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Kiali vs Kibana: What are the differences?
Introduction
Kiali and Kibana are two popular tools used for monitoring and visualization in the domain of software systems. While both tools provide valuable insights, they have distinct differences in terms of their capabilities and features. The key differences between Kiali and Kibana are:
Data Source: Kiali is specifically designed to monitor and visualize the service mesh data, primarily focusing on Istio. It provides an in-depth analysis of the traffic, health, and performance of microservices within the mesh. On the other hand, Kibana is a part of the Elastic Stack and is intended for analyzing and visualizing log data from various sources such as Elasticsearch, Logstash, and Beats. It offers a broader spectrum of log analysis with powerful search, aggregation, and dashboarding capabilities.
Focus on Service Mesh: Kiali, being built for Istio, provides a deep understanding of the service mesh topology, traffic flows, and observability features like distributed tracing. It allows users to visualize the dependencies between services, detect potential issues, and analyze the behavior of communication within the mesh. While Kibana can be used for analyzing logs related to service mesh deployments, it does not provide the same level of dedicated insight and visualization of the service mesh infrastructure.
Alerting and Monitoring: Kiali offers native support for monitoring and alerting on key performance indicators of the service mesh, such as error rates, latency, and throughput. It provides real-time notifications and customizable dashboards to track the health of microservices and troubleshoot issues. In contrast, Kibana relies on integrations with additional tools like Elasticsearch's Watcher or other external monitoring systems for achieving similar capabilities. It focuses more on log analysis rather than real-time monitoring and alerting specific to service mesh.
User Interface: Kiali offers a user-friendly web-based graphical interface that allows users to easily navigate and explore the service mesh topology. It provides interactive visualizations, traffic maps, and detailed metrics in a visually appealing manner. Kibana, on the other hand, offers a versatile and customizable user interface that can be tailored to specific log analysis needs. It provides powerful querying capabilities, advanced visualizations, and the ability to create detailed reports and dashboards for log-based insights.
Adoption and Community: Kiali has gained significant traction within the Istio community and is tightly integrated with Istio's control plane. It benefits from the active development and support from the Istio project and has a growing community of users. Kibana, being a part of the Elastic Stack, is widely adopted as a log analysis and visualization tool in various domains beyond service mesh deployments. It has a large ecosystem of plugins, extensive documentation, and a well-established community.
Ecosystem Integration: Kiali, being specifically designed for a service mesh like Istio, provides seamless integration with other Istio components and tools. It leverages Istio's telemetry and tracing capabilities to provide a holistic view of the service mesh. Kibana, on the other hand, can integrate with various data sources like Elasticsearch, Logstash, and Beats, allowing users to analyze and visualize log data from diverse systems, including service mesh deployments.
In summary, while both Kiali and Kibana offer monitoring and visualization capabilities, their key differences lie in their data sources, focus areas, monitoring capabilities, user interfaces, adoption within the community, and integration with wider ecosystems. Depending on the specific requirements and use cases, one tool may be more suitable than the other.
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.
Pros of Kiali
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 Kiali
Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
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