172 lines
6.1 KiB
Python
172 lines
6.1 KiB
Python
# Copyright 2017 - Nokia Networks
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain 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,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import eventlet
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from oslo_config import cfg
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from oslo_log import log as logging
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from glare.api.middleware import context
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from glare.common import exception
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from glare.common import store_api
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from glare.db.sqlalchemy import api as db_api
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from glare.i18n import _
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LOG = logging.getLogger(__name__)
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scrubber_opts = [
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cfg.IntOpt('scrub_time', default=0, min=0,
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help=_("""
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The amount of time, in seconds, to delay artifact scrubbing.
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When delayed delete is turned on, an artifact is put into
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``deleted`` state upon deletion until the scrubber deletes its data.
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Typically, soon
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after the artifact is put into ``deleted`` state, it is available
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for scrubbing. However, scrubbing can be delayed until a later point
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using this configuration option. This option denotes the time period
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an artifact spends in ``deleted`` state before it is available for
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scrubbing.
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It is important to realize that this has storage implications. The
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larger the ``scrub_time``, the longer the time to reclaim backend
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storage from deleted artifacts.
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Possible values:
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* Any non-negative integer
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Related options:
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* ``delayed_delete``
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""")),
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cfg.IntOpt('scrub_pool_size', default=1, min=1,
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help=_("""
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The size of thread pool to be used for scrubbing artifacts.
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When there are a large number of artifacts to scrub, it is
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beneficial to scrub artifacts in parallel so that the scrub queue
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stays in control and the backend storage is reclaimed in a timely
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fashion. This configuration option denotes the maximum number of
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artifacts to be scrubbed in parallel. The default value is one,
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which signifies serial scrubbing. Any value above one indicates
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parallel scrubbing.
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Possible values:
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* Any non-zero positive integer
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Related options:
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* ``delayed_delete``
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""")),
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]
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scrubber_cmd_opts = [
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cfg.IntOpt('wakeup_time', default=300, min=0,
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help=_("""
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Time interval, in seconds, between scrubber runs in daemon mode.
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Scrubber can be run either as a cron job or daemon. When run as a
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daemon, this configuration time specifies the time period between
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two runs. When the scrubber wakes up, it fetches and scrubs all
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``deleted`` artifacts that are available for scrubbing after taking
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``scrub_time`` into consideration.
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If the ``wakeup_time`` is set to a large number, there may be a large
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number of artifacts to be scrubbed for each run. Also, this impacts
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how quickly the backend storage is reclaimed.
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Possible values:
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* Any non-negative integer
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Related options:
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* ``daemon``
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* ``delayed_delete``
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"""))
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]
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scrubber_cmd_cli_opts = [
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cfg.BoolOpt('daemon',
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short='D',
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default=False,
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help=_("""
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Run scrubber as a daemon.
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This boolean configuration option indicates whether scrubber should
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run as a long-running process that wakes up at regular intervals to
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scrub artifacts. The wake up interval can be specified using the
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configuration option ``wakeup_time``.
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If this configuration option is set to ``False``, which is the
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default value, scrubber runs once to scrub artifacts and exits.
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In this case, if the operator wishes to implement continuous
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scrubbing of artifacts, scrubber needs to be scheduled as a cron job.
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Possible values:
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* True
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* False
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Related options:
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* ``wakeup_time``
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"""))
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]
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CONF = cfg.CONF
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CONF.register_opts(scrubber_opts, group='scrubber')
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class Daemon(object):
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def __init__(self, wakeup_time=300, threads=100):
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LOG.info("Starting Daemon: wakeup_time=%(wakeup_time)s "
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"threads=%(threads)s",
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{'wakeup_time': wakeup_time, 'threads': threads})
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self.wakeup_time = wakeup_time
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self.event = eventlet.event.Event()
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# This pool is used for periodic instantiation of scrubber
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self.daemon_pool = eventlet.greenpool.GreenPool(threads)
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def start(self, application):
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self._run(application)
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def wait(self):
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try:
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self.event.wait()
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except KeyboardInterrupt:
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LOG.info("Daemon Shutdown on KeyboardInterrupt")
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def _run(self, application):
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LOG.debug("Running scrubber application")
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self.daemon_pool.spawn_n(application.run, self.event)
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eventlet.spawn_after(self.wakeup_time, self._run, application)
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LOG.debug("Next run scheduled in %s seconds", self.wakeup_time)
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class Scrubber(object):
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def __init__(self):
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self.context = context.RequestContext()
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self.context.is_admin = True
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self.pool = eventlet.greenpool.GreenPool(
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CONF.scrubber.scrub_pool_size)
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def run(self, event=None):
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while True:
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artifacts = db_api._get_all(
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context=self.context,
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session=db_api.get_session(),
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limit=CONF.scrubber.scrub_pool_size,
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sort=[],
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filters=[('status', None, 'eq', None, 'deleted', 'and')])
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if not artifacts:
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break
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self.pool.imap(self._scrub_artifact, artifacts)
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@staticmethod
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def _scrub_artifact(af):
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LOG.info("Begin scrubbing of artifact %s", af.id)
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for blob in af.blobs:
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if not blob.external:
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try:
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store_api.delete_blob(blob.url, context=context)
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except exception.NotFound:
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# data has already been removed
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pass
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LOG.info("Blobs successfully deleted for artifact %s", af.id)
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# delete artifact itself
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db_api.delete(context, af.id, db_api.get_session())
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LOG.info("Artifact %s was scrubbed", af.id)
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