b8740a1d1e
### What changes were proposed in this pull request? This PR proposes applying `black` to pandas API on Spark codes, for improving static analysis. By executing the `./dev/reformat-python` in the spark home directory, all the code of the pandas API on Spark is fixed according to the static analysis rules. ### Why are the changes needed? This can be reduces the cost of static analysis during development. It has been used continuously for about a year in the Koalas project and its convenience has been proven. ### Does this PR introduce _any_ user-facing change? No, it's dev-only. ### How was this patch tested? Manually reformat the pandas API on Spark codes by running the `./dev/reformat-python`, and checked the `./dev/lint-python` is passed. Closes #32779 from itholic/SPARK-35499. Authored-by: itholic <haejoon.lee@databricks.com> Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
133 lines
4.8 KiB
Python
133 lines
4.8 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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The reference implementation of usage logger using the Python standard logging library.
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"""
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from inspect import Signature
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import logging
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from typing import Any, Optional
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def get_logger() -> Any:
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"""An entry point of the plug-in and return the usage logger."""
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return PandasOnSparkUsageLogger()
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def _format_signature(signature):
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return (
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"({})".format(", ".join([p.name for p in signature.parameters.values()]))
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if signature is not None
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else ""
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)
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class PandasOnSparkUsageLogger(object):
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"""
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The reference implementation of usage logger.
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The usage logger needs to provide the following methods:
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- log_success(self, class_name, name, duration, signature=None)
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- log_failure(self, class_name, name, ex, duration, signature=None)
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- log_missing(self, class_name, name, is_deprecated=False, signature=None)
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"""
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def __init__(self):
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self.logger = logging.getLogger("pyspark.pandas.usage_logger")
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def log_success(
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self, class_name: str, name: str, duration: float, signature: Optional[Signature] = None
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) -> None:
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"""
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Log the function or property call is successfully finished.
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:param class_name: the target class name
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:param name: the target function or property name
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:param duration: the duration to finish the function or property call
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:param signature: the signature if the target is a function, else None
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"""
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if self.logger.isEnabledFor(logging.INFO):
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msg = (
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"A {function} `{class_name}.{name}{signature}` was successfully finished "
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"after {duration:.3f} ms."
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).format(
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class_name=class_name,
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name=name,
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signature=_format_signature(signature),
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duration=duration * 1000,
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function="function" if signature is not None else "property",
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)
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self.logger.info(msg)
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def log_failure(
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self,
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class_name: str,
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name: str,
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ex: Exception,
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duration: float,
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signature: Optional[Signature] = None,
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) -> None:
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"""
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Log the function or property call failed.
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:param class_name: the target class name
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:param name: the target function or property name
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:param ex: the exception causing the failure
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:param duration: the duration until the function or property call fails
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:param signature: the signature if the target is a function, else None
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"""
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if self.logger.isEnabledFor(logging.WARNING):
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msg = (
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"A {function} `{class_name}.{name}{signature}` was failed "
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"after {duration:.3f} ms: {msg}"
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).format(
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class_name=class_name,
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name=name,
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signature=_format_signature(signature),
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msg=str(ex),
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duration=duration * 1000,
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function="function" if signature is not None else "property",
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)
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self.logger.warning(msg)
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def log_missing(
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self,
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class_name: str,
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name: str,
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is_deprecated: bool = False,
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signature: Optional[Signature] = None,
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) -> None:
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"""
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Log the missing or deprecated function or property is called.
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:param class_name: the target class name
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:param name: the target function or property name
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:param is_deprecated: True if the function or property is marked as deprecated
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:param signature: the original function signature if the target is a function, else None
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"""
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if self.logger.isEnabledFor(logging.INFO):
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msg = "A {deprecated} {function} `{class_name}.{name}{signature}` was called.".format(
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class_name=class_name,
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name=name,
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signature=_format_signature(signature),
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function="function" if signature is not None else "property",
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deprecated="deprecated" if is_deprecated else "missing",
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)
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self.logger.info(msg)
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