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Taikun OCP Guide

Table of Contents

Data collection

The main responsibility of Telemetry in OpenStack is to collect
information about the system that can be used by billing systems or
interpreted by analytic tooling.

Collected data can be stored in the form of samples or events in the
supported databases, which are listed in telemetry-supported-databases.

The available data collection mechanisms are:


Processing notifications from other OpenStack services, by consuming
messages from the configured message queue system.


Retrieve information directly from the hypervisor or by using the
APIs of other OpenStack services.


All OpenStack services send notifications about the executed
operations or system state. Several notifications carry information that
can be metered. For example, CPU time of a VM instance created by
OpenStack Compute service.

The notification agent is responsible for consuming notifications.
This component is responsible for consuming from the message bus and
transforming notifications into events and measurement samples.

By default, the notification agent is configured to build both events
and samples. To enable selective data models, set the required pipelines
using pipelines option under the [notification] section.

Additionally, the notification agent is responsible to send to any
supported publisher target such as gnocchi or panko. These services
persist the data in configured databases.

The different OpenStack services emit several notifications about the
various types of events that happen in the system during normal
operation. Not all these notifications are consumed by the Telemetry
service, as the intention is only to capture the billable events and
notifications that can be used for monitoring or profiling purposes. The
notifications handled are contained under the ceilometer.sample.endpoint namespace.


Some services require additional configuration to emit the
notifications. Please see the install_controller for more details.

Meter definitions

The Telemetry service collects a subset of the meters by filtering
notifications emitted by other OpenStack services. You can find the
meter definitions in a separate configuration file, called
ceilometer/data/meters.d/meters.yaml. This enables
operators/administrators to add new meters to Telemetry project by
updating the meters.yaml file without any need for
additional code changes.


The meters.yaml file should be modified with care.
Unless intended, do not remove any existing meter definitions from the
file. Also, the collected meters can differ in some cases from what is
referenced in the documentation.

It also support loading multiple meter definition files and allow
users to add their own meter definitions into several files according to
different types of metrics under the directory of

A standard meter definition looks like:

  - name: 'meter name'
    event_type: 'event name'
    type: 'type of meter eg: gauge, cumulative or delta'
    unit: 'name of unit eg: MB'
    volume: 'path to a measurable value eg: $.payload.size'
    resource_id: 'path to resource id eg: $.payload.id'
    project_id: 'path to project id eg: $.payload.owner'
    metadata: 'addiitonal key-value data describing resource'

The definition above shows a simple meter definition with some
fields, from which name, event_type,
type, unit, and volume are
required. If there is a match on the event type, samples are generated
for the meter.

The meters.yaml file contains the sample definitions for
all the meters that Telemetry is collecting from notifications. The
value of each field is specified by using JSON path in order to find the
right value from the notification message. In order to be able to
specify the right field you need to be aware of the format of the
consumed notification. The values that need to be searched in the
notification message are set with a JSON path starting with
$. For instance, if you need the size
information from the payload you can define it like

A notification message may contain multiple meters. You can use
* in the meter definition to capture all the meters and
generate samples respectively. You can use wild cards as shown in the
following example:

  - name: $.payload.measurements.[*].metric.[*].name
    event_type: 'event_name.*'
    type: 'delta'
    unit: $.payload.measurements.[*].metric.[*].unit
    volume: payload.measurements.[*].result
    resource_id: $.payload.target
    user_id: $.payload.initiator.id
    project_id: $.payload.initiator.project_id

In the above example, the name field is a JSON path with
matching a list of meter names defined in the notification message.

You can use complex operations on JSON paths. In the following
example, volume and resource_id fields perform
an arithmetic and string concatenation:

- name: 'compute.node.cpu.idle.percent'
  event_type: 'compute.metrics.update'
  type: 'gauge'
  unit: 'percent'
  volume: payload.metrics[?(@.name='cpu.idle.percent')].value * 100
  resource_id: $.payload.host + "_" + $.payload.nodename

You can use the timedelta plug-in to evaluate the
difference in seconds between two datetime fields from one

- name: 'compute.instance.booting.time'
  event_type: 'compute.instance.create.end'
 type: 'gauge'
 unit: 'sec'
   fields: [$.payload.created_at, $.payload.launched_at]
   plugin: 'timedelta'
 project_id: $.payload.tenant_id
 resource_id: $.payload.instance_id


The Telemetry service is intended to store a complex picture of the
infrastructure. This goal requires additional information than what is
provided by the events and notifications published by each service. Some
information is not emitted directly, like resource usage of the VM

Therefore Telemetry uses another method to gather this data by
polling the infrastructure including the APIs of the different OpenStack
services and other assets, like hypervisors. The latter case requires
closer interaction with the compute hosts. To solve this issue,
Telemetry uses an agent based architecture to fulfill the requirements
against the data collection.


