Source code for apache_beam.runners.interactive.recording_manager

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import logging
import threading
import time
import warnings

import pandas as pd

import apache_beam as beam
from apache_beam.dataframe.convert import to_pcollection
from apache_beam.dataframe.frame_base import DeferredBase
from apache_beam.portability.api.beam_runner_api_pb2 import TestStreamPayload
from apache_beam.runners.interactive import background_caching_job as bcj
from apache_beam.runners.interactive import interactive_environment as ie
from apache_beam.runners.interactive import interactive_runner as ir
from apache_beam.runners.interactive import pipeline_fragment as pf
from apache_beam.runners.interactive import pipeline_instrument as pi
from apache_beam.runners.interactive import utils
from apache_beam.runners.runner import PipelineState

_LOGGER = logging.getLogger(__name__)

[docs]class ElementStream: """A stream of elements from a given PCollection.""" def __init__( self, pcoll, # type: beam.pvalue.PCollection var, # type: str cache_key, # type: str max_n, # type: int max_duration_secs # type: float ): self._pcoll = pcoll self._cache_key = cache_key self._pipeline = pcoll.pipeline self._var = var self._n = max_n self._duration_secs = max_duration_secs # A small state variable that when True, indicates that no more new elements # will be yielded if read() is called again. self._done = False @property def var(self): # type: () -> str """Returns the variable named that defined this PCollection.""" return self._var @property def cache_key(self): # type: () -> str """Returns the cache key for this stream.""" return self._cache_key
[docs] def display_id(self, suffix): # type: (str) -> str """Returns a unique id able to be displayed in a web browser.""" return utils.obfuscate(self._cache_key, suffix)
[docs] def is_computed(self): # type: () -> boolean """Returns True if no more elements will be recorded.""" return self._pcoll in ie.current_env().computed_pcollections
[docs] def is_done(self): # type: () -> boolean """Returns True if no more new elements will be yielded.""" return self._done
[docs] def read(self, tail=True): # type: (boolean) -> Any """Reads the elements currently recorded.""" # Get the cache manager and wait until the file exists. cache_manager = ie.current_env().get_cache_manager(self._pipeline) # Retrieve the coder for the particular PCollection which will be used to # decode elements read from cache. coder = cache_manager.load_pcoder('full', self._cache_key) # Read the elements from the cache. # Import limiters here to prevent a circular import. from apache_beam.runners.interactive.options.capture_limiters import CountLimiter from apache_beam.runners.interactive.options.capture_limiters import ProcessingTimeLimiter reader, _ ='full', self._cache_key, tail=tail) # Because a single TestStreamFileRecord can yield multiple elements, we # limit the count again here in the to_element_list call. # # There are two ways of exiting this loop either a limiter was triggered or # all elements from the cache were read. In the latter situation, it may be # the case that the pipeline was still running. Thus, another invocation of # `read` will yield new elements. count_limiter = CountLimiter(self._n) time_limiter = ProcessingTimeLimiter(self._duration_secs) limiters = (count_limiter, time_limiter) for e in utils.to_element_list(reader, coder, include_window_info=True, n=self._n, include_time_events=True): # From the to_element_list we either get TestStreamPayload.Events if # include_time_events or decoded elements from the reader. Make sure we # only count the decoded elements to break early. if isinstance(e, TestStreamPayload.Event): time_limiter.update(e) else: count_limiter.update(e) yield e if any(l.is_triggered() for l in limiters): break # A limiter being triggered means that we have fulfilled the user's request. # This implies that reading from the cache again won't yield any new # elements. WLOG, this applies to the user pipeline being terminated. if any(l.is_triggered() for l in limiters) or ie.current_env().is_terminated(self._pipeline): self._done = True
