"-t": ""
runner:
- type: Duration
+ type: ProxDuration
+ # sampling interval
+ interval: 1
+ # sampled : yes OR sampled: no (DEFAULT yes)
+ sampled: yes
# we kill after duration, independent of test duration, so set this high
- duration: 300
+ duration: 3100
+ # Confirmation attempts
+ confirmation: 1
context:
type: Node
--- /dev/null
+# Copyright 2014: Mirantis Inc.
+# All Rights Reserved.
+#
+# Licensed 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.
+
+# yardstick comment: this is a modified copy of
+# rally/rally/benchmark/runners/constant.py
+
+"""A runner that runs a specific time before it returns
+"""
+
+from __future__ import absolute_import
+
+import os
+import multiprocessing
+import logging
+import traceback
+import time
+
+from yardstick.benchmark.runners import base
+from yardstick.common import exceptions as y_exc
+from yardstick.common import constants
+
+LOG = logging.getLogger(__name__)
+
+def _worker_process(queue, cls, method_name, scenario_cfg,
+ context_cfg, aborted, output_queue):
+
+ sequence = 1
+
+ runner_cfg = scenario_cfg['runner']
+
+ requested_interval = interval = runner_cfg.get("interval", 1)
+ duration = runner_cfg.get("duration", 60)
+ sampled = runner_cfg.get("sampled", False)
+
+ LOG.info("Worker START, duration is %ds", duration)
+ LOG.debug("class is %s", cls)
+
+ runner_cfg['runner_id'] = os.getpid()
+
+ benchmark = cls(scenario_cfg, context_cfg)
+ benchmark.setup()
+ method = getattr(benchmark, method_name)
+
+ sla_action = None
+ if "sla" in scenario_cfg:
+ sla_action = scenario_cfg["sla"].get("action", "assert")
+
+
+ start = time.time()
+ timeout = start + duration
+ while True:
+
+ LOG.debug("runner=%(runner)s seq=%(sequence)s START",
+ {"runner": runner_cfg["runner_id"], "sequence": sequence})
+
+ data = {}
+ errors = ""
+
+ benchmark.pre_run_wait_time(interval)
+
+ if sampled:
+ try:
+ pre_adjustment = time.time()
+ result = method(data)
+ post_adjustment = time.time()
+ if requested_interval > post_adjustment - pre_adjustment:
+ interval = requested_interval - (post_adjustment - pre_adjustment)
+ else:
+ interval = 0
+
+ except y_exc.SLAValidationError as error:
+ # SLA validation failed in scenario, determine what to do now
+ if sla_action == "assert":
+ raise
+ elif sla_action == "monitor":
+ LOG.warning("SLA validation failed: %s", error.args)
+ errors = error.args
+ # catch all exceptions because with multiprocessing we can have un-picklable exception
+ # problems https://bugs.python.org/issue9400
+ except Exception: # pylint: disable=broad-except
+ errors = traceback.format_exc()
+ LOG.exception("")
+ else:
+ if result:
+ # add timeout for put so we don't block test
+ # if we do timeout we don't care about dropping individual KPIs
+ output_queue.put(result, True, constants.QUEUE_PUT_TIMEOUT)
+
+ benchmark_output = {
+ 'timestamp': time.time(),
+ 'sequence': sequence,
+ 'data': data,
+ 'errors': errors
+ }
+
+ queue.put(benchmark_output, True, constants.QUEUE_PUT_TIMEOUT)
+ else:
+ LOG.debug("No sample collected ...Sequence %s", sequence)
+
+
+ sequence += 1
+
+ if (errors and sla_action is None) or time.time() > timeout or aborted.is_set():
+ LOG.info("Worker END")
+ break
+
+ try:
+ benchmark.teardown()
+ except Exception:
+ # catch any exception in teardown and convert to simple exception
+ # never pass exceptions back to multiprocessing, because some exceptions can
+ # be unpicklable
+ # https://bugs.python.org/issue9400
+ LOG.exception("")
+ raise SystemExit(1)
+
+ LOG.debug("queue.qsize() = %s", queue.qsize())
+ LOG.debug("output_queue.qsize() = %s", output_queue.qsize())
+ LOG.info("Exiting ProxDuration Runner...")
