scala spark NoClassDefFoundError - InitialPositionInStream - scala

Deploying a spark application written in scala to an EMR cluster with the following command and i cannot figure out why I am receiving a missing dependency error message when deployed to the EMR cluster instance.
error message:
User class threw exception: java.lang.NoClassDefFoundError: com/amazonaws/services/kinesis/clientlibrary/lib/worker/InitialPositionInStream
aws emr add-steps --cluster-id j-xxxxxxx --steps Type=spark,Name=ScalaStream,Args=[\
--class,"ScalaStream",\
--deploy-mode,cluster,\
--master,yarn,\
--jars,s3://xxx.xxx.xxx/aws-java-sdk-1.11.715.jar,\
--conf,spark.yarn.submit.waitAppCompletion=false,\
s3://xxx.xxxx.xxxx/simple-project_2.12-1.0.jar\
],ActionOnFailure=CONTINUE
and sbt file
name := "Simple Project"
version := "1.0"
scalaVersion := "2.12.8"
libraryDependencies += "org.apache.spark" % "spark-sql_2.12" % "2.4.4"
libraryDependencies += "org.apache.spark" % "spark-streaming_2.12" % "2.4.4"
libraryDependencies += "com.amazonaws" % "aws-java-sdk" % "1.11.715"
libraryDependencies += "org.apache.spark" % "spark-streaming-kinesis-asl_2.12" % "2.4.4"
partial code below
...
import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
...
val streamingContext = new StreamingContext(sparkContext, batchInterval)
// Populate the appropriate variables from the given args
val streamAppName = "xxxxxx"
val streamName = "xxxxxx"
val endpointUrl = "https://kinesis.xxxxx.amazonaws.com"
val regionName = "xx-xx-x"
val initialPosition = InitialPositionInStream.LATEST
val checkpointInterval = batchInterval
val storageLevel = StorageLevel.MEMORY_AND_DISK_2
val kinesisStream = KinesisUtils.createStream(streamingContext, streamAppName, streamAppName, endpointUrl, regionName, initialPosition, checkpointInterval, storageLevel)
val initialPosition = InitialPositionInStream.LATEST
val checkpointInterval = batchInterval
val storageLevel = StorageLevel.MEMORY_AND_DISK_2
val kinesisStream = KinesisUtils.createStream(streamingContext, streamAppName, streamAppName, endpointUrl, regionName, initialPosition, checkpointInterval, storageLevel)
20/02/05 21:43:10 ERROR ApplicationMaster: User class threw exception: java.lang.NoClassDefFoundError: com/amazonaws/services/kinesis/clientlibrary/lib/worker/InitialPositionInStream
java.lang.NoClassDefFoundError: com/amazonaws/services/kinesis/clientlibrary/lib/worker/InitialPositionInStream
at ScalaStream$.main(stream.scala:32)
at ScalaStream.main(stream.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:498)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:684)
Caused by: java.lang.ClassNotFoundException: com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 7 more
20/02/05 21:43:10 INFO ApplicationMaster: Final app status: FAILED, exitCode: 15, (reason: User class threw exception: java.lang.NoClassDefFoundError: com/amazonaws/services/kinesis/clientlibrary/lib/worker/InitialPositionInStream
at ScalaStream$.main(stream.scala:32)
at ScalaStream.main(stream.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:684)
Caused by: java.lang.ClassNotFoundException: com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 7 more
)
I've tried including the aws dependencies both in the sbt file and also --jars paramater of spark-submit but cannot see why the dependency is missing?

fixed by updating the following
sbt
name := "Simple Project"
version := "1.0"
scalaVersion := "2.12.8"
libraryDependencies += "org.apache.spark" % "spark-sql_2.12" % "2.4.4"
libraryDependencies += "org.apache.spark" % "spark-streaming_2.12" % "2.4.4"
libraryDependencies += "org.apache.spark" % "spark-streaming-kinesis-asl_2.12" % "2.4.4"
deploy script
aws emr add-steps --cluster-id j-xxxxxxx --steps Type=spark,Name=ScalaStream,Args=[\
--class,"ScalaStream",\
--deploy-mode,cluster,\
--master,yarn,\
--packages,\'org.apache.spark:spark-streaming-kinesis-asl_2.11:2.4.0,org.postgresql:postgresql:42.2.9,com.facebook.presto:presto-jdbc:0.60\',\
--conf,spark.yarn.submit.waitAppCompletion=false,\
--conf,yarn.log-aggregation-enable=true,\
--conf,spark.dynamicAllocation.enabled=true,\
--conf,spark.cores.max=4,\
--conf,spark.network.timeout=300,\
s3://xxx.xxx/simple-project_2.12-1.0.jar\
],ActionOnFailure=CONTINUE
the key being the --packages flag added to the aws emr add-steps. Mistakenly thought sbt package bundled the required dependencies.

