I have some Parquet files written using AvroParquetWriter (from Kafka Connect S3 connector).
One of the columns in the file aseg_lat has a schema DECIMAL(9, 7).
I can read that column perfectly fine using both PyArrow and PrestoSQL.
Trying to read it via Spark 3.0.0 running on AWS EMR I get the following error:
scala> var df2 = df.select("aseg_lat")
df2: org.apache.spark.sql.DataFrame = [aseg_lat: decimal(9,7)]
scala> df2.show()
20/08/25 12:03:35 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.
20/08/25 12:04:35 WARN TaskSetManager: Lost task 0.0 in stage 1.0 (TID 448, ip-172-30-2-50.ec2.internal, executor 8): org.apache.spark.sql.execution.QueryExecutionException: Parquet column cannot be converted in file <redacted>. Column: [aseg_lat], Expected: decimal(9,7), Found: BINARY
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:213)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:122)
at org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:559)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:345)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.sql.execution.datasources.SchemaColumnConvertNotSupportedException
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.constructConvertNotSupportedException(VectorizedColumnReader.java:298)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBinaryBatch(VectorizedColumnReader.java:603)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBatch(VectorizedColumnReader.java:268)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:285)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:183)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:122)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:207)
... 20 more
20/08/25 12:04:38 ERROR TaskSetManager: Task 0 in stage 1.0 failed 4 times; aborting job
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 451, ip-172-30-2-50.ec2.internal, executor 5): org.apache.spark.sql.execution.QueryExecutionException: Parquet column cannot be converted in file <redacted>. Column: [aseg_lat], Expected: decimal(9,7), Found: BINARY
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:213)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:122)
at org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:559)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:345)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.sql.execution.datasources.SchemaColumnConvertNotSupportedException
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.constructConvertNotSupportedException(VectorizedColumnReader.java:298)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBinaryBatch(VectorizedColumnReader.java:603)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBatch(VectorizedColumnReader.java:268)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:285)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:183)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:122)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:207)
... 20 more
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2175)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2124)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2123)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2123)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:990)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:990)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:990)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2355)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2304)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2293)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:792)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2093)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2133)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3664)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2737)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3655)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:106)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:207)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:88)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3653)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2737)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2944)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:338)
at org.apache.spark.sql.Dataset.show(Dataset.scala:864)
at org.apache.spark.sql.Dataset.show(Dataset.scala:823)
at org.apache.spark.sql.Dataset.show(Dataset.scala:832)
... 47 elided
Caused by: org.apache.spark.sql.execution.QueryExecutionException: Parquet column cannot be converted in file <redacted>. Column: [aseg_lat], Expected: decimal(9,7), Found: BINARY
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:213)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:122)
at org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:559)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_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$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:345)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.sql.execution.datasources.SchemaColumnConvertNotSupportedException
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.constructConvertNotSupportedException(VectorizedColumnReader.java:298)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBinaryBatch(VectorizedColumnReader.java:603)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBatch(VectorizedColumnReader.java:268)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:285)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:183)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:122)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:207)
... 20 more
I also tried using the Hive SerDe by setting spark.sql.hive.convertMetastoreParquet to false. That allows me to read the DECIMAL column but starts failing for other columns like Timestamps.
20/08/25 12:28:34 WARN DAGScheduler: Broadcasting large task binary with size 8.7 MiB
20/08/25 12:28:37 WARN TaskSetManager: Lost task 0.0 in stage 4.0 (TID 7, ip-172-30-2-50.ec2.internal, executor 6): java.lang.ClassCastException: org.apache.hadoop.io.LongWritable cannot be cast to org.apache.hadoop.hive.serde2.io.TimestampWritable
at org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableTimestampObjectInspector.getPrimitiveJavaObject(WritableTimestampObjectInspector.java:39)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$14(TableReader.scala:468)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$14$adapted(TableReader.scala:467)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$18(TableReader.scala:493)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:346)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
20/08/25 12:28:39 ERROR TaskSetManager: Task 0 in stage 4.0 failed 4 times; aborting job
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 4 times, most recent failure: Lost task 0.3 in stage 4.0 (TID 10, ip-172-30-2-50.ec2.internal, executor 6): java.lang.ClassCastException: org.apache.hadoop.io.LongWritable cannot be cast to org.apache.hadoop.hive.serde2.io.TimestampWritable
at org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableTimestampObjectInspector.getPrimitiveJavaObject(WritableTimestampObjectInspector.java:39)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$14(TableReader.scala:468)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$14$adapted(TableReader.scala:467)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$18(TableReader.scala:493)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:346)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
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)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2175)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2124)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2123)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2123)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:990)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:990)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:990)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2355)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2304)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2293)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:792)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2093)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2133)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3664)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2737)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3655)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:106)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:207)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:88)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3653)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2737)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2944)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:338)
at org.apache.spark.sql.Dataset.show(Dataset.scala:864)
at org.apache.spark.sql.Dataset.show(Dataset.scala:823)
at org.apache.spark.sql.Dataset.show(Dataset.scala:832)
... 47 elided
Caused by: java.lang.ClassCastException: org.apache.hadoop.io.LongWritable cannot be cast to org.apache.hadoop.hive.serde2.io.TimestampWritable
at org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableTimestampObjectInspector.getPrimitiveJavaObject(WritableTimestampObjectInspector.java:39)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$14(TableReader.scala:468)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$14$adapted(TableReader.scala:467)
at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$18(TableReader.scala:493)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:346)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Another observation was that changing the DECIMAL(9, 7) to DECIMAL(x, 7) (where x > 19) allows Spark to read the column but that is not a workable solution for me since I have multiple TBs of historical data written with DECIMAL(9, 7) which I'll need to re-process.
How can I read DECIMAL written by AvroParquetWriter from Spark?
Disabling Spark's vectorized parquet reader allows Spark to read such columns without issue. This was verified on both Spark 3.0.0 and Spark 2.4.4.
i.e. Set spark.sql.parquet.enableVectorizedReader to false in either the SparkSession or in spark-defaults.
Thanks to @mazaneicha for suggesting to try out this option.