Graphite vs Kibana vs StatsD

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Graphite
Graphite

286
222
+ 1
38
Kibana
Kibana

4.5K
3K
+ 1
227
StatsD
StatsD

192
126
+ 1
27

What is Graphite?

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

What is 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.

What is StatsD?

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).
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Why do developers choose Graphite?
Why do developers choose Kibana?
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    What are some alternatives to Graphite, Kibana, and StatsD?
    Graphene
    Graphene is a Python library for building GraphQL schemas/types fast and easily.
    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.
    Pencil
    A web application microframework for Rust
    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.
    See all alternatives
    Decisions about Graphite, Kibana, and StatsD
    StackShare Editors
    StackShare Editors
    Kibana
    Kibana
    Grafana
    Grafana
    Elasticsearch
    Elasticsearch
    Logstash
    Logstash
    Graphite
    Graphite
    Icinga
    Icinga

    One size definitely doesn’t fit all when it comes to open source monitoring solutions, and executing generally understood best practices in the context of unique distributed systems presents all sorts of problems. Megan Anctil, a senior engineer on the Technical Operations team at Slack gave a talk at an O’Reilly Velocity Conference sharing pain points and lessons learned at wrangling known technologies such as Icinga, Graphite, Grafana, and the Elastic Stack to best fit the company’s use cases.

    At the time, Slack used a few well-known monitoring tools since it’s Technical Operations team wasn’t large enough to build an in-house solution for all of these. Nor did the team think it’s sustainable to throw money at the problem, given the volume of information processed and the not-insignificant price and rigidity of many vendor solutions. With thousands of servers across multiple regions and millions of metrics and documents being processed and indexed per second, the team had to figure out how to scale these technologies to fit Slack’s needs.

    On the backend, they experimented with multiple clusters in both Graphite and ELK, distributed Icinga nodes, and more. At the same time, they’ve tried to build usability into Grafana that reflects the team’s mental models of the system and have found ways to make alerts from Icinga more insightful and actionable.

    See more
    Conor Myhrvold