3fb277d122
Found the issue while configuring devstack, the libs were having stackforge location. Change-Id: Ibd93eccb7d89cb6b06a1e8cc1d7d6f65ce52f352
391 lines
12 KiB
ReStructuredText
391 lines
12 KiB
ReStructuredText
..
|
|
Copyright 2015 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.
|
|
|
|
.. _plugins:
|
|
|
|
Rally Plugins
|
|
=============
|
|
|
|
How plugins work
|
|
----------------
|
|
|
|
Rally provides an opportunity to create and use a **custom benchmark scenario, runner or context** as a **plugin**:
|
|
|
|
.. image:: ./images/Rally-Plugins.png
|
|
:align: center
|
|
|
|
Plugins can be quickly written and used, with no need to contribute them to the actual Rally code. Just place a python module with your plugin class into the **/opt/rally/plugins** or **~/.rally/plugins** directory (or it's subdirectories), and it will be autoloaded.
|
|
|
|
|
|
Example: Benchmark scenario as a plugin
|
|
---------------------------------------
|
|
|
|
Let's create as a plugin a simple scenario which lists flavors.
|
|
|
|
Creation
|
|
^^^^^^^^
|
|
|
|
Inherit a class for your plugin from the base *Scenario* class and implement a scenario method inside it as usual. In our scenario, let us first list flavors as an ordinary user, and then repeat the same using admin clients:
|
|
|
|
.. code-block:: none
|
|
|
|
from rally.benchmark.scenarios import base
|
|
|
|
|
|
class ScenarioPlugin(base.Scenario):
|
|
"""Sample plugin which lists flavors."""
|
|
|
|
@base.atomic_action_timer("list_flavors")
|
|
def _list_flavors(self):
|
|
"""Sample of usage clients - list flavors
|
|
|
|
You can use self.context, self.admin_clients and self.clients which are
|
|
initialized on scenario instanse creation"""
|
|
self.clients("nova").flavors.list()
|
|
|
|
@base.atomic_action_timer("list_flavors_as_admin")
|
|
def _list_flavors_as_admin(self):
|
|
"""The same with admin clients"""
|
|
self.admin_clients("nova").flavors.list()
|
|
|
|
@base.scenario()
|
|
def list_flavors(self):
|
|
"""List flavors."""
|
|
self._list_flavors()
|
|
self._list_flavors_as_admin()
|
|
|
|
|
|
Placement
|
|
^^^^^^^^^
|
|
|
|
Put the python module with your plugin class into the **/opt/rally/plugins** or **~/.rally/plugins** directory or it's subdirectories and it will be autoloaded. You can also use a script **unpack_plugins_samples.sh** from **samples/plugins** which will automatically create the **~/.rally/plugins** directory.
|
|
|
|
|
|
Usage
|
|
^^^^^
|
|
|
|
You can refer to your plugin scenario in the benchmark task configuration files just in the same way as to any other scenarios:
|
|
|
|
.. code-block:: none
|
|
|
|
{
|
|
"ScenarioPlugin.list_flavors": [
|
|
{
|
|
"runner": {
|
|
"type": "serial",
|
|
"times": 5,
|
|
},
|
|
"context": {
|
|
"create_flavor": {
|
|
"ram": 512,
|
|
}
|
|
}
|
|
}
|
|
]
|
|
}
|
|
|
|
This configuration file uses the *"create_flavor"* context which we'll create as a plugin below.
|
|
|
|
|
|
Example: Context as a plugin
|
|
----------------------------
|
|
|
|
Let's create as a plugin a simple context which adds a flavor to the environment before the benchmark task starts and deletes it after it finishes.
|
|
|
|
Creation
|
|
^^^^^^^^
|
|
|
|
Inherit a class for your plugin from the base *Context* class. Then, implement the Context API: the *setup()* method that creates a flavor and the *cleanup()* method that deletes it.
|
|
|
|
.. code-block:: none
|
|
|
|
from rally.benchmark.context import base
|
|
from rally.common import log as logging
|
|
from rally import consts
|
|
from rally import osclients
|
|
|
|
LOG = logging.getLogger(__name__)
|
|
|
|
|
|
@base.context(name="create_flavor", order=1000)
|
|
class CreateFlavorContext(base.Context):
|
|
"""This sample create flavor with specified options before task starts and
|
|
delete it after task completion.
