OpenSearch vs. Elasticsearch: Similarities and 6 key differences

What Is OpenSearch?

OpenSearch is an open source search and analytics suite derived from Elasticsearch 7.10, the last open source version of Elasticsearch. Developed by Amazon Web Services (AWS), it aims to provide a community-driven alternative to Elasticsearch. OpenSearch includes core search and visualization capabilities and is compatible with Elasticsearch APIs. This allows users to migrate from Elasticsearch with minimal changes.

One of the key features of OpenSearch is its focus on security. It includes built-in security features such as encryption, authentication, and access control. Additionally, OpenSearch is highly extensible with a variety of plugins that enhance functionality. It also provides performance tuning options to cater to different use cases.

What is Elasticsearch?

Elasticsearch is an open source search and analytics engine developed in Java. Created by Elastic NV, it is designed for horizontal scalability, reliability, and real-time search capabilities. Elasticsearch forms the core component of the Elastic Stack, which includes Kibana, Logstash, and Beats, offering a complete data search and analysis platform. In 2018, Elasticsearch transitioned from a fully open source model to SSPL, a slightly more restrictive license, but which still allows free use and modification.

Elasticsearch supports complex search queries and provides near-instantaneous results, making it popular for applications that require full-text search, data analysis, and logging. Its architecture is distributed, meaning it can handle large volumes of data across multiple nodes. Elasticsearch is known for its flexibility and extensibility, allowing users to enhance functionality through a wide range of plugins and extensions.

Similarities between OpenSearch and Elasticsearch

OpenSearch and Elasticsearch share a common lineage, with OpenSearch being a fork of Elasticsearch 7.10. Both systems offer high-performance search capabilities and support complex search queries. They are built on similar architectures designed for horizontal scalability, real-time operations, and fault tolerance. This shared foundation ensures that users can employ many of the same tools, plugins, and APIs across both platforms.

In terms of functionality, both platforms provide data indexing, aggregations, and full-text search capabilities. They also support integration with various data sources and visualization tools, making them versatile in handling different types of data analysis tasks. The familiarity between the two means users transitioning from Elasticsearch to OpenSearch will find the learning curve manageable.

OpenSearch vs. Elasticsearch: Understanding the differences

1. Origins

OpenSearch emerged from a necessity when Elastic NV changed Elasticsearch’s license from Apache 2.0 to SSPL. Amazon Web Services led the development of OpenSearch to ensure continued access to an open source search and analytics suite. The primary goal was to maintain compatibility with Elasticsearch while providing a fully open source licensing model.

Elasticsearch was originally released in 2010 by Elastic NV as an open-source search engine. It rapidly gained popularity due to its scalability and search features. Over time, Elastic NV introduced various updates and paid enterprise features, evolving the platform significantly. The change in licensing from Apache 2.0 to SSPL marked a significant shift in its development approach, leading to the creation of forks like OpenSearch.

2. Features and functionality

Both OpenSearch and Elasticsearch offer strong search capabilities; however, they have diverged in specific areas. OpenSearch emphasizes built-in security features, such as fine-grained access controls and encryption, aimed at providing secure environments out of the box. Furthermore, OpenSearch includes OpenSearch Dashboards, a user interface for data visualization akin to Kibana.

Elasticsearch offers features through its commercial X-Pack extensions. These features include machine learning, alerting, and monitoring tools. While some of these features are available in OpenSearch, Elasticsearch’s enterprise extensions may provide enhanced functionality in certain areas. Both platforms support a wide range of plugins, allowing users to customize and extend their search capabilities to meet specific needs.

3. Security

OpenSearch integrates security features within the core package. This includes encryption at rest and in transit, role-based access control, and audit logging. Its focus on security ensures that sensitive data is protected without relying on additional third-party tools. These security measures are built-in and do not require separate installations.

Elasticsearch offers security features through its commercial X-Pack plugin. This includes features like role-based access control, IP whitelisting, and auditing, which are essential for enterprise security compliance. While these security features are robust, they are part of the paid subscription model. As a result, Elasticsearch’s security solutions might represent a higher total cost of ownership compared to OpenSearch.

4. Performance

Performance in both OpenSearch and Elasticsearch hinges on similar architectures designed for distributing loads across multiple nodes. OpenSearch has been specifically optimized for cloud environments, particularly on AWS infrastructure, which may provide performance benefits for users leveraging Amazon’s cloud services. Additionally, performance tuning and optimizations are readily configurable to suit various workloads.

Elasticsearch is known for its speed and efficient handling of large data sets. Its distributed nature ensures efficient query execution and high throughput. Performance can be further enhanced through hardware optimizations, and by leveraging Elastic’s commercial support plans. Overall, both platforms offer performance capabilities, but specific advantages may depend on the infrastructure and environment in use.

5. License and governance

Version 7.11 and later of Elasticsearch broke from the permissive Apache 2.0 licensing, moving to SSPL, which is a more restrictive license not considered open-source by OSI criteria. This shift was motivated by Elastic’s need to commercialize its offerings more tightly. OpenSearch, on the other hand, adheres to Apache 2.0, ensuring it remains free and open for use in any project or commercial product without restrictive licensing.

