Hi Nasrulla, Not sure what your new code is doing, but the symptom looks like you're creating a new data source that wraps around the builtin Parquet data source?
The problem here is, whole-stage codegen generated code for row-based input, but the actual input is columnar. In other words, in your setup, the vectorized Parquet reader is enabled (which produces columnar output), and you probably wrote a new operator that didn't properly interact with the columnar support, so that WSCG thought it should generate row-based code instead of columnar code. Hope it helps, Kris -- Kris Mok Software Engineer Databricks Inc. kris....@databricks.com databricks.com <http://databricks.com/> On Thu, Jun 11, 2020 at 5:41 PM Nasrulla Khan Haris <nasrulla.k...@microsoft.com.invalid> wrote: > HI Spark developer, > > > > I have a new baseRelation which Initializes ParquetFileFormat object and > when reading the data I am encountering Cast Exception below, however when > I disable codegen support with config “spark.sql.codegen.wholeStage"= > false, I do not encounter this exception. > > > > > > 20/06/11 17:35:39 INFO FileScanRDD: Reading File path: file:///D:/ > jvm/src/test/scala/resources/pems_sorted/station=402260/part-r-00245-ddaee723-f3f6-4f25-a34b-3312172aa6d7.snappy.parquet, > range: 0-50936, partition values: [402260] > > 20/06/11 17:35:39 INFO CodecPool: Got brand-new decompressor [.snappy] > > 20/06/11 17:35:40 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID > 0) > > java.lang.ClassCastException: org.apache.spark.sql.vectorized.ColumnarBatch > cannot be cast to org.apache.spark.sql.catalyst.InternalRow > > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.agg_doAggregateWithKeys_0$(Unknown > Source) > > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) > > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636) > > at > scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) > > at > org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125) > > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) > > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55) > > at org.apache.spark.scheduler.Task.run(Task.scala:123) > > at > org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) > > at > org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) > > at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) > > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > > at java.lang.Thread.run(Thread.java:748) > > > > > > Appreciate your inputs. > > > > Thanks, > > NKH >
