[SPARK-5469] restructure pyspark.sql into multiple files

All the DataTypes moved into pyspark.sql.types

The changes can be tracked by `--find-copies-harder -M25`
```
davieslocalhost:~/work/spark/python$ git diff --find-copies-harder -M25 --numstat master..
2       5       python/docs/pyspark.ml.rst
0       3       python/docs/pyspark.mllib.rst
10      2       python/docs/pyspark.sql.rst
1       1       python/pyspark/mllib/linalg.py
21      14      python/pyspark/{mllib => sql}/__init__.py
14      2108    python/pyspark/{sql.py => sql/context.py}
10      1772    python/pyspark/{sql.py => sql/dataframe.py}
7       6       python/pyspark/{sql_tests.py => sql/tests.py}
8       1465    python/pyspark/{sql.py => sql/types.py}
4       2       python/run-tests
1       1       sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala
```

Also `git blame -C -C python/pyspark/sql/context.py` to track the history.

Author: Davies Liu <davies@databricks.com>

Closes #4479 from davies/sql and squashes the following commits:

1b5f0a5 [Davies Liu] Merge branch 'master' of github.com:apache/spark into sql
2b2b983 [Davies Liu] restructure pyspark.sql

1 files changed, 300 insertions, 0 deletions

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
new file mode 100644
index 0000000000..d25c6365ed
--- /dev/null
+++ b/python/pyspark/sql/tests.py
@@ -0,0 +1,300 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Unit tests for pyspark.sql; additional tests are implemented as doctests in
+individual modules.
+"""
+import os
+import sys
+import pydoc
+import shutil
+import tempfile
+
+if sys.version_info[:2] <= (2, 6):
+    try:
+        import unittest2 as unittest
+    except ImportError:
+        sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
+        sys.exit(1)
+else:
+    import unittest
+
+
+from pyspark.sql import SQLContext, Column
+from pyspark.sql.types import IntegerType, Row, ArrayType, StructType, StructField, \
+    UserDefinedType, DoubleType, LongType
+from pyspark.tests import ReusedPySparkTestCase
+
+
+class ExamplePointUDT(UserDefinedType):
+    """
+    User-defined type (UDT) for ExamplePoint.
+    """
+
+    @classmethod
+    def sqlType(self):
+        return ArrayType(DoubleType(), False)
+
+    @classmethod
+    def module(cls):
+        return 'pyspark.tests'
+
+    @classmethod
+    def scalaUDT(cls):
+        return 'org.apache.spark.sql.test.ExamplePointUDT'
+
+    def serialize(self, obj):
+        return [obj.x, obj.y]
+
+    def deserialize(self, datum):
+        return ExamplePoint(datum[0], datum[1])
+
+
+class ExamplePoint:
+    """
+    An example class to demonstrate UDT in Scala, Java, and Python.
+    """
+
+    __UDT__ = ExamplePointUDT()
+
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+
+    def __repr__(self):
+        return "ExamplePoint(%s,%s)" % (self.x, self.y)
+
+    def __str__(self):
+        return "(%s,%s)" % (self.x, self.y)
+
+    def __eq__(self, other):
+        return isinstance(other, ExamplePoint) and \
+            other.x == self.x and other.y == self.y
+
+
+class SQLTests(ReusedPySparkTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        ReusedPySparkTestCase.setUpClass()
+        cls.tempdir = tempfile.NamedTemporaryFile(delete=False)
+        os.unlink(cls.tempdir.name)
+        cls.sqlCtx = SQLContext(cls.sc)
+        cls.testData = [Row(key=i, value=str(i)) for i in range(100)]
+        rdd = cls.sc.parallelize(cls.testData)
+        cls.df = cls.sqlCtx.inferSchema(rdd)
+
+    @classmethod
+    def tearDownClass(cls):
+        ReusedPySparkTestCase.tearDownClass()
+        shutil.rmtree(cls.tempdir.name, ignore_errors=True)
+
+    def test_udf(self):
+        self.sqlCtx.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType())
+        [row] = self.sqlCtx.sql("SELECT twoArgs('test', 1)").collect()
+        self.assertEqual(row[0], 5)
+
+    def test_udf2(self):
+        self.sqlCtx.registerFunction("strlen", lambda string: len(string), IntegerType())
+        self.sqlCtx.inferSchema(self.sc.parallelize([Row(a="test")])).registerTempTable("test")
