# Table of Contents

- [Grafana Alloy | Grafana Alloy documentation](#grafana-alloy-grafana-alloy-documentation)
- [Supported platforms | Grafana Alloy documentation](#supported-platforms-grafana-alloy-documentation)
- [Introduction to Grafana Alloy | Grafana Alloy documentation](#introduction-to-grafana-alloy-grafana-alloy-documentation)
- [Community components | Grafana Alloy documentation](#community-components-grafana-alloy-documentation)
- [Estimate Grafana Alloy resource usage | Grafana Alloy documentation](#estimate-grafana-alloy-resource-usage-grafana-alloy-documentation)
- [Get started | Grafana Alloy documentation](#get-started-grafana-alloy-documentation)
- [Alloy configuration syntax | Grafana Alloy documentation](#alloy-configuration-syntax-grafana-alloy-documentation)
- [Release cadence | Grafana Alloy documentation](#release-cadence-grafana-alloy-documentation)
- [Grafana Alloy backward compatibility | Grafana Alloy documentation](#grafana-alloy-backward-compatibility-grafana-alloy-documentation)
- [Component controller | Grafana Alloy documentation](#component-controller-grafana-alloy-documentation)
- [Components | Grafana Alloy documentation](#components-grafana-alloy-documentation)
- [Custom components | Grafana Alloy documentation](#custom-components-grafana-alloy-documentation)
- [Set up Grafana Alloy | Grafana Alloy documentation](#set-up-grafana-alloy-grafana-alloy-documentation)
- [Install Grafana Alloy | Grafana Alloy documentation](#install-grafana-alloy-grafana-alloy-documentation)
- [Clustering | Grafana Alloy documentation](#clustering-grafana-alloy-documentation)
- [Modules | Grafana Alloy documentation](#modules-grafana-alloy-documentation)
- [Deploy Grafana Alloy | Grafana Alloy documentation](#deploy-grafana-alloy-grafana-alloy-documentation)
- [Migrate to Grafana Alloy | Grafana Alloy documentation](#migrate-to-grafana-alloy-grafana-alloy-documentation)
- [Run Grafana Alloy | Grafana Alloy documentation](#run-grafana-alloy-grafana-alloy-documentation)
- [Configure Grafana Alloy clustering in an existing installation | Grafana Alloy documentation](#configure-grafana-alloy-clustering-in-an-existing-installation-grafana-alloy-documentation)
- [Set up meta-monitoring to collect Alloy telemetry | Grafana Alloy documentation](#set-up-meta-monitoring-to-collect-alloy-telemetry-grafana-alloy-documentation)
- [Configure Grafana Alloy | Grafana Alloy documentation](#configure-grafana-alloy-grafana-alloy-documentation)
- [Receive traces and metrics from Datadog-instrumented applications | Grafana Alloy documentation](#receive-traces-and-metrics-from-datadog-instrumented-applications-grafana-alloy-documentation)
- [Configure Grafana Alloy on Windows | Grafana Alloy documentation](#configure-grafana-alloy-on-windows-grafana-alloy-documentation)
- [Collect Kubernetes logs and forward them to Loki | Grafana Alloy documentation](#collect-kubernetes-logs-and-forward-them-to-loki-grafana-alloy-documentation)
- [Configure Grafana Alloy on Linux | Grafana Alloy documentation](#configure-grafana-alloy-on-linux-grafana-alloy-documentation)
- [Configure Grafana Alloy on Kubernetes | Grafana Alloy documentation](#configure-grafana-alloy-on-kubernetes-grafana-alloy-documentation)
- [Collect OpenTelemetry data and forward it to any OpenTelemetry-compatible endpoint | Grafana Alloy documentation](#collect-opentelemetry-data-and-forward-it-to-any-opentelemetry-compatible-endpoint-grafana-alloy-documentation)
- [Collect and forward data with Grafana Alloy | Grafana Alloy documentation](#collect-and-forward-data-with-grafana-alloy-grafana-alloy-documentation)
- [Monitor Microsoft Windows servers and desktops with Grafana Alloy | Grafana Alloy documentation](#monitor-microsoft-windows-servers-and-desktops-with-grafana-alloy-grafana-alloy-documentation)
- [Run Alloy as a non-root user in Kubernetes | Grafana Alloy documentation](#run-alloy-as-a-non-root-user-in-kubernetes-grafana-alloy-documentation)
- [Configure Grafana Alloy on macOS | Grafana Alloy documentation](#configure-grafana-alloy-on-macos-grafana-alloy-documentation)
- [Collect Prometheus metrics | Grafana Alloy documentation](#collect-prometheus-metrics-grafana-alloy-documentation)
- [Monitor metrics and logs with Grafana Alloy | Grafana Alloy documentation](#monitor-metrics-and-logs-with-grafana-alloy-grafana-alloy-documentation)

---

# Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Grafana Alloy

Open source

Grafana Alloy
=============

Grafana Alloy combines the strengths of the leading collectors into one place. Whether observing applications, infrastructure, or both, Grafana Alloy can collect, process, and export telemetry signals to scale and future-proof your observability approach.

* * *

Overview
========

[](/media/docs/alloy/alloy_diagram_v2.svg)

**Collect all your telemetry with one product**

Choosing the right tools to collect, process, and export telemetry data can be a confusing and costly experience. The broad range of telemetry you need to process and the collectors you choose can vary widely depending on your observability goals. In addition, you face the challenge of addressing the constantly evolving needs of your observability strategy. For example, you may initially only need application observability, but you then discover that you must add infrastructure observability. Many organizations manage and configure multiple collectors to address these challenges, introducing more complexity and potential errors in their obervability strategy.

**All signals, whether application, infrastructure, or both**

Grafana Alloy has native pipelines for leading telemetry signals, such as Prometheus and OpenTelemetry, and databases such as Loki and Pyroscope. This permits logs, metrics, traces, and even mature support for profiling.

**Enterprise strength observability**

Grafana Alloy improves reliability and provides advanced features for Enterprise needs, such as clusters of fleets and balancing workloads. Grafana [Fleet Management](/docs/grafana-cloud/send-data/fleet-management/)
 helps you manage multiple Grafana Alloy deployments at scale.

Explore
-------

[Introduction to Alloy\
\
Learn about what Alloy can do for you.](./introduction/)
[Install Alloy\
\
Learn how to install and uninstall Alloy on Docker, Kubernetes, Linux, macOS, or Windows.](./set-up/install/)
[Run Alloy\
\
Learn how to start, restart, and stop Alloy after you have installed it.](./set-up/run/)
[Configure Alloy\
\
Learn how to configure Alloy on Kubernetes, Linux, macOS, or Windows.](./configure/)
[Migrate to Alloy\
\
Learn how to migrate to Alloy from Grafana Agent Operator, Prometheus, Promtail, Grafana Agent Static, or Grafana Agent Flow.](./set-up/migrate/)
[Collect OpenTelemetry data\
\
Learn how to configure OpenTelemetry data delivery, configure batching, and receive OpenTelemetry data over OTLP.](./collect/opentelemetry-data/)
[Concepts\
\
Learn about components, modules, clustering, and the Alloy configuration syntax.](./get-started/)
[Reference\
\
Read the reference documentation about the command line tools, configuration blocks, components, and standard library.](./reference/)

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[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Supported platforms | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Introduction](/docs/alloy/latest/introduction/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Supported platforms

Open source

Supported platforms
===================

The following operating systems and hardware architecture are supported.

Linux
-----

*   Architectures: AMD64, ARM64
*   Within the Linux distribution lifecycle

Windows
-------

*   Minimum version: Windows Server 2016 or later, or Windows 10 or later.
*   Architectures: AMD64

macOS
-----

*   Minimum version: macOS 10.13 or later
*   Architectures: AMD64 on Intel, ARM64 on Apple Silicon

FreeBSD
-------

*   Within the FreeBSD lifecycle
*   Architectures: AMD64

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[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/introduction/supported-platforms.md)
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[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Introduction to Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Introduction

Open source [RSS](/docs/alloy/latest/introduction/index.xml)

Introduction to Grafana Alloy
=============================

Alloy is a flexible, high performance, vendor-neutral distribution of the [OpenTelemetry](https://opentelemetry.io/ecosystem/distributions/)
 Collector. It’s fully compatible with the most popular open source observability standards such as OpenTelemetry and Prometheus.

Alloy focuses on ease-of-use and the ability to adapt to the needs of power users.

Key features
------------

Some of the key features of Alloy include:

*   **Custom components:** You can use Alloy to create and share custom components. Custom components combine a pipeline of existing components into a single, easy-to-understand component that’s just a few lines long. You can use pre-built custom components from the community, ones packaged by Grafana, or create your own.
*   **Reusable components:** You can use the output of a component as the input for multiple other components.
*   **Chained components:** You can chain components together to form a pipeline.
*   **Single task per component:** The scope of each component is limited to one specific task.
*   **GitOps compatibility:** Alloy uses frameworks to pull configurations from Git, S3, HTTP endpoints, and just about any other source.
*   **Clustering support:** Alloy has native clustering support. Clustering helps distribute the workload and ensures you have high availability. You can quickly create horizontally scalable deployments with minimal resource and operational overhead.
*   **Security:** Alloy helps you manage authentication credentials and connect to HashiCorp Vaults or Kubernetes clusters to retrieve secrets.
*   **Debugging utilities:** Alloy provides troubleshooting support and an embedded [user interface](../troubleshoot/debug/)
     to help you identify and resolve configuration problems.

How does Alloy work as an OpenTelemetry collector?
--------------------------------------------------

[](/media/docs/alloy/flow-diagram-small-alloy.png)

### Collect

Alloy uses more than 120 components to collect telemetry data from applications, databases, and OpenTelemetry collectors. Alloy supports collection using multiple ecosystems, including OpenTelemetry and Prometheus.

Telemetry data can be either pushed to Alloy, or Alloy can pull it from your data sources.

### Transform

Alloy processes data and transforms it for sending.

You can use transformations to inject extra metadata into telemetry or filter out unwanted data.

### Write

Alloy sends data to OpenTelemetry-compatible databases or collectors, the Grafana stack, or Grafana Cloud.

Alloy can also write alerting rules in compatible databases.

Next steps
----------

*   [Install](../set-up/install/)
     Alloy.
*   Learn about the core [Concepts](../get-started/)
     of Alloy.
*   Follow the [tutorials](../tutorials/)
     for hands-on learning about Alloy.
*   Learn how to [collect and forward data](../collect/)
     with Alloy.
*   Check out the [reference](../reference/)
     documentation to find information about the Alloy components, configuration blocks, and command line tools.

Was this page helpful?
----------------------

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[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/introduction/_index.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/introduction/_index.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Community components | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Get started](/docs/alloy/latest/get-started/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Community components

Open source

Community components
====================

**Community components** are [components](../components/)
 implemented and maintained by the community.

While Grafana doesn’t offer commercial support for these components, they still undergo acceptance and review by the Alloy development team before being added to the repository.

To use these community components, you must explicitly pass the `--feature.community-components.enabled` flag to the `run` command.

**Community components** don’t have a stability level. They aren’t covered by the [backward compatibility strategy](../../introduction/backward-compatibility/)
.

> Warning
> 
> **Community components** without a maintainer may be disabled or removed if the components prevent or block the development of Alloy.

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[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Estimate Grafana Alloy resource usage | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Introduction](/docs/alloy/latest/introduction/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Estimate resource usage

Open source

Estimate Grafana Alloy resource usage
=====================================

This page provides guidance for expected resource usage of Alloy for each telemetry type, based on operational experience of some of the Alloy maintainers.

> Note
> 
> The resource usage depends on the workload, hardware, and the configuration used. The information on this page is a good starting point for most users, but your actual usage may be different.

Prometheus metrics
------------------

The Prometheus metrics resource usage depends mainly on the number of active series that need to be scraped and the scrape interval.

As a rule of thumb, **per each 1 million active series** and with the default scrape interval, you can expect to use approximately:

*   0.4 CPU cores
*   11 GiB of memory
*   1.5 MiB/s of total network bandwidth, send and receive

These recommendations are based on deployments that use [clustering](../../get-started/clustering/)
, but they broadly apply to other deployment modes. Refer to [Deploy Grafana Alloy](../../set-up/deploy/)
 for more information on how to deploy Alloy.

Loki logs
---------

Loki logs resource usage depends mainly on the volume of logs ingested.

As a rule of thumb, **per each 1 MiB/second of logs ingested**, you can expect to use approximately:

*   1 CPU core
*   120 MiB of memory

These recommendations are based on Kubernetes DaemonSet deployments on clusters with relatively small number of nodes and high logs volume on each. The resource usage can be higher per each 1 MiB/second of logs if you have a large number of small nodes due to the constant overhead of running the Alloy on each node.

Additionally, factors such as number of labels, number of files and average log line length may all play a role in the resource usage.

Pyroscope profiles
------------------

Pyroscope profiles resource usage depends mainly on the volume of profiles.

As a rule of thumb, **per each 100 profiles/second**, you can expect to use approximately:

*   1 CPU core
*   10 GiB of memory

Factors such as size of each profile and frequency of fetching them also play a role in the overall resource usage.

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----------------------

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[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Get started | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Get started

Open source [RSS](/docs/alloy/latest/get-started/index.xml)

Get started
===========

This section helps you get started with Grafana Alloy.

*   [Alloy configuration syntax](/docs/alloy/latest/get-started/configuration-syntax/)
    
*   [Community components](/docs/alloy/latest/get-started/community_components/)
    
*   [Components](/docs/alloy/latest/get-started/components/)
    
*   [Component controller](/docs/alloy/latest/get-started/component_controller/)
    
*   [Custom components](/docs/alloy/latest/get-started/custom_components/)
    
*   [Modules](/docs/alloy/latest/get-started/modules/)
    
*   [Clustering](/docs/alloy/latest/get-started/clustering/)
    

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[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Alloy configuration syntax | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Get started](/docs/alloy/latest/get-started/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Configuration syntax

Open source [RSS](/docs/alloy/latest/get-started/configuration-syntax/index.xml)

Alloy configuration syntax
==========================

Grafana Alloy dynamically configures and connects components with the Alloy configuration syntax. Alloy handles the collection, transformation, and delivery of telemetry data. Each component in the configuration handles one of those tasks or specifies how data flows and how the components are bound together.

[](/media/docs/alloy/flow-diagram-small-alloy.png)

The following simple example shows the basic concepts and how an Alloy configuration comes together into a pipeline.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    // Collection: mount a local directory with a certain path spec
    local.file_match "applogs" {
        path_targets = [{"__path__" = "/tmp/app-logs/app.log"}]
    }
    
    // Collection: Take the file match as input, and scrape those mounted log files
    loki.source.file "local_files" {
        targets    = local.file_match.applogs.targets
    
        // This specifies which component should process the logs next, the "link in the chain"
        forward_to = [loki.process.add_new_label.receiver]
    }
    
    // Transformation: pull some data out of the log message, and turn it into a label
    loki.process "add_new_label" {
        stage.logfmt {
            mapping = {
                "extracted_level" = "level",
            }
        }
    
        // Add the value of "extracted_level" from the extracted map as a "level" label
        stage.labels {
            values = {
                "level" = "extracted_level",
            }
        }
    
        // The next link in the chain is the local_loki "receiver" (receives the telemetry)
        forward_to = [loki.write.local_loki.receiver]
    }
    
    // Anything that comes into this component gets written to the loki remote API
    loki.write "local_loki" {
        endpoint {
            url = "http://loki:3100/loki/api/v1/push"
        }
    }

The Alloy syntax aims to reduce errors in configuration files by making configurations easier to read and write. The Alloy syntax uses blocks, attributes, and expressions. You can copy and paste the blocks from the documentation to help you get started as quickly as possible.

The Alloy syntax is declarative, so ordering components, blocks, and attributes doesn’t matter. The relationship between components determines the order of operations in the pipeline.

