.. _telemetry-data-pipelines:

=============================
Data processing and pipelines
=============================

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: ``samples`` or ``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_.

.. _telemetry-pipeline-configuration:

Pipeline configuration
~~~~~~~~~~~~~~~~~~~~~~

The notification agent supports two pipelines: one that handles samples and
another that handles events. The pipelines can be enabled and disabled by
setting `pipelines` option in the `[notifications]` section.

The actual configuration of each pipelines is, by default, stored in separate
configuration files: ``pipeline.yaml`` and ``event_pipeline.yaml``. The
location of the configuration files can be set by the ``pipeline_cfg_file`` and
``event_pipeline_cfg_file`` options listed in :ref:`configuring`

The meter pipeline definition looks like:

.. code-block:: yaml

   ---
   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 :ref:`telemetry-measurements`.
There is a possibility to define all the meters, or just included or excluded
meters, with which a source should operate:

-  To include all meters, use the ``*`` 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.

-  To define the list of meters, use either of the following:

   -  To define the list of included meters, use the ``meter_name``
      syntax.

   -  To define the list of excluded meters, use the ``!meter_name``
      syntax.

.. note::

   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:

-  Use only the wildcard symbol.

-  Use the list of included meters.

-  Use the list of excluded meters.

-  Use wildcard symbol with the list of excluded meters.

.. note::

   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:

.. list-table::
   :widths: 50 50
   :header-rows: 1

   * - Name of transformer
     - Reference name for configuration
   * - Accumulator
     - accumulator
   * - Aggregator
     - aggregator
   * - Arithmetic
     - arithmetic
   * - Rate of change
     - rate\_of\_change
   * - Unit conversion
     - unit\_conversion
   * - Delta
     - delta

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:

.. code-block:: yaml

   ---
   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.

.. _telemetry-transformers:

Transformers
------------

.. note::

   Transformers maintain data in memory and therefore do not guarantee
   durability in certain scenarios. A more durable and efficient solution
   may be achieved post-storage using solutions like Gnocchi.

The definition of transformers can contain the following fields:

name
    Name of the transformer.

parameters
    Parameters of the transformer.

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:

Rate of change transformer
``````````````````````````
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:

.. code-block:: yaml

   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 ``cpu_util`` meter
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
with unit ``%``, from sequential values of the ``cpu`` meter.

The definition for the disk I/O rate, which is also generated by the
rate of change transformer:

.. code-block:: yaml

   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"

Unit conversion transformer
```````````````````````````

Transformer to apply a unit conversion. It takes the volume of the meter
and multiplies it with the given ``scale`` expression. Also supports
``map_from`` and ``map_to`` like the rate of change transformer.

Sample configuration:

.. code-block:: yaml

   transformers:
       - name: "unit_conversion"
         parameters:
             target:
                 name: "disk.kilobytes"
                 unit: "KB"
                 scale: "volume * 1.0 / 1024.0"

With ``map_from`` and ``map_to``:

.. code-block:: yaml

   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"

Aggregator transformer
``````````````````````

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
attributes ``project_id``, ``user_id`` and ``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:

.. code-block:: yaml

   transformers:
       - name: "aggregator"
         parameters:
             retention_time: 60
             resource_metadata: last

To aggregate each 15 samples by ``user_id`` and ``resource_metadata`` and keep
the ``user_id`` of the first received sample and drop the
``resource_metadata``:

.. code-block:: yaml

   transformers:
       - name: "aggregator"
         parameters:
             size: 15
             user_id: first
             resource_metadata: drop

Accumulator transformer
```````````````````````

This transformer simply caches the samples until enough samples have
arrived and then flushes them all down the pipeline at once:

.. code-block:: yaml

   transformers:
       - name: "accumulator"
         parameters:
             size: 15

Multi meter arithmetic transformer
``````````````````````````````````

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 ``target``
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.

.. note::

   The calculation is limited to meters with the same interval.

Example configuration:

.. code-block:: yaml

   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:

.. code-block:: yaml

   transformers:
       - name: "arithmetic"
         parameters:
           target:
             name: "avg_cpu_per_core"
             unit: "ns"
             type: "cumulative"
             expr: "$(cpu) / ($(cpu).resource_metadata.cpu_number or 1)"

.. note::

   Expression evaluation gracefully handles NaNs and exceptions. In
   such a case it does not create a new sample but only logs a warning.

