Source code for apache_beam.runners.interactive.caching.read_cache

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"""Module to read cache of computed PCollections.

For internal use only; no backward-compatibility guarantees.
# pytype: skip-file

from typing import Tuple

import apache_beam as beam
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.runners.interactive import cache_manager as cache
from apache_beam.runners.interactive.caching.cacheable import Cacheable
from apache_beam.runners.interactive.caching.reify import unreify_from_cache
from apache_beam.runners.pipeline_context import PipelineContext
from apache_beam.transforms.ptransform import PTransform

[docs]class ReadCache: """Class that facilitates reading cache of computed PCollections. """ def __init__( self, pipeline: beam_runner_api_pb2.Pipeline, context: PipelineContext, cache_manager: cache.CacheManager, cacheable: Cacheable): self._pipeline = pipeline self._context = context self._cache_manager = cache_manager self._cacheable = cacheable self._key = repr(cacheable.to_key())
[docs] def read_cache(self) -> Tuple[str, str]: """Reads cache of the cacheable PCollection and wires the cache into the pipeline proto. Returns the pipeline-scoped ids of the cacheable PCollection and the cache reading output PCollection that replaces it. First, it creates a temporary pipeline instance on top of the existing component_id_map from the self._pipeline's context so that both pipelines share the context and have no conflict component ids. Second, it instantiates a _ReadCacheTransform to build the temporary pipeline with a subgraph under top level transforms that reads the cache of a cacheable PCollection. Third, it copies components of the subgraph from the temporary pipeline to self._pipeline, skipping components that are not in the temporary pipeline but presents in the component_id_map of self._pipeline. Since to_runner_api generates components for all entries in the component_id_map, those component ids from the context shared by self._pipeline need to be ignored. Last, it replaces inputs of all transforms that consume the cacheable PCollection with the output PCollection of the _ReadCacheTransform so that the whole pipeline computes with data from the cache. The pipeline fragment of reading the cacheable PCollection is now disconnected from the rest of the pipeline and can be pruned later. """ template, read_output = self._build_runner_api_template() output_id = self._context.pcollections.get_id(read_output) source_id = self._context.pcollections.get_id(self._cacheable.pcoll) # Copy cache reading subgraph from the template to the pipeline proto. for pcoll_id in template.components.pcollections: if pcoll_id in self._pipeline.components.pcollections: continue self._pipeline.components.pcollections[pcoll_id].CopyFrom( template.components.pcollections[pcoll_id]) for coder_id in template.components.coders: if coder_id in self._pipeline.components.coders: continue self._pipeline.components.coders[coder_id].CopyFrom( template.components.coders[coder_id]) for windowing_strategy_id in template.components.windowing_strategies: if (windowing_strategy_id in self._pipeline.components.windowing_strategies): continue self._pipeline.components.windowing_strategies[ windowing_strategy_id].CopyFrom( template.components.windowing_strategies[windowing_strategy_id]) template_root_transform_id = template.root_transform_ids[0] root_transform_id = self._pipeline.root_transform_ids[0] for transform_id in template.components.transforms: if (transform_id == template_root_transform_id or transform_id in self._pipeline.components.transforms): continue self._pipeline.components.transforms[transform_id].CopyFrom( template.components.transforms[transform_id]) self._pipeline.components.transforms[ root_transform_id].subtransforms.extend( template.components.transforms[template_root_transform_id]. subtransforms) # Replace all the input pcoll of source_id with output pcoll of output_id # from cache reading. for transform in self._pipeline.components.transforms.values(): inputs = transform.inputs if source_id in inputs.values(): keys_need_replacement = set() for key in inputs: if inputs[key] == source_id: keys_need_replacement.add(key) for key in keys_need_replacement: inputs[key] = output_id return source_id, output_id
def _build_runner_api_template( self) -> Tuple[beam_runner_api_pb2.Pipeline, beam.pvalue.PCollection]: transform = _ReadCacheTransform(self._cache_manager, self._key) tmp_pipeline = beam.Pipeline() tmp_pipeline.component_id_map = self._context.component_id_map read_output = tmp_pipeline | 'source_cache_' >> transform return tmp_pipeline.to_runner_api(), read_output
class _ReadCacheTransform(PTransform): """A composite transform encapsulates reading cache of PCollections. """ def __init__(self, cache_manager: cache.CacheManager, key: str): self._cache_manager = cache_manager self._key = key def expand(self, pcoll: beam.pvalue.PCollection) -> beam.pvalue.PCollection: return unreify_from_cache( pipeline=pcoll.pipeline, cache_key=self._key, cache_manager=self._cache_manager)