Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/4498#discussion_r24434802
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala ---
@@ -264,8 +262,80 @@ class SQLContext(@transient val sparkContext:
SparkContext)
}
@DeveloperApi
- def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame = {
- applySchema(rowRDD.rdd, schema);
+ def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame
= {
+ createDataFrame(rowRDD.rdd, schema)
+ }
+
+ /**
+ * Creates a [[DataFrame]] from an [[RDD]] containing [[Row]]s by
applying
+ * a seq of names of columns to this RDD, the data type for each column
will
+ * be inferred by the first row.
+ *
+ * @param rowRDD an RDD of Row
+ * @param columns names for each column
+ * @return DataFrame
+ */
+ def createDataFrame(rowRDD: RDD[Row], columns: Seq[String]): DataFrame =
{
--- End diff --
@yhuai `ScalaReflection.typeOfObject` would help a lot, but others in
JsonRDD can not fit here well.
I think this API will be useful when user has an RDD of Tuple/Seq, it can
be easily to be converted into RDD of Row, but don't want to figure out the sql
types manually.
I hadn't figure out the right scope of this API now, for example, it cannot
support nested StructType, should it support MapType and ArrayType? Maybe the
basic version is useful enough, the advanced version (use StructType) will help
all other rare cases.
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