[
https://issues.apache.org/jira/browse/SPARK-15473?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon updated SPARK-15473:
---------------------------------
Description:
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.
was:
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.
Summary: CSV fails to write and read back empty dataframe (was: CSV
fails to write empty dataframe)
> 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
>
> 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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