Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/1041#discussion_r13664158
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala ---
@@ -296,16 +296,25 @@ class SQLContext(@transient val sparkContext:
SparkContext)
* TODO: We only support primitive types, add support for nested types.
*/
private[sql] def inferSchema(rdd: RDD[Map[String, _]]): SchemaRDD = {
+ import scala.collection.JavaConversions._
+ def typeFor(obj: Any): DataType = obj match {
+ case c: java.lang.String => StringType
+ case c: java.lang.Integer => IntegerType
+ case c: java.lang.Long => LongType
+ case c: java.lang.Double => DoubleType
+ case c: java.lang.Boolean => BooleanType
+ case c: java.util.List[_] => ArrayType(typeFor(c.toList.head))
+ case c: java.util.Set[_] => ArrayType(typeFor(c.toList.head))
+ case c: java.util.Map[_, _] =>
--- End diff --
So I think a key question here is if nested dictionaries are Maps or
Structs. Right now the outermost dict is treated like a struct with named
attributes, so I'm kind of inclined to continue that trend. Although, I think
then we would want some way to create a map. What do you think?
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