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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.
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.
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.
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.
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.
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.
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.
Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:
- Must be able to get custom data from AS400,
- Able to display automation test results,
- System monitoring / Nginx API,
- Able to get data from 3rd parties DB.
Grafana is almost solving all the problems, except AS400 and no database to get automation test results.
You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.
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."
For our Predictive Analytics platform, we have used both Grafana and Kibana
- Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
- Kibana based reporting screenshot: https://imgur.com/vuVvZKN
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)
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
After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .
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.
Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).
@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.
Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.
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.
Pros of Graphite
- Render any graph16
- Great functions to apply on timeseries9
- Well supported integrations8
- Includes event tracking6
- Rolling aggregation makes storage managable3
Pros of Kibana
- Easy to setup88
- Free65
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- More "user-friendly"3
- Can build dashboards3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
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Cons of Graphite
Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3