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  5. Graphite vs Kibana

Graphite vs Kibana

OverviewDecisionsComparisonAlternatives

Overview

Graphite
Graphite
Stacks383
Followers419
Votes42
GitHub Stars6.0K
Forks1.3K
Kibana
Kibana
Stacks20.6K
Followers16.4K
Votes262
GitHub Stars20.8K
Forks8.5K

Graphite vs Kibana: What are the differences?

Introduction

Graphite and Kibana are both popular tools used in the field of data visualization and analysis. They have distinct features and functionalities that cater to different needs. This Markdown code provides a comparison between Graphite and Kibana, highlighting their key differences.

  1. Data Storage and Retrieval: Graphite is renowned for its efficient data storage and retrieval capabilities. It utilizes a custom database called Whisper, which is specifically designed for time-series data. On the other hand, Kibana integrates with Elasticsearch, a highly scalable search and analytics engine. This enables Kibana to retrieve and display data from various data sources with great agility and flexibility.

  2. Data Exploration and Visualization: While both Graphite and Kibana offer data exploration and visualization capabilities, they differ in their approaches. Graphite focuses on providing a rich set of predefined functions and a Graphite Query Language (GQL) for data exploration and graphing. Kibana, on the other hand, offers a more interactive and user-friendly interface for exploring and visualizing data. It provides a wide range of visualizations, including timelines, heat maps, and geospatial maps.

  3. Alerting and Monitoring: Graphite offers basic alerting functionality by allowing users to set thresholds on metrics and trigger notifications based on those thresholds. However, it lacks advanced monitoring features such as anomaly detection and machine learning-based alerting. In contrast, Kibana provides advanced alerting and monitoring capabilities by leveraging the capabilities of Elasticsearch. It supports anomaly detection, machine learning-based alerting, and customizable alerting rules for proactive monitoring.

  4. Community and Ecosystem: Graphite has been around for a longer period and has a well-established community and ecosystem. It has a wide array of plugins and integrations available, allowing users to extend its functionality as needed. Kibana, although relatively newer, also has a growing community and ecosystem. It benefits from the larger Elasticsearch ecosystem, which offers numerous plugins, connectors, and integrations for various data sources and systems.

  5. Querying and Filtering Capabilities: Graphite primarily focuses on time-series data and provides a powerful querying system for filtering and aggregating data based on time ranges and other metrics. It allows users to apply functions and operations to manipulate the data. Kibana, on the other hand, provides a more versatile querying and filtering system. It supports the querying of structured data using Elasticsearch's Query DSL, allowing users to apply complex queries, filters, and aggregations on a broader range of data.

  6. User Interface and Dashboarding: Graphite provides a straightforward user interface primarily focused on graphing and rendering time-series data. While it offers basic dashboarding capabilities through third-party tools like Grafana, its core focus is on data visualization. Conversely, Kibana excels in providing a feature-rich user interface with customizable dashboards as its core capability. It allows users to create interactive dashboards, combine visualizations, and easily share insights with others.

In summary, Graphite specializes in efficient data storage, time-series data exploration, and graphing, with a larger established community, while Kibana offers versatile data exploration, customizable dashboards, advanced alerting, and monitoring capabilities leveraging Elasticsearch's ecosystem.

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Advice on Graphite, Kibana

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
matteo1989it
matteo1989it

Jun 26, 2019

ReviewonKibanaKibanaGrafanaGrafanaElasticsearchElasticsearch

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
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

Graphite
Graphite
Kibana
Kibana

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

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.

carbon - a Twisted daemon that listens for time-series data;whisper - a simple database library for storing time-series data (similar in design to RRD);graphite webapp - A Django webapp that renders graphs on-demand using Cairo
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
6.0K
GitHub Stars
20.8K
GitHub Forks
1.3K
GitHub Forks
8.5K
Stacks
383
Stacks
20.6K
Followers
419
Followers
16.4K
Votes
42
Votes
262
Pros & Cons
Pros
  • 16
    Render any graph
  • 9
    Great functions to apply on timeseries
  • 8
    Well supported integrations
  • 6
    Includes event tracking
  • 3
    Rolling aggregation makes storage managable
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
    Works on top of elastic only
  • 4
    Elasticsearch is huge
  • 3
    Hardweight UI
Integrations
Sensu
Sensu
Nagios
Nagios
Logstash
Logstash
Windows Server
Windows Server
Netdata
Netdata
Riemann
Riemann
Diamond
Diamond
Telegraf
Telegraf
collectd
collectd
Ganglia
Ganglia
Logstash
Logstash
Elasticsearch
Elasticsearch
Beats
Beats

What are some alternatives to Graphite, Kibana?

Grafana

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

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.

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

Telegraf

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.

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