openstack-manuals/doc/admin-guide-cloud/source/telemetry-best-practices.rst

Telemetry best practices

The following are some suggested best practices to follow when deploying and configuring the Telemetry service. The best practices are divided into data collection and storage.

Data collection

1. The Telemetry module collects a continuously growing set of data. Not all the data will be relevant for a cloud administrator to monitor.

   • Based on your needs, you can edit the pipeline.yaml configuration file to include a selected number of meters while disregarding the rest.

   • By default, Telemetry service polls the service APIs every 10 minutes. You can change the polling interval on a per meter basis by editing the pipeline.yaml configuration file.

     Warning

     If the polling interval is too short, it will likely cause increase of stored data and the stress on the service APIs.

   • Expand the configuration to have greater control over different meter intervals.

     Note

     For more information, see the telemetry-pipeline-configuration.

2. If you are using the Kilo version of Telemetry, you can delay or adjust polling requests by enabling the jitter support. This adds a random delay on how the polling agents send requests to the service APIs. To enable jitter, set shuffle_time_before_polling_task in the ceilometer.conf configuration file to an integer greater than 0.

3. If you are using Juno or later releases, based on the number of resources that will be polled, you can add additional central and compute agents as necessary. The agents are designed to scale horizontally.

   Note

   For more information see, ha-deploy-services.

4. If you are using Juno or later releases, use the notifier:// publisher rather than rpc:// as there is a certain level of overhead that comes with RPC.

   Note

   For more information on RPC overhead, see RPC overhead info.

Data storage

1. We recommend that you avoid open-ended queries. In order to get better performance you can use reasonable time ranges and/or other query constraints for retrieving measurements.

   For example, this open-ended query might return an unpredictable amount of data:

   $ ceilometer sample-list --meter cpu -q 'resource_id=INSTANCE_ID_1'

   Whereas, this well-formed query returns a more reasonable amount of data, hence better performance:

   $ ceilometer sample-list --meter cpu -q 'resource_id=INSTANCE_ID_1;timestamp > 2015-05-01T00:00:00;timestamp < 2015-06-01T00:00:00'

2. You can install the API behind mod_wsgi, as it provides more settings to tweak, like threads and processes in case of WSGIDaemon.

   Note

   For more information on how to configure mod_wsgi, see the Telemetry Install Documentation.

3. The collection service provided by the Telemetry project is not intended to be an archival service. Set a Time to Live (TTL) value to expire data and minimize the database size. If you would like to keep your data for longer time period, you may consider storing it in a data warehouse outside of Telemetry.

   Note

   For more information on how to set the TTL, see telemetry-storing-samples.

4. We recommend that you do not use SQLAlchemy back end prior to the Juno release, as it previously contained extraneous relationships to handle deprecated data models. This resulted in extremely poor query performance.

5. We recommend that you do not run MongoDB on the same node as the controller. Keep it on a separate node optimized for fast storage for better performance. Also it is advisable for the MongoDB node to have a lot of memory.

   Note

   For more information on how much memory you need, see MongoDB FAQ.

6. Use replica sets in MongoDB. Replica sets provide high availability through automatic failover. If your primary node fails, MongoDB will elect a secondary node to replace the primary node, and your cluster will remain functional.

   For more information on replica sets, see the MongoDB replica sets docs.

7. Use sharding in MongoDB. Sharding helps in storing data records across multiple machines and is the MongoDB’s approach to meet the demands of data growth.

   For more information on sharding, see the MongoDB sharding docs.