Watcher Decision Engine has an external cluster data model (CDM) plugin interface which gives anyone the ability to integrate an external cluster data model collector (CDMC) in order to extend the initial set of cluster data model collectors Watcher provides.
This section gives some guidelines on how to implement and integrate custom cluster data model collectors within Watcher.
In order to create a new cluster data model collector, you have to:
BaseClusterDataModelCollector
class.execute()
abstract
method to return your entire cluster data model that this method should
build.notification_endpoints()
abstract property to
return the list of all the NotificationEndpoint
instances
that will be responsible for handling incoming notifications in order to
incrementally update your cluster data model.First of all, you have to extend the BaseClusterDataModelCollector
base class which defines the execute()
abstract method you will have to implement. This method is responsible for
building an entire cluster data model.
Here is an example showing how you can write a plugin called
DummyClusterDataModelCollector
:
# Filepath = <PROJECT_DIR>/thirdparty/dummy.py
# Import path = thirdparty.dummy
from watcher.decision_engine.model import model_root
from watcher.decision_engine.model.collector import base
class DummyClusterDataModelCollector(base.BaseClusterDataModelCollector):
def execute(self):
model = model_root.ModelRoot()
# Do something here...
return model
@property
def notification_endpoints(self):
return []
This implementation is the most basic one. So in order to get a better
understanding on how to implement a more advanced cluster data model collector,
have a look at the NovaClusterDataModelCollector
class.
As you may have noticed in the above example, we are reusing an existing model
provided by Watcher. However, this model can be easily customized by
implementing a new class that would implement the Model
abstract
base class. Here below is simple example on how to proceed in implementing a
custom Model:
# Filepath = <PROJECT_DIR>/thirdparty/dummy.py
# Import path = thirdparty.dummy
from watcher.decision_engine.model import base as modelbase
from watcher.decision_engine.model.collector import base
class MyModel(modelbase.Model):
def to_string(self):
return 'MyModel'
class DummyClusterDataModelCollector(base.BaseClusterDataModelCollector):
def execute(self):
model = MyModel()
# Do something here...
return model
@property
def notification_endpoints(self):
return []
Here below is the abstract Model
class that every single cluster data model
should implement:
watcher.decision_engine.model.base.
Model
[source]At this point, you have a fully functional cluster data model collector.
By default, cluster data model collectors define a period
option (see
get_config_opts()
) that corresponds
to the interval of time between each synchronization of the in-memory model.
However, in more complex implementation, you may want to define some
configuration options so one can tune the cluster data model collector to your
needs. To do so, you can implement the get_config_opts()
class method as followed:
from oslo_config import cfg
from watcher.decision_engine.model import model_root
from watcher.decision_engine.model.collector import base
class DummyClusterDataModelCollector(base.BaseClusterDataModelCollector):
def execute(self):
model = model_root.ModelRoot()
# Do something here...
return model
@property
def notification_endpoints(self):
return []
@classmethod
def get_config_opts(cls):
return super(
DummyClusterDataModelCollector, cls).get_config_opts() + [
cfg.StrOpt('test_opt', help="Demo Option.", default=0),
# Some more options ...
]
The configuration options defined within this class method will be included
within the global watcher.conf
configuration file under a section named by
convention: {namespace}.{plugin_name}
(see section Register a new
entry point). The namespace for CDMC plugins is
watcher_cluster_data_model_collectors
, so in our case, the watcher.conf
configuration would have to be modified as followed:
[watcher_cluster_data_model_collectors.dummy]
# Option used for testing.
test_opt = test_value
Then, the configuration options you define within this method will then be
injected in each instantiated object via the config
parameter of the
__init__()
method.
Here below is the abstract BaseClusterDataModelCollector
class that every
single cluster data model collector should implement:
watcher.decision_engine.model.collector.base.
BaseClusterDataModelCollector
(config, osc=None)[source]execute
()[source]Build a cluster data model
synchronize
()[source]Synchronize the cluster data model
Whenever called this synchronization will perform a drop-in replacement with the existing cluster data model
In order for the Watcher Decision Engine to load your new cluster data model
collector, the latter must be registered as a named entry point under the
watcher_cluster_data_model_collectors
entry point namespace of your
setup.py
file. If you are using pbr, this entry point should be placed in
your setup.cfg
file.
The name you give to your entry point has to be unique.
Here below is how to register DummyClusterDataModelCollector
using pbr:
[entry_points]
watcher_cluster_data_model_collectors =
dummy = thirdparty.dummy:DummyClusterDataModelCollector
At this point, you have a fully functional cluster data model collector.
However, this CDMC is only refreshed periodically via a background scheduler.
As you may sometimes execute a strategy with a stale CDM due to a high activity
on your infrastructure, you can define some notification endpoints that will be
responsible for incrementally updating the CDM based on notifications emitted
by other services such as Nova. To do so, you can implement and register a new
DummyEndpoint
notification endpoint regarding a dummy
event as shown
below:
from watcher.decision_engine.model import model_root
from watcher.decision_engine.model.collector import base
class DummyNotification(base.NotificationEndpoint):
@property
def filter_rule(self):
return filtering.NotificationFilter(
publisher_id=r'.*',
event_type=r'^dummy$',
)
def info(self, ctxt, publisher_id, event_type, payload, metadata):
# Do some CDM modifications here...
pass
class DummyClusterDataModelCollector(base.BaseClusterDataModelCollector):
def execute(self):
model = model_root.ModelRoot()
# Do something here...
return model
@property
def notification_endpoints(self):
return [DummyNotification(self)]
Note that if the event you are trying to listen to is published by a new
service, you may have to also add a new topic Watcher will have to subscribe to
in the notification_topics
option of the [watcher_decision_engine]
section.
The Watcher Decision Engine service will automatically discover any installed plugins when it is restarted. If a Python package containing a custom plugin is installed within the same environment as Watcher, Watcher will automatically make that plugin available for use.
At this point, you can use your new cluster data model plugin in your
strategy plugin by using the
collector_manager
property as followed:
# [...]
dummy_collector = self.collector_manager.get_cluster_model_collector(
"dummy") # "dummy" is the name of the entry point we declared earlier
dummy_model = dummy_collector.get_latest_cluster_data_model()
# Do some stuff with this model
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