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Chapter 3. Configuring the Time Series Database (Gnocchi) for Telemetry
Time series database (Gnocchi) is a multi-project, metrics, and resource database. It is designed to store metrics at a very large scale while providing access to metrics and resources information to operators and users.
3.1. Understanding the Time Series Database
This section defines the commonly used terms for the Time series database (Gnocchi)features.
- Aggregation method
-
A function used to aggregate multiple measures into an aggregate. For example, the
minaggregation method aggregates the values of different measures to the minimum value of all the measures in the time range. - Aggregate
- A data point tuple generated from several measures according to the archive policy. An aggregate is composed of a time stamp and a value.
- Archive policy
- An aggregate storage policy attached to a metric. An archive policy determines how long aggregates are kept in a metric and how aggregates are aggregated (the aggregation method).
- Granularity
- The time between two aggregates in an aggregated time series of a metric.
- Measure
- An incoming data point tuple sent to the Time series database by the API. A measure is composed of a time stamp and a value.
- Metric
- An entity storing aggregates identified by an UUID. A metric can be attached to a resource using a name. How a metric stores its aggregates is defined by the archive policy that the metric is associated to.
- Resource
- An entity representing anything in your infrastructure that you associate a metric with. A resource is identified by a unique ID and can contain attributes.
- Time series
- A list of aggregates ordered by time.
- Timespan
- The time period for which a metric keeps its aggregates. It is used in the context of archive policy.
3.2. Metrics
The Time series database (Gnocchi) stores metrics from Telemetry that designate anything that can be measured, for example, the CPU usage of a server, the temperature of a room or the number of bytes sent by a network interface.
A metric has the following properties:
- UUID to identify the metric
- Metric name
- Archive policy used to store and aggregate the measures
The Time series database stores the following metrics by default, as defined in the etc/ceilometer/polling.yaml file:
[root@controller-0 ~]# podman exec -ti ceilometer_agent_central cat /etc/ceilometer/polling.yaml
---
sources:
- name: some_pollsters
interval: 300
meters:
- cpu
- memory.usage
- network.incoming.bytes
- network.incoming.packets
- network.outgoing.bytes
- network.outgoing.packets
- disk.read.bytes
- disk.read.requests
- disk.write.bytes
- disk.write.requests
- hardware.cpu.util
- hardware.memory.used
- hardware.memory.total
- hardware.memory.buffer
- hardware.memory.cached
- hardware.memory.swap.avail
- hardware.memory.swap.total
- hardware.system_stats.io.outgoing.blocks
- hardware.system_stats.io.incoming.blocks
- hardware.network.ip.incoming.datagrams
- hardware.network.ip.outgoing.datagrams
The polling.yaml file also specifies the default polling interval of 300 seconds (5 minutes).
3.3. Time Series Database Components
Currently, Gnocchi uses the Identity service for authentication and Redis for incoming measure storage. To store the aggregated measures, Gnocchi relies on either Swift or Ceph (Object Storage). Gnocchi also leverages MySQL to store the index of resources and metrics.
The time series database provides the statsd deamon (gnocchi-statsd) that is compatible with the statsd protocol and can listen to the metrics sent over the network. To enable statsd support in Gnocchi, configure the [statsd] option in the configuration file. The resource ID parameter is used as the main generic resource where all the metrics are attached, a user and project ID that are associated with the resource and metrics, and an archive policy name that is used to create the metrics.
All the metrics are created dynamically as the metrics are sent to gnocchi-statsd, and attached with the provided name to the resource ID you configured.
3.4. Running the Time Series Database
Run the time series database by running the HTTP server and metric daemon:
# gnocchi-api
# gnocchi-metricd3.5. Running As A WSGI Application
You can run Gnocchi through a WSGI service such as mod_wsgi or any other WSGI application. You can use the gnocchi/rest/app.wsgi file, which is provided with Gnocchi, to enable Gnocchi as a WSGI application.
