1aa549ba98
This patch add profiling support for PySpark, it will show the profiling results before the driver exits, here is one example: ``` ============================================================ Profile of RDD<id=3> ============================================================ 5146507 function calls (5146487 primitive calls) in 71.094 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 5144576 68.331 0.000 68.331 0.000 statcounter.py:44(merge) 20 2.735 0.137 71.071 3.554 statcounter.py:33(__init__) 20 0.017 0.001 0.017 0.001 {cPickle.dumps} 1024 0.003 0.000 0.003 0.000 t.py:16(<lambda>) 20 0.001 0.000 0.001 0.000 {reduce} 21 0.001 0.000 0.001 0.000 {cPickle.loads} 20 0.001 0.000 0.001 0.000 copy_reg.py:95(_slotnames) 41 0.001 0.000 0.001 0.000 serializers.py:461(read_int) 40 0.001 0.000 0.002 0.000 serializers.py:179(_batched) 62 0.000 0.000 0.000 0.000 {method 'read' of 'file' objects} 20 0.000 0.000 71.072 3.554 rdd.py:863(<lambda>) 20 0.000 0.000 0.001 0.000 serializers.py:198(load_stream) 40/20 0.000 0.000 71.072 3.554 rdd.py:2093(pipeline_func) 41 0.000 0.000 0.002 0.000 serializers.py:130(load_stream) 40 0.000 0.000 71.072 1.777 rdd.py:304(func) 20 0.000 0.000 71.094 3.555 worker.py:82(process) ``` Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk by `sc.dump_profiles(path)`, such as ```python >>> sc._conf.set("spark.python.profile", "true") >>> rdd = sc.parallelize(range(100)).map(str) >>> rdd.count() 100 >>> sc.show_profiles() ============================================================ Profile of RDD<id=1> ============================================================ 284 function calls (276 primitive calls) in 0.001 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 4 0.000 0.000 0.000 0.000 serializers.py:198(load_stream) 4 0.000 0.000 0.000 0.000 {reduce} 12/4 0.000 0.000 0.001 0.000 rdd.py:2092(pipeline_func) 4 0.000 0.000 0.000 0.000 {cPickle.loads} 4 0.000 0.000 0.000 0.000 {cPickle.dumps} 104 0.000 0.000 0.000 0.000 rdd.py:852(<genexpr>) 8 0.000 0.000 0.000 0.000 serializers.py:461(read_int) 12 0.000 0.000 0.000 0.000 rdd.py:303(func) ``` The profiling is disabled by default, can be enabled by "spark.python.profile=true". Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump" Author: Davies Liu <davies.liu@gmail.com> Closes #2351 from davies/profiler and squashes the following commits: 7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles() 2b0daf2 [Davies Liu] fix docs 7a56c24 [Davies Liu] bugfix cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 09d02c3 [Davies Liu] Merge branch 'master' into profiler c23865c [Davies Liu] Merge branch 'master' into profiler 15d6f18 [Davies Liu] add docs for two configs dadee1a [Davies Liu] add docs string and clear profiles after show or dump 4f8309d [Davies Liu] address comment, add tests 0a5b6eb [Davies Liu] fix Python UDF 4b20494 [Davies Liu] add profile for python
274 lines
8 KiB
Python
274 lines
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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>>> from pyspark.context import SparkContext
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>>> sc = SparkContext('local', 'test')
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>>> a = sc.accumulator(1)
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>>> a.value
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1
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>>> a.value = 2
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>>> a.value
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2
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>>> a += 5
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>>> a.value
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7
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>>> sc.accumulator(1.0).value
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1.0
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>>> sc.accumulator(1j).value
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1j
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>>> rdd = sc.parallelize([1,2,3])
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>>> def f(x):
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... global a
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... a += x
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>>> rdd.foreach(f)
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>>> a.value
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13
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>>> b = sc.accumulator(0)
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>>> def g(x):
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... b.add(x)
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>>> rdd.foreach(g)
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>>> b.value
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6
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>>> from pyspark.accumulators import AccumulatorParam
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>>> class VectorAccumulatorParam(AccumulatorParam):
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... def zero(self, value):
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... return [0.0] * len(value)
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... def addInPlace(self, val1, val2):
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... for i in xrange(len(val1)):
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... val1[i] += val2[i]
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... return val1
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>>> va = sc.accumulator([1.0, 2.0, 3.0], VectorAccumulatorParam())
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>>> va.value
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[1.0, 2.0, 3.0]
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>>> def g(x):
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... global va
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... va += [x] * 3
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>>> rdd.foreach(g)
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>>> va.value
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[7.0, 8.0, 9.0]
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>>> rdd.map(lambda x: a.value).collect() # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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Py4JJavaError:...
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>>> def h(x):
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... global a
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... a.value = 7
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>>> rdd.foreach(h) # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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Py4JJavaError:...
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>>> sc.accumulator([1.0, 2.0, 3.0]) # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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Exception:...
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"""
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import select
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import struct
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import SocketServer
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import threading
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from pyspark.cloudpickle import CloudPickler
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from pyspark.serializers import read_int, PickleSerializer
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__all__ = ['Accumulator', 'AccumulatorParam']
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pickleSer = PickleSerializer()
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# Holds accumulators registered on the current machine, keyed by ID. This is then used to send
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# the local accumulator updates back to the driver program at the end of a task.