Related

How to handle the exception: "No suitable driver" while running spark-submit

I am trying to run a spark code which will read a table on postgres database and insert it into Hive table on HDFS. To do that I have set my connection properties in a properties file as below:
devUserName=usrname
devPassword=pwd
gpDriverClass=org.postgresql.Driver
gpDevUrl=jdbc:postgresql://ip:port/dbname?ssl=true&sslfactory=org.postgresql.ssl.NonValidatingFactory
And reading the properties file as:
val conFile = "testconnection.properties"
val properties = new Properties()
properties.load(new FileInputStream(conFile))
// GP Properties
val connectionUrl = properties.getProperty("gpDevUrl")
val devUserName = properties.getProperty("devUserName")
val devPassword = properties.getProperty("devPassword")
val driverClass = properties.getProperty("gpDriverClass")
I am submitting the jar file in the following way:
SPARK_MAJOR_VERSION=2 spark-submit --conf spark.ui.port=4090 --driver-class-path /home/usrname/jars/postgresql-42.1.4.jar --master=yarn --deploy-mode=cluster --driver-memory 40g --driver-cores 3 --executor-memory 40g --executor-cores 20 --num-executors 5 --files /usr/hdp/current/spark2-client/conf/hive-site.xml,testconnection.properties --jars /home/usrname/jars/postgresql-42.1.4.jar --class com.partition.source.YearPartition splinter_2.11-0.1.jar --keytab /home/usrname/usrname.keytab --principal usrname#DEV.COM --name Splinter --conf spark.executor.extraClassPath=/home/usrname/jars/postgresql-42.1.4.jar
The job fails with the following error message:
client token: Token { kind: YARN_CLIENT_TOKEN, service: }
diagnostics: User class threw exception: java.sql.SQLException: No suitable driver
ApplicationMaster host: 10.230.137.190
ApplicationMaster RPC port: 0
queue: default
start time: 1537545547309
final status: FAILED
tracking URL: http://ip:port/proxy/application_123456789_76/
user: fdlhdpetl
18/09/21 15:59:38 INFO Client: Deleted staging directory hdfs://dev/user/usrname/.sparkStaging/application_123456789_76
Exception in thread "main" org.apache.spark.SparkException: Application application_123456789_76 finished with failed status
at org.apache.spark.deploy.yarn.Client.run(Client.scala:1187)
at org.apache.spark.deploy.yarn.Client$.main(Client.scala:1233)
at org.apache.spark.deploy.yarn.Client.main(Client.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:782)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
18/09/21 15:59:38 INFO ShutdownHookManager: Shutdown hook called
18/09/21 15:59:38 INFO ShutdownHookManager: Deleting directory /tmp/spark-59934097-dfe9-4ad2-a66a-ff93d42cf838
My dependencies in the build.sbt file:
name := "Splinter"
version := "0.1"
scalaVersion := "2.11.8"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % "2.0.0" % "provided",
"org.apache.spark" %% "spark-sql" % "2.0.0" % "provided",
"org.json4s" %% "json4s-jackson" % "3.2.11" % "provided",
"org.apache.httpcomponents" % "httpclient" % "4.5.3"
)
// https://mvnrepository.com/artifact/org.postgresql/postgresql
libraryDependencies += "org.postgresql" % "postgresql" % "42.1.4"
Code used to read the table on postgres:
def prepareFinalDF(splitColumns:List[String], textList: ListBuffer[String], allColumns:String, dataMapper:Map[String, String], partition_columns:Array[String], spark:SparkSession): DataFrame = {
val execQuery = s"select ${allColumns}, 0 as ${flagCol} from schema.tablename where period_year='2017'"
val yearDF = spark.read.format("jdbc").option("url", connectionUrl).option("dbtable", s"(${execQuery}) as year2017").option("user", devUserName).option("password", devPassword).option("numPartitions",20).load()
val totalCols:List[String] = splitColumns ++ textList
val cdt = new ChangeDataTypes(totalCols, dataMapper)
hiveDataTypes = cdt.gpDetails()
prepareHiveTableSchema(hiveDataTypes, partition_columns)
val allColsOrdered = yearDF.columns.diff(partition_columns) ++ partition_columns
val allCols = allColsOrdered.map(colname => org.apache.spark.sql.functions.col(colname))
val resultDF = yearDF.select(allCols:_*)
val stringColumns = resultDF.schema.fields.filter(x => x.dataType == StringType).map(s => s.name)
val finalDF = stringColumns.foldLeft(resultDF) {
(tempDF, colName) => tempDF.withColumn(colName, regexp_replace(regexp_replace(col(colName), "[\r\n]+", " "), "[\t]+"," "))
}
finalDF
}
execQuery contains: select * from schema.tablename where period_year=2017
val dataDF = prepareFinalDF(splitColumns, textList, allColumns, dataMapper, partition_columns, spark)
dataDF.createOrReplaceTempView("preparedDF")
spark.sql("set hive.exec.dynamic.partition.mode=nonstrict")
spark.sql("set hive.exec.dynamic.partition=true")
spark.sql(s"INSERT OVERWRITE TABLE default.xx_gl_forecast PARTITION(${prtn_String_columns}) select * from preparedDF")
I checked the jar mentioned in the spark-submit and it is present in the dir given but it still fails when submitted.
Could anyone tell me what is the mistake I am doing here ? Is there any particular order to be followed while giving parameters in spark-submit ?
The JDBC driver for postgres has to be in the classpath not only of the spark driver but also in the executors. There are 3 ways to do it:
you build a fat jar that includes the driver. This means adding "org.postgresql" % "postgresql" % "42.1.4" to your projects' libraryDependencies.
you tell spark to download the dependency from maven. In your case, add --packages org.postgresql:postgresql:42.1.4 to your spark-submit (instead of --driver-class-path).
use --jars followed with the path to the drivers' jar. This will add the driver to both the driver's and executors' classpaths.

