Metric types

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, long-term downsampling behaves, and how that data displays within the Chronosphere app.

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.

Chronosphere types

The Chronosphere metric platform supports ingesting many different metric types.

Cumulative counter

A counter which stores a strictly increasing count of distinct events or a reset-to-zero on restart. Counters are a fundamental metric construct that keeps track of the number of times a certain event has occurred.

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.
  • Delta counters must use a metric_type=DELTA_COUNTER rollup rule with drop_raw=true to aggregate delta counters into a cumulative counter. Raw delta counter data can't be persisted.
  • 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.


A gauge is a basic metric type that tracks a value which changes over time.

Some examples of gauges are:

  • Current number of in-flight requests.
  • Current number of signed-in users.
  • Current amount of memory in use.


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 with drop_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

Chronosphere 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 Chronosphere metric type.


Prometheus TypeChronosphere Type
CounterCumulative counter
HistogramCumulative counter
Summary (quantile data points)Gauge
Summary (sum/count data points)Cumulative counter


OpenTelemetry TypeChronosphere Type
Cumulative monotonic sumCumulative counter
Delta monotonic sumDelta counter
Cumulative histogramCumulative counter
Delta histogramDelta counter
Summary (quantile data points)Gauge
Summary (sum/count data points)Cumulative counter
Cumulative non-monotonic sumGauge

Chronosphere does not support delta non-monotonic sums (also known as delta up-down counters).


Datadog TypeChronosphere Type
CountDelta counter


SignalFX TypeChronosphere Type
CumulativeCumulative counter
CounterDelta counter


Wavefront TypeChronosphere Type
CounterCumulative counter
Delta counterDelta counter
HistogramCumulative counter


StatsD TypeChronosphere Type
CounterDelta counter

GCP Metrics

GCP TypeChronosphere Type
CumulativeCumulative counter
DeltaDelta counter