Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/1774#discussion_r15827707
--- Diff: python/pyspark/sql.py ---
@@ -269,7 +269,7 @@ def __repr__(self):
class StructType(DataType):
"""Spark SQL StructType
- The data type representing rows.
+ The data type representing tuple or list values.
--- End diff --
This inconsistency is introduced by the difference between the JVM Row and
Python Row. For a JVM Row (both Scala and Java), fields in it are nameless and
we need to extract values by providing ordinals. However, a field in a Python
Row has its name. Right now, in Python, if users have an `RDD[Row]`, they need
to use `inferSchema` to create a `SchemaRDD`. If they have an `RDD[tuple]` or
`RDD[list]`, they need to use `applySchema`.
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