# Mapping Prometheus Metrics to Datadog Metrics

이 페이지는 아직 한국어로 제공되지 않으며 번역 작업 중입니다. 번역에 관한 질문이나 의견이 있으시면 언제든지 저희에게 연락해 주십시오.

## Overview

This page walks you through how Prometheus or OpenMetrics check metrics map to existing Datadog metric types.

## Prometheus and OpenMetrics metric types

• `counter`: A cumulative metric that represents a single monotonically increasing counter, whose value can only increase—or be reset to zero.
• `gauge`: A metric that represents a single numeric value, which can arbitrarily go up and down.
• `histogram`: Samples observations and counts them in configurable buckets; also provides a sum of all observed values.
• `summary`: Similar to `histogram`; samples observations, provides a sum of all observed values, and calculates configurable quantiles over a sliding time window.

## How Prometheus/OpenMetrics metrics map to Datadog metrics

counter`counter``count`
gauge`gauge``gauge`
histogram`_count`, `_sum`, `_bucket`The `_count`, `_sum`, and `_bucket` values of the histogram are each mapped to Datadog’s `count` type and include a `.count`, `.sum`, and `.bucket` suffix, respectively.
summary`_count`, `_sum`, `_created`The `_count` and `_sum` values are mapped to Datadog’s `count` type and include a `.count` and `.sum` suffix in their name, respectively. Quantile samples are mapped to a metric of type `gauge` with the `.quantile` suffix.

### Histogram

For Prometheus/OpenMetrics `histogram`, the `_count`, `_sum`, and `_bucket` values of the histogram are each mapped to Datadog’s `count` type and include a `.count`, `.sum`, and `.bucket` suffix in their names, respectively.

If the `histogram_buckets_as_distributions` parameter is `true`, `_bucket` samples are aggregated into a Datadog `distribution`. Datadog distribution metrics are based on the DDSketch algorithm and allow for more advanced statistical aggregations such as quantiles. For more information, see the Datadog Engineering Blog post on OpenMetrics and distribution metrics.

`collect_counters_with_distributions` can be used to send `_count` and `_sum` values as `count`s alongside the distribution.

### Summary

For Prometheus/OpenMetrics `summary`, `_count` and `_sum` values are mapped to Datadog’s `count` type and include a `.count` and `.sum` suffix in their name, respectively. Quantile samples are mapped to a metric of type `gauge` with the `.quantile` suffix.

### Counter

By default, Prometheus/OpenMetrics `counter` maps to Datadog’s `count`.

However, if the parameter `send_monotonic_counter` is `false`, then this metric is sent as `gauge`.

### Gauge

Prometheus/OpenMetrics `gauge` maps to Datadog’s `gauge`.

### Histogram

For Prometheus/OpenMetrics `histogram`, the `_count` and `_sum` values of the histogram are each mapped to Datadog’s `gauge` type and include a `.count` and `.sum` suffix in their name, respectively.

If the `send_histograms_buckets` parameter is `true`, `_bucket` samples are sent to Datadog with a `.bucket` suffix, and are also mapped to Datadog’s `gauge` by default.

Setting the `send_distribution_counts_as_monotonic` parameter to `true` causes the `_count` and `_bucket` metrics to be sent as type `count` instead. Setting `send_distribution_sums_as_monotonic` does the same for `_sum` metrics.

If the `send_distribution_buckets` parameter is `true`, `_bucket` samples are aggregated into a Datadog `distribution`. Datadog distribution metrics are based on the DDSketch algorithm, and allow for more advanced statistical aggregations such as quantiles. For more information, see the Datadog Engineering Blog post on OpenMetrics and distribution metrics.

### Summary

For Prometheus/OpenMetrics `summary`, `_count` and `_sum` values are mapped to Datadog’s `gauge` type by default, and include a `.count` and `.sum` suffix in their names, respectively. Quantile samples are mapped to a metric of type `gauge` with the `.quantile` suffix.

Setting the `send_distribution_counts_as_monotonic` parameter to `true` causes the `_count` and `_sum` metrics to be sent as type `count` instead. Setting `send_distribution_sums_as_monotonic` does the same for `_sum` metrics.

All `count` metrics are processed by the Agent as monotonic counts, meaning the Agent actually sends the difference between consecutive raw values. For more information, see Metric Submission: Custom Agent Check.