What is OpenSearch

OpenSearch is a community-driven, fully open-source search and analytics suite, forked from Elasticsearch 7.10.2 and Kibana 7.10.2. It provides a powerful, scalable platform used for real-time search, log analytics, observability, and security monitoring across various use cases.

Core Components and Architecture

The OpenSearch suite primarily consists of two core components:

1. OpenSearch (Search Engine): This is a distributed, multi-tenant full-text search and analytics engine. Built upon the Lucene library, it enables the storage, searching, and analysis of large volumes of data via RESTful APIs. Its core capabilities include:
   1. Full-Text Search: Provides high-performance, high-relevance text search.
   2. Aggregation Analytics: Supports complex data aggregation operations used for generating statistical reports and insights.
   3. Distributed Architecture: Data is sharded across multiple nodes in a cluster, ensuring high availability and horizontal scalability.
2. OpenSearch Dashboards (Visualization Interface): This is a browser-based visualization tool that allows users to explore data stored in OpenSearch, create dashboards, visual charts, and perform interactive analysis.

Key Features of OpenSearch

1. Broad Use Cases

OpenSearch serves as a "Swiss Army knife" for data processing, widely used in:

• Logs and Observability: Collecting, indexing, and analyzing logs, metrics, and traces from applications and infrastructure, forming the foundation of centralized logging systems.
• Security Analytics: Storing and querying security event information (like SIEM data) for threat detection and compliance monitoring.
• Enterprise Search: Providing high-performance internal search functionality for websites or applications.

2.Rich Plugin Ecosystem

The OpenSearch ecosystem is enhanced by a suite of plugins, including:

• Security Plugin: Provides fine-grained access control, multi-tenancy, and encrypted communication.
• Index State Management (ISM): Automates the index lifecycle, including rollover, shrink, and delete operations to optimize storage costs.
• Machine Learning and Vector Search: Supports Approximate Nearest Neighbor (ANN) search, allowing it to process vector embeddings and enable limited semantic search capabilities.

3.Open and Community-Driven

As a project under the Apache 2.0 license, OpenSearch guarantees openness and long-term availability, free from commercial licensing restrictions, and relies on an active developer community for continuous improvement.

Beyond Search: Introducing Velodb

While OpenSearch excels in specific domains like log analysis and dedicated full-text search, enterprises have growing demands for general-purpose data analytics and high-performance data ingestion.

Velodb is designed to meet these combined requirements:

Velodb is a comprehensive database that supports both analytics and retrieval. While OpenSearch is optimized for inverted index-based queries and log-style aggregations, Velodb is engineered to be a more versatile analytical platform with superior ingestion efficiency:

• General-Purpose Analytical Capabilities: Velodb boasts a powerful, general-purpose analytical engine capable of handling more complex SQL queries, deep OLAP (Online Analytical Processing) operations, and detailed data mining tasks that go beyond the specialized aggregations of OpenSearch. This positions Velodb as a more holistic platform for business intelligence and data science.
• Higher Write Throughput: Velodb's architecture is optimized for significantly higher write throughput. This allows it to more efficiently handle the massive, high-frequency data ingestion demands from real-time streams, IoT sensors, or large-scale applications, reducing data ingestion latency.
• Sub-Second Real-Time and Hybrid Retrieval: Velodb is designed to achieve real-time performance of less than 1 second for both complex analysis and retrieval tasks. It fully supports Hybrid Search, combining vector and keyword search for high-precision retrieval.
• Comprehensive Data Source Connectivity: Velodb supports seamless connection and operation on diverse external data ecosystems, including Lakehouse architectures (e.g., Delta Lake, Apache Iceberg) and traditional databases.

In summary, Velodb provides a unified, high-speed platform that addresses a broader spectrum of data needs—from general-purpose analysis and high-throughput ingestion to intelligent retrieval—offering an alternative for users seeking a more integrated and flexible data intelligence solution.