#
# Copyright (C) 2019 Databricks, Inc.
#
# Licensed 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 _string
from typing import Dict, Any, Optional
import inspect
import pandas as pd
from pyspark.sql import SparkSession, DataFrame as SDataFrame
from databricks import koalas as ks # For running doctests and reference resolution in PyCharm.
from databricks.koalas.utils import default_session
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series
__all__ = ["sql"]
from builtins import globals as builtin_globals
from builtins import locals as builtin_locals
[docs]def sql(query: str, globals=None, locals=None, **kwargs) -> DataFrame:
"""
Execute a SQL query and return the result as a Koalas DataFrame.
This function also supports embedding Python variables (locals, globals, and parameters)
in the SQL statement by wrapping them in curly braces. See examples section for details.
In addition to the locals, globals and parameters, the function will also attempt
to determine if the program currently runs in an IPython (or Jupyter) environment
and to import the variables from this environment. The variables have the same
precedence as globals.
The following variable types are supported:
* string
* int
* float
* list, tuple, range of above types
* Koalas DataFrame
* Koalas Series
* pandas DataFrame
Parameters
----------
query : str
the SQL query
globals : dict, optional
the dictionary of global variables, if explicitly set by the user
locals : dict, optional
the dictionary of local variables, if explicitly set by the user
kwargs
other variables that the user may want to set manually that can be referenced in the query
Returns
-------
Koalas DataFrame
Examples
--------
Calling a built-in SQL function.
>>> ks.sql("select * from range(10) where id > 7")
id
0 8
1 9
A query can also reference a local variable or parameter by wrapping them in curly braces:
>>> bound1 = 7
>>> ks.sql("select * from range(10) where id > {bound1} and id < {bound2}", bound2=9)
id
0 8
You can also wrap a DataFrame with curly braces to query it directly. Note that when you do
that, the indexes, if any, automatically become top level columns.
>>> mydf = ks.range(10)
>>> x = range(4)
>>> ks.sql("SELECT * from {mydf} WHERE id IN {x}")
id
0 0
1 1
2 2
3 3
Queries can also be arbitrarily nested in functions:
>>> def statement():
... mydf2 = ks.DataFrame({"x": range(2)})
... return ks.sql("SELECT * from {mydf2}")
>>> statement()
x
0 0
1 1
Mixing Koalas and pandas DataFrames in a join operation. Note that the index is dropped.
>>> ks.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=ks.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.
>>> myser = ks.Series({'a': [1.0, 2.0, 3.0], 'b': [15.0, 30.0, 45.0]})
>>> ks.sql("SELECT * from {myser}")
0
0 [1.0, 2.0, 3.0]
1 [15.0, 30.0, 45.0]
"""
if globals is None:
globals = _get_ipython_scope()
_globals = builtin_globals() if globals is None else dict(globals)
_locals = builtin_locals() if locals is None else dict(locals)
# The default choice is the globals
_dict = dict(_globals)
# The vars:
_scope = _get_local_scope()
_dict.update(_scope)
# Then the locals
_dict.update(_locals)
# Highest order of precedence is the locals
_dict.update(kwargs)
return SQLProcessor(_dict, query, default_session()).execute()
_CAPTURE_SCOPES = 2
def _get_local_scope():
# Get 2 scopes above (_get_local_scope -> sql -> ...) to capture the vars there.
try:
return inspect.stack()[_CAPTURE_SCOPES][0].f_locals
except Exception as e:
# TODO (rxin, thunterdb): use a more narrow scope exception.
# See https://github.com/databricks/koalas/pull/448
return {}
def _get_ipython_scope():
"""
Tries to extract the dictionary of variables if the program is running
in an IPython notebook environment.
