deb-ceilometer/ceilometer/pipeline.py
Pradeep Kilambi 6840237574 Use resource discovery for Network Services
This change migrates the network services pollsters to
leverage resource discovery through pipeline yaml. This
will also be more HA friendly to distribute work across
multiple central agents.

Change-Id: I4d454e98974438438c166051451b76ce9fbbc2a4
2014-07-21 12:11:50 -07:00

528 lines
19 KiB
Python

#
# Copyright 2013 Intel Corp.
# Copyright 2014 Red Hat, Inc
#
# Authors: Yunhong Jiang <yunhong.jiang@intel.com>
# 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 fnmatch
import itertools
import operator
import os
from oslo.config import cfg
import yaml
from ceilometer.openstack.common.gettextutils import _
from ceilometer.openstack.common import log
from ceilometer import publisher
from ceilometer import transformer as xformer
OPTS = [
cfg.StrOpt('pipeline_cfg_file',
default="pipeline.yaml",
help="Configuration file for pipeline definition."
),
]
cfg.CONF.register_opts(OPTS)
LOG = log.getLogger(__name__)
class PipelineException(Exception):
def __init__(self, message, pipeline_cfg):
self.msg = message
self.pipeline_cfg = pipeline_cfg
def __str__(self):
return 'Pipeline %s: %s' % (self.pipeline_cfg, self.msg)
class PublishContext(object):
def __init__(self, context, pipelines=None):
pipelines = pipelines or []
self.pipelines = set(pipelines)
self.context = context
def add_pipelines(self, pipelines):
self.pipelines.update(pipelines)
def __enter__(self):
def p(samples):
for p in self.pipelines:
p.publish_samples(self.context,
samples)
return p
def __exit__(self, exc_type, exc_value, traceback):
for p in self.pipelines:
p.flush(self.context)
class Source(object):
"""Represents a source of samples.
In effect it is a set of pollsters and/or notification handlers emitting
samples for a set of matching meters. Each source encapsulates meter name
matching, polling interval determination, optional resource enumeration or
discovery, and mapping to one or more sinks for publication.
"""
def __init__(self, cfg):
self.cfg = cfg
try:
self.name = cfg['name']
try:
self.interval = int(cfg['interval'])
except ValueError:
raise PipelineException("Invalid interval value", cfg)
# Support 'counters' for backward compatibility
self.meters = cfg.get('meters', cfg.get('counters'))
self.sinks = cfg.get('sinks')
except KeyError as err:
raise PipelineException(
"Required field %s not specified" % err.args[0], cfg)
if self.interval <= 0:
raise PipelineException("Interval value should > 0", cfg)
self.resources = cfg.get('resources') or []
if not isinstance(self.resources, list):
raise PipelineException("Resources should be a list", cfg)
self.discovery = cfg.get('discovery') or []
if not isinstance(self.discovery, list):
raise PipelineException("Discovery should be a list", cfg)
self._check_meters()
def __str__(self):
return self.name
def _check_meters(self):
"""Meter rules checking
At least one meaningful meter exist
Included type and excluded type meter can't co-exist at
the same pipeline
Included type meter and wildcard can't co-exist at same pipeline
"""
meters = self.meters
if not meters:
raise PipelineException("No meter specified", self.cfg)
if ([x for x in meters if x[0] not in '!*'] and
[x for x in meters if x[0] == '!']):
raise PipelineException(
"Both included and excluded meters specified",
cfg)
if '*' in meters and [x for x in meters if x[0] not in '!*']:
raise PipelineException(
"Included meters specified with wildcard",
self.cfg)
# (yjiang5) To support meters like instance:m1.tiny,
# which include variable part at the end starting with ':'.
# Hope we will not add such meters in future.
@staticmethod
def _variable_meter_name(name):
m = name.partition(':')
if m[1] == ':':
return m[1].join((m[0], '*'))
else:
return name
def support_meter(self, meter_name):
meter_name = self._variable_meter_name(meter_name)
# Special case: if we only have negation, we suppose the default is
# allow
default = all(meter.startswith('!') for meter in self.meters)
# Support wildcard like storage.* and !disk.*
# Start with negation, we consider that the order is deny, allow
if any(fnmatch.fnmatch(meter_name, meter[1:])
for meter in self.meters
if meter[0] == '!'):
return False
if any(fnmatch.fnmatch(meter_name, meter)
for meter in self.meters
if meter[0] != '!'):
return True
return default
def check_sinks(self, sinks):
if not self.sinks:
raise PipelineException(
"No sink defined in source %s" % self,
self.cfg)
for sink in self.sinks:
if sink not in sinks:
raise PipelineException(
"Dangling sink %s from source %s" % (sink, self),
self.cfg)
class Sink(object):
"""Represents a sink for the transformation and publication of samples.
Samples are emitted from a related source.
