5 Major Benefits of Using ElasticSearch

Selecting the right tool for information storage and retrieval can be very difficult given the number of tools available in the market. Some tools offer easy accessing the data, some allow us to create relations between the data, while others enable us to efficiently search data from a huge pool of information. ElasticSearch emerged as a combination of all these features, allowing users to quickly retrieve data records in any form, perform various kinds of searches regardless of their data type and analyze billions of data in just a couple of seconds. 

ElasticSearch is a highly scalable, enterprise-level and open source search engine that is based on Apache Lucene and runs in real time. Rather than following traditional text search setup, ElasticSearch provides an extended ability to make searches using query DSLs and APIs. Let’s discuss some of the major benefits of using ElasticSearch in your application. 

#1 High Performance

ElasticSearch is able to perform extremely fast searches by using distributed inverted indices. It caches all the queries for the result set, so for every search query that contains cached filter, it searches the result from the cache. Compared to typical SQL database that retrieves the searched data in more than 10 seconds, ElasticSearch can do it in less than 10 ms. 

#2 Easily Scalable

Based on a distributed architecture, ElasticSearch can be scaled up to thousands of servers and store thousands of gigabytes of data. It is built to run smoothly on any system or in any cluster containing a number of nodes. When you grow from a small cluster to a big cluster and very big cluster, the process is nearly automatic and requires a little planning, but still painless. 

#3 Distributed Architecture 

ElasticSearch is not only perfect for handling search queries, but it is powerful enough to handle large volumes of data with its distributed architecture. The distributed approach divides the indicies into shards, and shards are capable of creating innumerable replicas. As and when new documents are being added, routing and rebalancing operations are conducted automatically. 

#4 Document-oriented Database

ElasticSearch uses JSON (JavaScript Object Notation) as the serialization format for documents to store complex entities, and indexes all the records by itself. JSON serialization has become a standard format for NoSQL database and it is compatible with various programming languages. It is very easy, concise and simple to read, which results in higher performance. 

#5 Schema Free 

ElasticSearch is schema free, which means it doesn’t require any data definition, and uses some defaults to index the date unless you specify the data type. It accepts JSON documents and detects the data type, index the records and make it searchable. When the object with a new property is indexed later, it is automatically added to the mapping definitions. 

The Bottom Line

ElasticSearch has already acquired place in many organizations because of its excellent features. Although it is evolving and new functionalities are being added, its core peculiarity of offering high-performance search results and consistency is what makes it stand out.