Kibana is an important component of the ELK Stack, a widely used open source log analysis platform. It gives users the opportunity to search, visualize, and construct dashboards using the log data stored in Elasticsearch clusters.
Kibana’s core feature is data querying and analysis. By employing different techniques, individuals can look up the information indexed in Elasticsearch to track down certain occasions or words within their data for identifying the source of a problem and examining it. With the help of the queries, Kibana’s visualization abilities can be used to present data in diverse ways, including graphs, spreadsheets, maps, and other forms of visuals.
Grafana is a free and open source visualization software that can be used with a range of data repositories, though it is usually used with Graphite, InfluxDB, Prometheus, and Elasticsearch. It just so happens that Grafana was developed from Kibana, aiming to furnish assistance for metrics (or monitoring) that Kibana didn’t offer much, if any, of at the time.
In essence, Grafana is a comprehensive alternative to Graphite-web that makes it simple for users to make and modify dashboards. This particular Graphite target parser is one-of-a-kind, making it straightforward to modify metrics and functions. Through Grafana’s quick client-side rendering, users are able to generate detailed graphs with smart axis styles (like lines and points) over extended periods of time, utilizing Flot as the default selection.
1. Logs vs. Metrics (Logging vs. Monitoring)
The distinguishing factor between the two visualization tools comes from their intended use. Grafana has been designed with the purpose of evaluating and demonstrating metrics including CPU usage, memory, disk space and input/output usage. The platform does not allow full-text data querying. Kibana runs on top of Elasticsearch and is mainly utilized for examining log messages.
Creating a surveillance system, both methods can function effectively, yet there are still some distinctions that will be outlined in further detail. If you need logs for any purpose, like troubleshooting, forensics, development, or security, Kibana is the only thing that can help.
Both tools’ backers are trying to expand their scope. It is possible to send metrics information to Kibana and logging data to Grafana, although neither one is currently optimally suited for either job.
Grafana Labs, which is responsible for Grafana, has put out Loki, a system created to go along with the primary tool for more effectively sorting, displaying and studying logging. A disadvantage of Loki is that it does not catalogue the information in the logs. Instead of sorting them, it puts them into categories that match the log streams associated with them. This could be suitable for situations where it is easy to identify tags, such as for Kubernetes pod logs. Otherwise, the ELK Stack still has Grafana beat.
2. Setup, installation and configuration
Kibana and Grafana both have straightforward installation and setup processes. Both can be installed on Linux, Mac, Windows, Docker or constructed from its source. Kibana provides a variety of different methods of installation for each type of operating system, but overall, there is not much of a difference. A connection to your Elasticsearch instance is essential in order to use Kibana.
Grafana is set up using an .ini document, which is much simpler to work with than Kibana’s YAML configuration files that need to be written in a particular syntax. Grafana permits you to change configuration settings by utilizing environment variables.
Here is a tutorial on how to install Grafana and another tutorial on how to install Kibana.
3. Data sources and integrations
Grafana was created to function as a user interface for examining metrics. This means that it is compatible with a range of data stores for time-series data, including Graphite, Prometheus, InfluxDB, MySQL, PostgreSQL, and Elasticsearch, as well as other sources that are accessible via plugins. For every data source, Grafana has a tailor-made query editor that is optimized for the functions and abilities that that data source contains. Kibana is only able to be used with Elasticsearch and is not able to be used with any other kind of data source.
Kibana, in contrast, is only compatible with Elasticsearch and therefore does not accept any other data sources. To analyze data from other sources, it needs to be imported into the ELK Stack (via Filebeat or Metricbeat, then Logstash, then Elasticsearch) and then Kibana can be used to analyze it.
The following guide illustrates the process of moving MongoDB data to Kibana through Logstash and finally to the managed ELK Stack solution. The principle is similar to non-managed open source scenarios. If you want to know more about including Filebeat, have a look at the Filebeat tutorial; and if you need help on monitoring with Metricbeat, consult the Metricbeat tutorial.
4. Access control and authentication
Unless you pay for the X-Pack or use an open source solution such as SearchGuard, the Kibana dashboards that you create will be available and visible to everyone. Grafana has the capability to limit and manage user access to the dashboards, including the ability to employ an external SQL or LDAP server for authentication. Furthermore, Grafana’s API can be utilized for operations like storing a specified dashboard, generating users, and revising data sources. You can also generate particular API keys and assign them to specific positions.
Kibana is capable of utilizing its strong feature of inquiring and examining logs. You can search the data stored in Elasticsearch indices using either Lucene syntax, the Elasticsearch Query DSL, or the experimental Kuery. The results will be displayed in the main log display area in chronological order. Lucene is a highly effective query language, however it is not easy to use and requires some time to learn.
People who use Grafana employ a Query Editor to ask questions. The Query Editor for each data source is customized for that source, so the language used to query the data will differ depending on the source. Querying with Graphite will diverge from querying with Prometheus, for instance.
6. Dashboards and visualizations
Both Kibana and Grafana boast powerful visualization capabilities. Kibana provides a range of visualization options, enabling you to generate pie graphs, line graphs, tabular data, single metric pictures, geographical maps, time series and markdown displays, and mix all of those into one dashboard. Kibana dashboards are highly dynamic and customizable, allowing for data to be altered without delay and dashboards to be altered or opened as full-page displays. Kibana comes with pre-configured dashboards for different kinds of data, which helps to reduce the amount of time it takes to get started.
