Python GlobalWindows.windowed_value方法代码示例

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
  def finish_bundle(self):
    data = self._read_from_pubsub(self.source.timestamp_attribute)
    if data:
      output_pcollection = list(self._outputs)[0]
      bundle = self._evaluation_context.create_bundle(output_pcollection)
      # TODO(ccy): Respect the PubSub source's id_label field.
      for timestamp, message in data:
        if self.source.with_attributes:
          element = message
        else:
          element = message.data
        bundle.output(
            GlobalWindows.windowed_value(element, timestamp=timestamp))
      bundles = [bundle]
    else:
      bundles = []
    if self._applied_ptransform.inputs:
      input_pvalue = self._applied_ptransform.inputs[0]
    else:
      input_pvalue = pvalue.PBegin(self._applied_ptransform.transform.pipeline)
    unprocessed_bundle = self._evaluation_context.create_bundle(
        input_pvalue)

    # TODO(udim): Correct value for watermark hold.
    return TransformResult(self, bundles, [unprocessed_bundle], None,
                           {None: Timestamp.of(time.time())})

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def __iter__(self):
   output_stream = create_OutputStream()
   for encoded_key, values in self._table.items():
     key = self._key_coder.decode(encoded_key)
     self._post_grouped_coder.get_impl().encode_to_stream(
         GlobalWindows.windowed_value((key, values)), output_stream, True)
   return iter([output_stream.get()])

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def test_update_int(self):
   opcounts = OperationCounters(CounterFactory(), 'some-name',
                                coders.PickleCoder(), 0)
   self.verify_counters(opcounts, 0)
   opcounts.update_from(GlobalWindows.windowed_value(1))
   opcounts.update_collect()
   self.verify_counters(opcounts, 1)

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def test_update_multiple(self):
   coder = coders.PickleCoder()
   total_size = 0
   opcounts = OperationCounters(CounterFactory(), 'some-name',
                                coder, 0)
   self.verify_counters(opcounts, 0, float('nan'))
   value = GlobalWindows.windowed_value('abcde')
   opcounts.update_from(value)
   total_size += coder.estimate_size(value)
   value = GlobalWindows.windowed_value('defghij')
   opcounts.update_from(value)
   total_size += coder.estimate_size(value)
   self.verify_counters(opcounts, 2, (float(total_size) / 2))
   value = GlobalWindows.windowed_value('klmnop')
   opcounts.update_from(value)
   total_size += coder.estimate_size(value)
   self.verify_counters(opcounts, 3, (float(total_size) / 3))

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def test_update_str(self):
   coder = coders.PickleCoder()
   opcounts = OperationCounters(CounterFactory(), 'some-name',
                                coder, 0)
   self.verify_counters(opcounts, 0, float('nan'))
   value = GlobalWindows.windowed_value('abcde')
   opcounts.update_from(value)
   estimated_size = coder.estimate_size(value)
   self.verify_counters(opcounts, 1, estimated_size)

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def test_update_old_object(self):
   coder = coders.PickleCoder()
   opcounts = OperationCounters(CounterFactory(), 'some-name',
                                coder, 0)
   self.verify_counters(opcounts, 0, float('nan'))
   obj = OldClassThatDoesNotImplementLen()
   value = GlobalWindows.windowed_value(obj)
   opcounts.update_from(value)
   estimated_size = coder.estimate_size(value)
   self.verify_counters(opcounts, 1, estimated_size)

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def get_root_bundles(self):
   test_stream = self._applied_ptransform.transform
   bundles = []
   if len(test_stream.events) > 0:
     bundle = self._evaluation_context.create_bundle(
         pvalue.PBegin(self._applied_ptransform.transform.pipeline))
     # Explicitly set timestamp to MIN_TIMESTAMP to ensure that we hold the
     # watermark.
     bundle.add(GlobalWindows.windowed_value(0, timestamp=MIN_TIMESTAMP))
     bundle.commit(None)
     bundles.append(bundle)
   return bundles

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def finish_bundle(self):
   unprocessed_bundles = []
   hold = None
   if self.current_index < len(self.test_stream.events) - 1:
     unprocessed_bundle = self._evaluation_context.create_bundle(
         pvalue.PBegin(self._applied_ptransform.transform.pipeline))
     unprocessed_bundle.add(GlobalWindows.windowed_value(
         self.current_index + 1, timestamp=self.watermark))
     unprocessed_bundles.append(unprocessed_bundle)
     hold = self.watermark
   return TransformResult(
       self._applied_ptransform, self.bundles, unprocessed_bundles, None, hold)

