fd8af39713
## What changes were proposed in this pull request? `an -> a` Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one. ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13515 from zhengruifeng/an_a.
646 lines
27 KiB
Python
646 lines
27 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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import sys
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import operator
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import time
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from itertools import chain
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from datetime import datetime
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if sys.version < "3":
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from itertools import imap as map, ifilter as filter
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from py4j.protocol import Py4JJavaError
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from pyspark import RDD
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from pyspark.storagelevel import StorageLevel
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from pyspark.streaming.util import rddToFileName, TransformFunction
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from pyspark.rdd import portable_hash
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from pyspark.resultiterable import ResultIterable
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__all__ = ["DStream"]
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class DStream(object):
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"""
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A Discretized Stream (DStream), the basic abstraction in Spark Streaming,
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is a continuous sequence of RDDs (of the same type) representing a
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continuous stream of data (see L{RDD} in the Spark core documentation
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for more details on RDDs).
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DStreams can either be created from live data (such as, data from TCP
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sockets, Kafka, Flume, etc.) using a L{StreamingContext} or it can be
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generated by transforming existing DStreams using operations such as
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`map`, `window` and `reduceByKeyAndWindow`. While a Spark Streaming
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program is running, each DStream periodically generates a RDD, either
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from live data or by transforming the RDD generated by a parent DStream.
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DStreams internally is characterized by a few basic properties:
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- A list of other DStreams that the DStream depends on
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- A time interval at which the DStream generates an RDD
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- A function that is used to generate an RDD after each time interval
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"""
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def __init__(self, jdstream, ssc, jrdd_deserializer):
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self._jdstream = jdstream
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self._ssc = ssc
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self._sc = ssc._sc
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self._jrdd_deserializer = jrdd_deserializer
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self.is_cached = False
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self.is_checkpointed = False
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def context(self):
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"""
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Return the StreamingContext associated with this DStream
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"""
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return self._ssc
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def count(self):
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"""
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Return a new DStream in which each RDD has a single element
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generated by counting each RDD of this DStream.
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"""
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return self.mapPartitions(lambda i: [sum(1 for _ in i)]).reduce(operator.add)
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def filter(self, f):
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"""
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Return a new DStream containing only the elements that satisfy predicate.
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"""
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def func(iterator):
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return filter(f, iterator)
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return self.mapPartitions(func, True)
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def flatMap(self, f, preservesPartitioning=False):
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"""
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Return a new DStream by applying a function to all elements of
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this DStream, and then flattening the results
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"""
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def func(s, iterator):
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return chain.from_iterable(map(f, iterator))
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return self.mapPartitionsWithIndex(func, preservesPartitioning)
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def map(self, f, preservesPartitioning=False):
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"""
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Return a new DStream by applying a function to each element of DStream.
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"""
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def func(iterator):
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return map(f, iterator)
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return self.mapPartitions(func, preservesPartitioning)
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def mapPartitions(self, f, preservesPartitioning=False):
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"""
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Return a new DStream in which each RDD is generated by applying
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mapPartitions() to each RDDs of this DStream.
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"""
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def func(s, iterator):
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return f(iterator)
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return self.mapPartitionsWithIndex(func, preservesPartitioning)
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def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
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"""
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Return a new DStream in which each RDD is generated by applying
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mapPartitionsWithIndex() to each RDDs of this DStream.
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"""
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return self.transform(lambda rdd: rdd.mapPartitionsWithIndex(f, preservesPartitioning))
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def reduce(self, func):
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"""
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Return a new DStream in which each RDD has a single element
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generated by reducing each RDD of this DStream.
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"""
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return self.map(lambda x: (None, x)).reduceByKey(func, 1).map(lambda x: x[1])
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def reduceByKey(self, func, numPartitions=None):
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"""
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Return a new DStream by applying reduceByKey to each RDD.
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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return self.combineByKey(lambda x: x, func, func, numPartitions)
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def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
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numPartitions=None):
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"""
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Return a new DStream by applying combineByKey to each RDD.
