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Documentation Grafana Tempo Metrics from traces Span metrics Use metrics-generator

Use the metrics-generator to create metrics from spans

Part of the metrics-generator, the span metrics processor generates metrics from ingested tracing data, including request, error, and duration (RED) metrics.

Span metrics generate three metrics:

• A counter that computes requests
• A histogram that tracks the distribution of durations of all requests
• A counter that tracks the total size of spans ingested

Span metrics are of particular interest if your system is not monitored with metrics, but it has distributed tracing implemented. You get out-of-the-box metrics from your tracing pipeline.

Even if you already have metrics, span metrics can provide in-depth monitoring of your system. The generated metrics will show application level insight into your monitoring, as far as tracing gets propagated through your applications.

Last but not least, span metrics lower the entry barrier for using exemplars. An exemplar is a specific trace representative of measurement taken in a given time interval. Since traces and metrics co-exist in the metrics-generator, exemplars can be automatically added, providing additional value to these metrics.

How to run

To enable span metrics in Tempo or Grafana Enterprise Traces, enable the metrics generator and add an overrides section which enables the span-metrics processor. Refer to the configuration details.

If you want to enable metrics-generator for your Grafana Cloud account, refer to the Metrics-generator in Grafana Cloud documentation.

Enabling specific metrics (subprocessors)

Instead of enabling all span metrics, you can enable individual metric types using subprocessors in the overrides configuration:

• span-metrics-latency - Enables only the traces_spanmetrics_latency histogram
• span-metrics-count - Enables only the traces_spanmetrics_calls_total counter
• span-metrics-size - Enables only the traces_spanmetrics_size_total counter

Example overrides configuration:

YAML Copy

overrides:
  defaults:
    metrics_generator:
      processors:
        - span-metrics-latency
        - span-metrics-count
        # span-metrics-size omitted to disable size metrics

How it works

The span metrics processor works by inspecting every received span and computing the total count and the duration of spans for every unique combination of dimensions. Dimensions can be the service name, the operation, the span kind, the status code and any attribute present in the span.

This processor mirrored the implementation from the OpenTelemetry Collector of the processor with the same name. The OTel spanmetricsprocessor has since been deprecated and replaced with the span metric connector.

Metrics

The following metrics are exported:

By default, the metrics processor adds the following labels to each metric: service, span_name, span_kind, and status_code.

The status_message, job, and instance labels are optional and require additional configuration, as described in the sections below.

• service - The name of the service that generated the span
• span_name - The unique name of the span
• span_kind - The type of span, this can be one of five values:
  • SPAN_KIND_SERVER - The span was generated by a call from another service
  • SPAN_KIND_CLIENT - The span made a call to another service
  • SPAN_KIND_INTERNAL - The span does not have interaction outside of the service it was generated in
  • SPAN_KIND_PUBLISHER - The span created data that was pushed onto a bus or message broker
  • SPAN_KIND_CONSUMER - The span consumed data that was on a bus or messaging system
• status_code - The result of the span, this can be one of three values:
  • STATUS_CODE_UNSET - Result of the span was unset/unknown
  • STATUS_CODE_OK - The span operation completed successfully
  • STATUS_CODE_ERROR - The span operation completed with an error
• status_message (optionally enabled) - The message that details the reason for the status_code label
• job - The name of the job, a combination of namespace and service; only added if metrics_generator.processor.span_metrics.enable_target_info: true
• instance - The instance ID; only added if metrics_generator.processor.span_metrics.enable_target_info: true and metrics_generator.processor.span_metrics.enable_instance_label: true

Disabling intrinsic dimensions

You can control which intrinsic dimensions are included in your metrics. Disable any of the default intrinsic dimensions using the intrinsic_dimensions configuration. This is useful for reducing cardinality when certain labels are not needed.

YAML Copy

metrics_generator:
  processor:
    span_metrics:
      intrinsic_dimensions:
        service: true
        span_name: true
        span_kind: false # Disable span_kind label
        status_code: true
        status_message: false # Disabled by default

Adding custom dimensions

Additional user defined labels can be created using the dimensions configuration option. When a configured dimension collides with one of the default labels (for example, status_code), the label for the respective dimension is prefixed with double underscore (for example, __status_code).

Renaming dimensions with dimension_mappings

Custom labeling of dimensions is also supported using the dimension_mappings configuration option.

Understanding dimensions vs dimension_mappings:

Use dimensions when you want to add span attributes as labels using their default (sanitized) names. Use dimension_mappings when you want to rename attributes to custom label names or combine multiple attributes.

When using dimension_mappings, you do not need to also list the same attributes in dimensions. The dimension_mappings configuration reads directly from the original span attributes. You can use dimension_mappings to rename a single attribute to a different label name, or to combine multiple attributes into a single composite label.