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_accumulatorRegistry = {}
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def _deserialize_accumulator(aid, zero_value, accum_param):
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from pyspark.accumulators import _accumulatorRegistry
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accum = Accumulator(aid, zero_value, accum_param)
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accum._deserialized = True
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_accumulatorRegistry[aid] = accum
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return accum
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class Accumulator(object):
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"""
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A shared variable that can be accumulated, i.e., has a commutative and associative "add"
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operation. Worker tasks on a Spark cluster can add values to an Accumulator with the C{+=}
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operator, but only the driver program is allowed to access its value, using C{value}.
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Updates from the workers get propagated automatically to the driver program.
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While C{SparkContext} supports accumulators for primitive data types like C{int} and
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C{float}, users can also define accumulators for custom types by providing a custom
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L{AccumulatorParam} object. Refer to the doctest of this module for an example.
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"""
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def __init__(self, aid, value, accum_param):
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"""Create a new Accumulator with a given initial value and AccumulatorParam object"""
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from pyspark.accumulators import _accumulatorRegistry
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self.aid = aid
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self.accum_param = accum_param
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self._value = value
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self._deserialized = False
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_accumulatorRegistry[aid] = self
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def __reduce__(self):
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"""Custom serialization; saves the zero value from our AccumulatorParam"""
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param = self.accum_param
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return (_deserialize_accumulator, (self.aid, param.zero(self._value), param))
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@property
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def value(self):
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"""Get the accumulator's value; only usable in driver program"""
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if self._deserialized:
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raise Exception("Accumulator.value cannot be accessed inside tasks")
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return self._value
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@value.setter
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def value(self, value):
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"""Sets the accumulator's value; only usable in driver program"""
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if self._deserialized:
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raise Exception("Accumulator.value cannot be accessed inside tasks")
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self._value = value
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def add(self, term):
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"""Adds a term to this accumulator's value"""
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self._value = self.accum_param.addInPlace(self._value, term)
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def __iadd__(self, term):
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"""The += operator; adds a term to this accumulator's value"""
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self.add(term)
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return self
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def __str__(self):
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return str(self._value)
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def __repr__(self):
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return "Accumulator<id=%i, value=%s>" % (self.aid, self._value)
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class AccumulatorParam(object):
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"""
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Helper object that defines how to accumulate values of a given type.
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"""
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def zero(self, value):
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"""
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Provide a "zero value" for the type, compatible in dimensions with the
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provided C{value} (e.g., a zero vector)
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"""
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raise NotImplementedError
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def addInPlace(self, value1, value2):
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"""
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Add two values of the accumulator's data type, returning a new value;
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for efficiency, can also update C{value1} in place and return it.
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"""
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raise NotImplementedError
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class AddingAccumulatorParam(AccumulatorParam):
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"""
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An AccumulatorParam that uses the + operators to add values. Designed for simple types
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such as integers, floats, and lists. Requires the zero value for the underlying type
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as a parameter.
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"""
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def __init__(self, zero_value):
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self.zero_value = zero_value
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def zero(self, value):
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return self.zero_value
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def addInPlace(self, value1, value2):
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value1 += value2
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return value1
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# Singleton accumulator params for some standard types
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INT_ACCUMULATOR_PARAM = AddingAccumulatorParam(0)
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FLOAT_ACCUMULATOR_PARAM = AddingAccumulatorParam(0.0)
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COMPLEX_ACCUMULATOR_PARAM = AddingAccumulatorParam(0.0j)
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class PStatsParam(AccumulatorParam):
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"""PStatsParam is used to merge pstats.Stats"""
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@staticmethod
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def zero(value):
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return None
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@staticmethod
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def addInPlace(value1, value2):
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if value1 is None:
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return value2
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value1.add(value2)
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return value1
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class _UpdateRequestHandler(SocketServer.StreamRequestHandler):
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"""
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This handler will keep polling updates from the same socket until the
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server is shutdown.
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"""
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def handle(self):
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from pyspark.accumulators import _accumulatorRegistry
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while not self.server.server_shutdown:
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# Poll every 1 second for new data -- don't block in case of shutdown.
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r, _, _ = select.select([self.rfile], [], [], 1)
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if self.rfile in r:
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num_updates = read_int(self.rfile)
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for _ in range(num_updates):
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(aid, update) = pickleSer._read_with_length(self.rfile)
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_accumulatorRegistry[aid] += update
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# Write a byte in acknowledgement
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self.wfile.write(struct.pack("!b", 1))
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class AccumulatorServer(SocketServer.TCPServer):
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"""
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A simple TCP server that intercepts shutdown() in order to interrupt
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our continuous polling on the handler.
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"""
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server_shutdown = False
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def shutdown(self):
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self.server_shutdown = True
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SocketServer.TCPServer.shutdown(self)
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def _start_update_server():
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"""Start a TCP server to receive accumulator updates in a daemon thread, and returns it"""
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server = AccumulatorServer(("localhost", 0), _UpdateRequestHandler)
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thread = threading.Thread(target=server.serve_forever)
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thread.daemon = True
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thread.start()
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return server
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