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Posted to issues@spark.apache.org by "Dongjoon Hyun (JIRA)" <ji...@apache.org> on 2018/05/10 16:29:01 UTC
[jira] [Comment Edited] (SPARK-24204) Verify a write schema in
Json/Orc/ParquetFileFormat
[ https://issues.apache.org/jira/browse/SPARK-24204?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16470634#comment-16470634 ]
Dongjoon Hyun edited comment on SPARK-24204 at 5/10/18 4:28 PM:
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Thank you for pinging me, [~maropu]. Could you make a PR with your patch?
We need a general patch for JSON/Parquet/ORC like CSV.
was (Author: dongjoon):
Thank you for pinging me, [~maropu]. Could you make a PR with your patch?
> Verify a write schema in Json/Orc/ParquetFileFormat
> ---------------------------------------------------
>
> Key: SPARK-24204
> URL: https://issues.apache.org/jira/browse/SPARK-24204
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Takeshi Yamamuro
> Priority: Minor
>
> *SUMMARY*
> - CSV: Raising analysis exception.
> - JSON: dropping columns with null types
> - Parquet/ORC: raising runtime exceptions
> The native orc file format throws an exception with a meaningless message in executor-sides when unsupported types passed;
> {code}
> scala> val rdd = spark.sparkContext.parallelize(List(Row(1, null), Row(2, null)))
> scala> val schema = StructType(StructField("a", IntegerType) :: StructField("b", NullType) :: Nil)
> scala> val df = spark.createDataFrame(rdd, schema)
> scala> df.write.orc("/tmp/orc")
> java.lang.IllegalArgumentException: Can't parse category at 'struct<a:int,b:null^>'
> at org.apache.orc.TypeDescription.parseCategory(TypeDescription.java:223)
> at org.apache.orc.TypeDescription.parseType(TypeDescription.java:332)
> at org.apache.orc.TypeDescription.parseStruct(TypeDescription.java:327)
> at org.apache.orc.TypeDescription.parseType(TypeDescription.java:385)
> at org.apache.orc.TypeDescription.fromString(TypeDescription.java:406)
> at org.apache.spark.sql.execution.datasources.orc.OrcSerializer.org$apache$spark$sql$execution$datasources$orc$OrcSerializer$$createOrcValue(OrcSerializ
> er.scala:226)
> at org.apache.spark.sql.execution.datasources.orc.OrcSerializer.<init>(OrcSerializer.scala:36)
> at org.apache.spark.sql.execution.datasources.orc.OrcOutputWriter.<init>(OrcOutputWriter.scala:36)
> at org.apache.spark.sql.execution.datasources.orc.OrcFileFormat$$anon$1.newInstance(OrcFileFormat.scala:108)
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$SingleDirectoryWriteTask.newOutputWriter(FileFormatWriter.scala:376)
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$SingleDirectoryWriteTask.execute(FileFormatWriter.scala:387)
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply
> (FileFormatWriter.scala:278)
> {code}
> It seems to be better to verify a write schema in a driver side for users along with the CSV fromat;
> https://github.com/apache/spark/blob/76ecd095024a658bf68e5db658e4416565b30c17/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVFileFormat.scala#L65
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