Elasticsearch
Elasticsearch is a distributed search engine supporting various data types, including vectors, and provides scalable vector search capabilities, making it a popular choice for modern AI-powered applications. It can be extended with the k-NN plugin to provide scalable vector search using HNSW and Lucene, enabling hybrid semantic and keyword search capabilities.
About this tool
Elasticsearch
Elasticsearch is an open source, distributed, RESTful search and analytics engine, scalable data store, and vector database. It is at the core of the Elastic Stack and is widely used for a range of search, analytics, and monitoring applications.
Features
- Distributed and Scalable: Handles data from a single node up to hundreds of nodes, managing large datasets (hundreds of TBs, billions of vectors) with horizontal scalability.
- Vector Search: Native support for vector data and scalable vector search capabilities, including integration with the k-NN plugin for efficient similarity search using HNSW and Lucene.
- Hybrid Search: Supports combining vector (semantic) search with keyword (lexical) search for advanced information retrieval.
- Full-Text Search: Fast and relevant full-text search with fine-tuned relevancy and support for structured, unstructured, geo, and metric data.
- Analytics: Aggregations and analytics on large datasets, enabling trends and pattern analysis.
- Elasticsearch Query Language (ES|QL): Advanced query language for transforming and simplifying data investigation and workflows.
- AI Integration: The Elasticsearch Relevance Engine™ enables semantic search, integration with LLMs (Large Language Models), hybrid search, and use of transformer models.
- Resiliency: Built-in fault tolerance, cross-cluster replication, and high availability features.
- Flexible Data Storage: Supports local fast queries or remote storage (e.g., S3) for cost-effective data management; runtime fields for flexible data onboarding.
- Machine Learning: Automate anomaly detection and analytics on data.
- Security: Features for securing data and cluster access.
- Monitoring: Tools for monitoring the health and performance of the Elastic Stack.
- Time Series Data Management: Automate data lifecycle with index lifecycle management, frozen indices, and rollups.
- Client Libraries: Official and community-supported clients for multiple programming languages (Java, Python, .NET, SQL, PHP, etc.), with RESTful API and JSON support.
- Deployment Options: Available as open source, on-premises, hosted on Elastic Cloud (AWS, GCP, Azure), and as a fully managed serverless offering.
- Use Cases: Log monitoring, infrastructure monitoring, APM, synthetic monitoring, search and discovery, geospatial analysis, SIEM, and endpoint security.
Category
- Vector Database Engines
Tags
- Open-source
- Vector search
- Hybrid search
- Scalable
Pricing
- Not specified in provided content. (Elastic offers open source downloads, hosted cloud, and serverless options; specific pricing details can be found on their website.)
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Information
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