The mechanism by which data is processed is called a pipeline. Pipelines, at the configuration level, describe a coupling between sources of data and the corresponding sinks for transformation and publication of data. This functionality is handled by the notification agents.
A source is a producer of data:
events. In effect, it is a
set of notification handlers emitting datapoints for a set of matching meters
and event types.
Each source configuration encapsulates name matching and mapping to one or more sinks for publication.
A sink, on the other hand, is a consumer of data, providing logic for the transformation and publication of data emitted from related sources.
In effect, a sink describes a chain of handlers. The chain starts with zero or more transformers and ends with one or more publishers. The first transformer in the chain is passed data from the corresponding source, takes some action such as deriving rate of change, performing unit conversion, or aggregating, before publishing.
The pipeline configuration is, by default stored in separate configuration
event_pipeline.yaml next to
ceilometer.conf file. The meter pipeline and event pipeline
configuration files can be set by the
event_pipeline_cfg_file options listed in the Description of
configuration options for api table
section in the OpenStack Configuration Reference respectively. Multiple
pipelines can be defined in one pipeline configuration file.
The meter pipeline definition looks like:
--- sources: - name: 'source name' meters: - 'meter filter' sinks - 'sink name' sinks: - name: 'sink name' transformers: 'definition of transformers' publishers: - 'list of publishers'
There are several ways to define the list of meters for a pipeline source. The list of valid meters can be found in Measurements. There is a possibility to define all the meters, or just included or excluded meters, with which a source should operate:
*wildcard symbol. It is highly advisable to select only the meters that you intend on using to avoid flooding the metering database with unused data.
The OpenStack Telemetry service does not have any duplication check between pipelines, and if you add a meter to multiple pipelines then it is assumed the duplication is intentional and may be stored multiple times according to the specified sinks.
The above definition methods can be used in the following combinations:
At least one of the above variations should be included in the meters section. Included and excluded meters cannot co-exist in the same pipeline. Wildcard and included meters cannot co-exist in the same pipeline definition section.
The transformers section of a pipeline sink provides the possibility to add a list of transformer definitions. The available transformers are:
|Name of transformer||Reference name for configuration|
|Rate of change||rate_of_change|
The publishers section contains the list of publishers, where the samples data should be sent after the possible transformations.
Similarly, the event pipeline definition looks like:
--- sources: - name: 'source name' events: - 'event filter' sinks - 'sink name' sinks: - name: 'sink name' publishers: - 'list of publishers'
The event filter uses the same filtering logic as the meter pipeline.
The definition of transformers can contain the following fields:
The parameters section can contain transformer specific fields, like source and target fields with different subfields in case of the rate of change, which depends on the implementation of the transformer.
The following are supported transformers:
Transformer that computes the change in value between two data points in time.
In the case of the transformer that creates the
cpu_util meter, the
definition looks like:
transformers: - name: "rate_of_change" parameters: target: name: "cpu_util" unit: "%" type: "gauge" scale: "100.0 / (10**9 * (resource_metadata.cpu_number or 1))"
The rate of change transformer generates the
from the sample values of the
cpu counter, which represents
cumulative CPU time in nanoseconds. The transformer definition above
defines a scale factor (for nanoseconds and multiple CPUs), which is
applied before the transformation derives a sequence of gauge samples
%, from sequential values of the
The definition for the disk I/O rate, which is also generated by the rate of change transformer:
transformers: - name: "rate_of_change" parameters: source: map_from: name: "disk\\.(read|write)\\.(bytes|requests)" unit: "(B|request)" target: map_to: name: "disk.\\1.\\2.rate" unit: "\\1/s" type: "gauge"
Transformer to apply a unit conversion. It takes the volume of the meter
and multiplies it with the given
scale expression. Also supports
map_to like the rate of change transformer.
transformers: - name: "unit_conversion" parameters: target: name: "disk.kilobytes" unit: "KB" scale: "volume * 1.0 / 1024.0"
transformers: - name: "unit_conversion" parameters: source: map_from: name: "disk\\.(read|write)\\.bytes" target: map_to: name: "disk.\\1.kilobytes" scale: "volume * 1.0 / 1024.0" unit: "KB"
A transformer that sums up the incoming samples until enough samples have come in or a timeout has been reached.
Timeout can be specified with the
retention_time option. If you want
to flush the aggregation, after a set number of samples have been
aggregated, specify the size parameter.
The volume of the created sample is the sum of the volumes of samples
that came into the transformer. Samples can be aggregated by the
resource_metadata. To aggregate
by the chosen attributes, specify them in the configuration and set which
value of the attribute to take for the new sample (first to take the
first sample’s attribute, last to take the last sample’s attribute, and
drop to discard the attribute).
To aggregate 60s worth of samples by
resource_metadata and keep the
resource_metadata of the latest received sample:
transformers: - name: "aggregator" parameters: retention_time: 60 resource_metadata: last
To aggregate each 15 samples by
resource_metadata and keep
user_id of the first received sample and drop the
transformers: - name: "aggregator" parameters: size: 15 user_id: first resource_metadata: drop
This transformer simply caches the samples until enough samples have arrived and then flushes them all down the pipeline at once:
transformers: - name: "accumulator" parameters: size: 15
This transformer enables us to perform arithmetic calculations over one or more meters and/or their metadata, for example:
memory_util = 100 * memory.usage / memory
A new sample is created with the properties described in the
section of the transformer’s configuration. The sample’s
volume is the result of the provided expression. The calculation is
performed on samples from the same resource.