Grafana’s dashboards are the main reason why it is so widely used as a visualization tool. They are infamous for being completely versatile. The software has visuals referred to as panels, and people are able to put together a dashboard with panels that have various data sources. Grafana offers a range of panel types, such as graphs, singlestats, tables, heatmaps, and freetext. The individuals who utilize the software have access to a vast community of pre-made dashboards for different information types and sources.
In terms of functionality, both Grafana and Kibana have a variety of options that enable users to manipulate data in whatever manner they need. People can experiment with different panel colors, labels, X and Y axis, the dimensions of panels, and more. In conclusion, Grafana has more opportunities for personalization and its panel editors and collapsible rows make adjusting the settings simpler.
A key difference between Kibana and Grafana is alerts. Since version 4.x, Grafana has included an internal alarm system that allows people to apply specific criteria to dashboard panels that will lead to an alert being sent to the required notification destination (for instance, email, Slack, PagerDuty, personalized webhooks). Kibana does not come with an out-of-the-box alerting capability. Kibana users can choose to use a hosted ELK Stack from Logz.io, employ ElastAlert, or use X-Pack to include alerting.
The user and contributor base of both of these open source tools is very strong. When comparing the two projects on GitHub, it appears that Kibana has the upper hand. Grafana has approximately 14,000 changes in its source code while Kibana has over 17,000. Investigating the number of commits shows that Kibana is more active than the other project.
Grafana commits over time:
Kibana commits over time:
We can check out Google Trends to get an idea of how popular something is. Again, Kibana seemed to have the advantage until 2020. In recent times, Grafana has become more favored than Kibana due to several reasons. As the consequences of alterations to Kibana’s license cause various businesses and projects to modify their policies, that division might become greater.
Monitoring with Grafana
Configuring a dashboard
How do we configure such a dashboard from zero?
At first glance, it may seem complicated; however, it is important to remember that a dashboard is simply a group of panels. First, we will set up one panel, then the following panels will just require minor adjustments to the same process.
Let’s start with the configuration of a panel.
Grafana offers customizable panels with top-notch visuals.
To configure your metric from zero, you’ll need to;
- Get data : For this we will add a data source
- Configure query : Pick a function that provides data for your metric
- Customize panel : To make our metric more expressive
You need to specify a data source in order to obtain your data. This is a one time process. Once you’ve added a data source, you can leverage it for all your panels with no additional work.
Go to the data sources page if you want to include a datasource. Choose a data source from the list and input the necessary details. Grafana can be used with multiple data sources, each of which has its own set of parameters. You can look at the data sources page to locate the data source you need to use.
If you don’t see your source of information listed on the page, it is possible the plugin is not installed. Go to the plugins page and install the appropriate plugin for your Grafana.
We’ll use Prometheus for our RabbitMQ metrics because,
- Prometheus integrates well with RabbitMQ.
- It provides a vast amount of ready-to-use queries.
Prometheus has its own user interface, but it is better suited as a backend service for Grafana, as Grafana offers a superior user interface and user experience.
It is simple to bring in over 3000 dashboards to your Grafana. If you come across a dashboard that appeals to you, all you have to do is copy the numerical identifier (e.g. 2343) and paste it into the import area in your Grafana. With little-to-no configuration, your dashboard will be ready. This one is a quite powerful feature of Grafana.
Grafana has an HTTP API with countless endpoints. Taking into account that each panel and control board is a JSON item, this implies that there are an incalculable number of ways to modify and manage your Grafana. You can use the Dashboard API to perform Create, Read, Update, and Delete operations on your dashboards.
It is noteworthy to point out that Grafana is progressing rapidly. They’re releasing new features and bug fixed actively. So, it’s definitely worth hopping on the train.
Lack of features
Finally, we’ll discuss some of the drawbacks and absent features you can experience when utilizing Grafana. We’ll also talk about which version to use.
An issue with an alert system was present in earlier iterations of Grafana, where the diff and percent_diff functions were not working correctly. Set an alarm to alert you when your message count increases by 20% in the last 30 minutes. The percentage difference of query A from the last 30 minutes to present will be greater than 20. The 20% increase should function properly, yet you may still get an alert for a 20% decrease which could be quite irritating and unexpected. Fortunately this bug is fixed.
Grafana has experienced a vulnerability in its earlier releases. Thus it’s advised to use 7.0.2 or higher.
The alert message cannot be tailored to your needs, with only a few options available for personalization. The message section does not allow for the use of styling or variables. You cannot add buttons or any other elements, even though Slack is compatible with them.
No scheduling : You can’t turn off alerts. It is possible to suspend each one individually, however it is not possible to set up a timetable or temporarily deactivate them. You will be notified after 12 am or during Sunday morning.
No warning state : Alerting is a boolean state. You cannot send an alarm to your Slack. It’s either alerting (red) or not (green). A warning state is distinct from a pending state.
Without endpoint monitoring and alerting, it is impossible to gauge the status of your URL endpoints through Grafana. A plugin exists for that, but it can only be used for 3 endpoints. Otherwise, you’ll need to pay $100 monthly. Thats why we use PRTG tool for this need.
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