Thanks for your reply, Andy. Yes, that is what I concluded based on the Stack trace. The problem is stemming from Java implementation of generics, but I thought this will go away if you compiled against Java 1.8, which solves the issues of proper generic implementation.
Any ideas? Also, are you saying that in order for my example to work, I would need to move to Scala and have the UDT implemented in Scala? On Wed, Jan 20, 2016 at 10:27 AM, Andy Grove <andy.gr...@agildata.com> wrote: > Catalyst is expecting a class that implements scala.Row or scala.Product > and is instead finding a Java class. I've run into this issue a number of > times. Dataframe doesn't work so well with Java. Here's a blog post with > more information on this: > > http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/ > > > Thanks, > > Andy. > > -- > > Andy Grove > Chief Architect > AgilData - Simple Streaming SQL that Scales > www.agildata.com > > > On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <raghuki...@gmail.com> wrote: > >> Hi, >> >> I created a custom UserDefinedType in Java as follows: >> >> SQLPoint = new UserDefinedType<JavaPoint>() { >> //overriding serialize, deserialize, sqlType, userClass functions here >> } >> >> When creating a dataframe, I am following the manual mapping, I have a >> constructor for JavaPoint - JavaPoint(double x, double y) and a Customer >> record as follows: >> >> public class CustomerRecord { >> private int id; >> private String name; >> private Object location; >> >> //setters and getters follow here >> } >> >> Following the example in Spark source, when I create a RDD as follows: >> >> sc.textFile(inputFileName).map(new Function<String, CustomerRecord>() { >> //call method >> CustomerRecord rec = new CustomerRecord(); >> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y))); >> }); >> >> This results in a MatchError. The stack trace is as follows: >> >> scala.MatchError: [B@45aa3dd5 (of class [B) >> at >> >> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255) >> at >> >> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250) >> at >> >> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102) >> at >> >> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401) >> at >> >> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358) >> at >> >> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358) >> at >> >> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >> at >> >> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >> at >> >> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) >> at >> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) >> at >> scala.collection.TraversableLike$class.map(TraversableLike.scala:244) >> at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108) >> at >> >> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358) >> at >> >> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356) >> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312) >> at scala.collection.Iterator$class.foreach(Iterator.scala:727) >> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) >> at >> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) >> at >> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) >> at >> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) >> at scala.collection.TraversableOnce$class.to >> (TraversableOnce.scala:273) >> at scala.collection.AbstractIterator.to(Iterator.scala:1157) >> at >> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) >> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) >> at >> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) >> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) >> at >> >> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212) >> at >> >> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212) >> at >> >> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858) >> at >> >> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858) >> at >> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) >> at org.apache.spark.scheduler.Task.run(Task.scala:89) >> at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) >> 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) >> >> >> >> -- >> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html >> Sent from the Apache Spark User List mailing list archive at Nabble.com. >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> For additional commands, e-mail: user-h...@spark.apache.org >> >> >
