The article explains how continuous data generation and real‑time stream processing enable e‑commerce platforms like NetEase Kaola to handle massive Double 11 traffic, showcasing use cases, architectural shifts from batch to incremental computing, and the technical challenges of latency, accuracy, and fault tolerance.
The article details Suning’s decade‑long evolution of its membership platform for the Double‑11 shopping festival, covering early offline CS architecture, the transition to a WebSphere‑based e‑commerce system, vertical module splitting, data migration with Spark and Hive, and the implementation of same‑city multi‑active deployment.
This article examines the rise of organized “wool‑pulling” fraud groups during China’s Double‑11 shopping festival, outlines their tools and tactics, and details NetEase Cloud Security’s multi‑layered anti‑fraud system—including captcha, SMS verification, IP rules, device fingerprinting, rule engines, user profiling, network graph analysis, and blacklist strategies—to protect e‑commerce platforms.
This article examines JD.com’s technical strategies for the 2023 Double‑11 shopping festival, detailing dynamic load‑balancing and rate‑limiting mechanisms, evolving fault‑drill practices, and AI‑powered product and marketing enhancements that together ensure high‑concurrency stability and improved user experience.
This article explains a practical pipeline for extracting dominant colors from product images—using image resizing, MeanShift and FloodFill segmentation, region merging, Gaussian weighting, selection, and CIELab-based tagging—enabling accurate color-based search in e‑commerce platforms.
This article presents a comprehensive overview of Alibaba’s intelligent chatbot “Ali Xiaomi”, covering industry context, e‑commerce deployment, NLU architecture, intent‑matching layers, deep‑learning‑based intent classification, reinforcement‑learning‑driven recommendation, knowledge‑graph‑enhanced services, and hybrid retrieval‑generation dialogue models, with future outlooks for AI‑driven interaction.
The article describes how Dada Mall and its crowdsourcing task platform adopted a flexible plugin architecture to modularize product-specific logic, simplify maintenance, enable dynamic configuration, and support rapid business expansion without extensive refactoring.
This article explains how interaction designers can interpret and implement operational requirements—such as acquisition, retention, and activation—using the NetEase Yanxuan invitation feature as a case study, balancing business goals with clear, user‑friendly design.
In this interview, JD POP senior technical product manager Zhou Yinhua shares her experiences on driving innovation within product management, detailing the importance of innovative thinking, the challenges faced, the development of the POP merchant consulting platform, and how these practices have strengthened JD’s e‑commerce operations.
This article explores how to apply Domain‑Driven Design principles to structure and simplify a complex e‑commerce transaction system, detailing sub‑domain division, model refinement, core order‑service design, and the use of design patterns to achieve a maintainable backend architecture.
This article discusses JD.com's Y Business Unit's AI-driven supply chain optimization, focusing on sales prediction algorithms, inventory control, and the challenges of predicting new product sales.
This paper introduces a dynamic fusion algorithm that leverages multi‑dimensional logistic regression and rich consumer behavior features to infer infants' life stages from parental e‑commerce actions, demonstrating significant accuracy improvements over memory‑less baselines across multiple months of Taobao data.
This article details JD.com’s comprehensive strategies—including architecture design, pre‑event preparation, real‑time safeguards, and post‑event analysis—to ensure MySQL databases remain high‑performance and highly available during massive traffic spikes like 618 and Double‑11 sales.
This article traces Alibaba's e‑commerce platform evolution from a simple LAMP stack through Java migration, distributed caching, custom middleware, and multi‑active global deployment, highlighting the technical challenges, solutions like HSF RPC, TDDL sharding, and the high‑availability practices that now power Alibaba Cloud services.
Alibaba’s Vice President Gu XueMei explained at the 40th ACM SIGIR conference how deep interest networks, driven by big data and large‑scale deep learning, enable highly personalized e‑commerce experiences that dramatically reduce user churn and boost click‑through rates.
This article summarizes two SIGIR 2017 papers: one introduces a probabilistic latent‑class model for shopping‑pair push recommendations that improves e‑commerce click‑through rates by leveraging co‑purchase and view‑then‑purchase graphs, and the other presents IRGAN, a GAN‑based framework that unifies generative and discriminative information‑retrieval models, achieving state‑of‑the‑art results across web search, recommendation, and QA tasks.
