Learn about MetricSets in APM 🔗

MetricSets are categories of key indicators, such as request rate, error rate and durations, calculated based on your traces and spans in Splunk APM. There are two categories of MetricSets: Troubleshooting MetricSets (TMS), used for high-cardinality troubleshooting, and Monitoring MetricSets (MMS), used for real-time monitoring.

MetricSets are specific to Splunk APM, but Monitoring MetricSets are similar to the metric time-series used in Splunk Infrastructure Monitoring to populate charts and generate alerts. See the following Monitoring MetricSets section for more information.

Troubleshooting MetricSets 🔗

Troubleshooting MetricSets (TMS) are metric time series used for high-cardinality troubleshooting of identities in APM and for historical comparison among spans and workflows. Splunk APM generates Troubleshooting MetricSets based on indexed span tags. To learn how to index span tags to generate new Troubleshooting MetricSets, see Index span tags to generate Troubleshooting MetricSets.

Observability Cloud generates the following TMS for every identity in your service map:

Request rate
Error rate
Root cause error rate
Latency (minimum, maximum, P50, P90, P99)

These TMS appear in the Troubleshooting page, which contains the service map, and in Tag Spotlight. You can use TMS to filter the service map and create breakdowns across the values of a given indexed span tag.

Observability Cloud retains TMS for the same amount of time as raw traces. By default, the retention period is 8 days.

For more details about Troubleshooting MetricSets, see Reference and best practices for span tags and Troubleshooting MetricSets.

Monitoring MetricSets 🔗

Monitoring MetricSets (MMS) are a collection of metric time series (MTS) used in Splunk Infrastructure Monitoring. Use MMS to monitor and alert on the performance of your services in real time. You can also alert on TMS. MMS power the real-time APM Landing Page and the dashboard view, and they are the metrics that detectors monitor and use to generate alerts. Observability Cloud stores MMS for 13 months by default.

Each MMS includes six metrics. For each metric there is one MTS with responses sf_error: true or sf_error:false.

count
duration.min
duration.median
duration.p90
duration.p99
duration.max

While Observability Cloud generates TMS for every identity in APM, MMS are available only for a specific endpoint, for the aggregate of all endpoints in a service, or for a specific Business Workflow.

Endpoint-level MMS reflect the activity of a single endpoint in a service, while service-level MMS aggregate the activity of all of the endpoints in the service. Endpoint-level and service-level MMS are therefore limited to spans where the span.kind = SERVER or span.kind = CONSUMER.

Spans might lack a kind value, or have a different kind value, in the following situations:

The span originates in self-initiating operations or inferred services
An error in instrumentation occurs.

Workflow-level MMS aggregate data from all traces identified as part of a given Business Workflow.

Comparing Monitoring MetricSets and Troubleshooting MetricSets 🔗

Because endpoint-level and service-level MMS include a subset of the metrics that TMS include, you might notice that metric values for a service are slightly different depending on the context in Splunk APM. This is because MMS are the basis of the dashboard view, and MMS can only have a kind of SERVER or CONSUMER. In contrast, TMS are the basis of the Troubleshooting and Tag Spotlight views, and TMS aren’t restricted to specific metrics. For example, values for checkout service metrics displayed in the host dashboard might be different from the metrics displayed in the Troubleshooting view because there are multiple span kind values associated with this service that the MMS that power the dashboard do not monitor.

To restrict your TMS to endpoint-only data so that you can compare MMS and TMS directly, filter to a specific endpoint or break down the service map by endpoint.

Metrics and dimensions of Monitoring MetricSets 🔗

Each MMS has a set of metrics and dimensions for spans and traces you can use to monitor and alert on service performance.

To prevent overcounting metrics in aggregations, the built-in dashboards and charts in Splunk APM automatically exclude custom dimensionalized MMS. Custom dimensionalized MMS have a marker dimension, sf_dimensionalized:true, to enable this filtering. When you create your own dashboards and charts, you can exclude custom dimensionalized MMS by adding a filter on !sf_dimensionalized:true. If you do want to look at the time series of a custom dimensionalized MMS in your charts, filter on sf_dimensionalized:true and then aggregate by the custom dimension you want to look at.

The following tables provide the metrics and dimensions for MMS based on services, workflows, spans, and traces:

Service metrics and dimensions 🔗

Metrics	Dimensions
`service.request.count` `service.request.duration.ns.min` `service.request.duration.ns.median` `service.request.duration.ns.max` `service.request.duration.ns.p90` `service.request.duration.ns.p99`	`sf_environment` `sf_service` `sf_error`

Workflow metrics and dimensions 🔗

Metrics	Dimensions
`workflows.count` `workflows.duration.ns.min` `workflows.duration.ns.median` `workflows.duration.ns.max` `workflows.duration.ns.p90` `workflows.duration.ns.p99`	`sf_environment` `sf_workflow` `sf_error`

Span (endpoint-level) metrics and dimensions 🔗

Metrics	Dimensions
`spans.count` `spans.duration.ns.min` `spans.duration.ns.median` `spans.duration.ns.max` `spans.duration.ns.p90` `spans.duration.ns.p99`	`sf_environment` `sf_service` `sf_operation` `sf_kind` `sf_error` `sf_httpMethod`, where relevant

Trace metrics and dimensions 🔗

Metrics	Dimensions
`traces.count` `traces.duration.ns.min` `traces.duration.ns.median` `traces.duration.ns.max` `traces.duration.ns.p90` `traces.duration.ns.p99`	`sf_environment` `sf_service` `sf_operation` `sf_httpMethod` `sf_error`

Default identities and MetricSets 🔗

The total number of Troubleshooting MetricSets is the sum of all identities. The total number of Monitoring MetricSets is the sum of Service, Endpoint, and Workflow identities.

Splunk APM creates the following identities and MetricSets:

APM object	Description	Identity example	Troubleshooting MetricSet	Monitoring MetricSet
Service	Identities for all services	Service, 1	Yes	Yes
Endpoint	Identities for all endpoints	Service, 1.Endpoint, 1.HTTPMethod Service, 1.InitEndpoint, 1 if HTTPMethod is absent	Yes	Yes
Workflow	Identities for all initiating endpoints	Service, 1.InitEndpoint, 1.HTTPMethod Service, 1.InitEndpoint, 1 if HTTPMethod is absent	Yes	Yes
Edge	Identities for all edges between services	Service, 1.Endpoint, 1.HTTPMethod, >Service, 2.Endpoint, 2.HTTPMethod	Yes	No
Operation	Identities for all spans within services	Service, 1.Operation, 1	No	No

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