This presentation by Alibaba multi‑model database expert Zhang Yun explains how Phoenix enables SQL‑style querying over HBase, covering its architecture, query examples, performance considerations, and typical application scenarios.
This article presents a curated list of fifteen Alibaba open‑source and cloud‑based developer tools—including Java diagnostics, IDE plugins, chaos engineering, monitoring, static site generators, mobile compilation, and database utilities—detailing their usage scenarios, tutorials, and acquisition methods to boost development efficiency.
OceanBase, Alibaba’s home‑grown distributed relational database, survived early skepticism and near‑disbandment to replace Oracle in Ant Financial’s core systems, achieve record‑breaking transaction throughput, and now powers dozens of Chinese banks, illustrating the challenges and triumphs of building a modern DBMS.
The article explains Alibaba’s adoption of a ‘big middle platform, small front‑end’ strategy inspired by Supercell, detailing its organizational mechanism, data and product capabilities, and provides a visual overview of the architecture along with links to related in‑depth resources.
The article explains the renaming of the open‑source distributed transaction framework Fescar to Seata, outlines the background and evolution of distributed transactions at Alibaba and Ant Financial, and shares recent community milestones and future plans for the project.
The author critically examines common misconceptions about Spring Cloud Alibaba, discussing the compatibility of Dubbo with Feign, the choice of service registries like Nacos versus Eureka, and the role of circuit‑breaker tools such as Sentinel, while urging readers to think independently.
Alibaba Dragonwell is a free, long‑term‑support OpenJDK distribution announced at the Beijing Alibaba Cloud Summit, offering performance, security, and compatibility enhancements for large‑scale Java deployments while integrating Alibaba’s own innovations such as JWarmup and Java Flight Recorder.
The article explains how Alibaba introduced full‑link stress testing and a four‑step capacity‑planning process for the Double 11 shopping festival, detailing traffic‑prediction, system‑capacity evaluation, fine‑tuning via production‑environment load tests, and dynamic flow‑control mechanisms that together ensure system stability during massive traffic spikes.
This article explains Alibaba's intelligent, automated approach to managing large‑scale cloud‑native applications, covering challenges of scale, safety, and efficiency, and how AI‑driven decision making improves stability while reducing operational costs.
This article introduces Alibaba’s intelligent matting editor and its Semantic Human Matting (SHM) algorithm, detailing the integration of semantic segmentation and deep matting networks, the fusion module, training strategy, experimental results, and the deployment of an online fully‑automatic cutout tool for designers.
Alibaba’s MonkeyKing, inspired by Netflix’s Chaos Monkey, employs intentional fault injection—from random node kills to simulated network outages—to test and improve system robustness across IaaS, PaaS, and SaaS layers, offering a comprehensive model for reliability engineering in complex distributed environments.
An extensive curated list of 26 Alibaba open‑source projects—including Spring Cloud Alibaba, Druid, fastjson, Dubbo, JStorm, Nacos, Sentinel, and many others—covers essential tools for backend development, data processing, cloud‑native services, monitoring, and more, each with brief descriptions and GitHub links.
This article presents a curated collection of Alibaba’s open‑source Java projects, ranging from distributed service frameworks like Spring Cloud Alibaba and Dubbo, database tools such as Druid and TDDL, to monitoring, messaging, and cloud‑native components, each with brief descriptions and GitHub links.
Chinese cloud‑native pioneer Li Xiang, the first Chinese member of the CNCF Technical Oversight Committee, discusses his open‑source contributions, Alibaba’s large‑scale cluster management, the evolution of cloud‑native technologies, and offers advice for young engineers on mastering code, design, and emerging trends.
This article explains why chaos engineering is essential for distributed systems, distinguishes it from traditional fault testing, outlines practical inputs and principles, and details Alibaba's multi‑year experience with fault‑drill platforms, automation, and future plans to improve system reliability.
