[jira] [Commented] (SPARK-44988) Parquet INT64 (TIMESTAMP(NANOS,false)) throwing Illegal Parquet type

2023-10-09 Thread Miles Granger (Jira) 


[ 
https://issues.apache.org/jira/browse/SPARK-44988?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17773215#comment-17773215
 ] 

Miles Granger commented on SPARK-44988:
---

[~fanjia]that "worked" for me, but then of course need to cast the resulting 
bigint to a timestamp, which I feel is error prone. Would be nice if spark 
supported timestamp[ns] though.

> Parquet INT64 (TIMESTAMP(NANOS,false)) throwing Illegal Parquet type
> 
>
> Key: SPARK-44988
> URL: https://issues.apache.org/jira/browse/SPARK-44988
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 3.4.0, 3.4.1
>Reporter: Flavio Odas
>Priority: Critical
>
> This bug seems similar to https://issues.apache.org/jira/browse/SPARK-40819, 
> except that it's a problem with INT64 (TIMESTAMP(NANOS,false)), instead of 
> INT64 (TIMESTAMP(NANOS,true)).
> The error happens whenever I'm trying to read:
> {code:java}
> org.apache.spark.sql.AnalysisException: Illegal Parquet type: INT64 
> (TIMESTAMP(NANOS,false)).
>   at 
> org.apache.spark.sql.errors.QueryCompilationErrors$.illegalParquetTypeError(QueryCompilationErrors.scala:1762)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.illegalType$1(ParquetSchemaConverter.scala:206)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertPrimitiveField$2(ParquetSchemaConverter.scala:283)
>   at scala.Option.getOrElse(Option.scala:189)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertPrimitiveField(ParquetSchemaConverter.scala:224)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertField(ParquetSchemaConverter.scala:187)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertInternal$3(ParquetSchemaConverter.scala:147)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertInternal$3$adapted(ParquetSchemaConverter.scala:117)
>   at 
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
>   at scala.collection.immutable.Range.foreach(Range.scala:158)
>   at scala.collection.TraversableLike.map(TraversableLike.scala:286)
>   at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:108)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertInternal(ParquetSchemaConverter.scala:117)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convert(ParquetSchemaConverter.scala:87)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$readSchemaFromFooter$2(ParquetFileFormat.scala:493)
>   at scala.Option.getOrElse(Option.scala:189)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.readSchemaFromFooter(ParquetFileFormat.scala:493)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$mergeSchemasInParallel$2(ParquetFileFormat.scala:473)
>   at scala.collection.immutable.Stream.map(Stream.scala:418)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$mergeSchemasInParallel$1(ParquetFileFormat.scala:473)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$mergeSchemasInParallel$1$adapted(ParquetFileFormat.scala:464)
>   at 
> org.apache.spark.sql.execution.datasources.SchemaMergeUtils$.$anonfun$mergeSchemasInParallel$2(SchemaMergeUtils.scala:79)
>   at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:853)
>   at 
> org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:853)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
>   at 
> org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
>   at org.apache.spark.scheduler.Task.run(Task.scala:139)
>   at 
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
>   at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
>   at 
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) {code}



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[jira] [Commented] (SPARK-44988) Parquet INT64 (TIMESTAMP(NANOS,false)) throwing Illegal Parquet type

2023-08-31 Thread Jia Fan (Jira) 


[ 
https://issues.apache.org/jira/browse/SPARK-44988?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17760931#comment-17760931
 ] 

Jia Fan commented on SPARK-44988:
-

Have you tried setting spark.sql.legacy.parquet.nanosAsLong to true？

> Parquet INT64 (TIMESTAMP(NANOS,false)) throwing Illegal Parquet type
> 
>
> Key: SPARK-44988
> URL: https://issues.apache.org/jira/browse/SPARK-44988
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 3.4.0, 3.4.1
>Reporter: Flavio Odas
>Priority: Critical
>
> This bug seems similar to https://issues.apache.org/jira/browse/SPARK-40819, 
> except that it's a problem with INT64 (TIMESTAMP(NANOS,false)), instead of 
> INT64 (TIMESTAMP(NANOS,true)).
> The error happens whenever I'm trying to read:
> {code:java}
> org.apache.spark.sql.AnalysisException: Illegal Parquet type: INT64 
> (TIMESTAMP(NANOS,false)).
>   at 
> org.apache.spark.sql.errors.QueryCompilationErrors$.illegalParquetTypeError(QueryCompilationErrors.scala:1762)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.illegalType$1(ParquetSchemaConverter.scala:206)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertPrimitiveField$2(ParquetSchemaConverter.scala:283)
>   at scala.Option.getOrElse(Option.scala:189)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertPrimitiveField(ParquetSchemaConverter.scala:224)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertField(ParquetSchemaConverter.scala:187)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertInternal$3(ParquetSchemaConverter.scala:147)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertInternal$3$adapted(ParquetSchemaConverter.scala:117)
>   at 
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
>   at scala.collection.immutable.Range.foreach(Range.scala:158)
>   at scala.collection.TraversableLike.map(TraversableLike.scala:286)
>   at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:108)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertInternal(ParquetSchemaConverter.scala:117)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convert(ParquetSchemaConverter.scala:87)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$readSchemaFromFooter$2(ParquetFileFormat.scala:493)
>   at scala.Option.getOrElse(Option.scala:189)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.readSchemaFromFooter(ParquetFileFormat.scala:493)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$mergeSchemasInParallel$2(ParquetFileFormat.scala:473)
>   at scala.collection.immutable.Stream.map(Stream.scala:418)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$mergeSchemasInParallel$1(ParquetFileFormat.scala:473)
>   at 
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$mergeSchemasInParallel$1$adapted(ParquetFileFormat.scala:464)
>   at 
> org.apache.spark.sql.execution.datasources.SchemaMergeUtils$.$anonfun$mergeSchemasInParallel$2(SchemaMergeUtils.scala:79)
>   at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:853)
>   at 
> org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:853)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
>   at 
> org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
>   at org.apache.spark.scheduler.Task.run(Task.scala:139)
>   at 
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
>   at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
>   at 
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) {code}



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