Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/1346#discussion_r15767384
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala ---
@@ -89,6 +88,44 @@ class SQLContext(@transient val sparkContext:
SparkContext)
new SchemaRDD(this,
SparkLogicalPlan(ExistingRdd.fromProductRdd(rdd))(self))
/**
+ * :: DeveloperApi ::
+ * Creates a [[SchemaRDD]] from an [[RDD]] containing [[Row]]s by
applying a schema to this RDD.
+ * It is important to make sure that the structure of every [[Row]] of
the provided RDD matches
+ * the provided schema. Otherwise, there will be runtime exception.
+ * Example:
+ * {{{
+ * import org.apache.spark.sql._
+ * val sqlContext = new org.apache.spark.sql.SQLContext(sc)
+ *
+ * val schema =
+ * StructType(
+ * StructField("name", StringType, false) ::
+ * StructField("age", IntegerType, true) :: Nil)
+ *
--- End diff --
For the completeness of our data types, we need `StructType`
(`Seq[StructField]` is not a data type). For example, if the type of a filed is
a struct, we need to have a way to describe that the type of this field is a
struct. Also, because a row is basically a struct value, it is natural to use
`StructType` to represent a schema.
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