This article traces the evolution of Alibaba’s distributed data layer—from the early Cobar system to the modern TDDL framework and DRDS service—explaining their architectures, limitations, sharding principles, transaction‑boundary strategies, heterogeneous index tables, and the Jingwei data‑replication platform that together enable seamless scaling and high‑performance SQL processing across thousands of databases.
This article chronicles Lu Zhi’s nine‑year evolution from a fresh graduate at Alibaba to the open‑source lead of the SOFA middleware team, highlighting his early internships, open‑source experiments, the challenges of scaling a financial‑grade distributed system, and practical advice for aspiring contributors.
This article details Alibaba.com engineers' application of reinforcement learning, attention mechanisms, and weakly supervised techniques to extract product summaries, improve content quality, and significantly raise SEO rankings, supported by offline experiments, online A/B testing, and future research directions.
Fescar, Alibaba’s open‑source Fast & Easy Commit And Rollback (Seata) framework, provides a high‑performance, easy‑to‑use distributed transaction solution for microservice architectures, detailing its components, transaction lifecycle, and evolution from TXC to GTS, and illustrating its role in solving global transaction challenges.
This article introduces Fescar, Alibaba's open‑source distributed transaction framework for microservices, explains the challenges of global transactions, outlines its architecture and components, and reviews its evolution from TXC to GTS to Fescar.
This newsletter introduces Alibaba’s X‑DeepLearning deep‑learning framework, releases a large‑scale cluster dataset for research, announces the upcoming open‑source of the Blink streaming engine, showcases the futuristic FlyZoo Hotel, and shares a welcome letter to new hires, highlighting cutting‑edge technologies across AI, big data, and cloud operations.
Alibaba’s latest AI breakthroughs—including a voice assistant that understands hidden user intent, a new edge‑cloud computing whitepaper, record R&D spending, and a medical AI project for flu prediction—showcase the company’s rapid advances across artificial intelligence and cloud technologies.
The article explains why visualizing microservice architectures is essential for high availability, describes common and advanced visualization methods, discusses how to make visualization effective, handle architectural changes, identify key components, and leverage visual data for operations and reliability improvements.
Alibaba’s weekly roundup highlights its re‑election to the Java Global Management Committee, the upcoming open‑source X‑Deep Learning framework, a global mathematics competition, the launch of the DAMO Academy website, massive IPv6 deployment, and two major technical conferences slated for December.
This article shares Alibaba infrastructure leader Bi Xuan’s reflections on how broadening technical breadth, choosing depth, and aligning personal interests with business or foundational paths shape a software engineer’s growth, leadership potential, and long‑term career success.
The Lingjing system, built by Alibaba Infrastructure, provides an end‑to‑end hardware‑software performance diagnosis platform that collects fine‑grained metrics, visualizes data, automatically detects anomalies, and helps optimize resource utilization across complex data‑center stacks.
Alibaba’s campus face‑recognition system, handling over 200,000 daily scans, combines hardware upgrades, software image‑quality management, algorithmic grouping, and database‑photo enhancements to cut mis‑recognition to 0.1%, illustrating large‑scale AI deployment in security and payment scenarios.
The article chronicles Alibaba's ten‑year journey of building and scaling its big data platform—from early Oracle clusters and Hadoop‑based Cloud‑Ladder 1 to the self‑developed ODPS/MaxCompute, real‑time Blink engine, and the unified DataWorks ecosystem—highlighting key technical milestones, performance breakthroughs, and operational challenges that powered successive Double 11 shopping festivals.
This article chronicles Alibaba's ten‑year journey of building and scaling its big‑data platform—from early Oracle clusters and Hadoop, through the launch of ODPS and MaxCompute, to global cloud expansion and cutting‑edge streaming innovations that now power billions of transactions each Double‑11.
This article chronicles Alibaba's decade‑long journey of capacity planning for Double 11, detailing five evolutionary phases—from manual estimates to full‑link testing ecosystems—while balancing cost, stability, and efficiency in massive distributed systems.
