In the world of search technologies, Elasticsearch and OpenSearch have emerged as leading contenders offering powerful search capabilities and flexible deployments. So, let's compare Elasticsearch and Open search and take a look at their similarities, differences, and unique features that each one offers.
Introduction
Elasticsearch is a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data for lightning fast search, fine‑tuned relevancy, and powerful analytics that scale with ease.
OpenSearch is a distributed, community-driven, Apache 2.0-licensed, 100% open-source search and analytics suite used for a broad set of use cases like real-time application monitoring, log analytics, and website search. OpenSearch provides a highly scalable system for providing fast access and response to large volumes of data with an integrated visualization tool, OpenSearch Dashboards, that makes it easy for users to explore their data. OpenSearch is powered by the Apache Lucene search library, and it supports a number of search and analytics capabilities such as k-nearest neighbors (KNN) search, SQL, Anomaly Detection, Machine Learning Commons, Trace Analytics, full-text search, and more.
Deep Dive into Comparison
Scalability and Performance
Both Elasticsearch and OpenSearch are renowned for their ability to handle large volumes of data and to scale horizontally, they leverage distributed architecture and sharding techniques to achieve high performance and fault tolerance so it's a win for both.
Both are built upon Apache leucine. Leucine offers powerful indexing and query execution mechanisms enabling fast and efficient search operations so comparing performance when tuned and optimized will offer very similar results however tuning and finding correct documentation is much easier to do on Elasticsearch as documentation for OpenSearch at the moment is just absolutely horrendous but when it's all said and done they can perform about the same but as these two engines will continue to evolve in the future and more and more changes will be added we will just have to wait and see which one outperforms the other.
Deployment options
Both Elasticsearch and OpenSearch can be pulled and deployed on premise with relative ease. In this regard they are basically identical however when it comes to using a cloud service things begin to differ-
Elasticsearch as a service can be used on Azure, Google cloud and even AWS. It's essentially Cloud agnostic while OpenSearch as a service is only offered on AWS this can be a big deal if you find a better deal on a different Cloud platform and want to migrate with elastic you can pack up and go but with amazon not so much.
Cross-cluster searching
Elastic introduced cross-cluster searching enabling users to search across multiple clusters as if they were a single entity. OpenSearch carries forward this capability allowing seamless search operations across clusters however it is worth noting that AWS can only do this cross-cluster searching and CCR on clusters hosted by AWS OpenSearch.
Security Information and Event Management(SIEM)
A vital role in monitoring and analyzing security events, Elasticsearch has been widely integrated into SIEM Solutions offering powerful indexing and search capabilities for security log data. Elasticsearch comes with elastic SIEM out of the box and is ready to use . On the other hand because OpenSearch lacks integrated SIEM functionality consumers must use additional services or hire a third party for their security needs.
Ecosystem and Cost
OpenSearch is lagging from its origin to its lack of features to its lack of documentation and many other things, elastic is not perfect either while elastic may offer many of its tools for free. However, Elasticsearch is a for-profit organization as well and many of the other tools that you will need for Elasticsearch to function well are behind a paywall, for example - index life cycle management for Elasticsearch is free but cross-cluster replication is not, we have to get a license to unlock that feature.
OpenSearch and the community behind it are busy making alternatives for much of the proprietary software offered by elastic. And these alternatives are not exactly one-to-one copies of each other like Elasticsearch has time series data streams while OpenSearch has reintroduced segment replication, there are many tiny examples of these and these little things don't really make a huge difference so far but as the projects continue to evolve we can expect to see more and more divergence from one another.
Conclusion
When choosing which search engine is right, consider which additional tools and features you will need to calculate the price and check out the reliability of those additional tools to help you make the best choice. The add-ons and additional tools from OpenSearch are a little bit lackluster, but we will end up paying for additional tools with Elasticsearch and OpenSearch whichever way we go. With Elasticsearch we get a better experience.
Below is the clear understanding of both the searching giants:
| Services | Pros | Cons | Similarities |
| Elasticsearch | · Cloud-agnostic deployment. · Cross-cluster searching regardless of hosting. · SIEM functionality out of the box. | · Restrict some functionalities due to licensing. · Licensing makes it less cost-effective. | · Scalable. · High performance. · Ease Deployment (on-premise). · Diverging functionalities. |
| OpenSearch | · Range of unique features such as OpenSearch Dashboards, the OpenSearch Query Language (PPL) etc. · Fully open-source. · More cost-effective. | · Deployment limited to AWS. · Cross-cluster searching on clusters hosted by AWS OpenSearch only. · Lacks SIEM functionality. |
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