java.lang.NoClassDefFoundError: org/apache/spark/sq/sources/v2/StreamingWriteSupportProvider trying to pull from kafka topic in scala - scala

Question

I'm using a spark-shell instance to test the pulling of data from a client's kafka source. To launch the instance I am using the command spark-shell --jars spark-sql-kafka-0-10_2.11-2.5.0-palantir.8.jar, kafka_2.12-2.5.0.jar, kafka-clients-2.5.0.jar (all jars are present in the woring dir).
However, when I run the command val df = spark.read.format("kafka")........... after a few seconds it crashes with the below:
java.lang.NoClassDefFoundError: org/apache/spark/sql/sources/v2/StreamingWriteSupportProvider
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:760)
at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
at java.net.URLClassLoader.defineClass(URLClassLoader.java:455)
at java.net.URLClassLoader.access$100(URLClassLoader.java:73)
at java.net.URLClassLoader$1.run(URLClassLoader.java:367)
at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:360)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:344)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:370)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
at scala.collection.convert.Wrappers$JIteratorWrapper.next(Wrappers.scala:43)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.filterImpl(TraversableLike.scala:247)
at scala.collection.TraversableLike$class.filter(TraversableLike.scala:259)
at scala.collection.AbstractTraversable.filter(Traversable.scala:104)
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:533)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass$lzycompute(DataSource.scala:89)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass(DataSource.scala:89)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:304)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:146)
... 48 elided
Caused by: java.lang.ClassNotFoundException: org.apache.spark.sql.sources.v2.StreamingWriteSupportProvider
at java.net.URLClassLoader$1.run(URLClassLoader.java:372)
at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:360)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 79 more
HOWEVER - if I change the order of the jars in the spark-shell command to spark-shell --jars kafka_2.12-2.5.0.jar, kafka-clients-2.5.0.jar, spark-sql-kafka-0-10_2.11-2.5.0-palantir.8.jar, instead crashes with:
java.lang.NoClassDefFoundError: org/apache/kafka/common/serialization/ByteArrayDeserializer
at org.apache.spark.sql.kafka010.KafkaSourceProvider$.<init>(KafkaSourceProvider.scala:376)
at org.apache.spark.sql.kafka010.KafkaSourceProvider$.<clinit>(KafkaSourceProvider.scala)
at org.apache.spark.sql.kafka010.KafkaSourceProvider.validateBatchOptions(KafkaSourceProvider.scala:330)
at org.apache.spark.sql.kafka010.KafkaSourceProvider.createRelation(KafkaSourceProvider.scala:113)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:309)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:146)
... 48 elided
Caused by: java.lang.ClassNotFoundException: org.apache.kafka.common.serialization.ByteArrayDeserializer
at java.net.URLClassLoader$1.run(URLClassLoader.java:372)
at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:360)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 55 more
I am developing behind a very strict proxy managed by our client and am unable to user --packages instead, and I am at a bit of a loss here, am I unable to load all 3 dependencies at the launch of the shell? Am I missing another step somewhere?

Answer 1

In the Structured Streaming + Kafka Integration Guide it says:
For experimenting on spark-shell, you need to add this above library and its dependencies too when invoking spark-shell.
The library you are using seems to be customized and not publicly available in the maven central repository. That means, I can not look into its dependencies.
However, looking at the latest stable version 2.4.5 the dependencies according to maven central repository is kafka-clients version 2.0.0.

Answer 2

You are trying to import multiple scala versions 2.11 & 2.12 of different libraries.
Please add same version of scala libraries & check below how to import into spark-shell.
spark-shell --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.4.5,org.apache.kafka:kafka_2.11:2.4.1,org.apache.kafka:kafka-clients:2.4.1

Answer 3

One occasionally disruptive issue is dealing with dependency conflicts in cases where a user application and Spark itself both depend on the same library. This comes up relatively rarely, but when it does, it can be vexing for users. Typically, this will manifest itself when a NoSuchMethodError, a ClassNotFoundException, or some other JVM exception related to class loading is thrown during the execution of a Spark job. There are two solutions to this problem. The first is to modify your application to depend on the same version of the third-party library that Spark does. The second is to modify the packaging of your application using a procedure that is often called “shading.” The Maven build tool supports shading through advanced configuration of the plug-in shown in Example 7-5 (in fact, the shading capability is why the plugin is named maven-shade-plugin). Shading allows you to make a second copy of the conflicting package under a different namespace and rewrites your application’s code to use the renamed version. This somewhat brute-force technique is quite effective at resolving runtime dependency conflicts. For specific instructions on how to shade dependencies, see the documentation for your build tool.
I would try to know the scala version of the spark-shell because, it can be a scala version issue
scala> util.Properties.versionString
res3: String = version 2.11.8
if not, then check what spark version you are using and third-party library versions you are using as dependencies because, I am sure there is newest or oldest that your spark version doesn't support.
I hope it helps.