metrics-queries

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").