MongoDB的Collections
Ceilometer在MongoDB中共有这么几个Collections
- user
- { _id: user id
source: [ array of source ids reporting for the user ]
}
- project
- { _id: project id
source: [ array of source ids reporting for the project ]
}
- meter
- the raw incoming data
- resource
- the metadata for resources
- { _id: uuid of resource,
metadata: metadata dictionaries
user_id: uuid
project_id: uuid
meter: [ array of {counter_name: string, counter_type: string,
counter_unit: string} ]
}
其中meter是采集到的数据，其他的都是固定值
Collector对数据库的写数据
Collector在接收到采集的数据后，会调用record_metering_data()对数据进行写入，相应mongodb的代码在ceilometer.storage.impl_mongodb中
def record_metering_data(self, data):
self.db.user.update(
{'_id': data['user_id']},
{'$addToSet': {'source': data['source'],},},
upsert=True,
)
self.db.project.update(
{'_id': data['project_id']},
{'$addToSet': {'source': data['source'],},},
upsert=True,
)
self.db.resource.update(
{'_id': data['resource_id']},
{'$set': {'project_id': data['project_id'],
'user_id': data['user_id'],
'metadata': data['resource_metadata'],
'source': data['source'],},
'$addToSet': {'meter': {'counter_name': data['counter_name'],
'counter_type': data['counter_type'],
'counter_unit': data['counter_unit'],},},
},
upsert=True,
)
record = copy.copy(data)
self.db.meter.insert(record)
return
从上面代码可知，每次存储时都会更新user,project和resource，然后将数据完全写入到meter中，写入后的数据格式如下：
{
"counter_name": "disk.write.requests",
"user_id": "4ff44f4665564b2abcb8e1f1619f2b85",
"message_signature": "8473976666aecd078a281afed936839b737ceaf4bb63654759d63514bdc9ee03",
"timestamp": ISODate("2013-05-21T22:33:14.0Z"),
"resource_id": "b7fc623d-1d4a-4ac7-b96b-78c9d921fa74",
"resource_metadata": {
"ramdisk_id": "",
"display_name": "test",
"name": "instance-00000001",
"disk_gb": "",
"availability_zone": "",
"kernel_id": "",
"ephemeral_gb": "",
"host": "e781ff9ce97dcc328d8826cfb19a20c001b866cb20859653c2f481b1",
"memory_mb": "",
"instance_type": "42",
"vcpus": "",
"root_gb": "",
"image_ref": "da04e6dd-4cc7-4594-87d8-60927c07c396",
"architecture": "",
"os_type": "",
"reservation_id": "",
"image_ref_url": "http:\/\/192.168.0.6:8774\/676730085ab84296a9b4a7d68ee76078\/images\/da04e6dd-4cc7-4594-87d8-60927c07c396"
},
"source": "openstack",
"counter_unit": "request",
"counter_volume": NumberInt(1366),
"project_id": "be13e080970d44b280e4843e084bb2b1",
"message_id": "6cf1d76c-c266-11e2-a987-5eafb2e29593",
"counter_type": "cumulative"
}
这是一个disk.write.requests的数据，其中resource_metadata如果无变化的话，没个都会带这些数据，具体原因不详
另外，重要的东西在
- counter_unit 计量单位
- counter_volume 计量数值
- counter_type 计量类型
计量内容
在文档中讲了计量值和其单位
首先是计量类型：
- Cumulative 随时间的累计值（如cpu总时长）
- Gauge 离散项(floating IPs, image uploads)和变化的值 (disk I/O)
- Delta 随时间的变化量（带宽等）
计量单位比较多了，每个都不太一样，这个可以查询文档，如磁盘读写请求的单位为"request"
API对数据库的读操作
数据存储只是Ceilometer的一小部分，如果合理的利用和分析采集到的数据才比较重要，另外这部分也是暴露出来给开发者的部分
API服务以wsgi service方式运行在后端，Ceilometer有v1和v2两个版本的API，v1会被弃用，这里只讲v2部分
GET /v2/meters/cpu?q.op=ge&q.op=lt&q.op=eq&q.value=2013-05-19+23%3A00%3A00&q.value=2013-05-20+00%3A00%3A00&q.value=b7fc623d-1d4a-4ac7-b96b-78c9d921fa74&q.field=timestamp&q.field=timestamp&q.field=resource
这是一个我截取下来的请求，首先我们看到一个资源地址
GET /v2/meters/cpu
根据V2的Controller，我们可以看到是MetersController()对它进行处理的
class V2Controller(object):
resources = ResourcesController()
meters = MetersController()
alarms = AlarmsController()