Blocks
------

You use _Blocks_ to configure components and groups of attributes. Each block can contain any number of attributes or nested blocks. Blocks are steps in the overall pipeline expressed by the configuration.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    prometheus.remote_write "default" {
      endpoint {
        url = "http://localhost:9009/api/prom/push"
      }
    }

The preceding example has two blocks:

*   `prometheus.remote_write "default"`: A labeled block which instantiates a `prometheus.remote_write` component. The label is the string `"default"`.
*   `endpoint`: An unlabeled block inside the component that configures an endpoint to send metrics to. This block sets the `url` attribute to specify the endpoint.

Attributes
----------

You use _Attributes_ to configure individual settings. Attributes always take the form of `ATTRIBUTE_NAME = ATTRIBUTE_VALUE`.

The following example shows how to set the `log_level` attribute to `"debug"`.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    log_level = "debug"

Expressions
-----------

You use expressions to compute the value of an attribute. The simplest expressions are constant values like `"debug"`, `32`, or `[1, 2, 3, 4]`. The Alloy syntax supports complex expressions, for example:

*   Referencing the exports of components: `local.file.password_file.content`
*   Mathematical operations: `1 + 2`, `3 * 4`, `(5 * 6) + (7 + 8)`
*   Equality checks: `local.file.file_a.content == local.file.file_b.content`
*   Calling functions from Alloy’s standard library: `sys.env("HOME")` retrieves the value of the `HOME` environment variable.

You can use expressions for any attribute inside a component definition.

### Reference component exports

The most common expression is to reference the exports of a component, for example, `local.file.password_file.content`. You form a reference to a component’s exports by merging the component’s name (for example, `local.file`), label (for example, `password_file`), and export name (for example, `content`), delimited by a period.

Configuration syntax design goals
---------------------------------

Alloy is:

*   _Fast_: The configuration language is fast, so the component controller can quickly evaluate changes.
*   _Simple_: The configuration language is easy to read and write to minimize the learning curve.
*   _Easy to debug_: The configuration language gives detailed information when there’s a mistake in the configuration file.

The Alloy configuration syntax is a distinct language with custom syntax and features, such as first-class functions.

*   Blocks are a group of related settings and usually represent creating a component. Blocks have a name that consists of zero or more identifiers separated by `.`, an optional user label, and a body containing attributes and nested blocks.
*   Attributes appear within blocks and assign a value to a name.
*   Expressions represent a value, either literally or by referencing and combining other values. You use expressions to compute a value for an attribute.

Tooling
-------

You can use one or all of the following tools to help you write Alloy configuration files.

*   Editor support for:
    *   [VSCode](https://github.com/grafana/vscode-alloy)
        
    *   [Vim/Neovim](https://github.com/grafana/vim-alloy)
        
*   Code formatting using the [`alloy fmt` command](../../reference/cli/fmt/)
    

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---

# Release cadence | Grafana Alloy documentation

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Open source

Release cadence
===============

A new minor release is planned every six weeks for Alloy.

The release cadence is best-effort. If necessary, releases may be performed outside of this cadence, or a scheduled release date can be moved forwards or backwards.

Minor releases published on cadence include updating dependencies for upstream OpenTelemetry Collector code if new versions are available. Minor releases published outside of the release cadence may not include these dependency updates.

Patch and security releases may be created at any time.

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Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Grafana Alloy backward compatibility | Grafana Alloy documentation

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Open source

Grafana Alloy backward compatibility
====================================

Grafana Alloy follows [semantic versioning](https://semver.org/)
. [Exceptions](#exceptions)
 may arise that require a breaking change without a new major version. Alloy is stable, and we strive to maintain backward compatibility between minor and patch versions.

Documented functionality that’s released as _Generally available_ is covered by backward compatibility, including:

*   **User configuration**, including the Alloy configuration syntax, the semantics of the configuration file, and the command-line interface.
    
*   **APIs**, for any network or code API released as v1.0.0 or later.
    
*   **Observability data used in official dashboards**, where the official set of dashboards are found in the [`alloy-mixin/`](https://github.com/grafana/alloy/tree/main/operations/alloy-mixin)
     directory.
    

Exceptions
----------

We strive to maintain backward compatibility, but there are situations that may arise that require a breaking change without a new major version, deviating from [item 8 of the semver specification](https://semver.org/spec/v2.0.0.html#spec-item-8)
:

*   **Security**: A security issue may arise that requires breaking compatibility.
    
*   **Legal requirements**: If an exposed behavior violates a licensing or legal requirement, a breaking change may be required.
    
*   **Specification errors**: If a specification for a feature is found to be incomplete or inconsistent, fixing the specification may require a breaking change.
    
*   **Bugs**: If a bug is found that goes against the documented specification of that functionality, fixing the bug may require breaking compatibility for users who are relying on the incorrect behavior.
    
*   **Upstream changes**: Much of the functionality of Alloy is built on top of other software, such as OpenTelemetry Collector and Prometheus. If upstream software breaks compatibility, we may need to reflect this in Alloy.
    
*   **Community components**: Community components are components implemented and maintained by the community. They aren’t covered by the backward compatibility strategy.
    

We try, whenever possible, to resolve these issues without breaking compatibility.

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-----------------------------------

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\
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[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Component controller | Grafana Alloy documentation

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Open source

Component controller
====================

The _component controller_ is the core part of Alloy which manages components at runtime.

The component controller is responsible for:

*   Reading and validating the configuration file.
*   Managing the lifecycle of defined components.
*   Evaluating the arguments used to configure components.
*   Reporting the health of defined components.

Component graph
---------------

A relationship between [components](../components/)
 is created when an expression is used to set the argument of one component to an exported field of another component.

The set of all components and the relationships between them define a [Directed Acyclic Graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph)
 (DAG), which informs the component controller which references are valid and in what order components must be evaluated.

For a configuration file to be valid, components must not reference themselves or contain a cyclic reference.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    // INVALID: local.file.some_file can not reference itself:
    local.file "self_reference" {
      filename = local.file.self_reference.content
    }

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    // INVALID: cyclic reference between local.file.a and local.file.b:
    local.file "a" {
      filename = local.file.b.content
    }
    local.file "b" {
      filename = local.file.a.content
    }

Component evaluation
--------------------

A component is _evaluated_ when its expressions are computed into concrete values. The computed values configure the component’s runtime behavior. The component controller is finished loading once all components are evaluated, configured, and running.

The component controller only evaluates a given component after evaluating all of that component’s dependencies. Components that don’t depend on other components can be evaluated anytime during the evaluation process.

Component reevaluation
----------------------

A [component](../components/)
 is dynamic. A component can update its exports any number of times throughout its lifetime.

A _controller reevaluation_ is triggered when a component updates its exports. The component controller reevaluates any component that references the changed component, any components that reference those components, and so on, until all affected components are reevaluated.

Component health
----------------

At any given time, a component can have one of the following health states:

1.  Unknown: The default state. The component isn’t running yet.
2.  Healthy: The component is working as expected.
3.  Unhealthy: The component isn’t working as expected.
4.  Exited: The component has stopped and is no longer running.

By default, the component controller determines the health of a component. The component controller marks a component as healthy as long as that component is running and its most recent evaluation succeeded.

Some components can report their own component-specific health information. For example, the `local.file` component reports itself as unhealthy if the file it was watching gets deleted.

The overall health of a component is determined by combining the controller-reported health of the component with the component-specific health information.

An individual component’s health is independent of the health of any other components it references. A component can be marked as healthy even if it references an exported field of an unhealthy component.

Evaluation failures
-------------------

When a component fails to evaluate, it’s marked as unhealthy with the reason for why the evaluation failed.

When an evaluation fails, the component continues operating as normal. The component continues using its previous set of evaluated arguments and can continue exporting new values.

This behavior prevents failure propagation. If your `local.file` component, which watches API keys, suddenly stops working, other components continue using the last valid API key until the component returns to a healthy state.

In-memory traffic
-----------------

Components that expose HTTP endpoints, such as [`prometheus.exporter.unix`](../../reference/components/prometheus/prometheus.exporter.unix)
, can expose an internal address that completely bypasses the network and communicate in-memory. Components within the same process can communicate with one another without needing to be aware of any network-level protections such as authentication or mutual TLS.

The internal address defaults to `alloy.internal:12345`. If this address collides with a real target on your network, change it to something unique using the `--server.http.memory-addr` flag in the [run](../../reference/cli/run/)
 command.

Components must opt-in to using in-memory traffic. Refer to the individual documentation for components to learn if in-memory traffic is supported.

Configuration file updates
--------------------------

The `/-/reload` HTTP endpoint and the `SIGHUP` signal can inform the component controller to reload the configuration file. When this happens, the component controller synchronizes the set of running components with the ones in the configuration file, removing components no longer defined in the configuration file and creating new components added to the configuration file. All components managed by the controller are reevaluated after reloading.

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Incident management with Grafana IRM & SLOS in Grafana Cloud\
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[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Components | Grafana Alloy documentation

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![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Components

Open source

Components
==========

_Components_ are the building blocks of Alloy. Each component handles a single task, such as retrieving secrets or collecting Prometheus metrics.

Components are composed of the following:

*   **Arguments:** Settings that configure a component.
*   **Exports:** Named values that a component exposes to other components.

Each component has a name that describes what that component is responsible for. For example, the `local.file` component is responsible for retrieving the contents of files on disk.

You specify components in the configuration file by first providing the component’s name with a user-specified label, and then by giving arguments to configure the component.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    discovery.kubernetes "pods" {
      role = "pod"
    }
    
    discovery.kubernetes "nodes" {
      role = "node"
    }

You reference components by combining the component name with its label. For example, you can reference a `local.file` component labeled `foo` as `local.file.foo`.

The combination of a component’s name and its label must be unique within the configuration file. Combining component names with a label means you can define multiple instances of a component as long as each instance has a different label value.

Pipelines
---------

Most arguments for a component in a configuration file are constant values, such as setting a `log_level` attribute to the quoted string `"debug"`.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    log_level = "debug"

You use _expressions_ to dynamically compute the value of an argument at runtime. You can use expressions to retrieve the value of an environment variable (`log_level = sys.env("LOG_LEVEL")`) or to reference an exported field of another component (`log_level = local.file.log_level.content`).

You create a dependent relationship when a component’s argument references an exported field of another component. A component’s arguments now depend on another component’s exports. The input of the component is re-evaluated whenever the exports of the components it references are updated.

The flow of data through the set of references between components forms a _pipeline_.

An example pipeline may look like this:

1.  A `local.file` component watches a file that contains an API key.
2.  A `prometheus.remote_write` component is configured to receive metrics and forward them to an external database using the API key from the `local.file` for authentication.
3.  A `discovery.kubernetes` component discovers and exports Kubernetes Pods where metrics can be collected.
4.  A `prometheus.scrape` component references the exports of the previous component, and sends collected metrics to the `prometheus.remote_write` component.

[](/media/docs/alloy/diagram-concepts-example-pipeline.png)

The following configuration file represents the pipeline.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    // Get our API key from disk.
    //
    // This component has an exported field called "content", holding the content
    // of the file.
    //
    // local.file will watch the file and update its exports any time the
    // file changes.
    local.file "api_key" {
      filename  = "/var/data/secrets/api-key"
    
      // Mark this file as sensitive to prevent its value from being shown in the
      // UI.
      is_secret = true
    }
    
    // Create a prometheus.remote_write component, which other components can send
    // metrics to.
    //
    // This component exports a "receiver" value, which can be used by other
    // components to send metrics.
    prometheus.remote_write "prod" {
      endpoint {
        url = "https://prod:9090/api/v1/write"
    
        basic_auth {
          username = "admin"
    
          // Use the password file to authenticate with the production database.
          password = local.file.api_key.content
        }
      }
    }
    
    // Find Kubernetes pods where we can collect metrics.
    //
    // This component exports a "targets" value, which contains the list of
    // discovered pods.
    discovery.kubernetes "pods" {
      role = "pod"
    }
    
    // Collect metrics from Kubernetes pods and send them to prod.
    prometheus.scrape "default" {
      targets    = discovery.kubernetes.pods.targets
      forward_to = [prometheus.remote_write.prod.receiver]
    }

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Related documentation
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Related resources from Grafana Labs
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Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Custom components | Grafana Alloy documentation

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![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Custom components

Open source

Custom components
=================

_Custom components_ are a way to create new components from a pipeline of built-in and other custom components.

A custom component is composed of:

*   _Arguments_: Settings that configure the custom component.
*   _Exports_: Values that a custom component exposes to its consumers.
*   _Components_: Built-in and custom components that are run as part of the custom component.

Create custom components
------------------------

You can create a new custom component using [the `declare` configuration block](../../reference/config-blocks/declare/)
. The label of the block determines the name of the custom component.

The following custom configuration blocks can be used inside a `declare` block:

*   [argument](../../reference/config-blocks/argument/)
    : Create a new named argument, whose current value can be referenced using the expression `argument.NAME.value`. Argument values are determined by the user of a custom component.
*   [export](../../reference/config-blocks/export/)
    : Expose a new named value to custom component users.

Custom components are useful for reusing a common pipeline multiple times. To learn how to share custom components across multiple files, refer to [Modules](../modules/)
.

Example
-------

This example creates a custom component called `add`, which exports the sum of two arguments:

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    declare "add" {
        argument "a" { }
        argument "b" { }
    
        export "sum" {
            value = argument.a.value + argument.b.value
        }
    }
    
    add "example" {
        a = 15
        b = 17
    }
    
    // add.example.sum == 32

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[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Set up Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Set up

Open source [RSS](/docs/alloy/latest/set-up/index.xml)

Set up Grafana Alloy
====================

This section helps you install, migrate, run, and deploy Alloy.

*   [Install Grafana Alloy](/docs/alloy/latest/set-up/install/)
    
*   [Migrate to Grafana Alloy](/docs/alloy/latest/set-up/migrate/)
    
*   [Run Grafana Alloy](/docs/alloy/latest/set-up/run/)
    
*   [Deploy Grafana Alloy](/docs/alloy/latest/set-up/deploy/)
    

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----------------------

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[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Install Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Set up](/docs/alloy/latest/set-up/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Install

Open source [RSS](/docs/alloy/latest/set-up/install/index.xml)

Install Grafana Alloy
=====================

You can install Alloy on Docker, Kubernetes, Linux, macOS, or Windows.

Alloy supports the following architectures:

*   **Linux**: AMD64, ARM64
*   **Windows**: AMD64
*   **macOS**: AMD64 on Intel, ARM64 on Apple Silicon
*   **FreeBSD**: AMD64

> Note
> 
> Installing Alloy on other operating systems is possible, but isn’t recommended or supported.

*   [Run Grafana Alloy in a Docker container](/docs/alloy/latest/set-up/install/docker/)
    
*   [Deploy Grafana Alloy on Kubernetes](/docs/alloy/latest/set-up/install/kubernetes/)
    
*   [Install Grafana Alloy on Linux](/docs/alloy/latest/set-up/install/linux/)
    
*   [Install Grafana Alloy on macOS](/docs/alloy/latest/set-up/install/macos/)
    
*   [Install Grafana Alloy on Windows](/docs/alloy/latest/set-up/install/windows/)
    
*   [Deploy Grafana Alloy on OpenShift](/docs/alloy/latest/set-up/install/openshift/)
    
*   [Install Grafana Alloy with Ansible](/docs/alloy/latest/set-up/install/ansible/)
    
*   [Install Grafana Alloy with Chef](/docs/alloy/latest/set-up/install/chef/)
    
*   [Install Grafana Alloy with Puppet](/docs/alloy/latest/set-up/install/puppet/)
    
*   [Install Grafana Alloy as a standalone binary](/docs/alloy/latest/set-up/install/binary/)
    

Data collection
---------------

By default, Alloy sends anonymous usage information to Grafana Labs. Refer to [data collection](%22../../../../data-collection/)
 for more information about what data Grafana collects and how you can opt-out.

