Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/1598#discussion_r15713885
--- Diff: python/pyspark/sql.py ---
@@ -494,18 +838,18 @@ def __init__(self, sparkContext, sqlContext=None):
ValueError:...
>>> from datetime import datetime
- >>> allTypes = sc.parallelize([{"int": 1, "string": "string",
"double": 1.0, "long": 1L,
- ... "boolean": True, "time": datetime(2010, 1, 1, 1, 1, 1),
"dict": {"a": 1},
- ... "list": [1, 2, 3]}])
- >>> srdd = sqlCtx.inferSchema(allTypes).map(lambda x: (x.int,
x.string, x.double, x.long,
- ... x.boolean, x.time, x.dict["a"], x.list))
+ >>> allTypes = sc.parallelize([Row(int=1, string="string",
+ ... double=1.0, long=1L, boolean=True, list=[1, 2, 3],
+ ... time=datetime(2010, 1, 1, 1, 1, 1), dict={"a": 1})])
+ >>> srdd = sqlCtx.inferSchema(allTypes).map(lambda x: (x.int,
x.string,
+ ... x.double, x.long, x.boolean, x.time, x.dict["a"], x.list))
--- End diff --
It would be great to also add a SQL test here to make sure that types are
matching up with those expected in the execution engine. (though we might
change the names to avoid conflict with reserved words, as we have not
implemented identifier escaping). In particular the complex nested ones like
dict and list. Also it would be good to add a nested Row to the input types.
Something like:
```python
srdd.registerAsTable("pythonData")
sqlCtx.sql("SELECT dict['a'], list[0], nested.nestedField").collect() ...
```
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