665817bd4f
### What changes were proposed in this pull request? This pull request: - Adds following flags to the main mypy configuration: - [`strict_optional`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-strict_optional) - [`no_implicit_optional`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-no_implicit_optional) - [`disallow_untyped_defs`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-disallow_untyped_calls) These flags are enabled only for public API and disabled for tests and internal modules. Additionally, these PR fixes missing annotations. ### Why are the changes needed? Primary reason to propose this changes is to use standard configuration as used by typeshed project. This will allow us to be more strict, especially when interacting with JVM code. See for example https://github.com/apache/spark/pull/29122#pullrequestreview-513112882 Additionally, it will allow us to detect cases where annotations have unintentionally omitted. ### Does this PR introduce _any_ user-facing change? Annotations only. ### How was this patch tested? `dev/lint-python`. Closes #30382 from zero323/SPARK-33457. Authored-by: zero323 <mszymkiewicz@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
217 lines
7.4 KiB
Python
217 lines
7.4 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with 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,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from typing import overload
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from typing import (
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Callable,
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Generic,
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Hashable,
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Iterable,
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List,
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Optional,
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Tuple,
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TypeVar,
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Union,
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)
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import datetime
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from pyspark.rdd import RDD
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import pyspark.serializers
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from pyspark.storagelevel import StorageLevel
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import pyspark.streaming.context
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from py4j.java_gateway import JavaObject
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S = TypeVar("S")
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T = TypeVar("T")
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U = TypeVar("U")
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K = TypeVar("K", bound=Hashable)
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V = TypeVar("V")
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class DStream(Generic[T]):
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is_cached: bool
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is_checkpointed: bool
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def __init__(
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self,
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jdstream: JavaObject,
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ssc: pyspark.streaming.context.StreamingContext,
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jrdd_deserializer: pyspark.serializers.Serializer,
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) -> None: ...
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def context(self) -> pyspark.streaming.context.StreamingContext: ...
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def count(self) -> DStream[int]: ...
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def filter(self, f: Callable[[T], bool]) -> DStream[T]: ...
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def flatMap(
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self: DStream[T],
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f: Callable[[T], Iterable[U]],
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preservesPartitioning: bool = ...,
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) -> DStream[U]: ...
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def map(
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self: DStream[T], f: Callable[[T], U], preservesPartitioning: bool = ...
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) -> DStream[U]: ...
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def mapPartitions(
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self, f: Callable[[Iterable[T]], Iterable[U]], preservesPartitioning: bool = ...
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) -> DStream[U]: ...
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def mapPartitionsWithIndex(
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self,
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f: Callable[[int, Iterable[T]], Iterable[U]],
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preservesPartitioning: bool = ...,
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) -> DStream[U]: ...
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def reduce(self, func: Callable[[T, T], T]) -> DStream[T]: ...
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def reduceByKey(
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self: DStream[Tuple[K, V]],
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func: Callable[[V, V], V],
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[K, V]]: ...
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def combineByKey(
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self: DStream[Tuple[K, V]],
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createCombiner: Callable[[V], U],
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mergeValue: Callable[[U, V], U],
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mergeCombiners: Callable[[U, U], U],
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[K, U]]: ...
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def partitionBy(
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self: DStream[Tuple[K, V]],
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numPartitions: int,
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partitionFunc: Callable[[K], int] = ...,
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) -> DStream[Tuple[K, V]]: ...
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@overload
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def foreachRDD(self, func: Callable[[RDD[T]], None]) -> None: ...
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@overload
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def foreachRDD(self, func: Callable[[datetime.datetime, RDD[T]], None]) -> None: ...
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def pprint(self, num: int = ...) -> None: ...
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def mapValues(
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self: DStream[Tuple[K, V]], f: Callable[[V], U]
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) -> DStream[Tuple[K, U]]: ...
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def flatMapValues(
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self: DStream[Tuple[K, V]], f: Callable[[V], Iterable[U]]
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) -> DStream[Tuple[K, U]]: ...
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def glom(self) -> DStream[List[T]]: ...
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def cache(self) -> DStream[T]: ...
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def persist(self, storageLevel: StorageLevel) -> DStream[T]: ...
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def checkpoint(self, interval: int) -> DStream[T]: ...
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def groupByKey(
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self: DStream[Tuple[K, V]], numPartitions: Optional[int] = ...
