Fix some flaws in ceilometer docstrings
Change-Id: Iaed56b46cefff10f9a88fa5096390b19a297d237
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@ -646,7 +646,7 @@ class Statistics(_Base):
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# "invalid."
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#
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# If the timestamps are invalid, return None as a
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# sentinal indicating that there is something "funny"
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# sentinel indicating that there is something "funny"
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# about the range.
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if (self.duration_start and
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self.duration_end and
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@ -96,7 +96,7 @@ class Instance(object):
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The pollsters all expect an instance that looks like what the
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novaclient gives them, but the conductor API gives us a
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dictionary. This class makes an object from the dictonary so we
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dictionary. This class makes an object from the dictionary so we
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can pass it to the pollsters.
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"""
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def __init__(self, context, info):
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@ -75,7 +75,7 @@ class Pipeline(object):
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"""Sample handling pipeline
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Pipeline describes a chain of handlers. The chain starts with
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tranformer and ends with one or more publishers.
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transformer and ends with one or more publishers.
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The first transformer in the chain gets sample from data collector, i.e.
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pollster or notification handler, takes some action like dropping,
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@ -313,13 +313,13 @@ class PipelineManager(object):
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The top of the cfg is a list of pipeline definitions.
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Pipeline definition is an dictionary specifying the target samples,
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the tranformers involved, and the target publishers:
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the transformers involved, and the target publishers:
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{
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"name": pipeline_name
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"interval": interval_time
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"meters" : ["meter_1", "meter_2"],
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"resources": ["resource_uri1", "resource_uri2"],
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"tranformers":[
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"transformers":[
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{"name": "Transformer_1",
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"parameters": {"p1": "value"}},
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@ -348,9 +348,9 @@ class PipelineManager(object):
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the meters should be polled. It's optional and it's up to the
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specific pollster to decide how to use it.
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Transformer's name is plugin name in setup.py.
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Transformer's name is plugin name in setup.cfg.
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Publisher's name is plugin name in setup.py
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Publisher's name is plugin name in setup.cfg
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"""
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self.pipelines = [Pipeline(pipedef, transformer_manager)
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@ -130,7 +130,9 @@ def apply_metaquery_filter(session, query, metaquery):
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def make_query_from_filter(session, query, sample_filter, require_meter=True):
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"""Return a query dictionary based on the settings in the filter.
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:param filter: SampleFilter instance
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:param session: session used for original query
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:param query: Query instance
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:param sample_filter: SampleFilter instance
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:param require_meter: If true and the filter does not have a meter,
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raise an error.
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"""
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