Introduction to Kibana
Kibana is an open-source browser-based visualization tool mainly used to analyze large volumes of logs in the form of a line graph, bar graph, pie charts, heat maps, region maps, coordinate maps, gauge, goals, timelion etc. The visualization makes it easy to predict or to see the changes in trends of errors or other significant events of the input source. Kibana works in sync with Elasticsearch and Logstash which together form the so-called ELK stack.
Basic Things you will Study in this Tutorial:
•Features of Kibana
•Overview of ELK Stack
•Disadvantages of Kibana
•Kibana Aggregation
Features of Kibana
The features of Kibana were discussed below.
Visualization
Kibana has so many ways to visualize data in a simple manner. very few were normally used in vertical bar chart, high bar chart, pie chart, line graph, heat map etc.
Dashboard
When we have the visualizations prepared, all of them are often placed on one board – the Dashboard. perceptive completely different sections along provide you a transparent overall plan concerning what specifically is occurring.
Dev Tools
You can work together with your indexes using these tools. Beginners will add dummy indexes from dev tools and additionally add, update, delete the information, and use the indexes to form visualization.
Reports
All the information within the form of visualization can be converted into reports (CSV format), embedded within the code or within the form of URLs to be shared with others.
Filters and Search query
You can build the use of filters and search queries to urge the desired details for a selected input from a dashboard or visualization tool.
Plugins
You can add third-party plugins to feature add some new visualization or alternative UI addition in Kibana.
Coordinate and Region Maps
A coordinate and region map in Kibana helps to indicate the visualization on the geographical map giving a practical view of the information.
Timelion
Timelion, additionally known as timeline is one more visualization tool that is principally used for a time primarily information analysis. To work with the timeline, we need to use simple expression language that helps us connect with the index and additionally perform calculations on the information to get the results we want. It helps a lot as compared to information to the previous cycle in terms of the week , month, etc.
Canvas
Canvas is one more powerful feature in Kibana. By using canvas visualization, you can represent your knowledge in various color combinations, shapes, texts, multiple pages essentially known as work pad.
Overview of ELK Stack
•ELK stands for Elasticsearch, Logstash, and Kibana. ELK is one in every of the popular log management platform used worldwide for log analysis. within the ELK stack, Logstash extracts the working knowledge or alternative events from completely different input sources. It performs the tasks and later stores them in Elasticsearch.
•Kibana may be a visualization tool, that accesses the logs from Elasticsearch and is ready to show to the user within the type of line graph, bar graph, pie charts, etc.
•Logstash is accountable to gather the information from all the remote sources where the logs are filed and pushes identical to Elasticsearch.
•Elasticsearch acts as information where the data is collected and Kibana uses the information from Elasticsearch to represent the data to the user within the form of bar-graphs, pie charts, heat maps.
Disadvantages of Kibana
•Adding of plugins to Kibana can be very tedious if there is a version mismatch.
•You tend to face issues when you want to upgrade from an older version to a new one.
Kibana Aggregation
Aggregation refers to the collection of documents or a set of documents obtained from a particular search query or filter.
Whenever you perform any visualization, you need to decide the criteria, which means in which way you want to group the data to perform the metric on it.
In this section, we will discuss two types of Aggregation −
•Bucket Aggregation
•Metric Aggregation
A bucket mainly consists of a key and a document. When the aggregation is executed, the documents are placed in the respective bucket. So in the end, you should have a list of buckets, each with a list of documents.
Bucket Aggregation has the following list:
•Date Histogram
•Date Range
•Filters
•Histogram
•IPv4 Range
•Range
•Significant Terms
•Terms
Date Histogram
Date Histogram aggregation is used on a date field. So the index that you use to visualize, if you have a date field in that index than only this aggregation type can be used. This is a multi-bucket aggregation which means you can have some of the documents as a part of more than 1 bucket.
Date Range
You need a date field to use this aggregation type. Here we will have a date range, that is from date and to date are to be given. The buckets will have their documents based on the form and to date.
Filters
With Filter type aggregation, the buckets will be formed based on the filter. Here you will get a multi-bucket formed as based on the filter criteria one document can exist in one or more buckets.
Histogram
This type of aggregation is applied on a number field and it will group the documents in a bucket based on the interval applied.
IPv4 Range
This type of aggregation is used and mainly used for IP addresses.
Range
This type of Aggregation needs fields to be of type number. You need to specify the range and the documents will be listed in the buckets falling in the range. You can add more range if required by clicking on the Add Range button.
Significant Terms
This type of aggregation is mostly used on string fields.
Terms
This type of aggregation is used on all the available fields namely number, string, date, boolean, IP address, timestamp, etc. The most important thing is the aggregation we are going to use in all our visualization.
Metric Aggregation
Metric Aggregation mainly refers to the maths calculation done on the documents present in the bucket.