.. _architecture:

=====================
 System Architecture
=====================

.. index::
   single: agent; architecture
   double: compute agent; architecture
   double: collector; architecture
   double: data store; architecture
   double: database; architecture
   double: API; architecture

High-Level Architecture
=======================

.. The source for the following diagram can be found at: https://docs.google.com/presentation/d/1XiOiaq9zI_DIpxY1tlkysg9VAEw2r8aYob0bjG71pNg/edit?usp=sharing

.. figure:: ./ceilo-arch.png
   :width: 100%
   :align: center
   :alt: Architecture summary

   An overall summary of Ceilometer's logical architecture.

Each of Ceilometer's services are designed to scale horizontally. Additional
workers and nodes can added depending on the expected load. Ceilometer offers
five core services, the data agents designed to work independently from
collection and alarming, but also designed to work together as a
complete solution:

1. polling agent - daemon designed to poll OpenStack services and build Meters.
2. notification agent - daemon designed to listen to notifications on message queue
   and convert them to Events and Samples.
3. collector - daemon designed to gather and record event and metering data
   created by notification and polling agents.
4. api - service to query and view data recorded by collector service.
5. alarming - daemons to evaluate and notify based on defined alarming rules.


Gathering the data
==================

How is data collected?
----------------------

.. figure:: ./1-agents.png
   :width: 100%
   :align: center
   :alt: Collectors and agents

   This is a representation of how the collectors and agents gather data from
   multiple sources.

In a perfect world, each and every project that you want to instrument should
send events on the Oslo bus about anything that could be of interest to
you. Unfortunately, not all projects have implemented this and you will often
need to instrument other tools which may not use the same bus as OpenStack has
defined. The Ceilometer project created 2 methods to collect data:

1. :term:`Bus listener agent` which takes events generated on the
   notification bus and transforms them into Ceilometer samples. This
   is the preferred method of data collection. If you are working on some
   OpenStack related project and are using the Oslo library, you are kindly
   invited to come and talk to one of the project members to learn how you
   could quickly add instrumentation for your project.
2. :term:`Polling agents`, which is the less preferred method, will poll
   some API or other tool to collect information at a regular interval.
   Where the option exists to gather the same data by consuming notifications,
   then the polling approach is less preferred due to the load it can impose
   on the API services.

The first method is supported by the ceilometer-notification agent, which
monitors the message queues for notifications. Polling agents can be configured
either to poll local hypervisor or remote APIs (public REST APIs exposed by
services and host-level SNMP/IPMI daemons).

Notification Agents: Listening for data
---------------------------------------

.. index::
      double: notifications; architecture

The heart of the system is the notification daemon (agent-notification)
which monitors the message bus for data being provided by other
OpenStack components such as Nova, Glance, Cinder, Neutron, Swift, Keystone,
and Heat

The notification daemon loads one or more *listener* plugins, using the
namespace ``ceilometer.notification``. Each plugin can listen to any topics,
but by default it will listen to ``notifications.info``. The listeners grab
messages off the defined topics and redistributes them to the appropriate
plugins(endpoints) to be processed into Events and Samples.

Sample-oriented plugins provide a method to list the event types they're interested
in and a callback for processing messages accordingly. The registered name of the
callback is used to enable or disable it using the pipeline of the notification
daemon. The incoming messages are filtered based on their event type value before
being passed to the callback so the plugin only receives events it has
expressed an interest in seeing. For example, a callback asking for
``compute.instance.create.end`` events under
``ceilometer.compute.notifications`` would be invoked for those notification
events on the ``nova`` exchange using the ``notifications.info`` topic. Event
matching can also work using wildcards e.g. ``compute.instance.*``.

Similarly, if enabled, notifications are converted into Events which can be
filtered based on event_type declared by other services.

.. _polling:

Polling Agents: Asking for data
-------------------------------

.. index::
      double: polling; architecture

Polling for compute resources is handled by a polling agent running
on the compute node (where communication with the hypervisor is more
efficient), often referred to as the compute-agent. Polling via
service APIs for non-compute resources is handled by an agent running
on a cloud controller node, often referred to the central-agent.
A single agent can fulfill both roles in an all-in-one deployment.
Conversely, multiple instances of an agent may be deployed, in
which case the workload is shared. The polling agent
daemon is configured to run one or more *pollster* plugins using either the
``ceilometer.poll.compute`` and/or ``ceilometer.poll.central`` namespaces.

The agents periodically ask each pollster for instances of
``Sample`` objects. The frequency of polling is controlled via the pipeline
configuration. See :ref:`Pipeline-Configuration` for details.
The agent framework then passes the samples to the
pipeline for processing.

Please notice that there's an optional config called
``shuffle_time_before_polling_task`` in ceilometer.conf. Enable this by
setting an integer greater than zero to shuffle agents to start polling task,
so as to add some random jitter to the time of sending requests to nova
or other components to avoid large number of requests in short time.