During Alibaba’s Double‑11 shopping festivals, Tmall’s product‑detail and shop pages faced traffic spikes dozens of times higher than normal, prompting a multi‑stage architectural evolution—from early static‑page caching to unified web cache and finally full CDN staticization—delivering massive performance gains, high availability, and scalable cost‑effective solutions.
This article shares the end‑to‑end design and development experience of a startup e‑commerce platform, covering business requirement analysis, efficient development practices, security measures, performance and architecture optimizations, as well as team‑building lessons learned.
This article details JD.com's front‑end architecture for its homepage, covering the challenges of massive DOM growth, static page generation via CDN, layered page loading strategies, disaster‑recovery measures, performance optimizations, and the JDF engineering toolchain that enables modular development, automated builds, and efficient deployment in a high‑traffic e‑commerce environment.
This article consolidates e‑commerce platform architecture practices, covering design principles, multi‑level caching, indexing strategies, parallel and distributed computing, high availability, scaling techniques, resource optimization, static blueprint, component analysis, and supporting middleware such as load balancers, routers, HA, messaging, caching, buffering, search, and log collection.
This article presents a comprehensive analysis of horizontal database sharding, detailing the design decisions, partitioning dimensions, routing, pagination handling, lookup mapping, and deployment steps based on a real‑world implementation at a large e‑commerce platform, offering practical guidance for scaling order databases.
This article examines JD.com’s 618 shopping festival performance, the deployment of unmanned delivery robots, and the design and architecture of the ForceBot full‑link load‑testing system that enabled precise capacity planning and bottleneck detection for massive e‑commerce traffic.
This article examines JD.com's strategic redesign of its iPad shopping app in 2017, detailing user research, business-driven design decisions, the shortcomings of the old product‑detail page, and how the new 4.0 version improved readability, scalability, and conversion rates on a large‑screen mobile device.
This article explores the hidden, often unglamorous world of web crawling and anti‑crawling, detailing why companies need these technologies, the massive traffic they generate, the technical arms race between crawlers and defenders, and the evolving strategies and challenges that shape the industry today.
The second “Frontiers and Future of Information Retrieval” forum, co‑hosted by the Chinese Computer Society, Alibaba and academic committees, showcased how massive, structured e‑commerce data and AI algorithms are revolutionizing search, customer service, and research collaborations across the industry.
Alibaba’s ‘Ali Xiaomi’ chatbot leverages a layered architecture that integrates intent recognition, multi‑type matching, knowledge‑graph‑based entity management, deep reinforcement learning, and hybrid retrieval‑generation models to deliver high‑accuracy, scalable conversational services across e‑commerce, customer support, and intelligent recommendation scenarios.
This article examines e‑commerce product matching, comparing rule‑based methods, feature‑engineering models, and a pure data‑driven Factorization Machine approach, detailing their advantages, challenges, training techniques, and successive optimizations to improve matching accuracy and operational efficiency.
This article outlines how a massive e‑commerce platform like Taobao evolves its backend architecture step by step—from an all‑in‑one server through service separation, caching, load balancing, session management, database sharding, CDN acceleration, and finally distributed search—highlighting the motivations, trade‑offs, and key design considerations at each stage.
Alibaba’s e‑commerce search engine evolved over three years from offline batch models to a sophisticated AI-driven system that integrates real‑time feature ingestion, online learning, deep and reinforcement learning, enabling dynamic personalization and decision‑making that boosts conversion during high‑traffic events like Double 11.
At the 2017 QCon Beijing conference, Suning’s Silicon Valley Research Institute director Jim demonstrated how deep‑learning techniques can transform e‑commerce by vectorizing product data for smarter search relevance and by combining AI models with limited labeled data to build conversational chat‑bot platforms that understand user intent.
This guide walks through the evolution of a small .NET‑based e‑commerce system, covering its initial LAMP‑style setup, detailed backend architecture, logging and monitoring solutions, master‑slave database design, shared‑storage image server, mobile M‑site construction, capacity estimation methods, and caching strategies.
This article details Mogujie's end‑to‑end search system architecture, describing both offline and online components such as Topn, ABTest, QR, UPS, the search engine, precision ranking, and feature management, and walks through a concrete online request example from query to final ranked results.
This article analyses dozens of Chinese e‑commerce interactive marketing campaigns, comparing their design styles, value propositions, user‑experience patterns and technical tricks to explain why certain activities attract and retain users while others fail.
