chamikaramj commented on a change in pull request #14723:
URL: https://github.com/apache/beam/pull/14723#discussion_r649648168
##########
File path:
sdks/python/apache_beam/io/gcp/datastore/v1new/rampup_throttling_fn.py
##########
@@ -0,0 +1,80 @@
+import datetime
+import logging
+import time
+from typing import TypeVar
+
+from apache_beam import typehints
+from apache_beam.io.gcp.datastore.v1new import util
+from apache_beam.transforms import DoFn
+from apache_beam.utils.retry import FuzzedExponentialIntervals
+
+T = TypeVar('T')
+
+_LOG = logging.getLogger(__name__)
+
+
[email protected]_input_types(T)
[email protected]_output_types(T)
+class RampupThrottlingFn(DoFn):
+ """A ``DoFn`` that throttles ramp-up following an exponential function.
+
+ An implementation of a client-side throttler that enforces a gradual ramp-up,
+ broadly in line with Datastore best practices. See also
+ https://cloud.google.com/datastore/docs/best-practices#ramping_up_traffic.
+ """
+ def to_runner_api_parameter(self, unused_context):
+ from apache_beam.internal import pickler
+ config = {
+ 'num_workers': self._num_workers,
+ }
+ return 'beam:fn:rampup_throttling:v0', pickler.dumps(config)
+
+ _BASE_BUDGET = 500
+ _RAMP_UP_INTERVAL = datetime.timedelta(minutes=5)
+
+ def __init__(self, num_workers, *unused_args, **unused_kwargs):
+ """Initializes a ramp-up throttler transform.
+
+ Args:
+ num_workers: A hint for the expected number of workers, used to derive
+ the local rate limit.
+ """
+ super(RampupThrottlingFn, self).__init__(*unused_args, **unused_kwargs)
+ self._num_workers = num_workers
+ self._successful_ops = util.MovingSum(window_ms=1000, bucket_ms=1000)
+ self._first_instant = datetime.datetime.now()
+
+ def _calc_max_ops_budget(
+ self,
+ first_instant: datetime.datetime,
+ current_instant: datetime.datetime):
+ """Function that returns per-second budget according to best practices.
+
+ The exact function is `500 / num_shards * 1.5^max(0, (x-5)/5)`, where x is
+ the number of minutes since start time.
+ """
+ timedelta_since_first = current_instant - first_instant
+ growth = max(
+ 0.0, (timedelta_since_first - self._RAMP_UP_INTERVAL) /
+ self._RAMP_UP_INTERVAL)
+ max_ops_budget = int(self._BASE_BUDGET / self._num_workers * (1.5**growth))
Review comment:
It's not per worker but rather per bundle. The number of bundles is
decided by the runner (for example Dataflow) also the runner may use Dynamic
Work Rebalancing to create more bundles. So the number of bundles may be larger
or smaller than the actual number of workers. But the maximum number of active
bundles at a given time is bound by the number of workers (which can also
change if the runner supports autoscaling). Does your model still hold for this
case ?
##########
File path:
sdks/python/apache_beam/io/gcp/datastore/v1new/rampup_throttling_fn.py
##########
@@ -0,0 +1,101 @@
+#
+# 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.
+#
+
+import datetime
+import logging
+import time
+from typing import TypeVar
+
+from apache_beam import typehints
+from apache_beam.io.gcp.datastore.v1new import util
+from apache_beam.metrics.metric import Metrics
+from apache_beam.transforms import DoFn
+from apache_beam.utils.retry import FuzzedExponentialIntervals
+
+T = TypeVar('T')
+
+_LOG = logging.getLogger(__name__)
+
+
[email protected]_input_types(T)
[email protected]_output_types(T)
+class RampupThrottlingFn(DoFn):
+ """A ``DoFn`` that throttles ramp-up following an exponential function.
+
+ An implementation of a client-side throttler that enforces a gradual ramp-up,
+ broadly in line with Datastore best practices. See also
+ https://cloud.google.com/datastore/docs/best-practices#ramping_up_traffic.
+ """
+ def to_runner_api_parameter(self, unused_context):
Review comment:
I think the default implementation of "to_runner_api_parameter" should
be adequate.
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