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) -> DStream[Tuple[K, Iterable[V]]]: ...
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def countByValue(self) -> DStream[Tuple[T, int]]: ...
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def saveAsTextFiles(self, prefix: str, suffix: Optional[str] = ...) -> None: ...
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@overload
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def transform(self, func: Callable[[RDD[T]], RDD[U]]) -> TransformedDStream[U]: ...
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@overload
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def transform(
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self, func: Callable[[datetime.datetime, RDD[T]], RDD[U]]
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) -> TransformedDStream[U]: ...
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@overload
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def transformWith(
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self,
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func: Callable[[RDD[T], RDD[U]], RDD[V]],
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other: RDD[U],
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keepSerializer: bool = ...,
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) -> DStream[V]: ...
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@overload
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def transformWith(
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self,
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func: Callable[[datetime.datetime, RDD[T], RDD[U]], RDD[V]],
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other: RDD[U],
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keepSerializer: bool = ...,
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) -> DStream[V]: ...
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def repartition(self, numPartitions: int) -> DStream[T]: ...
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def union(self, other: DStream[U]) -> DStream[Union[T, U]]: ...
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def cogroup(
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self: DStream[Tuple[K, V]],
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other: DStream[Tuple[K, U]],
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[K, Tuple[List[V], List[U]]]]: ...
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def join(
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self: DStream[Tuple[K, V]],
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other: DStream[Tuple[K, U]],
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[K, Tuple[V, U]]]: ...
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def leftOuterJoin(
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self: DStream[Tuple[K, V]],
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other: DStream[Tuple[K, U]],
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[K, Tuple[V, Optional[U]]]]: ...
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def rightOuterJoin(
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self: DStream[Tuple[K, V]],
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other: DStream[Tuple[K, U]],
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[K, Tuple[Optional[V], U]]]: ...
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def fullOuterJoin(
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self: DStream[Tuple[K, V]],
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other: DStream[Tuple[K, U]],
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[K, Tuple[Optional[V], Optional[U]]]]: ...
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def slice(
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self, begin: Union[datetime.datetime, int], end: Union[datetime.datetime, int]
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) -> List[RDD[T]]: ...
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def window(
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self, windowDuration: int, slideDuration: Optional[int] = ...
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) -> DStream[T]: ...
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def reduceByWindow(
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self,
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reduceFunc: Callable[[T, T], T],
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invReduceFunc: Optional[Callable[[T, T], T]],
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windowDuration: int,
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slideDuration: int,
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) -> DStream[T]: ...
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def countByWindow(
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self, windowDuration: int, slideDuration: int
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) -> DStream[Tuple[T, int]]: ...
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def countByValueAndWindow(
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self,
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windowDuration: int,
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slideDuration: int,
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[T, int]]: ...
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def groupByKeyAndWindow(
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self: DStream[Tuple[K, V]],
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windowDuration: int,
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slideDuration: int,
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numPartitions: Optional[int] = ...,
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) -> DStream[Tuple[K, Iterable[V]]]: ...
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def reduceByKeyAndWindow(
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self: DStream[Tuple[K, V]],
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func: Callable[[V, V], V],
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invFunc: Optional[Callable[[V, V], V]],
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windowDuration: int,
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slideDuration: Optional[int] = ...,
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numPartitions: Optional[int] = ...,
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filterFunc: Optional[Callable[[Tuple[K, V]], bool]] = ...,
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) -> DStream[Tuple[K, V]]: ...
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def updateStateByKey(
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self: DStream[Tuple[K, V]],
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updateFunc: Callable[[Iterable[V], Optional[S]], S],
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numPartitions: Optional[int] = ...,
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initialRDD: Optional[RDD[Tuple[K, S]]] = ...,
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) -> DStream[Tuple[K, S]]: ...
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class TransformedDStream(DStream[U]):
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is_cached: bool
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is_checkpointed: bool
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func: Callable
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prev: DStream
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@overload
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def __init__(
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self: DStream[U], prev: DStream[T], func: Callable[[RDD[T]], RDD[U]]
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) -> None: ...
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@overload
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def __init__(
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self: DStream[U],
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prev: DStream[T],
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func: Callable[[datetime.datetime, RDD[T]], RDD[U]],
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) -> None: ...
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