The New Relic platform reports four main telemetry data types: metrics, events, logs, and traces.
This doc will help you understand some details about the structure of our Metric
data type. This data type is also referred to as "dimensional metrics." For a high level explanation of this data type, see Metric data type. And for how to query this type of data, see Query metrics.
Better understanding the details of the Metric
structure will help you understand what you see in New Relic, and help you create custom queries and charts of your data. The more you use advanced New Relic features, like reporting custom metrics and creating custom charts, the more this knowledge will come in handy.
Metric types
The metric type
determines how the data is aggregated over longer time windows and determines what fields and functions are available to analyze and query.
Metric types | Supported APIs | Description | Available query functions |
---|---|---|---|
| Measures the number of occurrences of an event. The count should be reset to You must specify a value for The value must be a positive double. Generally, you want to take the rate of the sum:
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| Equivalent to the | ||
| ✅ New Relic Events to Metrics (delta only) | Tracks the statistical distribution on a numeric attribute. This metric is re-aggregatable. For example, 1-minute data points from 60 minutes can be aggregated into a 1-hour data point, without degradation on accuracy. This type:
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| Represents a value that can increase or decrease with time. Examples of gauges include the temperature, CPU usage, and memory. For example, there is always a temperature, but you are periodically taking the temperature and reporting it. The value must fit into the range of a Java double. |
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| ✅ OpenTelemetry (legacy) | Used to report pre-aggregated data, or information on aggregated discrete events. A summary includes a You must specify a value for |
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| ❌ OpenTelemetry (legacy) | Tracks the number of unique values on a string or numeric attribute. This metric is re-aggregatable. For example, 1-minute data points from 60 minutes can be aggregated into a 1-hour data point, without degradation on accuracy. This type is generated only via the event-to-metrics service. |