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Grafana vs Graphite vs Kibana: What are the differences?
Introduction
Grafana, Graphite, and Kibana are popular open-source tools used for monitoring and visualizing data. While they serve similar purposes, there are significant differences between them.
Storage and Data Source: Grafana is a standalone monitoring tool that can integrate with different data sources, such as Graphite, Prometheus, and Elasticsearch. Graphite, on the other hand, is a time-series database that is primarily used for storing and querying numeric time-series data. Kibana is part of the ELK stack (Elasticsearch, Logstash, and Kibana) and is primarily used for analyzing log data stored in Elasticsearch.
Visualizations and Dashboards: Grafana provides a rich set of options for creating dynamic and interactive visualizations, including graphs, tables, heatmaps, and gauges. It allows users to build customizable dashboards by dragging and dropping different visual elements. Graphite, on the other hand, is more focused on graphing and charting, providing a simple interface for plotting time-series data. Kibana specializes in log data analysis and provides specific visualizations for log-based analytics, such as histograms, maps, and tag clouds.
Alerting and Notifications: Grafana has built-in alerting capabilities that allow users to set up rules based on metrics and receive notifications via various channels like email, Slack, or PagerDuty. Graphite, being primarily a database, does not have native alerting features. However, third-party tools can be used to set up alerts based on Graphite metrics. Kibana offers basic alerting capabilities through its Watcher feature, which can monitor Elasticsearch data and trigger actions based on predefined conditions.
Community and Ecosystem: Grafana has a large and active community, supported by a rich ecosystem of plugins and integrations. It has extensive documentation and a wide range of online resources, making it easy to find help and resources. Graphite also has an active community, but it may not have as many plugins and integrations available as Grafana. Kibana benefits from being part of the ELK stack, which has a significant user base and a range of community-driven plugins and resources.
Ease of Use: Grafana is known for its user-friendly and intuitive interface, making it easy for both beginners and advanced users to create dashboards and visualizations. It has a robust query editor and provides autocomplete suggestions. Graphite has a simpler interface focused on graphing, but it may require more technical expertise to set up and configure. Kibana has a relatively steep learning curve, especially for users without prior experience with Elasticsearch and the ELK stack.
Supported Use Cases: Grafana is widely used for monitoring and visualization in various domains, including infrastructure monitoring, application performance monitoring, and business intelligence. It is versatile and can integrate with multiple data sources, making it suitable for different use cases. Graphite is mainly used for time-series data storage and graphing, making it suitable for scenarios where historical trend analysis is critical. Kibana is primarily used for log analysis and enables users to search, analyze, and visualize log data in real-time.
In summary, Grafana is a versatile monitoring and visualization tool with a user-friendly interface, supporting various data sources and use cases. Graphite is focused on time-series data storage and graphing, while Kibana specializes in log analysis within the ELK stack environment.
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.
We're looking for a Monitoring and Logging tool. It has to support AWS (mostly 100% serverless, Lambdas, SNS, SQS, API GW, CloudFront, Autora, etc.), as well as Azure and GCP (for now mostly used as pure IaaS, with a lot of cognitive services, and mostly managed DB). Hopefully, something not as expensive as Datadog or New relic, as our SRE team could support the tool inhouse. At the moment, we primarily use CloudWatch for AWS and Pandora for most on-prem.
I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.
this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/
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.
Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.
I learned a lot from Grafana, especially the issue of data monitoring, as it is easy to use, I learned how to create quick and simple dashboards. InfluxDB, I didn't know any other types of DBMS, I only knew about relational DBMS or not, but the difference was the scalability of both, but with influxDB, I knew how a time series DBMS works and finally, Telegraf, which is from the same company as InfluxDB, as I used the Windows Operating System, Telegraf tools was the first in the industry, in addition, it has complete documentation, facilitating its use, I learned a lot about connections, without having to make scripts to collect the data.
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 Grafana
- Beautiful89
- Graphs are interactive68
- Free57
- Easy56
- Nicer than the Graphite web interface34
- Many integrations26
- Can build dashboards18
- Easy to specify time window10
- Can collaborate on dashboards10
- Dashboards contain number tiles9
- Open Source5
- Integration with InfluxDB5
- Click and drag to zoom in5
- Authentification and users management4
- Threshold limits in graphs4
- Alerts3
- It is open to cloud watch and many database3
- Simple and native support to Prometheus3
- Great community support2
- You can use this for development to check memcache2
- You can visualize real time data to put alerts2
- Grapsh as code0
- Plugin visualizationa0
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 Grafana
- No interactive query builder1
Cons of Graphite
Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
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Why do developers choose Kibana vs Grafana vs Graphite?
- Grafana is a general purpose dashboard tool that integrates with many data sources, including Graphite, InfluxDB, and OpenTSDB. Fans of Grafana call it beautiful and easy to use, and love its many integrations.
- Kibana is loved by fans of Elasticsearch; as part of the Elastic Stack it integrates seamlessly with other Elastic products. Fans also cite its ease of setup, pie chart capability, and user-friendliness as pros.
- Fans of Graphite appreciate its storage functions, integrations (including Grafana), and ability to render any graph.