An OTLP Sum represents a sum of reported measurements over a time window. For example, a Sum can be used to track the total number of connections made to a database or the total number of requests to an endpoint. Sums have two features that influence the mapping:

• Aggregation temporality, which can be cumulative or delta. Delta metrics have no overlap in their time windows, while cumulative metrics represent a time window from a fixed start point in time.
• Monotonicity. Monotonic sums never decrease and only support adding to the underlying count.

The default mapping is as follows:

1. For cumulative monotonic sums, the delta between consecutive points is calculated and reported to Datadog as a count. The first point is stored but omitted. To recover the value in the OTLP payload, use the cumsum arithmetic function.
2. Cumulative non-monotonic sums are exported as Datadog gauges.
3. Delta sums are exported as Datadog counts.

An OTLP Gauge represents a sampled value at a given time. Only the last value on a given time window is included in the OTLP metrics.

OTLP Gauges are mapped to Datadog Gauges, since they do not provide an aggregation semantic. Both integer and floating-point Gauge data points are mapped to floating point numbers in the Datadog format.

An OTLP Histogram represents the statistical distribution of a set of values on a given time window, by storing certain aggregation metrics such as the population sum or count together with a series of bucket counts. Histograms have one feature that influences the mapping:

• Aggregation temporality, which can be cumulative or delta. Delta metrics have no overlap in their time windows, while cumulative metrics represent a time window from a fixed start point in time.

The default mapping is as follows:

1. Delta histograms are reported as Datadog distributions. Read more about distributions to understand the available aggregations. Histograms with a count of 0 are dropped.
2. For cumulative histograms, the delta between consecutive points is calculated and reported to Datadog as a distribution. Deltas with a count of 0 are not reported. You may use the cumsum arithmetic function on individual aggregations to recover the value in the OTLP payload.

Note: Histogram metrics in OTLP are mapped by default to Distribution metrics. Because of how OTLP sends this data, percentile aggregations and the max and min (if not available on the original OTLP data) are approximations, not accurate calculations.

The Datadog Agent and the OpenTelemetry Collector Datadog exporter allow changing the Histogram export in the histogram subsection.

• If the mode is set to counters, the following metrics are produced:

<METRIC_NAME>.bucket, tagged by lower_bound and upper_bound

Bucket count in the time window for the bucket with the specified lower and upper bounds.
Datadog In-App Type: COUNT

• If the send_aggregation_metrics flag is enabled, the following metrics are produced:

<METRIC_NAME>.sum

Sum of the values submitted during the time window.
Datadog In-App Type: COUNT

<METRIC_NAME>.count

Number of values submitted during the time window.
Datadog In-App Type: COUNT

<METRIC_NAME>.min

Minimum of values submitted during the time window. Only available for delta OTLP Histograms. Available since: Datadog exporter v0.75.0 and Datadog Agent v6.45.0 and v7.45.0.
Datadog In-App Type: GAUGE

<METRIC_NAME>.max

Maximum of values submitted during the time window. Only available for delta OTLP Histograms. Available since: Datadog exporter v0.75.0 and Datadog Agent v6.45.0 and v7.45.0.
Datadog In-App Type: GAUGE

Note: send_aggregation_metrics is useful only when not using the distributions mode. Before the Datadog exporter v0.75.0 and the Datadog Agent v6.45.0 and v7.45.0 use send_count_sum_metrics instead.

An OTLP Summary is a legacy type that conveys quantile information about a population over a time window. OTLP Summary types are not produced by OpenTelemetry SDKs but may be produced by other components for backwards compatibility.

<METRIC_NAME>.sum

Sum of the values since the application started producing the metric.
Datadog In-App Type: COUNT

<METRIC_NAME>.count

Number of values in the population .
Datadog In-App Type: COUNT

<METRIC_NAME>.quantile, tagged by quantile

Value of a given quantile.
Datadog In-App Type: GAUGE

Suppose you are using an OpenTelemetry Counter instrument from a single application, which, by default, exports metrics of a cumulative monotonic Sum type. The following table summarizes Datadog behavior:

Suppose you are using an OpenTelemetry UpDownCounter instrument from a single application, which, by default, exports metrics of a cumulative Sum type. The following table summarizes Datadog behavior:

Suppose you are using an OpenTelemetry Gauge instrument, temperature, from a single application. The following table summarizes Datadog behavior:

Suppose you are using an OpenTelemetry Histogram instrument, request.response_time.histogram, from two web servers: webserver:web_1 and webserver:web_2. Suppose in a given collection period, webserver:web_1 reports the metric with the values [1,1,1,2,2,2,3,3], and webserver:web_2 reports the same metric with the values [1,1,2]. Over this collection period, the following five aggregations represent the global statistical distribution of all values collected from both web servers:

Read more about distributions to understand how to configure further aggregations.

Alternatively, if you are using the counters mode, the send_aggregation_metrics flag is enabled, and the histogram bucket boundaries are set to [-inf, 2, inf], the following metrics are reported:

Suppose you are submitting a legacy OTLP Summary metric, request.response_time.summary, from one web server. Suppose in a given collection period, the web server reports the metric with the values [1,1,1,2,2,2,3,3]. The following metrics would be reported, if min, max, and median quantiles are enabled: