Querying Metrics in Honeycomb

Metrics datasets in Honeycomb behave differently from tracing/event datasets. Operations that work on traces may fail or produce misleading results on metrics. This skill covers those differences so you construct correct, useful metrics queries.

Finding the Metrics Dataset

Metrics datasets are not identified by having "metrics" in their name. Many event datasets contain "metrics" in their slug (e.g., kafka-metrics, refinery-metrics, kubernetes-node-metrics). These are ordinary event datasets, not metrics datasets.

How to identify the real metrics dataset:

1. Call get_environment and look for rows where dataset_type = metrics. The slug is typically metrics but may differ per environment.
2. Alternatively, call get_dataset_columns on a candidate dataset — metrics datasets return a MetricInfo column showing type metadata like gauge, sum(cumulative,monotonic), or histogram(delta). Event datasets do not have this.

Do not guess the dataset. Always verify via get_environment or get_dataset_columns before constructing a metrics query. If the user says "metrics" but means an event dataset with metrics-like fields (e.g., telegraf, system_stats), the query rules below do not apply — those are event datasets and follow normal query patterns from the query-patterns skill.

Discovering Metrics and Their Attributes

Metrics datasets have a fundamentally different schema from event datasets. Each metric has its own set of resource and data point attributes. Two metrics in the same dataset may have completely different attributes available for filtering and grouping.

Workflow for discovering what to query:

1. Find metric names: Call get_dataset_columns on the metrics dataset (without metric_name). This returns metric names with their types in MetricInfo. Use find_columns with keywords to search for specific metrics (e.g., "cpu", "memory", "http request duration").

2. Find attributes for a specific metric: Call get_dataset_columns with the metric_name parameter set to the metric you want to query (e.g., metric_name: "k8s.pod.memory.usage"). This returns the resource attributes and data point attributes that co-occur with that metric, along with sample values. These are what you can use in WHERE and GROUP BY clauses.

3. Validate before querying: Not all attributes exist on all metrics. Always use step 2 to confirm available attributes before adding them to filters or breakdowns.

Allowed vs. Forbidden Operations on Metrics Datasets

The following operations are NOT allowed on metrics datasets:

Forbidden Operation	Why
COUNT (without column)	Counts metric events, not metric values — meaningless for metrics
RATE_SUM	Not supported on metrics datasets
RATE_AVG	Not supported on metrics datasets
RATE_MAX	Not supported on metrics datasets
CONCURRENCY	Requires span duration; metrics have no duration

Use these instead:

Goal	Use on Metrics
Visualize a gauge value	AVG(metric), MAX(metric), HEATMAP(metric)
Visualize a counter/sum	SUM(metric), AVG(metric), MAX(metric)
See distribution of values	HEATMAP(metric), P50(metric), P99(metric)
Track per-second rate of change	Override temporal aggregation with a calculated field (see below)
Percentile analysis	P50(metric), P90(metric), P99(metric)
Count of non-null values	COUNT(metric) (with a column specified)

Metric Types and Temporal Aggregation

Honeycomb automatically applies temporal aggregation to align raw metric values into query time steps. The function it applies depends on the metric type, visible in the MetricInfo column from get_dataset_columns.

Default Temporal Aggregation by Metric Type

MetricInfo	Type	Default Function	What It Does
gauge	Gauge	LAST()	Returns most recent value per time step
sum(cumulative,monotonic)	Monotonic cumulative sum	INCREASE()	Change between steps, handles counter resets
sum(cumulative)	Non-monotonic cumulative sum	LAST()	Most recent value (can go up or down)
sum(delta) or sum(delta,monotonic)	Delta sum	SUMMARIZE()	Sums all values in each step
histogram(cumulative)	Cumulative histogram	INCREASE()	Change per bucket between steps
histogram(delta)	Delta histogram	SUMMARIZE()	Sums bucket values in each step

These defaults are applied automatically — you do not need to configure them. The results you see from AVG, MAX, P99, etc. on a metrics dataset already reflect temporal aggregation having been applied first.

Overriding Temporal Aggregation

To override the default (e.g., to see RATE instead of INCREASE for a cumulative counter), use a query-scoped calculated field wrapping the metric name in a temporal aggregation function, then apply a spatial aggregation to that field in calculations.

{
  "calculated_fields": [
    { "name": "req_rate", "expression": "RATE($http.server.requests, 300)" }
  ],
  "calculations": [
    { "op": "AVG", "column": "req_rate" }
  ]
}

Supported temporal aggregation functions for calculated fields:

• LAST($metric) — most recent data point per step (gauges, non-monotonic sums)
• SUMMARIZE($metric) — sum all values per step with interpolation (delta metrics)
• INCREASE($metric[, range_interval_seconds]) — change in value across range, handles counter resets
• RATE($metric[, range_interval_seconds]) — per-second rate of change (INCREASE / time)

The optional range_interval_seconds parameter (integer, in seconds) controls the lookback window for calculating changes. Use it to smooth results or compensate for sparse data. When omitted, the query's granularity is used as the range interval.

Important: You must still apply a spatial aggregation (AVG, SUM, P99, HEATMAP, etc.) to the calculated field in calculations. The temporal aggregation function alone does not produce a visualization — it transforms the raw metric values, then the spatial aggregation summarizes across timeseries.