The article examines how Alibaba’s 2009 Double 11 promotion grew into the world’s largest shopping festival, mirroring China’s transition from an investment‑export‑driven economy to a consumption‑led one, while spurring advances in cloud computing, IoT, and digital manufacturing over the past decade.
Over ten years of Alibaba's Double 11, the company transformed a modest marketing event into a global e‑commerce platform by continuously improving backend architecture, scaling strategies, full‑link stress testing, multi‑active data centers, cloud migration, and real‑time incident response, offering valuable operational insights.
The article analyzes the massive technical challenges of Alibaba's Double 11 flash‑sale event and explains how cloud elasticity, distributed messaging, containerization, real‑time data processing, AI, front‑end optimization, caching, monitoring, and database sharding together enable billions of transactions within minutes.
Over ten years Alibaba's database team transformed from commercial Oracle‑based systems to open‑source AliSQL and finally to the self‑developed X‑DB, introducing distributed middleware, cloud resources, full‑link testing, intelligent optimization and storage‑compute separation to support the massive Double 11 traffic peaks.
Alibaba’s DAMO Academy chief database scientist Li Fei‑Fei was named an ACM Distinguished Scientist in 2018, recognizing his groundbreaking work on next‑generation distributed databases, unstructured data management, and data security that power Alibaba’s massive services and national smart‑city and meteorological platforms.
To meet Double‑Eleven’s sub‑second, billion‑item feed demands, Alibaba’s Xianyu selection system evolved from a Solr‑based search pipeline through offline batch and PostgreSQL attempts to a Blink‑powered real‑time stream platform using Niagara’s low‑latency LSM storage, delivering high‑throughput, personalized product feeds.
This article chronicles Alibaba's decade‑long journey of database innovations—from commercial and open‑source systems to a self‑developed distributed engine—highlighting the technical breakthroughs, performance optimizations, and intelligent features that powered successive Double 11 shopping festivals.
Spring Cloud Alibaba, an open‑source project from Alibaba, has launched its first preview version, offering Java developers integrated service discovery, configuration management, high‑availability protection, and seamless Alibaba Cloud product components, all built on Spring Boot and Spring Cloud patterns.
This article recounts a non‑technical graduate's two‑year self‑learning journey from electronics engineering to Java backend development, detailing the challenges, mindset shifts, and practical steps that ultimately led to landing an offer at Alibaba.
At the 2018 China Computer Conference, Alibaba CTO Zhang Jianfeng highlighted how digitalization underpins AI innovations across smart city infrastructure, new manufacturing, and retail, emphasizing massive data utilization, cognitive graph research, and immersive 3D store experiences.
The article chronicles OceanBase’s evolution from a research project to a commercial distributed relational database, detailing the technical challenges of meeting strict transaction, consistency, and availability requirements, the innovative solutions adopted, key milestones, and its impact on Alibaba’s ecosystem and the broader market.
This article explains how Alibaba adopted Apache Flink as a unified, low‑latency, high‑throughput big‑data engine, detailing its stream‑first design, state management, checkpointing, massive production deployment, community contributions, and upcoming plans for a unified API, SQL layer, broader language support, and AI integration.
This article presents a curated collection of Alibaba Group’s open‑source projects, spanning frontend design systems, Java libraries, database engines, distributed file and messaging systems, as well as tutorials, highlighting each project's purpose, key features, and GitHub repository links for developers seeking robust, production‑grade solutions.
This article explains how Alibaba’s mixed‑deployment (co‑location) technology combines online transaction services and offline compute workloads on shared physical servers, detailing its architecture, scheduling mechanisms, resource‑concession strategies, achieved performance gains, and future directions for large‑scale e‑commerce infrastructure.