"""
try:
from IPython import get_ipython
shell = get_ipython()
return shell.user_ns
except Exception as e:
# TODO (rxin, thunterdb): use a more narrow scope exception.
# See https://github.com/databricks/koalas/pull/448
return None
# Originally from pymysql package
_escape_table = [chr(x) for x in range(128)]
_escape_table[0] = "\\0"
_escape_table[ord("\\")] = "\\\\"
_escape_table[ord("\n")] = "\\n"
_escape_table[ord("\r")] = "\\r"
_escape_table[ord("\032")] = "\\Z"
_escape_table[ord('"')] = '\\"'
_escape_table[ord("'")] = "\\'"
def escape_sql_string(value: str) -> str:
"""Escapes value without adding quotes.
>>> escape_sql_string("foo\\nbar")
'foo\\\\nbar'
>>> escape_sql_string("'abc'de")
"\\\\'abc\\\\'de"
>>> escape_sql_string('"abc"de')
'\\\\"abc\\\\"de'
"""
return value.translate(_escape_table)
class SQLProcessor(object):
def __init__(self, scope: Dict[str, Any], statement: str, session: SparkSession):
self._scope = scope
self._statement = statement
# All the temporary views created when executing this statement
# The key is the name of the variable in {}
# The value is the cached Spark Dataframe.
self._temp_views = {} # type: Dict[str, SDataFrame]
# All the other variables, converted to a normalized form.
# The normalized form is typically a string
self._cached_vars = {} # type: Dict[str, Any]
# The SQL statement after:
# - all the dataframes have been have been registered as temporary views
# - all the values have been converted normalized to equivalent SQL representations
self._normalized_statement = None # type: Optional[str]
self._session = session
def execute(self) -> DataFrame:
"""
Returns a DataFrame for which the SQL statement has been executed by
the underlying SQL engine.
>>> str0 = 'abc'
>>> ks.sql("select {str0}")
abc
0 abc
>>> str1 = 'abc"abc'
>>> str2 = "abc'abc"
>>> ks.sql("select {str0}, {str1}, {str2}")
abc abc"abc abc'abc
0 abc abc"abc abc'abc
>>> strs = ['a', 'b']
>>> ks.sql("select 'a' in {strs} as cond1, 'c' in {strs} as cond2")
cond1 cond2
0 True False
"""
blocks = _string.formatter_parser(self._statement)
# TODO: use a string builder
res = ""
try:
for (pre, inner, _, _) in blocks:
var_next = "" if inner is None else self._convert(inner)
res = res + pre + var_next
self._normalized_statement = res
sdf = self._session.sql(self._normalized_statement)
finally:
for v in self._temp_views:
self._session.catalog.dropTempView(v)
return DataFrame(sdf)
def _convert(self, key) -> Any:
"""
Given a {} key, returns an equivalent SQL representation.
This conversion performs all the necessary escaping so that the string
returned can be directly injected into the SQL statement.
"""
# Already cached?
if key in self._cached_vars:
return self._cached_vars[key]
# Analyze:
if key not in self._scope:
raise ValueError(
"The key {} in the SQL statement was not found in global,"
" local or parameters variables".format(key)
)
var = self._scope[key]
fillin = self._convert_var(var)
self._cached_vars[key] = fillin
return fillin
def _convert_var(self, var) -> Any:
"""
Converts a python object into a string that is legal SQL.
"""
if isinstance(var, (int, float)):
return str(var)
if isinstance(var, Series):
return self._convert_var(var.to_dataframe())
if isinstance(var, pd.DataFrame):
return self._convert_var(ks.DataFrame(var))
if isinstance(var, DataFrame):
df_id = "koalas_" + str(id(var))
if df_id not in self._temp_views:
sdf = var.to_spark()
sdf.createOrReplaceTempView(df_id)
self._temp_views[df_id] = sdf
return df_id
if isinstance(var, str):
return '"' + escape_sql_string(var) + '"'
if isinstance(var, list):
return "(" + ", ".join([self._convert_var(v) for v in var]) + ")"
if isinstance(var, (tuple, range)):
return self._convert_var(list(var))
raise ValueError("Unsupported variable type {}: {}".format(type(var).__name__, str(var)))