apache_beam.runners package

Subpackages

• apache_beam.runners.dataflow package
  • Subpackages
    • apache_beam.runners.dataflow.internal package
      • Submodules
      • apache_beam.runners.dataflow.internal.apiclient module
      • apache_beam.runners.dataflow.internal.dependency module
      • apache_beam.runners.dataflow.internal.names module
      • Module contents
    • apache_beam.runners.dataflow.native_io package
      • Submodules
      • apache_beam.runners.dataflow.native_io.iobase module
      • Module contents
  • Submodules
  • apache_beam.runners.dataflow.dataflow_metrics module
  • apache_beam.runners.dataflow.dataflow_runner module
  • apache_beam.runners.dataflow.test_dataflow_runner module
  • Module contents
• apache_beam.runners.direct package
  • Submodules
  • apache_beam.runners.direct.bundle_factory module
  • apache_beam.runners.direct.clock module
  • apache_beam.runners.direct.consumer_tracking_pipeline_visitor module
  • apache_beam.runners.direct.direct_metrics module
  • apache_beam.runners.direct.direct_runner module
  • apache_beam.runners.direct.evaluation_context module
  • apache_beam.runners.direct.executor module
  • apache_beam.runners.direct.helper_transforms module
  • apache_beam.runners.direct.transform_evaluator module
  • apache_beam.runners.direct.transform_result module
  • apache_beam.runners.direct.watermark_manager module
  • Module contents

Submodules

apache_beam.runners.common module

Worker operations executor.

For internal use only; no backwards-compatibility guarantees.

class apache_beam.runners.common.DoFnContext(label, element=None, state=None)

Bases: object

For internal use only; no backwards-compatibility guarantees.

element

set_element(windowed_value)

timestamp

windows

class apache_beam.runners.common.DoFnInvoker(output_processor, signature)

Bases: object

An abstraction that can be used to execute DoFn methods.

A DoFnInvoker describes a particular way for invoking methods of a DoFn represented by a given DoFnSignature.

static create_invoker(output_processor, signature, context, side_inputs, input_args, input_kwargs)

Creates a new DoFnInvoker based on given arguments.

Parameters:

• signature – a DoFnSignature for the DoFn being invoked.
• context – Context to be used when invoking the DoFn (deprecated).
• side_inputs – side inputs to be used when invoking th process method.
• input_args – arguments to be used when invoking the process method
• input_kwargs – kwargs to be used when invoking the process method.

invoke_finish_bundle()

Invokes the DoFn.finish_bundle() method.

invoke_process(windowed_value)

Invokes the DoFn.process() function.

Parameters:windowed_value – a WindowedValue object that gives the element for which process() method should be invoked along with the window the element belongs to.

invoke_start_bundle()

Invokes the DoFn.start_bundle() method.

class apache_beam.runners.common.DoFnMethodWrapper(do_fn, method_name)

Bases: object

For internal use only; no backwards-compatibility guarantees.

Represents a method of a DoFn object.

class apache_beam.runners.common.DoFnRunner(fn, args, kwargs, side_inputs, windowing, context=None, tagged_receivers=None, logger=None, step_name=None, logging_context=None, state=None, scoped_metrics_container=None)

Bases: apache_beam.runners.common.Receiver

For internal use only; no backwards-compatibility guarantees.

A helper class for executing ParDo operations.

finish()

process(windowed_value)

receive(windowed_value)

start()

class apache_beam.runners.common.DoFnSignature(do_fn)

Bases: object

Represents the signature of a given DoFn object.

Signature of a DoFn provides a view of the properties of a given DoFn. Among other things, this will give an extensible way for for (1) accessing the structure of the DoFn including methods and method parameters (2) identifying features that a given DoFn support, for example, whether a given DoFn is a Splittable DoFn ( https://s.apache.org/splittable-do-fn) (3) validating a DoFn based on the feature set offered by it.

class apache_beam.runners.common.DoFnState(counter_factory)

Bases: object

For internal use only; no backwards-compatibility guarantees.

Keeps track of state that DoFns want, currently, user counters.

counter_for(aggregator)

Looks up the counter for this aggregator, creating one if necessary.

class apache_beam.runners.common.LoggingContext

Bases: object

For internal use only; no backwards-compatibility guarantees.

enter()

exit()

class apache_beam.runners.common.PerWindowInvoker(output_processor, signature, context, side_inputs, input_args, input_kwargs)

Bases: apache_beam.runners.common.DoFnInvoker

An invoker that processes elements considering windowing information.

invoke_process(windowed_value)

class apache_beam.runners.common.Receiver

Bases: object

For internal use only; no backwards-compatibility guarantees.

An object that consumes a WindowedValue.

This class can be efficiently used to pass values between the sdk and worker harnesses.

receive(windowed_value)

class apache_beam.runners.common.SimpleInvoker(output_processor, signature)

Bases: apache_beam.runners.common.DoFnInvoker

An invoker that processes elements ignoring windowing information.

invoke_process(windowed_value)

apache_beam.runners.common.as_receiver(maybe_receiver)

For internal use only; no backwards-compatibility guarantees.

apache_beam.runners.common.get_logging_context(maybe_logger, **kwargs)

apache_beam.runners.pipeline_context module

Utility class for serializing pipelines via the runner API.

For internal use only; no backwards-compatibility guarantees.

class apache_beam.runners.pipeline_context.PipelineContext(context_proto=None)

Bases: object

For internal use only; no backwards-compatibility guarantees.

Used for accessing and constructing the referenced objects of a Pipeline.

static from_runner_api(proto)

to_runner_api()

apache_beam.runners.runner module

PipelineRunner, an abstract base runner object.

class apache_beam.runners.runner.PipelineRunner

Bases: 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.

apply(transform, input)

Runner callback for a pipeline.apply call.

Parameters:

• 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.

apply_PTransform(transform, input)

run(pipeline)

Execute the entire pipeline or the sub-DAG reachable from a node.

run_transform(transform_node)

Runner callback for a pipeline.run call.

Parameters: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.

class apache_beam.runners.runner.PipelineState

Bases: object

State of the Pipeline, as returned by 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.

CANCELLED = 'CANCELLED'

DONE = 'DONE'

DRAINED = 'DRAINED'

DRAINING = 'DRAINING'

FAILED = 'FAILED'

RUNNING = 'RUNNING'

STOPPED = 'STOPPED'

UNKNOWN = 'UNKNOWN'

UPDATED = 'UPDATED'

class apache_beam.runners.runner.PipelineResult(state)

Bases: object

A PipelineResult provides access to info about a pipeline.

aggregated_values(aggregator_or_name)

Return a dict of step names to values of the Aggregator.

cancel()

Cancels the pipeline execution.

Raises:

• IOError – If there is a persistent problem getting job information.
• NotImplementedError – If the runner does not support this operation.

Returns:

The final state of the pipeline.

metrics()

Returns MetricsResult object to query metrics from the runner.

Raises:NotImplementedError – If the runner does not support this operation.

state

Return the current state of the pipeline execution.

wait_until_finish(duration=None)

Waits until the pipeline finishes and returns the final status.

Parameters:

duration – The time to wait (in milliseconds) for job to finish. If it is set to None, it will wait indefinitely until the job is finished.

Raises:

• IOError – If there is a persistent problem getting job information.
• NotImplementedError – If the runner does not support this operation.

Returns:

The final state of the pipeline, or None on timeout.

Module contents

Runner objects execute a Pipeline.

This package defines runners, which are used to execute a pipeline.