# Prometheus for Monitoring Microservices and Business Metrics

DevFeed: [Prometheus for Monitoring Microservices and Business Metrics](<https://devfeed.tech/articles/getting-down-to-business-with-prometheus-32007.md>)

Original publisher: [Read original article](<https://tech.finn.no2016/08/26/down-to-business-with-prometheus/>)

Author: Håvard Nesvold

Published: 2016-08-26T13:00:00Z

Content type: article

Language: en

Sources: [Finn.no](<https://devfeed.tech/sources/finn-no.md>)

Topics: [Prometheus](<https://devfeed.tech/topics/prometheus.md>), [Microservices](<https://devfeed.tech/topics/microservices.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Time Series](<https://devfeed.tech/topics/time-series.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>)

Tags: [grafana](<https://devfeed.tech/tags/grafana.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [prometheus](<https://devfeed.tech/tags/prometheus.md>), [time-series](<https://devfeed.tech/tags/time-series.md>)

## AI overview

FINN describes switching from StatsD and Graphite to Prometheus for monitoring time-series data in a microservices architecture. The article discusses Prometheus's scalability, Kubernetes support, and monitoring of both application and business metrics.

## Source excerpt

Having moved towards an architecture of microservices, FINN is already leveraging a number of technologies for identifying and dealing with service outages within this architectural style. With each piece of functionality being comprised of a growing number of individual services, specialized tools are required for detection, analysis and mitigation of errors, and we are already using our fair share: Zipkin, Hystrix, Kibana, Grafana and Sensu to name some. When it comes to metrics and monitoring of time series data, FINN has traditionally employed an infrastructure based on StatsD and Graphite. Recently, however, we opted to switch to Prometheus. Prometheus, which is heavily inspired by Google's Borgmon monitoring system, was originally developed by SoundCloud as a reaction to scaling issues experienced with just StatsD and Graphite. Taking into mind Adrian Cockcroft's rule #4 of monitoring: Monitoring systems need to be more available and scalable than the systems being monitored Prometheus stands out as a great choice for us. Not only is it directly supported by Kubernetes, the future container management platform of choice here at FINN, it has been engineered from the ground up to deal with issues of scale and stability. In this new area of monitoring microservices, Graphite seems to be losing ground. The switch to Prometheus has been rapidly implemented by all our autonomous service teams. Along with monitoring of the "traditional" application metrics like latency and memory usage, special care has also been taken to ensure implementation of adequate monitoring of business metrics. Business metrics monitoring The implications of one failing or partly broken service can be hard to evaluate when you are dealing with a large number of services. Since a complex problem now can be broken up into units that are truly independent, all individual parts can continue to work fine separately, while the end result is just not working. Business metrics monitoring is a key to