Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/4498#discussion_r24466017
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala ---
@@ -264,8 +262,81 @@ 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.
+ *
+ * It does not support nested StructType, use createDataFrame(rdd,
schema) instead.
+ *
+ * For example:
+ *
+ * {{{
+ * val sqlContext = new org.apache.spark.sql.SQLContext(sc)
+ *
+ * val people =
sc.textFile("examples/src/main/resources/people.txt").map(
+ * _.split(",")).map(p => Row(p(0), p(1).trim.toInt))
+ * val dataFrame = sqlContext.createDataFrame(people, Seq("name",
"age"))
+ * dataFrame.printSchema
+ * // root
+ * // |-- name: string (nullable = false)
+ * // |-- age: integer (nullable = true)
+ * }}}
+ *
+ * @param rowRDD an RDD of Row
+ * @param columns names for each column
+ * @return DataFrame
+ */
+ def createDataFrame(rowRDD: RDD[Row], columns: Seq[String]): DataFrame =
{
+ def inferType: PartialFunction[Any, DataType] =
ScalaReflection.typeOfObject orElse {
+ case map: Map[_, _] =>
--- End diff --
Is the `Map` at here a `scala.collection.Map` or `Predef.Map`
(`scala.collection.immutable.Map`)? (We should use `scala.collection.Map` at
here.)
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