Add performance degradation SLA plugin
This adds SLA plugin that finds minimum and maximum duration of iterations completed without errors during Rally task execution. Assuming that minimum duration is 100%, it calculates performance degradation against maximum duration. Example config: sla: performance_degradation: max_degradation: 75 Spec: sla_pd_plugin Change-Id: Ieedba7be72364f5599a3c0cf79f5f494a7391ea0
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parent
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@ -404,6 +404,8 @@
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max: 1
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max: 1
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min_iterations: 10
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min_iterations: 10
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sigmas: 10
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sigmas: 10
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performance_degradation:
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max_degradation: 50
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-
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-
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args:
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args:
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@ -13,6 +13,8 @@
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# License for the specific language governing permissions and limitations
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# License for the specific language governing permissions and limitations
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# under the License.
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# under the License.
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from __future__ import division
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import abc
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import abc
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import math
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import math
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@ -204,3 +206,36 @@ class IncrementComputation(StreamingAlgorithm):
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def result(self):
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def result(self):
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return self._count
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return self._count
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class DegradationComputation(StreamingAlgorithm):
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"""Calculates degradation from a stream of numbers
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Finds min and max values from a stream and then calculates
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ratio between them in percentage. Works only with positive numbers.
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"""
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def __init__(self):
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self.min_value = MinComputation()
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self.max_value = MaxComputation()
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def add(self, value):
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if value <= 0.0:
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raise ValueError("Unexpected value: %s" % value)
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self.min_value.add(value)
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self.max_value.add(value)
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def merge(self, other):
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min_result = other.min_value.result()
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if min_result is not None:
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self.min_value.add(min_result)
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max_result = other.max_value.result()
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if max_result is not None:
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self.max_value.add(max_result)
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def result(self):
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min_result = self.min_value.result()
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max_result = self.max_value.result()
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if min_result is None or max_result is None:
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return 0.0
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return (max_result / min_result - 1) * 100.0
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74
rally/plugins/common/sla/performance_degradation.py
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74
rally/plugins/common/sla/performance_degradation.py
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# Copyright 2016: Mirantis Inc.
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# All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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"""
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SLA (Service-level agreement) is set of details for determining compliance
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with contracted values such as maximum error rate or minimum response time.
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"""
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from __future__ import division
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from rally.common.i18n import _
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from rally.common import streaming_algorithms
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from rally.common import utils
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from rally import consts
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from rally.task import sla
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@sla.configure(name="performance_degradation")
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class PerformanceDegradation(sla.SLA):
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"""Calculates perfomance degradation based on iteration time
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This SLA plugin finds minimum and maximum duration of
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iterations completed without errors during Rally task execution.
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Assuming that minimum duration is 100%, it calculates
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performance degradation against maximum duration.
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"""
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CONFIG_SCHEMA = {
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"type": "object",
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"$schema": consts.JSON_SCHEMA,
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"properties": {
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"max_degradation": {
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"type": "number",
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"minimum": 0.0,
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},
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},
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"required": [
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"max_degradation",
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],
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"additionalProperties": False,
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}
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def __init__(self, criterion_value):
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super(PerformanceDegradation, self).__init__(criterion_value)
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self.max_degradation = self.criterion_value["max_degradation"]
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self.degradation = streaming_algorithms.DegradationComputation()
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def add_iteration(self, iteration):
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if not iteration.get("error"):
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self.degradation.add(iteration["duration"])
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self.success = self.degradation.result() <= self.max_degradation
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return self.success
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def merge(self, other):
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self.degradation.merge(other.degradation)
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self.success = self.degradation.result() <= self.max_degradation
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return self.success
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def details(self):
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return (_("Current degradation: %s%% - %s") %
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(utils.format_float_to_str(self.degradation.result() or 0.0),
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self.status()))
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@ -980,3 +980,50 @@ class SLAExtraFlagsTestCase(unittest.TestCase):
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"times": 5,
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"times": 5,
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"rps": 3,
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"rps": 3,
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"timeout": 6})
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"timeout": 6})
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class SLAPerfDegrTestCase(unittest.TestCase):
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def _get_sample_task_config(self, max_degradation=500):
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return {
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"Dummy.dummy_random_action": [
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{
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"args": {
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"actions_num": 5,
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"sleep_min": 0.5,
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"sleep_max": 2
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},
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"runner": {
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"type": "constant",
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"times": 10,
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"concurrency": 5
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},
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"sla": {
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"performance_degradation": {
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"max_degradation": max_degradation
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}
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}
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}
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]
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}
