Pipeline作用
Pipeline翻译过来是管道的意思，它在ceilometer中的作用类似一个过滤器一样，或者说是转换器。它是一般是一个方法链，这个方法链前面一部分是transformer，transformer实现数据转换等功能，它可以有多个。在链尾是publisher，它负责将数据发送到AMQP中去。
Pipeline定义
在Agent的构造函数中，第一个创建的属性就是pipeline_manager
self.pipeline_manager = pipeline.setup_pipeline(
transformer.TransformerExtensionManager(
'ceilometer.transformer',
),
publisher.PublisherExtensionManager(
'ceilometer.publisher',
),
)
其中，transformer和publisher来自setup.cfg中
ceilometer.transformer =
accumulator = ceilometer.transformer.accumulator:TransformerAccumulator
ceilometer.publisher =
meter_publisher = ceilometer.publisher.meter:MeterPublisher
meter = ceilometer.publisher.meter:MeterPublisher
udp = ceilometer.publisher.udp:UDPPublisher
Pipeline设置
它调用了ceilometer.pipeline中的setup_pipline(),setup_pipeline()通过导入pipeline.yaml，获得pipeline的配置，默认配置如下
name: meter_pipeline
interval: 600
counters:
- "*"
transformers:
publishers:
- meter
最后它创建了一个PipelineManager给self.pipeline_manager
PipelineManager(pipeline_cfg,transformer_manager,publisher_manager)
PipelineManager做的事情如下：
self.pipelines = [Pipeline(pipedef, publisher_manager,transformer_manager) for pipedef in cfg]
它遍历cfg中对pipeline的定义（基本都是一个），然后生成一个Pipeline对象数组
def __init__(self, cfg, publisher_manager, transformer_manager):
self.cfg = cfg
self.name = cfg['name']
self.interval = int(cfg['interval'])
self.counters = cfg['counters']
self.publishers = cfg['publishers']
self.transformer_cfg = cfg['transformers'] or []
self.publisher_manager = publisher_manager
self._check_counters()
self._check_publishers(cfg, publisher_manager)
self.transformers = self._setup_transformers(cfg, transformer_manager)
Pipeline的构造函数如上，它的作用是处理transformer和publisher
Pipeline使用
pipeline的使用位置在agent.py中
def setup_polling_tasks(self):
polling_tasks = {}
for pipeline, pollster in itertools.product(
self.pipeline_manager.pipelines,
self.pollster_manager.extensions):
for counter in pollster.obj.get_counter_names():
if pipeline.support_counter(counter):
polling_task = polling_tasks.get(pipeline.interval, None)
if not polling_task:
polling_task = self.create_polling_task()
polling_tasks[pipeline.interval] = polling_task
polling_task.add(pollster, [pipeline])
break
return polling_tasks
首先通过product生成pipeline和pollster的笛卡尔积，即将每一个pollster都和pipeline配对（一般只有一个pipeline）。
pipeline.support_counter(counter)用来检查这个counter是否同意进入pipeline
另外，每一个polling_task都在构造函数中
self.publish_context = pipeline.PublishContext(
agent_manager.context,
cfg.CONF.counter_source)
声明了一个pipeline.PublishContext()
在执行task.poll_and_publish前，会先执行
def add(self, pollster, pipelines):
self.publish_context.add_pipelines(pipelines)
self.pollsters.update([pollster])
即增加一个pipeline管理
最后是publish_context的使用位置
def poll_and_publish_instances(self, instances):
with self.publish_context as publisher:
for instance in instances:
if getattr(instance, 'OS-EXT-STS:vm_state', None) != 'error':
for pollster in self.pollsters:
publisher(list(pollster.obj.get_counters(
self.manager,
instance)))
这里用了with as作为pipeline的管理
在__enter__()中，定义了一个函数
def p(counters):
for p in self.pipelines:
p.publish_counters(self.context,
counters,
self.source)
这个函数执行pipeline中的publish_counters，然后最终的执行代码来自
ext.obj.publish_counters(ctxt, counters, source)
即publisher的publishcounters，在这里是`ceilometer.publisher.meter:publishcounters`，它负责将数据发送到AMQP中去
总结
Pipeline机制一定程度上保证了数据的安全性，并且可以统一数据格式，了解它对于了解Ceilometer的数据流有一定帮助