In the industry’s sea of database solutions, finding the right solution for your needs is crucial. Two prominent contenders in the database solutions are Elasticsearch and OpenSearch. The choice between them can seem confusing since OpenSearch is forked from Elasticsearch. Though they stem from a common ancestry, these databases have evolved into distinct platforms providing unique features. In this blog, we’ll explore key considerations like security features, licensing, pricing, community, support, and more to help you decide whether Elasticsearch or OpenSearch is the right fit for your project.
What is Elasticsearch?
Elastic developed Elasticsearch, a distributed, open-source analytics engine for all types of data, including structured and unstructured data, specialized for blazing fast search capabilities. Elasticsearch is the central part of the ELK stack. The ELK stack comprises Elasticsearch, Logstash, Kibana, and Beats, providing data storage, integration, visualization, and shipping solutions. This is a convenient approach for small and large companies handling big data.
Explore more about Elasticsearch and its use cases in this blog post: What is Elasticsearch used for?
What is OpenSearch?
OpenSearch was born as a forked project of Elasticsearch. Basically, the final open-source versions of Elasticsearch and Kibana 7.10.2 were forked, and those fork versions became OpenSearch and OpenSearch Dashboards 1.0. OpenSearch is a community-driven, open-source project managed by Amazon. It is Apache 2.0 licensed, which means you are free to use, contribute to, change, or update the source code. Like Elasticsearch, OpenSearch offers a multi-tenant architecture and analytics engine with full-text search and distributed search capabilities. OpenSearch also has visualization and analytics. Apart from these similarities, OpenSearch and Elasticsearch differ in many ways, which we will explore in this article.
Now that we’ve some basic understanding of Elasticsearch and OpenSearch, let’s dive into the feature comparison in detail.
Elasticsearch vs. OpenSearch: What’s the difference?
Let’s compare the following aspects of Elasticsearch and OpenSearch to understand which would better fit your next project.
- Features
- Query Types
- Community
- Licensing
- Security
- Performance
- Support and Documentation
- Pricing
Features
Both Elasticsearch and OpenSearch offer extensive features, including full-text search, distributed search, multi-tenancy, and robust analytics engines.
With a premium subscription, Elasticsearch offers some unique features, like geoshape aggregations and extra dashboard features for Kibana, including Canvas (a presentation tool) and Lens (smart suggestions for data visualization), which are currently unavailable with OpenSearch.
While Elasticsearch has been in the field longer than OpenSearch, OpenSearch is catching up and has some unique features to offer, like Trace Analytics. Trace Analytics allows users to analyze trace data from distributed applications. This can be particularly beneficial for troubleshooting and performance optimization.
Additionally, OpenSearch offers several free features only available in the premium versions of Elasticsearch. These include:
- Anomaly detection
- Centralized user accounts/access control
- Cross-cluster replication
- IP filtering
- Configurable retention period
- Machine learning features such as regression and classification
- Tableau connector
- JDBC driver
- ODBC driver
However, in OpenSearch, some functionalities, like anomaly detection alerting found in premium Elasticsearch, require the integration of additional plugins.
Query Types
Both Elasticsearch and OpenSearch are equipped to handle various standard query types, including full-text, term, and match queries. However, each offers unique capabilities in advanced query types.
Elasticsearch’s Advanced Query Capabilities
- Scripted Queries: Provide the flexibility to use custom scripts for complex search criteria.
- Span Queries: Ideal for locating specific sequences of terms within a document.
- Geo Queries: These allow you to locate documents based on geographic data, like proximity to a point or within a defined polygon.
- “More Like This” Queries: Useful for finding documents that are content-similar to a reference document.
- Specialized Queries: Cater to niche use cases, such as nested queries for searching within complex, nested data structures, and fuzzy queries to find terms with slight spelling differences.
OpenSearch’s Query Support
- Joining Queries: Allow for merging data from multiple indices or sources.
- Full-Text Queries: Enable searching for particular text strings within one or more documents.
- Term-Level Queries: Useful for finding documents that contain specific terms or phrases.
- Geographic and XY Queries: Facilitate searches based on geographical coordinates or shapes, such as locating documents within a certain radius of a point.
- Span Queries: Similar to Elasticsearch, these identify specific term sequences within a field.
In summary, while both Elasticsearch and OpenSearch offer robust querying capabilities for various use cases, Elasticsearch boasts a range of advanced query types that cater to more complex search needs. Meanwhile, OpenSearch provides solid support for standard query operations, including geospatial and text-based searches.
Community
Since Elasticsearch has been around longer, the community is large and active. The community actively contributes to product development, and support on the forums is also extensive. Elasticsearch has a community Git repository. However, only employees can commit to the codebase.
OpenSearch is completely open-source; hence, it is collaborative and community-driven. The contributions are made to the codebase, documentation, and plugins. The GitHub repo allows users to submit pull requests, commit, and leave feedback. OpenSearch has an active forum for discussions, announcements, Q&A, and feedback. Decision-making involves input from diverse contributors, fostering transparency and inclusivity.
