#
# 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.
#
"""
Infrastructure of options for pandas-on-Spark.
"""
from contextlib import contextmanager
import json
from typing import Any, Callable, Dict, Iterator, List, Tuple, Union
from pyspark._globals import _NoValue, _NoValueType
from pyspark.pandas.utils import default_session
__all__ = ["get_option", "set_option", "reset_option", "options", "option_context"]
class Option:
"""
Option class that defines an option with related properties.
This class holds all information relevant to the one option. Also,
Its instance can validate if the given value is acceptable or not.
It is currently for internal usage only.
Parameters
----------
key: str, keyword-only argument
the option name to use.
doc: str, keyword-only argument
the documentation for the current option.
default: Any, keyword-only argument
default value for this option.
types: Union[Tuple[type, ...], type], keyword-only argument
default is str. It defines the expected types for this option. It is
used with `isinstance` to validate the given value to this option.
check_func: Tuple[Callable[[Any], bool], str], keyword-only argument
default is a function that always returns `True` with an empty string.
It defines:
- a function to check the given value to this option
- the error message to show when this check is failed
When new value is set to this option, this function is called to check
if the given value is valid.
Examples
--------
>>> option = Option(
... key='option.name',
... doc="this is a test option",
... default="default",
... types=(float, int),
... check_func=(lambda v: v > 0, "should be a positive float"))
>>> option.validate('abc') # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
TypeError: The value for option 'option.name' was <class 'str'>;
however, expected types are [(<class 'float'>, <class 'int'>)].
>>> option.validate(-1.1)
Traceback (most recent call last):
...
ValueError: should be a positive float
>>> option.validate(1.1)
"""
def __init__(
self,
*,
key: str,
doc: str,
default: Any,
types: Union[Tuple[type, ...], type] = str,
check_func: Tuple[Callable[[Any], bool], str] = (lambda v: True, ""),
):
self.key = key
self.doc = doc
self.default = default
self.types = types
self.check_func = check_func
def validate(self, v: Any) -> None:
"""
Validate the given value and throw an exception with related information such as key.
"""
if not isinstance(v, self.types):
raise TypeError(
"The value for option '%s' was %s; however, expected types are "
"[%s]." % (self.key, type(v), str(self.types))
)
if not self.check_func[0](v):
raise ValueError(self.check_func[1])
# Available options.
#
# NOTE: if you are fixing or adding an option here, make sure you execute `show_options()` and
# copy & paste the results into show_options
# 'docs/source/user_guide/pandas_on_spark/options.rst' as well.
# See the examples below:
# >>> from pyspark.pandas.config import show_options
# >>> show_options()
_options: List[Option] = [
Option(
key="display.max_rows",
doc=(
"This sets the maximum number of rows pandas-on-Spark should output when printing out "
"various output. For example, this value determines the number of rows to be "
"shown at the repr() in a dataframe. Set `None` to unlimit the input length. "
"Default is 1000."
),
default=1000,
types=(int, type(None)),
check_func=(
lambda v: v is None or v >= 0,
"'display.max_rows' should be greater than or equal to 0.",
),
),
Option(
key="compute.max_rows",
doc=(
"'compute.max_rows' sets the limit of the current pandas-on-Spark DataFrame. "
"Set `None` to unlimit the input length. When the limit is set, it is executed "
"by the shortcut by collecting the data into the driver, and then using the pandas "
"API. If the limit is unset, the operation is executed by PySpark. Default is 1000."
),
default=1000,
types=(int, type(None)),
check_func=(
lambda v: v is None or v >= 0,
"'compute.max_rows' should be greater than or equal to 0.",
),
),
Option(
key="compute.shortcut_limit",
doc=(
"'compute.shortcut_limit' sets the limit for a shortcut. "
"It computes the specified number of rows and uses its schema. When the dataframe "
"length is larger than this limit, pandas-on-Spark uses PySpark to compute."
),
default=1000,
types=int,
check_func=(
lambda v: v >= 0,
"'compute.shortcut_limit' should be greater than or equal to 0.",
),
),
Option(
key="compute.ops_on_diff_frames",
doc=(
"This determines whether or not to operate between two different dataframes. "
"For example, 'combine_frames' function internally performs a join operation which "
"can be expensive in general. So, if `compute.ops_on_diff_frames` variable is not "
"True, that method throws an exception."
),
default=True,
types=bool,
),
Option(
key="compute.default_index_type",
doc=("This sets the default index type: sequence, distributed and distributed-sequence."),
default="distributed-sequence",
types=str,
check_func=(
lambda v: v in ("sequence", "distributed", "distributed-sequence"),
"Index type should be one of 'sequence', 'distributed', 'distributed-sequence'.",
),
),
Option(
key="compute.default_index_cache",
doc=(
"This sets the default storage level for temporary RDDs cached in "
"distributed-sequence indexing: 'NONE', 'DISK_ONLY', 'DISK_ONLY_2', "
"'DISK_ONLY_3', 'MEMORY_ONLY', 'MEMORY_ONLY_2', 'MEMORY_ONLY_SER', "
"'MEMORY_ONLY_SER_2', 'MEMORY_AND_DISK', 'MEMORY_AND_DISK_2', "
"'MEMORY_AND_DISK_SER', 'MEMORY_AND_DISK_SER_2', 'OFF_HEAP', "
"'LOCAL_CHECKPOINT'."
),
default="MEMORY_AND_DISK_SER",
types=str,
check_func=(
lambda v: v
in (
"NONE",
"DISK_ONLY",
"DISK_ONLY_2",
"DISK_ONLY_3",
"MEMORY_ONLY",
"MEMORY_ONLY_2",
"MEMORY_ONLY_SER",
"MEMORY_ONLY_SER_2",
"MEMORY_AND_DISK",
"MEMORY_AND_DISK_2",
"MEMORY_AND_DISK_SER",
"MEMORY_AND_DISK_SER_2",
"OFF_HEAP",
"LOCAL_CHECKPOINT",
),
"Index type should be one of 'NONE', 'DISK_ONLY', 'DISK_ONLY_2', "
"'DISK_ONLY_3', 'MEMORY_ONLY', 'MEMORY_ONLY_2', 'MEMORY_ONLY_SER', "
"'MEMORY_ONLY_SER_2', 'MEMORY_AND_DISK', 'MEMORY_AND_DISK_2', "
"'MEMORY_AND_DISK_SER', 'MEMORY_AND_DISK_SER_2', 'OFF_HEAP', "
"'LOCAL_CHECKPOINT'.",
),
),
Option(
key="compute.ordered_head",
doc=(
"'compute.ordered_head' sets whether or not to operate head with natural ordering. "
"pandas-on-Spark does not guarantee the row ordering so `head` could return some "
"rows from distributed partitions. If 'compute.ordered_head' is set to True, "
"pandas-on-Spark performs natural ordering beforehand, but it will cause a "
"performance overhead."
),
default=False,
types=bool,
),
Option(
key="compute.eager_check",
doc=(
"'compute.eager_check' sets whether or not to launch some Spark jobs just for the sake "
"of validation. If 'compute.eager_check' is set to True, pandas-on-Spark performs the "
"validation beforehand, but it will cause a performance overhead. Otherwise, "
"pandas-on-Spark skip the validation and will be slightly different from pandas. "
"Affected APIs: `Series.dot`, `Series.asof`, `Series.compare`, "
"`FractionalExtensionOps.astype`, `IntegralExtensionOps.astype`, "
"`FractionalOps.astype`, `DecimalOps.astype`, `skipna of statistical functions`."
),
default=True,
types=bool,
),
Option(
key="compute.isin_limit",
doc=(
"'compute.isin_limit' sets the limit for filtering by 'Column.isin(list)'. "
"If the length of the ‘list’ is above the limit, broadcast join is used instead "
"for better performance."
),
default=80,
types=int,
check_func=(
lambda v: v >= 0,
"'compute.isin_limit' should be greater than or equal to 0.",
),
),
Option(
key="compute.pandas_fallback",
doc=(
"'compute.pandas_fallback' sets whether or not to fallback automatically "
"to Pandas' implementation."
),
default=False,
types=bool,
),
Option(
key="plotting.max_rows",
doc=(
"'plotting.max_rows' sets the visual limit on top-n-based plots such as `plot.bar` "
"and `plot.pie`. If it is set to 1000, the first 1000 data points will be used "
"for plotting. Default is 1000."
),
default=1000,
types=int,
check_func=(
lambda v: v >= 0,
"'plotting.max_rows' should be greater than or equal to 0.",
),
),
Option(
key="plotting.sample_ratio",
doc=(
"'plotting.sample_ratio' sets the proportion of data that will be plotted for sample-"
"based plots such as `plot.line` and `plot.area`. "
"This option defaults to 'plotting.max_rows' option."
),
default=None,
types=(float, type(None)),
check_func=(
lambda v: v is None or 1 >= v >= 0,
"'plotting.sample_ratio' should be 1.0 >= value >= 0.0.",
),
),
Option(
key="plotting.backend",
doc=(
"Backend to use for plotting. Default is plotly. "
"Supports any package that has a top-level `.plot` method. "
"Known options are: [matplotlib, plotly]."
),
default="plotly",
types=str,
),
]
_options_dict: Dict[str, Option] = dict(zip((option.key for option in _options), _options))
_key_format = "pandas_on_Spark.{}".format
class OptionError(AttributeError, KeyError):
pass
def show_options() -> None:
"""
Make a pretty table that can be copied and pasted into public documentation.
This is currently for an internal purpose.
Examples
--------
>>> show_options() # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
================... =======... =====================...
Option Default Description
================... =======... =====================...
display.max_rows 1000 This sets the maximum...
...
================... =======... =====================...
