The Comprehensive Guide to OpenSearch for Observability

This guide covers OpenSearch, an open-source, distributed search, log analytics, and data visualization technology used by DevOps organizations.

Table of Contents

– The emergence and growth of OpenSearch

– The fundamental components of OpenSearch

– The main applications

– Differences Between OpenSearch and Elasticsearch

– Is it a good idea to switch to OpenSearch?

– Conclusion

In IT today, organizations need observability to monitor infrastructure and application performance, ensure good user experiences, and maintain system health. OpenSearch is an open-source tool that helps organizations improve their observability practices.

DevOps organizations need fast analysis capabilities. They collect log data, performance metrics, business insights, and security monitoring data. Good querying and analytics are essential for observability in cloud environments. This guide explains how OpenSearch addresses these needs and why observability matters for organizations of all sizes.

The emergence and growth of OpenSearch

Elasticsearch was one of the most famous enterprise search and analytics tools for many years. However, a significant shift occurred when Elastic, the organization behind Elasticsearch, introduced licensing changes. This guide examines the impact of these changes and how they led to the birth of OpenSearch, a distributed, community-driven, open-source search and analytics suite.

The fundamental components of OpenSearch

OpenSearch consists of two components: a search engine and data store called OpenSearch, and a user interface and visualization tool known as OpenSearch Dashboards. This section explores the key features that OpenSearch brings to observability. From full-text querying to advanced anomaly detection, you will discover how OpenSearch supports observability.

Open-Source Foundation:

• OpenSearch is built on an open-source foundation. It is released under the Apache License, version 2.0 (ALv2), which allows users to use, modify, extend, monetize, and resell the software without restrictions.

Community-Driven Development:

• A community of contributors develops OpenSearch. This approach ensures continuous innovation, rapid development, and broad support for various use cases.

Apache 2.0 Licensing:

• OpenSearch uses Apache 2.0, a permissive open-source license that encourages wide adoption and contribution. This licensing model supports an ecosystem of plugins, extensions, and integrations.

Compatibility with Elasticsearch:

• OpenSearch maintains compatibility with all versions of Elasticsearch up to version 7.10. This compatibility ensures a smooth transition for organizations already using Elasticsearch, making it easier to migrate to OpenSearch.

Components of OpenSearch:

• OpenSearch has two primary components:
  • OpenSearch: This data store and search engine provides indexing and searching capabilities. It allows users to ingest, store, and search large amounts of data.
  • OpenSearch Dashboards: This component provides visualization and a user interface for data analysis. It enables users to create custom dashboards, visualize data, and gain insights from their datasets.

Plugin Ecosystem:

• OpenSearch supports an ecosystem of plugins that extend its core functionality. These plugins cover many use cases, including search customization, security enhancements, performance analysis, and machine learning integration.

Robust Search Features:

• OpenSearch provides search features to customize the search experience, including full-text querying, autocomplete, scroll search for handling large datasets, and customizable scoring and ranking to fine-tune search results.

Application Analytics:

• OpenSearch offers tools for creating custom observability applications. Users can consolidate log events, trace data, and metric information into a single view to monitor system health. This capability allows for quick troubleshooting and issue resolution.

Trace Analytics:

• OpenSearch allows users to ingest and visualize OpenTelemetry data, enabling the tracking of event flows in distributed applications. This feature helps identify and address performance issues.

K-NN Search:

• OpenSearch offers k-NN (k-nearest neighbors) search capabilities using machine learning. This feature can perform similarity searches across billions of documents, making it useful for product recommendations, fraud detection, and content search applications.

Alerting System:

• OpenSearch has an alerting system that enables users to set up, manage, and monitor alerts based on specific conditions. The system can automatically notify stakeholders when issues or anomalies occur, helping organizations maintain system reliability.

Advanced Security Features:

• OpenSearch includes security features like encryption, authentication, authorization, and auditing. It supports integration with authentication providers like Active Directory, LDAP, SAML, Kerberos, and JSON web tokens. Fine-grained role-based access control is available for securing indices, documents, and fields.

Community Projects and Documentation:

• The OpenSearch community contributes to various projects and provides documentation to support users. These resources help developers and organizations get the most out of OpenSearch.

The main applications

OpenSearch is versatile and finds applications in many scenarios. Whether you are searching within applications, monitoring cloud-native environments, or securing your systems, OpenSearch has solutions for these use cases. This chapter explores real-world use cases and how OpenSearch is used in practice.

Differences Between OpenSearch and Elasticsearch

Licensing, vendor lock-in, feature sets, and community support are aspects where OpenSearch and Elasticsearch differ. We examine these differences to help you make an informed choice for your observability needs.

OpenSearch and Elasticsearch are both search and analytics engines, but there are significant differences between them. Here are the key distinctions:

1. Licensing:

– OpenSearch: OpenSearch is committed to open-source principles and is released under the Apache License, version 2.0 (ALv2). This license allows users to freely use, modify, extend, and distribute the software without restrictions.

– Elasticsearch: Elasticsearch, while initially open source, transitioned to non-open source licenses with the introduction of the Server Side Public License (SSPL) and the Elastic License (ELv2). These licenses are more restrictive and have raised concerns about using Elasticsearch in specific scenarios.