Delta transformer
`````````````````

This transformer calculates the change between two sample datapoints of a
resource. It can be configured to capture only the positive growth deltas.

Example configuration:

.. code-block:: yaml

   transformers:
       - name: "delta"
         parameters:
           target:
               name: "cpu.delta"
           growth_only: True

.. _publishing:

Publishers
----------

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:

gnocchi (default)
`````````````````

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 <http://gnocchi.xyz>`__.

panko
`````

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 ``panko://``.

notifier
````````

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.

The following customization options are available:

``per_meter_topic``
    The value of this parameter is 1. It is used for publishing the samples on
    additional ``metering_topic.sample_name`` topic queue besides the
    default ``metering_topic`` queue.

``policy``
    Used for configuring the behavior for the case, when the
    publisher fails to send the samples, where the possible predefined
    values are:

    default
        Used for waiting and blocking until the samples have been sent.

    drop
        Used for dropping the samples which are failed to be sent.

    queue
        Used for creating an in-memory queue and retrying to send the
        samples on the queue in the next samples publishing period (the
        queue length can be configured with ``max_queue_length``, where
        1024 is the default value).

``topic``
    The topic name of the queue to publish to. Setting this will override the
    default topic defined by ``metering_topic`` and ``event_topic`` options.
    This option can be used to support multiple consumers.

udp
```

This publisher can be specified in the form of ``udp://<host>:<port>/``. It
emits metering data over UDP.

file
````

The file publisher can be specified in the form of
``file://path?option1=value1&option2=value2``. This publisher
records metering data into a file.

.. note::

   If a file name and location is not specified, the ``file`` publisher
   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 ``file`` publisher:

``max_bytes``
    When this option is greater than zero, it will cause a rollover.
    When the specified size is about to be exceeded, the file is closed and a
    new file is silently opened for output. If its value is zero, rollover
    never occurs.

``backup_count``
    If this value is non-zero, an extension will be appended to the
    filename of the old log, as '.1', '.2', and so forth until the
    specified value is reached. The file that is written and contains
    the newest data is always the one that is specified without any
    extensions.

http
````

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, additional configuration options
can be passed in: ``http://localhost:80/?option1=value1&option2=value2``

The following options are availble:

``timeout``
    The number of seconds before HTTP request times out.

``max_retries``
    The number of times to retry a request before failing.

``batch``
    If false, the publisher will send each sample and event individually,
    whether or not the notification agent is configured to process in batches.

``verify_ssl``
    If false, the ssl certificate verification is disabled.

The default publisher is ``gnocchi``, without any additional options
specified. A sample ``publishers`` section in the
``/etc/ceilometer/pipeline.yaml`` looks like the following:

.. code-block:: yaml

   publishers:
       - gnocchi://
       - panko://
       - udp://10.0.0.2:1234
       - notifier://?policy=drop&max_queue_length=512&topic=custom_target

Pipeline Partitioning
~~~~~~~~~~~~~~~~~~~~~

.. note::

   Partitioning is only required if pipelines contain transformations. It has
   secondary benefit of supporting batching in certain publishers.

On large workloads, multiple notification agents can be deployed to handle the
flood of incoming messages from monitored services. If transformations are
enabled in the pipeline, the notification agents must be coordinated to ensure
related messages are routed to the same agent. To enable coordination, set the
``workload_partitioning`` value in ``notification`` section.

To distribute messages across agents, ``pipeline_processing_queues`` option
should be set. This value defines how many pipeline queues to create which will
then be distributed to the active notification agents. It is recommended that
the number of processing queues, at the very least, match the number of agents.

Increasing the number of processing queues will improve the distribution of
messages across the agents. It will also help batching which minimises the
requests to Gnocchi storage backend. It will also increase the load the on
message queue as it uses the queue to shard data.

.. warning::

   Decreasing the number of processing queues may result in lost data as any
   previously created queues may no longer be assigned to active agents. It
   is only recommended that you **increase** processing queues.
