To give a few more details of my environment in case that helps you reproduce:
* I'm running spark 1.0.1 downloaded as a tar ball, not built myself * I'm running in stand alone mode, with 1 master and 1 worker, both on the same machine (though the same error occurs with two workers on two machines) * I'm using spark-core and spark-sql 1.0.1 pulled via maven Here's my built.sbt: name := "spark-test" version := "1.0" scalaVersion := "2.10.4" resolvers += "Akka Repository" at "http://repo.akka.io/releases/" resolvers += "Cloudera Repository" at " https://repository.cloudera.com/artifactory/cloudera-repos/" libraryDependencies += "org.apache.spark" %% "spark-sql" % "1.0.1" % "provided" libraryDependencies += "org.apache.spark" %% "spark-core" % "1.0.1" % "provided" On Tue, Jul 15, 2014 at 12:21 PM, Zongheng Yang <zonghen...@gmail.com> wrote: > FWIW, I am unable to reproduce this using the example program locally. > > On Tue, Jul 15, 2014 at 11:56 AM, Keith Simmons <keith.simm...@gmail.com> > wrote: > > Nope. All of them are registered from the driver program. > > > > However, I think we've found the culprit. If the join column between two > > tables is not in the same column position in both tables, it triggers > what > > appears to be a bug. For example, this program fails: > > > > import org.apache.spark.SparkContext._ > > import org.apache.spark.SparkContext > > import org.apache.spark.SparkConf > > import org.apache.spark.sql.SQLContext > > import org.apache.spark.sql.SchemaRDD > > import org.apache.spark.sql.catalyst.types._ > > > > case class Record(value: String, key: Int) > > case class Record2(key: Int, value: String) > > > > object TestJob { > > > > def main(args: Array[String]) { > > run() > > } > > > > private def run() { > > val sparkConf = new SparkConf() > > sparkConf.setAppName("TestJob") > > sparkConf.set("spark.cores.max", "8") > > sparkConf.set("spark.storage.memoryFraction", "0.1") > > sparkConf.set("spark.shuffle.memoryFracton", "0.2") > > sparkConf.set("spark.executor.memory", "2g") > > > sparkConf.setJars(List("target/scala-2.10/spark-test-assembly-1.0.jar")) > > sparkConf.setMaster(s"spark://dev1.dev.pulse.io:7077") > > sparkConf.setSparkHome("/home/pulseio/spark/current") > > val sc = new SparkContext(sparkConf) > > > > val sqlContext = new org.apache.spark.sql.SQLContext(sc) > > import sqlContext._ > > > > val rdd1 = sc.parallelize((1 to 100).map(i => Record(s"val_$i", i))) > > val rdd2 = sc.parallelize((1 to 100).map(i => Record2(i, s"rdd_$i"))) > > rdd1.registerAsTable("rdd1") > > rdd2.registerAsTable("rdd2") > > > > sql("SELECT * FROM rdd1").collect.foreach { row => println(row) } > > > > sql("SELECT rdd1.key, rdd1.value, rdd2.value FROM rdd1 join rdd2 on > > rdd1.key = rdd2.key order by rdd1.key").collect.foreach { row => > > println(row) } > > > > sc.stop() > > } > > > > } > > > > If you change the definition of Record and Record2 to the following, it > > succeeds: > > > > case class Record(key: Int, value: String) > > case class Record2(key: Int, value: String) > > > > as does: > > > > case class Record(value: String, key: Int) > > case class Record2(value: String, key: Int) > > > > Let me know if you need anymore details. > > > > > > On Tue, Jul 15, 2014 at 11:14 AM, Michael Armbrust < > mich...@databricks.com> > > wrote: > >> > >> Are you registering multiple RDDs of case classes as tables > concurrently? > >> You are possibly hitting SPARK-2178 which is caused by SI-6240. > >> > >> > >> On Tue, Jul 15, 2014 at 10:49 AM, Keith Simmons < > keith.simm...@gmail.com> > >> wrote: > >>> > >>> HI folks, > >>> > >>> I'm running into the following error when trying to perform a join in > my > >>> code: > >>> > >>> java.lang.NoClassDefFoundError: Could not initialize class > >>> org.apache.spark.sql.catalyst.types.LongType$ > >>> > >>> I see similar errors for StringType$ and also: > >>> > >>> scala.reflect.runtime.ReflectError: value apache is not a package. > >>> > >>> Strangely, if I just work with a single table, everything is fine. I > can > >>> iterate through the records in both tables and print them out without a > >>> problem. > >>> > >>> Furthermore, this code worked without an exception in Spark 1.0.0 > >>> (thought the join caused some field corruption, possibly related to > >>> https://issues.apache.org/jira/browse/SPARK-1994). The data is > coming from > >>> a custom protocol buffer based format on hdfs that is being mapped > into the > >>> individual record types without a problem. > >>> > >>> The immediate cause seems to be a task trying to deserialize one or > more > >>> SQL case classes before loading the spark uber jar, but I have no idea > why > >>> this is happening, or why it only happens when I do a join. Ideas? > >>> > >>> Keith > >>> > >>> P.S. If it's relevant, we're using the Kryo serializer. > >>> > >>> > >> > > >