1b24bfbd73
This adds a new scheduler driver, CachingScheduler It currently subclasses the filter scheduler and only adds caching of calls to host_manager.get_all_host_states. It relies on there being a small number of schedulers, ideally one, calls to consume_from_instance within the filter scheduler to update the current cached list of hosts, and a low rate of deletes. It introduces the new config value: scheduler_driver_task_period The periodic task it controls is used by the caching scheduler to fetch an updated copy of host_manager.get_all_host_states. If your value for service_down_time is much smaller than scheduler_driver_task_period, there will be issues with hosts appearing to be dead, just because the list of hosts is being cached. Correct configuration of those two values can avoid this issue. DocImpact Part of blueprint caching-scheduler Change-Id: I2eb873ce8024a576e597205e2d52b5d5e8aee97a
78 lines
3.3 KiB
Python
78 lines
3.3 KiB
Python
# Copyright (c) 2014 Rackspace Hosting
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# All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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from nova.scheduler import filter_scheduler
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class CachingScheduler(filter_scheduler.FilterScheduler):
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"""Scheduler to test aggressive caching of the host list.
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Please note, this is a very opinionated scheduler. Be sure to
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review the caveats listed here before selecting this scheduler.
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The aim of this scheduler is to reduce server build times when
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you have large bursts of server builds, by reducing the time it
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takes, from the users point of view, to service each schedule
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request.
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There are two main parts to scheduling a users request:
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* getting the current state of the system
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* using filters and weights to pick the best host
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This scheduler tries its best to cache in memory the current
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state of the system, so we don't need to make the expensive
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call to get the current state of the system while processing
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a user's request, we can do that query in a periodic task
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before the user even issues their request.
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To reduce races, cached info of the chosen host is updated using
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the existing host state call: consume_from_instance
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Please note, the way this works, each scheduler worker has its own
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copy of the cache. So if you run multiple schedulers, you will get
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more retries, because the data stored on any additional scheduler will
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be more out of date, than if it was fetched from the database.
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In a similar way, if you have a high number of server deletes, the
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extra capacity from those deletes will not show up until the cache is
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refreshed.
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"""
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def __init__(self, *args, **kwargs):
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super(CachingScheduler, self).__init__(*args, **kwargs)
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self.all_host_states = None
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def run_periodic_tasks(self, context):
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"""Called from a periodic tasks in the manager."""
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elevated = context.elevated()
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# NOTE(johngarbutt) Fetching the list of hosts before we get
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# a user request, so no user requests have to wait while we
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# fetch the list of hosts.
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self.all_host_states = self._get_up_hosts(elevated)
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def _get_all_host_states(self, context):
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"""Called from the filter scheduler, in a template pattern."""
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if self.all_host_states is None:
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# NOTE(johngarbutt) We only get here when we a scheduler request
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# comes in before the first run of the periodic task.
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# Rather than raise an error, we fetch the list of hosts.
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self.all_host_states = self._get_up_hosts(context)
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return self.all_host_states
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def _get_up_hosts(self, context):
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all_hosts_iterator = self.host_manager.get_all_host_states(context)
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return list(all_hosts_iterator)
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