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Posted to issues@spark.apache.org by "Wenchen Fan (Jira)" <ji...@apache.org> on 2021/02/22 04:52:01 UTC
[jira] [Assigned] (SPARK-34167) Reading parquet with Decimal(8,2)
written as a Decimal64 blows up
[ 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
>
>
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