Source code for apache_beam.runners.runner

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"""PipelineRunner, an abstract base runner object."""

from __future__ import absolute_import

import logging
import os
import shelve
import shutil
import tempfile

__all__ = ['PipelineRunner', 'PipelineState', 'PipelineResult']

def _get_runner_map(runner_names, module_path):
  """Create a map of runner name in lower case to full import path to the
  runner class.
  return {runner_name.lower(): module_path + runner_name
          for runner_name in runner_names}

_TEST_RUNNER_PATH = 'apache_beam.runners.test.'

_KNOWN_DIRECT_RUNNERS = ('DirectRunner', 'EagerRunner')
_KNOWN_DATAFLOW_RUNNERS = ('DataflowRunner',)
_KNOWN_TEST_RUNNERS = ('TestDataflowRunner',)



def create_runner(runner_name):
  """For internal use only; no backwards-compatibility guarantees.

  Creates a runner instance from a runner class name.

    runner_name: Name of the pipeline runner. Possible values are:
      DirectRunner, DataflowRunner and TestDataflowRunner.

    A runner object.

    RuntimeError: if an invalid runner name is used.

  # Get the qualified runner name by using the lower case runner name. If that
  # fails try appending the name with 'runner' and check if it matches.
  # If that also fails, use the given runner name as is.
  runner_name = _RUNNER_MAP.get(
      _RUNNER_MAP.get(runner_name.lower() + 'runner', runner_name))

  if '.' in runner_name:
    module, runner = runner_name.rsplit('.', 1)
      return getattr(__import__(module, {}, {}, [runner], -1), runner)()
    except ImportError:
      if runner_name in _KNOWN_DATAFLOW_RUNNERS:
        raise ImportError(
            'Google Cloud Dataflow runner not available, '
            'please install apache_beam[gcp]')
    raise ValueError(
        'Unexpected pipeline runner: %s. Valid values are %s '
        'or the fully qualified name of a PipelineRunner subclass.' % (
            runner_name, ', '.join(_ALL_KNOWN_RUNNERS)))

