Shinken vs StatsD

Overview

StatsD

Stacks373

Followers293

Votes31

Shinken

Stacks17

Followers39

Votes0

Shinken vs StatsD: What are the differences?

Developers describe Shinken 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. On the other hand, StatsD is detailed as "Simple daemon for easy stats aggregation". StatsD is a front-end proxy for the Graphite/Carbon metrics server, originally written by Etsy's Erik Kastner. StatsD is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

Shinken and StatsD can be categorized as "Monitoring" tools.

Some of the features offered by Shinken are:

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

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

buckets: Each stat is in its own "bucket". They are not predefined anywhere. Buckets can be named anything that will translate to Graphite (periods make folders, etc)
values: Each stat will have a value. How it is interpreted depends on modifiers. In general values should be integer.
flush: After the flush interval timeout (defined by config.flushInterval, default 10 seconds), stats are aggregated and sent to an upstream backend service.

Shinken and StatsD are both open source tools. StatsD with 14.2K GitHub stars and 1.83K forks on GitHub appears to be more popular than Shinken with 1.08K GitHub stars and 355 GitHub forks.

According to the StackShare community, StatsD has a broader approval, being mentioned in 72 company stacks & 16 developers stacks; compared to Shinken, which is listed in 3 company stacks and 3 developer stacks.

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CLI (Node.js)

Manual

Detailed Comparison

StatsD	Shinken
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).	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.
Network daemon; Runs on the Node.js platform; Sends aggregates to one or more pluggable backend services	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
Stacks 373	Stacks 17
Followers 293	Followers 39
Votes 31	Votes 0
Pros & Cons
Pros 9 Open source 7 Single responsibility 5 Efficient wire format 3 Loads of integrations 3 Handles aggregation Cons 1 No authentication; cannot be used over Internet	No community feedback yet
Integrations
Node.js Docker Graphite	Nagios

What are some alternatives to StatsD, Shinken?

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.

Jaeger

Jaeger, a Distributed Tracing System

Related Comparisons

Some of the features offered by Shinken are:

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

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

buckets: Each stat is in its own "bucket". They are not predefined anywhere. Buckets can be named anything that will translate to Graphite (periods make folders, etc)
values: Each stat will have a value. How it is interpreted depends on modifiers. In general values should be integer.
flush: After the flush interval timeout (defined by config.flushInterval, default 10 seconds), stats are aggregated and sent to an upstream backend service.

Shinken vs StatsD