+
+class ProxDurationRunner(base.Runner):
+ """Run a scenario for a certain amount of time
+
+If the scenario ends before the time has elapsed, it will be started again.
+
+ Parameters
+ duration - amount of time the scenario will be run for
+ type: int
+ unit: seconds
+ default: 60 sec
+ interval - time to wait between each scenario invocation
+ type: int
+ unit: seconds
+ default: 1 sec
+ sampled - Sample data is required yes/no
+ type: boolean
+ unit: True/False
+ default: False
+ confirmation - Number of confirmation retries
+ type: int
+ unit: retry attempts
+ default: 0
+ """
+ __execution_type__ = 'ProxDuration'
+
+ def _run_benchmark(self, cls, method, scenario_cfg, context_cfg):
+ name = "{}-{}-{}".format(self.__execution_type__, scenario_cfg.get("type"), os.getpid())
+ self.process = multiprocessing.Process(
+ name=name,
+ target=_worker_process,
+ args=(self.result_queue, cls, method, scenario_cfg,
+ context_cfg, self.aborted, self.output_queue))
+ self.process.start()
INFLUXDB_IMAGE = get_param('influxdb.image', 'tutum/influxdb')
INFLUXDB_TAG = get_param('influxdb.tag', '0.13')
INFLUXDB_DASHBOARD_PORT = 8083
+QUEUE_PUT_TIMEOUT = 10
# grafana
GRAFANA_IP = get_param('grafana.ip', SERVER_IP)
from yardstick.network_services.traffic_profile.prox_profile import ProxProfile
from yardstick.network_services import constants
+from yardstick.common import constants as overall_constants
LOG = logging.getLogger(__name__)
# success, the binary search will complete on an integer multiple
# of the precision, rather than on a fraction of it.
- theor_max_thruput = 0
+ theor_max_thruput = actual_max_thruput = 0
result_samples = {}
+ rate_samples = {}
+ pos_retry = 0
+ neg_retry = 0
+ total_retry = 0
+ ok_retry = 0
# Store one time only value in influxdb
single_samples = {
successful_pkt_loss = 0.0
line_speed = traffic_gen.scenario_helper.all_options.get(
"interface_speed_gbps", constants.NIC_GBPS_DEFAULT) * constants.ONE_GIGABIT_IN_BITS
+
+ ok_retry = traffic_gen.scenario_helper.scenario_cfg["runner"].get("confirmation", 0)
for test_value in self.bounds_iterator(LOG):
- result, port_samples = self._profile_helper.run_test(pkt_size, duration,
- test_value,
- self.tolerated_loss,
- line_speed)
- self.curr_time = time.time()
- self.prev_time = self.curr_time
-
- if result.success:
- LOG.debug("Success! Increasing lower bound")
- self.current_lower = test_value
- successful_pkt_loss = result.pkt_loss
- samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
-
- # store results with success tag in influxdb
- success_samples = {'Success_' + key: value for key, value in samples.items()}
-
- # Store number of packets based statistics (we already have throughput)
- success_samples["Success_rx_total"] = int(result.rx_total)
- success_samples["Success_tx_total"] = int(result.tx_total)
- success_samples["Success_can_be_lost"] = int(result.can_be_lost)
- success_samples["Success_drop_total"] = int(result.drop_total)
- self.queue.put(success_samples)
-
- # Store Actual throughput for result samples
- result_samples["Result_Actual_throughput"] = \
- success_samples["Success_RxThroughput"]
- else:
- LOG.debug("Failure... Decreasing upper bound")
- self.current_upper = test_value
- samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
- # samples contains data such as Latency, Throughput, number of packets
- # Hence they should not be divided by the time difference
-
- if theor_max_thruput < samples["RequestedTxThroughput"]:
- theor_max_thruput = samples['RequestedTxThroughput']
- self.queue.put({'theor_max_throughput': theor_max_thruput})
-
- LOG.debug("Collect TG KPIs %s %s", datetime.datetime.now(), samples)
- self.queue.put(samples)
+ pos_retry = 0
