AI Kernel Feature Overview
As AI technology rapidly evolves, data infrastructure has become the core of AI applications. ApsaraDB for SelectDB is a high-performance, real-time analytical database designed for the AI era. It deeply integrates text search, vector retrieval, AI functions, and intelligent interaction capabilities via MCP Server to build a one-stop AI data stack for everything from data storage and retrieval to analytics. With its high-performance, cost-effective, and easy-to-integrate solution, ApsaraDB for SelectDB supports use cases such as Lakehouse for AI, semantic search, hybrid search and analytics, RAG, Agent Facing Analytics, and AI observability.
Lakehouse for AI
Use case: AI model development involves data preparation, feature engineering, and model evaluation, which typically requires processing massive amounts of data. In traditional architectures, data is frequently moved between data lakes and analytical engines. The Lakehouse architecture eliminates data silos and accelerates development by deeply integrating the open storage of a data lake with a real-time analytical engine, consolidating the entire AI development workflow onto a single platform.
Applications in the AI development workflow:
Large-scale data preparation: Use the efficient data processing capabilities of ApsaraDB for SelectDB to filter, sample, and clean petabyte-scale data in data lakes, and rapidly create high-quality training datasets.
Real-time feature engineering: Use the real-time analytics capabilities of ApsaraDB for SelectDB to perform online feature extraction, transformation, and aggregation, and provide a real-time feature service for model training and inference.
Model and data quality assessment: Conduct rapid, multi-dimensional analysis on test sets and online data to continuously monitor model performance and data drift.
ApsaraDB for SelectDB advantages:
Lakehouse architecture: Builds an open lakehouse based on open table formats like Iceberg and Paimon and their catalogs for unified management of analytical and AI data.
High-speed SQL engine: Serves as a real-time analytical engine that supports interactive queries and lightweight ETL, providing high-speed SQL processing for data preparation and feature engineering.
Seamless data flow: Reads from and writes to the data lake directly without data migration, enabling unified data management at the storage layer and flexible acceleration at the compute layer.
Semantic search
Use case: Semantic search uses vectorization to capture the deep meaning of text, enabling it to retrieve semantically relevant content even if a user's query terms do not exactly match the document content. This technology is crucial for use cases like cross-lingual retrieval, synonym recognition, and intent understanding, significantly improving search recall and user experience.
Typical applications:
Enterprise document retrieval: Employees describe a problem in natural language, and the system understands their intent and retrieves relevant policies, procedures, and knowledge from massive document repositories.
E-commerce product search: A user enters "breathable shoes for summer," and the system understands the need and retrieves relevant products, not just those that match the keywords.
Content recommendation platforms: Recommends articles and videos based on semantic similarity, discovering content that users might be interested in but have described with different terms.
ApsaraDB for SelectDB advantages:
High-performance vector retrieval: Supports HNSW and IVF algorithms to deliver sub-second responses for searches across hundreds of millions of vectors, meeting the demands of large-scale semantic search.
Enhanced hybrid search: Combines semantic search and keyword filtering in a single SQL query, balancing the broad recall of semantic search with the precision of keyword matching.
Multimodal extension: Supports semantic retrieval of multimodal content like images and audio, not just text.
Flexible quantization optimization: Uses SQ/PQ quantization techniques to significantly reduce storage and computation costs while maintaining retrieval accuracy.
Hybrid search and analytics
Use case: As the value of semi-structured and unstructured data such as customer reviews, chat logs, and production logs in business decision-making grows, traditional analytics solutions must evolve. Hybrid search and analytics combines full-text search, vector retrieval, and structured data analysis on a single platform. This approach enables both semantic search and multi-dimensional analysis with aggregate statistics.
Typical applications:
Customer insights: Combine customer review text retrieval with user behavior analytics to precisely identify customer needs and satisfaction trends.
Smart manufacturing: Integrate full-text search of production logs, device image recognition, and IoT metric analysis to enable fault prediction and quality optimization.
Internet of Vehicles (IoV): Enhance the smart cockpit experience by combining analysis of in-vehicle data, text mining of user feedback, and vector retrieval of driving behavior.
