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  1. Stackups
  2. DevOps
  3. Monitoring
  4. Monitoring Tools
  5. Prometheus vs Shinken

Prometheus vs Shinken

OverviewDecisionsComparisonAlternatives

Overview

Prometheus
Prometheus
Stacks4.8K
Followers3.8K
Votes239
GitHub Stars61.1K
Forks9.9K
Shinken
Shinken
Stacks17
Followers39
Votes0

Prometheus vs Shinken: What are the differences?

What is Prometheus? An open-source service monitoring system and time series database, developed by SoundCloud. Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

What is Shinken? Nagios compatible monitoring framework, written in Python. Shinken's main goal is to give users a flexible architecture for their monitoring system that is designed to scale to large environments. Shinken is backwards-compatible with the Nagios configuration standard and plugins. It works on any operating system and architecture that supports Python, which includes Windows, GNU/Linux and FreeBSD.

Prometheus and Shinken belong to "Monitoring Tools" category of the tech stack.

Some of the features offered by Prometheus are:

  • a multi-dimensional data model (timeseries defined by metric name and set of key/value dimensions)
  • a flexible query language to leverage this dimensionality
  • no dependency on distributed storage

On the other hand, Shinken provides the following key features:

  • Easy to install : install is mainly done with pip but some packages are available (deb / rpm) and we are planning to provide nightly build
  • Easy for new users : once installed, Shinken provide a simple command line interface to install new module and packs
  • Easy to migrate from Nagios : we want Nagios configuration and plugins to work in Shinken so that it is a “in place” replacement

Prometheus and Shinken are both open source tools. Prometheus with 24.6K GitHub stars and 3.49K forks on GitHub appears to be more popular than Shinken with 1.08K GitHub stars and 354 GitHub forks.

According to the StackShare community, Prometheus has a broader approval, being mentioned in 235 company stacks & 84 developers stacks; compared to Shinken, which is listed in 3 company stacks and 3 developer stacks.

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Advice on Prometheus, Shinken

Leonardo Henrique da
Leonardo Henrique da

Pleno QA Enginneer at SolarMarket

Dec 8, 2020

Decided

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

402k views402k
Comments
Raja Subramaniam
Raja Subramaniam

Aug 27, 2019

Needs adviceonPrometheusPrometheusKubernetesKubernetesSysdigSysdig

We have Prometheus as a monitoring engine as a part of our stack which contains Kubernetes cluster, container images and other open source tools. Also, I am aware that Sysdig can be integrated with Prometheus but I really wanted to know whether Sysdig or sysdig+prometheus will make better monitoring solution.

779k views779k
Comments
Susmita
Susmita

Senior SRE at African Bank

Jul 28, 2020

Needs adviceonGrafanaGrafana

Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:

  • Must be able to get custom data from AS400,
  • Able to display automation test results,
  • System monitoring / Nginx API,
  • Able to get data from 3rd parties DB.

Grafana is almost solving all the problems, except AS400 and no database to get automation test results.

869k views869k
Comments

Detailed Comparison

Prometheus
Prometheus
Shinken
Shinken

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Shinken's main goal is to give users a flexible architecture for their monitoring system that is designed to scale to large environments. Shinken is backwards-compatible with the Nagios configuration standard and plugins. It works on any operating system and architecture that supports Python, which includes Windows, GNU/Linux and FreeBSD.

Dimensional data; Powerful queries; Great visualization; Efficient storage; Precise alerting; Simple operation
Easy to install : install is mainly done with pip but some packages are available (deb / rpm) and we are planning to provide nightly build; Easy for new users : once installed, Shinken provide a simple command line interface to install new module and packs; Easy to migrate from Nagios : we want Nagios configuration and plugins to work in Shinken so that it is a “in place” replacement; Plugins provide great flexibility and are a big legacy codebase to use. It would be a shame not to use all this community work Multi-platform : python is available in a lot of OS. We try to write generic code to keep this possible; Utf8 compliant : python is here to do that. For now Shinken is compatible with 2.6-2.7 version but python 3.X is even more character encoding friendly; Independent from other monitoring solution : our goal is to provide a modular tool that can integrate with others through standard interfaces). Flexibility first; Flexible : in an architecture point view. It is very close to our scalability wish. Cloud computing is make architecture moving a lot, we have to fit to it; Fun to code : python ensure good code readability. Adding code should not be a pain when developing;
Statistics
GitHub Stars
61.1K
GitHub Stars
-
GitHub Forks
9.9K
GitHub Forks
-
Stacks
4.8K
Stacks
17
Followers
3.8K
Followers
39
Votes
239
Votes
0
Pros & Cons
Pros
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
Cons
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
No community feedback yet
Integrations
Grafana
Grafana
Nagios
Nagios

What are some alternatives to Prometheus, Shinken?

Grafana

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Kibana

Kibana

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Nagios

Nagios

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Netdata

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

Zabbix

Zabbix

Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.

Sensu

Sensu

Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.

Graphite

Graphite

Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand

Lumigo

Lumigo

Lumigo is an observability platform built for developers, unifying distributed tracing with payload data, log management, and real-time metrics to help you deeply understand and troubleshoot your systems.

StatsD

StatsD

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

Jaeger

Jaeger

Jaeger, a Distributed Tracing System

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