Each sink config is concerned *only* with the transformation rules
and publication conduits for samples.
In effect, a sink describes a chain of handlers. The chain starts
with zero or more transformers and ends with one or more publishers.
The first transformer in the chain is passed samples from the
corresponding source, takes some action such as deriving rate of
change, performing unit conversion, or aggregating, before passing
the modified sample to next step.
The subsequent transformers, if any, handle the data similarly.
At the end of the chain, publishers publish the data. The exact
publishing method depends on publisher type, for example, pushing
into data storage via the message bus providing guaranteed delivery,
or for loss-tolerant samples UDP may be used.
If no transformers are included in the chain, the publishers are
passed samples directly from the sink which are published unchanged.
"""
def __init__(self, cfg, transformer_manager):
self.cfg = cfg
try:
self.name = cfg['name']
# It's legal to have no transformer specified
self.transformer_cfg = cfg['transformers'] or []
except KeyError as err:
raise PipelineException(
"Required field %s not specified" % err.args[0], cfg)
if not cfg.get('publishers'):
raise PipelineException("No publisher specified", cfg)
self.publishers = []
for p in cfg['publishers']:
if '://' not in p:
# Support old format without URL
p = p + "://"
try:
self.publishers.append(publisher.get_publisher(p))
except Exception:
LOG.exception(_("Unable to load publisher %s"), p)
self.transformers = self._setup_transformers(cfg, transformer_manager)
def __str__(self):
return self.name
def _setup_transformers(self, cfg, transformer_manager):
transformer_cfg = cfg['transformers'] or []
transformers = []
for transformer in transformer_cfg:
parameter = transformer['parameters'] or {}
try:
ext = transformer_manager.get_ext(transformer['name'])
except KeyError:
raise PipelineException(
"No transformer named %s loaded" % transformer['name'],
cfg)
transformers.append(ext.plugin(**parameter))
LOG.info(_(
"Pipeline %(pipeline)s: Setup transformer instance %(name)s "
"with parameter %(param)s") % ({'pipeline': self,
'name': transformer['name'],
'param': parameter}))
return transformers
def _transform_sample(self, start, ctxt, sample):
try:
for transformer in self.transformers[start:]:
sample = transformer.handle_sample(ctxt, sample)
if not sample:
LOG.debug(_(
"Pipeline %(pipeline)s: Sample dropped by "
"transformer %(trans)s") % ({'pipeline': self,
'trans': transformer}))
return
return sample
except Exception as err:
LOG.warning(_("Pipeline %(pipeline)s: "
"Exit after error from transformer "
"%(trans)s for %(smp)s") % ({'pipeline': self,
'trans': transformer,
'smp': sample}))
LOG.exception(err)
def _publish_samples(self, start, ctxt, samples):
"""Push samples into pipeline for publishing.
:param start: The first transformer that the sample will be injected.
This is mainly for flush() invocation that transformer
may emit samples.
:param ctxt: Execution context from the manager or service.
:param samples: Sample list.
"""
transformed_samples = []
for sample in samples:
LOG.debug(_(
"Pipeline %(pipeline)s: Transform sample "
"%(smp)s from %(trans)s transformer") % ({'pipeline': self,
'smp': sample,
'trans': start}))
sample = self._transform_sample(start, ctxt, sample)
if sample:
transformed_samples.append(sample)
if transformed_samples:
for p in self.publishers:
try:
p.publish_samples(ctxt, transformed_samples)
except Exception:
LOG.exception(_(
"Pipeline %(pipeline)s: Continue after error "
"from publisher %(pub)s") % ({'pipeline': self,
'pub': p}))
def publish_samples(self, ctxt, samples):
for meter_name, samples in itertools.groupby(
sorted(samples, key=operator.attrgetter('name')),
operator.attrgetter('name')):
self._publish_samples(0, ctxt, samples)
def flush(self, ctxt):
"""Flush data after all samples have been injected to pipeline."""
for (i, transformer) in enumerate(self.transformers):
try:
self._publish_samples(i + 1, ctxt,
list(transformer.flush(ctxt)))
except Exception as err:
LOG.warning(_(
"Pipeline %(pipeline)s: Error flushing "
"transformer %(trans)s") % ({'pipeline': self,
'trans': transformer}))
LOG.exception(err)
class Pipeline(object):
"""Represents a coupling between a sink and a corresponding source."""
def __init__(self, source, sink):
self.source = source
self.sink = sink
self.name = str(self)
def __str__(self):
return (self.source.name if self.source.name == self.sink.name
else '%s:%s' % (self.source.name, self.sink.name))
def get_interval(self):
return self.source.interval
@property
def resources(self):
return self.source.resources
@property
def discovery(self):
return self.source.discovery
def support_meter(self, meter_name):
return self.source.support_meter(meter_name)
@property
def publishers(self):
return self.sink.publishers
def publish_sample(self, ctxt, sample):
self.publish_samples(ctxt, [sample])
def publish_samples(self, ctxt, samples):
supported = [s for s in samples if self.source.support_meter(s.name)]
self.sink.publish_samples(ctxt, supported)
def flush(self, ctxt):
self.sink.flush(ctxt)
class PipelineManager(object):
"""Pipeline Manager
Pipeline manager sets up pipelines according to config file
Usually only one pipeline manager exists in the system.
