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What is Elasticsearch ? Guide to understand Elastic Stack & Elasticsearch

Swati Rai | JAN 19, 2021

Elasticsearch – Elastic – kibana – logstash – elk- elastic stack – ELK Stack, if you have come across this blog then surely you would have bumped into these terms in one way or the other. But before diving in deeper, lets understand people’s insight on elasticsearch, “what is elasticsearch?”, as name suggest search people understand elasticsearch something similar to a Google for Amazons of the world, elasticsearch - an index, a one stop big data solution which is fast reliable and scalable, an analytics database. There are multiple synonym to this amazing technology and all are correct as this is directly proportional to your engagement with Elasticsearch, and yes Elasticsearch does it all. As per Database Engines ranks – Elasticsearch is the most common and reliable technology applied by developers for their databases in 2020.

elastic search

What is Elastic Stack and how it works ?

So before we dive into Elasticsearch lets us first understand what exactly is Elastic Stack (previously known as the ELK Stack).

Elastic Stack a powerful set of software products that ingest and store data from any source, and in any format, and perform search, analysis, and visualization in milliseconds or less. The Elastic Stack is designed for direct use by developers to power a variety of use cases.

Considered as the heart of the Elastic Stack “Elasticsearch”, - centrally stores user data for high-efficiency search, excellent relevancy, and powerful analytics that is highly scalable.

Core Products/Elements of Elastic Stack

The core products that define an Elastic stack are listed below:

  • Beat – Data Collection

  • Logstash – Data processing pipeline

  • Elastic Search – Search and analytics engine

  • Kibana – Dashboard to visualize data

Core Products of elastic stack

Why is Elastic Stack needed?

As per a data, humans generates 2.5 quintillion bytes of data every day and 1.7MB of data is generated by an individual in a second. The Data, now it’s a world of data. So we need a system that analyses our data. There are two terms to understand:

  • Analysis – In the analysis part, you’ll get results from the past data or the existing data that you have
  • Analytics – When you want to predict user requirements, you want graphs based visualization for better business clarity and also want to understand Data patterns

So these are two most important tools for any business, and to accumulate-stack-analyse your data Elastic Stack is the most powerful tool.

What is Elasticsearch ?


Elasticsearch is an open-source, RESTful, distributed search and analytics engine built on Apache Lucene. Since its release in 2010, Elasticsearch has quickly become the most popular search engine, and is commonly used for log analytics, full-text search, security intelligence, business analytics, and operational intelligence use cases. It is a part of the main solution Elastic Stack and works in with other elements like Logstash & Kibana.

How Does Elasticsearch works?

To understand the working of Elasticsearch, let’s try to understand the basic architecture of Elasticsearch first –

Basic Architecture of Elasticsearch :

  • Cluster: In Elasticserach, data is stored in nodes, there can be n number of nodes in a machine. And each node is related to the cluster. So the Cluster is a set of nodes

  • Node: A node is a single server that is a part of a cluster. A node stores data and participates in the cluster’s indexing and search capabilities. An Elasticsearch node can be configured in different ways like Master Node, Data Node, Client Node.

  • Documents: You store your data as documents which are JSON objects. So how these data organized in the cluster? The answer is indices. In the world of relational databases, documents can be compared to a row in a table.

  • Index: Elasticsearch Indices are logical partitions of documents and can be compared to a database in the world of relational databases.

  • Types: Each index has one or more mapping types that are used to divide documents into a logical group. It can be compared to a table in the world of relational databases. Every document is stored as an index. The index you can say is the collection of documents. That has similar characteristics for instance, the Department will have A index, and Employees have B index i.e, they are logically related.

  • Sharding: Sharding is just a way to divided index into smaller pieces. Each piece is known as a shard. Sharding is done at an index level. Shard is just like an index. For scalability. With sharding, you can store billions of documents within the one index.

  • Replicas: Elasticsearch allows you to make one or more copies of your index’s shards which are called “replica shards” or just “replicas”. Basically, a replica shard is a copy of a primary shard. Replicas provide redundant copies of your data to protect against hardware failure and increase capacity to serve read requests like searching or retrieving a document.

  • Inverted texts: Elasticsearch uses a data structure called an inverted index, which is designed to allow very fast full-text searches. An inverted index lists every unique word that appears in any document and identifies all of the documents each word occurs in.

Working of Elasticsearch –

Now let’s jump on how Elasticsearch works, raw data flows into Elasticsearch from a variety of sources, including logs, system metrics, and web applications. Data ingestion is the process by which this raw data is parsed, normalized, and enriched before it is indexed in Elasticsearch. An Elasticsearch index is a collection of documents that are related to each other. Elasticsearch stores data as JSON documents. Each document correlates a set of keys (names of fields or properties) with their corresponding values (strings, numbers, Booleans, dates, arrays of values, geolocations, or other types of data).