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----------------------

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[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Clustering | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Get started](/docs/alloy/latest/get-started/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Clustering

Open source

Clustering
==========

Clustering enables a fleet of Alloy deployments to work together for workload distribution and high availability. It helps create horizontally scalable deployments with minimal resource and operational overhead.

To achieve this, Alloy makes use of an eventually consistent model that assumes all participating Alloy deployments are interchangeable and converge on using the same configuration file.

The behavior of a standalone, non-clustered Alloy is the same as if it were a single-node cluster.

You configure clustering by passing `cluster` command-line flags to the [run](../../reference/cli/run/#clustering)
 command.

Use cases
---------

### Target auto-distribution

Target auto-distribution is the most basic use case of clustering. It allows scraping components running on all peers to distribute the scrape load between themselves. Target auto-distribution requires that all Alloy deployments in the same cluster can reach the same service discovery APIs and scrape the same targets.

You must explicitly enable target auto-distribution on components by defining a `clustering` block.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    prometheus.scrape "default" {
        clustering {
            enabled = true
        }
    
        ...
    }

A cluster state change is detected when a node joins or a node leaves. All participating components locally recalculate target ownership and re-balance the number of targets they’re scraping without explicitly communicating ownership over the network.

Target auto-distribution allows you to dynamically scale the number of Alloy deployments to distribute workload during peaks. It also provides resiliency because targets are automatically picked up by one of the node peers if a node leaves.

Alloy uses a local consistent hashing algorithm to distribute targets, meaning that, on average, only ~1/N of the targets are redistributed.

Refer to component reference documentation to discover whether it supports clustering, such as:

*   [`prometheus.scrape`](../../reference/components/prometheus/prometheus.scrape/#clustering-block)
    
*   [`pyroscope.scrape`](../../reference/components/pyroscope/pyroscope.scrape/#clustering-block)
    
*   [`prometheus.operator.podmonitors`](../../reference/components/prometheus/prometheus.operator.podmonitors/#clustering-block)
    
*   [`prometheus.operator.servicemonitors`](../../reference/components/prometheus/prometheus.operator.servicemonitors/#clustering-block)
    

Cluster monitoring and troubleshooting
--------------------------------------

You can use the Alloy UI [clustering page](../../troubleshoot/debug/#clustering-page)
 to monitor your cluster status. Refer to [Debug clustering issues](../../troubleshoot/debug/#debug-clustering-issues)
 for additional troubleshooting information.

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----------------------

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[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Modules | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Get started](/docs/alloy/latest/get-started/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Modules

Open source

Modules
=======

A _Module_ is a unit of Alloy configuration, which combines all the other concepts, containing a mix of configuration blocks, instantiated components, and custom component definitions. The module passed as an argument to [the `run` command](../../reference/cli/run/)
 is called the _main configuration_.

Modules can be [imported](#import-modules)
 to enable the reuse of [custom components](../custom_components/)
 defined by that module.

Import modules
--------------

A module can be _imported_, allowing the custom components defined by that module to be used by other modules, called the _importing module_. Modules can be imported from multiple locations using one of the `import` configuration blocks:

*   [`import.file`](../../reference/config-blocks/import.file/)
    : Imports a module from a file on disk.
*   [`import.git`](../../reference/config-blocks/import.git/)
    : Imports a module from a file located in a Git repository.
*   [`import.http`](../../reference/config-blocks/import.http/)
    : Imports a module from the response of an HTTP request.
*   [`import.string`](../../reference/config-blocks/import.string/)
    : Imports a module from a string.

> Warning
> 
> You can’t import a module that contains top-level blocks other than `declare` or `import`.

Modules are imported into a _namespace_ where the top-level custom components of the imported module are exposed to the importing module. The label of the import block specifies the namespace of an import. For example, if a configuration contains a block called `import.file "my_module"`, then custom components defined by that module are exposed as `my_module.CUSTOM_COMPONENT_NAME`. Imported namespaces must be unique across a given importing module.

If an import namespace matches the name of a built-in component namespace, such as `prometheus`, the built-in namespace is hidden from the importing module, and only components defined in the imported module may be used.

> Warning
> 
> If you choose a label that corresponds to an existing component for an `import` or a `declare` block, the component is shadowed and you won’t be able to use it in your configuration. For example, if you use the label `import.file "mimir"`, you won’t be able to use the existing components that start with `mimir` such as `mimir.rules.kubernetes` because it refers to the module imported via the `import` block.

Example
-------

This example module defines a component to filter out debug-level and info-level log lines:

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    declare "log_filter" {
      // argument.write_to is a required argument that specifies where filtered
      // log lines are sent.
      //
      // The value of the argument is retrieved in this file with
      // argument.write_to.value.
      argument "write_to" {
        optional = false
      }
    
      // loki.process.filter is our component which executes the filtering,
      // passing filtered logs to argument.write_to.value.
      loki.process "filter" {
        // Drop all debug- and info-level logs.
        stage.match {
          selector = `{job!=""} |~ "level=(debug|info)"`
          action   = "drop"
        }
    
        // Send processed logs to our argument.
        forward_to = argument.write_to.value
      }
    
      // export.filter_input exports a value to the module consumer.
      export "filter_input" {
        // Expose the receiver of loki.process so the module importer can send
        // logs to our loki.process component.
        value = loki.process.filter.receiver
      }
    }

You can save this module to a file called `helpers.alloy` and import it:

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    // Import our helpers.alloy module, exposing its custom components as
    // helpers.COMPONENT_NAME.
    import.file "helpers" {
      filename = "helpers.alloy"
    }
    
    loki.source.file "self" {
      targets = LOG_TARGETS
    
      // Forward collected logs to the input of our filter.
      forward_to = [helpers.log_filter.default.filter_input]
    }
    
    helpers.log_filter "default" {
      // Configure the filter to forward filtered logs to loki.write below.
      write_to = [loki.write.default.receiver]
    }
    
    loki.write "default" {
      endpoint {
        url = LOKI_URL
      }
    }

Security
--------

Since modules can load an arbitrary configuration from a potentially remote source, it’s important to carefully consider the security of your solution. The best practice is to ensure that the Alloy configuration can’t be changed by attackers. This includes the main Alloy configuration files as well as modules fetched from remote locations such as Git repositories or HTTP servers.

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----------------------

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[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/get-started/modules.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/get-started/modules.md)
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[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Deploy Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Set up](/docs/alloy/latest/set-up/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Deploy

Open source

Deploy Grafana Alloy
====================

Alloy is a flexible, vendor-neutral telemetry collector. This flexibility means that Alloy doesn’t enforce a specific deployment topology but can work in multiple scenarios.

This page lists common topologies used for Alloy deployments, when to consider using each topology, issues you may run into, and scaling considerations.

As a centralized collection service
-----------------------------------

Deploying Alloy as a centralized service is recommended for collecting application telemetry. This topology allows you to use a smaller number of collectors to coordinate service discovery, collection, and remote writing.

[](/media/docs/alloy/collection-diagram-alloy.png)

Using this topology requires deploying Alloy on separate infrastructure, and making sure that they can discover and reach these applications over the network. The main predictor for the size of an Alloy deployment is the number of active Prometheus metrics series it’s scraping. A rule of thumb is approximately 10 KB of memory for each series. We recommend you start looking towards horizontal scaling around the 1 million active series mark.

### Use Kubernetes StatefulSets

Deploying Alloy as a StatefulSet is the recommended option for Prometheus metrics collection. The persistent Pod identifiers make it possible to consistently match volumes with pods so that you can use them for the WAL directory.

You can also use a Kubernetes Deployment in cases where persistent storage isn’t required, such as a traces-only pipeline.

### Pros

*   Straightforward scaling using [clustering](../../configure/clustering/)
    
*   Minimizes the “noisy neighbor” effect
*   Easy to meta-monitor

### Cons

*   Requires running on separate infrastructure

### Use for

*   Scalable telemetry collection

### Don’t use for

*   Host-level metrics and logs

As a host daemon
----------------

Deploying one Alloy instance per machine is required for collecting machine-level Prometheus metrics and logs, such as node\_exporter hardware and network metrics or journald system logs.

[](/media/docs/alloy/host-diagram-alloy.png)

Each Alloy instance requires you to open an outgoing connection for each remote endpoint it’s shipping data to. This can lead to NAT port exhaustion on the egress infrastructure. Each egress IP can support up to (65535 - 1024 = 64511) outgoing connections on different ports. So, if all Alloys are sending Prometheus metrics and log data, an egress IP can support up to 32,255 collectors.

### Use Kubernetes DaemonSets

The simplest use case of the host daemon topology is a Kubernetes DaemonSet, and it’s required for node-level observability (for example cAdvisor metrics) and collecting Pod logs.

### Pros

*   Doesn’t require running on separate infrastructure
*   Typically leads to smaller-sized collectors
*   Lower network latency to instrumented applications

### Cons

*   Requires planning a process for provisioning Alloy on new machines, as well as keeping configuration up to date to avoid configuration drift
*   Not possible to scale independently when using Kubernetes DaemonSets
*   Scaling the topology can strain external APIs (like service discovery) and network infrastructure (like firewalls, proxy servers, and egress points)

### Use for

*   Collecting machine-level Prometheus metrics and logs (for example, node\_exporter hardware metrics, Kubernetes Pod logs)

### Don’t use for

*   Scenarios where Alloy grows so large it can become a noisy neighbor
*   Collecting an unpredictable amount of telemetry

As a container sidecar
----------------------

Deploying Alloy as a container sidecar is only recommended for short-lived applications or specialized Alloy deployments.

[](/media/docs/alloy/sidecar-diagram-alloy.png)

### Use Kubernetes Pod sidecars

In a Kubernetes environment, the sidecar model consists of deploying Alloy as an extra container on the Pod. The Pod’s controller, network configuration, enabled capabilities, and available resources are shared between the actual application and the sidecar Alloy.

### Pros

*   Doesn’t require running on separate infrastructure
*   Straightforward networking with partner applications

### Cons

*   Doesn’t scale separately
*   Makes resource consumption harder to monitor and predict
*   Each Alloy instance doesn’t have a life cycle of its own, making it harder to do things like recovering from network outages

### Use for

*   Serverless services
*   Job/batch applications that work with a push model
*   Air-gapped applications that can’t be otherwise reached over the network

### Don’t use for

*   Long-lived applications
*   Scenarios where the Alloy deployment size grows so large it can become a noisy neighbor

Process different types of telemetry in different Alloy instances
-----------------------------------------------------------------

If the load on Alloy is small, you can process all necessary telemetry signals in the same Alloy process. For example, a single Alloy deployment can process all of the incoming metrics, logs, traces, and profiles.

However, if the load on Alloy is big, it may be beneficial to process different telemetry signals in different deployments of Alloy.

This provides better stability due to the isolation between processes. For example, an overloaded Alloy instance processing traces won’t impact an Alloy instance processing metrics. Different types of signal collection require different methods for scaling:

*   “Pull” components such as `prometheus.scrape` and `pyroscope.scrape` are scaled using hashmod sharing or clustering.
*   “Push” components such as `otelcol.receiver.otlp` are scaled by placing a load balancer in front of the components.

### Traces

Scaling Alloy instances for tracing is very similar to [scaling OpenTelemetry Collector](https://opentelemetry.io/docs/collector/scaling/)
 instances. This similarity is because most Alloy components used for tracing are based on components from the OTel Collector.

#### When to scale

To decide whether scaling is necessary, check metrics such as:

*   `otelcol_receiver_refused_spans_total` from receivers such as `otelcol.receiver.otlp`.
*   `otelcol_receiver_refused_spans_total` from processors such as `otelcol.processor.batch`.
*   `otelcol_exporter_send_failed_spans_total` from exporters such as `otelcol.exporter.otlp` and `otelcol.exporter.loadbalancing`.

#### Stateful and stateless components

In the context of tracing, a “stateful component” is a component that needs to aggregate certain spans to work correctly. A “stateless Alloy” is an Alloy instance which doesn’t contain stateful components.

Scaling stateful Alloy instances is more difficult, because spans must be forwarded to a specific Alloy instance according to a span property such as trace ID or a `service.name` attribute. You can forward spans with `otelcol.exporter.loadbalancing`.

Examples of stateful components:

*   `otelcol.processor.tail_sampling`
*   `otelcol.connector.spanmetrics`
*   `otelcol.connector.servicegraph`

A “stateless component” doesn’t need to aggregate specific spans to work correctly. It can work correctly even if it only has some of the spans of a trace.

A stateless Alloy instance can be scaled without using `otelcol.exporter.loadbalancing`. For example, you could use an off-the-shelf load balancer to do a round-robin load balancing.

Examples of stateless components:

*   `otelcol.processor.probabilistic_sampler`
*   `otelcol.processor.transform`
*   `otelcol.processor.attributes`
*   `otelcol.processor.span`

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----------------------

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[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/set-up/deploy.md)
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[Help and support](/help/)
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Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
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Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Migrate to Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Set up](/docs/alloy/latest/set-up/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Migrate

Open source [RSS](/docs/alloy/latest/set-up/migrate/index.xml)

Migrate to Grafana Alloy
========================

This section details how to migrate to Alloy from other common solutions.

*   [Migrate Grafana Agent Static to Grafana Alloy](/docs/alloy/latest/set-up/migrate/from-static/)
    
*   [Migrate from Grafana Agent Operator to Grafana Alloy](/docs/alloy/latest/set-up/migrate/from-operator/)
    
*   [Migrate Grafana Agent Flow to Grafana Alloy](/docs/alloy/latest/set-up/migrate/from-flow/)
    
*   [Migrate from OpenTelemetry Collector to Grafana Alloy](/docs/alloy/latest/set-up/migrate/from-otelcol/)
    
*   [Migrate from Prometheus to Grafana Alloy](/docs/alloy/latest/set-up/migrate/from-prometheus/)
    
*   [Migrate from Promtail to Grafana Alloy](/docs/alloy/latest/set-up/migrate/from-promtail/)
    

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[Help and support](/help/)
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Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
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Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Run Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Set up](/docs/alloy/latest/set-up/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Run

Open source [RSS](/docs/alloy/latest/set-up/run/index.xml)

Run Grafana Alloy
=================

Use the following pages to learn how to start, restart, and stop Alloy after it’s installed. For installation instructions, refer to [Install Grafana Alloy](../install/)
.

*   [Run Grafana Alloy on Linux](/docs/alloy/latest/set-up/run/linux/)
    
*   [Run Grafana Alloy on macOS](/docs/alloy/latest/set-up/run/macos/)
    
*   [Run Grafana Alloy on Windows](/docs/alloy/latest/set-up/run/windows/)
    
*   [Run Grafana Alloy as a standalone binary](/docs/alloy/latest/set-up/run/binary/)
    

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----------------------

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[Help and support](/help/)
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Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Configure Grafana Alloy clustering in an existing installation | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Configure](/docs/alloy/latest/configure/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Clustering

Open source [RSS](/docs/alloy/latest/configure/clustering/index.xml)

Configure Alloy clustering in an existing installation
======================================================

You can configure Alloy to run with [clustering](../clustering/)
 so that individual Alloys can work together for workload distribution and high availability.

This topic describes how to add clustering to an existing installation.

Configure Alloy clustering with Helm Chart
------------------------------------------

This section guides you through enabling clustering when Alloy is installed on Kubernetes using the Alloy [Helm chart](../../set-up/install/kubernetes/)
.

### Before you begin

*   Ensure that your `values.yaml` file has `controller.type` set to `statefulset`.

