Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/2716#discussion_r19384056
--- Diff: python/pyspark/sql.py ---
@@ -995,19 +1038,22 @@ def registerFunction(self, name, f,
returnType=StringType()):
self._sc._javaAccumulator,
returnType.json())
- def inferSchema(self, rdd):
+ def inferSchema(self, rdd, samplingRatio=None):
"""Infer and apply a schema to an RDD of L{Row}.
- We peek at the first row of the RDD to determine the fields' names
- and types. Nested collections are supported, which include array,
- dict, list, Row, tuple, namedtuple, or object.
+ If `samplingRatio` is presented, it infer schema by all of the
sampled
+ dataset.
- All the rows in `rdd` should have the same type with the first one,
- or it will cause runtime exceptions.
+ Otherwise, it peeks first few rows of the RDD to determine the
fields'
+ names and types. Nested collections are supported, which include
array,
+ dict, list, Row, tuple, namedtuple, or object.
Each row could be L{pyspark.sql.Row} object or namedtuple or
objects,
using dict is deprecated.
+ If some of rows has different types with inferred types, it may
cause
+ runtime exceptions.
--- End diff --
When `samplingRatio` is specified, the schema is inferred by looking at the
types of each row in the sampled dataset. Otherwise, the first 100 rows of the
RDD are inspected. Nested collections are supported, which can include array,
dict, list, Row, tuple, namedtuple, or object.
Each row could be L{pyspark.sql.Row} object or namedtuple or objects.
Using top level dicts is deprecated, as this datatype is used to represent Maps.
If a single column has multiple distinct inferred types, it may cause
runtime exceptions.
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