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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def func(rdd):
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return rdd.combineByKey(createCombiner, mergeValue, mergeCombiners, numPartitions)
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return self.transform(func)
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def partitionBy(self, numPartitions, partitionFunc=portable_hash):
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"""
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Return a copy of the DStream in which each RDD are partitioned
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using the specified partitioner.
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"""
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return self.transform(lambda rdd: rdd.partitionBy(numPartitions, partitionFunc))
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def foreachRDD(self, func):
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"""
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Apply a function to each RDD in this DStream.
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"""
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if func.__code__.co_argcount == 1:
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old_func = func
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func = lambda t, rdd: old_func(rdd)
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jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer)
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api = self._ssc._jvm.PythonDStream
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api.callForeachRDD(self._jdstream, jfunc)
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def pprint(self, num=10):
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"""
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Print the first num elements of each RDD generated in this DStream.
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@param num: the number of elements from the first will be printed.
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"""
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def takeAndPrint(time, rdd):
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taken = rdd.take(num + 1)
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print("-------------------------------------------")
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print("Time: %s" % time)
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print("-------------------------------------------")
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for record in taken[:num]:
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print(record)
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if len(taken) > num:
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print("...")
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print("")
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self.foreachRDD(takeAndPrint)
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def mapValues(self, f):
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"""
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Return a new DStream by applying a map function to the value of
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each key-value pairs in this DStream without changing the key.
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"""
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map_values_fn = lambda kv: (kv[0], f(kv[1]))
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return self.map(map_values_fn, preservesPartitioning=True)
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def flatMapValues(self, f):
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"""
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Return a new DStream by applying a flatmap function to the value
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of each key-value pairs in this DStream without changing the key.
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"""
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flat_map_fn = lambda kv: ((kv[0], x) for x in f(kv[1]))
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return self.flatMap(flat_map_fn, preservesPartitioning=True)
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def glom(self):
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"""
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Return a new DStream in which RDD is generated by applying glom()
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to RDD of this DStream.
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"""
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def func(iterator):
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yield list(iterator)
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return self.mapPartitions(func)
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def cache(self):
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"""
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Persist the RDDs of this DStream with the default storage level
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(C{MEMORY_ONLY}).
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"""
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self.is_cached = True
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self.persist(StorageLevel.MEMORY_ONLY)
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return self
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def persist(self, storageLevel):
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"""
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Persist the RDDs of this DStream with the given storage level
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"""
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self.is_cached = True
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javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
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self._jdstream.persist(javaStorageLevel)
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return self
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def checkpoint(self, interval):
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"""
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Enable periodic checkpointing of RDDs of this DStream
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@param interval: time in seconds, after each period of that, generated
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RDD will be checkpointed
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"""
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self.is_checkpointed = True
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self._jdstream.checkpoint(self._ssc._jduration(interval))
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return self
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def groupByKey(self, numPartitions=None):
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"""
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Return a new DStream by applying groupByKey on each RDD.
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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return self.transform(lambda rdd: rdd.groupByKey(numPartitions))
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def countByValue(self):
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"""
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Return a new DStream in which each RDD contains the counts of each
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distinct value in each RDD of this DStream.
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"""
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return self.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x+y)
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def saveAsTextFiles(self, prefix, suffix=None):
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"""
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Save each RDD in this DStream as at text file, using string
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representation of elements.
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"""
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def saveAsTextFile(t, rdd):
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path = rddToFileName(prefix, suffix, t)
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try:
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rdd.saveAsTextFile(path)
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except Py4JJavaError as e:
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# after recovered from checkpointing, the foreachRDD may
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# be called twice
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if 'FileAlreadyExistsException' not in str(e):
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raise
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return self.foreachRDD(saveAsTextFile)
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# TODO: uncomment this until we have ssc.pickleFileStream()
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# def saveAsPickleFiles(self, prefix, suffix=None):
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# """
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# Save each RDD in this DStream as at binary file, the elements are
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# serialized by pickle.