|
|
|
|
To create your own context plugin, inherit it from
|
|
rally.benchmark.context.base.Context
|
|
"""
|
|
|
|
CONFIG_SCHEMA = {
|
|
"type": "object",
|
|
"$schema": consts.JSON_SCHEMA,
|
|
"additionalProperties": False,
|
|
"properties": {
|
|
"flavor_name": {
|
|
"type": "string",
|
|
},
|
|
"ram": {
|
|
"type": "integer",
|
|
"minimum": 1
|
|
},
|
|
"vcpus": {
|
|
"type": "integer",
|
|
"minimum": 1
|
|
},
|
|
"disk": {
|
|
"type": "integer",
|
|
"minimum": 1
|
|
}
|
|
}
|
|
}
|
|
|
|
def setup(self):
|
|
"""This method is called before the task start"""
|
|
try:
|
|
# use rally.osclients to get nessesary client instance
|
|
nova = osclients.Clients(self.context["admin"]["endpoint"]).nova()
|
|
# and than do what you need with this client
|
|
self.context["flavor"] = nova.flavors.create(
|
|
# context settings are stored in self.config
|
|
name=self.config.get("flavor_name", "rally_test_flavor"),
|
|
ram=self.config.get("ram", 1),
|
|
vcpus=self.config.get("vcpus", 1),
|
|
disk=self.config.get("disk", 1)).to_dict()
|
|
LOG.debug("Flavor with id '%s'" % self.context["flavor"]["id"])
|
|
except Exception as e:
|
|
msg = "Can't create flavor: %s" % e.message
|
|
if logging.is_debug():
|
|
LOG.exception(msg)
|
|
else:
|
|
LOG.warning(msg)
|
|
|
|
def cleanup(self):
|
|
"""This method is called after the task finish"""
|
|
try:
|
|
nova = osclients.Clients(self.context["admin"]["endpoint"]).nova()
|
|
nova.flavors.delete(self.context["flavor"]["id"])
|
|
LOG.debug("Flavor '%s' deleted" % self.context["flavor"]["id"])
|
|
except Exception as e:
|
|
msg = "Can't delete flavor: %s" % e.message
|
|
if logging.is_debug():
|
|
LOG.exception(msg)
|
|
else:
|
|
LOG.warning(msg)
|
|
|
|
|
|
|
|
Placement
|
|
^^^^^^^^^
|
|
|
|
Put the python module with your plugin class into the **/opt/rally/plugins** or **~/.rally/plugins** directory or it's subdirectories and it will be autoloaded. You can also use a script **unpack_plugins_samples.sh** from **samples/plugins** which will automatically create the **~/.rally/plugins** directory.
|
|
|
|
|
|
Usage
|
|
^^^^^
|
|
|
|
You can refer to your plugin context in the benchmark task configuration files just in the same way as to any other contexts:
|
|
|
|
.. code-block:: none
|
|
|
|
{
|
|
"Dummy.dummy": [
|
|
{
|
|
"args": {
|
|
"sleep": 0.01
|
|
},
|
|
"runner": {
|
|
"type": "constant",
|
|
"times": 5,
|
|
"concurrency": 1
|
|
},
|
|
"context": {
|
|
"users": {
|
|
"tenants": 1,
|
|
"users_per_tenant": 1
|
|
},
|
|
"create_flavor": {
|
|
"ram": 1024
|
|
}
|
|
}
|
|
}
|
|
]
|
|
}
|
|
|
|
Example: SLA as a plugin
|
|
------------------------
|
|
|
|
Let's create as a plugin an SLA (success criterion) which checks whether the range of the observed performance measurements does not exceed the allowed maximum value.
|
|
|
|
Creation
|
|
^^^^^^^^
|
|
|
|
Inherit a class for your plugin from the base *SLA* class and implement its API (the *check()* method):
|
|
|
|
.. code-block:: none
|
|
|
|
from rally.benchmark.sla import base
|
|
|
|
|
|
class MaxDurationRange(base.SLA):
|
|
"""Maximum allowed duration range in seconds."""
|
|
OPTION_NAME = "max_duration_range"
|
|
CONFIG_SCHEMA = {"type": "number", "minimum": 0.0,
|
|
"exclusiveMinimum": True}
|
|
|
|
@staticmethod
|
|
def check(criterion_value, result):
|
|
durations = [r["duration"] for r in result if not r.get("error")]
|
|
durations_range = max(durations) - min(durations)
|
|
success = durations_range <= criterion_value
|
|
msg = (_("Maximum duration range per iteration %ss, actual %ss")
|
|
% (criterion_value, durations_range))
|
|
return base.SLAResult(success, msg)
|
|
|
|
|
|
|
|
Placement
|
|
^^^^^^^^^
|
|
|
|
Put the python module with your plugin class into the **/opt/rally/plugins** or **~/.rally/plugins** directory or it's subdirectories and it will be autoloaded. You can also use a script **unpack_plugins_samples.sh** from **samples/plugins** which will automatically create the **~/.rally/plugins** directory.