+ neg_retry = 0
+ total_retry = 0
+
+ rate_samples["MAX_Rate"] = self.current_upper
+ rate_samples["MIN_Rate"] = self.current_lower
+ rate_samples["Test_Rate"] = test_value
+ self.queue.put(rate_samples, True, overall_constants.QUEUE_PUT_TIMEOUT)
+ while (pos_retry <= ok_retry) and (neg_retry <= ok_retry):
+
+ total_retry = total_retry + 1
+ result, port_samples = self._profile_helper.run_test(pkt_size, duration,
+ test_value,
+ self.tolerated_loss,
+ line_speed)
+ if (total_retry > (ok_retry * 3)) and (ok_retry is not 0):
+ LOG.info("Failure.!! .. RETRY EXCEEDED ... decrease lower bound")
+
+ successful_pkt_loss = result.pkt_loss
+ samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
+
+ self.current_upper = test_value
+ neg_retry = total_retry
+ elif result.success:
+ if (pos_retry < ok_retry) and (ok_retry is not 0):
+ neg_retry = 0
+ LOG.info("Success! ... confirm retry")
+
+ successful_pkt_loss = result.pkt_loss
+ samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
+
+ else:
+ LOG.info("Success! Increasing lower bound")
+ self.current_lower = test_value
+
+ successful_pkt_loss = result.pkt_loss
+ samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
+
+ # store results with success tag in influxdb
+ success_samples = \
+ {'Success_' + key: value for key, value in samples.items()}
+
+ success_samples["Success_rx_total"] = int(result.rx_total)
+ success_samples["Success_tx_total"] = int(result.tx_total)
+ success_samples["Success_can_be_lost"] = int(result.can_be_lost)
+ success_samples["Success_drop_total"] = int(result.drop_total)
+ success_samples["Success_RxThroughput"] = samples["RxThroughput"]
+ LOG.info(">>>##>>Collect SUCCESS TG KPIs %s %s",
+ datetime.datetime.now(), success_samples)
+ self.queue.put(success_samples, True, overall_constants.QUEUE_PUT_TIMEOUT)
+
+ # Store Actual throughput for result samples
+ actual_max_thruput = success_samples["Success_RxThroughput"]
+
+ pos_retry = pos_retry + 1
+
+ else:
+ if (neg_retry < ok_retry) and (ok_retry is not 0):
+
+ pos_retry = 0
+ LOG.info("failure! ... confirm retry")
+ else:
+ LOG.info("Failure... Decreasing upper bound")
+ self.current_upper = test_value
+
+ neg_retry = neg_retry + 1
+ samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
+
+ if theor_max_thruput < samples["TxThroughput"]:
+ theor_max_thruput = samples['TxThroughput']
+ self.queue.put({'theor_max_throughput': theor_max_thruput})
+
+ LOG.info(">>>##>>Collect TG KPIs %s %s", datetime.datetime.now(), samples)
+ self.queue.put(samples, True, overall_constants.QUEUE_PUT_TIMEOUT)
result_samples["Result_pktSize"] = pkt_size
result_samples["Result_theor_max_throughput"] = theor_max_thruput
+ result_samples["Result_Actual_throughput"] = actual_max_thruput
self.queue.put(result_samples)
t._run([scenario], False, "yardstick.out")
runner.run.assert_called_once()
+ @mock.patch.object(task, 'Context')
+ @mock.patch.object(task, 'base_runner')
+ def test_run_ProxDuration(self, mock_base_runner, *args):
+ scenario = {
+ 'host': 'athena.demo',
+ 'target': 'ares.demo',
+ 'runner': {
+ 'duration': 60,
+ 'interval': 1,
+ 'sampled': 'yes',
+ 'confirmation': 1,
+ 'type': 'ProxDuration'
+ },
+ 'type': 'Ping'
+ }
+
+ t = task.Task()
+ runner = mock.Mock()
+ runner.join.return_value = 0
+ runner.get_output.return_value = {}
+ runner.get_result.return_value = []
+ mock_base_runner.Runner.get.return_value = runner
+ t._run([scenario], False, "yardstick.out")
+ runner.run.assert_called_once()
+
@mock.patch.object(os, 'environ')
def test_check_precondition(self, mock_os_environ):
cfg = {
--- /dev/null
+##############################################################################
+# Copyright (c) 2018 Nokia and others.
