,payload1(237).toDouble,payload1(238).toDouble)
}).toDF(typedCols: _*)
//insert dataframe in cassandra table
df
.write
.format("org.apache.spark.sql.cassandra")
.mode(SaveMode.Append)
.options(Map("keyspace" -> "ztedb4g", "table" -> nameTable.toLowerCase)) // tolowercase because the name table comes in uppercase
.save()
df.show(1)
println(s"${df.count()} rows processed.")
}
}
}
}
}
ssc.start()
ssc.awaitTermination()
}
The producer works well and publishes the messages as I want it to, but when I execute the consumer to insert in a table called "Cellmodu" I get the following error:
Exception in thread "streaming-job-executor-11" java.lang.ClassFormatError: com/datastax/demo/cellmodu
at com.datastax.demo.SparkKafkaConsumerCellmodu$$anonfun$1.apply(SparkKafkaConsumerCellmodu.scala:90)
at com.datastax.demo.SparkKafkaConsumerCellmodu$$anonfun$1.apply(SparkKafkaConsumerCellmodu.scala:57)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:628)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:628)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:257)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
I keep getting this error over and over again for different streaming jobs and then nothing is inserted in my table, note that I tried to executre the exact same code for other tables with different case class of course that matches my table schema and it worked just fine, I don't understand why I get this error for few tables only like this one

as per your exception it is clearly java.lang.ClassFormatError error. First compare with other classes which are working fine in inserting to table. If you are using xml config information for Cellmodu, please check that if something wrong in it.

wait for multiple scala-async

i want to close csv writer when all the async block executed
import java.io.{FileReader, FileWriter}
import com.opencsv.{CSVReader, CSVWriter}
import org.jsoup.helper.StringUtil
import scala.async.Async.{async, await}
import scala.concurrent.ExecutionContext.Implicits.global
var rows = 0
reader.forEach(line => {
async {
val csv = new CSV(line(0), line(1), line(2), line(3), line(4));
entries(0) = csv.id
entries(1) = csv.name
val di = async(httpRequest(csv.id))
var di2 = "not available"
val di2Future = async(httpRequest(csv.name))
di2 = await(di2Future)
entries(2) = await(di)
entries(3) = di2
writer.writeNext(entries)
println(s"${csv.id} completed!!!! ")
rows += 1
}
})
writer.close();
in above code writer always closed first, so i want to execute all async block then close my csv writer.

Below is a skeleton solution.
val allResponses = reader.map(l => {
val entries = ??? // declare entires data structure here
for {
line <- async(l)
// line <- Future.succesful(l)
csv <- async {
val csv = CSV(line(0), line(1), line(2), line(3), line(4))
entries(0) = csv.id
entries(1) = csv.name
csv
}
di <- async {
httpRequest(csv.id)
}
di2 <- async {
httpRequest(csv.name)
}
e <- async {
entries(2) = di
entries(3) = di2
entries
}
} yield e
})
val t = Future.sequence(allResponses)
t.map(a => {
val writer = new FileWriter("file.txt")
a.foreach(i => {
writer.writeNext(i)
})
writer.close()
})
Hope this helps.

An async block produces a Future[A], where A in your case is Unit (which is the type of the assignment rows += 1).
In general, you can perform operations when a Future is complete like the following:
def myFuture: Future[Something] = ??? // your async process
myFuture.onComplete {
case Success(result) =>
???
case Failure(exception) =>
???
}
If you want to perform something regardless the status you can skip pattern matching:
myFuture.onComplete(_ => writer.close()) // e.g.

