[jira] [Resolved] (SPARK-15473) CSV fails to write and read back empty dataframe

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-15473?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-15473.
----------------------------------
    Resolution: Cannot Reproduce

Yup, I just double checked in the master too. Let me leave this resolved.

> CSV fails to write and read back empty dataframe
> ------------------------------------------------
>
>                 Key: SPARK-15473
>                 URL: https://issues.apache.org/jira/browse/SPARK-15473
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.0
>            Reporter: Hyukjin Kwon
>            Priority: Major
>
> Currently CSV data source fails to write and read empty data.
> The code below:
> {code}
> val emptyDf = spark.range(10).filter(_ => false)
> emptyDf.write
>   .format("csv")
>   .save(path.getCanonicalPath)
> val copyEmptyDf = spark.read
>   .format("csv")
>   .load(path.getCanonicalPath)
> copyEmptyDf.show()
> {code}
> throws an exception below:
> {code}
> Can not create a Path from an empty string
> java.lang.IllegalArgumentException: Can not create a Path from an empty string
> 	at org.apache.hadoop.fs.Path.checkPathArg(Path.java:127)
> 	at org.apache.hadoop.fs.Path.<init>(Path.java:135)
> 	at org.apache.hadoop.util.StringUtils.stringToPath(StringUtils.java:241)
> 	at org.apache.hadoop.mapred.FileInputFormat.setInputPaths(FileInputFormat.java:362)
> 	at org.apache.spark.SparkContext$$anonfun$hadoopFile$1$$anonfun$32.apply(SparkContext.scala:987)
> 	at org.apache.spark.SparkContext$$anonfun$hadoopFile$1$$anonfun$32.apply(SparkContext.scala:987)
> 	at org.apache.spark.rdd.HadoopRDD$$anonfun$getJobConf$6.apply(HadoopRDD.scala:178)
> 	at org.apache.spark.rdd.HadoopRDD$$anonfun$getJobConf$6.apply(HadoopRDD.scala:178)
> 	at scala.Option.map(Option.scala:146)
> {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()}}.
> Maybe, it should be able to read/write header for schemas as well as empty data.
> For Parquet and JSON, it works but CSV does not.



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