Blog
Building the future of OpenSearch together
As announced today, the OpenSearch Project is now part of the newly formed OpenSearch Software Foundation, a community-driven initiative under the Linux Foundation. This marks a major milestone in the history of the OpenSearch Project, and we couldn't be more excited to share our thoughts on what this means to...
Diving deep into distributed microservices with OpenSearch and OpenTelemetry
In modern distributed systems, understanding the interactions between microservices is crucial for identifying performance bottlenecks and diagnosing failures. In this blog post, we’ll demonstrate the new features introduced in the...
Transforming bucket aggregations: Our journey to 100x performance improvement
OpenSearch is widely used for data analytics, especially when working with time-series data. A core feature of time-series analysis is the date histogram aggregation, which groups documents by date or...
The new semantic field: Simplifying semantic search in OpenSearch
Semantic search improves result relevance by using a machine learning (ML) model to generate dense or sparse vector embeddings from unstructured text. Traditionally, enabling semantic search has required several manual...
Making ingestion smarter: System ingest pipelines in OpenSearch
OpenSearch 3.1 introduces the system ingest pipeline, a new capability designed specifically for plugin developers. It lets you automatically process documents during ingestion by defining system ingest processors that run...
Reducing hybrid query latency in OpenSearch 3.1 with efficient score collection
Hybrid queries combine the precision of traditional lexical search with the semantic power of vector search. In OpenSearch 3.1, we delivered significant latency reductions for hybrid queries by redesigning how...
Introduction to ML inference processors in OpenSearch: Review summarization and semantic search
In an era of AI revolutionizing how we interact with information, traditional keyword-based search has become increasingly insufficient. Users expect search engines to understand context, interpret natural language, and deliver...
Neural sparse models are now available in Hugging Face Sentence Transformers
OpenSearch’s neural sparse search transforms text into sparse token-weight pairs using transformer models, combining semantic search capabilities with efficient inverted indexing. This approach delivers high retrieval accuracy with low latency...