971f9c85c1
New pipeline transformer that enables us to perform arithmetic calculations
over one more meters and/or their metadata, for example:
memory_util = 100 * memory.usage / memory .
The calculation is limited to meters with the same interval.
Example configuration:
- name: "arithmetic"
parameters:
target:
name: "memory_util"
unit: "%"
type: "gauge"
expr: "100 * $(memory.usage) / $(memory)"
To demonstrate the use of metadata, here is the implementation of
a silly metric that shows average CPU time per core::
- name: "arithmetic"
parameters:
target:
name: "avg_cpu_per_core"
unit: "ns"
type: "cumulative"
expr: "$(cpu) / ($(cpu).resource_metadata.cpu_number or 1)"
Expression evaluation gracefully handles NaNs and exceptions. In such
a case it does not create a new sample but only logs a warning.
DocImpact: add documentation about using this new transformer
Implements: blueprint arithmetic-transformer
Change-Id: I1b637e5b1d1bb15ed3c3d7ec758d2a684eaccf21
94 lines
2.8 KiB
Python
94 lines
2.8 KiB
Python
#
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# Copyright 2013 Intel Corp.
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#
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# Author: Yunhong Jiang <yunhong.jiang@intel.com>
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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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import abc
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import collections
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import six
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from stevedore import extension
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class TransformerExtensionManager(extension.ExtensionManager):
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def __init__(self, namespace):
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super(TransformerExtensionManager, self).__init__(
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namespace=namespace,
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invoke_on_load=False,
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invoke_args=(),
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invoke_kwds={}
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)
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self.by_name = dict((e.name, e) for e in self.extensions)
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def get_ext(self, name):
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return self.by_name[name]
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@six.add_metaclass(abc.ABCMeta)
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class TransformerBase(object):
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"""Base class for plugins that transform the sample."""
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def __init__(self, **kwargs):
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"""Setup transformer.
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Each time a transformed is involved in a pipeline, a new transformer
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instance is created and chained into the pipeline. i.e. transformer
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instance is per pipeline. This helps if transformer need keep some
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cache and per-pipeline information.
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:param kwargs: The parameters that are defined in pipeline config file.
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"""
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super(TransformerBase, self).__init__()
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@abc.abstractmethod
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def handle_sample(self, context, sample):
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"""Transform a sample.
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:param context: Passed from the data collector.
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:param sample: A sample.
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"""
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def flush(self, context):
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"""Flush samples cached previously.
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:param context: Passed from the data collector.
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"""
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return []
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class Namespace(object):
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"""Encapsulates the namespace.
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Encapsulation is done by wrapping the evaluation of the configured rule.
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This allows nested dicts to be accessed in the attribute style,
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and missing attributes to yield false when used in a boolean expression.
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"""
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def __init__(self, seed):
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self.__dict__ = collections.defaultdict(lambda: Namespace({}))
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self.__dict__.update(seed)
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for k, v in self.__dict__.iteritems():
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if isinstance(v, dict):
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self.__dict__[k] = Namespace(v)
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def __getattr__(self, attr):
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return self.__dict__[attr]
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def __getitem__(self, key):
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return self.__dict__[key]
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def __nonzero__(self):
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return len(self.__dict__) > 0
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