In this talk, Alibaba engineer Yang Yi explains the evolution of resource operation and DevOps at Alibaba, covering the shift from manual tasks to containerized, automated platforms, the challenges of large‑scale scheduling, cost reduction strategies for events such as Double 11, and the move toward intelligent, ops‑less infrastructure.
Alibaba’s senior tech expert explains the concept, architecture, core AI capabilities, real‑world case studies, and future roadmap of the Tmall “Future Store”, a vision‑driven, sensor‑rich unmanned retail experience that merges computer‑vision, edge computing, and data‑driven operations.
The 13th OSDI conference in Carlsbad attracted over 650 attendees, featuring 47 accepted papers and three Best Papers—two led by Chinese students—while Alibaba showcased its latest OS kernel (AliKernel), next‑generation distributed storage system Pangu 2.0, and the large‑scale resource manager Sigma, sparking lively discussions among global experts.
This article explains how Alibaba adopted Apache Flink as a unified, low‑latency, high‑throughput big data engine, covering its origins, technical advantages over Spark, large‑scale deployment, state management, checkpointing, API unification, and future directions in streaming and batch processing.
This article explains Alibaba's mixed‑deployment (co‑location) technique, detailing its motivation, architecture, resource‑sharing mechanisms, scheduling strategies, performance results, and future directions for scaling and refining resource utilization across online and offline workloads.
This article recounts Alibaba middleware leader Bi Xuan’s reflections from the 2018 Hangzhou Cloud Expo, highlighting three key projects—HSF, HBase, and Pouch Container—while exploring the broader impact of open‑source technologies such as Linux, Kubernetes, Dubbo, RocketMQ, and OpenMessaging on modern cloud‑native software development.
This article chronicles Alibaba's eight‑year journey from monolithic applications to a fully containerized, image‑based infrastructure, detailing the evolution of PouchContainer, its technical architecture, large‑scale deployment, Docker compatibility, and the operational practices that enabled seamless migration across the group.
The article reviews Alibaba's large‑scale data‑center network advancements, covering its high‑performance evolution, programmable forwarding planes, massive RDMA deployment, automated control systems, AI‑driven self‑healing, and the Luoshen cloud network engine that underpins Alibaba Cloud services.
The 2018 Hangzhou Yunqi Conference featured a series of expert talks on intelligent operations, covering Alibaba's AI‑driven maintenance systems, robust supply‑chain optimization, data‑center automation, MSP transformation, and AI‑Ops practices, providing actionable insights for large‑scale infrastructure management.
The article examines how the shift from DT 1.0 to DT 2.0 drives new infrastructure challenges in computing, storage, and cooling, and describes Alibaba's heterogeneous‑computing platform, immersion liquid‑cooling technology, and the Xuanwu next‑generation server architecture as solutions.
At the 2018 Hangzhou Yunqi Conference, Alibaba's CTO Zhang Jianfeng outlined DAMO Academy’s expansive AI strategy, unveiling new research labs, a semiconductor venture, quantum computing initiatives, and a youth science award aimed at accelerating data, algorithm, and computing breakthroughs.
Alibaba’s monitoring evolution—from fragmented early tools to the standardized Sunfire platform and now AI‑powered full‑link observability—addresses scaling challenges, introduces business‑centric metrics, automated traceability, and intelligent anomaly detection, illustrating how massive, multi‑tenant infrastructures achieve unified, proactive operations at scale.
This article explains how Alibaba transforms source code into a running service by using the Aone platform to automate environment setup, building, packaging, testing, and staged deployment, while covering key terminology, quality‑assurance methods, test‑environment isolation, and pipeline orchestration for continuous delivery.
This article introduces Wide&Deep, PNN, DeepFM, and a novel Wide‑ResNet model for Alibaba’s 1688 “Guess You Like” recommendation, explains the underlying feature services and real‑time scoring pipeline, presents offline experiments showing AUC gains with batch normalization, and shares practical tuning insights.
