Introduction to What is Kibana

Kibana is basically used for visualization for large datasets in a proper way to understand deeper. We can use different types of methods to display results in Kibana like a line graph, bar graph, pie charts, heat maps, region maps, coordinate maps, gauge, goals, timeline, etc.

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Kibana is coming along with ELK stack which we called Elasticsearch, Logstash, and Kibana.

How to install Kibana?

To install the kibana go to the kibana main website and choose according to your system.

To install kibana in ubuntu/Linux use the following steps:

The first step is to install kibana, we must download either tar file or deb file.

After download, the tar file, keep that file in a particular location and unzip it. After unzipping it, just go to that folder and run the commands like below:

After that kibana will start and you can check it in localhost as shown in the image below:

Note: To run kibana, must you have running elasticsearch in your system, otherwise it will not run successfully.

Features of Kibana

Here are the features of Kibana mention below

1. Visualization

Kibana basically uses for the data visualized in different ways. Most of the methods which are easy to visualize data are vertical bar chart, horizontal bar chart, pie chart, line graph, heat map, etc.

2. Dashboard

When we start to visualize logs data or datasets, we can keep it on board. To see different kinds of option in one place will give more power to use data.

3. Dev Tools

Dev tools are the best way to learn for beginner’s elasticsearch. We can directly copy and paste examples from the “elasticsearch documents example” to “kibana” and we can see that what’s happening in real-time. It is also very simple and quick to add, update, delete the indexes from it.

4. Reports

The visualize data along with dashboards can be converted to useful information reports like CSV format and also in the form of URL to share code.

5. Filters and Search query

To write the query in elasticsearch is a little bit complex but with the help of the filters and search queries we can get the desired information that we want from the huge dataset.

6. Plugins

Kibana has very good in-built plugin features but there also an option to add plugin of the third party so that we can get more power to explore data.

7. Coordinate and Region Maps

This feature map in Kibana helps to visualization on the geographical map where they giving a realistic view of the data.

8. Timeline

Timeline is another visualization feature of kibana which basically used for the time data analysis. Sometimes we want to visualize how much data transfer speed during indexing in elasticsearch, so we can get all those details from this feature. It also helps to compare data with the previous cycle day, week and month, etc.

9. Canvas

This is another feature of kibana which helps to represent the data in different color combinations in different shapes, and texts.

Advantages of Kibana

Kibana has the most impressive feature that is open source which helps us to visualize a large volume of logs displays inline graphs, bar graphs, pie charts, heat maps, etc.
Kibana is very easy to understand for beginners.
We can easily convert our visualization to reports for dashboards.
We can easily use different colors to display complex data using canvas.
Kibana also has the facility of the timeline to visualization and compare data from previous records.

Disadvantages of Kibana

Sometimes adding third-party plugins to Kibana will be a very tedious job because of version mismatch.
Sometimes not only plugin version issues occur because sometimes when we upgrade then the elasticsearch version also mismatch error occurs.

Machine Learning with Kibana

It is very hard to recognize any cyberattack or sudden high peak in data graphs because, while monitoring, we used to depend on our eyes to search and trace the root cause of any problem and to get an idea of such issues, we have to play with time pickers for setting different time ranges. We can do that by searching and discovering the trends and peaks in the graphs, but there is a tool that can help us to pinpoint these issues and let us know about any such incidents easily.

Using the X-Pack machine learning feature, we can create the jobs for any such requirements and can obtain a complete insight by identifying the outliers. It can also help us to perform a predictive analysis by reading the data available in Elasticsearch, applying algorithms and then displaying it in Kibana.

Using Elastic Stack, we can get an insight into our data, such as the amount of traffic on a website by seeing the requests per second. This information can show us the trends, such as the peak time, but if we want to get details, such as what is causing this, or the reason behind this trend, we can use X-Pack machine learning. It can provide us these details (that are hidden behind the data) and can be explored using unsupervised machine learning for Elasticsearch.

X-Pack machine learning works on time series-based data and automates the analysis process by identifying the anomaly in data. We can apply it in real-time or can set it for batch processing. It uses proprietary machine learning algorithms for running the machine learning job.

In this blog, we will cover the description of machine learning jobs involving single metrics, multi-metric, population, and advanced jobs. We will also discover the data visualizer option through which we can get further insights into the data and, based on that understanding, we can decide the field to be analyzed for machine learning.

We will explore the anomaly explorer option by means of which we can see the maximum anomaly score over time. It shows the anomaly as a block, which is displayed in a different color.

Machine learning jobs

In Kibana if we want to run any machine learning analysis, first, we have to create a machine learning job and execute that in the same order to get the result. A job holds all the details with configuration and meta-data information used to perform the machine learning task.

There are one or more detectors for each job that applies the analytical function to specific fields of our data. The job also has different features that affect the anomaly consideration through types of events or entities. For example, entities are analyzed based on other entities in data, or relative to their own previous behavior.

In order to run machine learning jobs, we need to go to the UI section of the kibana and run different kinds of machine learning jobs. We can use these features when we click on the left side option machine learning, which will open another window as given below:

Create a Super Cool Dashboard from a Web Application

Here, we are going to discuss how to pull the data from RDBMS using Logstash and push it to Elasticsearch and then use the data in Kibana to create the dashboard.

Basically, I am going to explain how we can use Logstash to pull the data from the RDBMS and then send this data to Elasticsearch, from where we can read it in Kibana to create the dashboard. This approach is quite interesting as we are not making any change in the application to get the data but simply using the JDBC connector of Logstash that provides us the facility to pull the data directly from any RDBMS just by writing the queries after connecting to the database server.

For this type of dashboard creation, we need to create a star schema table and pull the data into it from other tables, the same approach we use to apply for reporting. Once that is done, we can use the JDBC connector to pull the data using queries in Logstash. Logstash pulls the data from RDBMS, and we can configure it to send the data directly in Elasticsearch. Then, we can create the index pattern in Kibana to use that index in order to create the dashboards.

The following diagram depicts the architecture of kibana:

The preceding diagram shows a web server that is connected to a database server for reading and write operations. We are using the JDBC input plugin of Logstash to fetch the data from the database by writing queries. This data is then sent to Elastic, and Kibana uses that data to create the dashboards. So, first of all, we need to understand how JDBC input plugin works.

Conclusion

Kibana is an open-source for the visualization large volume of data and also there is some third party plugin which makes it’s more powerful to control the data to get more information from the logs or datasets. Like kibana some other tools also available but the best thing about the kibana that it is a part of the ELK stack.

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