Spark Context creation error

I get the following set of errors when trying to create a spark context from my application.
Exception in thread "main" java.lang.NoClassDefFoundError: scala/Product$class
at org.apache.spark.SparkConf$DeprecatedConfig.<init>(SparkConf.scala:723)
at org.apache.spark.SparkConf$.<init>(SparkConf.scala:571)
at org.apache.spark.SparkConf$.<clinit>(SparkConf.scala)
at org.apache.spark.SparkConf.set(SparkConf.scala:92)
at org.apache.spark.SparkConf.set(SparkConf.scala:81)
at org.apache.spark.SparkConf.setAppName(SparkConf.scala:118)
at sparkEnvironment$.<init>(Ticket.scala:33)
at sparkEnvironment$.<clinit>(Ticket.scala)
at Ticket$.main(Ticket.scala:40)
at Ticket.main(Ticket.scala)
Caused by: java.lang.ClassNotFoundException: scala.Product$class
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:335)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 10 more
I'm using Spark 2.2.0 and Scala version 2.12.3. My build.sbt looks like follows :
scalaVersion := "2.12.3"
libraryDependencies += "com.typesafe" % "config" % "1.3.1"
libraryDependencies += "mysql" % "mysql-connector-java" % "5.1.36"
//Sparks dependencies
libraryDependencies += "org.apache.spark" % "spark-core_2.11" % "2.2.0"
libraryDependencies += "org.apache.spark" % "spark-mllib_2.10" % "2.2.0"
And the snippet in which I try to create the context is as follows :
object sparkEnvironment {
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
val conf : SparkConf = new SparkConf().setAppName("Ticketing").setMaster("local[2]")
val sc = new SparkContext(conf)
}
object Ticket {
def main(args: Array[String]): Unit = {
println(sc)
}
}
Spark 2.2.0 is built and distributed to work with Scala 2.11 by default.
To write applications in Scala, you will need to use a compatible Scala version (e.g. 2.11.X).
And your scala version is 2.12.X. That's why it is throwing exception.

java.lang.ClassNotFoundException: SparkSql at java.net.URLClassLoader.findClass(Unknown Source)

