# Metrics in OpenTelemetry for beginners: easily explained

This document is an introduction to Metrics in OpenTelemetry for beginners. It briefly explains what metrics are in the context of OpenTelemetry, describes the types of metrics in OpenTelemetry,  and outlines their lifecycle from generation to export to a metric storage, such as Prometheus, InfluxDB, and many others.

## What is OpenTelemetry

**OpenTelemetry** is an observability framework for generating and delivering telemetry data, such as metrics, logs, and traces, to an Observability storage.

## Benefits of using OpenTelemetry for metrics

When you build an OpenTelemetry infrastructure for metrics, you can enjoy the following benefits:

* Support of multiple programming languages
    
* Easy integration with different **metric backends** (storages)
    
* Flexibility across environments — Kubernetes, clouds, and bare metal
    
* **Unified** infrastructure for metrics, logs, and traces
    

## What is a metric?

**A metric** is a [time-series](https://en.wikipedia.org/wiki/Time_series) measure of a process or a state within software. Engineers use metrics to monitor software health, performance, and business efficiency.

## What kind of metrics exist in OpenTelemetry?

### Gauge

A Gauge is an instantaneous snapshot of data at each point in time.

For example, it can be the number of processes on the machine or the CPU temperature.

![Metrics in OpenTelemetry for beginners: easily explained gauge metrics](https://cdn.hashnode.com/res/hashnode/image/upload/v1761927806478/a6d857d5-3862-4e6d-9c27-20bb581f4ec0.png align="center")

### Sum

Sum is a numeric metric that represents the cumulative total of all reported measurements over a time interval or a cumulative value from the beginning of time. For example, it can be the total amount of requests served by a service in a minute (1), or a cumulative value of requests from the start (2).

![Metrics in OpenTelemetry for beginners: easily explained Sum metric](https://cdn.hashnode.com/res/hashnode/image/upload/v1761928612249/f56e07ba-2aa1-4f18-b416-2c6d4a2177d1.png align="center")

### Histogram

A histogram describes a distribution of metric values grouped into buckets, where each bucket represents a range of values and the count of measurements that fell into that range.  For example, buckets can represent server request latency intervals, and values - the count of requests that fall in the bucket.

![Metrics in OpenTelemetry: easily explained - Histogram](https://cdn.hashnode.com/res/hashnode/image/upload/v1761927888755/461af78c-d641-4e28-91ef-b77c8ea8271c.png align="center")

Each metric in OpenTelemetry contains the following information:

* **Metric name**
    
* **Attributes** - these can be a name of the application that produced a metric, an availability zone, the name of the server, the instrumentation name, and many others
    
* **Value type** of the point (integer, floating point, etc.)
    
* **Unit of measurement**
    
* **Points at each time interval**
    

Read more about the Metrics data model in OpenTelemetry in the official [documentation](https://opentelemetry.io/docs/specs/otel/metrics/data-model).

## How the OpenTelemetry metrics pipeline works

Let’s look at the metrics lifecycle within the Observability framework.

![Metrics in OpenTelemetry for beginners: easily explained - data flow](data:image/png;base64,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 align="center")

1. ### Metrics generation
    

Typically, your application needs to generate metrics. To do that, you can use a corresponding **OpenTelemetry SDK** – a set of tools for generating telemetry data and sending it to the OpenTelemetry Collector.

2. ### Metrics collection
    

The next step is exporting metrics to the [OpenTelemetry collector](https://opentelemetry.io/docs/collector/). OpenTelemetry Collector can collect telemetry data from multiple applications and process it all together. This step is not necessary - the application can send metrics directly to the Metrics Backend, especially if you are just trying it out.

3. ### Processing in the collector
    

OpenTelemetry Collector can [transform metrics](https://opentelemetry.io/docs/collector/transforming-telemetry/) before sending them to the Metrics backend. Transformation involves filtering, adding, or deleting attributes, as well as renaming metrics. You can write [your own processor](https://opentelemetry.io/docs/collector/building/) if you have a specific need.

4. ### Export to the Backend
    

Finally, OpenTelemetry Collector exports your metrics to the Metrics Backend.

