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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2021/11/22 06:34:25 UTC

[GitHub] [spark] HyukjinKwon opened a new pull request #34677: [WIP][SPARK-37436][PYTHON] Uses Python's standard string formatter for SQL API in pandas API on Spark

HyukjinKwon opened a new pull request #34677:
URL: https://github.com/apache/spark/pull/34677


   ### What changes were proposed in this pull request?
   
   This PR proposes to use [Python's standard string formatter](https://docs.python.org/3/library/string.html#custom-string-formatting) instead of hacky custom SQL parser for SQL API in pandas API on Spark
   
   ### Why are the changes needed?
   
   TBD
   
   ### Does this PR introduce _any_ user-facing change?
   
   TBD
   
   ### How was this patch tested?
   
   Doctests were added.


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Posted by GitBox <gi...@apache.org>.
HyukjinKwon removed a comment on pull request #34677:
URL: https://github.com/apache/spark/pull/34677#issuecomment-976353742


   retest this please


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[GitHub] [spark] HyukjinKwon commented on pull request #34677: [SPARK-37436][PYTHON] Uses Python's standard string formatter for SQL API in pandas API on Spark

Posted by GitBox <gi...@apache.org>.
HyukjinKwon commented on pull request #34677:
URL: https://github.com/apache/spark/pull/34677#issuecomment-976126088


   This is ready for a review. cc @ueshin @xinrong-databricks @itholic.


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[GitHub] [spark] HyukjinKwon commented on pull request #34677: [SPARK-37436][PYTHON] Uses Python's standard string formatter for SQL API in pandas API on Spark

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   retest this please


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[GitHub] [spark] xinrong-databricks commented on a change in pull request #34677: [SPARK-37436][PYTHON] Uses Python's standard string formatter for SQL API in pandas API on Spark

Posted by GitBox <gi...@apache.org>.
xinrong-databricks commented on a change in pull request #34677:
URL: https://github.com/apache/spark/pull/34677#discussion_r756326280



##########
File path: python/pyspark/pandas/sql_processor.py
##########
@@ -43,142 +43,6 @@ def sql(
     locals: Optional[Dict[str, Any]] = None,
     **kwargs: Any,
 ) -> DataFrame:
-    """

Review comment:
       I am curious why the docstring is removed. Do we always remove the docstring of deprecated API?




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[GitHub] [spark] HyukjinKwon commented on pull request #34677: [SPARK-37436][PYTHON] Uses Python's standard string formatter for SQL API in pandas API on Spark

Posted by GitBox <gi...@apache.org>.
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   Merged to master.


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[GitHub] [spark] xinrong-databricks commented on a change in pull request #34677: [SPARK-37436][PYTHON] Uses Python's standard string formatter for SQL API in pandas API on Spark

Posted by GitBox <gi...@apache.org>.
xinrong-databricks commented on a change in pull request #34677:
URL: https://github.com/apache/spark/pull/34677#discussion_r756326280



##########
File path: python/pyspark/pandas/sql_processor.py
##########
@@ -43,142 +43,6 @@ def sql(
     locals: Optional[Dict[str, Any]] = None,
     **kwargs: Any,
 ) -> DataFrame:
-    """

Review comment:
       I am curious why the docstring is removed.




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     * `class SQLStringFormatter(string.Formatter):`


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   retest this please


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   retest this please


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    * This patch passes all tests.
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[GitHub] [spark] xinrong-databricks commented on a change in pull request #34677: [SPARK-37436][PYTHON] Uses Python's standard string formatter for SQL API in pandas API on Spark

Posted by GitBox <gi...@apache.org>.
xinrong-databricks commented on a change in pull request #34677:
URL: https://github.com/apache/spark/pull/34677#discussion_r756328097



