OpenSearch vs. ElasticSearch: What to Choose?


OpenSearch? ElasticSearch? Which one to choose and are they any different at all? These are the kind of questions asked by many developers, no matter novice or expert. In this blog, we’ll unveil the mystery.


According to the DB-Engines ranking, ElasticSearch is ranked #8 out of all database management systems — and putting aside the fact that ElasticSearch is a full-text search engine and not a database management system, its ranking is still impressive.

ElasticSearch is a modern search and analytics engine originally authored by Shay Banon and built on top of the Lucene library. Originally released in early 2010, the search engine quickly developed a reputation for being a schema-free, cross-platform alternative for non-relational database management systems like MongoDB.

OpenSearch is built upon ElasticSearch, but with a twist — OpenSearch doesn’t only offer a search engine (hence the name), but also offers a variety of other tools its users can choose from. The development of OpenSearch is led by Amazon’s AWS and the software started in 2021 as being a fork of ElasticSearch and Kibana.

Why Use ElasticSearch or OpenSearch?

Before we dig into the features offered by both search engines — OpenSearch and ElasticSearch — we’ll walk you through why they became an option in the first place.

ElasticSearch was created by Shay Banon to be a scalable and distributable search solution for his wife's growing list of recipes — ElasticSearch is made to be a fit for all kinds of data: textual, numerical, geospatial, and the like. It's also built on Apache Lucene and offers a visualization tool, and many aggregations too.

OpenSearch was created on the same premise as ElasticSearch - the core catch here being that OpenSearch, as the name suggests, helps developers avoid being locked-in to a single vendor.

It is worth noting that neither ElasticSearch neither OpenSearch are database management systems — they’re full-text-based search engines offering a wide array of additional features unique to themselves (read on to learn more.)

When to Use OpenSearch?

The majority of use cases concerning OpenSearch have to do with website-based search engines, real-time application monitoring, and log analytics. Since OpenSearch offers more versatility than its counterpart ElasticSearch by acting as a 100% open-source search and analytics suite, it is a frequent choice for developers looking to ingest, aggregate, view, and work with their data for a number of use cases such as the ones mentioned above.

The primary difference between OpenSearch and ElasticSearch has to do with licensing — OpenSearch is a Server-Side Public License (SSPL.)

AWS themselves stated that they have decided to create OpenSearch as a fork of Kibana, but under a different license. OpenSearch being made by AWS, it is also closely related to Amazon itself and can be used either standalone, or as part of the Amazon OpenSearch Service.

When to Use ElasticSearch?

ElasticSearch use cases are simpler — and explaining when it should be used is, consequentially, more simple too. People elect to use ElasticSearch as an option when migrating from non-relational database management systems such as MongoDB — ElasticSearch, in essence, is a server that processes JSON requests and provides JSON-based data in return.

ElasticSearch works by organizing data into JSON documents, then grouping those documents into something called “indices” based on their characteristics. Indices are essentially data structures that map words to their locations in documents, and that’s one of the core reasons why ElasticSearch is so fast.

Also, keep in mind that ElasticSearch is not a DBMS like MySQL, TimescaleDB, or MongoDB - instead, it is just a component of a stack called ELK — the Elastic Stack. The Elastic Stack is a set of tools made for data storage and visualization when working with ElasticSearch and this toolset includes Logstash, Kibana, and, of course, Elasticsearch itself. Revised versions of the stack also include Beats. No, not headphones — those are agents that acquire data, then feed the acquired data into the Elasticsearch ecosystem.

How to Work with OpenSearch and ElasticSearch?

By now, the choice for you should be clear — and no matter which kind of search engine you will choose, it’s essential to acquire the right tools for working with them in the first place, too.

Fortunately for you, DbVisualizer is one of such tools — it’s the highest-rated SQL client on the market right now with customers including Netflix, Volkswagen, Tesla, and even NASA.

DbVisualizer can make your job working with any database management system a breeze — aside from being a SQL client, it can also help you observe your tables like a spreadsheet, help you create stunning ERD diagrams, and provide you with a graph-based view of the tables within your database, too. Our team provides you with a 21-day trial of the software as well — first three weeks are on us, so make sure to try the software out for yourself and see how you do!

Frequently Asked Questions

How is ElasticSearch Different From OpenSearch?

OpenSearch provides you with the option to use it as part of Amazon’s OpenSearch service or as a standalone tool.

OpenSearch is essentially a clone of ElasticSearch, just with an open-source license — hence the name.

What is ELK?

ELK is a shortened term referring to the Elastic stack — Grafana, Beats, Logstash, and ElasticSearch.

Why Should I Use ElasticSearch or OpenSearch?

ElasticSearch or OpenSearch should be used if you’re after a whole host of full-text search solutions and want to migrate to a toolset from a non-relational database management system such as MongoDB.

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About the author
Lukas Vileikis
Lukas Vileikis is an ethical hacker and a frequent conference speaker. He runs one of the biggest & fastest data breach search engines in the world -, frequently speaks at conferences and blogs in multiple places including his blog over at
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