> For the complete documentation index, see [llms.txt](https://gia-duong-duc-minh.gitbook.io/notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gia-duong-duc-minh.gitbook.io/notes/computer/microservices/application-metrics.md).

# Application metrics

## Context

You have applied the \[\[Microservice architecture]] pattern.

## Problem

How to understand the behavior of an application and troubleshoot problems?

## Forces

Any solution should have minimal runtime overhead

## Solution

Instrument a service to gather statistics about individual operations. Aggregate metrics in centralized metrics service, which provides reporting and alerting. There are two models for aggregating metrics:

* push - the service pushes metrics to the metrics service
* pull - the metrics services pulls metrics from the service

## Examples

* Instrumentation libraries:
  * Coda Hale/Yammer [Java Metrics Library](http://metrics.dropwizard.io/3.1.0/)
  * [Prometheus client libraries](https://prometheus.io/docs/instrumenting/clientlibs/)
* Metrics aggregation services
  * [Prometheus](https://prometheus.io/docs/introduction/overview/)
  * [AWS Cloud Watch](https://aws.amazon.com/cloudwatch/)

## Resulting context

This pattern has the following benefits:

* It provides deep insight into application behavior

This pattern has the following drawbacks:

* Metrics code is intertwined with business logic making it more complicated

This pattern has the following issues:

* Aggregating metrics can require significant infrastructure