### Steps

To configure clustering:

1.  Amend your existing `values.yaml` file to add `clustering.enabled=true` inside the `alloy` block.
    
    yaml  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        alloy:
          clustering:
            enabled: true
    
2.  Upgrade your installation to use the new `values.yaml` file:
    
    bash  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        helm upgrade <RELEASE_NAME> -f values.yaml
    
    Replace the following:
    
    *   _`<RELEASE_NAME>`_: The name of the installation you chose when you installed the Helm chart.
3.  Use the Alloy [UI](../../troubleshoot/debug/#component-detail-page)
     to verify the cluster status:
    
    1.  Click **Clustering** in the navigation bar.
        
    2.  Ensure that all expected nodes appear in the resulting table.
        

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[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/configure/clustering/_index.md)
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[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Set up meta-monitoring to collect Alloy telemetry | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Collect and forward data](/docs/alloy/latest/collect/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Collect Alloy telemetry

Open source

Set up meta-monitoring to collect Alloy telemetry
=================================================

You can configure Alloy to collect its own telemetry and forward it to the backend of your choosing.

This topic describes how to collect and forward metrics, logs, and traces data from Alloy.

Components and configuration blocks used in this topic
------------------------------------------------------

*   [`prometheus.exporter.self`](../../reference/components/prometheus/prometheus.exporter.self/)
    
*   [`prometheus.scrape`](../../reference/components/prometheus/prometheus.scrape/)
    
*   [`logging`](../../reference/config-blocks/logging/)
    
*   [`tracing`](../../reference/config-blocks/tracing/)
    

Before you begin
----------------

*   Identify where to send Alloy’s telemetry data.
*   Be familiar with the concept of [Components](../../get-started/components/)
     in Alloy.

Meta-monitoring metrics
-----------------------

Alloy exposes its internal metrics using the Prometheus exposition format.

The metrics are exposed on a `/metrics` endpoint on an address configurable via the `--server.http.listen-addr` [command line flag](../../reference/cli/run/)
. For example, `127.0.0.1:12345/metrics`. However, it’s also possible to access the same metrics using the `prometheus.exporter.self` component.

In this task, you use the [`prometheus.exporter.self`](../../reference/components/prometheus/prometheus.exporter.self/)
 and [`prometheus.scrape`](../../reference/components/prometheus/prometheus.scrape/)
 components to scrape Alloy’s internal metrics and forward it to compatible Alloy components.

1.  Add the following `prometheus.exporter.self` component to your configuration. The component accepts no arguments.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        prometheus.exporter.self "<SELF_LABEL>" {
        }
    
    Replace the following:
    
    *   _`<SELF_LABEL>`_: The label for the component such as `default` or `metamonitoring`. The label must be unique across all `prometheus.exporter.self` components in the same configuration file.
2.  Add the following `prometheus.scrape` component to your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        prometheus.scrape "<SCRAPE_LABEL>" {
          targets    = prometheus.exporter.self.<SELF_LABEL>.targets
          forward_to = [<METRICS_RECEIVER_LIST>]
        }
    
    Replace the following:
    
    *   _`<SCRAPE_LABEL>`_: The label for the scrape component such as `default`. The label must be unique across all `prometheus.scrape` components in the same configuration file.
    *   _`<METRICS_RECEIVER_LIST>`_: A comma-delimited list of component receivers to forward metrics to. For example, to send to a remote write component, use `prometheus.remote_write.WRITE_LABEL.receiver`. Similarly, to send data to a relabeling component, use `prometheus.relabel.PROCESS_LABEL.receiver`. To use data in the OTLP format, you can send data to a converter component, like `otelcol.receiver.prometheus.OTEL.receiver`.

The following example demonstrates configuring a possible sequence of components.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    prometheus.exporter.self "default" {
    }
    
    prometheus.scrape "metamonitoring" {
      targets    = prometheus.exporter.self.default.targets
      forward_to = [prometheus.remote_write.default.receiver]
    }
    
    prometheus.remote_write "default" {
      endpoint {
        url = "http://mimir:9009/api/v1/push"
      }
    }

Meta-monitoring logs
--------------------

The [logging](../../reference/config-blocks/logging/)
 block defines the logging behavior of Alloy.

In this task, you use the [logging](../../reference/config-blocks/logging/)
 block to forward Alloy’s logs to a compatible component. The block is specified without a label and can only be provided once per configuration file.

1.  Add the following `logging` configuration block to the top level of your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        logging {
          level    = "<LOG_LEVEL>"
          format   = "<LOG_FORMAT>"
          write_to = [<LOGS_RECEIVER_LIST>]
        }
    
    Replace the following:
    
    *   _`<LOG_LEVEL>`_: The log level to use for Alloy’s logs. If the attribute isn’t set, it defaults to `info`.
    *   _`<LOG_FORMAT>`_: The log format to use for Alloy’s logs. If the attribute isn’t set, it defaults to `logfmt`.
    *   _`<LOGS_RECEIVER_LIST>`_: A comma-delimited list of component receivers to forward logs to. For example, to send to a processing component, use `loki.process.PROCESS_LABEL.receiver`. Similarly, to send data to a relabeling component, use `loki.relabel.PROCESS_LABEL.receiver`. To use data in the OTLP format, you can send data to a converter component, like `otelcol.receiver.loki.OTEL.receiver`.

The following example demonstrates configuring the logging block and sending to a compatible component.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    logging {
      level    = "warn"
      format   = "json"
      write_to = [loki.write.default.receiver]
    }
    
    loki.write "default" {
      endpoint {
        url = "http://loki:3100/loki/api/v1/push"
      }
    }

Meta-monitoring traces
----------------------

The [tracing](../../reference/config-blocks/tracing/)
 block defines the tracing behavior of Alloy.

In this task, you use the [tracing](../../reference/config-blocks/tracing/)
 block to forward Alloy internal traces to a compatible component. The block is specified without a label and can only be provided once per configuration file.

1.  Add the following `tracing` configuration block to the top level of your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        tracing {
          sampling_fraction = <SAMPLING_FRACTION>
          write_to          = [<TRACES_RECEIVER_LIST>]
        }
    
    Replace the following:
    
    *   _`<SAMPLING_FRACTION>`_: The fraction of traces to keep. If the attribute isn’t set, it defaults to `0.1`.
    *   _`<TRACES_RECEIVER_LIST>`_: A comma-delimited list of component receivers to forward traces to. For example, to send to an OpenTelemetry exporter component use `otelcol.exporter.otlp.EXPORT_LABEL.input`.

The following example demonstrates configuring the tracing block and sending to a compatible component.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    tracing {
      sampling_fraction = 0.1
      write_to          = [otelcol.exporter.otlp.default.input]
    }
    
    otelcol.exporter.otlp "default" {
        client {
            endpoint = "tempo:4317"
        }
    }

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----------------------

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[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/collect/metamonitoring.md)
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[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Configure Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Configure

Open source [RSS](/docs/alloy/latest/configure/index.xml)

Configure Grafana Alloy
=======================

You can configure Alloy after it’s [installed](../set-up/install/)
. The default configuration file location for Alloy is:

*   Linux: `/etc/alloy/config.alloy`
*   macOS: `$(brew --prefix)/etc/alloy/config.alloy`
*   Windows: `%ProgramFiles%\GrafanaLabs\Alloy\config.alloy`

This section includes information that helps you configure Alloy.

*   [Configure Grafana Alloy clustering in an existing installation](/docs/alloy/latest/configure/clustering/)
    
*   [Configure Grafana Alloy on Kubernetes](/docs/alloy/latest/configure/kubernetes/)
    
*   [Configure Grafana Alloy on Linux](/docs/alloy/latest/configure/linux/)
    
*   [Configure Grafana Alloy on macOS](/docs/alloy/latest/configure/macos/)
    
*   [Configure Grafana Alloy on Windows](/docs/alloy/latest/configure/windows/)
    
*   [Run Alloy as a non-root user in Kubernetes](/docs/alloy/latest/configure/nonroot/)
    

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/configure/_index.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/configure/_index.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Receive traces and metrics from Datadog-instrumented applications | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Collect and forward data](/docs/alloy/latest/collect/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Collect Datadog traces and metrics

Open source

Receive traces and metrics from Datadog-instrumented applications
=================================================================

You can configure Alloy to collect [Datadog](https://www.datadoghq.com/)
 traces and metrics and forward them to any OpenTelemetry-compatible database.

This topic describes how to:

*   Configure Alloy to send traces and metrics.
*   Configure the Alloy Datadog Receiver.
*   Configure the Datadog Agent to forward traces and metrics to the Alloy Datadog Receiver.

Before you begin
----------------

*   Ensure that at least one instance of the [Datadog Agent](https://docs.datadoghq.com/agent/)
     is collecting metrics and traces.
*   Identify where to write the collected telemetry. Metrics can be written to [Prometheus](https://prometheus.io)
     or any other OpenTelemetry-compatible database such as Grafana Mimir, Grafana Cloud, or Grafana Enterprise Metrics. Traces can be written to Grafana Tempo, Grafana Cloud, or Grafana Enterprise Traces.
*   Be familiar with the concept of [Components](../../get-started/components/)
     in Alloy.

Configure Alloy to send traces and metrics
------------------------------------------

Before components can collect Datadog telemetry signals, you must have a component responsible for writing this telemetry somewhere.

The [otelcol.exporter.otlp](../../reference/components/otelcol/otelcol.exporter.otlp/)
 component is responsible for delivering OTLP data to OpenTelemetry-compatible endpoints.

1.  Add the following `otelcol.exporter.otlp` component to your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        otelcol.exporter.otlp "default" {
          client {
            endpoint = "<OTLP_ENDPOINT_URL>"
            auth     = otelcol.auth.basic.auth.handler
          }
        }
    
    Replace the following:
    
    *   _`<OTLP_ENDPOINT_URL>`_: The full URL of the OpenTelemetry-compatible endpoint where metrics and traces are sent, such as `https://otlp-gateway-prod-eu-west-2.grafana.net/otlp`.
2.  If your endpoint requires basic authentication, paste the following inside the `endpoint` block.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        basic_auth {
          username = "<USERNAME>"
          password = "<PASSWORD>"
        }
    
    Replace the following:
    
    *   _`<USERNAME>`_: The basic authentication username.
    *   _`<PASSWORD>`_: The basic authentication password or API key.

Configure the Alloy Datadog Receiver
------------------------------------

1.  Add the following `otelcol.processor.batch` component to your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        otelcol.processor.batch "default" {
          output {
            metrics = [otelcol.exporter.otlp.default.input]
            traces  = [otelcol.exporter.otlp.default.input]
          }
        }
    
2.  Add the following `otelcol.processor.deltatocumulative` component to your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        otelcol.processor.deltatocumulative "default" {
          max_stale = "<MAX_STALE>"
          max_streams = <MAX_STREAMS>
          output {
            metrics = [otelcol.processor.batch.default.input]
          }
        }
    
    Replace the following:
    
    *   _`<MAX_STALE>`_: How long until a series not receiving new samples is removed, such as “5m”.
    *   _`<MAX_STREAMS>`_: The upper limit of streams to track. New streams exceeding this limit are dropped.
3.  Add the following `otelcol.receiver.datadog` component to your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        otelcol.receiver.datadog "default" {
          endpoint = "<HOST>:<PORT>"
          output {
            metrics = [otelcol.processor.deltatocumulative.default.input]
            traces  = [otelcol.processor.batch.default.input]
          }
        }
    
    Replace the following:
    
    *   _`<HOST>`_: The host address where the receiver listens.
    *   _`<PORT>`_: The port where the receiver listens.
4.  If your endpoint requires basic authentication, paste the following inside the `endpoint` block.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        basic_auth {
          username = "<USERNAME>"
          password = "<PASSWORD>"
        }
    
    Replace the following:
    
    *   _`<USERNAME>`_: The basic authentication username.
    *   _`<PASSWORD>`_: The basic authentication password or API key.

Configure Datadog Agent to forward telemetry to the Alloy Datadog Receiver
--------------------------------------------------------------------------

You can set up your Datadog Agent to forward Datadog metrics and traces simultaneously to Alloy and Datadog.

We recommend this approach for current Datadog users who want to try using Alloy.

1.  Add the following environment variables to your Datadog-agent installation.
    
    bash  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        DD_ADDITIONAL_ENDPOINTS='{"http://<DATADOG_RECEIVER_HOST>:<DATADOG_RECEIVER_PORT>": ["datadog-receiver"]}'
        DD_APM_ADDITIONAL_ENDPOINTS='{"http://<DATADOG_RECEIVER_HOST>:<DATADOG_RECEIVER_PORT>": ["datadog-receiver"]}'
    
    `DD_ADDITIONAL_ENDPOINTS` is used for forwarding metrics, whereas `DD_APM_ADDITIONAL_ENDPOINTS` is for traces.
    
    Replace the following:
    
    *   _`<DATADOG_RECEIVER_HOST>`_: The hostname where the Alloy receiver is found.
    *   _`<DATADOG_RECEIVER_PORT>`_: The port where the Alloy receiver is exposed.

Alternatively, you might want your Datadog Agent to send metrics only to Alloy. You can do this by setting up your Datadog Agent in the following way:

1.  Replace the DD\_URL in the configuration YAML:
    
    yaml  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
         dd_url: http://<DATADOG_RECEIVER_HOST>:<DATADOG_RECEIVER_PORT>
    
    Or by setting an environment variable:
    
    bash  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        DD_DD_URL='{"http://<DATADOG_RECEIVER_HOST>:<DATADOG_RECEIVER_PORT>": ["datadog-receiver"]}'
    

Run Alloy
---------

The `otelcol.receiver.datadog` component is experimental. To use this component, you need to start Alloy with additional command line flags:

bash  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    alloy run config.alloy --stability.level=experimental

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/collect/datadog-traces-metrics.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/collect/datadog-traces-metrics.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Configure Grafana Alloy on Windows | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Configure](/docs/alloy/latest/configure/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Windows

Open source

Configure Grafana Alloy on Windows
==================================

To configure Alloy on Windows, perform the following steps:

1.  Edit the default configuration file at `%PROGRAMFILES%\GrafanaLabs\Alloy\config.alloy`.
    
2.  Restart the Alloy service:
    
    1.  Open the Windows Services manager:
        
        1.  Right click on the Start Menu and select **Run**.
            
        2.  Type `services.msc` and click **OK**.
            
    2.  Right click on the service called **Alloy**.
        
    3.  Click on **All Tasks > Restart**.
        

Change command-line arguments
-----------------------------

By default, the Alloy service will launch and pass the following arguments to the Alloy binary:

*   `run`
*   `%PROGRAMFILES%\GrafanaLabs\Alloy\config.alloy`
*   `--storage.path=%PROGRAMDATA%\GrafanaLabs\Alloy\data`

To change the set of command-line arguments passed to the Alloy binary, perform the following steps:

1.  Open the Registry Editor:
    
    1.  Right click on the Start Menu and select **Run**.
        
    2.  Type `regedit` and click **OK**.
        
2.  Navigate to the key at the path `HKEY_LOCAL_MACHINE\SOFTWARE\GrafanaLabs\Alloy`.
    
3.  Double-click on the value called **Arguments**\*.
    
4.  In the dialog box, enter the arguments to pass to the Alloy binary. Make sure that each argument is on its own line.
    
5.  Restart the Alloy service:
    
    1.  Open the Windows Services manager:
        
        1.  Right click on the Start Menu and select **Run**.
            
        2.  Type `services.msc` and click **OK**.
            
    2.  Right click on the service called **Alloy**.
        
    3.  Click on **All Tasks > Restart**.
        

Change environment variable values
----------------------------------

The Go runtime provides several ways to modify the execution of a binary using [environment variables](../../reference/cli/environment-variables/)
.

To change the environment variables used by Alloy, perform the following steps.

1.  Open the Registry Editor:
    
    1.  Right click on the Start Menu and select **Run**.
        
    2.  Type `regedit` and click **OK**.
        
2.  Navigate to the key at the path `HKEY_LOCAL_MACHINE\SOFTWARE\GrafanaLabs\Alloy`.
    
3.  Double-click on the multi-string value called **Environment**\*.
    
4.  In the dialog box, enter the environment variable values to pass to the Alloy binary. Make sure that each variable is on its own line.
    
