Source code for apache_beam.runners.interactive.interactive_runner

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"""A runner that allows running of Beam pipelines interactively.

This module is experimental. No backwards-compatibility guarantees.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import logging

import apache_beam as beam
from apache_beam import runners
from apache_beam.runners.direct import direct_runner
from apache_beam.runners.interactive import cache_manager as cache
from apache_beam.runners.interactive import pipeline_analyzer
from apache_beam.runners.interactive.display import display_manager
from apache_beam.runners.interactive.display import pipeline_graph_renderer

# size of PCollection samples cached.
SAMPLE_SIZE = 8


[docs]class InteractiveRunner(runners.PipelineRunner): """An interactive runner for Beam Python pipelines. Allows interactively building and running Beam Python pipelines. """ def __init__(self, underlying_runner=None, cache_dir=None, render_option=None): """Constructor of InteractiveRunner. Args: underlying_runner: (runner.PipelineRunner) cache_dir: (str) the directory where PCollection caches are kept render_option: (str) this parameter decides how the pipeline graph is rendered. See display.pipeline_graph_renderer for available options. """ self._underlying_runner = (underlying_runner or direct_runner.DirectRunner()) self._cache_manager = cache.FileBasedCacheManager(cache_dir) self._renderer = pipeline_graph_renderer.get_renderer(render_option) self._in_session = False
[docs] def set_render_option(self, render_option): """Sets the rendering option. Args: render_option: (str) this parameter decides how the pipeline graph is rendered. See display.pipeline_graph_renderer for available options. """ self._renderer = pipeline_graph_renderer.get_renderer(render_option)
[docs] def start_session(self): """Start the session that keeps back-end managers and workers alive. """ if self._in_session: return enter = getattr(self._underlying_runner, '__enter__', None) if enter is not None: logging.info('Starting session.') self._in_session = True enter() else: logging.error('Keep alive not supported.')
[docs] def end_session(self): """End the session that keeps backend managers and workers alive. """ if not self._in_session: return exit = getattr(self._underlying_runner, '__exit__', None) if exit is not None: self._in_session = False logging.info('Ending session.') exit(None, None, None)
[docs] def cleanup(self): self._cache_manager.cleanup()
[docs] def apply(self, transform, pvalueish): # TODO(qinyeli, BEAM-646): Remove runner interception of apply. return self._underlying_runner.apply(transform, pvalueish)
[docs] def run_pipeline(self, pipeline): if not hasattr(self, '_desired_cache_labels'): self._desired_cache_labels = set() # Invoke a round trip through the runner API. This makes sure the Pipeline # proto is stable. pipeline = beam.pipeline.Pipeline.from_runner_api( pipeline.to_runner_api(), pipeline.runner, pipeline._options) # Snapshot the pipeline in a portable proto before mutating it. pipeline_proto, original_context = pipeline.to_runner_api( return_context=True) pcolls_to_pcoll_id = self._pcolls_to_pcoll_id(pipeline, original_context) analyzer = pipeline_analyzer.PipelineAnalyzer(self._cache_manager, pipeline_proto, self._underlying_runner, pipeline._options, self._desired_cache_labels) # Should be only accessed for debugging purpose. self._analyzer = analyzer pipeline_to_execute = beam.pipeline.Pipeline.from_runner_api( analyzer.pipeline_proto_to_execute(), self._underlying_runner, pipeline._options) display = display_manager.DisplayManager( pipeline_proto=pipeline_proto, pipeline_analyzer=analyzer, cache_manager=self._cache_manager, pipeline_graph_renderer=self._renderer) display.start_periodic_update() result = pipeline_to_execute.run() result.wait_until_finish() display.stop_periodic_update() return PipelineResult(result, self, self._analyzer.pipeline_info(), self._cache_manager, pcolls_to_pcoll_id)
def _pcolls_to_pcoll_id(self, pipeline, original_context): """Returns a dict mapping PCollections string to PCollection IDs. Using a PipelineVisitor to iterate over every node in the pipeline, records the mapping from PCollections to PCollections IDs. This mapping will be used to query cached PCollections. Args: pipeline: (pipeline.Pipeline) original_context: (pipeline_context.PipelineContext) Returns: (dict from str to str) a dict mapping str(pcoll) to pcoll_id. """ pcolls_to_pcoll_id = {} from apache_beam.pipeline import PipelineVisitor # pylint: disable=import-error class PCollVisitor(PipelineVisitor): # pylint: disable=used-before-assignment """"A visitor that records input and output values to be replaced. Input and output values that should be updated are recorded in maps input_replacements and output_replacements respectively. We cannot update input and output values while visiting since that results in validation errors. """ def enter_composite_transform(self, transform_node): self.visit_transform(transform_node) def visit_transform(self, transform_node): for pcoll in transform_node.outputs.values(): pcolls_to_pcoll_id[str(pcoll)] = original_context.pcollections.get_id( pcoll) pipeline.visit(PCollVisitor()) return pcolls_to_pcoll_id
[docs]class PipelineResult(beam.runners.runner.PipelineResult): """Provides access to information about a pipeline.""" def __init__(self, underlying_result, runner, pipeline_info, cache_manager, pcolls_to_pcoll_id): super(PipelineResult, self).__init__(underlying_result.state) self._runner = runner self._pipeline_info = pipeline_info self._cache_manager = cache_manager self._pcolls_to_pcoll_id = pcolls_to_pcoll_id def _cache_label(self, pcoll): pcoll_id = self._pcolls_to_pcoll_id[str(pcoll)] return self._pipeline_info.cache_label(pcoll_id)
[docs] def wait_until_finish(self): # PipelineResult is not constructed until pipeline execution is finished. return
[docs] def get(self, pcoll): cache_label = self._cache_label(pcoll) if self._cache_manager.exists('full', cache_label): pcoll_list, _ = self._cache_manager.read('full', cache_label) return pcoll_list else: self._runner._desired_cache_labels.add(cache_label) # pylint: disable=protected-access raise ValueError('PCollection not available, please run the pipeline.')
[docs] def sample(self, pcoll): cache_label = self._cache_label(pcoll) if self._cache_manager.exists('sample', cache_label): return self._cache_manager.read('sample', cache_label) else: self._runner._desired_cache_labels.add(cache_label) # pylint: disable=protected-access raise ValueError('PCollection not available, please run the pipeline.')