What is Elasticsearch? [Working, Pros, and Applications]

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What is Elasticsearch? [Working, Pros, and Applications]

Anmol Jain
Last updated on May 22, 2024

    With data being the new oil of this digital world, it is a must that modern mobile, web, and data analytics applications should have the scalability and ability to handle vast amounts of data in near real-time. Elasticsearch is a robust solution that acts as an underlying technology or engine for developing applications with advanced search features and capabilities.

    However, what exactly is Elasticsearch? Many people think of it as an analytics engine, search engine, database, big data solution, or something like Google Chrome. All these answers are true.

    More precisely, it is a free and open-source search engine that enables everyone to find what they are looking for quickly without any hassle. From an individual looking to buy the best pair of shoes to an employee searching for specific documents or files from the company’s intranet, Elasticsearch can do it all.

    Furthermore, the Apache Lucene library forms the basis for this search engine. It is available under the open-source Server Side Public License and the Elastic license, and the proprietary Elastic license. It is among the most popular enterprise search engines.

    Through this blog post, we aim to explain what exactly Elasticsearch is and its various aspects.

    So, here we go!

    What is Elasticsearch? [Definition]

    It is an open-source, distributed full-text search and analytics engine developed by Elastic (formerly Elasticsearch N.V.). It supports all sorts of data, including structured, unstructured, geospatial, numerical, and textual. The most common use cases of this search engine are log analytics, business analytics, full-text search, and security intelligence.

    Furthermore, it is a central component of the Elastic Stack, which is a collection of free and open-source tools for data storage, ingestion, analysis, and visualization. Previously, the Elastic stack was referred to as ELK (Elasticsearch, Logstash, and Kibana). In addition, this stack incorporated the fourth product called Beats. Beats are the shipping agents to transmit data to Elasticsearch.

    This search and analytics engine, along with other components in the ELK stack, lets you store, search, and analyze heaps of data very quickly in near real-time and respond with results in just milliseconds. It collects any type of data from heterogeneous sources, stores it, and indexes it based on the user-specified mapping. Such indexing of data makes it possible for the engine to provide the search results in milliseconds.

    Also, rather than leveraging tables and schemas to store data. It supports unstructured data and does not use SQL for querying data. Hence, we can call it a NoSQL database.

    Moreover, it has an extensive REST API that enables you to store and search data. Additionally, we can think of this search and analytics engine as a server that processes JSON requests and returns JSON data.


    In 2004, Shay Banon created Compass, which was a predecessor to Elasticsearch. However, while developing the third version of Compass, he thought that it was essential to create a scalable search solution by rewriting the parts of Compass. With this in mind, he created a distributed solution from scratch by leveraging the JSON over the HTTP interface in 2010, which was the first version of Elasticsearch.

    Later, in 2012, Banon founded Elastic NV to offer commercial services and products associated with the recently created distributed solution. In 2015, Banon renamed Elastic NV to Elastic.

    How Does Elasticsearch Work?

    Initially, the raw data flows into the search engine. You might be wondering from where this raw data comes into the search engine. Logstash, one of the core components of the ELK stack, is responsible for collecting data from various sources, including web applications, logs, or system metrics, and sending data to Elasticsearch.

    Before sending data, Logstash integrates, parses, normalizes, and cleans or enriches the collected data. After enrichment, it passes the clean data to Elasticsearch for indexing based on the user-defined mappings. Once the data gets indexed, you can finally execute queries against your data to retrieve the desired information.

    Basically, Elasticsearch operates on the principle of shared-nothing architecture. It stores data in the form of JSON documents, where each document consists of a set of keys with their corresponding values. In addition, the data structure it primarily leverages is the inverted index governed by Apache Lucene’s APIs.

    The inverted index data structure generally maps each word to a list of documents containing that word. Therefore, when you enter the specific keyword, the search engine locates all those documents containing the keyword and returns a list as a result.

    Furthermore, another component of the ELK stack called Kibana is a data visualization and management tool. It has the ability to represent data graphically using various elements, such as graphs, pie charts, bar graphs, maps, and histograms.

    The latest and most recent component of the ELK stack is Beats, which are shipping agents that send data from multiple systems or machines to Logstash.

    Why Use Elasticsearch?

    The following are some reasons for using this search and analytics engine:

    • Fast

    As Apache Lucene forms the basis for this search engine, it is an extremely good full-text search. It is also a near real-time search platform. This means that the time between a document getting indexed till it getting searchable is very short, which is generally one second. As a result, this search engine is ideal for time-sensitive applications, such as infrastructure monitoring and security analytics.

    • Distributed

    As discussed earlier, this search engine stores data in the form of documents, and it distributes these documents across multiple containers, specifically referred to as shards. These shards are replicated, and hence, you can find copies of documents in case of hardware failure. Such a distributed nature of Elasticsearch makes it capable of handling petabytes of data and scaling out to hundreds and thousands of servers.

    • Wide Range of Features

    Besides resilience, scalability, and speed, this search engine provides many other features, such as index lifecycle management and data rollups for faster searching and retrieval of data.

    • Simplified Data Ingestion and Visualization

    The Elastic stack makes data ingestion and visualization easy and simple. Beats and Logstash collect, integrate, analyze, and clean raw data. Meanwhile, Kibana is a data visualization tool that makes it easier to represent your data graphically.

    Use Cases of Elasticsearch

    The following are some of the most common use cases of Elasticsearch:

    • Application Search: This search engine is ideal to use in the development of applications that require a platform for search, retrieval, and reporting of data. eBay is among the best examples of application search, which facilitates searching across 800 million listings and returning results in milliseconds.
    • Website Search: It is an effective tool for websites that require storing vast amounts of data for effective and accurate searches.
    • Enterprise Search: This search engine facilitates enterprise search, including blog search, eCommerce product search, people search, document search, and other kinds of search.
    • Logging and Log Analytics: One of the primary use cases of this search engine is to ingest and analyze log data in near real-time. In addition, it provides valuable insights to operate on log metrics.
    • Infrastructure Metrics and Container Monitoring: Numerous companies leverage the ELK stack to examine several metrics. This could entail collecting data based on a variety of performance parameters that differ depending on the use case.
    • Security Analytics: Security analysis is yet another prominent application of Elasticsearch. The ELK stack analyzes access logs and similar logging systems. This analysis provides a complete overview of what exactly is happening across your systems.
    • Business Analytics: The ELK stack comes with a set of tools, where some tools make it the best solution for business analytics.
    • Scraping and Analyzing Public Data: This search and analytics engine is capable of mining public data, such as social media conversations and comments on posts. It carries out real-time analysis of that data that helps businesses understand potential customers.


    Elasticsearch is a free-to-use search and analytics engine and one of the core components of the ELK stack. Many other tools, along with this search engine, together make an excellent solution for storage, search, data processing, analytics, and visualization.

    Today, many companies, including Facebook, Netflix, eBay, Walmart, and Slack, leverage this search engine to uncover the hidden potential of their data and derive valuable insights by processing it.

    Hopefully, you might have understood what exactly Elasticsearch is and its use cases through this blog post. Feel free to share your queries in the comments section regarding this topic, if any.

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    Yes, Elasticsearch is free and open-source to use under the Server Side Public License. However, it also comes with a proprietary license called the Elastic License.

    Elasticsearch supports the following programming languages: Java Go JavaScript with Node.js Python Ruby PHP Perl C# with .NET

    Elasticsearch supports 34 text languages and offers analyzers for each of them. Also, it can support many other text languages with the help of plugins.

    Yes, you can contribute to the Elasticsearch project, whether you are inside or outside of Elastic, as it is an open-source search engine.

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