.. 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 `_.