openstack/ceilometer
Code Issues Proposed changes

fix errors about words spelling.

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There are some errors about words spelling. Others is about wrong
referend.

Change-Id: Id04788f2736e2782d888f49bb93751b95eab0a41
This commit is contained in:
lipan 2018-04-10 15:30:57 +08:00
parent 754f39bdc1
commit 3b8c219a48
1 changed files with 9 additions and 9 deletions
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18
doc/source/contributor/plugins.rst
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@ -56,7 +56,7 @@ periodically calls their :func:`get_samples` method.
Currently we keep separate namespaces - ``ceilometer.poll.compute``
and ``ceilometer.poll.central`` for quick separation of what to poll depending
on where is polling agent running. For example, this will load, among others,
the :class:`ceilometer.compute.pollsters.cpu.CPUPollster`
the :class:`ceilometer.compute.pollsters.instance_stats.CPUPollster`
Pollster
--------
@ -68,12 +68,12 @@ returns a sequence of ``Sample`` objects as defined in the
:file:`ceilometer/sample.py` file.
Compute plugins are defined as subclasses of the
:class:`ceilometer.compute.pollsters.BaseComputePollster` class as defined in
the :file:`ceilometer/compute/pollsters/__init__.py` file.
:class:`ceilometer.compute.pollsters.GenericComputePollster` class as defined
in the :file:`ceilometer/compute/pollsters/__init__.py` file.
For example, in the ``CPUPollster`` plugin, the ``get_samples`` method takes
in a given list of resources representating instances on the local host, loops
through them and retrieves the `cputime` details from resource. Similarly,
in a given list of resources representing instances on the local host, loops
through them and retrieves the `cpu time` details from resource. Similarly,
other metrics are built by pulling the appropriate value from the given list
of resources.
@ -93,16 +93,16 @@ pipelines based on a given configuration file. Pipelines are required to define
Additionally, it must set ``get_main_endpoints`` which provides endpoints to be
added to the main queue listener in the notification agent. This main queue
endpoint inherits :class:`ceilometer.pipeline.base.MainNotificationEndpoint`
and is defines which notification priorites to listen, normalises the data,
and defines which notification priorities to listen, normalises the data,
and redirects the data for pipeline processing or requeuing depending on
`workload_partitioning` configuration.
If a pipeline is configured to support `workload_partitioning`, data from the
main queue endpoints are sharded and requeued in internal queues. The
main queue endpoints are shared and requeued in internal queues. The
notification agent configures a second notification consumer to handle these
internal queues and pushes data to endpoints defined by
``get_interim_endpoints`` in the pipeline manager. These interim endpoints
define how to handle the sharded, normalised data models for pipeline
define how to handle the shared, normalised data models for pipeline
processing
Both main queue and interim queue notification endpoints should implement:
@ -110,7 +110,7 @@ Both main queue and interim queue notification endpoints should implement:
``event_types``
A sequence of strings defining the event types the endpoint should handle
``process_notifications(self, priority, message)``
``process_notifications(self, priority, notifications)``
Receives an event message from the list provided to ``event_types`` and
returns a sequence of objects. Using the SampleEndpoint, it should yield
``Sample`` objects as defined in the :file:`ceilometer/sample.py` file.