5.  Restart the Alloy service:
    
    1.  Open the Windows Services manager (`services.msc`):
        
        1.  Right click on the Start Menu and select **Run**.
            
        2.  Type `services.msc` and click **OK**.
            
    2.  Right click on the service called **Alloy**.
        
    3.  Click on **All Tasks > Restart**.
        

Expose the UI to other machines
-------------------------------

By default, Alloy listens on the local network for its HTTP server. This prevents other machines on the network from being able to access the [UI for debugging](../../troubleshoot/debug/#alloy-ui)
.

To expose the UI to other machines, complete the following steps:

1.  Follow [Change command-line arguments](#change-command-line-arguments)
     to edit command line flags passed to Alloy.
    
2.  Add the following command line argument:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        --server.http.listen-addr=LISTEN_ADDR:12345
    
    Replace the following:
    
    *   _`<LISTEN_ADDR>`_: An IP address which other machines on the network have access to. For example, the IP address of the machine Alloy is running on.
        
        To listen on all interfaces, replace _`<LISTEN_ADDR>`_ with `0.0.0.0`.
        

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/configure/windows.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/configure/windows.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Collect Kubernetes logs and forward them to Loki | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Collect and forward data](/docs/alloy/latest/collect/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Collect Kubernetes logs

Open source

Collect Kubernetes logs and forward them to Loki
================================================

You can configure Alloy to collect logs and forward them to a [Loki](/oss/loki/)
 database.

This topic describes how to:

*   Configure logs delivery.
*   Collect logs from Kubernetes Pods.

Components used in this topic
-----------------------------

*   [`discovery.kubernetes`](../../reference/components/discovery/discovery.kubernetes/)
    
*   [`discovery.relabel`](../../reference/components/discovery/discovery.relabel/)
    
*   [`local.file_match`](../../reference/components/local/local.file_match/)
    
*   [`loki.source.file`](../../reference/components/loki/loki.source.file/)
    
*   [`loki.source.kubernetes`](../../reference/components/loki/loki.source.kubernetes/)
    
*   [`loki.source.kubernetes_events`](../../reference/components/loki/loki.source.kubernetes_events/)
    
*   [`loki.process`](../../reference/components/loki/loki.process/)
    
*   [`loki.write`](../../reference/components/loki/loki.write/)
    

Before you begin
----------------

*   Ensure that you are familiar with logs labelling when working with Loki.
*   Identify where to write collected logs. You can write logs to Loki endpoints such as Grafana Loki, Grafana Cloud, or Grafana Enterprise Logs.
*   Be familiar with the concept of [Components](../../get-started/components/)
     in Alloy.

Configure logs delivery
-----------------------

Before components can collect logs, you must have a component responsible for writing those logs somewhere.

The [`loki.write`](../../reference/components/loki/loki.write/)
 component delivers logs to a Loki endpoint. After you define a `loki.write` component, you can use other Alloy components to forward logs to it.

To configure a `loki.write` component for logs delivery, complete the following steps:

1.  Add the following `loki.write` component to your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        loki.write "<LABEL>" {
          endpoint {
            url = "<LOKI_URL>"
          }
        }
    
    Replace the following:
    
    *   _`<LABEL>`_: The label for the component, such as `default`. The label you use must be unique across all `loki.write` components in the same configuration file.
    *   _`<LOKI_URL>`_ : The full URL of the Loki endpoint where logs are sent, such as `https://logs-us-central1.grafana.net/loki/api/v1/push`.
2.  If your endpoint requires basic authentication, paste the following inside the `endpoint` block.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        basic_auth {
          username = "<USERNAME>"
          password = "<PASSWORD>"
        }
    
    Replace the following:
    
    *   _`<USERNAME>`_: The basic authentication username.
    *   _`<PASSWORD>`_: The basic authentication password or API key.
3.  If you have more than one endpoint to write logs to, repeat the `endpoint` block for additional endpoints.
    

The following simple example demonstrates configuring `loki.write` with multiple endpoints, mixed usage of basic authentication, and a `loki.source.file` component that collects logs from the filesystem on the Alloy container.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    loki.write "default" {
      endpoint {
        url = "http://localhost:3100/loki/api/v1/push"
      }
    
      endpoint {
        url = "https://logs-us-central1.grafana.net/loki/api/v1/push"
    
        // Get basic authentication based on environment variables.
        basic_auth {
          username = "<USERNAME>"
          password = "<PASSWORD>"
        }
      }
    }
    
    loki.source.file "example" {
      // Collect logs from the default listen address.
      targets = [\
        {__path__ = "/tmp/foo.txt", "color" = "pink"},\
        {__path__ = "/tmp/bar.txt", "color" = "blue"},\
        {__path__ = "/tmp/baz.txt", "color" = "grey"},\
      ]
    
      forward_to = [loki.write.default.receiver]
    }

Replace the following:

*   _`<USERNAME>`_: The remote write username.
*   _`<PASSWORD>`_: The remote write password.

For more information on configuring logs delivery, refer to [loki.write](../../reference/components/loki/loki.write/)
.

Collect logs from Kubernetes
----------------------------

You can configure Alloy to collect all kinds of logs from Kubernetes:

1.  System logs
2.  Pods logs
3.  Kubernetes Events

Thanks to the component architecture, you can follow one or all of the next sections to get those logs. After you have followed the [Configure logs delivery](#configure-logs-delivery)
 to ensure collected logs can be written somewhere, jump to the relevant sections.

### System logs

To get the system logs, you should use the following components:

*   [`local.file_match`](../../reference/components/local/local.file_match/)
    : Discovers files on the local filesystem.
*   [`loki.source.file`](../../reference/components/loki/loki.source.file/)
    : Reads log entries from files.
*   [`loki.write`](../../reference/components/loki/loki.write/)
    : Send logs to the Loki endpoint. You should have configured it in the [Configure logs delivery](#configure-logs-delivery)
     section.

Here is an example using those stages.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    // local.file_match discovers files on the local filesystem using glob patterns and the doublestar library. It returns an array of file paths.
    local.file_match "node_logs" {
      path_targets = [{\
          // Monitor syslog to scrape node-logs\
          __path__  = "/var/log/syslog",\
          job       = "node/syslog",\
          node_name = sys.env("HOSTNAME"),\
          cluster   = <CLUSTER_NAME>,\
      }]
    }
    
    // loki.source.file reads log entries from files and forwards them to other loki.* components.
    // You can specify multiple loki.source.file components by giving them different labels.
    loki.source.file "node_logs" {
      targets    = local.file_match.node_logs.targets
      forward_to = [loki.write.<WRITE_COMPONENT_NAME>.receiver]
    }

Replace the following values:

*   _`<CLUSTER_NAME>`_: The label for this specific Kubernetes cluster, such as `production` or `us-east-1`.
*   _`<WRITE_COMPONENT_NAME>`_: The name of your `loki.write` component, such as `default`.

### Pods logs

> Tip
> 
> You can get pods logs through the log files on each node. In this guide, you get the logs through the Kubernetes API because it doesn’t require system privileges for Alloy.

You need the following components:

*   [`discovery.kubernetes`](../../reference/components/discovery/discovery.kubernetes/)
    : Discover pods information and list them for components to use.
*   [`discovery.relabel`](../../reference/components/discovery/discovery.relabel/)
    : Enforce relabelling strategies on the list of pods.
*   [`loki.source.kubernetes`](../../reference/components/loki/loki.source.kubernetes/)
    : Tails logs from a list of Kubernetes pods targets.
*   [`loki.process`](../../reference/components/loki/loki.process/)
    : Modify the logs before sending them to the next component.
*   [`loki.write`](../../reference/components/loki/loki.write/)
    : Send logs to the Loki endpoint. You should have configured it in the [Configure logs delivery](#configure-logs-delivery)
     section.

Here is an example using those stages:

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    // discovery.kubernetes allows you to find scrape targets from Kubernetes resources.
    // It watches cluster state and ensures targets are continually synced with what is currently running in your cluster.
    discovery.kubernetes "pod" {
      role = "pod"
    }
    
    // discovery.relabel rewrites the label set of the input targets by applying one or more relabeling rules.
    // If no rules are defined, then the input targets are exported as-is.
    discovery.relabel "pod_logs" {
      targets = discovery.kubernetes.pod.targets
    
      // Label creation - "namespace" field from "__meta_kubernetes_namespace"
      rule {
        source_labels = ["__meta_kubernetes_namespace"]
        action = "replace"
        target_label = "namespace"
      }
    
      // Label creation - "pod" field from "__meta_kubernetes_pod_name"
      rule {
        source_labels = ["__meta_kubernetes_pod_name"]
        action = "replace"
        target_label = "pod"
      }
    
      // Label creation - "container" field from "__meta_kubernetes_pod_container_name"
      rule {
        source_labels = ["__meta_kubernetes_pod_container_name"]
        action = "replace"
        target_label = "container"
      }
    
      // Label creation -  "app" field from "__meta_kubernetes_pod_label_app_kubernetes_io_name"
      rule {
        source_labels = ["__meta_kubernetes_pod_label_app_kubernetes_io_name"]
        action = "replace"
        target_label = "app"
      }
    
      // Label creation -  "job" field from "__meta_kubernetes_namespace" and "__meta_kubernetes_pod_container_name"
      // Concatenate values __meta_kubernetes_namespace/__meta_kubernetes_pod_container_name
      rule {
        source_labels = ["__meta_kubernetes_namespace", "__meta_kubernetes_pod_container_name"]
        action = "replace"
        target_label = "job"
        separator = "/"
        replacement = "$1"
      }
    
      // Label creation - "container" field from "__meta_kubernetes_pod_uid" and "__meta_kubernetes_pod_container_name"
      // Concatenate values __meta_kubernetes_pod_uid/__meta_kubernetes_pod_container_name.log
      rule {
        source_labels = ["__meta_kubernetes_pod_uid", "__meta_kubernetes_pod_container_name"]
        action = "replace"
        target_label = "__path__"
        separator = "/"
        replacement = "/var/log/pods/*$1/*.log"
      }
    
      // Label creation -  "container_runtime" field from "__meta_kubernetes_pod_container_id"
      rule {
        source_labels = ["__meta_kubernetes_pod_container_id"]
        action = "replace"
        target_label = "container_runtime"
        regex = "^(\\S+):\\/\\/.+$"
        replacement = "$1"
      }
    }
    
    // loki.source.kubernetes tails logs from Kubernetes containers using the Kubernetes API.
    loki.source.kubernetes "pod_logs" {
      targets    = discovery.relabel.pod_logs.output
      forward_to = [loki.process.pod_logs.receiver]
    }
    
    // loki.process receives log entries from other Loki components, applies one or more processing stages,
    // and forwards the results to the list of receivers in the component's arguments.
    loki.process "pod_logs" {
      stage.static_labels {
          values = {
            cluster = "<CLUSTER_NAME>",
          }
      }
    
      forward_to = [loki.write.<WRITE_COMPONENT_NAME>.receiver]
    }

Replace the following values:

*   _`<CLUSTER_NAME>`_: The label for this specific Kubernetes cluster, such as `production` or `us-east-1`.
*   _`<WRITE_COMPONENT_NAME>`_: The name of your `loki.write` component, such as `default`.

### Kubernetes Cluster Events

You need the following components:

*   [`loki.source.kubernetes_events`](../../reference/components/loki/loki.source.kubernetes_events/)
    : Tails events from Kubernetes API.
*   [`loki.process`](../../reference/components/loki/loki.process/)
    : Modify the logs before sending them to the next component.
*   [`loki.write`](../../reference/components/loki/loki.write/)
    : Send logs to the Loki endpoint. You should have configured it in the [Configure logs delivery](#configure-logs-delivery)
     section.

Here is an example using those stages:

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    // loki.source.kubernetes_events tails events from the Kubernetes API and converts them
    // into log lines to forward to other Loki components.
    loki.source.kubernetes_events "cluster_events" {
      job_name   = "integrations/kubernetes/eventhandler"
      log_format = "logfmt"
      forward_to = [\
        loki.process.cluster_events.receiver,\
      ]
    }
    
    // loki.process receives log entries from other loki components, applies one or more processing stages,
    // and forwards the results to the list of receivers in the component's arguments.
    loki.process "cluster_events" {
      forward_to = [loki.write.<WRITE_COMPONENT_NAME>.receiver]
    
      stage.static_labels {
        values = {
          cluster = "<CLUSTER_NAME>",
        }
      }
    
      stage.labels {
        values = {
          kubernetes_cluster_events = "job",
        }
      }
    }

Replace the following values:

*   _`<CLUSTER_NAME>`_: The label for this specific Kubernetes cluster, such as `production` or `us-east-1`.
*   _`<WRITE_COMPONENT_NAME>`_: The name of your `loki.write` component, such as `default`.

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/collect/logs-in-kubernetes.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/collect/logs-in-kubernetes.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Configure Grafana Alloy on Linux | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Configure](/docs/alloy/latest/configure/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Linux

Open source

Configure Grafana Alloy on Linux
================================

To configure Alloy on Linux, perform the following steps:

1.  Edit the default configuration file at `/etc/alloy/config.alloy`.
    
2.  Run the following command in a terminal to reload the configuration file:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        sudo systemctl reload alloy
    

To change the configuration file used by the service, perform the following steps:

1.  Edit the environment file for the service:
    
    *   Debian or Ubuntu: edit `/etc/default/alloy`
    *   RHEL/Fedora or SUSE/openSUSE: edit `/etc/sysconfig/alloy`
2.  Change the contents of the `CONFIG_FILE` environment variable to point at the new configuration file.
    
3.  Restart the Alloy service:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        sudo systemctl restart alloy
    

Pass additional command-line flags
----------------------------------

By default, the Alloy service launches with the [run](../../reference/cli/run/)
 command, passing the following flags:

*   `--storage.path=/var/lib/alloy`

To pass additional command-line flags to the Alloy binary, perform the following steps:

1.  Edit the environment file for the service:
    
    *   Debian-based systems: edit `/etc/default/alloy`
    *   RedHat or SUSE-based systems: edit `/etc/sysconfig/alloy`
2.  Change the contents of the `CUSTOM_ARGS` environment variable to specify command-line flags to pass.
    
3.  Restart the Alloy service:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        sudo systemctl restart alloy
    

To see the list of valid command-line flags that can be passed to the service, refer to the documentation for the [run](../../reference/cli/run/)
 command.

Expose the UI to other machines
-------------------------------

By default, Alloy listens on the local network for its HTTP server. This prevents other machines on the network from being able to access the [UI for debugging](../../troubleshoot/debug/#alloy-ui)
.

To expose the UI to other machines, complete the following steps:

1.  Follow [Pass additional command-line flags](#pass-additional-command-line-flags)
     to edit command line flags passed to Alloy
    
2.  Add the following command line argument to `CUSTOM_ARGS`:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        --server.http.listen-addr=<LISTEN_ADDR>:12345
    
    Replace the following:
    
    *   _`<LISTEN_ADDR>`_: An IP address which other machines on the network have access to. For example, the IP address of the machine Alloy is running on.
        
        To listen on all interfaces, replace _`<LISTEN_ADDR>`_ with `0.0.0.0`.
        

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/configure/linux.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/configure/linux.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Configure Grafana Alloy on Kubernetes | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Configure](/docs/alloy/latest/configure/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Kubernetes

Open source

Configure Grafana Alloy on Kubernetes
=====================================

This page describes how to apply a new configuration to Alloy when running on Kubernetes with the Helm chart. It assumes that:

*   You have [installed Alloy on Kubernetes using the Helm chart](../../set-up/install/kubernetes/)
    .
*   You already have a Alloy configuration that you want to apply to your Helm chart installation.

Refer to [Collect and forward data](../../collect/)
 for information about configuring Alloy to collect and forward data.

