Elasticsearch index: how it works and uses

Elasticsearch index: how it works and 6 key uses

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What is Elasticsearch?

Elasticsearch can be said to be a distributed, non-proprietary, and analytics engine that is built on Apache Lucene and developed in Java. It began as a scalable version of the Lucene non-proprietary search framework, adding the ability to align with Lucence indices. Elasticsearch can store, search and explore a massive volume of data swiftly and in real-time, giving answers in split seconds. Elastic search can reach quick responses because instead of looking up the text directly, it searches the index.

In addition, Elasticsearch uses a structure based on documents instead of tables and schemas; it also comes with extensive REST APIs, which are used for keeping and looking up data. At its best, Elasticsearch can come as a server with the power to process JSON requests and give back JSON data.

ball of rubberbands with elasticsearch logo superimposed
Image Source: knowi.com

Buy Guide To Using The Elasticsearch Index

See practical guide to using the Elasticsearch distributed search engine for full-text search or real-time analytics of structured data.

How does Elasticsearch work?

To better understand how Elasticsearch works, there are some basic concepts of organizing information with its backend components.

The first and foremost concept is the Logical concept- which contains Documents. Documents can be described as the fundamental unit of information arranged in an Elasticsearch expressed in JSON.

JSON is the global internet data interchange format. Think of a document in the structure of a role in a relatable database, representing a specific entity- which is whatever data you’re looking for. With Elasticserarch, documents are not just text. They can be any arranged data encoded in JSON.

This data could be numbers, strings, and dates. Every document contains a unique ID alongside a given data type, which describes the specific type of entity the document is. For instance, a particular document can mean an encyclopedia write-up or log entries from the web.

Next, there are Indices. An index can be described as a group of documents with similar characteristics. An index can further be explained as a highest-level entity that can query against Elasticsearch. An index is similar to a database in a relational database schema. Any documents that are in an index are very much logically related. For example, in an e-commerce website, they can be an index for Customers, another one for Products, another one for Orders, etc. An index is recognized by a name used to refer to the index when performing indexing, searching, updating, and taking out operations against the document.

 Thirdly we have Inverted Index. An index is known as an inverted index, which is the mechanism in which all search engines operate. Furthermore, it is a data structure that keeps a mapping from content like words or numbers to its area in a document or a series of documents.

An inverted index does not control strings directly. Instead, it breaks each document into individual search terms, i.e., every word maps each search term to the documents those search terms happen within. For instance, in the diagram below, the term “best” appears in document 2, which is mapped to that document.

This presents a quick look-up of the area to search terms in a particular document. Using distributed inverted indices, Elasticsearch swiftly looks from massive data sets.

Visual Representation of an Inverted Index

Buy Guide To Using The Elasticsearch Index

See practical guide to using the Elasticsearch distributed search engine for full-text search or real-time analytics of structured data.

Backend Components


In Elasticsearch, a cluster refers to a group of several node instances which are intertwined together. The power of an Elastic cluster is within the dispensation of tasks, searching, and indexing in all the nodes in the cluster.


A node is part of a cluster. It is a single server. A node keeps data and engages in the cluster’s indexing and search faculty. An Elasticsearch node can be structured in different ways:

First, it can be structured as a Master Node. A Master Node controls the Elasticsearch cluster, and in addition, it is responsible for all of the cluster-wide operations like creating/ taking out an index and adding/ taking out nodes.

Next, as a Data Node. A data node keeps and executes data-related operations like search and aggregation.

Lastly, as a Client Node. A Client Node advances cluster requests to the master node in addition to data-related appeal to data nodes.


An Elasticsearch gives the ability to ramify the index into several pieces called shards. A shard is a fully-functional and free index that can host any other node without a cluster. By sharing the documents in an index around multiple shards and sharing those shards with several nodes. Elasticsearch allows redundancy, protecting against hardware neglect and increasing query volume in addition to nodes being included in a cluster.


A replica shards or a replica in an Elasticsearch allows for creating one or several copies of an index’s shards. A replica shard can further be described as a copy of a primary shard. Every document is an index that belongs to one primary shard. Replicas allow unwanted copies of data to protect against hardware neglect and expand capacity to serve read requests like looking up or recovering a document.

What is the Elastic Stack {ELK}

Elasticsearch is also the central component of the Elastic Stack, a set of open-source tools for data intake, enrichment, storage, and analysis, including visualization. The Elastic Stack is popularly referred to as the ELK stack after components like Elasticsearch, Longstash, and Kibana. While Elasticsearch is a search engine at its best, users who are using Elastic for log data can have a quick way to absorb and visualize data.

Buy Guide To Using The Elasticsearch Index

See practical guide to using the Elasticsearch distributed search engine for full-text search or real-time analytics of structured data.

What is Elasticsearch used for?

1. Application search: For applications that rely heavily on a search platform to access, retrieve, and report data.

2. Website search: Websites that store a lot of content find Elasticsearch useful for an effective and up-to-date search. This makes Elasticsearch a steadily gaining ground in the search domain sphere across sites.

3. Enterprise search: Elasticsearch grants enterprise-wide search, which includes searching the document, searching for E-commerce products, blog search, individual search including any other form of inquiry.

It has rapidly penetrated and taken the place of search solutions among the most popular websites people use daily. Elasticsearch is an excellent part of company intranets from an enterprise-specific perspective.

4. Logging and log analytics: As we’ve discussed, Elassticsearch is commonly utilized for nd analyzing log data in near-real-time and in a scalable manner. It also provides essential operational insights on log metrics to drive actions.

Infrastructure metrics and container monitoring- Several companies, use the ELK stack in analyzing different metrics. Metrics include collecting data from several performance parameters according to use case.

5. Security analytics: Another primary analytics application of Elasticsesrch is its security analysis. This includes access logs and similar logs concerning system security that can be examined with the ELK stack, which provides a complete picture of the happenings across the user’s system in real-time.

6. Business analytics: Several of the built-in features available within the ELK Stack make it a better business analytics tool option. Though, it should be known that there is a guide learning curve for applying such products in specific organizations. This occurs mainly in companies that possess several data sources apart from Elasticsearch. Kibana works exclusively with Elasticsearch data. However, Knowi is an alternative analytics platform that inherently integrates with Elasticsearch and makes even non-technical business users make envision and carry analytics on Elasticsearch data without the initial knowledge or prowess of the ELK Stack.

Big tech companies that use ELK Stack

1. Netflix

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The production company, Netflix depends on the ELK Stack to monitor and inspect customer service operations and security logs beyond several use cases. For instance, Elasticsearch is the underlying engine that aids Netflix’s messaging system. Furthermore, Netflix uses Elasticsearch for its automatic sharding and duplication, flexible schema, outstanding extension model, and ecosystem with various plugins. Netflix has gradually increased its use of Elasticsearch from small abandoned deployments to more than a dozen clusters incorporating several nodes.               

2. Ebay

EBay Headquarters Ahead Of Earnings Figures
Image Source: TheVerge.com

eBay has created countless business-critical text search and analytics use cases that use Elasticsearch as the backbone. eBay has made a custom Elasticsearch -as-a-Service platform to permit an Elasticsearch cluster that provides eBay’s internal OpenStack-based cloud platform.

3. Walmart

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The retail store Walmart uses the Elastic Stack to expose the hidden potential of its data to get insights concerning customer purchasing patterns and track store performance metrics, including holiday analytics and all in close real-time. Walmart also uses ELK’s security features for security with SSO, and this comes with notifying anomaly detection and observing DevOps.

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