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def __iter__(self):
   output_stream = create_OutputStream()
   if self._windowing.is_default():
     globally_window = GlobalWindows.windowed_value(None).with_value
     windowed_key_values = lambda key, values: [globally_window((key, values))]
   else:
     trigger_driver = trigger.create_trigger_driver(self._windowing, True)
     windowed_key_values = trigger_driver.process_entire_key
   coder_impl = self._post_grouped_coder.get_impl()
   key_coder_impl = self._key_coder.get_impl()
   for encoded_key, windowed_values in self._table.items():
     key = key_coder_impl.decode(encoded_key)
     for wkvs in windowed_key_values(key, windowed_values):
       coder_impl.encode_to_stream(wkvs, output_stream, True)
   return iter([output_stream.get()])

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
  def _flush_batch(self, destination):

    # Flush the current batch of rows to BigQuery.
    rows = self._rows_buffer[destination]
    table_reference = bigquery_tools.parse_table_reference(destination)

    if table_reference.projectId is None:
      table_reference.projectId = vp.RuntimeValueProvider.get_value(
          'project', str, '')

    logging.debug('Flushing data to %s. Total %s rows.',
                  destination, len(rows))

    while True:
      # TODO: Figure out an insertId to make calls idempotent.
      passed, errors = self.bigquery_wrapper.insert_rows(
          project_id=table_reference.projectId,
          dataset_id=table_reference.datasetId,
          table_id=table_reference.tableId,
          rows=rows,
          skip_invalid_rows=True)

      logging.debug("Passed: %s. Errors are %s", passed, errors)
      failed_rows = [rows[entry.index] for entry in errors]
      should_retry = any(
          bigquery_tools.RetryStrategy.should_retry(
              self._retry_strategy, entry.errors[0].reason)
          for entry in errors)
      rows = failed_rows

      if not should_retry:
        break
      else:
        retry_backoff = next(self._backoff_calculator)
        logging.info('Sleeping %s seconds before retrying insertion.',
                     retry_backoff)
        time.sleep(retry_backoff)

    self._total_buffered_rows -= len(self._rows_buffer[destination])
    del self._rows_buffer[destination]

    return [pvalue.TaggedOutput(BigQueryWriteFn.FAILED_ROWS,
                                GlobalWindows.windowed_value(
                                    (destination, row))) for row in failed_rows]

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def finish_bundle(self):
   data = self._read_from_pubsub()
   if data:
     output_pcollection = list(self._outputs)[0]
     bundle = self._evaluation_context.create_bundle(output_pcollection)
     # TODO(ccy): we currently do not use the PubSub message timestamp or
     # respect the PubSub source's id_label field.
     now = Timestamp.of(time.time())
     for message_data in data:
       bundle.output(GlobalWindows.windowed_value(message_data, timestamp=now))
     bundles = [bundle]
   else:
     bundles = []
   if self._applied_ptransform.inputs:
     input_pvalue = self._applied_ptransform.inputs[0]
   else:
     input_pvalue = pvalue.PBegin(self._applied_ptransform.transform.pipeline)
   unprocessed_bundle = self._evaluation_context.create_bundle(
       input_pvalue)
   return TransformResult(
       self._applied_ptransform, bundles,
       [unprocessed_bundle], None, {None: Timestamp.of(time.time())})

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def process_element(self, element):
   index = element.value
   self.watermark = element.timestamp
   assert isinstance(index, int)
   assert 0 <= index <= len(self.test_stream.events)
   self.current_index = index
   event = self.test_stream.events[self.current_index]
   if isinstance(event, ElementEvent):
     assert len(self._outputs) == 1
     output_pcollection = list(self._outputs)[0]
     bundle = self._evaluation_context.create_bundle(output_pcollection)
     for tv in event.timestamped_values:
       bundle.output(
           GlobalWindows.windowed_value(tv.value, timestamp=tv.timestamp))
     self.bundles.append(bundle)
   elif isinstance(event, WatermarkEvent):
     assert event.new_watermark >= self.watermark
     self.watermark = event.new_watermark
   elif isinstance(event, ProcessingTimeEvent):
     # TODO(ccy): advance processing time in the context's mock clock.
     pass
   else:
     raise ValueError('Invalid TestStream event: %s.' % event)

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def process_timer(self, timer_firing):
   """Default process_timer() impl. generating KeyedWorkItem element."""
   self.process_element(
       GlobalWindows.windowed_value(
           KeyedWorkItem(timer_firing.encoded_key,
                         timer_firings=[timer_firing])))

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def _read_values_to_bundles(reader):
   read_result = [GlobalWindows.windowed_value(e) for e in reader]
   return self._split_list_into_bundles(
       output_pcollection, read_result,
       _BoundedReadEvaluator.MAX_ELEMENT_PER_BUNDLE, lambda _: 1)

# 需要导入模块: from apache_beam.transforms.window import GlobalWindows [as 别名]
# 或者: from apache_beam.transforms.window.GlobalWindows import windowed_value [as 别名]
 def set(self, ts):
   from apache_beam.transforms.window import GlobalWindows
   self._receiver.receive(
       GlobalWindows.windowed_value(
           (self._key,
            dict(timestamp=timestamp.Timestamp.of(ts)))))