惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
Docker
F
Fortinet All Blogs
爱范儿
爱范儿
博客园 - Franky
MyScale Blog
MyScale Blog
罗磊的独立博客
博客园 - 三生石上(FineUI控件)
B
Blog
P
Proofpoint News Feed
IT之家
IT之家
宝玉的分享
宝玉的分享
D
DataBreaches.Net
S
SegmentFault 最新的问题
Microsoft Azure Blog
Microsoft Azure Blog
GbyAI
GbyAI
M
MIT News - Artificial intelligence
L
LangChain Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
月光博客
月光博客
雷峰网
雷峰网
Stack Overflow Blog
Stack Overflow Blog
量子位
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻

cs.DB updates on arXiv.org

Block-Sphere Vector Quantization GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction CogScale: Scalable Benchmark for Sequence Processing TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings IVF-TQ: Calibration-Free Streaming Vector Search via a Codebook-Free Residual Layer Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks Gaussian Relational Graph Transformer Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift A Horn extension of DL-Lite with NL data complexity 3D Primitives are a Spatial Language for VLMs Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance A CAP-like Trilemma for Large Language Models: Correctness, Non-bias, and Utility under Semantic Underdetermination EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models Toward Multi-Database Query Reasoning for Text2Cypher Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations PrepBench: How Far Are We from Natural-Language-Driven Data Preparation? Anatomy of a Query: W5H Dimensions and FAR Patterns for Text-to-SQL Evaluation Building informative materials datasets beyond targeted objectives
Implementing Decentralized Per-Partition Automatic Failov...
Josh Rowe, Mikael Horal, Hari Sudan Sundar, Muthukumaran Arumuga · 2025-05-21 · via cs.DB updates on arXiv.org

Azure Cosmos DB is a cloud-native distributed database, operating at a massive scale, powering Microsoft Cloud. Think 10s of millions of database partitions (replica-sets), 100+ PBs of data under management, 20M+ vCores. Failovers are an integral part of distributed databases to provide data availability during outages (partial or full regional outages). While failovers within a replica-set within a single region are well understood and commonly exercised, geo failovers in databases across regions are not as common and usually left as a disaster recovery scenario. An upcoming release of Azure Cosmos DB introduces a fine grained (partition-level) automatic failover solution for geo failovers that minimizes the Recovery Time Objective (RTO) and honors customer-chosen consistency level and Recovery Point Objective (RPO) at any scale. This is achieved thanks to a decentralized architecture which offers seamless horizontal scaling to allow us to handle outages ranging from node-level faults to full-scale regional outages. Our solution is designed to handle a broad spectrum of hardware and software faults, including node failures, crashes, power events and most network partitions, that span beyond the scope of a single fault domain or an availability zone.