Alibaba’s infrastructure team introduces a full‑link business monitoring approach that visualizes end‑to‑end health from a business perspective, unifies metrics, automates data collection, and leverages intelligent baseline alerts, enabling rapid issue detection, precise root‑cause analysis, and fine‑grained dimension monitoring across services.
Alibaba showcased its cutting‑edge network research at SIGCOMM 2018, highlighting programmable hardware, integrated software‑hardware designs, AI‑driven operations, the Hyper‑scale Edge Network vision, hierarchical network lifecycle management, large‑scale RDMA deployment, and advanced network visualization, positioning itself alongside leading global tech firms.
Alibaba, together with top Chinese universities and research institutes, unveiled the Cangjingge Knowledge Engine project, detailing its massive data assets, five‑module architecture, large‑scale knowledge construction techniques, and initial deployments in safety and tourism knowledge graphs to boost AI applications.
At SIGCOMM 2018 in Hungary, Alibaba showcased its pioneering network research, sharing visions on programmable hardware, integrated software‑hardware design, intelligent operations, the Hyper‑scale Edge Network, RDMA deployment, and network visualization, while presenting several influential papers and collaborating with leading academia.
The article chronicles three generations of Hangzhou‑origin engineers—from gritty early Alibaba pioneers to modern cloud and blockchain specialists—showing how their relentless problem‑solving, cultural resilience, and tech‑first mindset transformed China’s internet ecosystem and positioned the city as a global engineering hub.
A 2018 Liepin report reveals that Hangzhou topped the net inflow of internet engineers in China, driven by favorable talent policies, high happiness scores, competitive salaries, a strong tech ecosystem led by Alibaba, and shorter commutes, making it the new hotspot for engineering talent.
This article details Alibaba's real‑time computing platform, outlining its operational challenges, the unified automation platform Aquila, proactive fault‑elimination strategies, and ongoing moves toward intelligent, data‑driven management to support massive workloads during events like Double‑11.
This article explains how Alibaba Xianyu uses GeoHash encoding and optimized spatial indexing to efficiently match billions of user‑posted GPS points with tens of thousands of market‑area polygons, reducing computation from quadrillions to billions of operations through precise point‑polygon algorithms and fast neighbor lookups.
The author recounts a four‑month interview journey across three Alibaba departments, detailing each technical round, the specific Java‑centric questions asked, the on‑site coding tasks, and practical advice on preparation, self‑promotion, and leveraging open‑source contributions.
This article reviews the background of Alibaba's XiaoMi chatbot, explores how machine reading comprehension can be applied to e‑commerce scenarios such as rule interpretation and product inquiries, surveys key datasets and SQuAD‑based models, and discusses practical challenges and solutions for deploying these technologies in real‑world business environments.
Alibaba’s AI lab unveiled ‘Li Zhi’, a hyper‑realistic virtual human that can be created from just an hour of video, showcasing advanced facial reshaping, speech synthesis and multilingual interaction at CVPR 2018, and hinting at future applications from customer service to emotional computing.
Alibaba’s DAMO Academy unveiled the DFSMN speech‑recognition model, open‑sourced on GitHub, which sets a new 96.04% accuracy record on LibriSpeech, trains three times faster than LSTM, and powers real‑world demos like AI cashiers and metro ticket machines.
This article explains Alibaba's GOC‑driven operation‑assurance solution for new retail, covering the sector's evolution, unique reliability challenges, a four‑pillar support framework—including high‑availability, mobile ops, emergency response, and change control—and real‑world best practices from Hema Fresh.
On May 17, 2018, Alibaba became the sole Chinese representative on the Java Community Process (JCP) Executive Committee, giving China a voice in shaping global Java standards, highlighting its extensive Java deployment across e‑commerce, finance, and logistics, and its open‑source contributions like AlibabaJDK, Dubbo, and RocketMQ.