SparkSql.scala
import org.apache.spark.sql.SparkSession
object SparkSql
{
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().master("local[*]").appName("SparkSql1").getOrCreate()
//val data = spark.sparkContext.textFile(("C:\\Users\\blaskar\\Desktop\\Biswajit\\data\\sample.txt"))
val data = spark.read.format("csv").option("inferSchema","true").option("header","true").load("C:\\Users\\blaskar\\Desktop\\Biswajit\\data\\SampleCSVFile.csv")
val columnsRenamed = Seq("S.no", "Eldonbase", "platnium","Macintyre","count1","count2","count3","Nunavut","Storage","count4")
val original_data = data.toDF(columnsRenamed:_*).persist()
original_data.show(10)
}
}
build.sbt
name := "Spark-Sample1"
version := "1.0"
scalaVersion := "2.11.8"
libraryDependencies ++= Seq(
"org.apache.spark" % "spark-core_2.11" % "2.1.0" exclude("org.apache.hadoop", "hadoop-yarn-server-web-proxy"),
"org.apache.spark" % "spark-sql_2.11" % "2.1.0" exclude("org.apache.hadoop", "hadoop-yarn-server-web-proxy"))
VERSIONS OF SOFTWARE THE SYSTEM IS USING
spark: 2.1.0
scala: 2.11.8
I am using IntelliJ. The code is running fine but when I am submitting the job to my standalone cluster it is showing the below error:
SPARK-SUBMIT ERROR:
spark-submit --class "SparkSql" C:\Users\blaskar\Desktop\sbt_projects\Spark-Sample1
\out\artifacts\Spark_Sample1_jar\Spark-Sample1.jar
ERROR
java.lang.ClassNotFoundException: SparkSql
at java.net.URLClassLoader.findClass(Unknown Source)
at java.lang.ClassLoader.loadClass(Unknown Source)
at java.lang.ClassLoader.loadClass(Unknown Source)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Unknown Source)
at org.apache.spark.util.Utils$.classForName(Utils.scala:229)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:695)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

HBase TimeoutIOException

I am trying to fetch the HBase data using spark and scala. However, I am getting an error which I am not able to contemplate.
Code
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.HConstants
import org.apache.hadoop.hbase.client.{HBaseAdmin, Result}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.spark.SparkConf
object HBase {
def main(args: Array[String]): Unit = {
val tableName = "posts"
val sc = new SparkContext(new SparkConf().setAppName("HBaseReadWrite").setMaster("local[4]"))
val conf = HBaseConfiguration.create()
conf.set(HConstants.ZOOKEEPER_QUORUM, "localhost")
conf.set(TableInputFormat.INPUT_TABLE, tableName)
val admin = new HBaseAdmin(conf)
if(!admin.isTableAvailable(conf.get(tableName))) {
println("Table doesn't exist")
return
}
val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat],
classOf[ImmutableBytesWritable], classOf[Result])
println(hBaseRDD.map(x => x._2).map(result => Bytes.toString(result.getRow)).collect().take(5).mkString("\n"))
}
}
build.sbt
name := "NLPAnnotationController"
version := "1.0"
scalaVersion := "2.10.5"
resolvers += "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/"
resolvers += "sonatype snapshots" at "https://oss.sonatype.org/content/repositories/snapshots/"
organization := "com.scryAnalytics"
val hadoop_version = "0.98.19-hadoop2"
libraryDependencies ++= Seq(
"org.apache.spark" % "spark-core_2.10" % "1.2.0",
"org.apache.hbase" % "hbase-spark" % "1.2.0-cdh5.7.2",
"org.apache.hbase" % "hbase-client" % hadoop_version excludeAll(ExclusionRule(organization = "javax.servlet", name="javax.servlet-api"), ExclusionRule(organization = "org.mortbay.jetty", name="jetty"), ExclusionRule(organization = "org.mortbay.jetty", name="servlet-api-2.5")),
"org.apache.hbase" % "hbase-common" % hadoop_version excludeAll(ExclusionRule(organization = "javax.servlet", name="javax.servlet-api"), ExclusionRule(organization = "org.mortbay.jetty", name="jetty"), ExclusionRule(organization = "org.mortbay.jetty", name="servlet-api-2.5")),
"org.apache.hbase" % "hbase-server" % hadoop_version excludeAll(ExclusionRule(organization = "javax.servlet", name="javax.servlet-api"), ExclusionRule(organization = "org.mortbay.jetty", name="jetty"), ExclusionRule(organization = "org.mortbay.jetty", name="servlet-api-2.5")),
"org.scala-lang" % "scala-library" % "2.10.5",
"it.nerdammer.bigdata" % "spark-hbase-connector_2.10" % "1.0.3"
)
Error
Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hadoop/hbase/exceptions/TimeoutIOException
at HBase$.main(HBase.scala:20)
at HBase.main(HBase.scala)
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 com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.exceptions.TimeoutIOException
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308)
at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
I have tried changing the versions of the dependencies, still no progress.
Any help would be great. Thanks in advance.
Most probably, the jar file you provide to workers doesn't contain classes from your dependencies. Build "fat" jar using sbt-assembly and upload it to spark.