##########
File path: python/pyspark/pandas/sql_formatter.py
##########
@@ -0,0 +1,273 @@
+#
+# 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.
+#
+
+import os
+import string
+from typing import Any, Optional, Union, List, Sequence, Mapping, Tuple
+import uuid
+import warnings
+
+import pandas as pd
+
+from pyspark.pandas.internal import InternalFrame
+from pyspark.pandas.namespace import _get_index_map
+from pyspark.sql.functions import lit
+from pyspark import pandas as ps
+from pyspark.sql import SparkSession
+from pyspark.pandas.utils import default_session
+from pyspark.pandas.frame import DataFrame
+from pyspark.pandas.series import Series
+
+
+__all__ = ["sql"]
+
+
+# This is not used in this file. It's for legacy sql_processor.
+_CAPTURE_SCOPES = 3
+
+
+def sql(
+    query: str,
+    index_col: Optional[Union[str, List[str]]] = None,
+    **kwargs: Any,
+) -> DataFrame:
+    """
+    Execute a SQL query and return the result as a pandas-on-Spark DataFrame.
+
+    This function acts as a standard Python string formatter with understanding
+    the following variable types:
+
+        * pandas-on-Spark DataFrame
+        * pandas-on-Spark Series
+        * pandas DataFrame
+        * pandas Series
+        * string
+
+    Parameters
+    ----------
+    query : str
+        the SQL query
+    index_col : str or list of str, optional
+        Column names to be used in Spark to represent pandas-on-Spark's index. The index name
+        in pandas-on-Spark is ignored. By default, the index is always lost.
+
+        .. note:: If you want to preserve the index, explicitly use :func:`DataFrame.reset_index`,
+            and pass it to the sql statement with `index_col` parameter.
+
+            For example,
+
+            >>> psdf = ps.DataFrame({"A": [1, 2, 3], "B":[4, 5, 6]}, index=['a', 'b', 'c'])
+            >>> new_psdf = psdf.reset_index()
+            >>> ps.sql("SELECT * FROM {new_psdf}", index_col="index", new_psdf=new_psdf)
+            ... # doctest: +NORMALIZE_WHITESPACE
+                   A  B
+            index
+            a      1  4
+            b      2  5
+            c      3  6
+
+            For MultiIndex,
+
+            >>> psdf = ps.DataFrame(
+            ...     {"A": [1, 2, 3], "B": [4, 5, 6]},
+            ...     index=pd.MultiIndex.from_tuples(
+            ...         [("a", "b"), ("c", "d"), ("e", "f")], names=["index1", "index2"]
+            ...     ),
+            ... )
+            >>> new_psdf = psdf.reset_index()
+            >>> ps.sql("SELECT * FROM {new_psdf}", index_col=["index1", "index2"], new_psdf=new_psdf)
+            ... # doctest: +NORMALIZE_WHITESPACE
+                           A  B
+            index1 index2
+            a      b       1  4
+            c      d       2  5
+            e      f       3  6
+
+            Also note that the index name(s) should be matched to the existing name.
+    kwargs
+        other variables that the user want to set that can be referenced in the query
+
+    Returns
+    -------
+    pandas-on-Spark DataFrame
+
+    Examples
+    --------
+
+    Calling a built-in SQL function.
+
+    >>> ps.sql("SELECT * FROM range(10) where id > 7")
+       id
+    0   8
+    1   9
+
+    >>> ps.sql("SELECT * FROM range(10) WHERE id > {bound1} AND id < {bound2}", bound1=7, bound2=9)
+       id
+    0   8
+
+    >>> mydf = ps.range(10)
+    >>> x = tuple(range(4))
+    >>> ps.sql("SELECT {ser} FROM {mydf} WHERE id IN {x}", ser=mydf.id, mydf=mydf, x=x)
+       id
+    0   0
+    1   1
+    2   2
+    3   3
+
+    Mixing pandas-on-Spark and pandas DataFrames in a join operation. Note that the index is
+    dropped.
+
+    >>> ps.sql('''
+    ...   SELECT m1.a, m2.b
+    ...   FROM {table1} m1 INNER JOIN {table2} m2
+    ...   ON m1.key = m2.key
+    ...   ORDER BY m1.a, m2.b''',
+    ...   table1=ps.DataFrame({"a": [1,2], "key": ["a", "b"]}),
+    ...   table2=pd.DataFrame({"b": [3,4,5], "key": ["a", "b", "b"]}))
+       a  b
+    0  1  3
+    1  2  4
+    2  2  5
+
+    Also, it is possible to query using Series.
+
+    >>> psdf = ps.DataFrame({"A": [1, 2, 3], "B":[4, 5, 6]}, index=['a', 'b', 'c'])
+    >>> ps.sql("SELECT {mydf.A} FROM {mydf}", mydf=psdf)
+       A
+    0  1
+    1  2
+    2  3
+    """
+    if "PYSPARK_PANDAS_SQL_LEGACY" in os.environ:

Review comment:
       Do we intend not to check `PYSPARK_PANDAS_SQL_LEGACY` equals to 1 or not?




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