Working with logs is a very important component of testing since it helps to identify bottlenecks that any web application has. Kibana is used for a more simplified log analysis. This is open-source software designed for data visualization.

Kibana is a visualization tool that allows you to explore, visualize, and create dashboards over log data collected in Elasticsearch clusters.
Elastic is the company behind Kibana, as well as two other open-source tools out there – Elasticsearch and Logstash.

Logstash is responsible for processing and sending logs. The role of a search engine is performed by Elasticsearch. And visualization is performed with the help of Kibana.

All these tools can work closely together and are widely known as ELK Stack or Elastic Stack. The tester can access this system through a link that can be opened in any browser.

Today, ELK Stark is very popular among such well-known services as Uber, Amazon, Netflix, Microsoft, and Volkswagen, as well as among world quality control companies.

The main function of Kibana is to create requests and analyze data. In addition, its functionality allows you to visualize information using line graphs, heat maps, pie charts, and histograms, as well as geospatial support. Using different methods, you can search for diagnostics data stored in Elasticsearch.

Basic Options of Kibana

With Kibana, it is much easier to process a large amount of data, you can quickly create and deploy dynamic dashboards that display changes in the Elasticsearch query in real-time. Kibana is a powerful visualization tool and has a list of options that we will list below.

Discover is responsible for interactive data exploration (send search queries, explore data, set a time filter, and select the desired search results by filters).

Visualize. Kibana has a large list of visualization styles such as charts (pie, line), tables, geographical maps, etc.

Dashboards display a list of visualizations and searches. You can open the data panel in full format, edit and rearrange its content, share data, and save it.

Timelion visualizes data and analyzes it with an expression language. This option is designed to get all independent data into one interface. Now, the identification of requests, visual, and transformation processes are implemented in one place.

Machine Learning is a place where one can create tasks and see anomalies arising from infrastructure problems, cyber-attacks, or business problems.

APM helps users to monitor the productivity of applications and detect errors if any.

Dev Tools. Its developer toolkit interacts with the ELK Stack. That is, it has a console for programmers, due to this, when they write Elasticsearch commands on one of the tabs, the same commands will be displayed on the other.

Monitoring – is an option that is characterized by a list of such functions:

• Performance visualization of Kibana, Elasticsearch, Logstash, and Beats online;
• Analysis of previous performance indicators of these products;
• Ensuring detailed monitoring of Kibana.

Management – control configuration page of Kibana. Main options:

• Index pattern maintain initial settings and current configuration of indexes names;
• Saved objects keep all the visualization, information blocks, and search;
• Advanced settings in Kibana.

Pros and Cons of Kibana

The main advantages of Kibana are:

• Understandable graphics, diagrams, and reports;
• Starting most popular requests and bar charts in two clicks;
• Opportunity to find bugs in the most secret and hidden data;
• Various methods of data search;
• Secure access to information;
• Protection against data leakage;
• Visual card data;
• Prematurity reporting.

The disadvantages of Kibana include problems that arise based on different versions of the product. For example, to add plugins, you have to spend a lot of time.

With the help of visualization, users can trace the course of patterns, control the environment and resort to decisive action when anomalous phenomena occur. Today, we reviewed one of the most popular visualization tools, which have appeared in large numbers over time. The use of visualization tools in testing is very important because they provide the visibility that is needed to maximize data understanding and analysis.