|
|
|
|
|
|
Usage
|
|
^^^^^
|
|
|
|
You can refer to your SLA in the benchmark task configuration files just in the same way as to any other SLA:
|
|
|
|
.. code-block:: none
|
|
|
|
{
|
|
"Dummy.dummy": [
|
|
{
|
|
"args": {
|
|
"sleep": 0.01
|
|
},
|
|
"runner": {
|
|
"type": "constant",
|
|
"times": 5,
|
|
"concurrency": 1
|
|
},
|
|
"context": {
|
|
"users": {
|
|
"tenants": 1,
|
|
"users_per_tenant": 1
|
|
}
|
|
},
|
|
"sla": {
|
|
"max_duration_range": 2.5
|
|
}
|
|
}
|
|
]
|
|
}
|
|
|
|
|
|
Example: Scenario runner as a plugin
|
|
------------------------------------
|
|
|
|
Let's create as a plugin a scenario runner which runs a given benchmark scenario for a random number of times (chosen at random from a given range).
|
|
|
|
Creation
|
|
^^^^^^^^
|
|
|
|
Inherit a class for your plugin from the base *ScenarioRunner* class and implement its API (the *_run_scenario()* method):
|
|
|
|
.. code-block:: none
|
|
|
|
import random
|
|
|
|
from rally.benchmark.runners import base
|
|
from rally import consts
|
|
|
|
|
|
class RandomTimesScenarioRunner(base.ScenarioRunner):
|
|
"""Sample of scenario runner plugin.
|
|
|
|
Run scenario random number of times, which is choosen between min_times and
|
|
max_times.
|
|
"""
|
|
|
|
__execution_type__ = "random_times"
|
|
|
|
CONFIG_SCHEMA = {
|
|
"type": "object",
|
|
"$schema": consts.JSON_SCHEMA,
|
|
"properties": {
|
|
"type": {
|
|
"type": "string"
|
|
},
|
|
"min_times": {
|
|
"type": "integer",
|
|
"minimum": 1
|
|
},
|
|
"max_times": {
|
|
"type": "integer",
|
|
"minimum": 1
|
|
}
|
|
},
|
|
"additionalProperties": True
|
|
}
|
|
|
|
def _run_scenario(self, cls, method_name, context, args):
|
|
# runners settings are stored in self.config
|
|
min_times = self.config.get('min_times', 1)
|
|
max_times = self.config.get('max_times', 1)
|
|
|
|
for i in range(random.randrange(min_times, max_times)):
|
|
run_args = (i, cls, method_name,
|
|
base._get_scenario_context(context), args)
|
|
result = base._run_scenario_once(run_args)
|
|
# use self.send_result for result of each iteration
|
|
self._send_result(result)
|
|
|
|
|
|
|
|
Placement
|
|
^^^^^^^^^
|
|
|
|
Put the python module with your plugin class into the **/opt/rally/plugins** or **~/.rally/plugins** directory or it's subdirectories and it will be autoloaded. You can also use a script **unpack_plugins_samples.sh** from **samples/plugins** which will automatically create the **~/.rally/plugins** directory.
|
|
|
|
|
|
Usage
|
|
^^^^^
|
|
|
|
You can refer to your scenario runner in the benchmark task configuration files just in the same way as to any other runners. Don't forget to put you runner-specific parameters to the configuration as well (*"min_times"* and *"max_times"* in our example):
|
|
|
|
.. code-block:: none
|
|
|
|
{
|
|
"Dummy.dummy": [
|
|
{
|
|
"runner": {
|
|
"type": "random_times",
|
|
"min_times": 10,
|
|
"max_times": 20,
|
|
},
|
|
"context": {
|
|
"users": {
|
|
"tenants": 1,
|
|
"users_per_tenant": 1
|
|
}
|
|
}
|
|
}
|
|
]
|
|
}
|
|
|
|
|
|
|
|
|
|
Different plugin samples are available `here <https://github.com/openstack/rally/tree/master/samples/plugins>`_.
|