Source code for watcher.decision_engine.model.collector.base

# -*- encoding: utf-8 -*-
# Copyright (c) 2015 b<>com
# Authors: Jean-Emile DARTOIS <>
#          Vincent FRANCOISE <>
# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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A :ref:`Cluster Data Model <cluster_data_model_definition>` (or CDM) is a
logical representation of the current state and topology of the :ref:`Cluster
<cluster_definition>` :ref:`Managed resources <managed_resource_definition>`.

It is represented as a set of :ref:`Managed resources
<managed_resource_definition>` (which may be a simple tree or a flat list of
key-value pairs) which enables Watcher :ref:`Strategies <strategy_definition>`
to know the current relationships between the different :ref:`resources
<managed_resource_definition>` of the :ref:`Cluster <cluster_definition>`
during an :ref:`Audit <audit_definition>` and enables the :ref:`Strategy
<strategy_definition>` to request information such as:

- What compute nodes are in a given :ref:`Audit Scope
- What :ref:`Instances <instance_definition>` are hosted on a given compute
- What is the current load of a compute node?
- What is the current free memory of a compute node?
- What is the network link between two compute nodes?
- What is the available bandwidth on a given network link?
- What is the current space available on a given virtual disk of a given
  :ref:`Instance <instance_definition>` ?
- What is the current state of a given :ref:`Instance <instance_definition>`?
- ...

In a word, this data model enables the :ref:`Strategy <strategy_definition>`
to know:

- the current topology of the :ref:`Cluster <cluster_definition>`
- the current capacity for each :ref:`Managed resource
- the current amount of used/free space for each :ref:`Managed resource
- the current state of each :ref:`Managed resources

In the Watcher project, we aim at providing a some generic and basic
:ref:`Cluster Data Model <cluster_data_model_definition>` for each :ref:`Goal
<goal_definition>`, usable in the associated :ref:`Strategies
<strategy_definition>` through a plugin-based mechanism which are called
cluster data model collectors (or CDMCs). These CDMCs are responsible for
loading and keeping up-to-date their associated CDM by listening to events and
also periodically rebuilding themselves from the ground up. They are also
directly accessible from the strategies classes. These CDMs are used to:

- simplify the development of a new :ref:`Strategy <strategy_definition>` for a
  given :ref:`Goal <goal_definition>` when there already are some existing
  :ref:`Strategies <strategy_definition>` associated to the same :ref:`Goal
- avoid duplicating the same code in several :ref:`Strategies
  <strategy_definition>` associated to the same :ref:`Goal <goal_definition>`
- have a better consistency between the different :ref:`Strategies
  <strategy_definition>` for a given :ref:`Goal <goal_definition>`
- avoid any strong coupling with any external :ref:`Cluster Data Model
  <cluster_data_model_definition>` (the proposed data model acts as a pivot
  data model)

There may be various :ref:`generic and basic Cluster Data Models
<cluster_data_model_definition>` proposed in Watcher helpers, each of them
being adapted to achieving a given :ref:`Goal <goal_definition>`:

- For example, for a :ref:`Goal <goal_definition>` which aims at optimizing
  the network :ref:`resources <managed_resource_definition>` the :ref:`Strategy
  <strategy_definition>` may need to know which :ref:`resources
  <managed_resource_definition>` are communicating together.
- Whereas for a :ref:`Goal <goal_definition>` which aims at optimizing thermal
  and power conditions, the :ref:`Strategy <strategy_definition>` may need to
  know the location of each compute node in the racks and the location of each
  rack in the room.

Note however that a developer can use his/her own :ref:`Cluster Data Model
<cluster_data_model_definition>` if the proposed data model does not fit
his/her needs as long as the :ref:`Strategy <strategy_definition>` is able to
produce a :ref:`Solution <solution_definition>` for the requested :ref:`Goal
<goal_definition>`. For example, a developer could rely on the Nova Data Model
to optimize some compute resources.

The :ref:`Cluster Data Model <cluster_data_model_definition>` may be persisted
in any appropriate storage system (SQL database, NoSQL database, JSON file,
XML File, In Memory Database, ...). As of now, an in-memory model is built and
maintained in the background in order to accelerate the execution of

import abc
import copy
import threading
import time

from oslo_config import cfg
from oslo_log import log

from watcher.common import clients
from watcher.common.loader import loadable
from watcher.decision_engine.model import model_root

LOG = log.getLogger(__name__)

[docs] class BaseClusterDataModelCollector(loadable.LoadableSingleton, metaclass=abc.ABCMeta): STALE_MODEL = model_root.ModelRoot(stale=True)
[docs] def __init__(self, config, osc=None): super(BaseClusterDataModelCollector, self).__init__(config) self.osc = osc if osc else clients.OpenStackClients() self.lock = threading.RLock() self._audit_scope_handler = None self._cluster_data_model = None self._data_model_scope = None
@property def cluster_data_model(self): if self._cluster_data_model is None: self.lock.acquire() self._cluster_data_model = self.execute() self.lock.release() return self._cluster_data_model @cluster_data_model.setter def cluster_data_model(self, model): self.lock.acquire() self._cluster_data_model = model self.lock.release() @abc.abstractproperty def notification_endpoints(self): """Associated notification endpoints :return: Associated notification endpoints :rtype: List of :py:class:`~.EventsNotificationEndpoint` instances """ raise NotImplementedError() def set_cluster_data_model_as_stale(self): self.cluster_data_model = self.STALE_MODEL
[docs] @abc.abstractmethod def get_audit_scope_handler(self, audit_scope): """Get audit scope handler""" raise NotImplementedError()
[docs] @abc.abstractmethod def execute(self): """Build a cluster data model""" raise NotImplementedError()
[docs] @classmethod def get_config_opts(cls): return [ cfg.IntOpt( 'period', default=3600, help='The time interval (in seconds) between each ' 'synchronization of the model'), ]
def get_latest_cluster_data_model(self): LOG.debug("Creating copy") LOG.debug(self.cluster_data_model.to_xml()) return copy.deepcopy(self.cluster_data_model)
[docs] def synchronize(self): """Synchronize the cluster data model Whenever called this synchronization will perform a drop-in replacement with the existing cluster data model """ self.cluster_data_model = self.execute()
class BaseModelBuilder(object): def call_retry(self, f, *args, **kwargs): """Attempts to call external service Attempts to access data from the external service and handles exceptions. The retrieval should be retried in accordance to the value of api_call_retries :param f: The method that performs the actual querying for metrics :param args: Array of arguments supplied to the method :param kwargs: The amount of arguments supplied to the method :return: The value as retrieved from the external service """ num_retries = CONF.collector.api_call_retries timeout = CONF.collector.api_query_timeout for i in range(num_retries): try: return f(*args, **kwargs) except Exception as e: LOG.exception(e) self.call_retry_reset(e) LOG.warning("Retry {0} of {1}, error while calling service " "retry in {2} seconds".format(i+1, num_retries, timeout)) time.sleep(timeout) raise @abc.abstractmethod def call_retry_reset(self, exc): """Attempt to recover after encountering an error Recover from errors while calling external services, the exception can be used to make a better decision on how to best recover. """ pass @abc.abstractmethod def execute(self, model_scope): """Build the cluster data model limited to the scope and return it Builds the cluster data model with respect to the supplied scope. The schema of this scope will depend on the type of ModelBuilder. """ raise NotImplementedError()