Source code for apache_beam.runners.interactive.background_caching_job

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"""Module to build and run background caching job.

For internal use only; no backwards-compatibility guarantees.

A background caching job is a job that captures events for all capturable
sources of a given pipeline. With Interactive Beam, one such job is started when
a pipeline run happens (which produces a main job in contrast to the background
caching job) and meets the following conditions:

  #. The pipeline contains capturable sources, configured through
  #. No such background job is running.
  #. No such background job has completed successfully and the cached events are
     still valid (invalidated when capturable sources change in the pipeline).

Once started, the background caching job runs asynchronously until it hits some
capture limit configured in interactive_beam.options. Meanwhile, the main job
and future main jobs from the pipeline will run using the deterministic
replayable captured events until they are invalidated.

# pytype: skip-file

from __future__ import absolute_import

import logging
import threading
import time

import apache_beam as beam
from apache_beam.runners.interactive import interactive_environment as ie
from apache_beam.runners.interactive.caching import streaming_cache
from apache_beam.runners.runner import PipelineState

_LOGGER = logging.getLogger(__name__)

[docs]class BackgroundCachingJob(object): """A simple abstraction that controls necessary components of a timed and space limited background caching job. A background caching job successfully completes source data capture in 2 conditions: #. The job is finite and runs into DONE state; #. The job is infinite but hits an interactive_beam.options configured limit and gets cancelled into CANCELLED/CANCELLING state. In both situations, the background caching job should be treated as done successfully. """ def __init__(self, pipeline_result, limiters): self._pipeline_result = pipeline_result self._condition_checker = threading.Thread( target=self._background_caching_job_condition_checker, daemon=True) # Limiters are checks s.t. if any are triggered then the background caching # job gets cancelled. self._limiters = limiters self._condition_checker.start() def _background_caching_job_condition_checker(self): while not PipelineState.is_terminal(self._pipeline_result.state): if self._should_end_condition_checker(): self.cancel() break time.sleep(0.5) def _should_end_condition_checker(self): return any([l.is_triggered() for l in self._limiters])
[docs] def is_done(self): is_terminated = self._pipeline_result.state is PipelineState.DONE is_triggered = self._should_end_condition_checker() is_cancelling = ( self._pipeline_result.state in ( PipelineState.CANCELLED, PipelineState.CANCELLING)) return is_terminated or (is_triggered and is_cancelling)
[docs] def is_running(self): return self._pipeline_result.state is PipelineState.RUNNING
[docs] def cancel(self): """Cancels this background caching job. """ if not PipelineState.is_terminal(self._pipeline_result.state): try: self._pipeline_result.cancel() except NotImplementedError: # Ignore the cancel invocation if it is never implemented by the runner. pass
[docs]def attempt_to_run_background_caching_job(runner, user_pipeline, options=None): """Attempts to run a background caching job for a user-defined pipeline. The pipeline result is automatically tracked by Interactive Beam in case future cancellation/cleanup is needed. """ if is_background_caching_job_needed(user_pipeline): # Cancel non-terminal jobs if there is any before starting a new one. attempt_to_cancel_background_caching_job(user_pipeline) # Cancel the gRPC server serving the test stream if there is one. attempt_to_stop_test_stream_service(user_pipeline) # TODO(BEAM-8335): refactor background caching job logic from # pipeline_instrument module to this module and aggregate tests. from apache_beam.runners.interactive import pipeline_instrument as instr runner_pipeline = beam.pipeline.Pipeline.from_runner_api( user_pipeline.to_runner_api(use_fake_coders=True), runner, options) background_caching_job_result = beam.pipeline.Pipeline.from_runner_api( instr.build_pipeline_instrument( runner_pipeline).background_caching_pipeline_proto(), runner, options).run() limiters = ie.current_env().options.capture_control.limiters() ie.current_env().set_background_caching_job( user_pipeline, BackgroundCachingJob(background_caching_job_result, limiters=limiters))
[docs]def is_background_caching_job_needed(user_pipeline): """Determines if a background caching job needs to be started. It does several state checks and record state changes throughout the process. It is not idempotent to simplify the usage. """ job = ie.current_env().get_background_caching_job(user_pipeline) # Checks if the pipeline contains any source that needs to be cached. need_cache = has_source_to_cache(user_pipeline) # If this is True, we can invalidate a previous done/running job if there is # one. cache_changed = is_source_to_cache_changed(user_pipeline) # When capture replay is disabled, cache is always needed for capturable # sources (if any). if need_cache and not ie.current_env().options.enable_capture_replay: from apache_beam.runners.interactive.options import capture_control capture_control.evict_captured_data() return True return ( need_cache and # Checks if it's the first time running a job from the pipeline. ( not job or # Or checks if there is no previous job. # DONE means a previous job has completed successfully and the # cached events might still be valid. not ( job.is_done() or # RUNNING means a previous job has been started and is still # running. job.is_running()) or # Or checks if we can invalidate the previous job. cache_changed))
