aodh/ceilometer/transformer/conversions.py

# -*- encoding: utf-8 -*-
#
# Copyright © 2013 Red Hat, Inc
#
# Author: Eoghan Glynn <eglynn@redhat.com>
#
# 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
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.

import collections

from ceilometer.openstack.common.gettextutils import _  # noqa
from ceilometer.openstack.common import log
from ceilometer.openstack.common import timeutils
from ceilometer import sample
from ceilometer import transformer

LOG = log.getLogger(__name__)


class Namespace(object):
    """Encapsulates the namespace wrapping the evaluation of the
       configured scale factor. This allows nested dicts to be
       accessed in the attribute style, and missing attributes
       to yield false when used in a boolean expression.
    """
    def __init__(self, seed):
        self.__dict__ = collections.defaultdict(lambda: Namespace({}))
        self.__dict__.update(seed)
        for k, v in self.__dict__.iteritems():
            if isinstance(v, dict):
                self.__dict__[k] = Namespace(v)

    def __getattr__(self, attr):
        return self.__dict__[attr]

    def __getitem__(self, key):
        return self.__dict__[key]

    def __nonzero__(self):
        return len(self.__dict__) > 0


class ScalingTransformer(transformer.TransformerBase):
    """Transformer to apply a scaling conversion.
    """

    def __init__(self, source={}, target={}, **kwargs):
        """Initialize transformer with configured parameters.

        :param source: dict containing source sample unit
        :param target: dict containing target sample name, type,
                       unit and scaling factor (a missing value
                       connotes no change)
        """
        self.source = source
        self.target = target
        LOG.debug(_('scaling conversion transformer with source:'
                    ' %(source)s target: %(target)s:')
                  % {'source': source,
                     'target': target})
        super(ScalingTransformer, self).__init__(**kwargs)

    @staticmethod
    def _scale(s, scale):
        """Apply the scaling factor (either a straight multiplicative
           factor or else a string to be eval'd).
        """
        ns = Namespace(s.as_dict())

        return ((eval(scale, {}, ns) if isinstance(scale, basestring)
                 else s.volume * scale) if scale else s.volume)

    def _convert(self, s, growth=1):
        """Transform the appropriate sample fields.
        """
        scale = self.target.get('scale')
        return sample.Sample(
            name=self.target.get('name', s.name),
            unit=self.target.get('unit', s.unit),
            type=self.target.get('type', s.type),
            volume=self._scale(s, scale) * growth,
            user_id=s.user_id,
            project_id=s.project_id,
            resource_id=s.resource_id,
            timestamp=s.timestamp,
            resource_metadata=s.resource_metadata
        )

    def handle_sample(self, context, s):
        """Handle a sample, converting if necessary."""
        LOG.debug(_('handling sample %s'), (s,))
        if (self.source.get('unit', s.unit) == s.unit):
            s = self._convert(s)
            LOG.debug(_('converted to: %s'), (s,))
        return s


class RateOfChangeTransformer(ScalingTransformer):
    """Transformer based on the rate of change of a sample volume,
       for example taking the current and previous volumes of a
       cumulative sample and producing a gauge value based on the
       proportion of some maximum used.
    """

    def __init__(self, **kwargs):
        """Initialize transformer with configured parameters.
        """
        self.cache = {}
        super(RateOfChangeTransformer, self).__init__(**kwargs)

    def handle_sample(self, context, s):
        """Handle a sample, converting if necessary."""
        LOG.debug(_('handling sample %s'), (s,))
        key = s.name + s.resource_id
        prev = self.cache.get(key)
        timestamp = timeutils.parse_isotime(s.timestamp)
        self.cache[key] = (s.volume, timestamp)

        if prev:
            prev_volume = prev[0]
            prev_timestamp = prev[1]
            time_delta = timeutils.delta_seconds(prev_timestamp, timestamp)
            # we only allow negative deltas for noncumulative samples, whereas
            # for cumulative we assume that a reset has occurred in the interim
            # so that the current volume gives a lower bound on growth
            volume_delta = (s.volume - prev_volume
                            if (prev_volume <= s.volume or
                                s.type != sample.TYPE_CUMULATIVE)
                            else s.volume)
            rate_of_change = ((1.0 * volume_delta / time_delta)
                              if time_delta else 0.0)

            s = self._convert(s, rate_of_change)
            LOG.debug(_('converted to: %s'), (s,))
        else:
            LOG.warn(_('dropping sample with no predecessor: %s'),
                     (s,))
            s = None
        return s