aodh/ceilometer/pipeline.py
Lianhao Lu 0e26faeb69 Corrected import order
Enabled H306 in tox.ini and corrected the import order.

Change-Id: If77984e8c62b0ec0cfb3d068e9c1b0d4d7c3583d
2013-11-13 14:41:33 +08:00

364 lines
12 KiB
Python

# -*- encoding: utf-8 -*-
#
# Copyright © 2013 Intel Corp.
#
# Author: Yunhong Jiang <yunhong.jiang@intel.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 import log
from ceilometer import publisher
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=[]):
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 Pipeline(object):
"""Sample handling pipeline
Pipeline describes a chain of handlers. The chain starts with
tranformer and ends with one or more publishers.
The first transformer in the chain gets sample from data collector, i.e.
pollster or notification handler, takes some action like dropping,
aggregation, changing field etc, then passes the updated sample
to next step.
The subsequent transformers, if any, handle the data similarly.
In the end of the chain, publishers publish the data. The exact publishing
method depends on publisher type, for example, pushing into data storage
through message bus, sending to external CW software through CW API call.
If no transformer is included in the chain, the publishers get samples
from data collector and publish them directly.
"""
def __init__(self, cfg, transformer_manager):
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'))
# 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 self.interval <= 0:
raise PipelineException("Interval value should > 0", cfg)
self._check_meters()
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 _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)
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 %s: Setup transformer instance %s "
"with parameter %s",
self,
transformer['name'],
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 %s: Sample dropped by transformer %s",
self, transformer)
return
return sample
except Exception as err:
LOG.warning("Pipeline %s: Exit after error from transformer"
"%s for %s",
self, transformer, 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 %s: Transform sample %s from %s transformer",
self, sample, start)
sample = self._transform_sample(start, ctxt, sample)
if sample:
transformed_samples.append(sample)
if transformed_samples:
LOG.audit("Pipeline %s: Publishing samples", self)
for p in self.publishers:
try:
p.publish_samples(ctxt, transformed_samples)
except Exception:
LOG.exception("Pipeline %s: Continue after error "
"from publisher %s", self, p)
LOG.audit("Pipeline %s: Published samples", self)
def publish_sample(self, ctxt, sample):
self.publish_samples(ctxt, [sample])
def publish_samples(self, ctxt, samples):
for meter_name, samples in itertools.groupby(
sorted(samples, key=operator.attrgetter('name')),
operator.attrgetter('name')):
if self.support_meter(meter_name):
self._publish_samples(0, ctxt, samples)
# (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.
def _variable_meter_name(self, 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 it
# allow
if all(meter.startswith('!') for meter in self.meters):
default = True
else:
default = False
# 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 flush(self, ctxt):
"""Flush data after all samples have been injected to pipeline."""
LOG.audit("Flush pipeline %s", self)
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 %s: Error flushing "
"transformer %s",
self, transformer)
LOG.exception(err)
def get_interval(self):
return self.interval
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 top of the cfg is a list of pipeline definitions.
Pipeline definition is an dictionary specifying the target samples,
the tranformers involved, and the target publishers:
{
"name": pipeline_name
"interval": interval_time
"meters" : ["meter_1", "meter_2"],
"tranformers":[
{"name": "Transformer_1",
"parameters": {"p1": "value"}},
{"name": "Transformer_2",
"parameters": {"p1": "value"}},
]
"publishers": ["publisher_1", "publisher_2"]
}
Interval is how many seconds should the samples be injected to
the pipeline.
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.
Transformer's name is plugin name in setup.py.
Publisher's name is plugin name in setup.py
"""
self.pipelines = [Pipeline(pipedef, transformer_manager)
for pipedef in cfg]
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):
"""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)