Events and Event Processing

Events vs. Samples

In addition to Meters, and related Sample data, Ceilometer can also process Events.

While a Sample represents a single numeric datapoint, driving a Meter that represents the changes in that value over time, an Event represents the state of an object in an OpenStack service (such as an Instance in Nova, or an Image in Glance) at a point in time when something of interest has occurred. This can include non-numeric data, such as an instance’s flavor, or network address.

In general, Events let you know when something has changed about an object in an OpenStack system, such as the resize of an instance, or creation of an image.

While Samples can be relatively cheap (small), disposable (losing an individual sample datapoint won’t matter much), and fast, Events are larger, more informative, and should be handled more consistently (you do not want to lose one).

Event Structure

To facilitate downstream processing (billing and/or aggregation), a minimum required data set and format <format> has been defined for services, however events generally contain the following information:


A dotted string defining what event occurred, such as compute.instance.resize.start


A UUID for this event.


A timestamp of when the event occurred on the source system.


A flat mapping of key-value pairs. The event’s Traits contain most of the details of the event. Traits are typed, and can be strings, ints, floats, or datetimes.


(Optional) Mainly for auditing purpose, the full notification message can be stored (unindexed) for future evaluation.

Events from Notifications

Events are primarily created via the notifications system in OpenStack. OpenStack systems, such as Nova, Glance, Neutron, etc. will emit notifications in a JSON format to the message queue when some notable action is taken by that system. Ceilometer will consume such notifications from the message queue, and process them.

The general philosophy of notifications in OpenStack is to emit any and all data someone might need, and let the consumer filter out what they are not interested in. In order to make processing simpler and more efficient, the notifications are stored and processed within Ceilometer as Events. The notification payload, which can be an arbitrarily complex JSON data structure, is converted to a flat set of key-value pairs known as Traits. This conversion is specified by a config file, so that only the specific fields within the notification that are actually needed for processing the event will have to be stored as Traits.

Note that the Event format is meant for efficient processing and querying, there are other means available for archiving notifications (i.e. for audit purposes, etc), possibly to different datastores.

Converting Notifications to Events

In order to make it easier to allow users to extract what they need, the conversion from Notifications to Events is driven by a configuration file (specified by the flag definitions_cfg_file in ceilometer.conf).

This includes descriptions of how to map fields in the notification body to Traits, and optional plugins for doing any programmatic translations (splitting a string, forcing case, etc.)

The mapping of notifications to events is defined per event_type, which can be wildcarded. Traits are added to events if the corresponding fields in the notification exist and are non-null. (As a special case, an empty string is considered null for non-text traits. This is due to some openstack projects (mostly Nova) using empty string for null dates.)

If the definitions file is not present, a warning will be logged, but an empty set of definitions will be assumed. By default, any notifications that do not have a corresponding event definition in the definitions file will be converted to events with a set of minimal, default traits. This can be changed by setting the flag drop_unmatched_notifications in the ceilometer.conf file. If this is set to True, then any notifications that don’t have events defined for them in the file will be dropped. This can be what you want, the notification system is quite chatty by design (notifications philosophy is “tell us everything, we’ll ignore what we don’t need”), so you may want to ignore the noisier ones if you don’t use them.

There is a set of default traits (all are TEXT type) that will be added to all events if the notification has the relevant data:

  • service: (All notifications should have this) notification’s publisher

  • tenant_id

  • request_id

  • project_id

  • user_id

These do not have to be specified in the event definition, they are automatically added, but their definitions can be overridden for a given event_type.

Definitions file format

The event definitions file is in YAML format. It consists of a list of event definitions, which are mappings. Order is significant, the list of definitions is scanned in reverse order (last definition in the file to the first), to find a definition which matches the notification’s event_type. That definition will be used to generate the Event. The reverse ordering is done because it is common to want to have a more general wildcarded definition (such as compute.instance.*) with a set of traits common to all of those events, with a few more specific event definitions (like compute.instance.exists) afterward that have all of the above traits, plus a few more. This lets you put the general definition first, followed by the specific ones, and use YAML mapping include syntax to avoid copying all of the trait definitions.

Event Definitions

Each event definition is a mapping with two keys (both required):


This is a list (or a string, which will be taken as a 1 element list) of event_types this definition will handle. These can be wildcarded with unix shell glob syntax. An exclusion listing (starting with a ‘!’) will exclude any types listed from matching. If ONLY exclusions are listed, the definition will match anything not matching the exclusions.


This is a mapping, the keys are the trait names, and the values are trait definitions.

Trait Definitions

Each trait definition is a mapping with the following keys:


(optional) The data type for this trait. (as a string). Valid options are: text, int, float, and datetime. defaults to text if not specified.


A path specification for the field(s) in the notification you wish to extract for this trait. Specifications can be written to match multiple possible fields, the value for the trait will be derived from the matching fields that exist and have a non-null values in the notification. By default the value will be the first such field. (plugins can alter that, if they wish). This is normally a string, but, for convenience, it can be specified as a list of specifications, which will match the fields for all of them. (See Field Path Specifications for more info on this syntax.)


(optional) This is a mapping (For convenience, this value can also be specified as a string, which is interpreted as the name of a plugin to be loaded with no parameters) with the following keys:


(string) name of a plugin to load


(optional) Mapping of keyword arguments to pass to the plugin on initialization. (See documentation on each plugin to see what arguments it accepts.)

Field Path Specifications

The path specifications define which fields in the JSON notification body are extracted to provide the value for a given trait. The paths can be specified with a dot syntax (e.g. Square bracket syntax (e.g. payload[host]) is also supported. In either case, if the key for the field you are looking for contains special characters, like ‘.’, it will need to be quoted (with double or single quotes) like so:


The syntax used for the field specification is a variant of JSONPath, and is fairly flexible. (see: for more info)

Example Definitions file

- event_type: compute.instance.*
  traits: &instance_traits
      fields: payload.user_id
      fields: payload.instance_id
      fields: publisher_id
        name: split
          segment: 1
          max_split: 1
      fields: publisher_id
      plugin: split
      type: int
      fields: payload.instance_type_id
      fields: payload.image_meta.'org.openstack__1__architecture'
      type: datetime
      fields: payload.launched_at
      type: datetime
      fields: payload.deleted_at
- event_type:
    - compute.instance.exists
    - compute.instance.update
    <<: *instance_traits
      type: datetime
      fields: payload.audit_period_beginning
      type: datetime
      fields: payload.audit_period_ending

Trait plugins

Trait plugins can be used to do simple programmatic conversions on the value in a notification field, like splitting a string, lowercasing a value, converting a screwball date into ISO format, or the like. They are initialized with the parameters from the trait definition, if any, which can customize their behavior for a given trait. They are called with a list of all matching fields from the notification, so they can derive a value from multiple fields. The plugin will be called even if there are no fields found matching the field path(s), this lets a plugin set a default value, if needed. A plugin can also reject a value by returning None, which will cause the trait not to be added. If the plugin returns anything other than None, the trait’s value will be set to whatever the plugin returned (coerced to the appropriate type for the trait).

Building Notifications

In general, the payload format OpenStack services emit could be described as the Wild West. The payloads are often arbitrary data dumps at the time of the event which is often susceptible to change. To make consumption easier, the Ceilometer team offers: CADF, an open, cloud standard which helps model cloud events.