Kibana vs StatsD

Overview

StatsD

Stacks373

Followers293

Votes31

Kibana

Stacks20.6K

Followers16.4K

Votes262

GitHub Stars20.8K

Forks8.5K

Kibana vs StatsD: What are the differences?

Developers describe Kibana as "Explore & Visualize Your Data". 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. 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).

Kibana and StatsD can be primarily classified as "Monitoring" tools.

Some of the features offered by Kibana are:

Flexible analytics and visualization platform
Real-time summary and charting of streaming data
Intuitive interface for a variety of users

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.

"Easy to setup" is the top reason why over 76 developers like Kibana, while over 6 developers mention "Single responsibility" as the leading cause for choosing StatsD.

Kibana and StatsD are both open source tools. It seems that StatsD with 14.2K GitHub stars and 1.83K forks on GitHub has more adoption than Kibana with 12.4K GitHub stars and 4.8K GitHub forks.

According to the StackShare community, Kibana has a broader approval, being mentioned in 907 company stacks & 479 developers stacks; compared to StatsD, which is listed in 72 company stacks and 16 developer stacks.

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Advice on StatsD, Kibana

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.

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Comments

matteo1989it

Jun 26, 2019

Reviewon

Kibana

Grafana

Elasticsearch

I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics

757k views757k

Comments

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

Detailed Comparison

StatsD	Kibana
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).	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.
Network daemon; Runs on the Node.js platform; Sends aggregates to one or more pluggable backend services	Flexible analytics and visualization platform;Real-time summary and charting of streaming data;Intuitive interface for a variety of users;Instant sharing and embedding of dashboards
Statistics
GitHub Stars -	GitHub Stars 20.8K
GitHub Forks -	GitHub Forks 8.5K
Stacks 373	Stacks 20.6K
Followers 293	Followers 16.4K
Votes 31	Votes 262
Pros & Cons
Pros 9 Open source 7 Single responsibility 5 Efficient wire format 3 Handles aggregation 3 Loads of integrations Cons 1 No authentication; cannot be used over Internet	Pros 88 Easy to setup 65 Free 45 Can search text 21 Has pie chart 13 X-axis is not restricted to timestamp Cons 7 Unintuituve 4 Elasticsearch is huge 4 Works on top of elastic only 3 Hardweight UI
Integrations
Node.js Docker Graphite	Logstash Elasticsearch Beats

What are some alternatives to StatsD, Kibana?

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.

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

Telegraf

It is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics.

Related Comparisons

Some of the features offered by Kibana are:

Flexible analytics and visualization platform
Real-time summary and charting of streaming data
Intuitive interface for a variety of users

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.

"Easy to setup" is the top reason why over 76 developers like Kibana, while over 6 developers mention "Single responsibility" as the leading cause for choosing StatsD.

Kibana vs StatsD