Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/16854#discussion_r104330396
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala ---
@@ -399,6 +395,52 @@ class DataFrameReader private[sql](sparkSession:
SparkSession) extends Logging {
}
/**
+ * Loads an `Dataset[String]` storing CSV rows and returns the result as
a `DataFrame`.
+ *
+ * Unless the schema is specified using `schema` function, this function
goes through the
+ * input once to determine the input schema.
+ *
+ * @param csvDataset input Dataset with one CSV row per record
+ * @since 2.2.0
+ */
+ def csv(csvDataset: Dataset[String]): DataFrame = {
+ val parsedOptions: CSVOptions = new CSVOptions(
+ extraOptions.toMap,
+ sparkSession.sessionState.conf.sessionLocalTimeZone)
+ val filteredLines = CSVUtils.filterCommentAndEmpty(csvDataset,
parsedOptions)
+ val maybeFirstLine = filteredLines.take(1).headOption
+ if (maybeFirstLine.isEmpty) {
+ return sparkSession.emptyDataFrame
+ }
+
+ val firstLine = maybeFirstLine.get
+ val linesWithoutHeader: RDD[String] = filteredLines.rdd.mapPartitions(
+ CSVUtils.filterHeaderLine(_, firstLine, parsedOptions))
+
+ val schema = userSpecifiedSchema.getOrElse {
--- End diff --
Oh, sorry, I overlooked. It seems `TextInputCSVDataSource.infer` takes
input paths whereas we want `Dataset` here. Let me try to take a look and see
if we could reuse it.
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