Processing the data
===================

.. _multi-publisher:

Pipeline Manager
----------------

.. figure:: ./3-Pipeline.png
   :width: 100%
   :align: center
   :alt: Ceilometer pipeline

   The assembly of components making the Ceilometer pipeline

Ceilometer offers the ability to take data gathered by the agents, manipulate
it, and publish it in various combinations via multiple pipelines.

Transforming the data
---------------------

.. figure:: ./4-Transformer.png
   :width: 100%
   :align: center
   :alt: Transformer example

   Example of aggregation of multiple cpu time usage samples in a single
   cpu percentage sample

The data gathered from the polling and notifications agents contains a wealth
of data and if combined with historical or temporal context, can be used to
derive even more data. Ceilometer offers various transformers which can be used
to manipulate data in the pipeline.

Publishing the data
-------------------

.. figure:: ./5-multi-publish.png
   :width: 100%
   :align: center
   :alt: Multi-publish

   This figure shows how a sample can be published to multiple destinations.

Currently, processed data can be published using 4 different transports:
notifier, a notification based publisher which pushes samples to a message
queue which can be consumed by the collector or an external system; rpc, a
relatively secure, synchronous RPC based publisher; udp, which publishes
samples using UDP packets; and kafka, which publishes data to a Kafka message
queue to be consumed by any system that supports Kafka.


Storing the data
================

Collector Service
-----------------

The collector daemon gathers the processed event and metering data captured by
the notification and polling agents. It validates the incoming data and (if
the signature is valid) then writes the messages to a declared target:
database, file, or http.

.. _which-db:

Supported databases
-------------------

.. figure:: ./6-storagemodel.png
   :width: 100%
   :align: center
   :alt: Storage model

   An overview of the Ceilometer storage model.

Since the beginning of the project, a plugin model has been put in place
to allow for various types of database backends to be used. A list of supported
backends can be found in the :ref:`choosing_db_backend` section of the
documentation for more details.

In the Juno and Kilo release cycle, Ceilometer's database was divided into
three separate connections: alarm, event, and metering. This allows
deployers to either continue storing all data within a single database or to
divide the data into their own databases, tailored for its purpose. For
example, a deployer could choose to store alarms in an SQL backend while
storing events and metering data in a NoSQL backend.

.. note::

   We do not guarantee that we won't change the DB schema, so it is
   highly recommended to access the database through the API and not use
   direct queries.


Accessing the data
==================

API Service
-----------

If the collected data from polling and notification agents are stored in supported
database(s) (see the section :ref:`which-db`), it is possible that the schema of
these database(s) may evolve over time. For this reasons, we offer a REST API
and recommend that you access the collected data via the API rather than by
accessing the underlying database directly.

If the way in which you wish to access your data is not yet supported by the API,
please contact us with your feedback, so that we can improve the API
accordingly.

.. figure:: ./2-accessmodel.png
   :width: 100%
   :align: center
   :alt: data access model

   This is a representation of how to access data stored by Ceilometer

The :ref:`list of currently built in meters <measurements>` is available in
the developer documentation, and it is also relatively easy to add your own
(and eventually contribute it).

Ceilometer is part of OpenStack, but is not tied to OpenStack's definition of
"users" and "tenants." The "source" field of each sample refers to the authority
defining the user and tenant associated with the sample. Deployers can define
custom sources through a configuration file, and then create agents to collect
samples for new meters using those sources. This means that you can collect
data for applications running on top of OpenStack, such as a PaaS or SaaS
layer, and use the same tools for metering your entire cloud.

Moreover, end users can also
:ref:`send their own application specific data <user-defined-data>` into the
database through the REST API for a various set of use cases (see the section
"Alarming" later in this article).

.. _send their own application centric data: ./webapi/v2.html#user-defined-data


Evaluating the data
===================

Alarming Service
----------------

The alarming component of Ceilometer, first delivered in the Havana
version, allows you to set alarms based on threshold evaluation for a
collection of samples. An alarm can be set on a single meter, or on a
combination. For example, you may want to trigger an alarm when the memory
consumption reaches 70% on a given instance if the instance has been up for
more than 10 min. To setup an alarm, you will call
:ref:`Ceilometer's API server <alarms-api>` specifying the alarm conditions and
an action to take.

Of course, if you are not administrator of the cloud itself, you can only set
alarms on meters for your own components. You can also
:ref:`send your own meters <user-defined-data>` from within your instances,
meaning that you can trigger alarms based on application centric data.

There can be multiple form of actions, but two have been implemented so far:

1. :term:`HTTP callback`: you provide a URL to be called whenever the alarm has
   been set off. The payload of the request contains all the details of why the
   alarm was triggered.
2. :term:`log`: mostly useful for debugging, stores alarms in a log file.

For more details on this, we recommend that you read the blog post by
Mehdi Abaakouk `Autoscaling with Heat and Ceilometer`_. Particular attention
should be given to the section "Some notes about deploying alarming" as the
database setup (using a separate database from the one used for metering)
will be critical in all cases of production deployment.

.. _Autoscaling with Heat and Ceilometer: http://techs.enovance.com/5991/autoscaling-with-heat-and-ceilometer
