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 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 and Configure Controller Services for more details.
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
meters.yaml file without any need for additional code changes.
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
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:
--- metric: - 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,
are required. If there is a match on the event type, samples are generated
for the meter.
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
size information from the payload you can define it like
A notification message may contain multiple meters. You can use
the meter definition to capture all the meters and generate samples
respectively. You can use wild cards as shown in the following example:
--- metric: - 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,
resource_id fields perform an arithmetic
and string concatenation:
--- metric: - 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 + "_" + $.payload.nodename
You can use the
timedelta plug-in to evaluate the difference in seconds
datetime fields from one notification.
--- metric: - name: 'compute.instance.booting.time' event_type: 'compute.instance.create.end' type: 'gauge' unit: 'sec' volume: fields: [$.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 instances.
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 agent.
The polling definition may look like the following:
--- sources: - name: 'source name' interval: 'how often the samples should be generated' meters: - 'meter filter' resources: - 'list of resource URLs' discovery: - '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 polling.
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 components.
Running ceilometer-agent-compute is exactly the same as:
$ ceilometer-polling --polling-namespaces compute
Running ceilometer-agent-central is exactly the same as:
$ ceilometer-polling --polling-namespaces central
Running ceilometer-agent-ipmi is exactly the same as:
$ ceilometer-polling --polling-namespaces ipmi
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 and Configure Compute Services section in the Installation Tutorials and Guides.
The list of supported hypervisors can be found in 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 OpenStack Compute. The support column provides the information about which meter is available for each hypervisor supported by the Telemetry service.
This agent is responsible for polling public REST APIs to retrieve additional information on OpenStack resources not already surfaced via notifications.
Some of the services polled with this agent are:
OpenStack Object Storage
OpenStack Block Storage
To install and configure this service use the Install and configure for Red Hat Enterprise Linux and CentOS 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 Introduction to dynamic pollster subsystem.
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
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 IPMI meters.
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.