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.
The Metrics 2.0 beta introduces a more structured, more powerful, OpenTelemetry-aligned query model. Highlights include:
RATE(), INCREASE(), SUMMARIZE(), and LAST() through calculated fields to better analyze time series behavior. 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.
all datasets in $environment). Environment-wide queries will return results from all other datasets in the environment. This helps avoid mixing early Metrics data with production datasets during the beta period.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.
To get started:
Metrics dataset using the Query Builder.LAST(), SUMMARIZE() or INCREASE()) based on the metadata from your telemetry.RATE() and INCREASE(), overriding the defaults.all datasets in $environment).
Environment-wide queries will return results from all other datasets in the environment.
This helps avoid mixing early Metrics data with production datasets during the beta period. 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.
Metrics 2.0 builds on your existing OTLP metrics pipeline and expands how you can explore your data.
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 double-counting and 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. Metrics 2.0 stores histograms as distributed data structures, enabling more accurate and flexible analysis:
You can now create Triggers based on metrics queries to alert on trends or anomalies in your telemetry data.
all datasets in $environment) during the beta.
This helps avoid mixing early Metrics data with production datasets during the beta period. 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.
You’re welcome to request access to the beta or share feedback at any time:
#discuss-metrics.