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Grafana vs Kiali: What are the differences?
Introduction:
Grafana and Kiali are two popular open-source software platforms used in the field of monitoring and observability for cloud-native applications. While both tools serve a similar purpose, there are several key differences between them.
Visualization and Dashboarding Capabilities: Grafana is primarily known for its powerful visualization and dashboarding capabilities. It provides a wide range of options for creating interactive and customizable visualizations, including charts, graphs, tables, and more. On the other hand, Kiali focuses more on providing a comprehensive overview of the Istio service mesh, offering detailed insights into the network topology, traffic flow, and application dependencies.
Scope and Focus: Grafana is a more general-purpose monitoring and visualization tool that can be used with various data sources, such as time-series databases, log management systems, and application performance monitoring. It offers extensive integration capabilities and supports a wide range of plugins, making it suitable for monitoring diverse sets of data. In contrast, Kiali is specifically designed for monitoring and troubleshooting microservices-based architectures using Istio as the service mesh. It provides deep insights into Istio-specific metrics, such as traffic routing, circuit breaking, and load balancing.
Ease of Installation and Configuration: Grafana is relatively easy to install and configure, with comprehensive documentation and support from a large community. It offers multiple deployment options, including self-hosted and cloud-based setups. Additionally, Grafana provides a user-friendly interface for creating dashboards and configuring data sources, making it easier for users to get started. Kiali, on the other hand, requires Istio to be properly installed and configured before it can be used. This additional complexity makes the initial setup and configuration process more involved compared to Grafana.
Customizability and Extensibility: Grafana provides users with extensive customization options, allowing them to personalize dashboards, create custom panels, and define alert rules. It also supports a wide range of plugins developed by the community, enabling users to extend its functionality further. In contrast, Kiali has a more limited scope for customization and extension. While it provides a good set of pre-defined visualizations and metrics, it may not offer the same level of flexibility and extensibility as Grafana.
Target Audience: Grafana is widely adopted by both developers and operations teams, as well as individuals looking for a versatile monitoring and visualization tool. Its user-friendly interface and extensive community support make it suitable for a wide range of use cases. Kiali, on the other hand, is primarily targeted towards developers and operations teams working with microservices architectures using Istio. It provides specialized features and insights specific to Istio, making it more relevant for users with Istio-based setups.
Community and Ecosystem: Grafana has a large and vibrant community, with a vast ecosystem of plugins, dashboards, and extensions developed by the community. This ensures that users have access to a wide range of resources, tutorials, and best practices. Kiali, being a more specialized tool, has a smaller community and ecosystem compared to Grafana. However, it benefits from the overall popularity and adoption of Istio, which helps in addressing specific challenges and issues faced by users.
In summary, Grafana offers powerful and versatile visualization capabilities with a wide range of integrations, making it suitable for general-purpose monitoring and observability tasks. Kiali, on the other hand, focuses specifically on Istio-based microservices architectures, providing deep insights into the Istio service mesh. The choice between Grafana and Kiali depends on the specific requirements and use case of the application or infrastructure being monitored.
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.
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/
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.
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 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 Kiali
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Cons of Grafana
- No interactive query builder1