# -*- encoding: utf-8 -*- # # Copyright © 2013 Intel Corp. # # Author: Yunhong Jiang # # 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 itertools import os from oslo.config import cfg import yaml from ceilometer.openstack.common import log 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, source, pipelines=[]): self.pipelines = set(pipelines) self.context = context self.source = source def add_pipelines(self, pipelines): self.pipelines.update(pipelines) def __enter__(self): def p(counters): for p in self.pipelines: p.publish_counters(self.context, counters, self.source) return p def __exit__(self, exc_type, exc_value, traceback): for p in self.pipelines: p.flush(self.context, self.source) 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 counter from data collector, i.e. pollster or notification handler, takes some action like dropping, aggregation, changing field etc, then passes the updated counter 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 counters from data collector and publish them directly. """ def __init__(self, cfg, publisher_manager, transformer_manager): self.cfg = cfg try: self.name = cfg['name'] try: self.interval = int(cfg['interval']) except ValueError: raise PipelineException("Invalid interval value", cfg) self.counters = cfg['counters'] self.publishers = cfg['publishers'] # It's legal to have no transformer specified self.transformer_cfg = cfg['transformers'] or [] self.publisher_manager = publisher_manager 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_counters() self._check_publishers(cfg, publisher_manager) self.transformers = self._setup_transformers(cfg, transformer_manager) def __str__(self): return self.name def _check_counters(self): """Counter rules checking At least one meaningful counter exist Included type and excluded type counter can't co-exist at the same pipeline Included type counter and wildcard can't co-exist at same pipeline """ counters = self.counters if not counters: raise PipelineException("No counter specified", self.cfg) if [x for x in counters if x[0] not in '!*'] and \ [x for x in counters if x[0] == '!']: raise PipelineException( "Both included and excluded counters specified", cfg) if '*' in counters and [x for x in counters if x[0] not in '!*']: raise PipelineException( "Included counters specified with wildcard", self.cfg) def _check_publishers(self, cfg, publisher_manager): if not self.publishers: raise PipelineException( "No publisher specified", cfg) if not set(self.publishers).issubset(set(publisher_manager.names())): raise PipelineException( "Publishers %s invalid" % set(self.publishers).difference( set(self.publisher_manager.names())), 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 _publish_counters_to_one_publisher(self, ext, ctxt, counters, source): try: ext.obj.publish_counters(ctxt, counters, source) except Exception as err: LOG.warning("Pipeline %s: Continue after error " "from publisher %s", self, ext.name) LOG.exception(err) def _transform_counter(self, start, ctxt, counter, source): try: for transformer in self.transformers[start:]: counter = transformer.handle_sample(ctxt, counter, source) if not counter: LOG.debug("Pipeline %s: Counter dropped by transformer %s", self, transformer) return return counter except Exception as err: LOG.warning("Pipeline %s: Exit after error from transformer" "%s for %s", self, transformer, counter) LOG.exception(err) def _publish_counters(self, start, ctxt, counters, source): """Push counter into pipeline for publishing. param start: the first transformer that the counter will be injected. This is mainly for flush() invocation that transformer may emit counters param ctxt: execution context from the manager or service param counters: counter list param source: counter source """ transformed_counters = [] for counter in counters: LOG.audit("Pipeline %s: Transform counter %s from %s transformer", self, counter, start) counter = self._transform_counter(start, ctxt, counter, source) if counter: transformed_counters.append(counter) LOG.audit("Pipeline %s: Publishing counters", self) self.publisher_manager.map(self.publishers, self._publish_counters_to_one_publisher, ctxt=ctxt, counters=transformed_counters, source=source, ) LOG.audit("Pipeline %s: Published counters", self) def publish_counter(self, ctxt, counter, source): self.publish_counters(ctxt, [counter], source) def publish_counters(self, ctxt, counters, source): for counter_name, counters in itertools.groupby( sorted(counters, key=lambda c: c.name), lambda c: c.name): if self.support_counter(counter_name): self._publish_counters(0, ctxt, counters, source) # (yjiang5) To support counters like instance:m1.tiny, # which include variable part at the end starting with ':'. # Hope we will not add such counters in future. def _variable_counter_name(self, name): m = name.partition(':') if m[1] == ':': return m[1].join((m[0], '*')) else: return name def support_counter(self, counter_name): counter_name = self._variable_counter_name(counter_name) if ('!' + counter_name) in self.counters: return False if '*' in self.counters: return True elif self.counters[0][0] == '!': return not ('!' + counter_name) in self.counters else: return counter_name in self.counters def flush(self, ctxt, source): """Flush data after all counter have been injected to pipeline.""" LOG.audit("Flush pipeline %s", self) for (i, transformer) in enumerate(self.transformers): try: self._publish_counters(i + 1, ctxt, list(transformer.flush(ctxt, source)), source) 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, publisher_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 counters, the tranformers involved, and the target publishers: { "name": pipeline_name "interval": interval_time "counters" : ["counter_1", "counter_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 counters be injected to the pipeline. Valid counter format is '*', '!counter_name', or 'counter_name'. '*' is wildcard symbol means any counters; '!counter_name' means "counter_name" will be excluded; 'counter_name' means 'counter_name' will be included. The 'counter_name" is Counter namedtuple's name field. For counter names with variable like "instance:m1.tiny", it's "instance:*", as returned by get_counter_list(). Valid counters definition is all "included counter names", all "excluded counter names", wildcard and "excluded counter 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, publisher_manager, transformer_manager) for pipedef in cfg] def publisher(self, context, source): """Build a new Publisher for these manager pipelines. :param context: The context. :param source: Counter source. """ return PublishContext(context, source, self.pipelines) def setup_pipeline(transformer_manager, publisher_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, publisher_manager)