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Kibana
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StatsD

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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.

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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What are some alternatives to Kibana and StatsD?
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.
Loggly
The world's most popular cloud-based log management service delivers application intelligence.
Graylog
Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.
Splunk
Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
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.
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Decisions about Kibana and StatsD
StackShare Editors
StackShare Editors
Icinga
Icinga
Graphite
Graphite
Logstash
Logstash
Elasticsearch
Elasticsearch
Grafana
Grafana
Kibana
Kibana

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.

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StackShare Editors
StackShare Editors
Flask
Flask
AWS EC2
AWS EC2
Celery
Celery
Datadog
Datadog
PagerDuty
PagerDuty
Airflow
Airflow
StatsD
StatsD
Grafana
Grafana

Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

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Łukasz Korecki
Łukasz Korecki
CTO & Co-founder at EnjoyHQ · | 6 upvotes · 30.7K views
atEnjoyHQEnjoyHQ
Stackdriver
Stackdriver
Clojure
Clojure
StatsD
StatsD
Google Compute Engine
Google Compute Engine
collectd
collectd

We use collectd because of it's low footprint and great capabilities. We use it to monitor our Google Compute Engine machines. More interestingly we setup collectd as StatsD replacement - all our Clojure services push application-level metrics using our own metrics library and collectd pushes them to Stackdriver

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Grafana
Grafana
Splunk
Splunk
Kibana
Kibana

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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Elasticsearch
Elasticsearch
Grafana
Grafana
Kibana
Kibana

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

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Kibana
Kibana
Grafana
Grafana

For our Predictive Analytics platform, we have used both Grafana and Kibana

Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

  • Creating and organizing visualization panels
  • Templating the panels on dashboards for repetetive tasks
  • Realtime monitoring, filtering of charts based on conditions and variables
  • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
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Amazon CloudWatch
Amazon CloudWatch
PagerDuty
PagerDuty
Grafana
Grafana
Graphite
Graphite
StatsD
StatsD
Sentry
Sentry

A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we’re looking to migrate all of these to our internal monitoring system soon).

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Interest over time
Reviews of Kibana and StatsD
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How developers use Kibana and StatsD
Avatar of Stream
Stream uses StatsDStatsD

StatsD is used to track the number of messages we're publishing and the type of realtime subscribers. So it shows the number of longpoll connections, the number of websocket connections etc. It also tracks how Redis is performing.

Avatar of Clarabridge Engage
Clarabridge Engage uses KibanaKibana

Used for graphing internal logging data; including metrics related to how fast we serve pages and execute MySQL/ElasticSearch queries.

Avatar of Wirkn Inc.
Wirkn Inc. uses KibanaKibana

Our Kibana instances uses our ElasticSearch search data to help answer any complicated questions we have about our data.

Avatar of Hevelop
Hevelop uses KibanaKibana

Kibana is our tools to query data in Elasticsearch clusters set up as catalog search engine.

Avatar of Diogo Silva
Diogo Silva uses KibanaKibana

Perfect for exploring and visualizing the data available at ElasticSearch

Avatar of Chris Hartwig
Chris Hartwig uses StatsDStatsD

Business and system counters go through StatsD and are pushed to InfluxDB

Avatar of Tongliang Liu
Tongliang Liu uses KibanaKibana

Log visualization. Wish it could add built-in alert functionality.

Avatar of Tongliang Liu
Tongliang Liu uses StatsDStatsD

Arm yourself with sensor all over your application

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