Elasticsearch and OpenSearch are often compared side-by-side, with many claiming that the core difference between them boils down to vendor dependency. But what does that mean for businesses, developers, and users who rely on powerful search engines? In this blog, we’ll explore why Elasticsearch and OpenSearch are so similar, why vendor dependency matters, and how it can impact your choice of search engine.
Understanding the Fork: Elasticsearch to OpenSearch
The story begins in 2021 when Elastic changed Elasticsearch’s license from Apache 2.0 (fully open-source) to the more restrictive Server Side Public License (SSPL). This move was meant to prevent cloud providers from offering Elasticsearch as a service without contributing back to the Elastic community. In response, Amazon Web Services (AWS) forked Elasticsearch, creating OpenSearch under the Apache 2.0 license, ensuring it stayed fully open-source.
Key Similarities Between Elasticsearch and OpenSearch
Because OpenSearch is a fork, it retains nearly all the core capabilities of Elasticsearch. Both tools are designed for:
Scalable, real-time search: They can handle complex search queries across massive datasets.
Data analytics: With their roots in Apache Lucene, both platforms offer comprehensive analytics, from anomaly detection to full-text search.
Distributed architecture: Both are built to scale horizontally, allowing them to manage huge datasets by simply adding more nodes to the cluster.
Performance optimization: Elasticsearch and OpenSearch handle complex queries with speed and accuracy, suitable for logs, security data, and business intelligence.
As a result, functionally and operationally, these two search engines often feel the same to users, especially those using them for standard search and analytics applications.
Vendor Dependency: What’s the Real Difference?
The licensing split led to a broader discussion about vendor dependency, as the shift impacted support, integration, and community direction.
Support and EcosystemElasticsearch remains under Elastic’s development and ecosystem, which includes premium tools like Kibana, Beats, and Logstash. These tools are designed to work seamlessly with Elasticsearch but may have limitations when used with OpenSearch. OpenSearch has its own equivalent tools, like OpenSearch Dashboards, but is still building its ecosystem.
Managed Services and Cloud IntegrationElastic Cloud is Elastic’s managed service offering, ideal for those who want a streamlined experience with Elasticsearch on major cloud platforms. Meanwhile, AWS OpenSearch Service (previously called Amazon Elasticsearch Service) has full integration with AWS. This level of cloud integration means that users of one ecosystem may experience lock-in, or find it cumbersome to switch.
Open Source vs. License-Controlled UpdatesVendor dependency also appears in terms of update cadence and feature releases. Elastic often develops new features for Elasticsearch that may only be available under their SSPL license, such as advanced machine learning features. OpenSearch, on the other hand, continues to prioritize open-source contributions, meaning businesses can depend on it staying fully open-source.
Community and InnovationVendor dependency impacts community contributions and innovation pace. Elastic has a larger community from years of established development, which means it has more robust support, plugins, and customizations. OpenSearch’s community is rapidly growing, driven largely by AWS, but also by developers prioritizing open-source solutions. Vendor dependency here means users must decide between Elastic’s mature community or OpenSearch’s community-focused open-source model.
Use Cases and How Vendor Dependency Matters
For many businesses, vendor dependency isn’t just a philosophical issue; it has direct implications for their use cases:
E-commerce and Log Analytics: Both Elasticsearch and OpenSearch are capable of handling e-commerce search and log analytics, so vendor dependency may matter less for these applications. Elastic’s proprietary features, however, might be attractive if advanced ML capabilities are required.
Real-time Monitoring and Security: Organizations prioritizing open-source solutions for security data are often drawn to OpenSearch, as it’s free from proprietary licensing issues.
Cloud-based Applications: AWS users often find OpenSearch more appealing due to its native integration, while multi-cloud or self-hosted environments might benefit more from the broader flexibility that Elasticsearch offers.
Is Elasticsearch Still Better?
For businesses where high-level support, advanced proprietary features, and robust integrations are critical, Elasticsearch remains the top choice. Elastic’s focused development brings new features faster, and its ecosystem can provide additional value.
However, if you prioritize vendor independence, a fully open-source model, and integration with AWS, then OpenSearch is likely a better fit. OpenSearch’s Apache 2.0 licensing means there’s no worry about SSPL restrictions, and it is continually evolving to close the feature gap with Elasticsearch.
Conclusion: Choose What Fits Your Needs
The choice between Elasticsearch and OpenSearch ultimately comes down to your organization’s needs, values, and infrastructure:
Elastic’s ecosystem is best for enterprises needing cutting-edge features, enhanced community support, and are comfortable with SSPL licensing.
OpenSearch offers the freedom of a truly open-source solution with seamless AWS integration, ideal for those wanting control without vendor lock-in.
In most cases, Elasticsearch leads with a mature ecosystem and features, making it ideal for critical business use cases. Yet, OpenSearch continues to evolve, making vendor dependency one of the few major differences that separate these powerful search engines.
Comments