Elasticsearch vs. OpenSearch: 6 Key Differences and How to Choose

What is Elasticsearch?

Elasticsearch is an open-source, RESTful, distributed search and analytics engine built on Apache Lucene. It’s designed to handle large volumes of data, making it a popular choice for log and event data management. Elasticsearch is also known for its real-time capabilities, allowing users to explore, analyze, and visualize data patterns as they occur.

In addition to log and event data management, Elasticsearch is commonly used for full-text search and operational intelligence use cases. It aims to be scalable, resilient, and fast, allowing you to index and search data in near-real time. Elasticsearch also supports complex queries to perform detailed analysis and supports multi-tenancy for easy management of multiple indices.

One of the key features of Elasticsearch is its distributed nature. This means that indices can be divided into shards, with each shard being a self-contained index. This feature facilitates the handling of large datasets by allowing the data to be distributed across a cluster of servers.

What Is AWS OpenSearch? 

OpenSearch is an open source project created by AWS in 2021, as a fork of Elasticsearch 7.10.2. This means it has the same basic functionalities as Elasticsearch, but since then the project has diverged from Elasticsearch in several ways.

On top of OpenSearch, Amazon provides a fully managed search and analytics service called AWS OpenSearch. It includes OpenSearch and OpenSearch Dashboards (a community-driven, open-source data visualization and user interface suite derived from Kibana 7.10). Users pay only for the resources they run on AWS, with no additional charge for the search and visualization software.

As a fully managed service, AWS OpenSearch is intended to be easy to set up and manage. This allows companies to deploy, secure, and run OpenSearch at scale. Users can set up and configure their OpenSearch clusters with a few clicks in the AWS Management Console, and AWS handles tasks like patching, upgrading, and backup.

AWS OpenSearch also provides security features at no additional cost, including encryption, user authentication and access control, and audit logging. It offers automated snapshots, high availability, and easy scaling, allowing it to handle large amounts of data.

This is part of a series of articles about ELK Stack.

The Drama Behind OpenSearch: Tensions Between Elastic and AWS

Elasticsearch, released under the Apache 2.0 license in 2010, became the preferred enterprise search engine globally. Commonly used with Logstash and Kibana (forming the ELK stack), it excels in log analytics applications such as monitoring applications, analyzing security logs, and tracking user behavior. Recognizing its potential, Amazon introduced Amazon Elasticsearch Service (Amazon ES) in 2015, a managed cloud service allowing AWS users to deploy scalable Elasticsearch clusters and manage data activities in the cloud.

However, tensions rose between Elastic N.V., the company behind Elasticsearch, and Amazon. Elastic N.V. accused Amazon of trademark infringement and misleading marketing, leading to a lawsuit in 2019. The dispute culminated in significant developments in 2021: In January, Elastic N.V. shifted Elasticsearch’s licensing to the Server Side Public License (SSPL) and the Elastic License with the release of version 7.11. This move aimed to prevent companies, including Amazon, from offering Elasticsearch as a service without partnering with Elastic. 

In response, Amazon forked the last open-source version of Elasticsearch (7.10.2) in April 2021, initiating a new open-source project, OpenSearch. Alongside this, Amazon introduced the Amazon OpenSearch Service, thereby navigating around the licensing changes and continuing to offer a search engine solution to its cloud customers.

Elasticsearch vs. OpenSearch: Key Differences 

While Elasticsearch and AWS OpenSearch share a common lineage and core functionality, they have some key differences that set them apart.

1. Data Ingestion

Both Elasticsearch and AWS OpenSearch provide powerful data ingestion capabilities, but they approach this task differently. Elasticsearch supports various data types and structures and uses ingest nodes for pre-processing documents before indexing. It also supports bulk data ingestion, making it a good choice for large-scale data analytics.

OpenSearch focuses on ease of use and integration with other AWS services. It provides a managed pipeline for data ingestion, simplifying the process of moving data into the system. AWS OpenSearch also integrates seamlessly with services like AWS Kinesis, AWS Glue, and AWS Lambda, providing a complete data processing pipeline.

2. Client Libraries

Elasticsearch has a wide range of client libraries in many programming languages, such as Java, Python, .NET, PHP, Perl, Ruby, and JavaScript. This makes it easy for developers to integrate Elasticsearch into their applications, regardless of the programming language they are using.

AWS OpenSearch has its own set of client libraries. As of the time of this writing, it offers clients for Python, Java, JavaScript (Node.js), Go, Ruby, PHP, .NET, and Rust. In addition, OpenSearch is technically compatible with Elasticsearch clients because it is essentially the same platform as Elasticsearch. However, Elasticsearch has added license restrictions that block its clients from connecting to OpenSearch.

3. Performance

Both Elasticsearch and AWS OpenSearch are designed to handle large amounts of data and deliver fast, reliable search results. They both use the same underlying engine (Lucene) and offer similar features such as sharding, replication, and a distributed architecture to ensure high performance.

However, OpenSearch has the added advantage of being part of a fully managed service. This means that it can leverage AWS’s global infrastructure for improved performance, scalability, and reliability. AWS provides performance monitoring tools, automated backups, and disaster recovery features as part of the OpenSearch service, helping to ensure high performance and data safety.

4. Licensing and Pricing

When it comes to licensing, both Elasticsearch and OpenSearch have undergone significant changes in recent years. In 2021, Elasticsearch changed its license from Apache 2.0 to Server Side Public License (SSPL). This move sparked controversy in the open-source community as the SSPL is not recognized as an open-source license by the Open Source Initiative (OSI). In response, AWS forked the last Apache-licensed version of Elasticsearch to create OpenSearch, which remains under the Apache 2.0 license.

The pricing models of Elasticsearch and OpenSearch are another point of divergence. Elasticsearch, managed by Elastic, offers a tiered pricing model. It includes a free tier with basic features and paid tiers that unlock more advanced functionalities. OpenSearch, as a project managed by AWS, is free to use at all levels of functionality. However, users incur costs if they choose to use AWS services for hosting and managing their OpenSearch instances.

5. Support and Documentation

Elasticsearch has a rich collection of documentation available, covering everything from basic setup to advanced usage scenarios. It also boasts a large and active community that can provide support. Elastic, the company behind Elasticsearch, offers paid support options as well.

OpenSearch is a relatively new project, and its documentation is still growing. That said, AWS has committed to maintaining comprehensive documentation for OpenSearch, and the existing Elasticsearch documentation remains largely applicable. OpenSearch also benefits from the support of the extensive AWS community. Like Elastic, AWS offers paid support options for OpenSearch.

6. Security

Elasticsearch initially offered advanced security features only in its paid tiers. However, following the licensing change, Elastic announced that these features would be available for free. These include SSL encryption, role-based access control, and audit logging.

OpenSearch, in contrast, includes security features as a part of its core offering, as long as you choose to run it on AWS. It offers functionalities similar to Elasticsearch, such as encryption, user authentication, and fine-grained access control. Given its management by AWS, users can also benefit from the robust security and compliance infrastructure of the AWS cloud.

Elasticsearch vs. OpenSearch: How to Choose? 

Choosing between Elasticsearch and OpenSearch largely depends on your specific needs and circumstances. If you prefer a mature product with a well-established community and extensive documentation, Elasticsearch might be the better choice. On the other hand, if you value the principles of open-source software and prefer a tool that offers advanced features for free, OpenSearch could be the better fit.

It’s also important to consider your hosting environment. If you’re already using AWS services, OpenSearch might offer smoother integration and management. Conversely, if you’re using another cloud provider or hosting your search engine on-premises, Elasticsearch might provide more flexibility.

Finally, it’s worth considering the future direction of both projects. Elasticsearch continues to be the global leader in enterprise search, and has a long track record of innovation in the space. Amazon’s OpenSearch, with a more limited community of contributors and less focus from its corporate sponsor, will probably gradually lag behind Elasticsearch in future innovation.

Coralogix as an Alternative

Coralogix is a SaaS, full-stack observability platform with cutting-edge architecture that eliminates the need for expensive indexing and hot storage. With a rich suite of products including log analytics, powerful alerting, custom dashboards, APM, RUM, SIEM and more, along with world-class, 24/7 customer support and a straightforward pricing model, Coralogix is a great alternative to either Opensearch or Elasticsearch. Read more in this in-depth comparison.