[ https://issues.apache.org/jira/browse/SPARK-34167?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wenchen Fan reassigned SPARK-34167: ----------------------------------- Assignee: Raza Jafri > Reading parquet with Decimal(8,2) written as a Decimal64 blows up > ----------------------------------------------------------------- > > Key: SPARK-34167 > URL: https://issues.apache.org/jira/browse/SPARK-34167 > Project: Spark > Issue Type: Bug > Components: Input/Output > Affects Versions: 3.0.1 > Reporter: Raza Jafri > Assignee: Raza Jafri > Priority: Major > Fix For: 3.2.0 > > Attachments: > part-00000-7fecd321-b247-4f7e-bff5-c2e4d8facaa0-c000.snappy.parquet, > part-00000-940f44f1-f323-4a5e-b828-1e65d87895aa-c000.snappy.parquet > > > When reading a parquet file written with Decimals with precision < 10 as a > 64-bit representation, Spark tries to read it as an INT and fails. I > generated this file using [https://github.com/rapidsai/cudf.] It allowed me > to create a Decimal(8,2) backed by a 64-bit representation (LongDecimal). I > have attached the files that can be read successfully using a 3rd party > parquet reader (I used > [nathonhowell/parquet-tools|https://hub.docker.com/r/nathanhowell/parquet-tools]) > > Steps to reproduce: > Read the attached file that has a single Decimal(8,2) column with 10 values > {code:java} > scala> spark.read.parquet("/tmp/pyspark_tests/936454/PARQUET_DATA").show > ... > Caused by: java.lang.NullPointerException > at > org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.putLong(OnHeapColumnVector.java:327) > at > org.apache.spark.sql.execution.datasources.parquet.VectorizedRleValuesReader.readLongs(VectorizedRleValuesReader.java:370) > at > org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readLongBatch(VectorizedColumnReader.java:514) > at > org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBatch(VectorizedColumnReader.java:256) > at > org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:273) > at > org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:171) > at > org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:173) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93) > at > org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:497) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:756) > at > org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340) > at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:337) > 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:480) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1426) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:483) > 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) > ... > {code} > > > Here are my findings. The *{{VectorizedParquetRecordReader}}* starts to read > in the long value from parquet file correctly because its basing the read on > the > [requestedSchema|https://github.com/apache/spark/blob/e6f019836c099398542b443f7700f79de81da0d5/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java#L150] > which is a *MessageType* and has the underlying data stored correctly as > {{INT64}} where as the *WritableColumnVector* is initialized based on the > [batchSchema|https://github.com/apache/spark/blob/e6f019836c099398542b443f7700f79de81da0d5/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java#L151] > which is coming from {{org.apache.spark.sql.parquet.row.requested_schema}} > that is set by the reader which is a *{{StructType}}* and only has > {{Decimal(__,__)}} in it. > [https://github.com/apache/spark/blob/a44e008de3ae5aecad9e0f1a7af6a1e8b0d97f4e/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java#L224] > > So we can see the problem above is the *WritableColumnVector* is initialized > to store an int array, while the *VectorizedParquetReader* method calls the > *readLongBatch* method which in turn calls the > *VectorizedRleValuesReader.readLongs* which reads the long values and tries > to call *WritableColumnVector.putLong* which will throw a NPE because > *WritableColumnVector* wasn't initialized to store a long array. > In the case where the file has a dictionaryPage a different exception is > thrown > > {code:java} > Caused by: java.lang.UnsupportedOperationException: > org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainLongDictionary > at org.apache.parquet.column.Dictionary.decodeToInt(Dictionary.java:45) > at > org.apache.spark.sql.execution.datasources.parquet.ParquetDictionary.decodeToInt(ParquetDictionary.java:31) > at > org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.getInt(OnHeapColumnVector.java:298) > at > org.apache.spark.sql.execution.vectorized.WritableColumnVector.getDecimal(WritableColumnVector.java:353) > 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$1(SparkPlan.scala:340) > at org.apache.spark.rdd.RDD.$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) > {code} > In this case we have to make sure the correct dictionary is initialized i.e. > *PlainIntDictionary* by setting the correct type in the *ColumnDescriptor* > > Attached are two files, one with Decimal(8,2) ther other with Decimal(1,1) > both written as Decimal backed by INT64. Decimal(1,1) results in a different > exception but same for the same reason > > -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org