12 KiB
Rally Plugins
How plugins work
Rally provides an opportunity to create and use a custom benchmark scenario, runner or context as a plugin:
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 list 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:
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 instance 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:
{
"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.
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:
{
"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):
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:
{
"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):
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):
{
"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.