rally/doc/source/plugins.rst

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 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:

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:

{
    "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.