The Gnocchi API tier runs using WSGI. This means it can be run using Apache httpd and mod_wsgi, or another HTTP daemon such as uwsgi. Configure the number of processes and threads according to the number of CPUs you have, usually around 1.5 × number of CPUs. If one server is not enough, you can spawn any number of new API servers to scale Gnocchi out, even on different machines.
3.6. metricd Workers
By default, the gnocchi-metricd daemon spans all your CPU power to maximize CPU utilization when computing metric aggregation. You can use the gnocchi status command to query the HTTP API and get the cluster status for metric processing. This command displays the number of metrics to process, known as the processing backlog for the gnocchi-metricd. As long as this backlog is not continuously increasing, that means that gnocchi-metricd can cope with the amount of metric that are being sent. If the number of measure to process is continuously increasing, you might need to temporarily increase the number of the gnocchi-metricd daemons. You can run any number of metricd daemons on any number of servers.
For director-based deployments, you can adjust certain metric processing parameters in your environment file:
-
MetricProcessingDelay- Adjusts the delay period between iterations of metric processing. -
GnocchiMetricdWorkers- Configure the number ofmetricdworkers.
3.7. Monitoring the Time Series Database
The /v1/status endpoint of the HTTP API returns various information, such as the number of measures to process (measures backlog), which you can easily monitor. To verify good health of the overall system, ensure that the HTTP server and the gnocchi-metricd daemon are running and are not writing errors in their log files.
3.8. Backing up and Restoring the Time Series Database
To recover from an unfortunate event, backup both the index and the storage. You must create a database dump (PostgreSQL or MySQL), and create snapshots or copies of your data storage (Ceph, Swift or your file system). The procedure to restore is: restore your index and storage backups, re-install Gnocchi if necessary, and restart it.
3.9. Batch deleting old resources from Gnocchi
To remove outdated measures, create the archive policy to suit your requirements. To batch delete resources, metrics and measures, use the CLI or REST API. For example, to delete resources and all their associated metrics that were terminated 30 days ago, run the following command:
openstack metric resource batch delete "ended_at < '-30days'"3.10. Capacity metering using the Telemetry service
The OpenStack Telemetry service provides usage metrics that you can use for billing, charge-back, and show-back purposes. Such metrics data can also be used by third-party applications to plan for capacity on the cluster and can also be leveraged for auto-scaling virtual instances using OpenStack Heat. For more information, see Auto Scaling for Instances.
You can use the combination of Ceilometer and Gnocchi for monitoring and alarms. This is supported on small-size clusters and with known limitations. For real-time monitoring, Red Hat OpenStack Platform ships with agents that provide metrics data, and can be consumed by separate monitoring infrastructure and applications. For more information, see Monitoring Tools Configuration.
3.10.1. Viewing measures
List all the measures for a particular resource:
# openstack metric measures show --resource-id UUID METER_NAMEList only measures for a particular resource, within a range of timestamps:
# openstack metric measures show --aggregation mean --start <START_TIME> --stop <STOP_TIME> --resource-id UUID METER_NAMEThe timestamp variables <START_TIME> and <STOP_TIME> use the format iso-dateThh:mm:ss.
3.10.2. Creating new measures
You can use measures to send data to the Telemetry service, and they do not need to correspond to a previously-defined meter. For example:
# openstack metrics measures add -m 2015-01-12T17:56:23@42 --resource-id UUID METER_NAME3.10.3. Example: Viewing cloud usage measures
This example shows the average memory usage of all instances for each project.
