Github user goldmedal commented on a diff in the pull request:
https://github.com/apache/spark/pull/19339#discussion_r140779203
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala ---
@@ -456,6 +456,40 @@ class DataFrameReader private[sql](sparkSession:
SparkSession) extends Logging {
}
/**
+ * Loads a `JavaRDD[String]` storing storing CSV rows and returns the
result as a `DataFrame`.
+ *
+ * If the schema is not specified using `schema` function and
`inferSchema` option is enabled,
+ * this function goes through the input once to determine the input
schema.
+ *
+ * If the schema is not specified using `schema` function and
`inferSchema` option is disabled,
+ * it determines the columns as string types and it reads only the first
line to determine the
+ * names and the number of fields.
+ *
+ * @param csvRDD input RDD with one CSV row per record
+ * @since 2.2.0
+ */
+ @deprecated("Use csv(Dataset[String]) instead.", "2.2.0")
+ def csv(csvRDD: JavaRDD[String]): DataFrame = csv(csvRDD.rdd)
+
+ /**
+ * Loads a `RDD[String]` storing storing CSV rows and returns the result
as a `DataFrame`.
+ *
+ * If the schema is not specified using `schema` function and
`inferSchema` option is enabled,
+ * this function goes through the input once to determine the input
schema.
+ *
+ * If the schema is not specified using `schema` function and
`inferSchema` option is disabled,
+ * it determines the columns as string types and it reads only the first
line to determine the
+ * names and the number of fields.
+ *
+ * @param csvRDD input RDD with one CSV row per record
+ * @since 2.2.0
+ */
+ @deprecated("Use csv(Dataset[String]) instead.", "2.2.0")
+ def csv(csvRDD: RDD[String]): DataFrame = {
--- End diff --
Thanks for your reviewing :)
umm.. I followed `spark.read.json`'s way to add them. Although
`json(jsonRDD :RDD[String]` has been deprecated, PySpark still use it to create
a `DataFrame`. I think adding a private wrapper in Scala maybe better because
not only PySpark but SparkR maybe need it.
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