"""
def __init__(self, cfg, transformer_manager):
"""Setup the pipelines according to config.
The configuration is supported in one of two forms:
1. Deprecated: the source and sink configuration are conflated
as a list of consolidated pipelines.
The pipelines are defined as a list of dictionaries each
specifying the target samples, the transformers involved,
and the target publishers, for example:
[{"name": pipeline_1,
"interval": interval_time,
"meters" : ["meter_1", "meter_2"],
"resources": ["resource_uri1", "resource_uri2"],
"transformers": [
{"name": "Transformer_1",
"parameters": {"p1": "value"}},
{"name": "Transformer_2",
"parameters": {"p1": "value"}},
],
"publishers": ["publisher_1", "publisher_2"]
},
{"name": pipeline_2,
"interval": interval_time,
"meters" : ["meter_3"],
"publishers": ["publisher_3"]
},
]
2. Decoupled: the source and sink configuration are separately
specified before being linked together. This allows source-
specific configuration, such as resource discovery, to be
kept focused only on the fine-grained source while avoiding
the necessity for wide duplication of sink-related config.
The configuration is provided in the form of separate lists
of dictionaries defining sources and sinks, for example:
{"sources": [{"name": source_1,
"interval": interval_time,
"meters" : ["meter_1", "meter_2"],
"resources": ["resource_uri1", "resource_uri2"],
"sinks" : ["sink_1", "sink_2"]
},
{"name": source_2,
"interval": interval_time,
"meters" : ["meter_3"],
"sinks" : ["sink_2"]
},
],
"sinks": [{"name": sink_1,
"transformers": [
{"name": "Transformer_1",
"parameters": {"p1": "value"}},
{"name": "Transformer_2",
"parameters": {"p1": "value"}},
],
"publishers": ["publisher_1", "publisher_2"]
},
{"name": sink_2,
"publishers": ["publisher_3"]
},
]
}
The semantics of the common individual configuration elements
are identical in the deprecated and decoupled version.
The interval determines the cadence of sample injection into
the pipeline where samples are produced under the direct control
of an agent, i.e. via a polling cycle as opposed to incoming
notifications.
Valid meter format is '*', '!meter_name', or 'meter_name'.
'*' is wildcard symbol means any meters; '!meter_name' means
"meter_name" will be excluded; 'meter_name' means 'meter_name'
will be included.
The 'meter_name" is Sample name field. For meter names with
variable like "instance:m1.tiny", it's "instance:*".
Valid meters definition is all "included meter names", all
"excluded meter names", wildcard and "excluded meter names", or
only wildcard.
The resources is list of URI indicating the resources from where
the meters should be polled. It's optional and it's up to the
specific pollster to decide how to use it.
Transformer's name is plugin name in setup.cfg.
Publisher's name is plugin name in setup.cfg
"""
self.pipelines = []
if 'sources' in cfg or 'sinks' in cfg:
if not ('sources' in cfg and 'sinks' in cfg):
raise PipelineException("Both sources & sinks are required",
cfg)
LOG.info(_('detected decoupled pipeline config format'))
sources = [Source(s) for s in cfg.get('sources', [])]
sinks = dict((s['name'], Sink(s, transformer_manager))
for s in cfg.get('sinks', []))
for source in sources:
source.check_sinks(sinks)
for target in source.sinks:
self.pipelines.append(Pipeline(source,
sinks[target]))
else:
LOG.warning(_('detected deprecated pipeline config format'))
for pipedef in cfg:
source = Source(pipedef)
sink = Sink(pipedef, transformer_manager)
self.pipelines.append(Pipeline(source, sink))
def publisher(self, context):
"""Build a new Publisher for these manager pipelines.
:param context: The context.
"""
return PublishContext(context, self.pipelines)
def setup_pipeline(transformer_manager=None):
"""Setup pipeline manager according to yaml config file."""
cfg_file = cfg.CONF.pipeline_cfg_file
if not os.path.exists(cfg_file):
cfg_file = cfg.CONF.find_file(cfg_file)
LOG.debug(_("Pipeline config file: %s"), cfg_file)
with open(cfg_file) as fap:
data = fap.read()
pipeline_cfg = yaml.safe_load(data)
LOG.info(_("Pipeline config: %s"), pipeline_cfg)
return PipelineManager(pipeline_cfg,
transformer_manager or
xformer.TransformerExtensionManager(
'ceilometer.transformer',
))