The name field can use either dots (.) or underscores (_), as both are converted to underscores (_) in the final Prometheus metric labels. For example, both env and env.label result in env_label in Prometheus metrics.

The following example shows how to rename the deployment.environment attribute to a shorter label called env, for example:

YAML Copy

dimension_mappings:
  - name: env
    source_labels: ["deployment.environment"]

This example shows how to combine the service.name, service.namespace, and service.version attributes into a single label called service_instance. The join parameter specifies the separator used to join the attribute values together.

YAML Copy

dimension_mappings:
  - name: service_instance
    source_labels: ["service.name", "service.namespace", "service.version"]
    join: "/"

With this configuration, if a span has the following attribute values:

• service.name = "abc"
• service.namespace = "def"
• service.version = "ghi"

The resulting metric label is service_instance="abc/def/ghi".

An optional metric called traces_target_info using all resource level attributes as dimensions can be enabled in the enable_target_info configuration option.

Excluding dimensions from target_info

When enable_target_info is enabled, all resource attributes are included as labels on the traces_target_info metric. To reduce cardinality, you can exclude specific attributes using the target_info_excluded_dimensions configuration:

YAML Copy

metrics_generator:
  processor:
    span_metrics:
      enable_target_info: true
      target_info_excluded_dimensions:
        - "telemetry.sdk.version"
        - "process.runtime.version"

Handling sampled traces

If you use a ratio-based sampler, you can use the custom sampler below to not lose metric information. However, you also need to set metrics_generator.processor.span_metrics.span_multiplier_key to "X-SampleRatio".

Go Copy

package tracer
import (
	"go.opentelemetry.io/otel/attribute"
	tracesdk "go.opentelemetry.io/otel/sdk/trace"
)

type RatioBasedSampler struct {
	innerSampler        tracesdk.Sampler
	sampleRateAttribute attribute.KeyValue
}

func NewRatioBasedSampler(fraction float64) RatioBasedSampler {
	innerSampler := tracesdk.TraceIDRatioBased(fraction)
	return RatioBasedSampler{
		innerSampler:        innerSampler,
		sampleRateAttribute: attribute.Float64("X-SampleRatio", fraction),
	}
}

func (ds RatioBasedSampler) ShouldSample(parameters tracesdk.SamplingParameters) tracesdk.SamplingResult {
	sampler := ds.innerSampler
	result := sampler.ShouldSample(parameters)
	if result.Decision == tracesdk.RecordAndSample {
		result.Attributes = append(result.Attributes, ds.sampleRateAttribute)
	}
	return result
}

func (ds RatioBasedSampler) Description() string {
	return "Ratio Based Sampler which gives information about sampling ratio"
}

Filtering

In some cases, you may want to reduce the number of metrics produced by the spanmetrics processor. You can configure the processor to use an include filter to match criteria that must be present in the span in order to be included. Following the include filter, you can use an exclude filter to reject portions of what was previously included by the filter policy.

Currently, only filtering by resource and span attributes with the following value types is supported.

• bool
• double
• int
• string

Additionally, these intrinsic span attributes may be filtered upon:

• name
• status (code)
• kind

The following intrinsic kinds are available for filtering.

• SPAN_KIND_SERVER
• SPAN_KIND_INTERNAL
• SPAN_KIND_CLIENT
• SPAN_KIND_PRODUCER
• SPAN_KIND_CONSUMER

Intrinsic keys can be acted on directly when implementing a filter policy. For example:

YAML Copy

---
metrics_generator:
  processor:
    span_metrics:
      filter_policies:
        - include:
            match_type: strict
            attributes:
              - key: kind
                value: SPAN_KIND_SERVER

In this example, spans which are of kind “server” are included for metrics export.

When selecting spans based on non-intrinsic attributes, it is required to specify the scope of the attribute, similar to how it is specified in TraceQL. For example, if the resource contains a location attribute which is to be used in a filter policy, then the reference needs to be specified as resource.location. This requires users to know and specify which scope an attribute is to be found and avoids the ambiguity of conflicting values at differing scopes. The following may help illustrate.

YAML Copy

---
metrics_generator:
  processor:
    span_metrics:
      filter_policies:
        - include:
            match_type: strict
            attributes:
              - key: resource.location
                value: earth

In the above examples, we are using match_type of strict, which is a direct comparison of values. You can use regex, an additional option for match_type, to build a regular expression to match against.

YAML Copy

---
metrics_generator:
  processor:
    span_metrics:
      filter_policies:
        - include:
            match_type: regex
            attributes:
              - key: resource.location
                value: eu-.*
        - exclude:
            match_type: regex
            attributes:
              - key: resource.tier
                value: dev-.*

In the above, we first include all spans which have a resource.location that begins with eu- with the include statement, and then exclude those with begin with dev-. In this way, a flexible approach to filtering can be achieved to ensure that only metrics which are important are generated.

Example