Elasticsearch uses a data structure called an inverted index, which is designed to allow very fast full-text searches. An inverted index lists every unique word that appears in any document and identifies all of the documents each word occurs in.

During the indexing process, Elasticsearch stores documents and builds an inverted index to make the document data searchable in near real-time. Indexing is initiated with the index API, through which you can add or update a JSON document in a specific index.

What is Beat?

beat in Elasticsearch

Beats is a collection of lightweight, single-purpose data shipping agents used to send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch. Beats are great for gathering data as they can sit on your servers, with your containers, or deploy as functions then centralize data in Elasticsearch. For example, Filebeat can sit on your server, monitor log files as they come in, parses them, and import into Elasticsearch in near-real-time.

What is Logstash ?

Logstash core product of Elasticsearch

Logstash, one of the core products of the Elastic Stack, is used to aggregate and process data and send it to Elasticsearch. Logstash is an open source, server-side data processing pipeline that enables you to ingest data from multiple sources simultaneously and enrich and transform it before it is indexed into Elasticsearch. For example, since data is often scattered across different systems in various formats, Logstash allows you to tie different systems together like web servers, databases, Amazon services, etc. and publish data to wherever it needs to go in a continuous streaming fashion.

What is Kibana ?

Kibana tool for Elasticsearch

Kibana is a data visualization and management tool for Elasticsearch that provides real-time histograms, line graphs, pie charts, and maps. Kibana also includes advanced applications such as Canvas, which allows users to create custom dynamic infographics based on their data, and Elastic Maps for visualizing geospatial data.

Where can Elasticsearch be used ?

The speed and scalability of Elasticsearch and its ability to index many types of content mean that it can be used for a number of use cases here are some of the cases where Elaticsearch can be used extensively:

  • 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

What are the benefits of Elasticsearch ?

Elasticsearch is fast: Elasticsearch offers simple REST based APIs, a simple HTTP interface, and uses schema-free JSON documents, making it easy to get started and quickly build applications for a variety of use-cases.

High Performance: The distributed nature of Elasticsearch enables it to process large volumes of data in parallel, quickly finding the best matches for your queries. The distributed nature of Elasticsearch allows it to scale out to hundreds (or even thousands) of servers and handle petabytes of data.

Complimentary Set of Features: Elasticsearch comes integrated with Kibana, a popular visualization and reporting tool. It also offers integration with Beats and Logstash, while enable you to easily transform source data and load it into your Elasticsearch cluster. You can also use a number of open-source Elasticsearch plugins such as language analyzers and suggesters to add rich functionality to your applications.

Near Real-Time Operations: Elasticsearch operations such as reading or writing data usually take less than a second to complete. This lets you use Elasticsearch for near real-time use cases such as application monitoring and anomaly detection.

Is Elasticsearch free?

Yes, the open source features of Elasticsearch are free to use under the Apache 2 license. Additional free features are available under the Elastic license, and paid subscriptions provide access to support as well as advanced features such as alerting and machine learning.

What programming languages does Elasticsearch support?

Elasticsearch supports a variety of languages and official clients are available for:

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

Elasticsearch Used Cases in BFSI –

India’s leading Banking player was juggling with constant security breaches, and they wanted to eliminate blind spots and strengthen their overall security maturity. With Elastic Stack they were able to detect abnormal or suspicious user behaviour and pattern in their infrastructure, analyse it and keep the system safe. NSEIT helped them build and implement an end-to-end solution. Goldman Sachs are using Elasticsearch for tracking and analysing stock trades to provide better financial guidance. Softbank Payment Services are harnessing the power of Elasticsearch to monitor transactions and services performance while also monitoring for fraud.

Other used Cases of Elasticsearch-

When you hail a ride using Uber, Elastic helps power the systems that locate nearby riders and drivers. When you shop online on Ebay, Elastic helps power finding the right products to add to your cart. When you look for a partner on Tinder, Elastic helps power the algorithms that guide you to a match. When you search for your favourite shows/series on Netflix, Elasticsearch use cases to monitor and analyse customer service operations and security logs. When you use LinkedIn, in the backend Elasticsearch is doing its job of monitoring performance and security.

Conclusion –

Elasticsearch as tool or a platform is humongous and has got multiple applications across enterprises. This blog aims to cover the basics of Elasticsearch and aims to serve as a guide or one stop solution to all possible questions related to Elasticsearch or Elastic Stack. But to know more about Elasticsearch or if you want to use it in your ecosystem, do contact NSEIT, as we are proud Elastic Partners.

Swati Rai

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