Modern applications require an efficient and scalable search and data analysis system. One of the most popular solutions in this field is OpenSearch—an open-source platform developed as an alternative to Elasticsearch. With its extensive data processing, integration, and security capabilities, OpenSearch serves as an excellent foundation for modern applications.

What is OpenSearch?

OpenSearch is a free and open-source platform for search and data analysis. It consists of several key components, including a search engine, OpenSearch Dashboards for data visualization, and a set of plugins that extend its functionality.

Source: OpenSearch

More information about OpenSearch is available here.

Why build applications on OpenSearch?

Building applications on OpenSearch has numerous advantages, especially in terms of search, data analysis, and scalability. Here are a few reasons why you should consider using OpenSearch:

• Open-source code – OpenSearch is an open-source project, meaning you have full access to its code and can customize it to suit your needs. This provides greater flexibility in developing custom solutions.
• High performance and scalability – OpenSearch is optimized for handling large datasets, offering fast search and analysis even with vast amounts of data. Additionally, its distributed nature allows for easy scaling to accommodate growing demands.
• Ease of integration – OpenSearch offers a wide range of integrations with popular tools and frameworks, simplifying application development.
• Flexible queries – OpenSearch's query language, which closely resembles Elasticsearch’s, enables advanced searches, filtering, aggregation, and data analysis in a straightforward manner.
• Security – OpenSearch provides security features such as access control, encryption of data in transit and at rest—essential for applications handling sensitive data.
• Community and support – OpenSearch has a growing community and support from AWS, ensuring access to documentation, examples, and discussion forums to help resolve issues.
• Data consolidation – The ability to integrate multiple data sources within a single system improves search efficiency and analysis capabilities.

How to build an application on OpenSearch?

Choosing the environment and configuration

The first step in building an OpenSearch-based application is selecting the appropriate environment. OpenSearch can be deployed locally, in a Docker container, or as a cloud service, such as AWS OpenSearch Service. Key configuration aspects include defining the number of replicas and shards for indexes, as well as data storage policies. A well-designed cluster architecture helps optimize performance and scalability.

Indexing and processing data

Once OpenSearch is running, the next step is data indexing. The platform supports various data sources, from system logs to information from IoT systems and web applications. OpenSearch provides pipelines, enabling real-time data processing—such as deduplication and enriching records with additional attributes. With built-in language analyzers, text processing can be tailored to specific languages and applications.

Searching and analyzing data

A key feature of OpenSearch is its ability to efficiently search for information. Various query types are available, including full-text searches, aggregations, and SQL queries. Additionally, OpenSearch includes machine learning-based anomaly detection, helping identify unusual patterns in data, such as suspicious financial transactions or anomalies in network traffic. With geolocation query capabilities, OpenSearch is particularly useful for applications requiring mapping and spatial data analysis.

Application integration

To enable an application to communicate effectively with OpenSearch, one of the available APIs can be used. OpenSearch provides a REST API and client libraries for popular programming languages such as Python, Java, and Node.js. This allows for seamless implementation of data search and indexing operations. OpenSearch Dashboards enable data visualization and the creation of interactive analytics panels, allowing users to monitor and explore data without needing to write queries.

Source: OpenSearch

Security and scalability

Data security is a crucial aspect of any application. OpenSearch includes several protection mechanisms, including role-based access control (RBAC), TLS encryption, and integration with authentication systems such as SAML and OpenID. Built-in access management enables precise control over who can access specific indexes and functions.

In terms of scalability, OpenSearch allows for flexible resource management through data sharding and index replication. Features like Index State Management (ISM) facilitate automated data retention management, which is particularly useful in applications where data volumes grow dynamically.

OpenSearch is a powerful tool for search and data analysis, making it an excellent foundation for modern applications. With support for various data sources, flexible integration options, and advanced security features, it is a great alternative to commercial search systems. Choosing OpenSearch enables the development of scalable and high-performance applications while avoiding licensing issues and commercial restrictions. If you’re looking for a flexible and open solution for data analysis, OpenSearch might be the perfect choice.

Source: OpenSearch

At Hawatel, we have experience in designing solution architectures, implementing them, and developing custom applications based on OpenSearch. Want to learn more? Contact us!