Senior Alibaba engineer Xiao Xie recounts his decade‑long journey from the early Taobao Mall project to leading the Cloud Computing “Flying‑Sky Eight” team, detailing pivotal initiatives like the Five‑Color Stone integration, full‑link stress testing for Double‑11, and the evolution toward self‑developed, distributed cloud technologies.
Alibaba’s recent Search and Computing Open Day revealed how AI, big‑data algorithms and multi‑objective optimization are reshaping its e‑commerce ecosystem, boosting commercial efficiency, supporting diverse merchants, and driving innovative retail experiences for billions of users.
This article outlines Alibaba's DevOps transformation, describing the three operational stages, four foundations of automated operations, CI/CD implementation, essential system characteristics, development‑defined operations, config‑driven changes, and the tools that enable high‑availability, efficiency, and scalability.
In April 2018, Tsinghua University and Alibaba launched a joint Natural Interaction Experience Lab to explore next‑generation human‑machine interaction, aiming to give machines five senses and emotional understanding, and to apply this research across retail, smart homes, autonomous driving, and other emerging AI‑driven scenarios.
In this keynote, Alibaba's chief scientist explains how voice AI serves as the natural interface for IoT, detailing the company's strategy to connect billions of devices through cloud infrastructure, AI-driven perception, and multimodal interaction across consumer and industrial applications.
OceanBase, developed by Alibaba and Ant Financial, is a groundbreaking distributed relational database that replaced traditional IOE systems, achieved record‑breaking transaction throughput during China’s Double 11 shopping festivals, and showcases innovative use of Paxos, high availability, scalability, and cost‑effective cloud‑native architecture.
This article details Alibaba’s Shenma Search intelligent dialogue system, covering its content taxonomy, platform architecture, TaskBot/QABot/ChatBot engines, knowledge‑graph infrastructure, production pipelines, and performance metrics that enable AI‑driven information services across devices like Tmall Genie.
This article explains Alibaba's co‑location (混部) technology that mixes online services and batch compute on the same physical servers, detailing its background, key characteristics, scheduling architecture, resource isolation mechanisms, cost‑saving formulas, and future roadmap, showing how it boosts utilization and reduces expenses.
Alibaba’s Tair, a high‑performance distributed key/value cache, evolved through multiple versions to support massive traffic during Double 11, employing multi‑region deployment, hotspot hashing, memory merging, user‑space networking, and client optimizations that dramatically cut latency, improve scalability, and reduce operational costs.
Alibaba senior technical expert Houyi explains how intelligent network automation, rapid fault detection, automatic isolation, and traffic‑optimizing technologies were applied during Double 11 to dramatically improve stability, reduce costs, and enhance overall network performance across millions of devices.
In 2017 Alibaba's AliMe chatbot platform expanded from a single‑company solution to a multilingual, multi‑channel AI service, introducing platform‑level SaaS/PaaS capabilities, a seven‑layer front‑end architecture, modular back‑end design, advanced intent recognition, knowledge‑graph‑driven product management, reinforcement‑learning‑based recommendation, and machine‑reading comprehension for enterprise and consumer use cases.
This article traces Alibaba's e‑commerce technical evolution—from early LAMP‑based monoliths through Java‑centric 2.0 systems to a massive multi‑IDC, service‑oriented distributed architecture with HSF, Pandora, and advanced database sharding solutions—highlighting the challenges and innovations that enabled its massive scalability.
This article shares Alibaba's Python engineer job requirements, detailed interview questions covering list operations, word frequency, GIL, multithreading, design patterns, and common Python concepts, along with concise answers to help candidates prepare effectively.
This article explains how Alibaba’s supply‑chain algorithms and data‑driven operations enable rapid order processing, accurate demand forecasting, dynamic inventory allocation, and efficient warehouse fulfillment during the massive traffic of Double 11, highlighting the challenges faced and the solutions implemented.