[docs]class PipelineRunner(object): """A runner of a pipeline object. The base runner provides a run() method for visiting every node in the pipeline's DAG and executing the transforms computing the PValue in the node. A custom runner will typically provide implementations for some of the transform methods (ParDo, GroupByKey, Create, etc.). It may also provide a new implementation for clear_pvalue(), which is used to wipe out materialized values in order to reduce footprint. """
[docs] def run(self, pipeline): """Execute the entire pipeline or the sub-DAG reachable from a node.""" # Imported here to avoid circular dependencies. # pylint: disable=wrong-import-order, wrong-import-position from apache_beam.pipeline import PipelineVisitor class RunVisitor(PipelineVisitor): def __init__(self, runner): self.runner = runner def visit_transform(self, transform_node): try: self.runner.run_transform(transform_node) except: logging.error('Error while visiting %s', transform_node.full_label) raise pipeline.visit(RunVisitor(self))
[docs] def apply(self, transform, input): """Runner callback for a pipeline.apply call. Args: transform: the transform to apply. input: transform's input (typically a PCollection). A concrete implementation of the Runner class may want to do custom pipeline construction for a given transform. To override the behavior for a transform class Xyz, implement an apply_Xyz method with this same signature. """ for cls in transform.__class__.mro(): m = getattr(self, 'apply_%s' % cls.__name__, None) if m: return m(transform, input) raise NotImplementedError( 'Execution of [%s] not implemented in runner %s.' % (transform, self))
[docs] def apply_PTransform(self, transform, input): # The base case of apply is to call the transform's expand. return transform.expand(input)
[docs] def run_transform(self, transform_node): """Runner callback for a call. Args: transform_node: transform node for the transform to run. A concrete implementation of the Runner class must implement run_Abc for some class Abc in the method resolution order for every non-composite transform Xyz in the pipeline. """ for cls in transform_node.transform.__class__.mro(): m = getattr(self, 'run_%s' % cls.__name__, None) if m: return m(transform_node) raise NotImplementedError( 'Execution of [%s] not implemented in runner %s.' % ( transform_node.transform, self))
class PValueCache(object): """For internal use only; no backwards-compatibility guarantees. Local cache for arbitrary information computed for PValue objects.""" def __init__(self, use_disk_backed_cache=False): # Cache of values computed while a runner executes a pipeline. This is a # dictionary of PValues and their computed values. Note that in principle # the runner could contain PValues from several pipelines without clashes # since a PValue is associated with one and only one pipeline. The keys of # the dictionary are tuple of PValue instance addresses obtained using id() # and tag names converted to strings. self._use_disk_backed_cache = use_disk_backed_cache if use_disk_backed_cache: self._tempdir = tempfile.mkdtemp() self._cache =, 'shelve')) else: self._cache = {} def __del__(self): if self._use_disk_backed_cache: self._cache.close() shutil.rmtree(self._tempdir) def __len__(self): return len(self._cache) def to_cache_key(self, transform, tag): return transform.full_label, tag def _ensure_pvalue_has_real_producer(self, pvalue): """Ensure the passed-in PValue has the real_producer attribute. Args: pvalue: A PValue instance whose cached value is requested. During the runner's execution only the results of the primitive transforms are cached. Whenever we are looking for a PValue that is the output of a composite transform we need to find the output of its rightmost transform part. """ if not hasattr(pvalue, 'real_producer'): real_producer = pvalue.producer while real_producer =[-1] pvalue.real_producer = real_producer def is_cached(self, pobj): from apache_beam.pipeline import AppliedPTransform if isinstance(pobj, AppliedPTransform): transform = pobj tag = None else: self._ensure_pvalue_has_real_producer(pobj) transform = pobj.real_producer tag = pobj.tag return self.to_cache_key(transform, tag) in self._cache def cache_output(self, transform, tag_or_value, value=None): if value is None: value = tag_or_value tag = None else: tag = tag_or_value self._cache[ self.to_cache_key(transform, tag)] = [value, transform.refcounts[tag]] def get_pvalue(self, pvalue, decref=True): """Gets the value associated with a PValue from the cache.""" self._ensure_pvalue_has_real_producer(pvalue) try: value_with_refcount = self._cache[self.key(pvalue)] if decref: value_with_refcount[1] -= 1 logging.debug('PValue computed by %s (tag %s): refcount: %d => %d', pvalue.real_producer.full_label, self.key(pvalue)[1], value_with_refcount[1] + 1, value_with_refcount[1]) if value_with_refcount[1] <= 0: self.clear_pvalue(pvalue) return value_with_refcount[0] except KeyError: if (pvalue.tag is not None and self.to_cache_key(pvalue.real_producer, None) in self._cache): # This is an undeclared, empty output of a DoFn executed # in the local runner before this output was referenced. return [] else: raise def get_unwindowed_pvalue(self, pvalue, decref=True): return [v.value for v in self.get_pvalue(pvalue, decref)] def clear_pvalue(self, pvalue): """Removes a PValue from the cache.""" if self.is_cached(pvalue): del self._cache[self.key(pvalue)] def key(self, pobj): self._ensure_pvalue_has_real_producer(pobj) return self.to_cache_key(pobj.real_producer, pobj.tag)
[docs]class PipelineState(object): """State of the Pipeline, as returned by :attr:`PipelineResult.state`. This is meant to be the union of all the states any runner can put a pipeline in. Currently, it represents the values of the dataflow API JobState enum. """ UNKNOWN = 'UNKNOWN' # not specified STARTING = 'STARTING' # not yet started STOPPED = 'STOPPED' # paused or not yet started RUNNING = 'RUNNING' # currently running DONE = 'DONE' # successfully completed (terminal state) FAILED = 'FAILED' # failed (terminal state) CANCELLED = 'CANCELLED' # explicitly cancelled (terminal state) UPDATED = 'UPDATED' # replaced by another job (terminal state) DRAINING = 'DRAINING' # still processing, no longer reading data DRAINED = 'DRAINED' # draining completed (terminal state) PENDING = 'PENDING' # the job has been created but is not yet running. CANCELLING = 'CANCELLING' # job has been explicitly cancelled and is
# in the process of stopping
[docs]class PipelineResult(object): """A :class:`PipelineResult` provides access to info about a pipeline.""" def __init__(self, state): self._state = state @property def state(self): """Return the current state of the pipeline execution.""" return self._state
[docs] def wait_until_finish(self, duration=None): """Waits until the pipeline finishes and returns the final status. Args: duration (int): The time to wait (in milliseconds) for job to finish. If it is set to :data:`None`, it will wait indefinitely until the job is finished. Raises: ~exceptions.IOError: If there is a persistent problem getting job information. ~exceptions.NotImplementedError: If the runner does not support this operation. Returns: The final state of the pipeline, or :data:`None` on timeout. """ raise NotImplementedError
[docs] def cancel(self): """Cancels the pipeline execution. Raises: ~exceptions.IOError: If there is a persistent problem getting job information. ~exceptions.NotImplementedError: If the runner does not support this operation. Returns: The final state of the pipeline. """ raise NotImplementedError
[docs] def metrics(self): """Returns :class:`~apache_beam.metrics.metric.MetricResults` object to query metrics from the runner. Raises: ~exceptions.NotImplementedError: If the runner does not support this operation. """ raise NotImplementedError
# pylint: disable=unused-argument
[docs] def aggregated_values(self, aggregator_or_name): """Return a dict of step names to values of the Aggregator.""" logging.warn('%s does not implement aggregated_values', self.__class__.__name__) return {}