For detailed reference on temporal aggregation functions, counter reset handling, and range_interval_seconds, see: ${CLAUDE_PLUGIN_ROOT}/skills/metrics-queries/references/temporal-aggregation.md

Querying Histogram Metrics

OpenTelemetry histograms are stored as a collection of sub-fields. For a histogram named http.server.duration, Honeycomb creates:

Field	Meaning
http.server.duration.count	Total number of data points
http.server.duration.sum	Sum of all values
http.server.duration.avg	Mean value (sum/count)
http.server.duration.p50	Median (50th percentile)
http.server.duration.p99	99th percentile
http.server.duration.p001 through .p999	Full range of percentiles

Two ways to query histograms:

1. Use the parent column name directly with percentile or distribution operations. This is the recommended approach:

   { "op": "P99", "column": "http.server.duration" }
   

   { "op": "HEATMAP", "column": "http.server.duration" }
   

2. Use sub-fields with MAX when you want the worst-case pre-computed percentile across all timeseries in a step:

   { "op": "MAX", "column": "http.server.duration.p99" }
   

   This returns the highest p99 value reported by any single timeseries in the time step, which differs from P99(http.server.duration) which computes the 99th percentile across all data.

When to use which:

• For most analysis: use P99(parent_column) or HEATMAP(parent_column)
• For worst-case bounds across hosts/pods: use MAX(parent_column.p99)
• For throughput from histograms: use SUM(parent_column.count) or AVG(parent_column.count)

Query Math with Metrics

Query math (compound queries with named calculations and formulas) works on metrics datasets the same way it works on event datasets. Name your calculations, add per-calculation filters if needed, and define formulas to combine them.

Common metrics formula patterns:

Utilization percentage

{
  "calculations": [
    { "op": "AVG", "column": "k8s.pod.memory.usage", "name": "used" },
    { "op": "AVG", "column": "k8s.pod.memory.available", "name": "available" }
  ],
  "formulas": [
    { "name": "utilization_pct", "expression": "$used / ($used + $available) * 100" }
  ],
  "breakdowns": ["k8s.pod.name"],
  "orders": [{ "column": "utilization_pct", "order": "descending" }],
  "limit": 20
}

Histogram tail ratio

{
  "calculations": [
    { "op": "P50", "column": "http.server.duration", "name": "median" },
    { "op": "P99", "column": "http.server.duration", "name": "tail" }
  ],
  "formulas": [
    { "name": "tail_ratio", "expression": "$tail / $median" }
  ],
  "breakdowns": ["service.name"]
}

Error rate from counters (with temporal aggregation override)

{
  "calculated_fields": [
    { "name": "error_rate", "expression": "RATE($http.server.errors)" },
    { "name": "request_rate", "expression": "RATE($http.server.requests)" }
  ],
  "calculations": [
    { "op": "SUM", "column": "error_rate", "name": "errors_per_sec" },
    { "op": "SUM", "column": "request_rate", "name": "requests_per_sec" }
  ],
  "formulas": [
    { "name": "error_pct", "expression": "$errors_per_sec / $requests_per_sec * 100" }
  ]
}

For more query examples, see: ${CLAUDE_PLUGIN_ROOT}/skills/metrics-queries/references/metrics-query-examples.md

Granularity for Metrics

Metrics arrive at known, regular intervals (e.g., every 10s, 30s, or 60s). Granularity matters more for metrics than for traces:

• Align granularity with the reporting interval. If metrics report every 60 seconds, use a granularity that divides evenly into 60 (e.g., 60, 120, 300). Misaligned granularity causes uneven bucket sizes that produce noisy results.
• Spiky-looking graphs usually mean the granularity is finer than the reporting interval. Increase granularity or, in the UI, enable "Omit Missing Values" to produce continuous lines.
• RATE operations and granularity: RATE_SUM (on event datasets) is particularly sensitive to granularity choice — inconsistent data points per bucket produce variable results.

Common Pitfalls

1. Using COUNT on metrics. COUNT counts the number of metric events, not the metric value. Use AVG, SUM, MAX, or HEATMAP instead.
2. Using RATE_AVG/RATE_SUM/RATE_MAX on metrics datasets. These are not allowed. To get a rate, use a calculated field with RATE($metric) and then apply a spatial aggregation like AVG or SUM.
3. Assuming all metrics share the same attributes. Each metric has its own set of resource and data point attributes. Always call get_dataset_columns with metric_name to discover what's available for a specific metric before adding filters or breakdowns.
4. Confusing event datasets with the metrics dataset. Datasets named kafka-metrics, refinery-metrics, etc. are event datasets. Check dataset_type from get_environment.
5. Querying histogram sub-fields when the parent column works. Use P99(http.server.duration) rather than AVG(http.server.duration.p99) unless you specifically need worst-case bounds.
6. Not specifying an aggregate function. Metrics queries without a spatial aggregation in SELECT default to COUNT, which is meaningless for metrics.

Additional Resources

Reference Files

• ${CLAUDE_PLUGIN_ROOT}/skills/metrics-queries/references/metrics-query-examples.md — Metrics query cookbook with run_query examples for common scenarios
• ${CLAUDE_PLUGIN_ROOT}/skills/metrics-queries/references/temporal-aggregation.md — Deep reference on temporal aggregation functions, counter resets, and range_interval_seconds
• ${CLAUDE_PLUGIN_ROOT}/skills/metrics-queries/references/metric-types.md — OpenTelemetry metric types, how they map to Honeycomb, and what the MetricInfo values mean

Cross-References

• For general query construction patterns (calculated fields, relational fields, result interpretation): query-patterns skill
• For investigating production issues using metrics alongside traces: production-investigation skill
• For SLO interpretation and burn alert design: slos-and-triggers skill
• For instrumenting applications to send metrics: otel-instrumentation skill