# openstack metric measures aggregation --resource-type instance --groupby project_id -m memory3.10.4. View Existing Alarms
To list the existing Telemetry alarms, use the aodh command. For example:
# aodh alarm list
+--------------------------------------+--------------------------------------------+----------------------------+-------------------+----------+---------+
| alarm_id | type | name | state | severity | enabled |
+--------------------------------------+--------------------------------------------+----------------------------+-------------------+----------+---------+
| 922f899c-27c8-4c7d-a2cf-107be51ca90a | gnocchi_aggregation_by_resources_threshold | iops-monitor-read-requests | insufficient data | low | True |
+--------------------------------------+--------------------------------------------+----------------------------+-------------------+----------+---------+To list the meters assigned to a resource, specify the UUID of the resource (an instance, image, or volume, among others). For example:
# gnocchi resource show 5e3fcbe2-7aab-475d-b42c-a440aa42e5ad3.10.5. Create an Alarm
You can use aodh to create an alarm that activates when a threshold value is reached. In this example, the alarm activates and adds a log entry when the average CPU utilization for an individual instance exceeds 80%. A query is used to isolate the specific instance’s id (94619081-abf5-4f1f-81c7-9cedaa872403) for monitoring purposes:
# aodh alarm create --type gnocchi_aggregation_by_resources_threshold --name cpu_usage_high --metric cpu_util --threshold 80 --aggregation-method sum --resource-type instance --query '{"=": {"id": "94619081-abf5-4f1f-81c7-9cedaa872403"}}' --alarm-action 'log://'
+---------------------------+-------------------------------------------------------+
| Field | Value |
+---------------------------+-------------------------------------------------------+
| aggregation_method | sum |
| alarm_actions | [u'log://'] |
| alarm_id | b794adc7-ed4f-4edb-ace4-88cbe4674a94 |
| comparison_operator | eq |
| description | gnocchi_aggregation_by_resources_threshold alarm rule |
| enabled | True |
| evaluation_periods | 1 |
| granularity | 60 |
| insufficient_data_actions | [] |
| metric | cpu_util |
| name | cpu_usage_high |
| ok_actions | [] |
| project_id | 13c52c41e0e543d9841a3e761f981c20 |
| query | {"=": {"id": "94619081-abf5-4f1f-81c7-9cedaa872403"}} |
| repeat_actions | False |
| resource_type | instance |
| severity | low |
| state | insufficient data |
| state_timestamp | 2016-12-09T05:18:53.326000 |
| threshold | 80.0 |
| time_constraints | [] |
| timestamp | 2016-12-09T05:18:53.326000 |
| type | gnocchi_aggregation_by_resources_threshold |
| user_id | 32d3f2c9a234423cb52fb69d3741dbbc |
+---------------------------+-------------------------------------------------------+
To edit an existing threshold alarm, use the aodh alarm update command. For example, to increase the alarm threshold to 75%:
# aodh alarm update --name cpu_usage_high --threshold 753.10.6. Disable or Delete an Alarm
To disable an alarm:
# aodh alarm update --name cpu_usage_high --enabled=falseTo delete an alarm:
# aodh alarm delete --name cpu_usage_high3.10.7. Example: Monitor the disk activity of instances
The following example demonstrates how to use an Aodh alarm to monitor the cumulative disk activity for all the instances contained within a particular project.
1. Review the existing projects, and select the appropriate UUID of the project you need to monitor. This example uses the admin project:
$ openstack project list
+----------------------------------+----------+
| ID | Name |
+----------------------------------+----------+
| 745d33000ac74d30a77539f8920555e7 | admin |
| 983739bb834a42ddb48124a38def8538 | services |
| be9e767afd4c4b7ead1417c6dfedde2b | demo |
+----------------------------------+----------+
2. Use the project’s UUID to create an alarm that analyses the sum() of all read requests generated by the instances in the admin project (the query can be further restrained with the --query parameter).