The calculation is limited to meters with the same interval.
transformers: - name: "arithmetic" parameters: target: name: "memory_util" unit: "%" type: "gauge" expr: "100 * $(memory.usage) / $(memory)"
To demonstrate the use of metadata, the following implementation of a novel meter shows average CPU time per core:
transformers: - name: "arithmetic" parameters: target: name: "avg_cpu_per_core" unit: "ns" type: "cumulative" expr: "$(cpu) / ($(cpu).resource_metadata.cpu_number or 1)"
Expression evaluation gracefully handles NaNs and exceptions. In such a case it does not create a new sample but only logs a warning.
This transformer calculates the change between two sample datapoints of a resource. It can be configured to capture only the positive growth deltas.
transformers: - name: "delta" parameters: target: name: "cpu.delta" growth_only: True
The Telemetry service provides several transport methods to transfer the data collected to an external system. The consumers of this data are widely different, like monitoring systems, for which data loss is acceptable and billing systems, which require reliable data transportation. Telemetry provides methods to fulfill the requirements of both kind of systems.
The publisher component makes it possible to save the data into persistent storage through the message bus or to send it to one or more external consumers. One chain can contain multiple publishers.
To solve this problem, the multi-publisher can be configured for each data point within the Telemetry service, allowing the same technical meter or event to be published multiple times to multiple destinations, each potentially using a different transport.
The following publisher types are supported:
When the gnocchi publisher is enabled, measurement and resource information is pushed to gnocchi for time-series optimized storage. Gnocchi must be registered in the Identity service as Ceilometer discovers the exact path via the Identity service.
More details on how to enable and configure gnocchi can be found on its official documentation page.
Event data in Ceilometer can be stored in panko which provides an HTTP REST
interface to query system events in OpenStack. To push data to panko,
set the publisher to
direct://?dispatcher=panko. Beginning in panko’s
Pike release, the publisher can be set as
The notifier publisher can be specified in the form of
notifier://?option1=value1&option2=value2. It emits data over AMQP using
oslo.messaging. Any consumer can then subscribe to the published topic
for additional processing.
Prior to Ocata, the collector would consume this publisher but has since been deprecated and therefore not required.
The following customization options are available:
metering_topic.sample_nametopic queue besides the default
Used for configuring the behavior for the case, when the publisher fails to send the samples, where the possible predefined values are:
max_queue_length, where 1024 is the default value).
event_topicoptions. This option can be used to support multiple consumers.
This publisher can be specified in the form of
emits metering data over UDP.
The file publisher can be specified in the form of
file://path?option1=value1&option2=value2. This publisher
records metering data into a file.
If a file name and location is not specified, the
does not log any meters, instead it logs a warning message in
the configured log file for Telemetry.
The following options are available for the
The Telemetry service supports sending samples to an external HTTP
target. The samples are sent without any modification. To set this
option as the notification agents’ target, set
http:// as a publisher
endpoint in the pipeline definition files. The HTTP target should be set along
with the publisher declaration. For example, addtional configuration options
can be passed in:
The following options are availble:
The default publisher is
gnocchi, without any additional options
specified. A sample
publishers section in the
/etc/ceilometer/pipeline.yaml looks like the following:
publishers: - gnocchi:// - panko:// - udp://10.0.0.2:1234 - notifier://?policy=drop&max_queue_length=512&topic=custom_target - direct://?dispatcher=http
The following publishers are deprecated as of Ocata and may be removed in subsequent releases.
This publisher can be specified in the form of
The dispatcher’s options include:
gnocchi. It emits data in the configured dispatcher directly, default
configuration (the form is
direct://) is database dispatcher.
In the Mitaka release, this method can only emit data to the database
dispatcher, and the form is
We recommened you use oslo.messaging if possible as it provides consistent OpenStack API.
kafka publisher can be specified in the form of:
This publisher sends metering data to a kafka broker. The kafka publisher
offers similar options as
If the topic parameter is missing, this publisher brings out
metering data under a topic name,
ceilometer. When the port
number is not specified, this publisher uses 9092 as the
This functionality was replaced by
When the database dispatcher is configured as a data store, you have the
option to set a
time_to_live option (ttl) for samples. By default
the ttl value for samples is set to -1, which means that they
are kept in the database forever.
The time to live value is specified in seconds. Each sample has a time
stamp, and the
ttl value indicates that a sample will be deleted
from the database when the number of seconds has elapsed since that
sample reading was stamped. For example, if the time to live is set to
600, all samples older than 600 seconds will be purged from the
Certain databases support native TTL expiration. In cases where this is
not possible, a command-line script, which you can use for this purpose
ceilometer-expirer. You can run it in a cron job, which helps to keep
your database in a consistent state.
The level of support differs in case of the configured back end:
|Database||TTL value support||Note|
|MongoDB||Yes||MongoDB has native TTL support for deleting samples that are older than the configured ttl value.|
|SQL-based back ends||Yes||
|HBase||No||Telemetry’s HBase support does not include native TTL
|DB2 NoSQL||No||DB2 NoSQL does not have native TTL