ZeroMQ word count app gives error when you compile in spark 1.2.1

I'm trying to setup zeromq data stream to spark. Basically I took the ZeroMQWordCount.scala app an tried to recompile it and run it.
I have zeromq 2.1 installed, and spark 1.2.1
here is my scala code:
package org.apache.spark.examples.streaming
import akka.actor.ActorSystem
import akka.actor.actorRef2Scala
import akka.zeromq._
import akka.zeromq.Subscribe
import akka.util.ByteString
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.zeromq._
import scala.language.implicitConversions
import org.apache.spark.SparkConf
object ZmqBenchmark {
def main(args: Array[String]) {
if (args.length < 2) {
System.err.println("Usage: ZmqBenchmark <zeroMQurl> <topic>")
System.exit(1)
}
//StreamingExamples.setStreamingLogLevels()
val Seq(url, topic) = args.toSeq
val sparkConf = new SparkConf().setAppName("ZmqBenchmark")
// Create the context and set the batch size
val ssc = new StreamingContext(sparkConf, Seconds(2))
def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator
// For this stream, a zeroMQ publisher should be running.
val lines = ZeroMQUtils.createStream(ssc, url, Subscribe(topic), bytesToStringIterator _)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()
}
}
and this is my .sbt file for dependencies:
name := "ZmqBenchmark"
version := "1.0"
scalaVersion := "2.10.4"
resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/"
resolvers += "Sonatype (releases)" at "https://oss.sonatype.org/content/repositories/releases/"
libraryDependencies += "org.apache.spark" % "spark-core_2.10" % "1.2.1"
libraryDependencies += "org.apache.spark" %% "spark-streaming" % "1.2.1"
libraryDependencies += "org.apache.spark" % "spark-streaming-zeromq_2.10" % "1.2.1"
libraryDependencies += "com.typesafe.akka" %% "akka-actor" % "2.2.0"
libraryDependencies += "org.zeromq" %% "zeromq-scala-binding" % "0.0.6"
libraryDependencies += "com.typesafe.akka" % "akka-zeromq_2.10.0-RC5" % "2.1.0-RC6"
libraryDependencies += "org.apache.spark" % "spark-examples_2.10" % "1.1.1"
libraryDependencies += "org.spark-project.zeromq" % "zeromq-scala-binding_2.11" % "0.0.7-spark"
The application compiles without any errors using sbt package, however when i run the application with spark submit, i get an error:
zaid#zaid-VirtualBox:~/spark-1.2.1$ ./bin/spark-submit --master local[*] ./zeromqsub/example/target/scala-2.10/zmqbenchmark_2.10-1.0.jar tcp://127.0.0.1:5553 hello
15/03/06 10:21:11 WARN Utils: Your hostname, zaid-VirtualBox resolves to a loopback address: 127.0.1.1; using 192.168.220.175 instead (on interface eth0)
15/03/06 10:21:11 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/spark/streaming/zeromq/ZeroMQUtils$
at ZmqBenchmark$.main(ZmqBenchmark.scala:78)
at ZmqBenchmark.main(ZmqBenchmark.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: org.apache.spark.streaming.zeromq.ZeroMQUtils$
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
... 9 more
Any ideas why this happens? i know the app should work because when i run the same example using the $/run-example $ script and point to the ZeroMQWordCount app from spark, it runs without the exception. My guess is the sbt file is incorrect, what else do I need to have in the sbt file?
Thanks
You are using ZeroMQUtils.createStream but the line
Caused by: java.lang.ClassNotFoundException: org.apache.spark.streaming.zeromq.ZeroMQUtils
shows that the bytecode for ZeroMQUtils was not located. When the spark examples are run, they are run against a jar file (like spark-1.2.1/examples/target/scala-2.10/spark-examples-1.2.1-hadoop1.0.4.jar) including the ZeroMQUtils class. A solution would be to use the --jars flag so spark-submit command can find the bytecode. In your case, this could be something like
spark-submit --jars /opt/spark/spark-1.2.1/examples/target/scala-2.10/spark-examples-1.2.1-hadoop1.0.4.jar--master local[*] ./zeromqsub/example/target/scala-2.10/zmqbenchmark_2.10-1.0.jar tcp://127.0.0.1:5553 hello
assuming that you have installed spark-1.2.1 in /opt/spark.