Understand your metrics usage with the metrics usage report 🔗
To get a detailed breakdown of your metric time series (MTS) creation and usage, you can request a usage report for a specific time interval by contacting your tech support member or your account team. By default, the time interval is 30 days.
You can use the detailed metrics usage report to optimize your usage of custom metrics.
If you are on a usage-based pricing plan, the system counts your metrics as custom metrics. By using the report to find and clean high cardinality metrics, you can better control your costs and query performance.
If you are on a host-based pricing plan with high utilization of custom metrics, you can use the report to lower your custom metrics usage. Learn more about this subscription plan in Infrastructure Monitoring subscription usage (Host and metric plans).
Format of the report 🔗
For each metric in the report, you can see the columns shown in the following table.
Metric identifiers 🔗
Column |
Description |
|---|---|
Metric name |
The name of the metric |
Category type |
The category of the metric, in the format of a number. It only applies to host-based subscription plans. In data points per minute (DPM) subscription plans, the system counts all metrics ase considered custom metrics. |
The following table has an overview of metric category types. To learn more about metric categories, see Metric categories.
Category type |
Description |
|---|---|
0 |
No information about the category type of the metric.
Note: Category type information for metrics is only available after 03/16/2023. Any metrics created before that date has category type
0. |
1 |
Host |
2 |
Container |
3 |
Custom
Metrics reported to Observability Cloud outside of those reported by default, such as host, container, or bundled metrics. Custom metrics might result in increased data ingest costs.
|
4 |
Hi-resolution |
5 |
Internal |
6 |
Tracing metrics |
7 |
Bundled
In host-based subscription plans, additional metrics sent through Infrastructure Monitoring public cloud integrations that are not attributed to specific hosts or containers.
|
8 |
APM hosts |
9 |
APM container |
10 |
APM identity |
11 |
APM bundled metrics |
12 |
APM Troubleshooting MetricSets
This category is not part of the report.
|
13 |
APM Monitoring MetricSets
This category is not part of the report.
|
14 |
Infrastructure Monitoring function |
15 |
APM function |
16 |
RUM Troubleshooting MetricSets
This category is not part of the report.
|
17 |
RUM Monitoring MetricSets |
18 |
Network Explorer metrics |
19 |
Runtime metrics |
20 |
Synthetics metrics |
Note
In subscription plans based on metric time series (MTS), all metrics are categorized as custom metrics and billed accordingly.
Usage statistics 🔗
Column |
Description |
|---|---|
Detectors |
Number of unique detectors running during the interval for the metric. Detectors created before the interval are also counted. |
Charts |
Number of unique charts containing the metric that users viewed in dashboards during the interval. If users viewed the same chart multiple times, all the views count only once towards the total charts count. |
MTS creation statistics 🔗
Column |
Description |
|---|---|
MTS count |
The number of MTS created for the metric during the interval. |
Common dimensions |
A list of common dimension name combinations for the MTS created along with a number of MTS created with that combination. |
Dimension cardinality |
The approximate cardinality of each dimension for the MTS created for the metric. Error rate is less than 2% when the cardinality is high. |
MTS per token |
The token used to create the MTS and the number of MTS created for the metric using that token.
|
Example MTS |
Three examples of MTS for the metric containing the dimension key-value pairs of the metric. |
Leverage metrics usage report to optimize your metrics volume 🔗
Using the statistics in the metrics usage report, you can gain more visibility into and control over your metrics in the following ways:
Understand the cardinality of your metrics and determine the top cardinality metrics.
Find high cardinality metrics that aren’t frequently used in charts and dashboards. You can optimize your metrics volume by aggregating or dropping these metrics with metrics pipeline management.
Identify dimensions that are the main drivers of high cardinality. You can drop these dimensions and ingest only the aggregated metrics with metrics pipeline management.
Determine which teams are ingesting high cardinality metrics. You can work with those teams to optimize your metrics.
With in-depth insights into your metrics usage and creation, you can make the most use out of metrics pipeline management by aggregating or dropping metrics which are the main cost drivers.
To learn more about metrics pipeline management, see Introduction to metrics pipeline management.