+        [res] = self.sqlCtx.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect()
+        self.assertEqual(4, res[0])
+
+    def test_udf_with_array_type(self):
+        d = [Row(l=range(3), d={"key": range(5)})]
+        rdd = self.sc.parallelize(d)
+        self.sqlCtx.inferSchema(rdd).registerTempTable("test")
+        self.sqlCtx.registerFunction("copylist", lambda l: list(l), ArrayType(IntegerType()))
+        self.sqlCtx.registerFunction("maplen", lambda d: len(d), IntegerType())
+        [(l1, l2)] = self.sqlCtx.sql("select copylist(l), maplen(d) from test").collect()
+        self.assertEqual(range(3), l1)
+        self.assertEqual(1, l2)
+
+    def test_broadcast_in_udf(self):
+        bar = {"a": "aa", "b": "bb", "c": "abc"}
+        foo = self.sc.broadcast(bar)
+        self.sqlCtx.registerFunction("MYUDF", lambda x: foo.value[x] if x else '')
+        [res] = self.sqlCtx.sql("SELECT MYUDF('c')").collect()
+        self.assertEqual("abc", res[0])
+        [res] = self.sqlCtx.sql("SELECT MYUDF('')").collect()
+        self.assertEqual("", res[0])
+
+    def test_basic_functions(self):
+        rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])
+        df = self.sqlCtx.jsonRDD(rdd)
+        df.count()
+        df.collect()
+        df.schema()
+
+        # cache and checkpoint
+        self.assertFalse(df.is_cached)
+        df.persist()
+        df.unpersist()
+        df.cache()
+        self.assertTrue(df.is_cached)
+        self.assertEqual(2, df.count())
+
+        df.registerTempTable("temp")
+        df = self.sqlCtx.sql("select foo from temp")
+        df.count()
+        df.collect()
+
+    def test_apply_schema_to_row(self):
+        df = self.sqlCtx.jsonRDD(self.sc.parallelize(["""{"a":2}"""]))
+        df2 = self.sqlCtx.applySchema(df.map(lambda x: x), df.schema())
+        self.assertEqual(df.collect(), df2.collect())
+
+        rdd = self.sc.parallelize(range(10)).map(lambda x: Row(a=x))
+        df3 = self.sqlCtx.applySchema(rdd, df.schema())
+        self.assertEqual(10, df3.count())
+
+    def test_serialize_nested_array_and_map(self):
+        d = [Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})]
+        rdd = self.sc.parallelize(d)
+        df = self.sqlCtx.inferSchema(rdd)
+        row = df.head()
+        self.assertEqual(1, len(row.l))
+        self.assertEqual(1, row.l[0].a)
+        self.assertEqual("2", row.d["key"].d)
+
+        l = df.map(lambda x: x.l).first()
+        self.assertEqual(1, len(l))
+        self.assertEqual('s', l[0].b)
+
+        d = df.map(lambda x: x.d).first()
+        self.assertEqual(1, len(d))
+        self.assertEqual(1.0, d["key"].c)
+
+        row = df.map(lambda x: x.d["key"]).first()
+        self.assertEqual(1.0, row.c)
+        self.assertEqual("2", row.d)
+
+    def test_infer_schema(self):
+        d = [Row(l=[], d={}),
+             Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")}, s="")]
+        rdd = self.sc.parallelize(d)
+        df = self.sqlCtx.inferSchema(rdd)
+        self.assertEqual([], df.map(lambda r: r.l).first())
+        self.assertEqual([None, ""], df.map(lambda r: r.s).collect())
+        df.registerTempTable("test")
+        result = self.sqlCtx.sql("SELECT l[0].a from test where d['key'].d = '2'")
+        self.assertEqual(1, result.head()[0])
+
+        df2 = self.sqlCtx.inferSchema(rdd, 1.0)
+        self.assertEqual(df.schema(), df2.schema())
+        self.assertEqual({}, df2.map(lambda r: r.d).first())
+        self.assertEqual([None, ""], df2.map(lambda r: r.s).collect())
+        df2.registerTempTable("test2")
+        result = self.sqlCtx.sql("SELECT l[0].a from test2 where d['key'].d = '2'")
+        self.assertEqual(1, result.head()[0])
+
+    def test_struct_in_map(self):
+        d = [Row(m={Row(i=1): Row(s="")})]
+        rdd = self.sc.parallelize(d)
+        df = self.sqlCtx.inferSchema(rdd)
+        k, v = df.head().m.items()[0]
+        self.assertEqual(1, k.i)
+        self.assertEqual("", v.s)
+
+    def test_convert_row_to_dict(self):
