def __init__(self,
sources,
max_reader_threads=MAX_SOURCE_READER_THREADS,
read_counter=None):
self.sources = sources
self.num_reader_threads = min(max_reader_threads, len(self.sources))
# Queue for sources that are to be read.
self.sources_queue = Queue.Queue()
for source in sources:
self.sources_queue.put(source)
# Queue for elements that have been read.
self.element_queue = Queue.Queue(ELEMENT_QUEUE_SIZE)
# Queue for exceptions encountered in reader threads; to be rethrown.
self.reader_exceptions = Queue.Queue()
# Whether we have already iterated; this iterable can only be used once.
self.already_iterated = False
# Whether an error was encountered in any source reader.
self.has_errored = False
self.read_counter = read_counter or opcounters.TransformIOCounter()
self.reader_threads = []
self._start_reader_threads()
def _read_side_inputs(self, tags_and_types):
"""Generator reading side inputs in the order prescribed by tags_and_types.
Args:
tags_and_types: List of tuples (tag, type). Each side input has a string
tag that is specified in the worker instruction. The type is actually
a boolean which is True for singleton input (read just first value)
and False for collection input (read all values).
Yields:
With each iteration it yields the result of reading an entire side source
either in singleton or collection mode according to the tags_and_types
argument.
"""
# Only call this on the old path where side_input_maps was not
# provided directly.
assert self.side_input_maps is None
# Get experiments active in the worker to check for side input metrics exp.
experiments = RuntimeValueProvider.get_value('experiments', list, [])
# We will read the side inputs in the order prescribed by the
# tags_and_types argument because this is exactly the order needed to
# replace the ArgumentPlaceholder objects in the args/kwargs of the DoFn
# getting the side inputs.
#
# Note that for each tag there could be several read operations in the
# specification. This can happen for instance if the source has been
# sharded into several files.
for i, (side_tag, view_class, view_options) in enumerate(tags_and_types):
sources = []
# Using the side_tag in the lambda below will trigger a pylint warning.
# However in this case it is fine because the lambda is used right away
# while the variable has the value assigned by the current iteration of
# the for loop.
# pylint: disable=cell-var-from-loop
for si in itertools.ifilter(
lambda o: o.tag == side_tag, self.spec.side_inputs):
if not isinstance(si, operation_specs.WorkerSideInputSource):
raise NotImplementedError('Unknown side input type: %r' % si)
sources.append(si.source)
# The tracking of time spend reading and bytes read from side inputs is
# behind an experiment flag to test its performance impact.
if 'sideinput_io_metrics' in experiments:
si_counter = opcounters.SideInputReadCounter(
self.counter_factory,
self.state_sampler,
declaring_step=self.name_context.step_name,
# Inputs are 1-indexed, so we add 1 to i in the side input id
input_index=i + 1)
else:
si_counter = opcounters.TransformIOCounter()
iterator_fn = sideinputs.get_iterator_fn_for_sources(
sources, read_counter=si_counter)
# Backwards compatibility for pre BEAM-733 SDKs.
if isinstance(view_options, tuple):
if view_class == pvalue.AsSingleton:
has_default, default = view_options
view_options = {'default': default} if has_default else {}
else:
view_options = {}
yield apache_sideinputs.SideInputMap(
view_class, view_options, sideinputs.EmulatedIterable(iterator_fn))