Kibana/Elastic Query language

What is Query Language?

A query language gives an approach to pose an inquiry. Query language refers to any computer programming language that demands and receives information from the database by sending queries. A query language is fundamentally made for making and adjusting information in and out from a database. It is a specific language for requesting data from a database.

For example, the query SELECT ALL WHERE percentage > 60 AND name = "Daniel" retrieves all records in which the name-field is "Daniel" having marks higher than 60%. SQL (Structured Query Language) is the most widely used Query Language at present.

What is Elastic Stack?

Elastic Stack is also known as ELK Stack. The ELK stack is a group of three open-source projects. The three, including Elasticsearch, Logstash (gathers all types of data from the different sources and makes it available for further use), and Kibana, discussed later in writing. Despite each one of these three technologies being a separate venture, they are built to work exceptionally well together.

ELK stack is also useful to resolve issues related to the centralized logging system, which means that all of your log data being pushed to one central unit. Data can be taken from any source using ELK Stack. It allows users to examine, evaluate, and visualize that data in real-time. Elastic Stack architecture starts from Logstash and leads to Elasticsearch and then Kibana. Major websites like Netflix, LinkedIn, and Wikipedia are using ELK stack for their business.

Elastic Search:

Elasticsearch is used as an Open-Source. It is a document-oriented database based on Lucene library designed to store and manage the document. Elasticsearch APIs are directly related to Lucene, and it is using the same as Lucene operations name. Shay Banon, in 2004, created the forerunner to Elasticsearch called Compass. Every feature of Elasticsearch is visible as a REST API (Index API, Get API, Search API, Put Mapping API).

Elasticsearch stores data in the JSON document format. JSON stands for JavaScript Object Notation. Using JSON, You can also nest other queries based on your needs. Elasticsearch is developed in Java, basically used where there are heaps of content, and we need to look at any information for the best match with a particular expression. It assists in auto-completing by suggesting words in the search box on partially typed words. This is carried out in real-time, based on the search history. Also, it allows them to start typing a few characters and receive a list of suggested queries as they type. It can also be used to index any kind of diverse data.

In comparison to SQL, Elasticsearch database management systems can take only 10ms to fetch required search query data. Whereas SQL can take more than 10 seconds. Important Terms used in Elastic Search are:

·     Cluster: A Cluster is a collection of one or more Elasticsearch nodes instances that are linked together.

·     Node: It refers to an instance of Elasticsearch.

·     Index: It is a collection of documents having similar features.

·     Document: A Document is a fundamental piece of information that is to be indexed.

·     Shard: In order to distribute the index, an index is broken into shards.

Kibana Query Language:

Kibana is an open-source for Elasticsearch, Used for Data Visualization. It completes the ELK Stack, Being the last in ELK stack architecture after Logstash and Elastic Search. It is a Powerful front-end console and is capable of visualizing indexed information from the elastic cluster. It also offers various interactive diagrams and graphs to help us view the queries better. It can be used for searching and viewing the data stored in Elasticsearch directories.

 It also helps us to interact with this data. Users can also use Kibana to perform progressive data analysis and visualize it in the form of various Tables, Charts, or Maps. Kibana's query language is generally based on the Lucene query syntax. Lucene is a query language that can be used to filter the message. It can be categorized into three parts: Field, Term, and Operators/Modifiers.

Up until version 6.2, Lucene syntax was the only way to query in Kibana. Another query language was introduced from version 6.2 called Kuery, or as it's been now called KQL (Kibana Querying Language) to improve the searching experience. Since version 7.0, KQL is used as a default language for querying in Kibana, but the user can also switch to Lucene to his liking.

Different methods can be used for performing searches on your data in kibana. Kibana also offers real-time analysis, charting, and summarization of data having a user-friendly interface. It is an excellent front-end dashboard which is capable of visualizing indexed material from the elastic cluster.

The most common search types in kibana are:

·     Free text searches:

Free text search works within all fields, including the source field, which includes all the other fields. The free text queries allow you to search and examine the text body such as the body of an email. It is used for searching an exact string. Case sensitivity is not crucial in this type of search. This group contains queries such as intervals query, match query, match_bool_prefix query, match_phrase query, match_phrase_prefix query, multi_match query, common terms query, query_string query, simple_query_string query.

·     Field-level searches:

Field-level searches are used for searching for data inside specific fields. It allows us to search for a string within a particular field. Field-level searches are case sensitive, depending on the type of fields, and cannot use wildcard searches. The syntaxes generally use in this type of searches are like, "name: Google", "bytes: [65 to 88]". Users can search a range within a field by using [] and {}.

·     Logical statements:

Logical statements are combined into searches by using these statements. Proper format, i.e., Using Capital letters, is a must to define logical terms like AND or OR. You can use parentheses to limit logical statements. AND, OR, and NOT are the three most commonly used Logical statements.

·     Proximity searches:

Proximity queries allow us to state an edit distance for words appearing in a different order in any phrase. It is used for searching terms within precise character proximity. Proximity queries in Kibana are executed with a (~) following the words you are looking for and are placed in quotation marks.