+#
+# All rights reserved. This program and the accompanying materials
+# are made available under the terms of the Apache License, Version 2.0
+# which accompanies this distribution, and is available at
+# http://www.apache.org/licenses/LICENSE-2.0
+##############################################################################
+
+import mock
+import unittest
+import multiprocessing
+import os
+import time
+
+from yardstick.benchmark.runners import proxduration
+from yardstick.common import exceptions as y_exc
+
+
+class ProxDurationRunnerTest(unittest.TestCase):
+ class MyMethod(object):
+ SLA_VALIDATION_ERROR_SIDE_EFFECT = 1
+ BROAD_EXCEPTION_SIDE_EFFECT = 2
+
+ def __init__(self, side_effect=0):
+ self.count = 101
+ self.side_effect = side_effect
+
+ def __call__(self, data):
+ self.count += 1
+ data['my_key'] = self.count
+ if self.side_effect == self.SLA_VALIDATION_ERROR_SIDE_EFFECT:
+ raise y_exc.SLAValidationError(case_name='My Case',
+ error_msg='my error message')
+ elif self.side_effect == self.BROAD_EXCEPTION_SIDE_EFFECT:
+ raise y_exc.YardstickException
+ return self.count
+
+ def setUp(self):
+ self.scenario_cfg = {
+ 'runner': {'interval': 0, "duration": 0},
+ 'type': 'some_type'
+ }
+
+ self.benchmark = mock.Mock()
+ self.benchmark_cls = mock.Mock(return_value=self.benchmark)
+
+ def _assert_defaults__worker_run_setup_and_teardown(self):
+ self.benchmark_cls.assert_called_once_with(self.scenario_cfg, {})
+ self.benchmark.setup.assert_called_once()
+ self.benchmark.teardown.assert_called_once()
+
+ @mock.patch.object(os, 'getpid')
+ @mock.patch.object(multiprocessing, 'Process')
+ def test__run_benchmark_called_with(self, mock_multiprocessing_process,
+ mock_os_getpid):
+ mock_os_getpid.return_value = 101
+
+ runner = proxduration.ProxDurationRunner({})
+ benchmark_cls = mock.Mock()
+ runner._run_benchmark(benchmark_cls, 'my_method', self.scenario_cfg,
+ {})
+ mock_multiprocessing_process.assert_called_once_with(
+ name='ProxDuration-some_type-101',
+ target=proxduration._worker_process,
+ args=(runner.result_queue, benchmark_cls, 'my_method',
+ self.scenario_cfg, {}, runner.aborted, runner.output_queue))
+
+ @mock.patch.object(os, 'getpid')
+ def test__worker_process_runner_id(self, mock_os_getpid):
+ mock_os_getpid.return_value = 101
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+
+ self.assertEqual(self.scenario_cfg['runner']['runner_id'], 101)
+
+ def test__worker_process_called_with_cfg(self):
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+
+ def test__worker_process_called_with_cfg_loop(self):
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 0.01}
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+ self.assertGreater(self.benchmark.my_method.call_count, 2)
+
+ def test__worker_process_called_without_cfg(self):
+ scenario_cfg = {'runner': {}}
+
+ aborted = multiprocessing.Event()
+ aborted.set()
+
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ scenario_cfg, {}, aborted, mock.Mock())
+
+ self.benchmark_cls.assert_called_once_with(scenario_cfg, {})
+ self.benchmark.setup.assert_called_once()
+ self.benchmark.teardown.assert_called_once()
+
+ def test__worker_process_output_queue(self):
+ self.benchmark.my_method = mock.Mock(return_value='my_result')
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ output_queue = multiprocessing.Queue()
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), output_queue)