ApsaraDB for SelectDB advantages:
Unified architecture: Handles structured analytics, full-text search, and vector retrieval on a single platform, eliminating the need for data migration and integration of disparate systems.
Hybrid query performance: Supports executing vector similarity search, keyword filtering, and aggregate analysis within a single SQL query, delivering excellent performance.
Flexible schema support: The VARIANT data type natively supports dynamic JSON structures, and Light Schema Change lets you modify fields and indexes in seconds.
Full-stack optimization: Provides end-to-end optimization from the inverted index and vector index to the MPP execution engine, balancing retrieval precision with analytical efficiency.
RAG (Retrieval-Augmented Generation)
Use case: RAG provides context to large language models by retrieving relevant information from an external knowledge base. This approach effectively mitigates issues like model hallucination and stale knowledge. A vector database is a core component of RAG systems, and must quickly retrieve the most relevant document snippets from massive knowledge bases and support high-concurrency user queries.
Typical applications:
Enterprise knowledge base: Build an intelligent Q&A system based on internal documents and manuals to provide employees with quick, accurate answers to natural language queries.
Intelligent customer service assistant: Combine a product knowledge base with historical cases to provide precise response suggestions for customer service agents or chatbots.
Intelligent document assistant: Quickly locates relevant content within large document collections to assist with research, writing, and decision-making.
ApsaraDB for SelectDB advantages:
High-concurrency performance: The distributed architecture supports high-concurrency vector retrieval, effectively handling concurrent access from a large number of users.
Hybrid search capability: Supports vector similarity search and keyword filtering in a single SQL query, balancing semantic recall with precise matching.
Elastic scalability: Retrieval performance scales linearly as the cluster expands, supporting smooth scaling from millions to tens of billions of vectors.
Integrated solution: Manages vector data, original documents, and business data on a single platform, simplifying the data architecture for RAG applications.
Agent facing analytics
Use case: With the rise of AI Agent technology, AI is increasingly automating analytical decisions. Unlike traditional human-led analysis, Agent Facing Analytics requires a data platform with exceptional real-time and high-concurrency capabilities. The platform must complete data queries in milliseconds to support decisions and handle concurrent requests from large numbers of agents.
Typical applications:
Real-time fraud detection
Intelligent ad placement
Personalized recommendations
ApsaraDB for SelectDB advantages:
Sub-second data latency: Supports real-time data ingestion and updates, ensuring agent decisions are based on the latest data.
Millisecond query response: Delivers an average query latency of under 100 ms, meeting the real-time decision-making needs of agents.
High concurrency (10,000+ QPS): Supports over 10,000 queries per second (QPS), easily handling concurrent queries from massive numbers of agents.
Native agent integration: Integrates seamlessly with AI agents via MCP Server, simplifying the development and integration process.
AI observability
Use case: AI model training and production workloads generate massive volumes of logs, metrics, and trace data. As a key part of AI infrastructure, an AI observability system analyzes this data to pinpoint issues and continuously optimize performance. It must handle multiple challenges, including high-throughput ingestion of petabyte-scale data, millisecond-level retrieval responses, and cost control.
Typical applications:
Model training monitoring: Track training metrics and resource consumption in real time to quickly identify training anomalies and performance bottlenecks.
Inference service tracing: Record the complete path of each inference request to analyze latency sources and error patterns.
AI application log analysis: Perform full-text search and aggregate analysis on massive application logs to support troubleshooting and behavioral insights.
ApsaraDB for SelectDB advantages:
High performance: Supports sustained data ingestion of petabytes per day (10 GB/s) and uses an inverted index to accelerate log retrieval, achieving responses in seconds.
Cost optimization: Achieves a compression ratio of 5:1 to 10:1, reducing storage costs by 50%–80%, and supports low-cost storage for cold data.
Flexible schema: Light Schema Change lets you modify fields and indexes in seconds, and the VARIANT type natively supports dynamic JSON structures.
Ecosystem-friendly: Compatible with the OpenTelemetry and ELK ecosystems, and supports integration with mainstream visualization tools like Grafana and Kibana.