Shinken vs Metricbeat

Overview

Shinken

Stacks17

Followers39

Votes0

Metricbeat

Stacks48

Followers125

Votes3

Metricbeat vs Shinken: What are the differences?

Developers describe Metricbeat as "A Lightweight Shipper for Metrics". Collect metrics from your systems and services. From CPU to memory, Redis to NGINX, and much more, It is a lightweight way to send system and service statistics. On the other hand, Shinken is detailed as "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.

Metricbeat and Shinken can be primarily classified as "Monitoring" tools.

Some of the features offered by Metricbeat are:

System-Level Monitoring
system-level CPU usage statistics
Network IO statistics

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

Shinken is an open source tool with 1.08K GitHub stars and 356 GitHub forks. Here's a link to Shinken's open source repository on GitHub.

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Advice on Shinken, Metricbeat

Sunil

Team Lead at XYZ

Jun 15, 2020

Needs adviceon

Prometheus

Grafana

Linux

Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.

Is it OK to use Metricbeat for Linux server or can we use Prometheus?

What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?

Regards, Sunil.

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Comments

Detailed Comparison

Shinken	Metricbeat
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.	Collect metrics from your systems and services. From CPU to memory, Redis to NGINX, and much more, It is a lightweight way to send system and service statistics.
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;	System-Level Monitoring; system-level CPU usage statistics; Network IO statistics
Statistics
Stacks 17	Stacks 48
Followers 39	Followers 125
Votes 0	Votes 3
Pros & Cons
No community feedback yet	Pros 2 Simple 1 Easy to setup
Integrations
Nagios	Redis Linux NGINX Windows

What are some alternatives to Shinken, Metricbeat?

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 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.

Prometheus

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.

Nagios

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

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

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

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

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).