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Metric filter policies control which metrics pass through Edge. Use them to drop noisy metrics, reduce cardinality, and cut costs.

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Basic Structure

{
  "id": "policy-id",
  "name": "Human-readable name",
  "metric": {
    "match": [...],
    "keep": true
  }
}
Every metric policy has:
  • match: One or more matchers that identify target metrics
  • keep: Boolean (true to keep, false to drop)
Unlike log policies, metric policies don’t support percentage sampling or rate limiting. The keep value is a simple boolean.

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Matchers

Matchers identify which metrics a policy applies to. When a policy has multiple matchers, all must match (AND logic).

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Metric Fields

Match on well-known metric fields:
FieldDescription
nameThe metric name
descriptionMetric description
unitMetric unit (e.g., ms, bytes, 1)
scope_nameInstrumentation scope name
scope_versionInstrumentation scope version
{
  "id": "match-cpu-metrics",
  "name": "Match system CPU metrics",
  "metric": {
    "match": [
      {
        "metric_field": "name",
        "regex": "^system\\.cpu\\."
      }
    ],
    "keep": false
  }
}

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Metric Type

Match on metric type:
TypeDescription
METRIC_TYPE_GAUGEInstantaneous measurement
METRIC_TYPE_SUMCumulative or delta sum
METRIC_TYPE_HISTOGRAMDistribution of values
METRIC_TYPE_EXPONENTIAL_HISTOGRAMExponential bucket histogram
METRIC_TYPE_SUMMARYPre-calculated quantiles
{
  "id": "match-histograms",
  "name": "Match histogram metrics",
  "metric": {
    "match": [
      {
        "metric_type": "METRIC_TYPE_HISTOGRAM"
      }
    ],
    "keep": false
  }
}

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Aggregation Temporality

Match on how metrics report aggregated values:
TemporalityDescription
AGGREGATION_TEMPORALITY_DELTAReports change since last report
AGGREGATION_TEMPORALITY_CUMULATIVEReports total since start
{
  "id": "match-delta-metrics",
  "name": "Match delta temporality metrics",
  "metric": {
    "match": [
      {
        "aggregation_temporality": "AGGREGATION_TEMPORALITY_DELTA"
      }
    ],
    "keep": true
  }
}

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Datapoint Attributes

Match on data point attributes (dimensions/labels): 
{
  "id": "match-prod-web-01",
  "name": "Match metrics from prod-web-01",
  "metric": {
    "match": [
      {
        "datapoint_attribute": "host.name",
        "exact": "prod-web-01"
      }
    ],
    "keep": true
  }
}

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Resource Attributes

Match on resource attributes: 
{
  "id": "match-api-gateway",
  "name": "Match api-gateway service metrics",
  "metric": {
    "match": [
      {
        "resource_attribute": "service.name",
        "exact": "api-gateway"
      }
    ],
    "keep": true
  }
}

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Scope Attributes

Match on instrumentation scope attributes: 
{
  "id": "match-otel-go",
  "name": "Match OpenTelemetry Go metrics",
  "metric": {
    "match": [
      {
        "scope_attribute": "library.name",
        "exact": "opentelemetry-go"
      }
    ],
    "keep": true
  }
}

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Match Types

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Exact Match

Match the exact string value: 
{
  "id": "match-request-duration",
  "name": "Match HTTP request duration metric",
  "metric": {
    "match": [
      {
        "metric_field": "name",
        "exact": "http.request.duration"
      }
    ],
    "keep": true
  }
}

​
Regex Match

Match using RE2 regular expressions: 
{
  "id": "drop-debug-metrics",
  "name": "Drop debug-prefixed metrics",
  "metric": {
    "match": [
      {
        "metric_field": "name",
        "regex": "^debug\\."
      }
    ],
    "keep": false
  }
}

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Exists Match

Match on field presence: 
{
  "id": "match-internal-flag",
  "name": "Match metrics with internal attribute",
  "metric": {
    "match": [
      {
        "datapoint_attribute": "internal",
        "exists": true
      }
    ],
    "keep": false
  }
}

​
Negation

Invert any match with negate: 
{
  "id": "drop-non-system-metrics",
  "name": "Drop metrics not starting with system",
  "metric": {
    "match": [
      {
        "metric_field": "name",
        "regex": "^system\\.",
        "negate": true
      }
    ],
    "keep": false
  }
}

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Policy Precedence

When multiple policies match the same metric, keep: false takes precedence over keep: true.

