Test Spark Scala with Maven Got Error: java.lang.NoClassDefFoundError - scala

Question

I tried to Test Spark Scala on Scala IDE (eclipse) with Maven but keep getting error:
Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hadoop/fs/FSDataInputStream
at org.apache.spark.SparkConf.loadFromSystemProperties(SparkConf.scala:73)
at org.apache.spark.SparkConf.<init>(SparkConf.scala:68)
at org.apache.spark.SparkConf.<init>(SparkConf.scala:55)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:904)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
at com.SimpleApp$.main(SimpleApp.scala:7)
at com.SimpleApp.main(SimpleApp.scala)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.fs.FSDataInputStream
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:349)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 9 more
The program I try is the Quick Start code, from the Spark Documentation:
import org.apache.spark.sql.SparkSession
object SimpleApp {
def main(args: Array[String]) {
val logFile = "YOUR_SPARK_HOME/README.md" // Should be some file on your system
val spark = SparkSession.builder.appName("Simple Application").getOrCreate()
val logData = spark.read.textFile(logFile).cache()
val numAs = logData.filter(line => line.contains("a")).count()
val numBs = logData.filter(line => line.contains("b")).count()
println(s"Lines with a: $numAs, Lines with b: $numBs")
spark.stop()
}
}
I use Spark 2.2.0 and Scala 2.11.7. The pom.xml file is:
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.2.0</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>2.2.0</version>
</dependency>
I followed a solution from another thread: NoClassDefFoundError com.apache.hadoop.fs.FSDataInputStream when execute spark-shell
But it doesn't work for me. The content in my spark-env.sh file is:
# If 'hadoop' binary is on your PATH
export SPARK_DIST_CLASSPATH=$(hadoop classpath)
# With explicit path to 'hadoop' binary
export SPARK_DIST_CLASSPATH=$(/usr/local/hadoop/bin/hadoop classpath)
# Passing a Hadoop configuration directory
export SPARK_DIST_CLASSPATH=$(hadoop --config /usr/local/hadoop/etc/hadoop classpath)
Could anybody help me with this? Appreciate your help.
Devesh's answer solve parts of my problem. However, I got other problems:
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
18/08/17 10:34:03 INFO SparkContext: Running Spark version 2.2.0
18/08/17 10:34:03 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/08/17 10:34:03 WARN Utils: Your hostname, toshiba0 resolves to a loopback address: 127.0.1.1; using 192.168.1.217 instead (on interface wlp2s0)
18/08/17 10:34:03 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
18/08/17 10:34:03 ERROR SparkContext: Error initializing SparkContext.
org.apache.spark.SparkException: A master URL must be set in your configuration
at org.apache.spark.SparkContext.<init>(SparkContext.scala:376)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2509)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:909)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
at com.SimpleApp$.main(SimpleApp.scala:11)
at com.SimpleApp.main(SimpleApp.scala)
18/08/17 10:34:03 INFO SparkContext: Successfully stopped SparkContext
Exception in thread "main" org.apache.spark.SparkException: A master URL must be set in your configuration
at org.apache.spark.SparkContext.<init>(SparkContext.scala:376)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2509)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:909)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
at com.SimpleApp$.main(SimpleApp.scala:11)
at com.SimpleApp.main(SimpleApp.scala)
I don't know why Spark says my loopback address is 127.0.1.1, I checked my configuration: /etc/network/interfaces, it's auto loopback, and I ping 127.0.0.1. It works.
I followed the solution from this link Error initializing SparkContext: A master URL must be set in your configuration
and put the following code, because I use my laptop. It still doesn't work.
val conf = new SparkConf().setMaster("local[2]")
Don't know what happen to my settings. Thank you!

Answer 1

Just add following in maven pom.xml file
<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.7.0</version>
</dependency>
In previous versions of Spark, you had to create a SparkConf and SparkContext to interact with Spark whereas in Spark 2.0 onwards the same effects can be achieved through SparkSession, without explicitly creating SparkConf, SparkContext or SQLContext, as they’re encapsulated within the SparkSession
** sample code snippet:-**
import org.apache.spark.sql.SparkSession
object SimpleApp {
def main(args: Array[String]) {
val logFile = "YOUR_SPARK_HOME/README.md" // some file on system
val spark = SparkSession
.builder
.appName("Simple Application")
.master("local[2]")
.getOrCreate()
val logData = spark.read.textFile(logFile).cache()
val numAs = logData.filter(line => line.contains("a")).count()
val numBs = logData.filter(line => line.contains("b")).count()
println(s"Lines with a: $numAs, Lines with b: $numBs")
}
}