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def test_sla_fail(self):
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rally = utils.Rally()
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cfg = self._get_sample_task_config(max_degradation=1)
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config = utils.TaskConfig(cfg)
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rally("task start --task %s" % config.filename)
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self.assertRaises(utils.RallyCliError, rally, "task sla_check")
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def test_sla_success(self):
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rally = utils.Rally()
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config = utils.TaskConfig(self._get_sample_task_config())
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rally("task start --task %s" % config.filename)
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rally("task sla_check")
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expected = [
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{"benchmark": "Dummy.dummy_random_action",
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"criterion": "performance_degradation",
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"detail": mock.ANY,
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"pos": 0, "status": "PASS"},
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]
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data = rally("task sla_check --json", getjson=True)
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self.assertEqual(expected, data)
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@ -274,3 +274,47 @@ class IncrementComputationTestCase(test.TestCase):
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self.assertEqual(single_inc._count, merged_inc._count)
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self.assertEqual(single_inc._count, merged_inc._count)
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self.assertEqual(single_inc.result(), merged_inc.result())
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self.assertEqual(single_inc.result(), merged_inc.result())
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@ddt.ddt
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class DegradationComputationTestCase(test.TestCase):
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@ddt.data(
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([], None, None, 0.0),
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([30.0, 30.0, 30.0, 30.0], 30.0, 30.0, 0.0),
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([45.0, 45.0, 45.0, 30.0], 30.0, 45.0, 50.0),
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([15.0, 10.0, 20.0, 19.0], 10.0, 20.0, 100.0),
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([30.0, 56.0, 90.0, 73.0], 30.0, 90.0, 200.0))
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@ddt.unpack
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def test_add(self, stream, min_value, max_value, result):
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comp = algo.DegradationComputation()
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for value in stream:
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comp.add(value)
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self.assertEqual(min_value, comp.min_value.result())
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self.assertEqual(max_value, comp.max_value.result())
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self.assertEqual(result, comp.result())
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@ddt.data(-10.0, -1.0, -1, 0.0, 0)
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def test_add_raise(self, value):
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comp = algo.DegradationComputation()
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self.assertRaises(ValueError, comp.add, value)
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@ddt.data(([39.0, 30.0, 32.0], [49.0, 40.0, 51.0], 30.0, 51.0, 70.0),
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([31.0, 30.0, 32.0], [39.0, 45.0, 43.0], 30.0, 45.0, 50.0),
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([], [31.0, 30.0, 45.0], 30.0, 45.0, 50.0),
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([31.0, 30.0, 45.0], [], 30.0, 45.0, 50.0),
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([], [], None, None, 0.0))
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@ddt.unpack
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def test_merge(self, stream1, stream2, min_value, max_value, result):
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comp1 = algo.DegradationComputation()
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for value in stream1:
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comp1.add(value)
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comp2 = algo.DegradationComputation()
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for value in stream2:
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comp2.add(value)
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comp1.merge(comp2)
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self.assertEqual(min_value, comp1.min_value.result())
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self.assertEqual(max_value, comp1.max_value.result())
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self.assertEqual(result, comp1.result())
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@ -0,0 +1,92 @@
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# Copyright 2016: Mirantis Inc.
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# All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import ddt
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import jsonschema
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from rally.plugins.common.sla import performance_degradation as perfdegr
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from tests.unit import test
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@ddt.ddt
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class PerformanceDegradationTestCase(test.TestCase):
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def setUp(self):
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super(PerformanceDegradationTestCase, self).setUp()
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self.sla = perfdegr.PerformanceDegradation({"max_degradation": 50})
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def test_config_schema(self):
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properties = {
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"performance_degradation": {}
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}
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self.assertRaises(
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jsonschema.ValidationError,
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perfdegr.PerformanceDegradation.validate,
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properties)
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properties["performance_degradation"]["max_degradation"] = -1
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self.assertRaises(
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jsonschema.ValidationError,
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perfdegr.PerformanceDegradation.validate,
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properties)
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properties["performance_degradation"]["max_degradation"] = 1000.0
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perfdegr.PerformanceDegradation.validate(properties)
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@ddt.data(([39.0, 30.0, 32.0, 49.0, 47.0, 43.0], False, "Failed"),
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([31.0, 30.0, 32.0, 39.0, 45.0, 43.0], True, "Passed"),
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([], True, "Passed"))
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@ddt.unpack
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def test_iterations(self, durations, result, status):
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for duration in durations:
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self.sla.add_iteration({"duration": duration})
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self.assertIs(self.sla.success, result)
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self.assertIs(self.sla.result()["success"], result)
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self.assertEqual(status, self.sla.status())
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@ddt.data(([39.0, 30.0, 32.0], [49.0, 40.0, 51.0], False, "Failed"),
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([31.0, 30.0, 32.0], [39.0, 45.0, 43.0], True, "Passed"),
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([31.0, 30.0, 32.0], [32.0, 49.0, 30.0], False, "Failed"),
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([], [31.0, 30.0, 32.0], True, "Passed"),
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([31.0, 30.0, 32.0], [], True, "Passed"),
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([], [], True, "Passed"),
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([35.0, 30.0, 49.0], [], False, "Failed"),
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([], [35.0, 30.0, 49.0], False, "Failed"))
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@ddt.unpack
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def test_merge(self, durations1, durations2, result, status):
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for duration in durations1:
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self.sla.add_iteration({"duration": duration})
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sla2 = perfdegr.PerformanceDegradation({"max_degradation": 50})
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for duration in durations2:
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sla2.add_iteration({"duration": duration})
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self.sla.merge(sla2)
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self.assertIs(self.sla.success, result)
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self.assertIs(self.sla.result()["success"], result)
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self.assertEqual(status, self.sla.status())
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def test_details(self):
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self.assertEqual("Current degradation: 0.0% - Passed",
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self.sla.details())
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for duration in [39.0, 30.0, 32.0]:
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self.sla.add_iteration({"duration": duration})
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self.assertEqual("Current degradation: 30.0% - Passed",
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self.sla.details())
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self.sla.add_iteration({"duration": 75.0})
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self.assertEqual("Current degradation: 150.0% - Failed",
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self.sla.details())
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