Polling rules are defined by the polling.yaml file. It defines the pollsters to
enable and the interval they should be polled.

Each source configuration encapsulates meter name matching which
matches against the entry point of pollster. It also includes: polling
interval determination, optional resource enumeration or discovery.

All samples generated by polling are placed on the queue to be
handled by the pipeline configuration loaded in the notification

The polling definition may look like the following:

  - name: 'source name'
    interval: 'how often the samples should be generated'
      - 'meter filter'
      - 'list of resource URLs'
      - 'list of discoverers'

The interval parameter in the sources section defines the
cadence of sample generation in seconds.

Polling plugins are invoked according to each source’s section whose
meters parameter matches the plugin’s meter name. Its matching
logic functions the same as pipeline filtering.

The optional resources section of a polling source allows a
list of static resource URLs to be configured. An amalgamated list of
all statically defined resources are passed to individual pollsters for

The optional discovery section of a polling source contains
the list of discoverers. These discoverers can be used to dynamically
discover the resources to be polled by the pollsters.

If both resources and discovery are set, the final
resources passed to the pollsters will be the combination of the dynamic
resources returned by the discoverers and the static resources defined
in the resources section.


There are three types of agents supporting the polling mechanism, the
compute agent, the central agent, and the
IPMI agent. Under the hood, all the types of polling agents
are the same ceilometer-polling agent, except that they
load different polling plug-ins (pollsters) from different namespaces to
gather data. The following subsections give further information
regarding the architectural and configuration details of these

Running ceilometer-agent-compute is exactly the same

$ ceilometer-polling --polling-namespaces compute

Running ceilometer-agent-central is exactly the same

$ ceilometer-polling --polling-namespaces central

Running ceilometer-agent-ipmi is exactly the same as:

$ ceilometer-polling --polling-namespaces ipmi

Compute agent

This agent is responsible for collecting resource usage data of VM
instances on individual compute nodes within an OpenStack deployment.
This mechanism requires a closer interaction with the hypervisor,
therefore a separate agent type fulfills the collection of the related
meters, which is placed on the host machines to retrieve this
information locally.

A Compute agent instance has to be installed on each and every
compute node, installation instructions can be found in the install_compute section in
the Installation Tutorials and Guides.

The list of supported hypervisors can be found in telemetry-supported-hypervisors. The Compute agent
uses the API of the hypervisor installed on the compute hosts.
Therefore, the supported meters may be different in case of each
virtualization back end, as each inspection tool provides a different
set of meters.

The list of collected meters can be found in telemetry-compute-meters. The
support column provides the information about which meter is available
for each hypervisor supported by the Telemetry service.

Central agent

This agent is responsible for polling public REST APIs to retrieve
additional information on OpenStack resources not already surfaced via

Some of the services polled with this agent are:

  • OpenStack Networking
  • OpenStack Object Storage
  • OpenStack Block Storage

To install and configure this service use the install_rdo section in the
Installation Tutorials and Guides.

Although Ceilometer has a set of default polling agents, operators
can add new pollsters dynamically via the dynamic pollsters subsystem

IPMI agent

This agent is responsible for collecting IPMI sensor data and Intel
Node Manager data on individual compute nodes within an OpenStack
deployment. This agent requires an IPMI capable node with the ipmitool
utility installed, which is commonly used for IPMI control on various
Linux distributions.

An IPMI agent instance could be installed on each and every compute
node with IPMI support, except when the node is managed by the Bare
metal service and the conductor.send_sensor_data option is
set to true in the Bare metal service. It is no harm to
install this agent on a compute node without IPMI or Intel Node Manager
support, as the agent checks for the hardware and if none is available,
returns empty data. It is suggested that you install the IPMI agent only
on an IPMI capable node for performance reasons.

The list of collected meters can be found in telemetry-bare-metal-service.


Do not deploy both the IPMI agent and the Bare metal service on one
compute node. If conductor.send_sensor_data is set, this
misconfiguration causes duplicated IPMI sensor samples.


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