Governance structures of the two also differ significantly. OpenSearch is community-driven, with a more inclusive contribution model managed by AWS but open to contributions from diverse groups and individuals. Conversely, Elastic NV centrally governs Elasticsearch, overseeing development and policy decisions, which includes constraints on features meant to drive commercial licensing.

6. Ecosystem and community

OpenSearch’s ecosystem heavily piggybacks on its roots in Elasticsearch, ensuring compatibility with many existing tools and extensions. Its community-focused governance model fosters continual open-source enhancements and community-driven initiatives. The ecosystem includes OpenSearch Dashboards, which mirrors functionalities similar to Kibana for data visualization.

Elasticsearch enjoys a more mature and broader ecosystem due to its longer history. The Elastic Stack integrates a range of tools such as Kibana for visualization, Logstash for data ingestion, and Beats for lightweight data shippers. However, many advanced tools and support come with a commercial license, which can be a limiting factor for open-source proponents. The community around Elasticsearch remains strong but is somewhat segmented between open-source users and paying customers.

Tips from the expert

Kassian Wren

Open Source Technology Evangelist

In my experience, here are tips that can help you better navigate the decision between OpenSearch and Elasticsearch:

Leverage OpenSearch’s AWS optimizations: If you’re heavily invested in AWS, take full advantage of OpenSearch’s cloud optimizations. AWS-specific integrations, such as IAM roles and S3-backed snapshots, can provide operational efficiency and cost savings.
Monitor community-driven updates: OpenSearch’s open governance means that new features and plugins can emerge rapidly from the community. Regularly monitor these updates as they may introduce innovative solutions that don’t exist in Elasticsearch.
Customize performance tuning beyond defaults: Both platforms offer performance tuning options, but don’t rely solely on default configurations. Deep dive into JVM tuning, shard management, and index lifecycle policies specific to your workloads to squeeze out maximum performance.
Consider multi-cloud or hybrid environments: If your architecture involves multi-cloud or on-prem setups, Elasticsearch’s broader ecosystem might offer more flexibility and better support for non-AWS environments, though at the cost of licensing constraints.
Explore plugin ecosystems thoroughly: Investigate third-party plugins beyond the core offerings of both platforms. Sometimes, these lesser-known plugins can address specific business needs, such as custom authentication methods or advanced analytics, without the need for enterprise licenses.
Understand licensing and avoid vendor lock in: OpenSearch remains fully open-source under the Apache 2.0 license, offering greater flexibility for modification and redistribution.

Elasticsearch vs. OpenSearch: How to choose?

Choosing between OpenSearch and Elasticsearch can significantly impact your project’s search and analytics capabilities. Each platform offers unique strengths, shaped by their development histories, licensing models, and community support. Here are some key considerations:

Deployment flexibility: OpenSearch is optimized for cloud environments, particularly AWS, which can lead to performance benefits and cost savings for users leveraging AWS infrastructure. Elasticsearch offers flexibility across various environments but may require additional configuration for optimal performance in non-native cloud setups.
Integration with existing tools: OpenSearch is highly compatible with tools that were originally built for Elasticsearch. However, it also focuses on maintaining and expanding this compatibility to support a broader range of open-source tools and systems, promoting a more flexible integration landscape.
Documentation and learning resources: OpenSearch benefits from extensive community contributions to its documentation and learning resources, often providing a wide variety of perspectives and use cases. Elasticsearch’s documentation, while comprehensive and professional, is centrally managed by Elastic NV and may include more enterprise-focused content.
Future roadmap and innovation: OpenSearch’s roadmap is publicly available and shaped by community input, allowing users to influence future developments. Elasticsearch’s roadmap is driven by Elastic NV’s strategic goals, which may focus on commercial product enhancements and enterprise features.
Compliance and security needs: If your project requires strict compliance with open-source licensing (e.g., for legal or philosophical reasons), OpenSearch’s adherence to Apache 2.0 is a significant advantage. Elasticsearch’s SSPL licensing, while offering security features, might not meet the open-source criteria for all organizations.

Empowering organizations with comprehensive support for OpenSearch

At the heart of a successful data-driven organization lies an efficient search and analytics engine. Instaclustr’s managed services offer comprehensive support for OpenSearch, a high-performing, fully open-source platform that provides organizations with reliable and scalable search and analytics capabilities.

Managed Service Offering: Instaclustr’s end-to-end support ensures your OpenSearch clusters are always up, secure, and scalable. By leveraging our managed service, your organization can focus on core business objectives, free from the complexities and challenges of infrastructure management.
Expert Assistance: Our team of experienced engineers is proficient in OpenSearch and its applications. They provide expert guidance in cluster configuration, performance tuning, and troubleshooting to optimize your cluster performance and resolve any potential issues rapidly.
24/7 Monitoring and Support: We proactively monitor your OpenSearch clusters to detect and address any potential issues, ensuring smooth operations and minimal downtime. At any hour of the day, our support team stands ready to assist, resolve issues, and keep your OpenSearch environment running optimally.
Embrace the power of open-source search and analytics with Instaclustr’s comprehensive support for OpenSearch. Let Instaclustr shoulder the burden of managing your OpenSearch environment, so you can focus on driving your business forward.

Discover more about Instaclustr support for OpenSearch: