Source code for apache_beam.runners.interactive.display.display_manager

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"""Manages displaying pipeline graph and execution status on the frontend.

This module is experimental. No backwards-compatibility guarantees.

# pytype: skip-file

import collections
import threading
import time
from typing import TYPE_CHECKING

from apache_beam.runners.interactive.display import interactive_pipeline_graph

  import IPython  # pylint: disable=import-error
  from IPython import get_ipython  # pylint: disable=import-error
  from IPython.display import display as ip_display  # pylint: disable=import-error
  # _display_progress defines how outputs are printed on the frontend.
  _display_progress = ip_display


    def _formatter(string, pp, cycle):  # pylint: disable=unused-argument

    if get_ipython():
      plain = get_ipython().display_formatter.formatters['text/plain']  # pylint: disable=undefined-variable
      plain.for_type(str, _formatter)

except ImportError:
  IPython = None
  _display_progress = print

[docs]class DisplayManager(object): """Manages displaying pipeline graph and execution status on the frontend.""" def __init__( self, pipeline_proto, pipeline_analyzer, cache_manager, pipeline_graph_renderer): """Constructor of DisplayManager. Args: pipeline_proto: (Pipeline proto) pipeline_analyzer: (PipelineAnalyzer) the pipeline analyzer that corresponds to this round of execution. This will provide more detailed informations about the pipeline cache_manager: (interactive_runner.CacheManager) DisplayManager fetches the latest status of pipeline execution by querying cache_manager. pipeline_graph_renderer: (pipeline_graph_renderer.PipelineGraphRenderer) decides how a pipeline graph is rendered. """ # Every parameter except cache_manager is expected to remain constant. self._analyzer = pipeline_analyzer self._cache_manager = cache_manager self._pipeline_graph = interactive_pipeline_graph.InteractivePipelineGraph( pipeline_proto, required_transforms=self._analyzer.tl_required_trans_ids(), referenced_pcollections=self._analyzer.tl_referenced_pcoll_ids(), cached_pcollections=self._analyzer.caches_used()) self._renderer = pipeline_graph_renderer # _text_to_print keeps track of information to be displayed. self._text_to_print = collections.OrderedDict() self._text_to_print['summary'] = ( 'Using %s cached PCollections\nExecuting %s of %s ' 'transforms.') % ( len(self._analyzer.caches_used()), ( len(self._analyzer.tl_required_trans_ids()) - len(self._analyzer.read_cache_ids()) - len(self._analyzer.write_cache_ids())), len( pipeline_proto.components.transforms[ pipeline_proto.root_transform_ids[0]].subtransforms)) self._text_to_print.update( {pcoll_id: "" for pcoll_id in self._analyzer.tl_referenced_pcoll_ids()}) # _pcollection_stats maps pcoll_id to # { 'cache_label': cache_label, version': version, 'sample': pcoll_in_list } self._pcollection_stats = {} for pcoll_id in self._analyzer.tl_referenced_pcoll_ids(): self._pcollection_stats[pcoll_id] = { 'cache_label': self._analyzer.pipeline_info().cache_label(pcoll_id), 'version': -1, 'sample': [] } self._producers = {} for _, transform in pipeline_proto.components.transforms.items(): for pcoll_id in transform.outputs.values(): if pcoll_id not in self._producers or '/' not in transform.unique_name: self._producers[pcoll_id] = transform.unique_name # For periodic update. self._lock = threading.Lock() self._periodic_update = False
[docs] def update_display(self, force=False): """Updates display on the frontend. Retrieves the latest execution status by querying CacheManager and updates display on the fronend. The assumption is that there is only one pipeline in a cell, because it clears up everything in the cell output every update cycle. Args: force: (bool) whether to force updating when no stats change happens. """ with self._lock: stats_updated = False for pcoll_id, stats in self._pcollection_stats.items(): cache_label = stats['cache_label'] version = stats['version'] if force or not self._cache_manager.is_latest_version( version, 'sample', cache_label): pcoll_list, version ='sample', cache_label) stats['sample'] = list(pcoll_list) stats['version'] = version stats_updated = True if pcoll_id in self._analyzer.tl_referenced_pcoll_ids(): self._text_to_print[pcoll_id] = ( str( '%s produced %s' % ( self._producers[pcoll_id], interactive_pipeline_graph.format_sample(pcoll_list, 5)))) if force or stats_updated: self._pipeline_graph.update_pcollection_stats(self._pcollection_stats) if IPython: from IPython import display display.clear_output(True) rendered_graph = self._renderer.render_pipeline_graph( self._pipeline_graph) display.display(display.HTML(rendered_graph)) _display_progress('Running...') for text in self._text_to_print.values(): if text != "": _display_progress(text)
[docs] def start_periodic_update(self): """Start a thread that periodically updates the display.""" self.update_display(True) self._periodic_update = True def _updater(): while self._periodic_update: self.update_display() time.sleep(.02) t = threading.Thread(target=_updater) t.daemon = True t.start()
[docs] def stop_periodic_update(self): """Stop periodically updating the display.""" self.update_display(True) self._periodic_update = False