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# """
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# def saveAsPickleFile(t, rdd):
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# path = rddToFileName(prefix, suffix, t)
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# try:
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# rdd.saveAsPickleFile(path)
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# except Py4JJavaError as e:
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# # after recovered from checkpointing, the foreachRDD may
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# # be called twice
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# if 'FileAlreadyExistsException' not in str(e):
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# raise
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# return self.foreachRDD(saveAsPickleFile)
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def transform(self, func):
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"""
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Return a new DStream in which each RDD is generated by applying a function
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on each RDD of this DStream.
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`func` can have one argument of `rdd`, or have two arguments of
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(`time`, `rdd`)
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"""
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if func.__code__.co_argcount == 1:
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oldfunc = func
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func = lambda t, rdd: oldfunc(rdd)
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assert func.__code__.co_argcount == 2, "func should take one or two arguments"
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return TransformedDStream(self, func)
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def transformWith(self, func, other, keepSerializer=False):
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"""
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Return a new DStream in which each RDD is generated by applying a function
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on each RDD of this DStream and 'other' DStream.
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`func` can have two arguments of (`rdd_a`, `rdd_b`) or have three
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arguments of (`time`, `rdd_a`, `rdd_b`)
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"""
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if func.__code__.co_argcount == 2:
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oldfunc = func
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func = lambda t, a, b: oldfunc(a, b)
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assert func.__code__.co_argcount == 3, "func should take two or three arguments"
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jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer, other._jrdd_deserializer)
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dstream = self._sc._jvm.PythonTransformed2DStream(self._jdstream.dstream(),
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other._jdstream.dstream(), jfunc)
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jrdd_serializer = self._jrdd_deserializer if keepSerializer else self._sc.serializer
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return DStream(dstream.asJavaDStream(), self._ssc, jrdd_serializer)
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def repartition(self, numPartitions):
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"""
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Return a new DStream with an increased or decreased level of parallelism.
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"""
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return self.transform(lambda rdd: rdd.repartition(numPartitions))
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@property
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def _slideDuration(self):
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"""
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Return the slideDuration in seconds of this DStream
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"""
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return self._jdstream.dstream().slideDuration().milliseconds() / 1000.0
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def union(self, other):
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"""
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Return a new DStream by unifying data of another DStream with this DStream.
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@param other: Another DStream having the same interval (i.e., slideDuration)
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as this DStream.
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"""
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if self._slideDuration != other._slideDuration:
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raise ValueError("the two DStream should have same slide duration")
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return self.transformWith(lambda a, b: a.union(b), other, True)
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def cogroup(self, other, numPartitions=None):
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"""
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Return a new DStream by applying 'cogroup' between RDDs of this
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DStream and `other` DStream.
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Hash partitioning is used to generate the RDDs with `numPartitions` partitions.
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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return self.transformWith(lambda a, b: a.cogroup(b, numPartitions), other)
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def join(self, other, numPartitions=None):
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"""
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Return a new DStream by applying 'join' between RDDs of this DStream and
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`other` DStream.
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Hash partitioning is used to generate the RDDs with `numPartitions`
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partitions.
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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return self.transformWith(lambda a, b: a.join(b, numPartitions), other)
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def leftOuterJoin(self, other, numPartitions=None):
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"""
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Return a new DStream by applying 'left outer join' between RDDs of this DStream and
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`other` DStream.
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Hash partitioning is used to generate the RDDs with `numPartitions`
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partitions.
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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return self.transformWith(lambda a, b: a.leftOuterJoin(b, numPartitions), other)
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def rightOuterJoin(self, other, numPartitions=None):
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"""
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Return a new DStream by applying 'right outer join' between RDDs of this DStream and
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`other` DStream.
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Hash partitioning is used to generate the RDDs with `numPartitions`
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partitions.
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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return self.transformWith(lambda a, b: a.rightOuterJoin(b, numPartitions), other)
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def fullOuterJoin(self, other, numPartitions=None):
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"""
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Return a new DStream by applying 'full outer join' between RDDs of this DStream and
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`other` DStream.
