cbb7f04aef
For now, this only adds MarshalSerializer, but it lays the groundwork for other supporting custom serializers. Many of these mechanisms can also be used to support deserialization of different data formats sent by Java, such as data encoded by MsgPack. This also fixes a bug in SparkContext.union().
229 lines
7 KiB
Python
229 lines
7 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 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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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 _UpdateRequestHandler(SocketServer.StreamRequestHandler):
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def handle(self):
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from pyspark.accumulators import _accumulatorRegistry
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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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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 = SocketServer.TCPServer(("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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