Alibaba's Tmall International leverages blockchain technology to create a global traceability system that assigns each imported product a tamper‑proof digital identity, enhancing transparency, authenticity verification, and supply‑chain coordination across dozens of countries and thousands of brands.
Alibaba senior technologist Houyi explains how the company used AI‑powered network intelligence, automated fault detection, traffic scheduling, and smart routing to dramatically improve stability, reduce costs, and boost efficiency during the massive Double 11 shopping event.
The article reviews Alibaba’s 2017 open‑source contributions, highlighting nine major projects—including Dubbo, RocketMQ, Druid, Fastjson, ApsaraCache, Pouch, Dragonfly, Ant Design, and Egg—detailing their features, community impact, and how they advance high‑performance distributed systems, cloud‑native computing, and modern application development.
Alibaba’s UED outsourcing platform integrates AI-driven image verification, DDD‑based architecture, CQRS, and API interfaces to streamline design task distribution, real‑time budgeting, automated settlement, and quality assessment, enabling efficient, controllable, and intelligent management of over 700 external designers across the group.
Over nine years, Alibaba transformed its Double 11 e‑commerce platform from a centralized system to a highly elastic, cloud‑native architecture, employing distributed design, multi‑active regions, unified scheduling, containerization with Pouch, and hybrid deployment to dramatically cut costs and boost peak throughput.
The article details Alibaba's Taobao Personalization Platform (TPP) intelligent scheduling system, explaining its architecture, optimization algorithms, convergence logic, and performance results that dramatically improve CPU utilization and automate scaling during both regular operation and high‑traffic events like Double‑11.
Alibaba’s eXtreme Parameter Server (XPS) platform tackles the challenges of training models on billions of samples and trillions of features by employing streaming learning, feature hashing, dynamic sparsity, asynchronous checkpointing, and high‑performance communication, enabling efficient, fault‑tolerant distributed AI at massive scale.
The article explains how Alibaba’s Edge Network, using the NetO intelligent traffic scheduling system, combines real‑time internet quality monitoring, SDN‑enhanced BGP routing, and extensive peering to optimize both inbound and outbound paths for millions of global Double 11 shoppers, ensuring a seamless user experience.
Alibaba’s internal collaboration platform, Ali Inside‑Out, evolved from a simple portal in 2013 to a cloud‑native, AI‑enhanced ecosystem that unifies search, feed, and global deployment, leveraging Alibaba’s tech stack—including Spring Boot, ISEARCH, NEO4J, and worldwide ADNS—to boost employee productivity and cultural engagement.
Alibaba has open‑sourced its lightweight container runtime Pouch under Apache 2.0, detailing its evolution from early LXC experiments to a million‑scale deployment, highlighting strong isolation, rich “rich container” features, P2P image distribution, and its integration with Kubernetes and other cloud‑native ecosystems.
The article explains how Alibaba tackled the slowdown of Moore's law and TCO constraints by progressively customizing X86 CPUs—from selecting vendor SKUs to requesting bespoke specifications and adding micro‑code features—to boost performance and efficiency for massive events like Double 11.
This article analyzes a real Alibaba internal financial‑system incident where a one‑cent accounting error arose from concurrent database writes after a distributed‑lock timeout, details the root‑cause investigation, and presents two remediation strategies—adjusting timeout settings and strengthening idempotent controls—to prevent similar issues.
A tiny 0.01‑yuan discrepancy in an Alibaba product revealed duplicate settlement records, exposing a distributed‑lock timeout and concurrency issue that led to double commits, and the article walks through the root‑cause analysis, reverse engineering of the process, and two remediation strategies focusing on timeout adjustments and idempotency controls.
Alibaba’s newly launched DAMO Academy, unveiled at the 2017 Cloud Expo, aims to invest 100 billion yuan over three years in frontier technologies—from AI and quantum computing to cloud services—by establishing global research centers, fostering academic partnerships, and aligning scientific breakthroughs with commercial growth.
