github.com/apache/beam/sdks/v2@v2.48.2/python/apache_beam/transforms/periodicsequence.py

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import math
import time

import apache_beam as beam
from apache_beam.io.restriction_trackers import OffsetRange
from apache_beam.io.restriction_trackers import OffsetRestrictionTracker
from apache_beam.io.watermark_estimators import ManualWatermarkEstimator
from apache_beam.runners import sdf_utils
from apache_beam.transforms import core
from apache_beam.transforms import window
from apache_beam.transforms.ptransform import PTransform
from apache_beam.transforms.window import TimestampedValue
from apache_beam.utils import timestamp
from apache_beam.utils.timestamp import MAX_TIMESTAMP
from apache_beam.utils.timestamp import Timestamp


class ImpulseSeqGenRestrictionProvider(core.RestrictionProvider):
  def initial_restriction(self, element):
    start, end, interval = element
    if isinstance(start, Timestamp):
      start = start.micros / 1000000
    if isinstance(end, Timestamp):
      end = end.micros / 1000000

    assert start <= end
    assert interval > 0
    total_outputs = math.ceil((end - start) / interval)
    return OffsetRange(0, total_outputs)

  def create_tracker(self, restriction):
    return OffsetRestrictionTracker(restriction)

  def restriction_size(self, unused_element, restriction):
    return restriction.size()

  # On drain, immediately stop emitting new elements
  def truncate(self, unused_element, unused_restriction):
    return None


class ImpulseSeqGenDoFn(beam.DoFn):
  '''
  ImpulseSeqGenDoFn fn receives tuple elements with three parts:

  * first_timestamp = first timestamp to output element for.
  * last_timestamp = last timestamp/time to output element for.
  * fire_interval = how often to fire an element.

  For each input element received, ImpulseSeqGenDoFn fn will start
  generating output elements in following pattern:

  * if element timestamp is less than current runtime then output element.
  * if element timestamp is greater than current runtime, wait until next
    element timestamp.

  ImpulseSeqGenDoFn can't guarantee that each element is output at exact time.
  ImpulseSeqGenDoFn guarantees that elements would not be output prior to
  given runtime timestamp.
  '''
  @beam.DoFn.unbounded_per_element()
  def process(
      self,
      element,
      restriction_tracker=beam.DoFn.RestrictionParam(
          ImpulseSeqGenRestrictionProvider()),
      watermark_estimator=beam.DoFn.WatermarkEstimatorParam(
          ManualWatermarkEstimator.default_provider())):
    '''
    :param element: (start_timestamp, end_timestamp, interval)
    :param restriction_tracker:
    :return: yields elements at processing real-time intervals with value of
      target output timestamp for the element.
    '''
    start, _, interval = element

    if isinstance(start, Timestamp):
      start = start.micros / 1000000

    assert isinstance(restriction_tracker, sdf_utils.RestrictionTrackerView)

    current_output_index = restriction_tracker.current_restriction().start
    current_output_timestamp = start + interval * current_output_index
    current_time = time.time()
    watermark_estimator.set_watermark(
        timestamp.Timestamp(current_output_timestamp))

    while current_output_timestamp <= current_time:
      if restriction_tracker.try_claim(current_output_index):
        yield current_output_timestamp
        current_output_index += 1
        current_output_timestamp = start + interval * current_output_index
        current_time = time.time()
        watermark_estimator.set_watermark(
            timestamp.Timestamp(current_output_timestamp))
      else:
        return

    restriction_tracker.defer_remainder(
        timestamp.Timestamp(current_output_timestamp))


class PeriodicSequence(PTransform):
  '''
  PeriodicSequence transform receives tuple elements with three parts:

  * first_timestamp = first timestamp to output element for.
  * last_timestamp = last timestamp/time to output element for.
  * fire_interval = how often to fire an element.

  For each input element received, PeriodicSequence transform will start
  generating output elements in following pattern:

  * if element timestamp is less than current runtime then output element.
  * if element timestamp is greater than current runtime, wait until next
    element timestamp.

  PeriodicSequence can't guarantee that each element is output at exact time.
  PeriodicSequence guarantees that elements would not be output prior to given
  runtime timestamp.
  The PCollection generated by PeriodicSequence is unbounded.
  '''
  def __init__(self):
    pass

  def expand(self, pcoll):
    return (
        pcoll
        | 'GenSequence' >> beam.ParDo(ImpulseSeqGenDoFn())
        | 'MapToTimestamped' >> beam.Map(lambda tt: TimestampedValue(tt, tt)))


class PeriodicImpulse(PTransform):
  '''
  PeriodicImpulse transform generates an infinite sequence of elements with
  given runtime interval.

  PeriodicImpulse transform behaves same as {@link PeriodicSequence} transform,
  but can be used as first transform in pipeline.
  The PCollection generated by PeriodicImpulse is unbounded.
  '''
  def __init__(
      self,
      start_timestamp=Timestamp.now(),
      stop_timestamp=MAX_TIMESTAMP,
      fire_interval=360.0,
      apply_windowing=False):
    '''
    :param start_timestamp: Timestamp for first element.
    :param stop_timestamp: Timestamp after which no elements will be output.
    :param fire_interval: Interval at which to output elements.
    :param apply_windowing: Whether each element should be assigned to
      individual window. If false, all elements will reside in global window.
    '''
    self.start_ts = start_timestamp
    self.stop_ts = stop_timestamp
    self.interval = fire_interval
    self.apply_windowing = apply_windowing

  def expand(self, pbegin):
    result = (
        pbegin
        | 'ImpulseElement' >> beam.Create(
            [(self.start_ts, self.stop_ts, self.interval)])
        | 'GenSequence' >> beam.ParDo(ImpulseSeqGenDoFn())
        | 'MapToTimestamped' >> beam.Map(lambda tt: TimestampedValue(tt, tt)))
    if self.apply_windowing:
      result = result | 'ApplyWindowing' >> beam.WindowInto(
          window.FixedWindows(self.interval))
    return result