# aodh alarm create --type gnocchi_aggregation_by_resources_threshold --name iops-monitor-read-requests --metric disk.read.requests.rate --threshold 42000 --aggregation-method sum --resource-type instance --query '{"=": {"project_id": "745d33000ac74d30a77539f8920555e7"}}'
+---------------------------+-----------------------------------------------------------+
| Field | Value |
+---------------------------+-----------------------------------------------------------+
| aggregation_method | sum |
| alarm_actions | [] |
| alarm_id | 192aba27-d823-4ede-a404-7f6b3cc12469 |
| comparison_operator | eq |
| description | gnocchi_aggregation_by_resources_threshold alarm rule |
| enabled | True |
| evaluation_periods | 1 |
| granularity | 60 |
| insufficient_data_actions | [] |
| metric | disk.read.requests.rate |
| name | iops-monitor-read-requests |
| ok_actions | [] |
| project_id | 745d33000ac74d30a77539f8920555e7 |
| query | {"=": {"project_id": "745d33000ac74d30a77539f8920555e7"}} |
| repeat_actions | False |
| resource_type | instance |
| severity | low |
| state | insufficient data |
| state_timestamp | 2016-11-08T23:41:22.919000 |
| threshold | 42000.0 |
| time_constraints | [] |
| timestamp | 2016-11-08T23:41:22.919000 |
| type | gnocchi_aggregation_by_resources_threshold |
| user_id | 8c4aea738d774967b4ef388eb41fef5e |
+---------------------------+-----------------------------------------------------------+3.10.8. Example: Monitor CPU usage
If you want to monitor an instance’s performance, you would start by examining the gnocchi database to identify which metrics you can monitor, such as memory or CPU usage. For example, run gnocchi resource show against an instance to identify which metrics can be monitored:
Query the available metrics for a particular instance UUID:
$ gnocchi resource show --type instance d71cdf9a-51dc-4bba-8170-9cd95edd3f66 --------------------------------------------------------------------------------------------+ | Field | Value | --------------------------------------------------------------------------------------------+ | created_by_project_id | 44adccdc32614688ae765ed4e484f389 | | created_by_user_id | c24fa60e46d14f8d847fca90531b43db | | creator | c24fa60e46d14f8d847fca90531b43db:44adccdc32614688ae765ed4e484f389 | | display_name | test-instance | | ended_at | None | | flavor_id | 14c7c918-df24-481c-b498-0d3ec57d2e51 | | flavor_name | m1.tiny | | host | overcloud-compute-0 | | id | d71cdf9a-51dc-4bba-8170-9cd95edd3f66 | | image_ref | e75dff7b-3408-45c2-9a02-61fbfbf054d7 | | metrics | compute.instance.booting.time: c739a70d-2d1e-45c1-8c1b-4d28ff2403ac | | | cpu.delta: 700ceb7c-4cff-4d92-be2f-6526321548d6 | | | cpu: 716d6128-1ea6-430d-aa9c-ceaff2a6bf32 | | | cpu_l3_cache: 3410955e-c724-48a5-ab77-c3050b8cbe6e | | | cpu_util: b148c392-37d6-4c8f-8609-e15fc15a4728 | | | disk.allocation: 9dd464a3-acf8-40fe-bd7e-3cb5fb12d7cc | | | disk.capacity: c183d0da-e5eb-4223-a42e-855675dd1ec6 | | | disk.ephemeral.size: 15d1d828-fbb4-4448-b0f2-2392dcfed5b6 | | | disk.iops: b8009e70-daee-403f-94ed-73853359a087 | | | disk.latency: 1c648176-18a6-4198-ac7f-33ee628b82a9 | | | disk.read.bytes.rate: eb35828f-312f-41ce-b0bc-cb6505e14ab7 | | | disk.read.bytes: de463be7-769b-433d-9f22-f3265e146ec8 | | | disk.read.requests.rate: 588ca440-bd73-4fa9-a00c-8af67262f4fd | | | disk.read.requests: 53e5d599-6cad-47de-b814-5cb23e8aaf24 | | | disk.root.size: cee9d8b1-181e-4974-9427-aa7adb3b96d9 | | | disk.usage: 4d724c99-7947-4c6d-9816-abbbc166f6f3 | | | disk.write.bytes.rate: 45b8da6e-0c89-4a6c-9cce-c95d49d9cc8b | | | disk.write.bytes: c7734f1b-b43a-48ee-8fe4-8a31b641b565 | | | disk.write.requests.rate: 96ba2f22-8dd6-4b89-b313-1e0882c4d0d6 | | | disk.write.requests: 553b7254-be2d-481b-9d31-b04c93dbb168 | | | memory.bandwidth.local: 187f29d4-7c70-4ae2-86d1-191d11490aad | | | memory.bandwidth.total: eb09a4fc-c202-4bc3-8c94-aa2076df7e39 | | | memory.resident: 97cfb849-2316-45a6-9545-21b1d48b0052 | | | memory.swap.in: f0378d8f-6927-4b76-8d34-a5931799a301 | | | memory.swap.out: c5fba193-1a1b-44c8-82e3-9fdc9ef21f69 | | | memory.usage: 7958d06d-7894-4ca1-8c7e-72ba572c1260 | | | memory: a35c7eab-f714-4582-aa6f-48c92d4b79cd | | | perf.cache.misses: da69636d-d210-4b7b-bea5-18d4959e95c1 | | | perf.cache.references: e1955a37-d7e4-4b12-8a2a-51de4ec59efd | | | perf.cpu.cycles: 5d325d44-b297-407a-b7db-cc9105549193 | | | perf.instructions: 973d6c6b-bbeb-4a13-96c2-390a63596bfc | | | vcpus: 646b53d0-0168-4851-b297-05d96cc03ab2 | | original_resource_id | d71cdf9a-51dc-4bba-8170-9cd95edd3f66 | | project_id | 3cee262b907b4040b26b678d7180566b | | revision_end | None | | revision_start | 2017-11-16T04:00:27.081865+00:00 | | server_group | None | | started_at | 2017-11-16T01:09:20.668344+00:00 | | type | instance | | user_id | 1dbf5787b2ee46cf9fa6a1dfea9c9996 | --------------------------------------------------------------------------------------------+In this result, the
metricsvalue lists the components you can monitor using Aodh alarms, for examplecpu_util.To monitor CPU usage, you will need the
cpu_utilmetric. To see more information on this metric:$ gnocchi metric show --resource d71cdf9a-51dc-4bba-8170-9cd95edd3f66 cpu_util -------------------------------------------------------------------------------------------------------+ | Field | Value | -------------------------------------------------------------------------------------------------------+ | archive_policy/aggregation_methods | std, count, min, max, sum, mean | | archive_policy/back_window | 0 | | archive_policy/definition | - points: 8640, granularity: 0:05:00, timespan: 30 days, 0:00:00 | | archive_policy/name | low | | created_by_project_id | 44adccdc32614688ae765ed4e484f389 | | created_by_user_id | c24fa60e46d14f8d847fca90531b43db | | creator | c24fa60e46d14f8d847fca90531b43db:44adccdc32614688ae765ed4e484f389 | | id | b148c392-37d6-4c8f-8609-e15fc15a4728 | | name | cpu_util | | resource/created_by_project_id | 44adccdc32614688ae765ed4e484f389 | | resource/created_by_user_id | c24fa60e46d14f8d847fca90531b43db | | resource/creator | c24fa60e46d14f8d847fca90531b43db:44adccdc32614688ae765ed4e484f389 | | resource/ended_at | None | | resource/id | d71cdf9a-51dc-4bba-8170-9cd95edd3f66 | | resource/original_resource_id | d71cdf9a-51dc-4bba-8170-9cd95edd3f66 | | resource/project_id | 3cee262b907b4040b26b678d7180566b | | resource/revision_end | None | | resource/revision_start | 2017-11-17T00:05:27.516421+00:00 | | resource/started_at | 2017-11-16T01:09:20.668344+00:00 | | resource/type | instance | | resource/user_id | 1dbf5787b2ee46cf9fa6a1dfea9c9996 | | unit | None | -------------------------------------------------------------------------------------------------------+-
archive_policy- Defines the aggregation interval for calculating thestd, count, min, max, sum, meanvalues.