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Examples

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Drop Debug Metrics

{
  "id": "drop-debug-metrics",
  "name": "Drop debug-prefixed metrics",
  "metric": {
    "match": [
      {
        "metric_field": "name",
        "regex": "^debug\\."
      }
    ],
    "keep": false
  }
}

​
Drop System Load Metrics

{
  "id": "drop-system-load",
  "name": "Drop system load metrics",
  "metric": {
    "match": [
      {
        "metric_field": "name",
        "regex": "^system\\.load"
      }
    ],
    "keep": false
  }
}

​
Drop Histogram Metrics

{
  "id": "drop-histograms",
  "name": "Drop all histogram metrics",
  "metric": {
    "match": [
      {
        "metric_type": "METRIC_TYPE_HISTOGRAM"
      }
    ],
    "keep": false
  }
}

​
Drop by Datapoint Attribute

{
  "id": "drop-internal-metrics",
  "name": "Drop metrics with internal flag",
  "metric": {
    "match": [
      {
        "datapoint_attribute": "internal",
        "exists": true
      }
    ],
    "keep": false
  }
}

​
Drop by Service

{
  "id": "drop-test-service-metrics",
  "name": "Drop metrics from test services",
  "metric": {
    "match": [
      {
        "resource_attribute": "service.name",
        "regex": "^test-"
      }
    ],
    "keep": false
  }
}

​
Drop High-Cardinality Metrics

{
  "id": "drop-per-request-metrics",
  "name": "Drop per-request ID metrics",
  "metric": {
    "match": [
      {
        "datapoint_attribute": "request.id",
        "exists": true
      }
    ],
    "keep": false
  }
}

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Keep Only Specific Metrics

Using negation to drop everything except what you want: 
{
  "id": "keep-only-business-metrics",
  "name": "Drop non-business metrics",
  "metric": {
    "match": [
      {
        "metric_field": "name",
        "regex": "^business\\.",
        "negate": true
      }
    ],
    "keep": false
  }
}

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Combined Conditions

{
  "id": "drop-noisy-api-metrics",
  "name": "Drop high-frequency API metrics from non-prod",
  "metric": {
    "match": [
      {
        "metric_field": "name",
        "regex": "^http\\."
      },
      {
        "resource_attribute": "deployment.environment",
        "exact": "production",
        "negate": true
      }
    ],
    "keep": false
  }
}

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Common Use Cases

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Cost Reduction

Drop metrics you never query:
  • Debug and internal metrics
  • Per-request ID dimensions (high cardinality)
  • Redundant system metrics
  • Metrics from test/dev environments

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Cardinality Control

High-cardinality metrics (many unique label combinations) are expensive. Drop metrics with:
  • Unique request IDs as labels
  • User IDs as labels
  • Timestamps as labels
  • Unbounded string values as labels

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Compliance

Drop metrics that might contain sensitive information:
  • Metrics with PII in labels
  • Metrics from sensitive services
  • Internal topology information

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Best Practices

  1. Start with observability: Know what you’re dropping before you drop it
  2. Use exact matches when possible: Faster than regex
  3. Target high-volume metrics: Focus on metrics that cost the most
  4. Be careful with negation: Easy to accidentally drop important metrics
  5. Test in staging: Verify policies before production deployment

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Next Steps

Log Filter

Filter logs

Log Transform

Transform log data