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Hash partitioning is used to generate the RDDs with `numPartitions`
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partitions.
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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return self.transformWith(lambda a, b: a.fullOuterJoin(b, numPartitions), other)
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def _jtime(self, timestamp):
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""" Convert datetime or unix_timestamp into Time
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"""
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if isinstance(timestamp, datetime):
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timestamp = time.mktime(timestamp.timetuple())
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return self._sc._jvm.Time(long(timestamp * 1000))
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def slice(self, begin, end):
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"""
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Return all the RDDs between 'begin' to 'end' (both included)
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`begin`, `end` could be datetime.datetime() or unix_timestamp
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"""
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jrdds = self._jdstream.slice(self._jtime(begin), self._jtime(end))
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return [RDD(jrdd, self._sc, self._jrdd_deserializer) for jrdd in jrdds]
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def _validate_window_param(self, window, slide):
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duration = self._jdstream.dstream().slideDuration().milliseconds()
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if int(window * 1000) % duration != 0:
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raise ValueError("windowDuration must be multiple of the slide duration (%d ms)"
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% duration)
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if slide and int(slide * 1000) % duration != 0:
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raise ValueError("slideDuration must be multiple of the slide duration (%d ms)"
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% duration)
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def window(self, windowDuration, slideDuration=None):
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"""
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Return a new DStream in which each RDD contains all the elements in seen in a
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sliding window of time over this DStream.
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@param windowDuration: width of the window; must be a multiple of this DStream's
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batching interval
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@param slideDuration: sliding interval of the window (i.e., the interval after which
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the new DStream will generate RDDs); must be a multiple of this
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DStream's batching interval
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"""
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self._validate_window_param(windowDuration, slideDuration)
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d = self._ssc._jduration(windowDuration)
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if slideDuration is None:
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return DStream(self._jdstream.window(d), self._ssc, self._jrdd_deserializer)
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s = self._ssc._jduration(slideDuration)
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return DStream(self._jdstream.window(d, s), self._ssc, self._jrdd_deserializer)
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def reduceByWindow(self, reduceFunc, invReduceFunc, windowDuration, slideDuration):
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"""
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Return a new DStream in which each RDD has a single element generated by reducing all
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elements in a sliding window over this DStream.
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if `invReduceFunc` is not None, the reduction is done incrementally
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using the old window's reduced value :
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1. reduce the new values that entered the window (e.g., adding new counts)
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2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
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This is more efficient than `invReduceFunc` is None.
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@param reduceFunc: associative and commutative reduce function
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@param invReduceFunc: inverse reduce function of `reduceFunc`; such that for all y,
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and invertible x:
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`invReduceFunc(reduceFunc(x, y), x) = y`
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@param windowDuration: width of the window; must be a multiple of this DStream's
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batching interval
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@param slideDuration: sliding interval of the window (i.e., the interval after which
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the new DStream will generate RDDs); must be a multiple of this
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DStream's batching interval
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"""
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keyed = self.map(lambda x: (1, x))
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reduced = keyed.reduceByKeyAndWindow(reduceFunc, invReduceFunc,
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windowDuration, slideDuration, 1)
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return reduced.map(lambda kv: kv[1])
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def countByWindow(self, windowDuration, slideDuration):
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"""
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Return a new DStream in which each RDD has a single element generated
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by counting the number of elements in a window over this DStream.
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windowDuration and slideDuration are as defined in the window() operation.
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This is equivalent to window(windowDuration, slideDuration).count(),
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but will be more efficient if window is large.