+        row = Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})
+        self.assertEqual(1, row.asDict()['l'][0].a)
+        rdd = self.sc.parallelize([row])
+        df = self.sqlCtx.inferSchema(rdd)
+        df.registerTempTable("test")
+        row = self.sqlCtx.sql("select l, d from test").head()
+        self.assertEqual(1, row.asDict()["l"][0].a)
+        self.assertEqual(1.0, row.asDict()['d']['key'].c)
+
+    def test_infer_schema_with_udt(self):
+        from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
+        row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
+        rdd = self.sc.parallelize([row])
+        df = self.sqlCtx.inferSchema(rdd)
+        schema = df.schema()
+        field = [f for f in schema.fields if f.name == "point"][0]
+        self.assertEqual(type(field.dataType), ExamplePointUDT)
+        df.registerTempTable("labeled_point")
+        point = self.sqlCtx.sql("SELECT point FROM labeled_point").head().point
+        self.assertEqual(point, ExamplePoint(1.0, 2.0))
+
+    def test_apply_schema_with_udt(self):
+        from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
+        row = (1.0, ExamplePoint(1.0, 2.0))
+        rdd = self.sc.parallelize([row])
+        schema = StructType([StructField("label", DoubleType(), False),
+                             StructField("point", ExamplePointUDT(), False)])
+        df = self.sqlCtx.applySchema(rdd, schema)
+        point = df.head().point
+        self.assertEquals(point, ExamplePoint(1.0, 2.0))
+
+    def test_parquet_with_udt(self):
+        from pyspark.sql.tests import ExamplePoint
+        row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
+        rdd = self.sc.parallelize([row])
+        df0 = self.sqlCtx.inferSchema(rdd)
+        output_dir = os.path.join(self.tempdir.name, "labeled_point")
+        df0.saveAsParquetFile(output_dir)
+        df1 = self.sqlCtx.parquetFile(output_dir)
+        point = df1.head().point
+        self.assertEquals(point, ExamplePoint(1.0, 2.0))
+
+    def test_column_operators(self):
+        ci = self.df.key
+        cs = self.df.value
+        c = ci == cs
+        self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column))
+        rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci)
+        self.assertTrue(all(isinstance(c, Column) for c in rcc))
+        cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7, ci and cs, ci or cs]
+        self.assertTrue(all(isinstance(c, Column) for c in cb))
+        cbool = (ci & ci), (ci | ci), (~ci)
+        self.assertTrue(all(isinstance(c, Column) for c in cbool))
+        css = cs.like('a'), cs.rlike('a'), cs.asc(), cs.desc(), cs.startswith('a'), cs.endswith('a')
+        self.assertTrue(all(isinstance(c, Column) for c in css))
+        self.assertTrue(isinstance(ci.cast(LongType()), Column))
+
+    def test_column_select(self):
+        df = self.df
+        self.assertEqual(self.testData, df.select("*").collect())
+        self.assertEqual(self.testData, df.select(df.key, df.value).collect())
+        self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect())
+
+    def test_aggregator(self):
+        df = self.df
+        g = df.groupBy()
+        self.assertEqual([99, 100], sorted(g.agg({'key': 'max', 'value': 'count'}).collect()[0]))
+        self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect())
+
+        from pyspark.sql import Dsl
+        self.assertEqual((0, u'99'), tuple(g.agg(Dsl.first(df.key), Dsl.last(df.value)).first()))
+        self.assertTrue(95 < g.agg(Dsl.approxCountDistinct(df.key)).first()[0])
+        self.assertEqual(100, g.agg(Dsl.countDistinct(df.value)).first()[0])
+
+    def test_help_command(self):
+        # Regression test for SPARK-5464
+        rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])
+        df = self.sqlCtx.jsonRDD(rdd)
+        # render_doc() reproduces the help() exception without printing output
+        pydoc.render_doc(df)
+        pydoc.render_doc(df.foo)
+        pydoc.render_doc(df.take(1))
+
+
+if __name__ == "__main__":
+    unittest.main()