[SPARK-2010] Support for nested data in PySpark SQL

JIRA issue https://issues.apache.org/jira/browse/SPARK-2010

This PR adds support for nested collection types in PySpark SQL, including
array, dict, list, set, and tuple. Example,

```
>>> from array import array
>>> from pyspark.sql import SQLContext
>>> sqlCtx = SQLContext(sc)
>>> rdd = sc.parallelize([
...         {"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
...         {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}])
>>> srdd = sqlCtx.inferSchema(rdd)
>>> srdd.collect() == [{"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
...                    {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}]
True
>>> rdd = sc.parallelize([
...         {"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
...         {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}])
>>> srdd = sqlCtx.inferSchema(rdd)
>>> srdd.collect() == \
... [{"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
...  {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}]
True
```

Author: Kan Zhang <kzhang@apache.org>

Closes #1041 from kanzhang/SPARK-2010 and squashes the following commits:

1b2891d [Kan Zhang] [SPARK-2010] minor doc change and adding a TODO
504f27e [Kan Zhang] [SPARK-2010] Support for nested data in PySpark SQL

1 files changed, 19 insertions, 10 deletions

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 378ff54531..131c130bbb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -298,19 +298,28 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   /**
    * Peek at the first row of the RDD and infer its schema.
-   * TODO: We only support primitive types, add support for nested types.
+   * TODO: consolidate this with the type system developed in SPARK-2060.
    */
   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.head))
+      case c: java.util.Set[_] => ArrayType(typeFor(c.head))
+      case c: java.util.Map[_, _] =>
+        val (key, value) = c.head
+        MapType(typeFor(key), typeFor(value))
+      case c if c.getClass.isArray =>
+        val elem = c.asInstanceOf[Array[_]].head
+        ArrayType(typeFor(elem))
+      case c => throw new Exception(s"Object of type $c cannot be used")
+    }
     val schema = rdd.first().map { case (fieldName, obj) =>
-      val dataType = obj.getClass match {
-        case c: Class[_] if c == classOf[java.lang.String] => StringType
-        case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
-        case c: Class[_] if c == classOf[java.lang.Long] => LongType
-        case c: Class[_] if c == classOf[java.lang.Double] => DoubleType
-        case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType
-        case c => throw new Exception(s"Object of type $c cannot be used")
-      }
-      AttributeReference(fieldName, dataType, true)()
+      AttributeReference(fieldName, typeFor(obj), true)()
     }.toSeq
 
     val rowRdd = rdd.mapPartitions { iter =>