def __init__(self, cache=None, blocking=False):
# Cache of CloudWorkflowStep protos generated while the runner
# "executes" a pipeline.
self._cache = cache if cache is not None else PValueCache()
self.blocking = blocking
self.result = None
self._unique_step_id = 0
def run(self, pipeline):
try:
self._cache = PValueCache(use_disk_backed_cache=True)
return super(DirectPipelineRunner, self).run(pipeline)
finally:
del self._cache
self._cache = self._null_cache
def __init__(self, cache=None):
# Cache of values computed while the runner executes a pipeline.
self._cache = cache if cache is not None else PValueCache()
self._counter_factory = counters.CounterFactory()
# Element counts used for debugging footprint issues in the direct runner.
# The values computed are used only for logging and do not take part in
# any decision making logic. The key for the counter dictionary is either
# the full label for the transform producing the elements or a tuple
# (full label, output tag) for ParDo transforms since they can output values
# on multiple outputs.
self.debug_counters = {}
self.debug_counters['element_counts'] = collections.Counter()