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Merge pull request apache#387 from mateiz/python-accumulators
Add accumulators to PySpark
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,178 @@ | ||
| """ | ||
| >>> from pyspark.context import SparkContext | ||
| >>> sc = SparkContext('local', 'test') | ||
| >>> a = sc.accumulator(1) | ||
| >>> a.value | ||
| 1 | ||
| >>> a.value = 2 | ||
| >>> a.value | ||
| 2 | ||
| >>> a += 5 | ||
| >>> a.value | ||
| 7 | ||
| >>> rdd = sc.parallelize([1,2,3]) | ||
| >>> def f(x): | ||
| ... global a | ||
| ... a += x | ||
| >>> rdd.foreach(f) | ||
| >>> a.value | ||
| 13 | ||
| >>> class VectorAccumulatorParam(object): | ||
| ... def zero(self, value): | ||
| ... return [0.0] * len(value) | ||
| ... def addInPlace(self, val1, val2): | ||
| ... for i in xrange(len(val1)): | ||
| ... val1[i] += val2[i] | ||
| ... return val1 | ||
| >>> va = sc.accumulator([1.0, 2.0, 3.0], VectorAccumulatorParam()) | ||
| >>> va.value | ||
| [1.0, 2.0, 3.0] | ||
| >>> def g(x): | ||
| ... global va | ||
| ... va += [x] * 3 | ||
| >>> rdd.foreach(g) | ||
| >>> va.value | ||
| [7.0, 8.0, 9.0] | ||
| >>> rdd.map(lambda x: a.value).collect() # doctest: +IGNORE_EXCEPTION_DETAIL | ||
| Traceback (most recent call last): | ||
| ... | ||
| Py4JJavaError:... | ||
| >>> def h(x): | ||
| ... global a | ||
| ... a.value = 7 | ||
| >>> rdd.foreach(h) # doctest: +IGNORE_EXCEPTION_DETAIL | ||
| Traceback (most recent call last): | ||
| ... | ||
| Py4JJavaError:... | ||
| >>> sc.accumulator([1.0, 2.0, 3.0]) # doctest: +IGNORE_EXCEPTION_DETAIL | ||
| Traceback (most recent call last): | ||
| ... | ||
| Exception:... | ||
| """ | ||
|
|
||
| import struct | ||
| import SocketServer | ||
| import threading | ||
| from pyspark.cloudpickle import CloudPickler | ||
| from pyspark.serializers import read_int, read_with_length, load_pickle | ||
|
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| # Holds accumulators registered on the current machine, keyed by ID. This is then used to send | ||
| # the local accumulator updates back to the driver program at the end of a task. | ||
| _accumulatorRegistry = {} | ||
|
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||
|
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| def _deserialize_accumulator(aid, zero_value, accum_param): | ||
| from pyspark.accumulators import _accumulatorRegistry | ||
| accum = Accumulator(aid, zero_value, accum_param) | ||
| accum._deserialized = True | ||
| _accumulatorRegistry[aid] = accum | ||
| return accum | ||
|
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| class Accumulator(object): | ||
| """ | ||
| A shared variable that can be accumulated, i.e., has a commutative and associative "add" | ||
| operation. Worker tasks on a Spark cluster can add values to an Accumulator with the C{+=} | ||
| operator, but only the driver program is allowed to access its value, using C{value}. | ||
| Updates from the workers get propagated automatically to the driver program. | ||
| While C{SparkContext} supports accumulators for primitive data types like C{int} and | ||
| C{float}, users can also define accumulators for custom types by providing a custom | ||
| C{AccumulatorParam} object with a C{zero} and C{addInPlace} method. Refer to the doctest | ||
| of this module for an example. | ||
| """ | ||
|
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||
| def __init__(self, aid, value, accum_param): | ||
| """Create a new Accumulator with a given initial value and AccumulatorParam object""" | ||
| from pyspark.accumulators import _accumulatorRegistry | ||
| self.aid = aid | ||
| self.accum_param = accum_param | ||
| self._value = value | ||
| self._deserialized = False | ||
| _accumulatorRegistry[aid] = self | ||
|
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||
| def __reduce__(self): | ||
| """Custom serialization; saves the zero value from our AccumulatorParam""" | ||
| param = self.accum_param | ||
| return (_deserialize_accumulator, (self.aid, param.zero(self._value), param)) | ||
|
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||
| @property | ||
| def value(self): | ||
| """Get the accumulator's value; only usable in driver program""" | ||
| if self._deserialized: | ||
| raise Exception("Accumulator.value cannot be accessed inside tasks") | ||
| return self._value | ||
|
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||
| @value.setter | ||
| def value(self, value): | ||
| """Sets the accumulator's value; only usable in driver program""" | ||
| if self._deserialized: | ||
| raise Exception("Accumulator.value cannot be accessed inside tasks") | ||
| self._value = value | ||
|
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| def __iadd__(self, term): | ||
| """The += operator; adds a term to this accumulator's value""" | ||
| self._value = self.accum_param.addInPlace(self._value, term) | ||
| return self | ||
|
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| def __str__(self): | ||
| return str(self._value) | ||
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| class AddingAccumulatorParam(object): | ||
| """ | ||
| An AccumulatorParam that uses the + operators to add values. Designed for simple types | ||
| such as integers, floats, and lists. Requires the zero value for the underlying type | ||
| as a parameter. | ||
| """ | ||
|
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| def __init__(self, zero_value): | ||
| self.zero_value = zero_value | ||
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| def zero(self, value): | ||
| return self.zero_value | ||
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| def addInPlace(self, value1, value2): | ||
| value1 += value2 | ||
| return value1 | ||
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| # Singleton accumulator params for some standard types | ||
| INT_ACCUMULATOR_PARAM = AddingAccumulatorParam(0) | ||
| DOUBLE_ACCUMULATOR_PARAM = AddingAccumulatorParam(0.0) | ||
| COMPLEX_ACCUMULATOR_PARAM = AddingAccumulatorParam(0.0j) | ||
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| class _UpdateRequestHandler(SocketServer.StreamRequestHandler): | ||
| def handle(self): | ||
| from pyspark.accumulators import _accumulatorRegistry | ||
| num_updates = read_int(self.rfile) | ||
| for _ in range(num_updates): | ||
| (aid, update) = load_pickle(read_with_length(self.rfile)) | ||
| _accumulatorRegistry[aid] += update | ||
| # Write a byte in acknowledgement | ||
| self.wfile.write(struct.pack("!b", 1)) | ||
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| def _start_update_server(): | ||
| """Start a TCP server to receive accumulator updates in a daemon thread, and returns it""" | ||
| server = SocketServer.TCPServer(("localhost", 0), _UpdateRequestHandler) | ||
| thread = threading.Thread(target=server.serve_forever) | ||
| thread.daemon = True | ||
| thread.start() | ||
| return server | ||
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| def _test(): | ||
| import doctest | ||
| doctest.testmod() | ||
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| if __name__ == "__main__": | ||
| _test() |
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