Ant Data leverages Ant Group's leading technological innovation in big data, databases, and multimedia, with years of industry practice. Through long-term technology planning and continuous innovation, we strive to build world-class data technology and products.
This article explains the challenges of high‑dimensional vector retrieval, introduces quantization techniques—especially the binary RaBitQ method and its MRQ extension—detailing their compression ratios, speed gains, compatibility with search indexes, and real‑world performance results in the VSAG system.
The article presents an in‑depth overview of DeepInsight Copilot, an AI‑driven business intelligence product that streamlines data, information, insight, and decision‑recommendation stages, detailing its functional modules, intelligent agents, multi‑generation technical evolution, architecture, model fine‑tuning, and future challenges and solutions in data analysis.
The article examines the rapid growth of unstructured data, explains the fundamentals and resource‑intensive nature of vector retrieval, presents Ant Group’s engineering practices—including hybrid HNSW‑DiskANN indexing, performance tricks like BSA pruning and memory prefetching, sparse‑vector and feedback‑driven recall improvements—and outlines the open‑source VSAG roadmap and ecosystem integrations.
Ray Flow Insight is an Ant Group open‑source tool that visualizes Ray's distributed programming primitives—Actors, Tasks, and Objects—to turn complex reinforcement‑learning systems from opaque "black boxes" into transparent, debuggable workflows, providing logical, physical, distributed stack, and flame‑graph views for performance analysis and optimization.
This article presents a comprehensive exploration of using Apache Paimon and Flink to design lake tables that support minute‑level latency, low cost, and unified batch‑stream processing for advertising data, covering schema design, partitioning strategies, performance trade‑offs, cost analysis, and operational best practices.
This article provides an in‑depth technical analysis of DeepSeek's open‑source 3FS distributed file system, focusing on the StorageService architecture, space pooling, allocation mechanisms, reference counting, fragmentation handling, and the RDMA‑based read/write data path.
This article examines the challenges of AI storage for massive models, describes Ant Group's multi‑cloud, high‑availability PCache architecture, and details its GPU‑mixed deployment, metadata services, data‑link optimizations, and performance results that enable petabyte‑scale training with low cost and high stability.
This article provides an in‑depth analysis of DeepSeek's open‑source 3FS distributed storage system, focusing on its network communication module, RDMA‑based design, core classes such as IBSocket, Listener, and IOWorker, and how these innovations advance high‑performance AI infrastructure.
The article provides a comprehensive English overview of 3FS, an AI‑focused distributed file system that leverages FoundationDB for metadata, CRAQ for chunk replication, and a hybrid Fuse/native client architecture, detailing its design, components, fault handling, and performance considerations for large‑scale training workloads.
The article examines how database services must evolve to meet the high‑availability, real‑time response, horizontal scalability, application agility, security compliance, and cost‑optimization demands of modern online businesses, using Ant Group’s multi‑generation architecture and technologies such as distributed middleware, HTAP/HSAP, and polyglot persistence as examples.