Configure the Helm chart
------------------------

To modify Alloy’s Helm chart configuration, perform the following steps:

1.  Create a local `values.yaml` file with a Helm chart configuration.
    
    1.  You can use your own copy of the values file or download a copy of the default [`values.yaml`](https://raw.githubusercontent.com/grafana/alloy/main/operations/helm/charts/alloy/values.yaml)
        .
        
    2.  Make changes to your `values.yaml` to customize settings for the Helm chart.
        
        Refer to the inline documentation in the default [`values.yaml`](https://raw.githubusercontent.com/grafana/alloy/main/operations/helm/charts/alloy/values.yaml)
         for more information about each option.
        
2.  Run the following command in a terminal to upgrade your Alloy installation:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        helm upgrade --namespace <NAMESPACE> <RELEASE_NAME> grafana/alloy -f <VALUES_PATH>
    
    Replace the following:
    
    *   _`<NAMESPACE>`_: The namespace you used for your Alloy installation.
    *   _`<RELEASE_NAME>`_: The name you used for your Alloy installation.
    *   _`<VALUES_PATH>`_: The path to your copy of `values.yaml` to use.

Kustomize considerations
------------------------

If you are using [Kustomize](https://kubernetes.io/docs/tasks/manage-kubernetes-objects/kustomization/)
 to inflate and install the [Helm chart](https://github.com/grafana/alloy/tree/main/operations/helm/charts/alloy)
, be careful when using a `configMapGenerator` to generate the ConfigMap containing the configuration. By default, the generator appends a hash to the name and patches the resource mentioning it, triggering a rolling update.

This behavior is undesirable for Alloy because the startup time can be significant, for example, when your deployment has a large metrics Write-Ahead Log. You can use the [Helm chart](https://github.com/grafana/alloy/tree/main/operations/helm/charts/alloy)
 sidecar container to watch the ConfigMap and trigger a dynamic reload.

The following is an example snippet of a `kustomization` that disables this behavior:

yaml  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    configMapGenerator:
      - name: alloy
        files:
          - config.alloy
        options:
          disableNameSuffixHash: true

Configure Alloy
---------------

This section describes how to modify the Alloy configuration, which is stored in a ConfigMap in the Kubernetes cluster. There are two methods to perform this task.

### Method 1: Modify the configuration in the values.yaml file

Use this method if you prefer to embed your Alloy configuration in the Helm chart’s `values.yaml` file.

1.  Modify the configuration file contents directly in the `values.yaml` file:
    
    yaml  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        alloy:
          configMap:
            content: |-
              // Write your Alloy config here:
              logging {
                level = "info"
                format = "logfmt"
              }
    
2.  Run the following command in a terminal to upgrade your Alloy installation:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        helm upgrade --namespace <NAMESPACE> <RELEASE_NAME> grafana/alloy -f <VALUES_PATH>
    
    Replace the following:
    
    *   _`<NAMESPACE>`_: The namespace you used for your Alloy installation.
    *   _`<RELEASE_NAME>`_: The name you used for your Alloy installation.
    *   _`<VALUES_PATH>`_: The path to your copy of `values.yaml` to use.

### Method 2: Create a separate ConfigMap from a file

Use this method if you prefer to write your Alloy configuration in a separate file.

1.  Write your configuration to a file, for example, `config.alloy`.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        // Write your Alloy config here:
        logging {
          level = "info"
          format = "logfmt"
        }
    
2.  Create a ConfigMap called `alloy-config` from the above file:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        kubectl create configmap --namespace <NAMESPACE> alloy-config "--from-file=config.alloy=./config.alloy"
    
    Replace the following:
    
    *   _`<NAMESPACE>`_: The namespace you used for your Alloy installation.
3.  Modify Helm Chart’s configuration in your `values.yaml` to use the existing ConfigMap:
    
    yaml  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        alloy:
          configMap:
            create: false
            name: alloy-config
            key: config.alloy
    
4.  Run the following command in a terminal to upgrade your Alloy installation:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        helm upgrade --namespace <NAMESPACE> <RELEASE_NAME> grafana/alloy -f <VALUES_PATH>
    
    Replace the following:
    
    *   _`<NAMESPACE>`_: The namespace you used for your Alloy installation.
    *   _`<RELEASE_NAME>`_: The name you used for your Alloy installation.
    *   _`<VALUES_PATH>`_: The path to your copy of `values.yaml` to use.

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/configure/kubernetes.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/configure/kubernetes.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Collect OpenTelemetry data and forward it to any OpenTelemetry-compatible endpoint | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Collect and forward data](/docs/alloy/latest/collect/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Collect OpenTelemetry data

Open source

Collect OpenTelemetry data and forward it to any OpenTelemetry-compatible endpoint
==================================================================================

You can configure Alloy to collect [OpenTelemetry](https://opentelemetry.io)
\-compatible data and forward it to any OpenTelemetry-compatible endpoint.

This topic describes how to:

*   Configure OpenTelemetry data delivery.
*   Configure batching.
*   Receive OpenTelemetry data over OTLP.

Components used in this topic
-----------------------------

*   [`otelcol.auth.basic`](../../reference/components/otelcol/otelcol.auth.basic/)
    
*   [`otelcol.exporter.otlp`](../../reference/components/otelcol/otelcol.exporter.otlp/)
    
*   [`otelcol.exporter.otlphttp`](../../reference/components/otelcol/otelcol.exporter.otlphttp/)
    
*   [`otelcol.processor.batch`](../../reference/components/otelcol/otelcol.processor.batch/)
    
*   [`otelcol.receiver.otlp`](../../reference/components/otelcol/otelcol.receiver.otlp/)
    

Before you begin
----------------

*   Ensure that you have basic familiarity with instrumenting applications with OpenTelemetry.
*   Have a set of OpenTelemetry applications ready to push telemetry data to Alloy.
*   Identify where Alloy writes received telemetry data.
*   Be familiar with the concept of [Components](../../get-started/components/)
     in Alloy.

Configure an OpenTelemetry Protocol exporter
--------------------------------------------

Before components can receive OpenTelemetry data, you must have a component responsible for exporting the OpenTelemetry data. An OpenTelemetry _exporter component_ is responsible for writing (exporting) OpenTelemetry data to an external system.

In this task, you use the [`otelcol.exporter.otlp`](../../reference/components/otelcol/otelcol.exporter.otlp/)
 component to send OpenTelemetry data to a server using the OpenTelemetry Protocol (OTLP). After an exporter component is defined, you can use other Alloy components to forward data to it.

> Tip
> 
> Refer to the list of available [Components](../../get-started/components/)
>  for the full list of `otelcol.exporter` components that you can use to export OpenTelemetry data.

To configure an `otelcol.exporter.otlp` component for exporting OpenTelemetry data using OTLP, complete the following steps:

1.  Add the following `otelcol.exporter.otlp` component to your configuration file:
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        otelcol.exporter.otlp "<EXPORTER_LABEL>" {
          client {
            endpoint = "<HOST>:<PORT>"
          }
        }
    
    Replace the following:
    
    *   _`<EXPORTER_LABEL>`_: The label for the component, such as `default`. The label you use must be unique across all `otelcol.exporter.otlp` components in the same configuration file.
    *   _`<HOST>`_: The hostname or IP address of the server to send OTLP requests to.
    *   _`<PORT>`_: The port of the server to send OTLP requests to.
2.  If your server requires basic authentication, complete the following:
    
    1.  Add the following `otelcol.auth.basic` component to your configuration file:
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            otelcol.auth.basic "<BASIC_AUTH_LABEL>" {
              username = "<USERNAME>"
              password = "<PASSWORD>"
            }
        
        Replace the following:
        
        *   _`<BASIC_AUTH_LABEL>`_: The label for the component, such as `default`. The label you use must be unique across all `otelcol.auth.basic` components in the same configuration file.
        *   _`<USERNAME>`_: The basic authentication username.
        *   _`<PASSWORD>`_: The basic authentication password or API key.
    2.  Add the following line inside of the `client` block of your `otelcol.exporter.otlp` component:
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            auth = otelcol.auth.basic.<BASIC_AUTH_LABEL>.handler
        
        Replace the following:
        
        *   _`<BASIC_AUTH_LABEL>`_: The label for the `otelcol.auth.basic` component.
3.  If you have more than one server to export metrics to, create an `otelcol.exporter.otlp` component for each additional server.
    

> Note
> 
> `otelcol.exporter.otlp` sends data using OTLP over gRPC (HTTP/2). To send to a server using HTTP/1.1, follow the preceding steps, but use the [`otelcol.exporter.otlphttp`](../../reference/components/otelcol/otelcol.exporter.otlphttp/)
>  component instead.

The following example demonstrates configuring `otelcol.exporter.otlp` with authentication and a component that forwards data to it:

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    otelcol.exporter.otlp "default" {
      client {
        endpoint = "my-otlp-grpc-server:4317"
        auth     = otelcol.auth.basic.credentials.handler
      }
    }
    
    otelcol.auth.basic "credentials" {
      // Retrieve credentials using environment variables.
    
      username = sys.env("BASIC_AUTH_USER")
      password = sys.env("API_KEY")
    }
    
    otelcol.receiver.otlp "example" {
      grpc {
        endpoint = "127.0.0.1:4317"
      }
    
      http {
        endpoint = "127.0.0.1:4318"
      }
    
      output {
        metrics = [otelcol.exporter.otlp.default.input]
        logs    = [otelcol.exporter.otlp.default.input]
        traces  = [otelcol.exporter.otlp.default.input]
      }
    }

For more information on writing OpenTelemetry data using the OpenTelemetry Protocol, refer to [`otelcol.exporter.otlp`](../../reference/components/otelcol/otelcol.exporter.otlp/)
.

Configure batching
------------------

Production-ready Alloy configurations shouldn’t send OpenTelemetry data directly to an exporter for delivery. Instead, data is usually sent to one or more _processor components_ that perform various transformations on the data.

Ensuring data is batched is a production-readiness step to improve data compression and reduce the number of outgoing network requests to external systems.

In this task, you configure an [`otelcol.processor.batch`](../../reference/components/otelcol/otelcol.processor.batch/)
 component to batch data before sending it to the exporter.

> Note
> 
> Refer to the list of available [Components](../../get-started/components/)
>  for the full list of `otelcol.processor` components that you can use to process OpenTelemetry data. You can chain processors by having one processor send data to another processor.

To configure an `otelcol.processor.batch` component, complete the following steps:

1.  Follow [Configure an OpenTelemetry Protocol exporter](#configure-an-opentelemetry-protocol-exporter)
     to ensure received data can be written to an external system.
    
2.  Add the following `otelcol.processor.batch` component into your configuration file:
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        otelcol.processor.batch "<PROCESSOR_LABEL>" {
          output {
            metrics = [otelcol.exporter.otlp.<EXPORTER_LABEL>.input]
            logs    = [otelcol.exporter.otlp.<EXPORTER_LABEL>.input]
            traces  = [otelcol.exporter.otlp.>EXPORTER_LABEL>.input]
          }
        }
    
    Replace the following:
    
    *   _`<PROCESSOR_LABEL>`_: The label for the component, such as `default`. The label you use must be unique across all `otelcol.processor.batch` components in the same configuration file.
    *   _`<EXPORTER_LABEL>`_: The label for your `otelcol.exporter.otlp` component.
    
    1.  To disable one of the telemetry types, set the relevant type in the `output` block to the empty list, such as `metrics = []`.
        
    2.  To send batched data to another processor, replace the components in the `output` list with the processor components to use.
        

The following example demonstrates configuring a sequence of `otelcol.processor` components before being exported.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    otelcol.processor.memory_limiter "default" {
      check_interval = "1s"
      limit          = "1GiB"
    
      output {
        metrics = [otelcol.processor.batch.default.input]
        logs    = [otelcol.processor.batch.default.input]
        traces  = [otelcol.processor.batch.default.input]
      }
    }
    
    otelcol.processor.batch "default" {
      output {
        metrics = [otelcol.exporter.otlp.default.input]
        logs    = [otelcol.exporter.otlp.default.input]
        traces  = [otelcol.exporter.otlp.default.input]
      }
    }
    
    otelcol.exporter.otlp "default" {
      client {
        endpoint = "my-otlp-grpc-server:4317"
      }
    }

For more information on configuring OpenTelemetry data batching, refer to [`otelcol.processor.batch`](../../reference/components/otelcol/otelcol.processor.batch/)
.

Configure an OpenTelemetry Protocol receiver
--------------------------------------------

You can configure Alloy to receive OpenTelemetry metrics, logs, and traces. An OpenTelemetry _receiver_ component is responsible for receiving OpenTelemetry data from an external system.

In this task, you use the [`otelcol.receiver.otlp`](../../reference/components/otelcol/otelcol.receiver.otlp/)
 component to receive OpenTelemetry data over the network using the OpenTelemetry Protocol (OTLP). You can configure a receiver component to forward received data to other Alloy components.

> Refer to the list of available [Components](../../get-started/components/)
>  for the full list of `otelcol.receiver` components that you can use to receive OpenTelemetry-compatible data.

To configure an `otelcol.receiver.otlp` component for receiving OTLP data, complete the following steps:

1.  Follow [Configure an OpenTelemetry Protocol exporter](#configure-an-opentelemetry-protocol-exporter)
     to ensure received data can be written to an external system.
    
2.  Optional: Follow [Configure batching](#configure-batching)
     to improve compression and reduce the total amount of network requests.
    
3.  Add the following `otelcol.receiver.otlp` component to your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        otelcol.receiver.otlp "<LABEL>" {
          output {
            metrics = [<COMPONENT_INPUT_LIST>]
            logs    = [<COMPONENT_INPUT_LIST>]
            traces  = [<COMPONENT_INPUT_LIST>]
          }
        }
    
    Replace the following:
    
    *   _`<LABEL>`_: The label for the component, such as `default`. The label you use must be unique across all `otelcol.receiver.otlp` components in the same configuration file.
    *   _`<COMPONENT_INPUT_LIST>`_: A comma-delimited list of component inputs to forward received data to. For example, to send data to a batch processor component, use `otelcol.processor.batch.PROCESSOR_LABEL.input`. To send data directly to an exporter component, use `otelcol.exporter.otlp.EXPORTER_LABEL.input`.
    
    1.  To allow applications to send OTLP data over gRPC on port `4317`, add the following to your `otelcol.receiver.otlp` component.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            grpc {
              endpoint = "<HOST>:4317"
            }
        
        Replace the following:
        
        *   _`<HOST>`_: A host to listen to traffic on. Use a narrowly scoped listen address whenever possible. To listen on all network interfaces, replace _`<HOST>`_ with `0.0.0.0`.
    2.  To allow applications to send OTLP data over HTTP/1.1 on port `4318`, add the following to your `otelcol.receiver.otlp` component.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            http {
              endpoint = "<HOST>:4318"
            }
        
        Replace the following:
        
        *   _`<HOST>`_: The host to listen to traffic on. Use a narrowly scoped listen address whenever possible. To listen on all network interfaces, replace _`<HOST>`_ with `0.0.0.0`.
    3.  To disable one of the telemetry types, set the relevant type in the `output` block to the empty list, such as `metrics = []`.
        

The following example demonstrates configuring `otelcol.receiver.otlp` and sending it to an exporter:

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    otelcol.receiver.otlp "example" {
      grpc {
        endpoint = "127.0.0.1:4317"
      }
    
      http {
        endpoint = "127.0.0.1:4318"
      }
    
      output {
        metrics = [otelcol.processor.batch.example.input]
        logs    = [otelcol.processor.batch.example.input]
        traces  = [otelcol.processor.batch.example.input]
      }
    }
    
    otelcol.processor.batch "example" {
      output {
        metrics = [otelcol.exporter.otlp.default.input]
        logs    = [otelcol.exporter.otlp.default.input]
        traces  = [otelcol.exporter.otlp.default.input]
      }
    }
    
    otelcol.exporter.otlp "default" {
      client {
        endpoint = "my-otlp-grpc-server:4317"
      }
    }

For more information on receiving OpenTelemetry data using the OpenTelemetry Protocol, refer to [`otelcol.receiver.otlp`](../../reference/components/otelcol/otelcol.receiver.otlp/)
.

