Source code for apache_beam.runners.direct.util

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""Utility classes used by the DirectRunner.

For internal use only. No backwards compatibility guarantees.
"""

# pytype: skip-file

from __future__ import absolute_import

from builtins import object


[docs]class TransformResult(object): """Result of evaluating an AppliedPTransform with a TransformEvaluator.""" def __init__( self, transform_evaluator, uncommitted_output_bundles, unprocessed_bundles, counters, keyed_watermark_holds, undeclared_tag_values=None): self.transform = transform_evaluator._applied_ptransform self.uncommitted_output_bundles = uncommitted_output_bundles self.unprocessed_bundles = unprocessed_bundles self.counters = counters # Mapping of key -> earliest hold timestamp or None. Keys should be # strings or None. # # For each key, we receive as its corresponding value the earliest # watermark hold for that key (the key can be None for global state), past # which the output watermark for the currently-executing step will not # advance. If the value is None or utils.timestamp.MAX_TIMESTAMP, the # watermark hold will be removed. self.keyed_watermark_holds = keyed_watermark_holds or {} # Only used when caching (materializing) all values is requested. self.undeclared_tag_values = undeclared_tag_values # Populated by the TransformExecutor. self.logical_metric_updates = None step_context = transform_evaluator._execution_context.get_step_context() self.partial_keyed_state = step_context.partial_keyed_state
[docs]class TimerFiring(object): """A single instance of a fired timer.""" def __init__( self, encoded_key, window, name, time_domain, timestamp, dynamic_timer_tag=''): self.encoded_key = encoded_key self.window = window self.name = name self.time_domain = time_domain self.timestamp = timestamp self.dynamic_timer_tag = dynamic_timer_tag def __repr__(self): return 'TimerFiring({!r}, {!r}, {}, {}, {})'.format( self.encoded_key, self.name, self.time_domain, self.timestamp, self.dynamic_timer_tag)
[docs]class KeyedWorkItem(object): """A keyed item that can either be a timer firing or a list of elements.""" def __init__(self, encoded_key, timer_firings=None, elements=None): self.encoded_key = encoded_key self.timer_firings = timer_firings or [] self.elements = elements or [] def __repr__(self): return 'KeyedWorkItem({!r}, {}, {})'.format( self.encoded_key, self.timer_firings, self.elements)