Metric types
Each ingested and aggregated metric has a metric type, a critical piece of metadata that indicates what kind of data a metric represents. Metric types affect how the data aggregates for storage, longterm downsampling behaves, and how that data displays within the Chronosphere Observability Platform.
If a metric is ingested or aggregated with the wrong type, you can get unexpected results when aggregating or querying the data, or data loss when persisting data in long term storage.
Observability Platform types
Observability Platform supports ingesting many different metric types.
Cumulative counter
A counter that stores a strictly increasing count of distinct events or a resettozero on restart. Counters are a fundamental metric construct that keeps track of the number of times a certain event has occurred.
 Each datapoint stores the running total at the given timestamp.
 Aggregated using
metric_type=CUMULATIVE_COUNTER
rollup rules  Queryable as a Prometheus counter (opens in a new tab).
 Subject to longterm downsampling.
 View incoming cumulative counters with a
__metric_type__:cumulative_counter
filter in the Live Telemetry Analyzer.
Examples of cumulative counters include:
 Number of requests served
 Number of user logins
 Number of heap allocations
Delta counter
A counter where each data point stores a strictly positive delta increment at the given timestamp. With this method, the client only keeps track of how many events since the last emission or flush of the metric value and only emit that value. It doesn't keep a running sum.
 Stores the same kinds of data as cumulative counters, but with a different format.
 Aggregated with
metric_type=DELTA_COUNTER
rollup rules. Raw delta counter data is automatically aggregated unless explicitly dropped by a rollup rule.  Queryable as a Prometheus counter (opens in a new tab).
 View incoming delta counters with a
__metric_type__:delta_counter
filter in the Live Telemetry Analyzer.
An example of a delta counter is:
 A client sends a 1 every time a request is served to count the number of requests served.
Gauge
A gauge is a basic metric type that tracks a value which changes over time.
 Each datapoint stores the value at the given timestamp. It's impossible to have two values at the same point in time.
 Aggregated with
metric_type=GAUGE
rollup rules.  Queryable as a Prometheus gauge (opens in a new tab).
 Subject to longterm downsampling.
 View incoming gauges with a
__metric_type__:gauge
filter in the Live Telemetry Analyzer.
Some examples of gauges are:
 Current number of inflight requests.
 Current number of signedin users.
 Current amount of memory in use.
Histogram
The frequency of values in a set of measurements that fall within certain ranges called buckets. A histogram uses several buckets defined in a bucket layout. Observability Platform processes and persists each histogram as a single structured value and time series.
Observability Platform supports OpenTelemetry exponential histograms and Prometheus native histograms, and also supports both cumulative and delta temporality. The histogram's bucket layout defines the histogram metric type in Observability Platform.
You can query histograms using PromQL histogram functions (opens in a new tab). They're subject to the same longterm downsampling as other metric types.
Observability Platform processes and persists Prometheus histograms as individual Cumulative Counter or Delta Counter time series. To query both histograms and legacy Prometheus histograms, see Querying histograms.
Some examples of histograms include:
 Distributions of request latencies
 Distributions of payload sizes
Metric Type  Bucket Layout  Temporality  Description 

Cumulative exponential histogram  Exponential  Cumulative  A cumulative histogram with exponential bucket layout summarizes a set of measurements by defining nonoverlapping, exponentially increasing bucket ranges and counting the frequency of observations within each bucket range. It includes the total count and sum of all observations. See Cumulative exponential histograms. 
Delta exponential histogram  Exponential  Delta  A delta exponential histogram has the same properties of a cumulative exponential histogram. The only difference is the delta temporality, where the instrumentation client only summarizes and sends observations since the last emission or flush of the histogram metric value. See Delta exponential histograms. 
Histograms are limited in these ways:
 The maximum bucket limit is 160 buckets.
 The maximum exponential histogram scale is 8, and the minimum scale is 4.
Cumulative exponential histograms
The cumulative exponential histogram type is compatible with both OpenTelemetry cumulative exponential histograms and legacy Prometheus native histograms with exponential bucket layouts.

Observability Platform automatically reduces the exponential histogram scale when the histogram exceeds the 160bucket limit. When downscaling, it doubles the exponential histogram bucket width to summarize the observations with fewer but wider buckets, effectively reducing the exponential histogram's resolution.
When aggregating or querying exponential histograms with different bucket scales, Observability Platform downscales them to the lowest common histogram scale. Zerothreshold handling similarly widens scales until values fit.

Observability Platform aggregates them with
metric_type=cumulative_exponential_histogram
rollup rules. 
You can view incoming measurements by using a
__metric_type__:cumulative_exponential_histogram
filter in the Live Telemetry Analyzer.
Delta exponential histograms
The delta exponential histogram type is compatible only with OpenTelemetry delta exponential histograms.
 Observability Platform aggregates them with
metric_type=delta_exponential_histogram
rollup rules.  You can view incoming measurements by using a
__metric_type__:delta_exponential_histogram
filter in the Live Telemetry Analyzer.
Measurement
A raw observation with some discrete value. Measurements differ from counters as each measurement event has a discrete, arbitrary value. Measurements also differ from a gauge, as there can be multiple values at any point in time.
 You must use a
metric_type=MEASUREMENT
rollup rule withdrop_raw=true
to aggregate measurements into a persistable metric type. Raw measurement data can't be persisted.  The
MEASUREMENT
metric type is best used when all values should be aggregated at once across many matching time series.  View incoming measurements with a
__metric_type__:measurement
filter in the Live Telemetry Analyzer.
Some examples of measurements are:
 The latency of an individual request
 The payload size of an individual request
Supported formats
Observability Platform can ingest metrics from many different external metric clients. Each external metric client has their own concept of "metric type" which is mapped into a Observability Platform metric type.
Prometheus
Prometheus Type  Observability Platform Type 

Gauge  Gauge 
Counter  Cumulative counter 
Histogram  Cumulative counter 
Summary (quantile data points)  Gauge 
Summary (sum/count data points)  Cumulative counter 
Native histogram with exponential bucket layout  Cumulative exponential histogram 
OpenTelemetry
OpenTelemetry Type  Observability Platform Type 

Gauge  Gauge 
Cumulative monotonic sum  Cumulative counter 
Delta monotonic sum  Delta counter 
Cumulative histogram  Cumulative counter 
Delta histogram  Delta counter 
Summary (quantile data points)  Gauge 
Summary (sum/count data points)  Cumulative counter 
Cumulative nonmonotonic sum  Gauge 
Cumulative exponential histogram  Cumulative exponential histogram 
Delta exponential histogram  Delta exponential histogram 
Observability Platform doesn't support delta nonmonotonic sums (also known as delta updown counters).
Datadog
Datadog Type  Observability Platform Type 

Gauge  Gauge 
Count  Delta counter 
Histogram  Measurement 
Distribution  Measurement 
SignalFX
SignalFX Type  Observability Platform Type 

Gauge  Gauge 
Cumulative  Cumulative counter 
Counter  Delta counter 
Wavefront
Wavefront Type  Observability Platform Type 

Gauge  Gauge 
Counter  Cumulative counter 
Delta counter  Delta counter 
Histogram  Cumulative counter 
StatsD
StatsD Type  Observability Platform Type 

Gauge  Gauge 
Counter  Delta counter 
Timer  Measurement 
GCP Metrics
GCP Type  Observability Platform Type 

Gauge  Gauge 
Cumulative  Cumulative counter 
Delta  Delta counter 