<details data-node-type="hn-details-summary"><summary>Note</summary><div data-type="detailsContent">This is a simplified architecture. In real-world scenarios, the data pipeline might include multiple collectors, such as Kafka.</div></details>

## How OpenTelemetry instrumentation generates Metrics

### For the server application

The exact options for generating metrics depend on the chosen programming language, but the OpenTelemetry community maintains a unified approach. **Opentelemetry instrumentation** is a set of libraries, SDKs, and API for generating metrics and other telemetry data.

Two kinds of instrumentation are generally available:

* **Code instrumentation**. Check the development status and availability for your language [here](https://opentelemetry.io/docs/languages/#status-and-releases).
    
* **Zero-code** **instrumentation**. For some programming languages, you can instrument your application without touching the code. Check the availability of zero-code instrumentation for your language [here](https://opentelemetry.io/docs/zero-code/). It means that once Zero-code instrumentation is configured, some common use-case metrics work out of the box, and it can also mean that you can use a metric-generation library integrated with OpenTelemetry to produce OpenTelemetry metrics. An example of such a library is [Java Micrometers](https://micrometer.io/).
    

In a generic scenario, instrumentation generates metrics and other telemetry data and sends them to the OpenTelemetry collector using the [OpenTelemetry (OTLP) protocol](https://github.com/open-telemetry/opentelemetry-proto/tree/main/docs). At this stage, metrics contain information, defined in the [OpenTelemetry data model](https://opentelemetry.io/docs/specs/otel/metrics/data-model/#opentelemetry-protocol-data-model).

OpenTelemetry instrumentation has flexible configuration options; in most cases, it utilizes environment variables that follow the same naming specifications across multiple programming languages. Check the [OpenTelemetry Exporter Configuration](https://opentelemetry.io/docs/specs/otel/protocol/exporter/) and [Environment variable specification](https://opentelemetry.io/docs/specs/otel/configuration/sdk-environment-variables/) to find out more.

### Other platforms

OpenTelemetry also has a set of instrumentations for specific platforms, which don’t run as a server application, such as:

* [AWS lambda](https://opentelemetry.io/docs/platforms/faas/)
    
* [Kubernetes infrastructure](https://opentelemetry.io/docs/platforms/kubernetes/)
    
* [Client-side application](https://opentelemetry.io/docs/platforms/client-apps/)
    

## How OpenTelemetry collector collects Metrics

OpenTelemetry Collector collects metrics from multiple applications. The canonical approach is to collect telemetry data using [`otlpreceiver`](https://github.com/open-telemetry/opentelemetry-collector/tree/main/receiver/otlpreceiver) via the **OTLP protocol**, mentioned earlier; however, in principle, it can collect metrics in any format if a corresponding receiver exists.

An example of such a receiver is [Prometheus-Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/prometheusreceiver). Instead of instrumenting your application, you can implement the exposure of Prometheus metrics in it, and configure the OpenTelemetry collector to collect these Prometheus metrics via the Prometheus protocol. Several receivers exist in the [opentelemetry-collector-contrib](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver) repository, and you can also implement your own receiver if needed.

## How OpenTelemetry collector processes Metrics

The OpenTelemetry collector can transform metrics using [processors](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor).

Some useful processors include:

* [k8sattributesprocessor](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/k8sattributesprocessor): enrich telemetry data with Kubernetes information, such as cluster name, region, deployment name, etc.
    
* [filterprocessor](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/filterprocessor): filter out incorrect data
    
* [attributeprocessor](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/attributesprocessor): add, rename, and delete metric attributes
    

You can also write a custom processor if you have specific needs.

## Destinations where metrics can be exported

An OpenTelemetry collector can write metrics into any metric storage that supports OTLP format, such as. But, as always, it is not limited to that. It can export data in any format if a corresponding processor exists.

Example of metric exporters:

* [prometheusremotewriteexporter](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/prometheusremotewriteexporter): can write metrics to a storage that supports the Prometheus Remote Write protocol, such as Uptrace, VictoriaMetrics, Thanos, and others
    
* [influxdbexporter](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/influxdbexporter): write metrics to InfluxDb
    
* [clickhoseexporter](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/clickhouseexporter): write metrics to clickhouse
    

## Conclusion

This is a brief overview of how metrics work in OpenTelemetry, and you are now ready to try it out in real- world scenarios. Do you plan to set up OpenTelemetry Collector in production? Learn [how to choose the OpenTelemetry collector distribution](https://elenanur.dev/how-to-choose-the-opentelemetry-collector-distribution)