[docs]def is_cache_complete(pipeline_id): # type: (str) -> bool """Returns True if the backgrond cache for the given pipeline is done. """ user_pipeline = ie.current_env().pipeline_id_to_pipeline(pipeline_id) job = ie.current_env().get_background_caching_job(user_pipeline) is_done = job and job.is_done() cache_changed = is_source_to_cache_changed( user_pipeline, update_cached_source_signature=False) return is_done and not cache_changed
[docs]def has_source_to_cache(user_pipeline): """Determines if a user-defined pipeline contains any source that need to be cached. If so, also immediately wrap current cache manager held by current interactive environment into a streaming cache if this has not been done. The wrapping doesn't invalidate existing cache in any way. This can help determining if a background caching job is needed to write cache for sources and if a test stream service is needed to serve the cache. Throughout the check, if source-to-cache has changed from the last check, it also cleans up the invalidated cache early on. """ from apache_beam.runners.interactive import pipeline_instrument as instr # TODO(BEAM-8335): we temporarily only cache replaceable unbounded sources. # Add logic for other cacheable sources here when they are available. has_cache = instr.has_unbounded_sources(user_pipeline) if has_cache: if not isinstance(ie.current_env().cache_manager(), streaming_cache.StreamingCache): ie.current_env().set_cache_manager( streaming_cache.StreamingCache( ie.current_env().cache_manager()._cache_dir, is_cache_complete=is_cache_complete, sample_resolution_sec=1.0)) return has_cache
[docs]def attempt_to_cancel_background_caching_job(user_pipeline): """Attempts to cancel background caching job for a user-defined pipeline. If no background caching job needs to be cancelled, NOOP. Otherwise, cancel such job. """ job = ie.current_env().get_background_caching_job(user_pipeline) if job: job.cancel()
[docs]def attempt_to_stop_test_stream_service(user_pipeline): """Attempts to stop the gRPC server/service serving the test stream. If there is no such server started, NOOP. Otherwise, stop it. """ if is_a_test_stream_service_running(user_pipeline): ie.current_env().evict_test_stream_service_controller(user_pipeline).stop()
[docs]def is_a_test_stream_service_running(user_pipeline): """Checks to see if there is a gPRC server/service running that serves the test stream to any job started from the given user_pipeline. """ return ie.current_env().get_test_stream_service_controller( user_pipeline) is not None
[docs]def is_source_to_cache_changed( user_pipeline, update_cached_source_signature=True): """Determines if there is any change in the sources that need to be cached used by the user-defined pipeline. Due to the expensiveness of computations and for the simplicity of usage, this function is not idempotent because Interactive Beam automatically discards previously tracked signature of transforms and tracks the current signature of transforms for the user-defined pipeline if there is any change. When it's True, there is addition/deletion/mutation of source transforms that requires a new background caching job. """ # By default gets empty set if the user_pipeline is first time seen because # we can treat it as adding transforms. recorded_signature = ie.current_env().get_cached_source_signature( user_pipeline) current_signature = extract_source_to_cache_signature(user_pipeline) is_changed = not current_signature.issubset(recorded_signature) # The computation of extract_unbounded_source_signature is expensive, track on # change by default. if is_changed and update_cached_source_signature: options = ie.current_env().options # No info needed when capture replay is disabled. if options.enable_capture_replay: if not recorded_signature: def sizeof_fmt(num, suffix='B'): for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']: if abs(num) < 1000.0: return "%3.1f%s%s" % (num, unit, suffix) num /= 1000.0 return "%.1f%s%s" % (num, 'Yi', suffix) 'Interactive Beam has detected unbounded sources in your pipeline. ' 'In order to have a deterministic replay, a segment of data will ' 'be recorded from all sources for %s seconds or until a total of ' '%s have been written to disk.', options.capture_duration.total_seconds(), sizeof_fmt(options.capture_size_limit)) else: 'Interactive Beam has detected a new streaming source was ' 'added to the pipeline. In order for the cached streaming ' 'data to start at the same time, all captured data has been ' 'cleared and a new segment of data will be recorded.') ie.current_env().cleanup() ie.current_env().set_cached_source_signature( user_pipeline, current_signature) return is_changed
[docs]def extract_source_to_cache_signature(user_pipeline): """Extracts a set of signature for sources that need to be cached in the user-defined pipeline. A signature is a str representation of urn and payload of a source. """ from apache_beam.runners.interactive import pipeline_instrument as instr # TODO(BEAM-8335): we temporarily only cache replaceable unbounded sources. # Add logic for other cacheable sources here when they are available. unbounded_sources_as_applied_transforms = instr.unbounded_sources( user_pipeline) unbounded_sources_as_ptransforms = set( map(lambda x: x.transform, unbounded_sources_as_applied_transforms)) _, context = user_pipeline.to_runner_api( return_context=True, use_fake_coders=True) signature = set( map( lambda transform: str(transform.to_runner_api(context)), unbounded_sources_as_ptransforms)) return signature