Opening existing embedded neo4j database

I am trying to use scala over the embedded jave Neo4j api. I am having trouble opening the database for reading on subsequent occasions. The code below should create two nodes and an edge every time it runs, but return all of them at the begining of each run. So, 0 nodes first time, 2 nodes second time, 4 third time etc.
import org.neo4j.tooling.GlobalGraphOperations
import org.neo4j.graphdb.factory.GraphDatabaseFactory
import org.neo4j.graphdb.RelationshipType
object tester extends App{
val DB_PATH = "data/neo4j"
object KNOWS extends RelationshipType {
override def name(): String = "KNOWS"
}
val graphDb = new GraphDatabaseFactory().newEmbeddedDatabase(DB_PATH) //seems to reset the whole directory
println(graphDb)
try {
println("Begin")
val tx = graphDb.beginTx() // Database operations go here
println(GlobalGraphOperations.at(graphDb).getAllNodes.iterator)
val nodes = GlobalGraphOperations.at(graphDb).getAllNodes.iterator
while (nodes.hasNext()) {
println(nodes.next())
}
nodes.close()
val relT = GlobalGraphOperations.at(graphDb).getAllRelationships.iterator
while (relT.hasNext()) {
println(relT.next())
}
println("Success - Begin")
tx.success()
}
try {
val tx = graphDb.beginTx() // Database operations go here
val firstNode = graphDb.createNode
val secondNode = graphDb.createNode
val relationship = firstNode.createRelationshipTo(secondNode, KNOWS)
println(firstNode)
println(secondNode)
println(relationship)
println(relationship.getType.name)
tx.success()
println("Success")
}
println("End")
try {
val tx = graphDb.beginTx() // Database operations go here
println(GlobalGraphOperations.at(graphDb).getAllNodes.iterator)
val nodes = GlobalGraphOperations.at(graphDb).getAllNodes.iterator
while (nodes.hasNext()) {
println(nodes.next())
}
nodes.close()
val relT = GlobalGraphOperations.at(graphDb).getAllRelationships.iterator
while (relT.hasNext()) {
println(relT.next())
}
println("Success - End")
tx.success()
}
graphDb.shutdown()
}
However, every time it simply seems to give an empty database and then the 2 new nodes. What's going on here?
EmbeddedGraphDatabase [data/neo4j]
Begin
org.neo4j.tooling.GlobalGraphOperations$1$1#74c49a90
Success - Begin
Node[2]
Node[3]
Relationship[1]
KNOWS
Success
End
org.neo4j.tooling.GlobalGraphOperations$1$1#2ec0df08
Node[2]
Node[3]
Relationship[1]
Success - End
Process finished with exit code 0

This is happening because you are not closing the transaction. You can do this by calling tx.close(). Also I think that instantiating tx inside try is not exactly how it should be. Here is a working version of your program:
import org.neo4j.tooling.GlobalGraphOperations
import org.neo4j.graphdb.factory.GraphDatabaseFactory
import org.neo4j.graphdb.RelationshipType
object tester extends App{
val DB_PATH = "data/neo4j"
object KNOWS extends RelationshipType {
override def name(): String = "KNOWS"
}
val graphDb = new GraphDatabaseFactory().newEmbeddedDatabase(DB_PATH)
println(graphDb)
val tx1 = graphDb.beginTx() // Database operations go here
try {
println("Will list all nodes")
println("1 - Begin")
println("GlobalGraphOperations.at(graphDb).getAllNodes.iterator")
val nodes = GlobalGraphOperations.at(graphDb).getAllNodes.iterator
while (nodes.hasNext()) {
println(nodes.next())
}
nodes.close()
val relT = GlobalGraphOperations.at(graphDb).getAllRelationships.iterator
while (relT.hasNext()) {
println(relT.next())
}
println("1 - Success - Begin")
tx1.success()
}
finally {
tx1.close()
}
val tx2 = graphDb.beginTx() // Database operations go here
try {
val firstNode = graphDb.createNode
val secondNode = graphDb.createNode
val relationship = firstNode.createRelationshipTo(secondNode, KNOWS)
println(firstNode)
println(secondNode)
println(relationship)
println(relationship.getType.name)
tx2.success()
println("2 - Success")
}
finally {
tx2.close()
}
println("2 - End")
val tx3 = graphDb.beginTx() // Database operations go here
try {
println(GlobalGraphOperations.at(graphDb).getAllNodes.iterator)
val nodes = GlobalGraphOperations.at(graphDb).getAllNodes.iterator
while (nodes.hasNext()) {
println(nodes.next())
}
nodes.close()
val relT = GlobalGraphOperations.at(graphDb).getAllRelationships.iterator
while (relT.hasNext()) {
println(relT.next())
}
println("3 - Success - End")
tx3.success()
}
finally {
tx3.close()
}
graphDb.shutdown()
}
EXTRA
I tried to bring your program closer to the "scala-style". Also, I tried to remove boilerplate and repeated code. To accomplish this I:
Used JavaConverters to handle Java collections and Iterables like we handle them in Scala
Created a method withTransaction to get automatic resource management for our transaction in scala.
This is the result:
import org.neo4j.tooling.GlobalGraphOperations
import org.neo4j.graphdb.factory.GraphDatabaseFactory
import org.neo4j.graphdb.RelationshipType
import org.neo4j.graphdb.Transaction
import scala.collection.JavaConverters._
object tester extends App{
val DB_PATH = "data/neo4j"
object KNOWS extends RelationshipType {
override def name(): String = "KNOWS"
}
def withTransaction (doWithTransaction: Transaction => Unit) {
val tempTx = graphDb.beginTx()
try {
doWithTransaction(tempTx)
}
finally {
tempTx.close()
}
}
val graphDb = new GraphDatabaseFactory().newEmbeddedDatabase(DB_PATH)
println(graphDb)
withTransaction { tx =>
println("1 - Begin")
val nodes = GlobalGraphOperations.at(graphDb).getAllNodes
for (node <- nodes.asScala)
println(node)
val relTs = GlobalGraphOperations.at(graphDb).getAllRelationships
for (irelT <- relTs.asScala)
println(irelT)
println("1 - Success - Begin")
tx.success()
}
withTransaction { tx =>
val firstNode = graphDb.createNode
val secondNode = graphDb.createNode
val relationship = firstNode.createRelationshipTo(secondNode, KNOWS)
println(firstNode)
println(secondNode)
println(relationship)
println(relationship.getType.name)
tx.success()
println("2 - Success")
}
println("2 - End")
withTransaction { tx =>
println(GlobalGraphOperations.at(graphDb).getAllNodes.iterator)
val nodes = GlobalGraphOperations.at(graphDb).getAllNodes
for (node <- nodes.asScala)
println(node)
val relTs = GlobalGraphOperations.at(graphDb).getAllRelationships
for (irelT <- relTs.asScala)
println(irelT)
println("3 - Success - End")
tx.success()
}
graphDb.shutdown()
}