+ time.sleep(0.1)
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+ self.assertEquals(output_queue.get(), 'my_result')
+
+ def test__worker_process_output_queue_multiple_iterations(self):
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ self.benchmark.my_method = self.MyMethod()
+
+ output_queue = multiprocessing.Queue()
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), output_queue)
+ time.sleep(0.1)
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+ self.assertGreater(self.benchmark.my_method.count, 103)
+
+ count = 101
+ while not output_queue.empty():
+ count += 1
+ self.assertEquals(output_queue.get(), count)
+
+ def test__worker_process_queue(self):
+ self.benchmark.my_method = self.MyMethod()
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ queue = multiprocessing.Queue()
+ timestamp = time.time()
+ proxduration._worker_process(queue, self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+ time.sleep(0.1)
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+
+ result = queue.get()
+ self.assertGreater(result['timestamp'], timestamp)
+ self.assertEqual(result['errors'], '')
+ self.assertEqual(result['data'], {'my_key': 102})
+ self.assertEqual(result['sequence'], 1)
+
+ def test__worker_process_queue_multiple_iterations(self):
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ self.benchmark.my_method = self.MyMethod()
+
+ queue = multiprocessing.Queue()
+ timestamp = time.time()
+ proxduration._worker_process(queue, self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+ time.sleep(0.1)
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+ self.assertGreater(self.benchmark.my_method.count, 103)
+
+ count = 0
+ while not queue.empty():
+ count += 1
+ result = queue.get()
+ self.assertGreater(result['timestamp'], timestamp)
+ self.assertEqual(result['errors'], '')
+ self.assertEqual(result['data'], {'my_key': count + 101})
+ self.assertEqual(result['sequence'], count)
+
+ def test__worker_process_except_sla_validation_error_no_sla_cfg(self):
+ self.benchmark.my_method = mock.Mock(
+ side_effect=y_exc.SLAValidationError)
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+
+ def test__worker_process_except_sla_validation_error_sla_cfg_monitor(self):
+ self.scenario_cfg['sla'] = {'action': 'monitor'}
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ self.benchmark.my_method = mock.Mock(
+ side_effect=y_exc.SLAValidationError)
+
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+
+ def test__worker_process_raise_sla_validation_error_sla_cfg_default(self):
+ self.scenario_cfg['sla'] = {}
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ self.benchmark.my_method = mock.Mock(
+ side_effect=y_exc.SLAValidationError)
+
+ with self.assertRaises(y_exc.SLAValidationError):
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls,
+ 'my_method', self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+
+ self.benchmark_cls.assert_called_once_with(self.scenario_cfg, {})
+ self.benchmark.setup.assert_called_once()
+ self.benchmark.my_method.assert_called_once_with({})
+
+ def test__worker_process_raise_sla_validation_error_sla_cfg_assert(self):
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ self.scenario_cfg['sla'] = {'action': 'assert'}
+ self.benchmark.my_method = mock.Mock(
+ side_effect=y_exc.SLAValidationError)
+
