Metrics

Supported Metric Types

Dash0 supports all standard OpenTelemetry metric types, with the exception of exponential histograms. The supported types include:

1. Counter: Monotonically increasing values that track the occurrence of events
2. Gauge: Values that can increase or decrease over time
3. Histogram: Samples observations into configurable buckets
4. Summary (Legacy): Similar to histograms but with client-side calculated quantiles

Reference: OTLP Metric Data Model

Compatibility with Time Series (Prometheus)

Dash0 fully adheres to the official OpenTelemetry and Prometheus metric compatibility standards, ensuring that your existing metrics infrastructure integrates seamlessly with our platform. Prometheus metrics need to be converted to the OTLP format before sending them to Dash0. This conversion can typically be done using the Prometheus receiver in the OpenTelemetry Collector.

Reference: Prometheus Metric points to OTLP

Querying Metrics in Dash0

Dash0 provides a flexible query interface that supports standard PromQL syntax. Since OTLP naming convention and Prometheus metric naming convention are different, Dash0 extends PromQL with the ability to query OpenTelemetry metrics through label matchers:

• OTLP metric names can be queried using the otel_metric_nameCopy label.
• Prometheus-compatible labels are created from OTLP attributes by replacing all non-alphanumeric characters with an underscore _Copy.

Example:

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{otel_metric_name="jvm.memory.used", service_name="catalog", jvm_memory_type="heap"}

For each OTLP metric, Dash0 also generates a Prometheus-compatible metric name. For example, the OTLP metric jvm.memory.usedCopy is mapped to jvm_memory_used_bytesCopy, which can be directly queried in PromQL.

Example:

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jvm_memory_used_bytes{service_name="catalog", jvm_memory_type="heap"}

The promQL query will fail with a parsing error if you use the otel metric name or attribute directly. For instance, the following query will return an unexpected character: '.'Copy parsing error.

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jvm.memory.used.bytes{service.name="catalog", jvm.memory.type="heap"}

Working with Histogram Metrics

Histograms are particularly valuable for measuring distributions of values like request durations, response sizes, or other variable measurements. Dash0 provides comprehensive support for histogram metrics, allowing you to analyze your data in multiple ways:

• Observation count: Query the total number of observations using the _countCopy suffix

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http_request_duration_seconds_count

• Sum of observations: Access the sum of all observed values using the _sumCopy suffix

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http_request_duration_seconds_sum

• Bucketed observations: Examine the distribution of values across predefined buckets using the _bucketCopy suffix. Use the leCopy (less than or equal) label to select specific bucket boundaries. The below query returns the count of observations that fell into buckets with upper bounds less than or equal to 0.5 seconds.

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http_request_duration_seconds_bucket{le="0.5"}

• Histogram Functions: Dash0 also supports various PromQL histogram functions that make it easy to calculate average or percentiles

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histogram_quantile(0.9, rate({otel_metric_name = "http_requests_duration_seconds", otel_metric_type = "histogram"}[5m]))

Working with Summary Metrics

Like histograms, summary metrics track both counts and sums of observations, but they also calculate quantiles on the client side. In Dash0, summary metrics are accessed through:

• Observation count: Using the _countCopy suffix

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http_request_duration_seconds_count

• Sum of observations: Using the _sumCopy suffix

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http_request_duration_seconds_sum

• Quantiles: Directly querying the summary with quantile labels

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{otel_metric_name="http_request_duration_seconds", otel_metric_type="summary", quantile="0.9"}

• Average: Calculate the mean value by dividing the sum by the count

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http_request_duration_seconds_sum / http_request_duration_seconds_count