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"""
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return self.map(lambda x: 1).reduceByWindow(operator.add, operator.sub,
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windowDuration, slideDuration)
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def countByValueAndWindow(self, windowDuration, slideDuration, numPartitions=None):
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"""
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Return a new DStream in which each RDD contains the count of distinct elements in
|
|
RDDs in a sliding window over this DStream.
|
|
|
|
@param windowDuration: width of the window; must be a multiple of this DStream's
|
|
batching interval
|
|
@param slideDuration: sliding interval of the window (i.e., the interval after which
|
|
the new DStream will generate RDDs); must be a multiple of this
|
|
DStream's batching interval
|
|
@param numPartitions: number of partitions of each RDD in the new DStream.
|
|
"""
|
|
keyed = self.map(lambda x: (x, 1))
|
|
counted = keyed.reduceByKeyAndWindow(operator.add, operator.sub,
|
|
windowDuration, slideDuration, numPartitions)
|
|
return counted.filter(lambda kv: kv[1] > 0)
|
|
|
|
def groupByKeyAndWindow(self, windowDuration, slideDuration, numPartitions=None):
|
|
"""
|
|
Return a new DStream by applying `groupByKey` over a sliding window.
|
|
Similar to `DStream.groupByKey()`, but applies it over a sliding window.
|
|
|
|
@param windowDuration: width of the window; must be a multiple of this DStream's
|
|
batching interval
|
|
@param slideDuration: sliding interval of the window (i.e., the interval after which
|
|
the new DStream will generate RDDs); must be a multiple of this
|
|
DStream's batching interval
|
|
@param numPartitions: Number of partitions of each RDD in the new DStream.
|
|
"""
|
|
ls = self.mapValues(lambda x: [x])
|
|
grouped = ls.reduceByKeyAndWindow(lambda a, b: a.extend(b) or a, lambda a, b: a[len(b):],
|
|
windowDuration, slideDuration, numPartitions)
|
|
return grouped.mapValues(ResultIterable)
|
|
|
|
def reduceByKeyAndWindow(self, func, invFunc, windowDuration, slideDuration=None,
|
|
numPartitions=None, filterFunc=None):
|
|
"""
|
|
Return a new DStream by applying incremental `reduceByKey` over a sliding window.
|
|
|
|
The reduced value of over a new window is calculated using the old window's reduce value :
|
|
1. reduce the new values that entered the window (e.g., adding new counts)
|
|
2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
|
|
|
|
`invFunc` can be None, then it will reduce all the RDDs in window, could be slower
|
|
than having `invFunc`.
|
|
|
|
@param func: associative and commutative reduce function
|
|
@param invFunc: inverse function of `reduceFunc`
|
|
@param windowDuration: width of the window; must be a multiple of this DStream's
|
|
batching interval
|
|
@param slideDuration: sliding interval of the window (i.e., the interval after which
|
|
the new DStream will generate RDDs); must be a multiple of this
|
|
DStream's batching interval
|
|
@param numPartitions: number of partitions of each RDD in the new DStream.
|
|
@param filterFunc: function to filter expired key-value pairs;
|
|
only pairs that satisfy the function are retained
|
|
set this to null if you do not want to filter
|
|
"""
|
|
self._validate_window_param(windowDuration, slideDuration)
|
|
if numPartitions is None:
|
|
numPartitions = self._sc.defaultParallelism
|
|
|
|
reduced = self.reduceByKey(func, numPartitions)
|
|
|
|
if invFunc:
|
|
def reduceFunc(t, a, b):
|
|
b = b.reduceByKey(func, numPartitions)
|
|
r = a.union(b).reduceByKey(func, numPartitions) if a else b
|
|
if filterFunc:
|
|
r = r.filter(filterFunc)
|
|
return r
|
|
|
|
def invReduceFunc(t, a, b):
|
|
b = b.reduceByKey(func, numPartitions)
|
|
joined = a.leftOuterJoin(b, numPartitions)
|
|
return joined.mapValues(lambda kv: invFunc(kv[0], kv[1])
|
|
if kv[1] is not None else kv[0])
|
|
|
|
jreduceFunc = TransformFunction(self._sc, reduceFunc, reduced._jrdd_deserializer)
|
|
jinvReduceFunc = TransformFunction(self._sc, invReduceFunc, reduced._jrdd_deserializer)
|
|
if slideDuration is None:
|
|
slideDuration = self._slideDuration
|
|
dstream = self._sc._jvm.PythonReducedWindowedDStream(
|
|
reduced._jdstream.dstream(),
|
|
jreduceFunc, jinvReduceFunc,
|
|
self._ssc._jduration(windowDuration),
|
|
self._ssc._jduration(slideDuration))
|
|
return DStream(dstream.asJavaDStream(), self._ssc, self._sc.serializer)
|
|
else:
|
|
return reduced.window(windowDuration, slideDuration).reduceByKey(func, numPartitions)
|
|
|
|
def updateStateByKey(self, updateFunc, numPartitions=None, initialRDD=None):
|
|
"""
|
|
Return a new "state" DStream where the state for each key is updated by applying
|
|
the given function on the previous state of the key and the new values of the key.
|
|
|
|
@param updateFunc: State update function. If this function returns None, then
|
|
corresponding state key-value pair will be eliminated.
|
|
"""
|
|
if numPartitions is None:
|
|
numPartitions = self._sc.defaultParallelism
|
|
|
|
if initialRDD and not isinstance(initialRDD, RDD):
|
|
initialRDD = self._sc.parallelize(initialRDD)
|
|
|
|
def reduceFunc(t, a, b):
|
|
if a is None:
|
|
g = b.groupByKey(numPartitions).mapValues(lambda vs: (list(vs), None))
|
|
else:
|
|
g = a.cogroup(b.partitionBy(numPartitions), numPartitions)
|
|
g = g.mapValues(lambda ab: (list(ab[1]), list(ab[0])[0] if len(ab[0]) else None))
|
|
state = g.mapValues(lambda vs_s: updateFunc(vs_s[0], vs_s[1]))
|
|
return state.filter(lambda k_v: k_v[1] is not None)
|
|
|
|
jreduceFunc = TransformFunction(self._sc, reduceFunc,
|
|
self._sc.serializer, self._jrdd_deserializer)
|
|
if initialRDD:
|
|
initialRDD = initialRDD._reserialize(self._jrdd_deserializer)
|
|
dstream = self._sc._jvm.PythonStateDStream(self._jdstream.dstream(), jreduceFunc,
|
|
initialRDD._jrdd)
|
|
else:
|
|
dstream = self._sc._jvm.PythonStateDStream(self._jdstream.dstream(), jreduceFunc)
|
|
|
|
return DStream(dstream.asJavaDStream(), self._ssc, self._sc.serializer)
|
|
|
|
|
|
class TransformedDStream(DStream):
|
|
"""
|
|
TransformedDStream is a DStream generated by an Python function
|
|
transforming each RDD of a DStream to another RDDs.
|
|
|
|
Multiple continuous transformations of DStream can be combined into
|
|
one transformation.
|
|
"""
|
|
def __init__(self, prev, func):
|
|
self._ssc = prev._ssc
|
|
self._sc = self._ssc._sc
|
|
self._jrdd_deserializer = self._sc.serializer
|
|
self.is_cached = False
|
|
self.is_checkpointed = False
|
|
self._jdstream_val = None
|
|
|
|
# Using type() to avoid folding the functions and compacting the DStreams which is not
|
|
# not strictly an object of TransformedDStream.
|
|
# Changed here is to avoid bug in KafkaTransformedDStream when calling offsetRanges().
|
|
if (type(prev) is TransformedDStream and
|
|
not prev.is_cached and not prev.is_checkpointed):
|
|
prev_func = prev.func
|
|
self.func = lambda t, rdd: func(t, prev_func(t, rdd))
|
|
self.prev = prev.prev
|
|
else:
|
|
self.prev = prev
|
|
self.func = func
|
|
|
|
@property
|
|
def _jdstream(self):
|
|
if self._jdstream_val is not None:
|
|
return self._jdstream_val
|
|
|
|
jfunc = TransformFunction(self._sc, self.func, self.prev._jrdd_deserializer)
|
|
dstream = self._sc._jvm.PythonTransformedDStream(self.prev._jdstream.dstream(), jfunc)
|
|
self._jdstream_val = dstream.asJavaDStream()
|
|
return self._jdstream_val
|