+ with self.assertRaises(y_exc.SLAValidationError):
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls,
+ 'my_method', self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+
+ self.benchmark_cls.assert_called_once_with(self.scenario_cfg, {})
+ self.benchmark.setup.assert_called_once()
+ self.benchmark.my_method.assert_called_once_with({})
+
+ def test__worker_process_queue_on_sla_validation_error_monitor(self):
+ self.scenario_cfg['sla'] = {'action': 'monitor'}
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ self.benchmark.my_method = self.MyMethod(
+ side_effect=self.MyMethod.SLA_VALIDATION_ERROR_SIDE_EFFECT)
+
+ queue = multiprocessing.Queue()
+ timestamp = time.time()
+ proxduration._worker_process(queue, self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+ time.sleep(0.1)
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+
+ result = queue.get()
+ self.assertGreater(result['timestamp'], timestamp)
+ self.assertEqual(result['errors'], ('My Case SLA validation failed. '
+ 'Error: my error message',))
+ self.assertEqual(result['data'], {'my_key': 102})
+ self.assertEqual(result['sequence'], 1)
+
+ def test__worker_process_broad_exception(self):
+ self.benchmark.my_method = mock.Mock(
+ side_effect=y_exc.YardstickException)
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+
+ def test__worker_process_queue_on_broad_exception(self):
+ self.benchmark.my_method = self.MyMethod(
+ side_effect=self.MyMethod.BROAD_EXCEPTION_SIDE_EFFECT)
+
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+ queue = multiprocessing.Queue()
+ timestamp = time.time()
+ proxduration._worker_process(queue, self.benchmark_cls, 'my_method',
+ self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+ time.sleep(0.1)
+
+ self._assert_defaults__worker_run_setup_and_teardown()
+
+ result = queue.get()
+ self.assertGreater(result['timestamp'], timestamp)
+ self.assertNotEqual(result['errors'], '')
+ self.assertEqual(result['data'], {'my_key': 102})
+ self.assertEqual(result['sequence'], 1)
+
+ def test__worker_process_benchmark_teardown_on_broad_exception(self):
+ self.benchmark.teardown = mock.Mock(
+ side_effect=y_exc.YardstickException)
+
+ self.scenario_cfg["runner"] = {"sampled": True, "duration": 1}
+
+ with self.assertRaises(SystemExit) as raised:
+ proxduration._worker_process(mock.Mock(), self.benchmark_cls,
+ 'my_method', self.scenario_cfg, {},
+ multiprocessing.Event(), mock.Mock())
+ self.assertEqual(raised.exception.code, 1)
+ self._assert_defaults__worker_run_setup_and_teardown()
fail_tuple = ProxTestDataTuple(10.0, 1, 2, 3, 4, [5.6, 5.7, 5.8], 850, 1000, 123.4)
traffic_generator = mock.MagicMock()
+ attrs1 = {'get.return_value' : 10}
+ traffic_generator.scenario_helper.all_options.configure_mock(**attrs1)
+
+ attrs2 = {'__getitem__.return_value' : 10, 'get.return_value': 10}
+ traffic_generator.scenario_helper.scenario_cfg["runner"].configure_mock(**attrs2)
profile_helper = mock.MagicMock()
profile_helper.run_test = target
profile._profile_helper = profile_helper
profile.execute_traffic(traffic_generator)
+
self.assertEqual(round(profile.current_lower, 2), 74.69)
self.assertEqual(round(profile.current_upper, 2), 76.09)
- self.assertEqual(len(runs), 7)
+ self.assertEqual(len(runs), 77)
# Result Samples inc theor_max
result_tuple = {'Result_Actual_throughput': 5e-07,
- 'Result_theor_max_throughput': 0.00012340000000000002,
