Source code for pyspark.accumulators

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import sys
import select
import struct
import socketserver as SocketServer
import threading
from pyspark.serializers import read_int, PickleSerializer

__all__ = ['Accumulator', 'AccumulatorParam']

pickleSer = PickleSerializer()

# 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 = {}

def _deserialize_accumulator(aid, zero_value, accum_param):
    from pyspark.accumulators import _accumulatorRegistry
    # If this certain accumulator was deserialized, don't overwrite it.
    if aid in _accumulatorRegistry:
        return _accumulatorRegistry[aid]
        accum = Accumulator(aid, zero_value, accum_param)
        accum._deserialized = True
        _accumulatorRegistry[aid] = accum
        return accum

[docs]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 `+=` operator, but only the driver program is allowed to access its value, using `value`. Updates from the workers get propagated automatically to the driver program. While :class:`SparkContext` supports accumulators for primitive data types like :class:`int` and :class:`float`, users can also define accumulators for custom types by providing a custom :py:class:`AccumulatorParam` object. Refer to its doctest for an example. Examples -------- >>> a = sc.accumulator(1) >>> a.value 1 >>> a.value = 2 >>> a.value 2 >>> a += 5 >>> a.value 7 >>> sc.accumulator(1.0).value 1.0 >>> sc.accumulator(1j).value 1j >>> rdd = sc.parallelize([1,2,3]) >>> def f(x): ... global a ... a += x >>> rdd.foreach(f) >>> a.value 13 >>> b = sc.accumulator(0) >>> def g(x): ... b.add(x) >>> rdd.foreach(g) >>> b.value 6 >>> 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): ... TypeError: ... """ 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 def __reduce__(self): """Custom serialization; saves the zero value from our AccumulatorParam""" param = self.accum_param return (_deserialize_accumulator, (self.aid,, param)) @property def value(self): """Get the accumulator's value; only usable in driver program""" if self._deserialized: raise RuntimeError("Accumulator.value cannot be accessed inside tasks") return self._value @value.setter def value(self, value): """Sets the accumulator's value; only usable in driver program""" if self._deserialized: raise RuntimeError("Accumulator.value cannot be accessed inside tasks") self._value = value
[docs] def add(self, term): """Adds a term to this accumulator's value""" self._value = self.accum_param.addInPlace(self._value, term)
def __iadd__(self, term): """The += operator; adds a term to this accumulator's value""" self.add(term) return self def __str__(self): return str(self._value) def __repr__(self): return "Accumulator<id=%i, value=%s>" % (self.aid, self._value)
[docs]class AccumulatorParam(object): """ Helper object that defines how to accumulate values of a given type. Examples -------- >>> from pyspark.accumulators import AccumulatorParam >>> class VectorAccumulatorParam(AccumulatorParam): ... def zero(self, value): ... return [0.0] * len(value) ... def addInPlace(self, val1, val2): ... for i in range(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 = sc.parallelize([1,2,3]) >>> rdd.foreach(g) >>> va.value [7.0, 8.0, 9.0] """
[docs] def zero(self, value): """ Provide a "zero value" for the type, compatible in dimensions with the provided `value` (e.g., a zero vector) """ raise NotImplementedError
[docs] def addInPlace(self, value1, value2): """ Add two values of the accumulator's data type, returning a new value; for efficiency, can also update `value1` in place and return it. """ raise NotImplementedError
class AddingAccumulatorParam(AccumulatorParam): """ 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. """ def __init__(self, zero_value): self.zero_value = zero_value def zero(self, value): return self.zero_value def addInPlace(self, value1, value2): value1 += value2 return value1 # Singleton accumulator params for some standard types INT_ACCUMULATOR_PARAM = AddingAccumulatorParam(0) FLOAT_ACCUMULATOR_PARAM = AddingAccumulatorParam(0.0) COMPLEX_ACCUMULATOR_PARAM = AddingAccumulatorParam(0.0j) class _UpdateRequestHandler(SocketServer.StreamRequestHandler): """ This handler will keep polling updates from the same socket until the server is shutdown. """ def handle(self): from pyspark.accumulators import _accumulatorRegistry auth_token = self.server.auth_token def poll(func): while not self.server.server_shutdown: # Poll every 1 second for new data -- don't block in case of shutdown. r, _, _ =[self.rfile], [], [], 1) if self.rfile in r: if func(): break def accum_updates(): num_updates = read_int(self.rfile) for _ in range(num_updates): (aid, update) = pickleSer._read_with_length(self.rfile) _accumulatorRegistry[aid] += update # Write a byte in acknowledgement self.wfile.write(struct.pack("!b", 1)) return False def authenticate_and_accum_updates(): received_token = if isinstance(received_token, bytes): received_token = received_token.decode("utf-8") if (received_token == auth_token): accum_updates() # we've authenticated, we can break out of the first loop now return True else: raise ValueError( "The value of the provided token to the AccumulatorServer is not correct.") # first we keep polling till we've received the authentication token poll(authenticate_and_accum_updates) # now we've authenticated, don't need to check for the token anymore poll(accum_updates) class AccumulatorServer(SocketServer.TCPServer): def __init__(self, server_address, RequestHandlerClass, auth_token): SocketServer.TCPServer.__init__(self, server_address, RequestHandlerClass) self.auth_token = auth_token """ A simple TCP server that intercepts shutdown() in order to interrupt our continuous polling on the handler. """ server_shutdown = False def shutdown(self): self.server_shutdown = True SocketServer.TCPServer.shutdown(self) self.server_close() def _start_update_server(auth_token): """Start a TCP server to receive accumulator updates in a daemon thread, and returns it""" server = AccumulatorServer(("localhost", 0), _UpdateRequestHandler, auth_token) thread = threading.Thread(target=server.serve_forever) thread.daemon = True thread.start() return server if __name__ == "__main__": import doctest from pyspark.context import SparkContext globs = globals().copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: globs['sc'] = SparkContext('local', 'test') (failure_count, test_count) = doctest.testmod( globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: sys.exit(-1)