[jira] [Updated] (SPARK-15474) ORC data source fails to write and read back empty dataframe

2017-10-10 Thread Dongjoon Hyun (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-15474?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Dongjoon Hyun updated SPARK-15474:
--
Affects Version/s: 2.2.0

>  ORC data source fails to write and read back empty dataframe
> -
>
> Key: SPARK-15474
> URL: https://issues.apache.org/jira/browse/SPARK-15474
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0, 2.1.1, 2.2.0
>Reporter: Hyukjin Kwon
>
> Currently ORC data source fails to write and read empty data.
> The code below:
> {code}
> val emptyDf = spark.range(10).limit(0)
> emptyDf.write
>   .format("orc")
>   .save(path.getCanonicalPath)
> val copyEmptyDf = spark.read
>   .format("orc")
>   .load(path.getCanonicalPath)
> copyEmptyDf.show()
> {code}
> throws an exception below:
> {code}
> Unable to infer schema for ORC at 
> /private/var/folders/9j/gf_c342d7d150mwrxvkqnc18gn/T/spark-5b7aa45b-a37d-43e9-975e-a15b36b370da.
>  It must be specified manually;
> org.apache.spark.sql.AnalysisException: Unable to infer schema for ORC at 
> /private/var/folders/9j/gf_c342d7d150mwrxvkqnc18gn/T/spark-5b7aa45b-a37d-43e9-975e-a15b36b370da.
>  It must be specified manually;
>   at 
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$16.apply(DataSource.scala:352)
>   at 
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$16.apply(DataSource.scala:352)
>   at scala.Option.getOrElse(Option.scala:121)
>   at 
> org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:351)
>   at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:130)
>   at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:140)
>   at 
> org.apache.spark.sql.sources.HadoopFsRelationTest$$anonfun$32$$anonfun$apply$mcV$sp$47.apply(HadoopFsRelationTest.scala:892)
>   at 
> org.apache.spark.sql.sources.HadoopFsRelationTest$$anonfun$32$$anonfun$apply$mcV$sp$47.apply(HadoopFsRelationTest.scala:884)
>   at 
> org.apache.spark.sql.test.SQLTestUtils$class.withTempPath(SQLTestUtils.scala:114)
> {code}
> Note that this is a different case with the data below
> {code}
> val emptyDf = spark.createDataFrame(spark.sparkContext.emptyRDD[Row], schema)
> {code}
> In this case, any writer is not initialised and created. (no calls of 
> {{WriterContainer.writeRows()}}.
> For Parquet and JSON, it works but ORC does not.



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[jira] [Updated] (SPARK-15474) ORC data source fails to write and read back empty dataframe

2017-10-10 Thread Dongjoon Hyun (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-15474?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Dongjoon Hyun updated SPARK-15474:
--
Affects Version/s: 2.1.1

>  ORC data source fails to write and read back empty dataframe
> -
>
> Key: SPARK-15474
> URL: https://issues.apache.org/jira/browse/SPARK-15474
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0, 2.1.1
>Reporter: Hyukjin Kwon
>
> Currently ORC data source fails to write and read empty data.
> The code below:
> {code}
> val emptyDf = spark.range(10).limit(0)
> emptyDf.write
>   .format("orc")
>   .save(path.getCanonicalPath)
> val copyEmptyDf = spark.read
>   .format("orc")
>   .load(path.getCanonicalPath)
> copyEmptyDf.show()
> {code}
> throws an exception below:
> {code}
> Unable to infer schema for ORC at 
> /private/var/folders/9j/gf_c342d7d150mwrxvkqnc18gn/T/spark-5b7aa45b-a37d-43e9-975e-a15b36b370da.
>  It must be specified manually;
> org.apache.spark.sql.AnalysisException: Unable to infer schema for ORC at 
> /private/var/folders/9j/gf_c342d7d150mwrxvkqnc18gn/T/spark-5b7aa45b-a37d-43e9-975e-a15b36b370da.
>  It must be specified manually;
>   at 
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$16.apply(DataSource.scala:352)
>   at 
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$16.apply(DataSource.scala:352)
>   at scala.Option.getOrElse(Option.scala:121)
>   at 
> org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:351)
>   at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:130)
>   at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:140)
>   at 
> org.apache.spark.sql.sources.HadoopFsRelationTest$$anonfun$32$$anonfun$apply$mcV$sp$47.apply(HadoopFsRelationTest.scala:892)
>   at 
> org.apache.spark.sql.sources.HadoopFsRelationTest$$anonfun$32$$anonfun$apply$mcV$sp$47.apply(HadoopFsRelationTest.scala:884)
>   at 
> org.apache.spark.sql.test.SQLTestUtils$class.withTempPath(SQLTestUtils.scala:114)
> {code}
> Note that this is a different case with the data below
> {code}
> val emptyDf = spark.createDataFrame(spark.sparkContext.emptyRDD[Row], schema)
> {code}
> In this case, any writer is not initialised and created. (no calls of 
> {{WriterContainer.writeRows()}}.
> For Parquet and JSON, it works but ORC does not.



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