the issue is that you are specifying a relative path. May be that everytime you run a clean and build you empty your target directory (or dist, or whatever your ide, development framework use as distribution directory) so the database is empty since it is created from scratch everytime. Try with an absolute path.

Is there a cleaner way to do this Group Query in MongoDB from Groovy?

I'm working on learning MongoDB. Language of choice for the current run at it is Groovy.
Working on Group Queries by trying to answer the question of which pet is the most needy one.
Below is my first attempt and it's awful. Any help cleaning this up (or simply confirming that there isn't a cleaner way to do it) would be much appreciated.
Thanks in advance!
package mongo.pets
import com.gmongo.GMongo
import com.mongodb.BasicDBObject
import com.mongodb.DBObject
class StatsController {
def dbPets = new GMongo().getDB('needsHotel').getCollection('pets')
//FIXME OMG THIS IS AWFUL!!!
def index = {
def petsNeed = 'a walk'
def reduce = 'function(doc, aggregator) { aggregator.needsCount += doc.needs.length }'
def key = new BasicDBObject()
key.put("name", true)
def initial = new BasicDBObject()
initial.put ("needsCount", 0)
def maxNeeds = 0
def needyPets = []
dbPets.group(key, new BasicDBObject(), initial, reduce).each {
if (maxNeeds < it['needsCount']) {
maxNeeds = it['needsCount']
needyPets = []
needyPets += it['name']
} else if (maxNeeds == it['needsCount']) {
needyPets += it['name']
}
}
def needyPet = needyPets
[petsNeedingCount: dbPets.find([needs: petsNeed]).count(), petsNeed: petsNeed, mostNeedyPet: needyPet]
}
}

It should be possible to be change the whole method to this (but I don't have MongoDB to test it)
def index = {
def petsNeed = 'a walk'
def reduce = 'function(doc, aggregator) { aggregator.needsCount += doc.needs.length }'
def key = [ name: true ] as BasicDBObject
def initial = [ needsCount: 0 ] as BasicDBObject
def allPets = dbPets.group( key, new BasicDBObject(), initial, reduce )
def maxNeeds = allPets*.needsCount.collect { it as Integer }.max()
def needyPet = allPets.findAll { maxNeeds == it.needsCount as Integer }.name
[petsNeedingCount: dbPets.find([needs: petsNeed]).count(), petsNeed: petsNeed, mostNeedyPet: needyPet]
}

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