+ 'Result_theor_max_throughput': 7.5e-07,
'Result_pktSize': 200}
profile.queue.put.assert_called_with(result_tuple)
fail_tuple = ProxTestDataTuple(10.0, 1, 2, 3, 4, [5.6, 5.7, 5.8], 850, 1000, 123.4)
traffic_generator = mock.MagicMock()
+ attrs1 = {'get.return_value': 10}
+ traffic_generator.scenario_helper.all_options.configure_mock(**attrs1)
+
+ attrs2 = {'__getitem__.return_value': 0, 'get.return_value': 0}
+ traffic_generator.scenario_helper.scenario_cfg["runner"].configure_mock(**attrs2)
profile_helper = mock.MagicMock()
profile_helper.run_test = target
# Result Samples
- result_tuple = {"Result_theor_max_throughput": 0, "Result_pktSize": 200}
+ result_tuple = {'Result_Actual_throughput': 0, "Result_theor_max_throughput": 0,
+ "Result_pktSize": 200}
profile.queue.put.assert_called_with(result_tuple)
# Check for success_ tuple (None expected)
for k in call_detail:
if "Success_" in k:
self.assertRaises(AttributeError)
+
+ def test_execute_4(self):
+
+ def target(*args, **_):
+ runs.append(args[2])
+ if args[2] < 0 or args[2] > 100:
+ raise RuntimeError(' '.join([str(args), str(runs)]))
+ if args[2] > 75.0:
+ return fail_tuple, {}
+ return success_tuple, {}
+
+ tp_config = {
+ 'traffic_profile': {
+ 'packet_sizes': [200],
+ 'test_precision': 2.0,
+ 'tolerated_loss': 0.001,
+ },
+ }
+
+ runs = []
+ success_tuple = ProxTestDataTuple(10.0, 1, 2, 3, 4, [5.1, 5.2, 5.3], 995, 1000, 123.4)
+ fail_tuple = ProxTestDataTuple(10.0, 1, 2, 3, 4, [5.6, 5.7, 5.8], 850, 1000, 123.4)
+
+ traffic_generator = mock.MagicMock()
+ attrs1 = {'get.return_value': 100000}
+ traffic_generator.scenario_helper.all_options.configure_mock(**attrs1)
+
+ attrs2 = {'__getitem__.return_value': 0, 'get.return_value': 0}
+ traffic_generator.scenario_helper.scenario_cfg["runner"].configure_mock(**attrs2)
+
+ profile_helper = mock.MagicMock()
+ profile_helper.run_test = target
+
+ profile = ProxBinSearchProfile(tp_config)
+ profile.init(mock.MagicMock())
+ profile._profile_helper = profile_helper
+
+ profile.execute_traffic(traffic_generator)
+ self.assertEqual(round(profile.current_lower, 2), 74.69)
+ self.assertEqual(round(profile.current_upper, 2), 76.09)
+ self.assertEqual(len(runs), 7)
+
+ # Result Samples inc theor_max
+ result_tuple = {'Result_Actual_throughput': 5e-07,
+ 'Result_theor_max_throughput': 7.5e-07,
+ 'Result_pktSize': 200}
+
+ profile.queue.put.assert_called_with(result_tuple)
+
+ success_result_tuple = {"Success_CurrentDropPackets": 0.5,
+ "Success_DropPackets": 0.5,
+ "Success_LatencyAvg": 5.3,
+ "Success_LatencyMax": 5.2,
+ "Success_LatencyMin": 5.1,
+ "Success_PktSize": 200,
+ "Success_RxThroughput": 7.5e-07,
+ "Success_Throughput": 7.5e-07,
+ "Success_TxThroughput": 0.00012340000000000002}
+
+ calls = profile.queue.put(success_result_tuple)
+ profile.queue.put.assert_has_calls(calls)
+
+ success_result_tuple2 = {"Success_CurrentDropPackets": 0.5,
+ "Success_DropPackets": 0.5,
+ "Success_LatencyAvg": 5.3,
+ "Success_LatencyMax": 5.2,
+ "Success_LatencyMin": 5.1,
+ "Success_PktSize": 200,
+ "Success_RxThroughput": 7.5e-07,
+ "Success_Throughput": 7.5e-07,
+ "Success_TxThroughput": 123.4,
+ "Success_can_be_lost": 409600,
+ "Success_drop_total": 20480,
+ "Success_rx_total": 4075520,
+ "Success_tx_total": 4096000}
+
+ calls = profile.queue.put(success_result_tuple2)
+ profile.queue.put.assert_has_calls(calls)
Code Review / yardstick.git / commitdiff