OpenTelemetry metrics are largely compatible with New Relic dimensional metrics. We support OpenTelemetry metrics v0.10. All of the supported metric types include an independent set of associated attributes (name-value pairs) which map directly to dimensions you can use to facet or filter metric data at query time. OpenTelemetry metrics are accompanied by a set of resource attributes that identify the originating entity that produced them and map to dimensions for faceting and filtering.
The OpenTelemetry data model for metrics defines a number of different metric types: sum, gauge, histogram, and summary.
Sum metrics
OpenTelemetry sums are a scalar metric that is the sum of all data points over a given time window. Sums have a notion of temporality indicating whether reported values incorporate previous measurements (cumulative temporality) or not (delta temporality).
In addition, sums can either be monotonic (only go up or only go down) or non-monotonic (go up and down).
Delta sums
Delta sums are handled differently depending on whether they are monotonic or non-monotonic:
- Monotonic delta sums are mapped to the count metric type.
- Non-monotonic delta sums are not supported. Things measured in this fashion do not generate meaningful data as they are most likely summed to be used. For more discussion, see this community pull request and our docs on gauges versus counters.
Cumulative sums
Cumulative sums are handled differently depending on whether they're monotonic or non-monotonic.
- Non-monotonic cumulative sums are mapped to the gauge metric type.
- Monotonic cumulative sums are mapped to our cumulativeCount metric type.
Sum configuration examples
To understand how to configure aggregation temporality, see these examples using the Java and Python OpenTelemetry SDKs.
Gauge metrics
OpenTelemetry gauge metric data points represent a sampled value at a given time. These values are converted to the New Relic gauge metric type. OpenTelemetry gauges do not have an aggregation temporality, but the sampled values can be aggregated at query time.
Histogram metrics
OpenTelemetry histograms compactly represent a population of recorded values along with a total count and sum. Optionally, histograms may include a series of buckets with explicit bounds and a count value for that bucket's population.
All OpenTelemetry histograms are converted to New Relic's distribution metric type, which is backed by a scaled exponential base 2 histogram (see NrSketch for a more thorough explanation).
Delta histograms
For exponential histograms, we are able to do a direct translation to NrSketch.
For explicit bounds histograms, counts from OpenTelemetry histogram buckets are assigned to New Relic's distribution metric buckets using linear interpolation. Also, OpenTelemetry has negative and positive infinity bound buckets which we represent in New Relic as zero-width buckets. We do this because we do not have a representation for negative and positive infinity. For example, an OpenTelemetry bucket with bounds \[-∞, 10) will be represented by a \[10,10) zero width New Relic bucket. You may see exaggerated bucket counts at the endpoints of your distribution due to this translation.
Cumulative histograms
Cumulative histograms are converted to a Delta histogram. For more on the translation process, see Cumulative metrics.
Summary metrics
OpenTelemetry summary metric data points are used to represent quantile summaries (for example, P99 latency). These map directly to the New Relic summary metric type, however the only quantiles we maintain are the 0.0 and 1.0 quantiles for min and max. If percentiles are something you wish to capture, prefer Histogram metrics.
Summary metric data points include count, sum, and quantile values, with 0.0 as min and 1.0 as max. OpenTelemetry provides summary metrics for compatibility with other formats.
Start time
The startTimeUnixNano field is optional according to the OpenTelemetry specification. For delta aggregation temporality, when present it is used for the timestamp on the resulting New Relic metric, and the duration is calculated as timeUnixNano - startTimeUnixNano. The duration field is used to calculate the queryable endTimeStamp attribute on the New Relic metric, but it serves no other semantic purpose.
If startTimeUnixNano is not provided, then timeUnixNano is used for the timestamp field on the resulting New Relic metric, and the duration field is set to zero.
For cumulative aggregation temporality, startTimeUnixNano represents the first observation of the time series and is used to detect resets. timeUnixNano is interpreted as the timestamp of the cumulative data point. Cumulative data points are then converted to delta aggregation temporality.
Array values for attributes
OpenTelemetry metrics and other signals may include attributes that consist of a homogenous array of primitive types. New Relic supports non-nested homogeneous arrays with less than 65 elements.
Exemplars
OpenTelemetry defines exemplar values that allow other signals, like traces, to be connected to a metric event and provide context. Exemplars are not supported by New Relic.
How to query metrics
Consider these tips for building metric NRQL queries in New Relic.
Query cumulative sums stored as cumulativeCounts
Here are some ways to query your data:
Query cumulative sums stored as gauges
New Relic customers that reported cumulative sums before April 4, 2023, have their cumulative sums converted to our gauge type.
If your cumulative sums are stored as the gauge type, here are some ways to query your data:
Query gauge metrics
When New Relic converts cumulative sums to gauges, you can query them using either the latest() or derivative() NRQL functions. The function you choose depends on whether you want to see the raw value or compute the rate of change.
Query histogram metrics
New Relic histograms translated from OpenTelemetry metrics have the same query semantics as other New Relic histograms. Namely, the histogram() NRQL function can be used to represent the histogram with a configurable number of buckets and bucket width. We translate cumulative histograms into delta histograms, so they are queried the same way a delta histogram would be queried. Note for explicit bounds histograms, that you may see larger bucket counts at the endpoint buckets. This is because we are adding negative and positive infinity bound OpenTelemetry buckets into a zero width New Relic bucket.
Important
The TIMESERIES keyword is not supported for New Relic histograms unless charing percentiles().