Doc on how to add notification endpoints

In this changeset, I updated the CDMC plugin documentation to explain
how to implement and register new notification endpoints.

Change-Id: Ib8c014e82051647edef5c1272f63429f76673227
Partially-Implements: blueprint cluster-model-objects-wrapper
This commit is contained in:
Vincent Françoise 2016-08-24 12:14:54 +02:00
parent 77b7fae41e
commit f238167edc

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@ -10,10 +10,10 @@
Build a new cluster data model collector Build a new cluster data model collector
======================================== ========================================
Watcher Decision Engine has an external cluster data model plugin interface Watcher Decision Engine has an external cluster data model (CDM) plugin
which gives anyone the ability to integrate an external cluster data model interface which gives anyone the ability to integrate an external cluster data
collector in order to extend the initial set of cluster data model collectors model collector (CDMC) in order to extend the initial set of cluster data model
Watcher provides. collectors Watcher provides.
This section gives some guidelines on how to implement and integrate custom This section gives some guidelines on how to implement and integrate custom
cluster data model collectors within Watcher. cluster data model collectors within Watcher.
@ -22,6 +22,17 @@ cluster data model collectors within Watcher.
Creating a new plugin Creating a new plugin
===================== =====================
In order to create a new model, you have to:
- Extend the :py:class:`~.base.BaseClusterDataModelCollector` class.
- Implement its :py:meth:`~.BaseClusterDataModelCollector.execute` abstract
method to return your entire cluster data model that this method should
build.
- Implement its :py:meth:`~.Goal.notification_endpoints` abstract property to
return the list of all the :py:class:`~.base.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 :class:`~.BaseClusterDataModelCollector` First of all, you have to extend the :class:`~.BaseClusterDataModelCollector`
base class which defines the :py:meth:`~.BaseClusterDataModelCollector.execute` base class which defines the :py:meth:`~.BaseClusterDataModelCollector.execute`
abstract method you will have to implement. This method is responsible for abstract method you will have to implement. This method is responsible for
@ -46,6 +57,10 @@ Here is an example showing how you can write a plugin called
# Do something here... # Do something here...
return model return model
@property
def notification_endpoints(self):
return []
This implementation is the most basic one. So in order to get a better 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, understanding on how to implement a more advanced cluster data model collector,
have a look at the :py:class:`~.NovaClusterDataModelCollector` class. have a look at the :py:class:`~.NovaClusterDataModelCollector` class.
@ -54,12 +69,12 @@ Define configuration parameters
=============================== ===============================
At this point, you have a fully functional cluster data model collector. At this point, you have a fully functional cluster data model collector.
By default, cluster data model collectors define an ``period`` option (see By default, cluster data model collectors define a ``period`` option (see
:py:meth:`~.BaseClusterDataModelCollector.get_config_opts`) that corresponds :py:meth:`~.BaseClusterDataModelCollector.get_config_opts`) that corresponds
to the interval of time between each synchronization of the in-memory model. to the interval of time between each synchronization of the in-memory model.
However, in more complex implementation, you may want to define some However, in more complex implementation, you may want to define some
configuration options so one can tune the cluster data model collector to its configuration options so one can tune the cluster data model collector to your
needs. To do so, you can implement the :py:meth:`~.Loadable.get_config_opts` needs. To do so, you can implement the :py:meth:`~.Loadable.get_config_opts`
class method as followed: class method as followed:
@ -77,6 +92,10 @@ class method as followed:
# Do something here... # Do something here...
return model return model
@property
def notification_endpoints(self):
return []
@classmethod @classmethod
def get_config_opts(cls): def get_config_opts(cls):
return super( return super(
@ -87,7 +106,9 @@ class method as followed:
The configuration options defined within this class method will be included The configuration options defined within this class method will be included
within the global ``watcher.conf`` configuration file under a section named by within the global ``watcher.conf`` configuration file under a section named by
convention: ``{namespace}.{plugin_name}``. In our case, the ``watcher.conf`` convention: ``{namespace}.{plugin_name}`` (see section :ref:`Register a new
entry point <register_new_cdmc_entrypoint>`). 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: configuration would have to be modified as followed:
.. code-block:: ini .. code-block:: ini
@ -113,14 +134,16 @@ single cluster data model collector should implement:
:noindex: :noindex:
.. _register_new_cdmc_entrypoint:
Register a new entry point Register a new entry point
========================== ==========================
In order for the Watcher Applier to load your new cluster data model collector, In order for the Watcher Decision Engine to load your new cluster data model
the cluster data model collector must be registered as a named entry point collector, the latter must be registered as a named entry point under the
under the ``decision_engine.model.collector`` entry point of your ``setup.py`` ``watcher_cluster_data_model_collectors`` entry point namespace of your
file. If you are using pbr_, this entry point should be placed in your ``setup.py`` file. If you are using pbr_, this entry point should be placed in
``setup.cfg`` file. your ``setup.cfg`` file.
The name you give to your entry point has to be unique. The name you give to your entry point has to be unique.
@ -135,6 +158,56 @@ Here below is how to register ``DummyClusterDataModelCollector`` using pbr_:
.. _pbr: http://docs.openstack.org/developer/pbr/ .. _pbr: http://docs.openstack.org/developer/pbr/
Add new notification endpoints
==============================
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:
.. code-block:: python
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.
Using cluster data model collector plugins Using cluster data model collector plugins
========================================== ==========================================