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

Grafana vs Shinken

OverviewDecisionsComparisonAlternatives

Overview

Grafana
Grafana
Stacks18.4K
Followers14.6K
Votes415
GitHub Stars70.7K
Forks13.1K
Shinken
Shinken
Stacks17
Followers39
Votes0

Grafana vs Shinken: What are the differences?

Introduction

Grafana and Shinken are both software tools used for monitoring and visualizing metrics in an IT environment. While they share some similarities, there are key differences that set them apart.

  1. Architecture: Grafana is primarily a visualization tool that connects to various data sources and presents the collected data in customizable dashboards. It does not have built-in monitoring capabilities but can integrate with other monitoring tools. On the other hand, Shinken is a complete monitoring solution that includes data collection, alerting, and visualization components. It is designed to monitor various types of infrastructure and services from a central server.

  2. Data Visualization: Grafana provides a wide range of options for visualizing data, including charts, graphs, tables, and maps. It offers a highly flexible and customizable interface, allowing users to create visually appealing and informative dashboards. Shinken, on the other hand, does not have as extensive visualization capabilities as Grafana. It focuses more on the monitoring aspect, providing basic visualization options such as tables and graphs.

  3. Alerting and Notification: Shinken excels in alerting and notification capabilities. It allows users to set up complex alerting rules based on predefined thresholds or custom criteria. It provides multiple notification options, such as email, SMS, and integration with third-party tools like Slack. Grafana, on the other hand, relies on integrations with alerting tools like Prometheus or InfluxDB for alerting and notification functionalities.

  4. Community and Ecosystem: Grafana has a larger and more active community compared to Shinken. This allows for a wider range of community-developed plugins, extensions, and integrations. Grafana also has extensive documentation and online resources available, making it easy for users to find help and support. Shinken has a smaller community and a more limited ecosystem of plugins and extensions.

  5. Ease of Use: Grafana offers a user-friendly and intuitive interface, making it easy for both beginners and experienced users to create dashboards and visualizations. It provides drag-and-drop functionality and a rich set of pre-built panels and templates. Shinken, on the other hand, has a steeper learning curve and requires more configuration and setup to get started. It is more suited for experienced users or organizations with specific monitoring requirements.

  6. Scalability: Shinken is designed to handle large-scale monitoring environments with thousands of hosts and services. It supports distributed monitoring, allowing for the deployment of multiple servers for load balancing and fault tolerance. Grafana, on the other hand, relies on external data sources for data collection and does not have built-in scalability features. Scalability in Grafana depends on the scalability of the underlying data sources.

In summary, Grafana is a powerful data visualization tool with flexible dashboards and a large community, while Shinken is a comprehensive monitoring solution with strong alerting capabilities and scalability for large deployments.

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Advice on Grafana, 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
StackShare
StackShare

Jun 25, 2019

Needs advice

From a StackShare Community member: “We need better analytics & insights into our Elasticsearch cluster. Grafana, which ships with advanced support for Elasticsearch, looks great but isn’t officially supported/endorsed by Elastic. Kibana, on the other hand, is made and supported by Elastic. I’m wondering what people suggest in this situation."

663k views663k
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

Grafana
Grafana
Shinken
Shinken

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.

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.

Create, edit, save & search dashboards;Change column spans and row heights;Drag and drop panels to rearrange;Use InfluxDB or Elasticsearch as dashboard storage;Import & export dashboard (json file);Import dashboard from Graphite;Templating
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
70.7K
GitHub Stars
-
GitHub Forks
13.1K
GitHub Forks
-
Stacks
18.4K
Stacks
17
Followers
14.6K
Followers
39
Votes
415
Votes
0
Pros & Cons
Pros
  • 89
    Beautiful
  • 68
    Graphs are interactive
  • 57
    Free
  • 56
    Easy
  • 34
    Nicer than the Graphite web interface
Cons
  • 1
    No interactive query builder
No community feedback yet
Integrations
Graphite
Graphite
InfluxDB
InfluxDB
Nagios
Nagios

What are some alternatives to Grafana, Shinken?

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.

Prometheus

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

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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