[docs]class Recording: """A group of PCollections from a given pipeline run.""" def __init__( self, user_pipeline, # type: beam.Pipeline pcolls, # type: List[beam.pvalue.PCollection] result, # type: beam.runner.PipelineResult pipeline_instrument, # type: beam.runners.interactive.PipelineInstrument max_n, # type: int max_duration_secs, # type: float ): self._user_pipeline = user_pipeline self._result = result self._result_lock = threading.Lock() self._pcolls = pcolls pcoll_var = lambda pcoll: pipeline_instrument.cacheable_var_by_pcoll_id( pipeline_instrument.pcolls_to_pcoll_id.get(str(pcoll), None)) self._streams = { pcoll: ElementStream( pcoll, pcoll_var(pcoll), pipeline_instrument.cache_key(pcoll), max_n, max_duration_secs) for pcoll in pcolls } self._start = time.time() self._duration_secs = max_duration_secs self._set_computed = bcj.is_cache_complete(str(id(user_pipeline))) # Run a separate thread for marking the PCollections done. This is because # the pipeline run may be asynchronous. self._mark_computed = threading.Thread(target=self._mark_all_computed) self._mark_computed.daemon = True self._mark_computed.start() def _mark_all_computed(self): # type: () -> None """Marks all the PCollections upon a successful pipeline run.""" if not self._result: return while not PipelineState.is_terminal(self._result.state): with self._result_lock: bcj = ie.current_env().get_background_caching_job(self._user_pipeline) if bcj and bcj.is_done(): self._result.wait_until_finish() elif time.time() - self._start >= self._duration_secs: self._result.cancel() self._result.wait_until_finish() elif all(s.is_done() for s in self._streams.values()): self._result.cancel() self._result.wait_until_finish() time.sleep(0.1) # Mark the PCollection as computed so that Interactive Beam wouldn't need to # re-compute. if self._result.state is PipelineState.DONE and self._set_computed: ie.current_env().mark_pcollection_computed(self._pcolls)
[docs] def is_computed(self): # type: () -> boolean """Returns True if all PCollections are computed.""" return all(s.is_computed() for s in self._streams.values())
[docs] def stream(self, pcoll): # type: (beam.pvalue.PCollection) -> ElementStream """Returns an ElementStream for a given PCollection.""" return self._streams[pcoll]
[docs] def computed(self): # type: () -> None """Returns all computed ElementStreams.""" return {p: s for p, s in self._streams.items() if s.is_computed()}
[docs] def uncomputed(self): # type: () -> None """Returns all uncomputed ElementStreams.""" return {p: s for p, s in self._streams.items() if not s.is_computed()}
[docs] def cancel(self): # type: () -> None """Cancels the recording.""" with self._result_lock: self._result.cancel()
[docs] def wait_until_finish(self): # type: () -> None """Waits until the pipeline is done and returns the final state. This also marks any PCollections as computed right away if the pipeline is successful. """ if not self._result: return beam.runners.runner.PipelineState.DONE self._mark_computed.join() return self._result.state
[docs] def describe(self): # type: () -> dict[str, int] """Returns a dictionary describing the cache and recording.""" cache_manager = ie.current_env().get_cache_manager(self._user_pipeline) size = sum( cache_manager.size('full', s.cache_key) for s in self._streams.values()) return {'size': size, 'duration': self._duration_secs}
[docs]class RecordingManager: """Manages recordings of PCollections for a given pipeline.""" def __init__(self, user_pipeline, pipeline_var=None, test_limiters=None): # type: (beam.Pipeline, str, list[Limiter]) -> None self.user_pipeline = user_pipeline # type: beam.Pipeline self.pipeline_var = pipeline_var if pipeline_var else '' # type: str self._recordings = set() # type: set[Recording] self._start_time_sec = 0 # type: float self._test_limiters = test_limiters if test_limiters else [] def _watch(self, pcolls): # type: (List[beam.pvalue.PCollection]) -> None """Watch any pcollections not being watched. This allows for the underlying caching layer to identify the PCollection as something to be cached. """ watched_pcollections = set() watched_dataframes = set() for watching in ie.current_env().watching(): for _, val in watching: if isinstance(val, beam.pvalue.PCollection): watched_pcollections.add(val) elif isinstance(val, DeferredBase): watched_dataframes.add(val) # Convert them all in a single step for efficiency. for pcoll in to_pcollection(*watched_dataframes, always_return_tuple=True): watched_pcollections.add(pcoll) for pcoll in pcolls: if pcoll not in watched_pcollections: ie.current_env().watch( {'anonymous_pcollection_{}'.format(id(pcoll)): pcoll}) def _clear(self, pipeline_instrument): # type: (List[beam.pvalue.PCollection]) -> None """Clears the recording of all non-source PCollections.""" cache_manager = ie.current_env().get_cache_manager(self.user_pipeline) # Only clear the PCollections that aren't being populated from the # BackgroundCachingJob. computed = ie.current_env().computed_pcollections cacheables = [ c for c in pipeline_instrument.cacheables.values() if c.pcoll.pipeline is self.user_pipeline and c.pcoll not in computed ] all_cached = set(str(c.to_key()) for c in cacheables) source_pcolls = getattr(cache_manager, 'capture_keys', set()) to_clear = all_cached - source_pcolls self._clear_pcolls(cache_manager, set(to_clear)) def _clear_pcolls(self, cache_manager, pcolls): for pc in pcolls: cache_manager.clear('full', pc)
