OpenSearch Core is a high-performance search engine built on the robust indexing and search capabilities of Apache Lucene.
It allows you to ingest large volumes of content in diverse formats using OpenSearch Data Prepper, index complex multidimensional data, and perform efficient searches that return highly accurate results.
You can then explore and visualize your data seamlessly with OpenSearch Dashboards.
Robust search and indexing based on Apache Lucene
OpenSearch is a fast, flexible, open-source suite for search and analytics. From log data to real-time monitoring, it delivers enterprise-grade features like security, alerting, and machine learning—without vendor lock-in. Its distributed design scales easily and keeps you in control.
Apache Lucene™ is a high-performance, full-featured search engine library written entirely in Java. It is a technology suitable for nearly any application that requires structured search, full-text search, faceting, nearest-neighbor search across high-dimensionality vectors, spell correction or query suggestions.
OpenSearch Core components
The OpenSearch Core architecture is made up of clusters, nodes, indexes, shards, and documents. At the top level is the OpenSearch cluster, a distributed network of nodes, each responsible for different cluster operations based on its type. Data nodes are responsible for storing indexes—logical groupings of documents—and handling tasks like data ingestion, search, and aggregation.
Each index is divided into shards, which include both primary and replica data. Shards are distributed across multiple machines, enabling horizontal scaling for improved performance and efficient use of storage.
OpenSearch’s sharding strategy
OpenSearch Clusters are highly scalable and exceptionally resilient.
Handle growing data volumes effortlessly with horizontal scaling and distributed architecture.
Fully open-source with support for custom plugins, fine-tuned queries, and community-driven innovation.
Run lightning-fast indexing, searches, and analytics on streaming or historical data for instant visibility.
Built-in security features, role-based access control, audit logging, and compliance.
Perform traditional keyword-based queries using the Okapi BM25 algorithm.
Incorporate text embedding models and advanced vector search capabilities for interpreting the meaning and context of queries.
Combine keyword and semantic search to improve search relevance and accuracy.
Execute k-NN searches and manage vector data alongside other data types with OpenSearch Core’s integrated vector database.
Render your search data and use natural language instructions to create visualizations with OpenSearch Dashboards.
Supports enterprise workloads using both horizontal and vertical scaling, as well as native vector capabilities that can handle billions of vectors.
Add new features and capabilities to OpenSearch core using its expansive library of pre-built plug-ins, with more added on a regular basis.
OpenSearch Core is built on Apache Lucene’s scalable, high-performance search library, continuously updated to meet evolving performance requirements.
OpenSearch Core is highly customizable, making it adaptable to a diverse range of search applications and AI use cases.
With dozens of plugins available, and more supported with new releases, you can easily add new features or build your own.
Check out the installation quickstart to get started using OpenSearch right away.
Access numerous bundled plugins or install additional plugins to customize your OpenSearch platform.
Review the OpenSearch performance benchmarks to view the results of ongoing performance testing.
Find answers to your questions, help others in the community, and join the conversation.
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Highlights of projects built by the community.
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