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Metrics 2.0 Beta Enablement Guide

Welcome to the Metrics 2.0 beta! This experience is a significant upgrade to how Honeycomb ingests and queries time series metrics, and is built on top of the OpenTelemetry Metrics Data Model. This guide outlines what’s new, how the beta works, and what to try out.

What’s New in Metrics 2.0 

The Metrics 2.0 beta introduces a more structured, more powerful, OpenTelemetry-aligned query model. Highlights include:

  • Support for OTLP Metric Types. Native support for gauges, cumulative and delta sums, and histograms.
  • Temporal Aggregation Functions Access RATE(), INCREASE(), SUMMARIZE(), and LAST() through calculated fields to better analyze time series behavior.
  • Automatic Aggregation Defaults Honeycomb chooses the appropriate temporal aggregate for each metric type.
  • Query-Scoped Calculated Fields for Metrics Use temporal logic in your queries without modifying your dataset schema.
  • Trigger Support for Metrics Alert on latency, utilization, and more using metrics-based queries and existing integrations.
  • Native Histogram Support Histograms are stored as distributed structures and support accurate percentiles and flexible merging across time and groupings.

How the Beta Works 

Once you are opted in, Honeycomb handles the rest:

  • No Instrumentation Changes Needed You don’t need to modify your code—just opt in.
  • Dual Ingest Automatically Enabled All OTLP metrics are duplicated to a new dataset named Metrics. If you already have a dataset with this name, you may see the beta dataset labeled as OTLP Metrics or Metrics Data.
  • No Additional Cost Honeycomb covers all storage and query costs for the duplicated metrics data during the beta.
  • Your Role in the Beta Try out the new functionality, tell us what is working well, and share what needs improvement.

What to Try 

To get started:

  1. Explore the new Metrics dataset using the Query Builder.
  2. Add query-scoped calculated fields and apply temporal functions, like RATE() and INCREASE().
  3. Add queries to Boards and validate your visualizations.
  4. Create triggers from your metrics queries and test alerting behavior.
  5. Share feedback on anything confusing, surprising, or cool!
Known Limitations
Support for time series math (for example, arithmetic across time series) and nested queries is coming in a future release.

Enabled Features in this Beta 

Metrics 2.0 introduces foundational improvements to how you ingest, query, and alert on metric data in Honeycomb. The following features are enabled as part of this beta.

Ingest & Query Enhancements 

Metrics 2.0 builds on your existing OTLP metrics pipeline and expands how you can explore your data.

  • Metrics ingestion and querying are enabled for all data sent through our OTLP metrics pipeline.
  • Expanded query capabilities: The query engine now supports:
    • Temporal aggregation: Analyze how a single time series changes over time.
    • Spatial aggregation: Compare values across multiple time series at the same point in time.

Temporal Aggregation Functions (via Calculated Fields) 

Metrics 2.0 introduces new temporal functions that make it easier to explore time series behavior directly in your queries.

  • LAST(metric): Returns the most recent value for each step, based on timestamp.
  • SUMMARIZE(metric): Sums all data points in a step, interpolating at step boundaries to avoid gaps. A single data point may be partially represented in two steps but fully counted in both.
  • INCREASE(metric [, range_interval_seconds]): Calculates the difference between the first and last values within each step individually. Automatically accounts for counter resets.
  • RATE(metric [, range_interval_seconds]): Calculates the rate of change by dividing INCREASE by the number of seconds in the time range, resulting in the rate of increase per second.
Important
These functions are currently available only through query-scoped calculated fields.

Native Histogram Support 

Metrics 2.0 stores histograms as distributed data structures, enabling more accurate and flexible analysis:

  • Compute completely accurate percentiles (for example, p99, p95) directly in queries.
  • Merge histograms across time windows or grouped dimensions.
  • Seamlessly handle histograms with cumulative sums.

Trigger (Alerting) Support 

You can now create Triggers based on metrics queries to alert on trends or anomalies in your telemetry data.

  • Alert on latency, utilization, or any other metric-based expression in a query.
  • Send alerts via PagerDuty, Slack, webhooks, or email.
  • Enjoy the same reliability and flexibility as event-based Triggers.
Important
Support for nested queries and arithmetic across time series is not yet available.

What’s Not Supported 

  • Saving temporal functions as persistent calculated fields (query-scoped use only).
  • Grouping/filtering by metric columns (only attributes for now).
  • Nesting temporal aggregates or using arithmetic across time series.
  • Creating SLOs using metrics.
  • Deleting metrics datasets while enrolled in the beta.

Feedback Welcome 

This is an active beta, so your feedback is crucial! We’d love to hear from you—whether it’s a bug, a friction point, or something you found unexpectedly powerful.

Ready to Join or Share Feedback? 

You’re welcome to request access to the beta or share feedback at any time:

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