Spark Unit Tests error on Window expression - apache-spark

Question

I'm currently using unit tests with Spark under IntelliJ, DataFrameSuiteBase
and SharedSparkContext.
I'm under Windows and all is perfect as long as the Spark operations don't
use the org.apache.spark.sql.expressions.Window objet.
For instance :
val accu = anosGroupees.select($"col1", $"avg(col2)",
sum($"avg(col2)").over(Window.orderBy("col3").rowsBetween(Long.MinValue,
-1)).as("mycolumn"))
The error is :
Task not serializable
org.apache.spark.SparkException: Task not serializable
at
org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:304)
at
org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:294)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2055)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:707)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:706)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:706)
at org.apache.spark.sql.execution.Window.doExecute(Window.scala:245)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at
org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at
org.apache.spark.sql.execution.Coalesce.doExecute(basicOperators.scala:250)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at
org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at
org.apache.spark.sql.execution.joins.BroadcastHashJoin$$anonfun$broadcastFuture$1$$anonfun$apply$1.apply(BroadcastHashJoin.scala:82)
at
org.apache.spark.sql.execution.joins.BroadcastHashJoin$$anonfun$broadcastFuture$1$$anonfun$apply$1.apply(BroadcastHashJoin.scala:79)
at
org.apache.spark.sql.execution.SQLExecution$.withExecutionId(SQLExecution.scala:100)
at
org.apache.spark.sql.execution.joins.BroadcastHashJoin$$anonfun$broadcastFuture$1.apply(BroadcastHashJoin.scala:79)
at
org.apache.spark.sql.execution.joins.BroadcastHashJoin$$anonfun$broadcastFuture$1.apply(BroadcastHashJoin.scala:79)
at
scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
at
scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.ClassCastException:
org.apache.hive.com.esotericsoftware.kryo.Kryo cannot be cast to
com.esotericsoftware.kryo.Kryo
at
org.apache.spark.sql.hive.HiveShim$HiveFunctionWrapper.serializePlan(HiveShim.scala:155)
at
org.apache.spark.sql.hive.HiveShim$HiveFunctionWrapper.writeExternal(HiveShim.scala:168)
at
java.io.ObjectOutputStream.writeExternalData(ObjectOutputStream.java:1459)
at
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1430)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at
java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at
java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at
java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1378)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1174)
at
java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at
java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at
java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
at
org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
at
org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:101)
at
org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:301)
... 39 more
What do you think about that ? Is there any classpath problem ?
If I run this code on a yarn cluster, there's no problem. It's only occured
on Windows under a maven project. The dependencies in the pom are :
<dependencies>
<dependency>
<groupId>ai.h2o</groupId>
<artifactId>sparkling-water-core_2.10</artifactId>
<version>1.5.10</version>
</dependency>
<dependency>
<groupId>ai.h2o</groupId>
<artifactId>h2o-scala_2.10</artifactId>
<version>3.8.0.3</version>
</dependency>
<dependency>
<groupId>ai.h2o</groupId>
<artifactId>h2o-app</artifactId>
<version>3.8.0.3</version>
</dependency>
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>15.0</version>
</dependency>
<dependency>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
</dependency>
<dependency>
<groupId>org.scalatest</groupId>
<artifactId>scalatest_2.10</artifactId>
<version>2.2.6</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-simple</artifactId>
<version>1.7.14</version>
</dependency>
<dependency>
<groupId>com.holdenkarau</groupId>
<artifactId>spark-testing-base_2.10</artifactId>
<version>1.5.2_0.3.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>1.6.0</version>
</dependency>
<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch-hadoop</artifactId>
<version>2.1.2</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-mllib_2.10</artifactId>
<version>1.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.10</artifactId>
<version>1.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive_2.10</artifactId>
<version>1.6.0</version>
</dependency>
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-csv_2.10</artifactId>
<version>1.3.0</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-csv</artifactId>
<version>1.2</version>
</dependency>
<dependency>
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>2.4</version>
</dependency>
<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch-spark_2.10</artifactId>
<version>2.1.2</version>
</dependency>
</dependencies>
Regards,
Brice

Answer 1

The root cause is a CLassCastException: org.apache.hive.com.esotericsoftware.kryo.Kryo cannot be cast to
com.esotericsoftware.kryo.Kryo.
I have encountered the same problem in Java.
Hive-Exec jar is packaged with a Kryo implementation (shaded). In the Spark-Hive jar HiveShim class links to another Kryo class. A workaround in the UnitTest is to reassign the runtimeSerializationKryo:
// For JUnit4 you can use BeforeClass
#BeforeClass
public static void otherKryoImpl() {
// com.esotericsoftware.kryo.Kryo << Not the: ORG.APACHE.HIVE Shaded version.
// In the class HiveShim (spark-hive_2.10) the com.esotericsoftware.kryo.Kryo is expected
// org.apache.hadoop.hive.ql.exec.Utilities
Utilities.runtimeSerializationKryo = new ThreadLocal() { // Not typed to avoid compiler warning.
#Override
protected synchronized Kryo initialValue() {
Kryo kryo = new Kryo();
// copy stuff from original implementation...
return kryo;
};
};
}

Answer 2

i have encountered the same problem.
and i fix it :-)
it seams a classpath problem ;
just put spark lib to the first place of your extra driver(executor) path