"""
import textwrap
header = ["Option", "Default", "Description"]
row_format = "{:<31} {:<23} {:<53}"
print(row_format.format("=" * 31, "=" * 23, "=" * 53))
print(row_format.format(*header))
print(row_format.format("=" * 31, "=" * 23, "=" * 53))
for option in _options:
doc = textwrap.fill(option.doc, 53)
formatted = "".join([line + "\n" + (" " * 56) for line in doc.split("\n")]).rstrip()
print(row_format.format(option.key, repr(option.default), formatted))
print(row_format.format("=" * 31, "=" * 23, "=" * 53))
[docs]def get_option(key: str, default: Union[Any, _NoValueType] = _NoValue) -> Any:
"""
Retrieves the value of the specified option.
Parameters
----------
key : str
The key which should match a single option.
default : object
The default value if the option is not set yet. The value should be JSON serializable.
Returns
-------
result : the value of the option
Raises
------
OptionError : if no such option exists and the default is not provided
"""
_check_option(key)
if default is _NoValue:
default = _options_dict[key].default
_options_dict[key].validate(default)
spark_session = default_session()
return json.loads(spark_session.conf.get(_key_format(key), default=json.dumps(default)))
[docs]def set_option(key: str, value: Any) -> None:
"""
Sets the value of the specified option.
Parameters
----------
key : str
The key which should match a single option.
value : object
New value of option. The value should be JSON serializable.
Returns
-------
None
"""
_check_option(key)
_options_dict[key].validate(value)
spark_session = default_session()
spark_session.conf.set(_key_format(key), json.dumps(value))
[docs]def reset_option(key: str) -> None:
"""
Reset one option to their default value.
Pass "all" as an argument to reset all options.
Parameters
----------
key : str
If specified only option will be reset.
Returns
-------
None
"""
_check_option(key)
default_session().conf.unset(_key_format(key))
[docs]@contextmanager
def option_context(*args: Any) -> Iterator[None]:
"""
Context manager to temporarily set options in the `with` statement context.
You need to invoke ``option_context(pat, val, [(pat, val), ...])``.
Examples
--------
>>> with option_context('display.max_rows', 10, 'compute.max_rows', 5):
... print(get_option('display.max_rows'), get_option('compute.max_rows'))
10 5
>>> print(get_option('display.max_rows'), get_option('compute.max_rows'))
1000 1000
"""
if len(args) == 0 or len(args) % 2 != 0:
raise ValueError("Need to invoke as option_context(pat, val, [(pat, val), ...]).")
opts = dict(zip(args[::2], args[1::2]))
orig_opts = {key: get_option(key) for key in opts}
try:
for key, value in opts.items():
set_option(key, value)
yield
finally:
for key, value in orig_opts.items():
set_option(key, value)
def _check_option(key: str) -> None:
if key not in _options_dict:
raise OptionError(
"No such option: '{}'. Available options are [{}]".format(
key, ", ".join(list(_options_dict.keys()))
)
)
class DictWrapper:
"""provide attribute-style access to a nested dict"""
def __init__(self, d: Dict[str, Option], prefix: str = ""):
object.__setattr__(self, "d", d)
object.__setattr__(self, "prefix", prefix)
def __setattr__(self, key: str, val: Any) -> None:
prefix = object.__getattribute__(self, "prefix")
d = object.__getattribute__(self, "d")
if prefix:
prefix += "."
canonical_key = prefix + key
candidates = [
k for k in d.keys() if all(x in k.split(".") for x in canonical_key.split("."))
]
if len(candidates) == 1 and candidates[0] == canonical_key:
set_option(canonical_key, val)
else:
raise OptionError(
"No such option: '{}'. Available options are [{}]".format(
key, ", ".join(list(_options_dict.keys()))
)
)
def __getattr__(self, key: str) -> Union["DictWrapper", Any]:
prefix = object.__getattribute__(self, "prefix")
d = object.__getattribute__(self, "d")
if prefix:
prefix += "."
canonical_key = prefix + key
candidates = [
k for k in d.keys() if all(x in k.split(".") for x in canonical_key.split("."))
]
if len(candidates) == 1 and candidates[0] == canonical_key:
return get_option(canonical_key)
elif len(candidates) == 0:
raise OptionError(
"No such option: '{}'. Available options are [{}]".format(
key, ", ".join(list(_options_dict.keys()))
)
)
else:
return DictWrapper(d, canonical_key)
def __dir__(self) -> List[str]:
prefix = object.__getattribute__(self, "prefix")
d = object.__getattribute__(self, "d")
if prefix == "":
candidates = d.keys()
offset = 0
else:
candidates = [k for k in d.keys() if all(x in k.split(".") for x in prefix.split("."))]
offset = len(prefix) + 1 # prefix (e.g. "compute.") to trim.
return [c[offset:] for c in candidates]
options = DictWrapper(_options_dict)
def _test() -> None:
import os
import doctest
import sys
from pyspark.sql import SparkSession
import pyspark.pandas.config
os.chdir(os.environ["SPARK_HOME"])
globs = pyspark.pandas.config.__dict__.copy()
globs["ps"] = pyspark.pandas
spark = (
SparkSession.builder.master("local[4]").appName("pyspark.pandas.config tests").getOrCreate()
)
(failure_count, test_count) = doctest.testmod(
pyspark.pandas.config,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()