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/collect/opentelemetry-data.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/collect/opentelemetry-data.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Collect and forward data with Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Collect and forward data

Open source [RSS](/docs/alloy/latest/collect/index.xml)

Collect and forward data with Grafana Alloy
===========================================

*   [Choose a Grafana Alloy component](/docs/alloy/latest/collect/choose-component/)
    
*   [Set up meta-monitoring to collect Alloy telemetry](/docs/alloy/latest/collect/metamonitoring/)
    
*   [Receive traces and metrics from Datadog-instrumented applications](/docs/alloy/latest/collect/datadog-traces-metrics/)
    
*   [Collect Kubernetes logs and forward them to Loki](/docs/alloy/latest/collect/logs-in-kubernetes/)
    
*   [Collect Prometheus metrics](/docs/alloy/latest/collect/prometheus-metrics/)
    
*   [Collect OpenTelemetry data and forward it to any OpenTelemetry-compatible endpoint](/docs/alloy/latest/collect/opentelemetry-data/)
    
*   [Collect OpenTelemetry data and forward to Grafana](/docs/alloy/latest/collect/opentelemetry-to-lgtm-stack/)
    
*   [Collect Amazon Elastic Container Service or AWS Fargate OpenTelemetry data](/docs/alloy/latest/collect/ecs-opentelemetry-data/)
    

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/collect/_index.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/collect/_index.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Monitor Microsoft Windows servers and desktops with Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Monitor metrics and logs](/docs/alloy/latest/monitor/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Monitor Windows

Open source

Monitor Microsoft Windows servers and desktops with Grafana Alloy
=================================================================

Microsoft Windows provides tools like Performance Monitor and Event Viewer to track system performance metrics and event logs. With Alloy, you can collect your performance metrics and event logs, forward them to a Grafana stack, and create dashboards to monitor your Windows performance and events.

The [`alloy-scenarios`](https://github.com/grafana/alloy-scenarios/)
 repository contains complete examples of Alloy deployments. Clone the repository and use the examples to understand how Alloy collects, processes, and exports telemetry signals.

In this example scenario, Alloy collects Windows performance metrics and event logs and forwards them to a Loki destination.

Before you begin
----------------

Ensure you have the following:

*   [Docker](https://www.docker.com/)
    
*   [Git](https://git-scm.com/)
    
*   A Windows Server or Desktop. This scenario monitors a computer running Windows.
*   Windows administrator access. You need administrator access to install Alloy and configure it to collect metrics and logs.

> Note
> 
> You need administrator privileges to run `docker` commands.

Clone and deploy the example
----------------------------

Follow these steps to clone the scenarios repository and deploy the monitoring example:

1.  Clone the Alloy scenarios repository.
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        git clone https://github.com/grafana/alloy-scenarios.git
    
2.  Start Docker to deploy the Grafana stack.
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        cd alloy-scenarios/windows
        docker compose up -d
    
    Verify the status of the Docker containers:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        docker ps
    
3.  [Install Alloy](/docs/alloy/latest/set-up/install/windows/)
     on Windows.
    
4.  Replace the default `config.alloy` file with the preconfigured `config.alloy` file included in the `alloy-scenarios/windows` directory. For detailed steps explaining how to stop and start the Alloy service, refer to [Configure Alloy on Windows](/docs/alloy/latest/configure/windows/)
    .
    
    1.  Stop the Alloy service.
    2.  Replace the `config.alloy` file in `C:\Program Files\GrafanaLabs\Alloy` with the `config.alloy` file from the `alloy-scenarios/windows` directory.
    3.  Start the Alloy service.
5.  (Optional) To access the Alloy UI from a remote computer, add `--server.http.listen-addr=0.0.0.0:12345` to the Alloy runtime arguments. For detailed steps explaining how to update this command-line argument, refer to [Expose the UI to other machines](/docs/alloy/latest/configure/windows/#expose-the-ui-to-other-machines)
    . This step makes the Alloy UI available at `http://<WINDOWS_IP_ADDRESS>:12345`.
    
6.  (Optional) Stop Docker to shut down the Grafana stack when you finish exploring this example.
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        docker compose down
    

Monitor and visualize your data
-------------------------------

Use Grafana to monitor your deployment’s health and visualize your data.

### Monitor Alloy

To monitor the health of your Alloy deployment, open your browser and go to [http://localhost:12345](http://localhost:12345)
.

For more information about the Alloy UI, refer to [Debug Grafana Alloy](/docs/alloy/latest/troubleshoot/debug/)
.

### Visualize your data

To explore metrics, open your browser and go to [http://localhost:3000/explore/metrics](http://localhost:3000/explore/metrics)
.

To use the Grafana Logs Drilldown, open your browser and go to [http://localhost:3000/a/grafana-lokiexplore-app](http://localhost:3000/a/grafana-lokiexplore-app)
.

To create a [dashboard](/docs/grafana/latest/getting-started/build-first-dashboard/#create-a-dashboard)
 to visualize metrics and logs, open your browser and go to [http://localhost:3000/dashboards](http://localhost:3000/dashboards)
.

Understand the Alloy configuration
----------------------------------

This example uses a `config.alloy` file to configure the Alloy components for metrics and logging. You can find the `config.alloy` file in the cloned repository at `alloy-scenarios/windows/`.

The configuration includes `livedebugging` to stream real-time data to the Alloy UI.

### Configure metrics

The metrics configuration in this example requires three components:

*   `prometheus.exporter.windows`
*   `prometheus.scrape`
*   `prometheus.remote_write`

#### `prometheus.exporter.windows`

The \[`prometheus.exporter.windows`\]\[prometheus.exporter.windows\] component exposes hardware and OS metrics for Windows-based systems. In this example, the component requires the following arguments:

*   `enabled_collectors`: The list of collectors to enable.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    prometheus.exporter.windows "default" {
      enabled_collectors = ["cpu","cs","logical_disk","net","os","service","system", "memory", "scheduled_task", "tcp"]
    }

#### `prometheus.scrape`

The \[`prometheus.scrape`\]\[prometheus.scrape\] component scrapes Windows metrics and forwards them to a receiver. In this example, the component requires the following arguments:

*   `targets`: The target to scrape metrics from.
*   `forward_to`: The destination to forward metrics to.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    prometheus.scrape "example" {
      targets    = prometheus.exporter.windows.default.targets
      forward_to = [prometheus.remote_write.demo.receiver]
    }

#### `prometheus.remote_write`

The \[`prometheus.remote_write`\]\[prometheus.remote\_write\] component sends metrics to a Prometheus server. In this example, the component requires the following argument:

*   `url`: Defines the full URL endpoint to send metrics to.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    prometheus.remote_write "demo" {
      endpoint {
        url = "http://localhost:9090/api/v1/write"
      }
    }

### Configure logging

The logging configuration in this example requires three components:

*   `loki.source.windowsevent`
*   `loki.process`
*   `loki.write`

#### `loki.source.windowsevent`

The [`loki.source.windowsevent`](/docs/alloy/latest/reference/components/loki/loki.source.windowsevent/)
 component reads events from Windows Event Logs and forwards them to other Loki components. In this example, the component requires the following arguments:

*   `eventlog_name`: The event log to read from.
*   `use_incoming_timestamp`: Assigns the current timestamp to the log.
*   `forward_to`: The list of receivers to send log entries to.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    loki.source.windowsevent "application"  {
        eventlog_name = "Application"
        use_incoming_timestamp = true
        forward_to = [loki.process.endpoint.receiver]
    }

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    loki.source.windowsevent "System"  {
        eventlog_name = "System"
        use_incoming_timestamp = true
        forward_to = [loki.process.endpoint.receiver]
    }

#### `loki.process`

The [`loki.process`](/docs/alloy/latest/reference/components/loki/loki.process/)
 component receives log entries from other Loki components, applies one or more processing stages, and forwards the results to the list of receivers. In this example, the component requires the following arguments:

*   `forward_to`: The list of receivers to send log entries to.
*   `expressions`: The key-value pairs that define the name of the data extracted and the value that it’s populated with.
*   `values`: The key-value pairs that define the label to set and how to look them up.
*   `source`: Name from extracted values map to use for the timestamp.
*   `overwrite_existing`: Overwrite the existing extracted data fields.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    loki.process "endpoint" {
      forward_to = [loki.write.endpoint.receiver]
      stage.json {
          expressions = {
              message = "",
              Overwritten = "",
              source = "",
              computer = "",
              eventRecordID = "",
              channel = "",
              component_id = "",
              execution_processId = "",
              execution_processName = "",
          }
      }
    
      stage.structured_metadata {
          values = {
              "eventRecordID" = "",
              "channel" = "",
              "component_id" = "",
              "execution_processId" = "",
              "execution_processName" = "",
          }
      }
    
      stage.eventlogmessage {
          source = "message"
          overwrite_existing = true
      }
    
      stage.labels {
          values = {
              "service_name" = "source",
          }
      }
    
      stage.output {
        source = "message"
      }
    
    }

#### `loki.write`

The [`loki.write`](/docs/alloy/latest/reference/components/loki/loki.write/)
 component writes the logs out to a Loki destination. In this example, the component requires the following argument:

*   `url`: Defines the full URL endpoint in Loki to send logs to.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    loki.write "endpoint" {
        endpoint {
            url ="http://localhost:3100/loki/api/v1/push"
        }
    }

### Configure `livedebugging`

Livedebugging streams real-time data from components directly to the Alloy UI. Refer to the [Troubleshooting documentation](/docs/alloy/latest/troubleshoot/debug/#live-debugging-page)
 for more details about this feature.

#### `livedebugging`

`livedebugging` is disabled by default. Enable it explicitly through the `livedebugging` configuration block to make debugging data visible in the Alloy UI.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    livedebugging {
      enabled = true
    }

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/monitor/monitor-windows.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/monitor/monitor-windows.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Run Alloy as a non-root user in Kubernetes | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Configure](/docs/alloy/latest/configure/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Non-root user

Open source

Run Alloy as a non-root user in Kubernetes
==========================================

The [Alloy Docker image](https://hub.docker.com/r/grafana/alloy)
 contains two users:

*   A `root` user.
*   A non-root user named `alloy` with UID `473` and gid `473`.

By default, the `alloy` binary runs as `root`. This is because some Alloy components like [beyla.ebpf](../../reference/components/beyla/beyla.ebpf/)
 require root permissions.

You can configure a non-root user when you deploy Alloy in Kubernetes.

Configure Alloy to run as a non-root user in Kubernetes
-------------------------------------------------------

> Note
> 
> Running Alloy as a non-root user won’t work if you are using components like [beyla.ebpf](../../reference/components/beyla/beyla.ebpf/)
>  that require root rights.

To run Alloy as a non-root user, configure a [security context](https://kubernetes.io/docs/tasks/configure-pod-container/security-context/)
 for the Alloy container. If you are using the [Grafana Helm chart](../../configure/kubernetes/#configure-the-helm-chart)
 you can add the following snippet to `values.yaml`:

yaml  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    alloy:
      securityContext:
        runAsUser: 473
        runAsGroup: 473

This configuration makes the Alloy binary run with UID 473 and GID 473 rather than as root.

Is the root user a security risk?
---------------------------------

Not really. The Linux kernel prevents Docker containers from accessing host resources. For example, Docker containers see a virtual file system rather than the host filesystem, a virtual network rather than the host network, a virtual process namespace rather than the host’s processes. Even if the user inside the Docker container is `root` it can’t break out of this virtual environment.

However, if there was a bug in the Linux kernel that allowed Docker containers to break out of the virtual environment, it would likely be easier to exploit this bug with a root user than with a non-root user. It’s worth noting that the attacker would not only need to find such a Linux kernel bug, but would also need to find a way to make Alloy exploit that bug.

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/configure/nonroot.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/configure/nonroot.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Configure Grafana Alloy on macOS | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Configure](/docs/alloy/latest/configure/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) macOS

Open source

Configure Grafana Alloy on macOS
================================

To configure Alloy on macOS, perform the following steps:

1.  Edit the default configuration file at `$(brew --prefix)/etc/alloy/config.alloy`.
    
2.  Run the following command in a terminal to restart the Alloy service:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        brew services restart alloy
    

Configure the Alloy service
---------------------------

> Note
> 
> Due to limitations in Homebrew, customizing the service used by Alloy on macOS requires changing the Homebrew formula and reinstalling Alloy.

To customize the Alloy service on macOS, perform the following steps:

1.  Run the following command in a terminal:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        brew edit alloy
    
    This opens the Alloy Homebrew Formula in an editor.
    
2.  Modify the `service` section as desired to change things such as:
    
    *   The configuration file used by Alloy.
    *   Flags passed to the Alloy binary.
    *   Location of log files.
3.  Save the modified file.
    
4.  Reinstall the Alloy Formula by running the following command in a terminal:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        brew reinstall --formula alloy
    
5.  Restart the Alloy service by running the command in a terminal:
    
    shell  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        brew services restart alloy
    

Expose the UI to other machines
-------------------------------

By default, Alloy listens on the local network for its HTTP server. This prevents other machines on the network from being able to access the [UI for debugging](../../troubleshoot/debug/#alloy-ui)
.

To expose the UI to other machines, complete the following steps:

1.  Follow [Configure the Alloy service](#configure-the-alloy-service)
     to edit command line flags passed to Alloy.
    
2.  Modify the line inside the `service` block containing `--server.http.listen-addr=127.0.0.1:12345`, replacing `127.0.0.1` with the IP address which other machines on the network have access to. For example, the IP address of the machine Alloy is running on.
    
    To listen on all interfaces, replace `127.0.0.1` with `0.0.0.0`.
    

Was this page helpful?
----------------------

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[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/configure/macos.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/configure/macos.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Collect Prometheus metrics | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Collect and forward data](/docs/alloy/latest/collect/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Collect Prometheus metrics

Open source

Collect Prometheus metrics
==========================

You can configure Alloy to collect [Prometheus](https://prometheus.io)
 metrics and forward them to any Prometheus-compatible database.

This topic describes how to:

*   Configure metrics delivery.
*   Collect metrics from Kubernetes Pods.

Components used in this topic
-----------------------------

*   [`discovery.kubernetes`](../../reference/components/discovery/discovery.kubernetes/)
    
*   [`prometheus.remote_write`](../../reference/components/prometheus/prometheus.remote_write/)
    
*   [`prometheus.scrape`](../../reference/components/prometheus/prometheus.scrape/)
    

Before you begin
----------------

*   Ensure that you have basic familiarity with instrumenting applications with Prometheus.
*   Have a set of Prometheus exports or applications exposing Prometheus metrics that you want to collect metrics from.
*   Identify where to write collected metrics. Metrics can be written to Prometheus or Prometheus-compatible endpoints such as Grafana Mimir, Grafana Cloud, or Grafana Enterprise Metrics.
*   Be familiar with the concept of [Components](../../get-started/components/)
     in Alloy.

Configure metrics delivery
--------------------------

Before components can collect Prometheus metrics, you must have a component responsible for writing those metrics somewhere.

The [`prometheus.remote_write`](../../reference/components/prometheus/prometheus.remote_write/)
 component is responsible for delivering Prometheus metrics to one or more Prometheus-compatible endpoints. After a `prometheus.remote_write` component is defined, you can use other Alloy components to forward metrics to it.