[docs] def clear(self): # type: () -> None """Clears all cached PCollections for this RecordingManager.""" cache_manager = ie.current_env().get_cache_manager(self.user_pipeline) if cache_manager: cache_manager.cleanup()
[docs] def cancel(self): # type: (None) -> None """Cancels the current background recording job.""" bcj.attempt_to_cancel_background_caching_job(self.user_pipeline) for r in self._recordings: r.wait_until_finish() self._recordings = set() # The recordings rely on a reference to the BCJ to correctly finish. So we # evict the BCJ after they complete. ie.current_env().evict_background_caching_job(self.user_pipeline)
[docs] def describe(self): # type: () -> dict[str, int] """Returns a dictionary describing the cache and recording.""" cache_manager = ie.current_env().get_cache_manager(self.user_pipeline) capture_size = getattr(cache_manager, 'capture_size', 0) descriptions = [r.describe() for r in self._recordings] size = sum(d['size'] for d in descriptions) + capture_size start = self._start_time_sec bcj = ie.current_env().get_background_caching_job(self.user_pipeline) if bcj: state = bcj.state else: state = PipelineState.STOPPED return { 'size': size, 'start': start, 'state': state, 'pipeline_var': self.pipeline_var }
[docs] def record_pipeline(self): # type: () -> bool """Starts a background caching job for this RecordingManager's pipeline.""" runner = self.user_pipeline.runner if isinstance(runner, ir.InteractiveRunner): runner = runner._underlying_runner # Make sure that sources without a user reference are still cached. ie.current_env().add_user_pipeline(self.user_pipeline) pi.watch_sources(self.user_pipeline) # Attempt to run background caching job to record any sources. if ie.current_env().is_in_ipython: warnings.filterwarnings( 'ignore', 'options is deprecated since First stable release. References to ' '<pipeline>.options will not be supported', category=DeprecationWarning) if bcj.attempt_to_run_background_caching_job( runner, self.user_pipeline, options=self.user_pipeline.options, limiters=self._test_limiters): self._start_time_sec = time.time() return True return False
[docs] def record(self, pcolls, max_n, max_duration): # type: (List[beam.pvalue.PCollection], int, Union[int,str]) -> Recording """Records the given PCollections.""" # Assert that all PCollection come from the same user_pipeline. for pcoll in pcolls: assert pcoll.pipeline is self.user_pipeline, ( '{} belongs to a different user-defined pipeline ({}) than that of' ' other PCollections ({}).'.format( pcoll, pcoll.pipeline, self.user_pipeline)) if isinstance(max_duration, str) and max_duration != 'inf': max_duration_secs = pd.to_timedelta(max_duration).total_seconds() else: max_duration_secs = max_duration # Make sure that all PCollections to be shown are watched. If a PCollection # has not been watched, make up a variable name for that PCollection and # watch it. No validation is needed here because the watch logic can handle # arbitrary variables. self._watch(pcolls) pipeline_instrument = pi.PipelineInstrument(self.user_pipeline) self.record_pipeline() # Get the subset of computed PCollections. These do not to be recomputed. computed_pcolls = set( pcoll for pcoll in pcolls if pcoll in ie.current_env().computed_pcollections) # Start a pipeline fragment to start computing the PCollections. uncomputed_pcolls = set(pcolls).difference(computed_pcolls) if uncomputed_pcolls: # Clear the cache of the given uncomputed PCollections because they are # incomplete. self._clear(pipeline_instrument) warnings.filterwarnings( 'ignore', 'options is deprecated since First stable release. References to ' '<pipeline>.options will not be supported', category=DeprecationWarning) pf.PipelineFragment(list(uncomputed_pcolls), self.user_pipeline.options).run() result = ie.current_env().pipeline_result(self.user_pipeline) else: result = None recording = Recording( self.user_pipeline, pcolls, result, pipeline_instrument, max_n, max_duration_secs) self._recordings.add(recording) return recording