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Kibana Elasticsearch

In this section, we will learn what is Elasticsearch, uses of Elasticsearch, working of Elasticsearch, and its deployment options.

What is Elasticsearch?

The Elasticsearch is an open source, distributed search and analytics engine for all type of data, that includes the textual data, numerical data, geospatial data, structured data, and even unstructured data. It is basically based on Apache Lucene.

The Elasticsearch was first published in 2010, by Elasticsearch N.V. Elasticsearch is the core component of the Elastic Stack. The Elastic Stack is a collection of open source tools for data ingestion, enrichment, storage, analysis and visualization, known for its easy REST APIs, distributed design, speed, and scalability. It is also known as the ELK Stack, which now provides a rich set of lightweight shipping agents which is known as Beats that is used for sending Elasticsearch data.

Uses of the Elasticsearch

The main purpose for the use of the Elasticsearch is its speed and scalability, and its un beatable ability to index multiple content types, which simply means that it can be used in the following:

  • Application search
  • Website search
  • Enterprise search
  • Logging and log analytics
  • Infrastructure metrics and container monitoring
  • Application performance monitoring
  • Geospatial data analysis and visualization
  • Security analytics
  • Business analytics

Working of the Elasticsearch

In Elasticsearch the raw data can be inserted from different sources that includes the logs data, the system metrics data and also the web applications data. In Elasticsearch the Data ingestion is the process that parses the data and then normalizes it. After that, it enriches these raw data before it is indexed in Elasticsearch for the further use. Users can run complex queries against their data once indexed in Elasticsearch, and use aggregations to retrieve complex summaries of their data. One can create powerful visualizations of the data with the help of Kibana tool. It can also share the dashboards among its team members and can also manage the Elastic Stack in it.

Meaning Index in Elasticsearch

An Elasticsearch index is a list of the relevant documents. Elasticsearch stores data as documents relating to JSON. The record compares a set of keys (field names or properties) with their respective values (strings, numbers, Booleans, dates, value ranges, geolocations, or other data types).

Elasticsearch uses a data structure called an inverted index to allow very quick full-text search.

Use of Logstash in Elasticsearch

In Elasticsearch, Logstash is the core products of the Elastic Stack. It is used for aggregating, storing, and submitting data to Elasticsearch. Logstash is an open source, server-side data processing pipeline that allows us to perform ingest, enrich, and convert data, simultaneously from multiple sources until it is indexed to Elasticsearch.

Use of Kibana and the Elasticsearch

Kibana is Elasticsearch's data visualization and management tool that provides histograms, line graphs, pie charts, and maps in real time. Kibana also provides specialized software such as Canvas, that enables users to build custom dynamic data-based infographics, and Elastic Maps for geospatial data visualisation.

Why use Elasticsearch?

  • The quest for elasticise is quick. Since Elasticsearch is built on top of Lucene it excels in searching for full-text. It is also a quasi-real-time search tool. It means the delay from the time a document is indexed before it is searchable is very small usually a second. As a result, Elasticsearch is well suited for time-sensitive cases such as security analytics and monitoring infrastructure.
  • Elasticsearch is naturally distributed. The documents stored in Elasticsearch are distributed across different containers known as shards, which are duplicated in case of hardware failure to provide redundant copies of the data.
  • Elasticsearch consists a vast set of attributes. Elasticsearch has a range of powerful built-in features that make storing and searching data much more efficient, such as data rollups and index lifecycle management, in addition to its speed, scalability, and resiliency.
  • The Elastic Stack in the Kibana simplifies the ingesting of the dataset, visualizing of data, and also reporting the results from the dataset in Kibana. Integrating with Beats and Logstash allows the processing of data simple before indexing into Elasticsearch. And Kibana provides real-time visualization of data from Elasticsearch, as well as UIs for fast access to data from application performance monitoring (APM), logs, and infrastructure metrics.

Is Elasticsearch free?

Yes, the Elasticsearch is free to use under the Apache 2 license. There is one more additional free feature that are available under the Elastic license are like paid subscriptions that provide support access in the Elasticsearch and also the features like alerting and machine learning.

Who can contribute his work to project of Elasticsearch?

Elasticsearch is a project managed by Elastic. The code base in Elasticsearch includes the in- and out-of-Elastic contributions from developers' side.

Anyone inside the Elasticsearch GitHub repository may submit a pull request. Elasticsearch also conducts a straightforward review of all pull requests that are received by it before integrating all those pull request into the code base.

Elasticsearch's Deploy Options

The technology Elasticsearch can be deployed as a hosted means or as a managed service via Elasticsearch Service that are available on cloud platform like Amazon Web Services (AWS), Google Cloud, and Alibaba Cloud, or it can be easily downloaded from the internet and installed on our own hardware or in the cloud platform as per the need. If anyone wants to dig up more about the Elasticsearch, he can look up to the documentation about Elasticsearch that consists of each and every instruction for downloading the Elasticsearch, to installing the Elasticsearch, and also about how to configure the Elasticsearch.

Elastic also provides Elastic Cloud Enterprise (which can be deployed on public or private clouds, virtual machines, or bare metal hardware) as well as a Private Subscription Level for customers who want to access, control, and track their deployments from a single console but choose not to use a public cloud platform.

Programming Languages Supported by Kibana or Elasticsearch

Elasticsearch supports the following programming languages:

  • Java programming language
  • JavaScript (Node.js) programming language
  • Go programming language
  • .NET (C#) programming language
  • PHP programming language
  • Perl programming language
  • Python programming language
  • Ruby programming language

What text languages supports Elasticsearch?

Elasticsearch supports 34 languages from Arabic to Thai, and offers analyzers for each script. The entire list can be found in the documentation on the Elasticsearch Language Analyzer. Custom plugins can be added to support additional languages.

Is Elasticsearch offering REST APIs?

Yes, the Elasticsearch also provides a comprehensive and efficient set of REST APIs that performs the different operations like cluster health check, CRUD (Create, Read, Update and Delete) in the web app, in the indices search operations. It is also available for performing the advanced search operations, such as filtering and aggregations in the web app or wherever it is needed.

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