To configure a `prometheus.remote_write` component for metrics delivery, complete the following steps:

1.  Add the following `prometheus.remote_write` component to your configuration file.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        prometheus.remote_write "<LABEL>" {
          endpoint {
            url = "<PROMETHEUS_URL>"
          }
        }
    
    Replace the following:
    
    *   _`<LABEL>`_: The label for the component, such as `default`. The label you use must be unique across all `prometheus.remote_write` components in the same configuration file.
    *   _`<PROMETHEUS_URL>`_ The full URL of the Prometheus-compatible endpoint where metrics are sent, such as `https://prometheus-us-central1.grafana.net/api/v1/write` for Prometheus or `https://mimir-us-central1.grafana.net/api/v1/push/` for Mimir. The endpoint URL depends on the database you use.
2.  If your endpoint requires basic authentication, paste the following inside the `endpoint` block.
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        basic_auth {
          username = "<USERNAME>"
          password = "<PASSWORD>"
        }
    
    Replace the following:
    
    *   _`<USERNAME>`_: The basic authentication username.
    *   _`<PASSWORD>`_: The basic authentication password or API key.
3.  If you have more than one endpoint to write metrics to, repeat the `endpoint` block for additional endpoints.
    

The following example demonstrates configuring `prometheus.remote_write` with multiple endpoints and mixed usage of basic authentication, and a `prometheus.scrape` component which forwards metrics to it.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    prometheus.remote_write "default" {
      endpoint {
        url = "http://localhost:9090/api/prom/push"
      }
    
      endpoint {
        url = "https://prometheus-us-central1.grafana.net/api/prom/push"
    
        // Get basic authentication based on environment variables.
        basic_auth {
          username = sys.env("<REMOTE_WRITE_USERNAME>")
          password = sys.env("<REMOTE_WRITE_PASSWORD>")
        }
      }
    }
    
    prometheus.scrape "example" {
      // Collect metrics from the default listen address.
      targets = [{\
        __address__ = "127.0.0.1:12345",\
      }]
    
      forward_to = [prometheus.remote_write.default.receiver]
    }

For more information on configuring metrics delivery, refer to [prometheus.remote\_write](../../reference/components/prometheus/prometheus.remote_write/)
.

Collect metrics from Kubernetes Pods
------------------------------------

Alloy can be configured to collect metrics from Kubernetes Pods by:

1.  Discovering Kubernetes Pods to collect metrics from.
2.  Collecting metrics from those discovered Pods.

To collect metrics from Kubernetes Pods, complete the following steps:

1.  Follow [Configure metrics delivery](#configure-metrics-delivery)
     to ensure collected metrics can be written somewhere.
    
2.  Discover Kubernetes Pods:
    
    1.  Add the following `discovery.kubernetes` component to your configuration file to discover every Pod in the cluster across all Namespaces.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            discovery.kubernetes "<DISCOVERY_LABEL>" {
              role = "pod"
            }
        
        Replace the following
        
        *   _`<DISCOVERY_LABEL>`_: The label for the component, such as `pods`. The label you use must be unique across all `discovery.kubernetes` components in the same configuration file.
        
        This generates one Prometheus target for every exposed port on every discovered Pod.
        
    2.  To limit the Namespaces that Pods are discovered in, add the following block inside the `discovery.kubernetes` component.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            namespaces {
              own_namespace = true
              names         = [<NAMESPACE_NAMES>]
            }
        
        Replace the following:
        
        *   _`<NAMESPACE_NAMES>`_: A comma-delimited list of strings representing Namespaces to search. Each string must be wrapped in double quotes. For example, `"default","kube-system"`.
        
        If you don’t want to search for Pods in the Namespace Alloy is running in, set `own_namespace` to `false`.
        
    3.  To use a field selector to limit the number of discovered Pods, add the following block inside the `discovery.kubernetes` component.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            selectors {
              role  = "pod"
              field = "<FIELD_SELECTOR>"
            }
        
        Replace the following:
        
        *   _`<FIELD_SELECTOR>`_: The Kubernetes field selector to use, such as `metadata.name=my-service`. For more information on field selectors, refer to the Kubernetes documentation on [Field Selectors](https://kubernetes.io/docs/concepts/overview/working-with-objects/field-selectors/)
            .
        
        Create additional `selectors` blocks for each field selector you want to apply.
        
    4.  To use a label selector to limit the number of discovered Pods, add the following block inside the `discovery.kubernetes` component.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            selectors {
              role  = "pod"
              label = "LABEL_SELECTOR"
            }
        
        Replace the following:
        
        *   _`<LABEL_SELECTOR>`_: The Kubernetes label selector, such as `environment in (production, qa)`. For more information on label selectors, refer to the Kubernetes documentation on [Labels and Selectors](https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/#set-based-requirement)
            .
        
        Create additional `selectors` blocks for each label selector you want to apply.
        
3.  Collect metrics from discovered Pods:
    
    1.  Add the following `prometheus.scrape` component to your configuration file.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            prometheus.scrape "<SCRAPE_LABEL>" {
              targets    = discovery.kubernetes.<DISCOVERY_LABEL>.targets
              forward_to = [prometheus.remote_write.<REMOTE_WRITE_LABEL>.receiver]
            }
        
        Replace the following:
        
        *   _`<SCRAPE_LABEL>`_: The label for the component, such as `pods`. The label you use must be unique across all `prometheus.scrape` components in the same configuration file.
        *   _`<DISCOVERY_LABEL>`_: The label for the `discovery.kubernetes` component.
        *   _`<REMOTE_WRITE_LABEL>`_: The label for your `prometheus.remote_write` component.

The following example demonstrates configuring Alloy to collect metrics from running production Kubernetes Pods in the `default` Namespace.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    discovery.kubernetes "pods" {
      role = "pod"
    
      namespaces {
        own_namespace = false
    
        names = ["default"]
      }
    
      selectors {
        role  = "pod"
        label = "environment in (production)"
      }
    }
    
    prometheus.scrape "pods" {
      targets    = discovery.kubernetes.pods.targets
      forward_to = [prometheus.remote_write.default.receiver]
    }
    
    prometheus.remote_write "default" {
      endpoint {
        url = "http://localhost:9090/api/prom/push"
      }
    }

For more information on configuring Kubernetes service delivery and collecting metrics, refer to [`discovery.kubernetes`](../../reference/components/discovery/discovery.kubernetes/)
 and [`prometheus.scrape`](../../reference/components/prometheus/prometheus.scrape/)
.

Collect metrics from Kubernetes Services
----------------------------------------

You can configure Alloy to collect metrics from Kubernetes Services by:

1.  Discovering Kubernetes Services to collect metrics from.
2.  Collecting metrics from those discovered Services.

To collect metrics from Kubernetes Services, complete the following steps.

1.  Follow [Configure metrics delivery](#configure-metrics-delivery)
     to ensure collected metrics can be written somewhere.
    
2.  Discover Kubernetes Services:
    
    1.  Add the following `discovery.kubernetes` component to your configuration file to discover every Services in the cluster across all Namespaces.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            discovery.kubernetes "<DISCOVERY_LABEL>" {
              role = "service"
            }
        
        Replace the following:
        
        *   _`<DISCOVERY_LABEL>`_: A label for the component, such as `services`. The label you use must be unique across all `discovery.kubernetes` components in the same configuration file.
        
        This generates one Prometheus target for every exposed port on every discovered Service.
        
    2.  To limit the Namespaces that Services are discovered in, add the following block inside the `discovery.kubernetes` component.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            namespaces {
              own_namespace = true
              names         = [<NAMESPACE_NAMES>]
            }
        
        Replace the following:
        
        *   _`<NAMESPACE_NAMES>`_: A comma-delimited list of strings representing Namespaces to search. Each string must be wrapped in double quotes. For example, `"default","kube-system"`.
        
        If you don’t want to search for Services in the Namespace Alloy is running in, set `own_namespace` to `false`.
        
    3.  To use a field selector to limit the number of discovered Services, add the following block inside the `discovery.kubernetes` component.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            selectors {
              role  = "service"
              field = "<FIELD_SELECTOR>"
            }
        
        Replace the following:
        
        *   _`<FIELD_SELECTOR>`_: The Kubernetes field selector, such as `metadata.name=my-service`. For more information on field selectors, refer to the Kubernetes documentation on [Field Selectors](https://kubernetes.io/docs/concepts/overview/working-with-objects/field-selectors/)
            .
        
        Create additional `selectors` blocks for each field selector you want to apply.
        
    4.  To use a label selector to limit the number of discovered Services, add the following block inside the `discovery.kubernetes` component.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            selectors {
              role  = "service"
              label = "<LABEL_SELECTOR>"
            }
        
        Replace the following:
        
        *   _`<LABEL_SELECTOR>`_: The Kubernetes label selector, such as `environment in (production, qa)`. For more information on label selectors, refer to the Kubernetes documentation on [Labels and Selectors](https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/#set-based-requirement)
            .
        
        Create additional `selectors` blocks for each label selector you want to apply.
        
3.  Collect metrics from discovered Services:
    
    1.  Add the following `prometheus.scrape` component to your configuration file.
        
        alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
        
            prometheus.scrape "<SCRAPE_LABEL>" {
              targets    = discovery.kubernetes.<DISCOVERY_LABEL>.targets
              forward_to = [prometheus.remote_write.<REMOTE_WRITE_LABEL>.receiver]
            }
        
        Replace the following:
        
        *   _`<SCRAPE_LABEL>`_: The label for the component, such as `services`. The label you use must be unique across all `prometeus.scrape` components in the same configuration file.
        *   _`<DISCOVERY_LABEL>`_: The label for the `discovery.kubernetes` component.
        *   _`<REMOTE_WRITE_LABEL>`_: The label for your `prometheus.remote_write` component.

The following example demonstrates configuring Alloy to collect metrics from running production Kubernetes Services in the `default` Namespace.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    discovery.kubernetes "services" {
      role = "service"
    
      namespaces {
        own_namespace = false
    
        names = ["default"]
      }
    
      selectors {
        role  = "service"
        label = "environment in (production)"
      }
    }
    
    prometheus.scrape "services" {
      targets    = discovery.kubernetes.services.targets
      forward_to = [prometheus.remote_write.default.receiver]
    }
    
    prometheus.remote_write "default" {
      endpoint {
        url = "http://localhost:9090/api/prom/push"
      }
    }

For more information on configuring Kubernetes service delivery and collecting metrics, refer to [`discovery.kubernetes`](../../reference/components/discovery/discovery.kubernetes/)
 and [`prometheus.scrape`](../../reference/components/prometheus/prometheus.scrape/)
.

Collect metrics from custom targets
-----------------------------------

You can configure Alloy to collect metrics from a custom set of targets without the need for service discovery.

To collect metrics from a custom set of targets, complete the following steps.

1.  Follow [Configure metrics delivery](#configure-metrics-delivery)
     to ensure collected metrics can be written somewhere.
    
2.  Add the following `prometheus.scrape` component to your configuration file:
    
    alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy
    
        prometheus.scrape "<SCRAPE_LABEL>" {
          targets    = [<TARGET_LIST>]
          forward_to = [prometheus.remote_write.<REMOTE_WRITE_LABEL>.receiver]
        }
    
    Replace the following:
    
    *   \_`<SCRAPE_LABEL>`: The label for the component, such as `custom_targets`. The label you use must be unique across all `prometheus.scrape` components in the same configuration file.
        
    *   _`<TARGET_LIST>`_: A comma-delimited list of [Objects](../../get-started/configuration-syntax/expressions/types_and_values/#objects)
         denoting the Prometheus target. Each object must conform to the following rules:
        
        *   There must be an `__address__` key denoting the `HOST:PORT` of the target to collect metrics from.
        *   To explicitly specify which protocol to use, set the `__scheme__` key to `"http"` or `"https"`. If the `__scheme__` key isn’t provided, the protocol to use is inherited by the settings of the `prometheus.scrape` component. The default is `"http"`.
        *   To explicitly specify which HTTP path to collect metrics from, set the `__metrics_path__` key to the HTTP path to use. If the `__metrics_path__` key isn’t provided, the path to use is inherited by the settings of the `prometheus.scrape` component. The default is `"/metrics"`.
        *   Add additional keys as desired to inject extra labels to collected metrics. Any label starting with two underscores (`__`) is dropped prior to scraping.
    *   _`<REMOTE_WRITE_LABEL>`_: The label for your `prometheus.remote_write` component.
        

The following example demonstrates configuring `prometheus.scrape` to collect metrics from a custom set of endpoints.

alloy  ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

    prometheus.scrape "custom_targets" {
      targets = [\
        {\
          __address__ = "prometheus:9090",\
        },\
        {\
          __address__ = "mimir:8080",\
          __scheme__  = "https",\
        },\
        {\
          __address__      = "custom-application:80",\
          __metrics_path__ = "/custom-metrics–path",\
        },\
        {\
          __address__ = "alloy:12345",\
          application = "alloy",\
          environment = "production",\
        },\
      ]
    
      forward_to = [prometheus.remote_write.default.receiver]
    }
    
    prometheus.remote_write "default" {
      endpoint {
        url = "http://localhost:9090/api/prom/push"
      }
    }

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/collect/prometheus-metrics.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/collect/prometheus-metrics.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---

# Monitor metrics and logs with Grafana Alloy | Grafana Alloy documentation

Menu

[Documentation](/docs/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) [Grafana Alloy](/docs/alloy/latest/)
![breadcrumb arrow](/static/assets/img/icons/grafana-icon-breadcrumb-arrow-gray.svg) Monitor metrics and logs

Open source [RSS](/docs/alloy/latest/monitor/index.xml)

Monitor metrics and logs with Grafana Alloy
===========================================

This section provides detailed examples that show you how you can use Grafana Alloy to monitor various data sources.

*   [Monitor Docker containers with Grafana Alloy](/docs/alloy/latest/monitor/monitor-docker-containers/)
    
*   [Monitor Microsoft Windows servers and desktops with Grafana Alloy](/docs/alloy/latest/monitor/monitor-windows/)
    
*   [Monitor logs from a local file with Grafana Alloy](/docs/alloy/latest/monitor/monitor-logs-from-file/)
    
*   [Monitor TCP logs with Grafana Alloy](/docs/alloy/latest/monitor/monitor-logs-over-tcp/)
    
*   [Monitor RFC5424-compliant syslog messages with Grafana Alloy](/docs/alloy/latest/monitor/monitor-syslog-messages/)
    
*   [Monitor structured logs with Grafana Alloy](/docs/alloy/latest/monitor/monitor-structured-logs/)
    
*   [Monitor Kubernetes logs with Grafana Alloy](/docs/alloy/latest/monitor/monitor-kubernetes-logs/)
    

Was this page helpful?
----------------------

 ![👍](/media/images/svg/thumbs-up.svg) Yes ![👎](/media/images/svg/thumbs-up.svg) No

[Suggest an edit in GitHub](https://github.com/grafana/alloy/edit/main/docs/sources/monitor/_index.md)
[Create a GitHub issue](https://github.com/grafana/alloy/issues/new?title=Documentation%20feedback:%20/docs/sources/monitor/_index.md)
[Email docs@grafana.com](mailto:docs@grafana.com)
[Help and support](/help/)
[Community](/community/)

Related documentation
---------------------

Related resources from Grafana Labs
-----------------------------------

Additional helpful documentation, links, and articles:

[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Incident management with Grafana IRM & SLOS in Grafana Cloud\
\
This webinar shows you how to prioritize critical resources through service level objective(SLO)-driven incident response and management (IRM).](/go/webinar/getting-started-with-grafana-incident-response-and-management-irm-and-slo-emea/)
[![webinar icon](/static/assets/img/icons/grafana-icon-card-webinar.svg)\
\
30 Apr\
\
Building advanced Grafana dashboards\
\
In this webinar, we’ll demo how to build and format Grafana dashboards.](/go/webinar/building-advanced-grafana-dashboards/)
[![video icon](/static/assets/img/icons/grafana-icon-card-video.svg)\
\
60 min\
\
Mastering OpenTelemetry instrumentation and Grafana\
\
Join our advanced OpenTelemetry webinar to learn about code-based instrumentation, best practices, and unlocking new use cases with Grafana and Grafana Cloud.](/go/webinar/mastering-code-based-instrumentation-with-opentelemetry-and-grafana/)

---