-
Use Aodh to create a monitoring task that queries
cpu_util. This task will trigger events based on the settings you specify. For example, to raise a log entry when an instance’s CPU spikes over 80% for an extended duration:aodh alarm create \ --project-id 3cee262b907b4040b26b678d7180566b \ --name high-cpu \ --type gnocchi_resources_threshold \ --description 'High CPU usage' \ --metric cpu_util \ --threshold 80.0 \ --comparison-operator ge \ --aggregation-method mean \ --granularity 300 \ --evaluation-periods 1 \ --alarm-action 'log://' \ --ok-action 'log://' \ --resource-type instance \ --resource-id d71cdf9a-51dc-4bba-8170-9cd95edd3f66 +---------------------------+--------------------------------------+ | Field | Value | +---------------------------+--------------------------------------+ | aggregation_method | mean | | alarm_actions | [u'log://'] | | alarm_id | 1625015c-49b8-4e3f-9427-3c312a8615dd | | comparison_operator | ge | | description | High CPU usage | | enabled | True | | evaluation_periods | 1 | | granularity | 300 | | insufficient_data_actions | [] | | metric | cpu_util | | name | high-cpu | | ok_actions | [u'log://'] | | project_id | 3cee262b907b4040b26b678d7180566b | | repeat_actions | False | | resource_id | d71cdf9a-51dc-4bba-8170-9cd95edd3f66 | | resource_type | instance | | severity | low | | state | insufficient data | | state_reason | Not evaluated yet | | state_timestamp | 2017-11-16T05:20:48.891365 | | threshold | 80.0 | | time_constraints | [] | | timestamp | 2017-11-16T05:20:48.891365 | | type | gnocchi_resources_threshold | | user_id | 1dbf5787b2ee46cf9fa6a1dfea9c9996 | +---------------------------+--------------------------------------+-
comparison-operator- Thegeoperator defines that the alarm will trigger if the CPU usage is greater than (or equal to) 80%. -
granularity- Metrics have an archive policy associated with them; the policy can have various granularities (for example, 5 minutes aggregation for 1 hour + 1 hour aggregation over a month). Thegranularityvalue must match the duration described in the archive policy. -
evaluation-periods- Number ofgranularityperiods that need to pass before the alarm will trigger. For example, setting this value to2will mean that the CPU usage will need to be over 80% for two polling periods before the alarm will trigger. [u'log://']- This value will log events to your Aodh log file.NoteYou can define different actions to run when an alarm is triggered (
alarm_actions), and when it returns to a normal state (ok_actions), such as a webhook URL.
-
To check if your alarm has been triggered, query the alarm’s history:
aodh alarm-history show 1625015c-49b8-4e3f-9427-3c312a8615dd --fit-width +----------------------------+------------------+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+ | timestamp | type | detail | event_id | +----------------------------+------------------+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+ | 2017-11-16T05:21:47.850094 | state transition | {"transition_reason": "Transition to ok due to 1 samples inside threshold, most recent: 0.0366665763", "state": "ok"} | 3b51f09d-ded1-4807-b6bb-65fdc87669e4 | +----------------------------+------------------+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+
3.10.9. Manage Resource Types
Telemetry resource types that were previously hardcoded can now be managed by the gnocchi client. You can use the gnocchi client to create, view, and delete resource types, and you can use the gnocchi API to update or delete attributes.
1. Create a new resource-type:
$ gnocchi resource-type create testResource01 -a bla:string:True:min_length=123
+----------------+------------------------------------------------------------+
| Field | Value |
+----------------+------------------------------------------------------------+
| attributes/bla | max_length=255, min_length=123, required=True, type=string |
| name | testResource01 |
| state | active |
+----------------+------------------------------------------------------------+2. Review the configuration of the resource-type:
$ gnocchi resource-type show testResource01
+----------------+------------------------------------------------------------+
| Field | Value |
+----------------+------------------------------------------------------------+
| attributes/bla | max_length=255, min_length=123, required=True, type=string |
| name | testResource01 |
| state | active |
+----------------+------------------------------------------------------------+3. Delete the resource-type:
$ gnocchi resource-type delete testResource01You cannot delete a resource type if a resource is using it.
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