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https://issues.apache.org/jira/browse/SPARK-25343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16605439#comment-16605439
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Hyukjin Kwon commented on SPARK-25343:
--------------------------------------
The workaround should be like:
{code}
val ds: Dataset[List[String]] = ...
val csv = ds.mapPartition { iter =>
val writer = new CsvWriter(...)
iter.map { line => writer.writeRowToString(line) }
}
val df = spark.read.csv(csv.as[String])
{code}
If performance doesn't matter much but still works.
> Extend CSV parsing to Dataset[List[String]]
> -------------------------------------------
>
> Key: SPARK-25343
> URL: https://issues.apache.org/jira/browse/SPARK-25343
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.3.1
> Reporter: Frank Kemmer
> Priority: Minor
>
> With the cvs() method it is currenty possible to create a Dataframe from
> Dataset[String], where the given string contains comma separated values. This
> is really great.
> But very often we have to parse files where we have to split the values of a
> line by very individual value separators and regular expressions. The result
> is a Dataset[List[String]]. This list corresponds to what you would get,
> after splitting the values of a CSV string at the separators.
> It would be great, if the csv() method would also accept such a Dataset as
> input especially given a target schema. The csv parser usually casts the
> separated values against the schema and can sort out lines where the values
> of the columns do not fit with the schema.
> This is especially interesting with PERMISSIVE mode and a column for corrupt
> records which then should contain the input list of strings as a dumped JSON
> string.
> This is the functionality I am looking for and I think the CSV parser is very
> close to it.
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