2. Community and Governance:

– OpenSearch: OpenSearch is developed as a community-driven project with a transparent and open governance model. It encourages contributions from the community and aims to create a level playing field for all users and contributors.

– Elasticsearch: Elasticsearch development is primarily controlled by Elastic NV, the organization behind it. Only Elastic NV employees can commit changes to the Elasticsearch codebase, which limits community contributions.

3. Access Controls:

– OpenSearch: OpenSearch includes access controls for centralized management as part of its core features, offering fine-grained access control to indices, documents, and fields. These access controls are available for all users.

– Elasticsearch: In Elasticsearch, similar access control features are available but are considered premium features, requiring a paid subscription to access.

4. Security Features:

– OpenSearch: OpenSearch provides security features, including encryption, authentication, authorization, and auditing. These features are available for free as part of the open-source package.

– Elasticsearch: Elasticsearch also offers security features, but like access controls, many of them are premium features that come with a cost.

5. Support and Tools:

– OpenSearch: OpenSearch offers phone support and helpful tools through the community, which is available for free. Additionally, OpenSearch is available as a managed service from multiple providers.

– Elasticsearch: Elasticsearch support, tools, and managed service offerings are typically part of a paid subscription with Elastic NV.

6. Machine Learning Integration:

– OpenSearch: OpenSearch provides machine learning capabilities through the ML Commons Library, which integrates directly with the platform.

– Elasticsearch: Elasticsearch offers machine learning tools, but they are part of its premium features.

7. Availability as a Managed Service:

– OpenSearch: OpenSearch is available as a managed service from various providers, including AWS, Oracle, and Aiven, making it easier for users to offload infrastructure management.

– Elasticsearch: While Elasticsearch can be deployed in various ways, including on-premises and in the cloud, Elastic NV is the primary provider of managed Elasticsearch services.

In summary, OpenSearch and Elasticsearch share similar core functionalities, but OpenSearch remains fully open source, provides broader access to key features, and has a more inclusive and community-driven ecosystem. The choice between the two often depends on factors like licensing preferences, budget considerations, and the level of support and additional features required for specific use cases.

Is it a good idea to switch to OpenSearch?

Migrating from Elasticsearch to OpenSearch offers benefits including freedom, innovation, and potential cost savings. This section guides you through the process of transitioning and helps you decide when and how to make the switch.

Conclusion

This guide summarizes the significance of OpenSearch in observability, explores the future of open-source observability, and describes the role that OpenSearch plays in shaping it.

FAQ

For answers to common questions about OpenSearch and its implications for observability, please refer to the FAQ section at the end of this guide.

Question 1.

Q.: What is OpenSearch, and why is it gaining popularity among DevOps organizations? **A.: OpenSearch is an open-source, distributed search, log analytics, and data visualization technology that has been gaining traction among DevOps organizations. It provides features for robust observability in complex IT environments.**

A.: OpenSearch is an open-source, distributed search, log analytics, and data visualization technology that is popular among DevOps organizations due to its comprehensive observability capabilities. It offers tools for monitoring and managing IT infrastructure and applications effectively.

Question 2.

Q.: What led to the development of OpenSearch, and how does it differ from Elasticsearch? A.: OpenSearch was developed in response to Elasticsearch transition to non-open source licenses. Elasticsearch, which had been widely used, introduced licensing changes, prompting the need for an open-source alternative. OpenSearch retains open-source principles, making it an attractive choice for those concerned about the evolving Elasticsearch licensing.

A.: OpenSearch was created as an open-source alternative to Elasticsearch when Elasticsearch changed its licensing to non-open source. OpenSearch adheres to open-source principles, making it an appealing option for those seeking open, community-driven search and analytics tools.

Question 3.

Q.: What are the primary features of OpenSearch that make it suitable for observability? A.: OpenSearch offers features for observability, including full-text querying, application analytics, SQL query capabilities, asynchronous search, Piped Processing Language (PPL), Data Prepper for data collection, machine learning libraries, dashboards, and more. These features allow users to gather, analyze, and visualize data effectively.

Question 4.

Q.: In what use cases can OpenSearch be applied effectively for observability? **A.: OpenSearch can be applied in various observability use cases, such as application search, log analytics, end-to-end monitoring of Kubernetes, cloud-native SIEM solutions, business analytics, and observability for cloud infrastructure and applications. It handles data collection, anomaly detection, root-cause analysis, and remediation well.**

Question 5.

Q.: What are the critical differences between OpenSearch and Elasticsearch? A.: One significant difference is that OpenSearch remains open source, while Elasticsearch introduced non-open source licenses. OpenSearch also emphasizes vendor neutrality, active community contributions, access controls for centralized management (a premium feature in Elasticsearch), and a full suite of security features available for free, unlike Elasticsearch.

Question 6.

Q.: Can you elaborate on the benefits of migrating to OpenSearch from Elasticsearch? A.: Migrating to OpenSearch offers several advantages, including the freedom and flexibility of open-source software, avoidance of vendor lock-in, lower costs, enhanced security, transparency, faster time to market, and access to an expanding community of users. OpenSearch also provides features that are premium in Elasticsearch.