Licensing
Recently, Elastic transitioned its licensing to Server Side Public License (SSPL) for its core features. Before that, it was licensed under Apache 2.0 license. The move has sparked debates within the community about the implications of this license on the open-source nature of Elasticsearch since the SSPL is not recognized as an open-source license by the Open Source Initiative (OSI).
OpenSearch embraces the Apache 2.0 license, maintaining a solid commitment to being open-source. This licensing choice ensures that the code remains freely available for anyone to use, modify, and distribute. It also implies fewer restrictions on using the software, making it a more flexible option for product development.
Security
When we talk about databases, security is of great importance. Elasticsearch offers basic security features in its free version, such as password protection and encryption. The advanced security features like Role-Based Access Control, Field and Document Level Security, and Audit Logging are available only in the premium versions.
In contrast, being completely free, OpenSearch offers a full suite of security features immediately. This includes role-based access control, multi-tenancy, audit logging, and encryption.
Security features are significant for a business since they safeguard sensitive data and restrict unauthorized users from accessing it. Thus, the security features could be a deciding point between Elasticsearch and OpenSearch since OpenSearch offers the entire security suite.
Performance
Elasticsearch uses a distributed architecture to parallelize search operations, leading to quick and relevant results. Its horizontal scalability allows you to add nodes to the cluster, distributing the workload and enhancing performance as data volume increases. It utilizes caching and advanced indexing techniques to optimize query performance, especially for frequently accessed data.
Being a fork of Elasticsearch, OpenSearch, apart from similar performance optimizations, maintains compatibility while introducing optimizations and improvements. It also has lower latency for search queries. It benefits from ongoing enhancements contributed by the community.
Documentation
Elasticsearch has well-maintained and diverse documentation from blogs, guides, videos, newsletters, a Slack channel, a YouTube channel, and more. If you’re stuck with something while working with ElasticSearch, you can go to their documentation and find an answer immediately.
OpenSearch, on the other hand, has many gaps since it is new to the market. However, these gaps will likely be filled soon, given the vast community of OpenSearch.
Pricing
Elasticsearch’s move to the Server Side Public License (SSPL) for its core features has raised concerns about potential implications for commercial use. The SSPL is more restrictive than traditional open-source licenses. Elastic offers subscription plans with additional features, support, and security enhancements. Pricing for these plans can vary based on the level of support and features required.
OpenSearch relies on community-driven support, reducing the dependency on commercial licensing for essential features. Users can customize and extend the software based on their requirements.
Comparison Table
| Aspect | Elasticsearch | OpenSearch |
| Features | Full-text search, distributed search, multi-tenancy, robust analytics engines. | Similar core features to Elasticsearch. |
| Unique premium features: geoshape aggregations, extra Kibana dashboard features. | Unique features: Trace Analytics, anomaly detection, centralized user accounts, cross-cluster replication, IP filtering, configurable retention period, machine learning features, Tableau connector, JDBC driver, ODBC driver. | |
| Query Types | Scripted, span, geo, “More Like This”, specialized, and fuzzy queries. | Joining, full-text, term-level, geographic and XY, span queries. |
| Community | Large, active community. Contributions mainly by employees. | Fully open-source, collaborative, community-driven. Open to all contributions. |
| Licensing | Transitioned to Server Side Public License (SSPL). | Apache 2.0 license, fully open source. |
| Security | Basic features in the free version. Advanced features in premium versions. | A full suite of security features is available for free. |
| Performance | Distributed architecture, caching, and advanced indexing for optimized performance. | Similar optimizations to Elasticsearch, and ongoing enhancements by the community. |
| Support and Documentation | Extensive documentation, including blogs, guides, videos, newsletters, Slack channel, and YouTube channel. | Newer, with some gaps in documentation, but rapidly improving. |
| Pricing | Subscription plans for additional features, support, and security. Concerns about commercial use due to SSPL. | Community-driven support, no commercial licensing for essential features. |
At Knowi
Knowi is a Business Intelligence (BI) platform that unifies analytics across structured, unstructured, and semi-structured data. Knowi has firmly established itself as a leader in the Elasticsearch and OpenSearch domains. Knowi also provides Elasticsearch analytics; give it a try if you want to elevate your business data with insights, embedded analytics, and visualizations. Make sure to contact us for any of your database queries.
Conclusion
Choosing between Elasticsearch and OpenSearch depends on your requirements, considerations, and values. OpenSearch may be ideal if you prioritize a fully open-source solution with a robust community-driven ethos and are on a budget. We also have to consider the learning curve involved in switching to OpenSearch. However, if you have an existing Elasticsearch infrastructure and are comfortable with the recent licensing changes, sticking with Elasticsearch might make sense. If you’re still confused about which database would suit you, schedule a quick call with Knowi to discuss your requirements. Book a demo today with Knowi and embark on a transformative analytics journey.
