Source code for apache_beam.runners.interactive.augmented_pipeline

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"""Module to augment interactive flavor into the given pipeline.

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

import copy
from typing import Dict
from typing import Optional
from typing import Set

import apache_beam as beam
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.runners.interactive import interactive_environment as ie
from apache_beam.runners.interactive import background_caching_job
from apache_beam.runners.interactive.caching.cacheable import Cacheable
from apache_beam.runners.interactive.caching.read_cache import ReadCache
from apache_beam.runners.interactive.caching.write_cache import WriteCache

[docs]class AugmentedPipeline: """A pipeline with augmented interactive flavor that caches intermediate PCollections defined by the user, reads computed PCollections as source and prunes unnecessary pipeline parts for fast computation. """ def __init__( self, user_pipeline: beam.Pipeline, pcolls: Optional[Set[beam.pvalue.PCollection]] = None): """ Initializes a pipelilne for augmenting interactive flavor. Args: user_pipeline: a beam.Pipeline instance defined by the user. pcolls: cacheable pcolls to be computed/retrieved. If the set is empty, all intermediate pcolls assigned to variables are applicable. """ assert not pcolls or all(pcoll.pipeline is user_pipeline for pcoll in pcolls), 'All %s need to belong to %s' % (pcolls, user_pipeline) self._user_pipeline = user_pipeline self._pcolls = pcolls self._cache_manager = ie.current_env().get_cache_manager( self._user_pipeline, create_if_absent=True) if background_caching_job.has_source_to_cache(self._user_pipeline): self._cache_manager = ie.current_env().get_cache_manager( self._user_pipeline) _, self._context = self._user_pipeline.to_runner_api(return_context=True) self._context.component_id_map = copy.copy( self._user_pipeline.component_id_map) self._cacheables = self.cacheables() @property def augmented_pipeline(self) -> beam_runner_api_pb2.Pipeline: return self.augment() # TODO(BEAM-10708): Support generating a background recording job that # contains unbound source recording transforms only. @property def background_recording_pipeline(self) -> beam_runner_api_pb2.Pipeline: raise NotImplementedError
[docs] def cacheables(self) -> Dict[beam.pvalue.PCollection, Cacheable]: """Finds all the cacheable intermediate PCollections in the pipeline with their metadata. """ c = {} for watching in ie.current_env().watching(): for key, val in watching: if (isinstance(val, beam.pvalue.PCollection) and val.pipeline is self._user_pipeline and (not self._pcolls or val in self._pcolls)): c[val] = Cacheable( var=key, pcoll=val, version=str(id(val)), producer_version=str(id(val.producer))) return c
[docs] def augment(self) -> beam_runner_api_pb2.Pipeline: """Augments the pipeline with cache. Always calculates a new result. For a cacheable PCollection, if cache exists, read cache; else, write cache. """ pipeline = self._user_pipeline.to_runner_api() # Find pcolls eligible for reading or writing cache. readcache_pcolls = set() for pcoll, cacheable in self._cacheables.items(): key = repr(cacheable.to_key()) if (self._cache_manager.exists('full', key) and pcoll in ie.current_env().computed_pcollections): readcache_pcolls.add(pcoll) writecache_pcolls = set( self._cacheables.keys()).difference(readcache_pcolls) # Wire in additional transforms to read cache and write cache. for readcache_pcoll in readcache_pcolls: ReadCache( pipeline, self._context, self._cache_manager, self._cacheables[readcache_pcoll]).read_cache() for writecache_pcoll in writecache_pcolls: WriteCache( pipeline, self._context, self._cache_manager, self._cacheables[writecache_pcoll]).write_cache() # TODO(BEAM-10708): Support streaming, add pruning logic, and integrate # pipeline fragment logic. return pipeline