The 2017 Suning Internet Product Manager Forum gathered over 500 professionals to share data‑driven product, marketing, and O2O strategies, offering practical insights from leading e‑commerce and tech companies on growth hacking, precise targeting, and innovative retail solutions.
The article explains how a high‑traffic e‑commerce platform like Taobao relies on distributed storage, search engines, massive caching, load‑balancing, CDN, sophisticated advertising and analytics systems, all of which require large teams of top engineers to design, implement, and operate.
This interview with Alibaba researcher Xu Yinghui reveals how the company built large‑scale deep reinforcement learning systems for search, recommendation, logistics and online advertising, detailing team structures, technical breakthroughs, training challenges, and future directions such as multi‑agent learning and GAN integration.
This article introduces the PH‑MAB mechanism that combines public and private transaction histories to improve multi‑armed bandit‑based recommendation systems, explains its truthful mechanism‑design foundation, and shows how it reduces regret and boosts platform revenue compared to traditional epsilon‑greedy approaches.
This article explains the concept and construction of user personas, explores the essence and algorithms of recommendation systems, compares movie and e‑commerce scenarios, and details NetEase Yanxuan's practical CTR‑based recommendation model with extensive feature engineering.
This article outlines a comprehensive PDCA‑based methodology for e‑commerce platforms to proactively prevent issues, quickly detect anomalies, and execute rapid decisions during large‑scale promotions, covering system goal definition, performance evaluation, capacity planning, SLA management, and team/process maturity.
This article explores how Taobao leverages reinforcement learning, multi‑armed bandits, and reward‑shaping techniques to improve large‑scale e‑commerce search ranking and recommendation, detailing problem modeling, algorithm designs such as Tabular Q‑learning and DDPG, experimental results from Double‑11, and advanced models like GBDT+FTRL and Wide‑&‑Deep.
Alibaba leveraged reinforcement learning, highlighted by MIT Technology Review’s 2017 breakthrough list, to transform its e‑commerce search and recommendation systems during Double 11, deploying large‑scale online and batch training pipelines, dynamic market segmentation, and real‑time decision models that boosted click‑through rates by up to 20 %.
This article explains how Alibaba leverages AI, big‑data analytics, and advanced recommendation algorithms to enable real‑time visitor clustering, personalized storefronts, and tailored content across its Customer Operation Platform, Double 11 promotion pages, QianNiu headlines, and service market, delivering significant conversion and engagement gains.
Alibaba’s merchant division introduced a scalable graph‑embedding approach for its “thousands‑of‑people‑one‑face” recommendation module, enabling personalized product suggestions within sparse shop data, improving click‑through rates by 30% and conversions by 60%, and presenting theoretical insights validated at AAAI 2017.
This article examines VIPshop's transformation from a single‑application LAMP system to a vertically split and finally distributed service‑oriented architecture, detailing the business model, key design requirements, platform governance, and the technical services that enable a scalable e‑commerce operation.
This article explores how Alibaba's Taobao leverages reinforcement learning, Markov decision processes, and reward shaping to improve large‑scale product search ranking and recommendation, detailing problem modeling, algorithm designs such as Tabular Q‑learning and DDPG, experimental results, and advanced recommendation models like GBDT‑FTRL and Wide‑Deep.
This article chronicles Xiaomi's e‑commerce platform evolution, detailing the shift from a simple monolithic design to a modular, sharded, and cloud‑native architecture that leverages async messaging, horizontal database partitioning, flash‑sale systems, dual‑data‑center caching, and sophisticated monitoring to handle massive traffic spikes.
The article reviews Alipay's 2017 AR‑driven marketing campaign, surveys the rapid adoption of AR/VR/MR by major e‑commerce platforms, and explains the technical foundations and distinctions of vision‑based, marker‑less, and location‑based augmented reality, virtual reality, and mixed reality systems.
Alibaba's Double 11 festival broke sales records with 120.7 billion RMB, and the article details the backend engineering challenges and solutions—such as database sharding, SQL optimization, multi‑level caching, and modular architecture—that enabled the platform to handle millions of orders per second while preserving data consistency and performance.
An illustrated guide explores the recommendation algorithms powering e‑commerce platforms, drawing on Xavier Amatriain’s CMU Machine Learning summer school lectures to explain collaborative filtering, content‑based, and hybrid approaches, their practical implementations, and the impact on user experience and sales.
The article explains how Taobao’s content‑centric strategy, embodied in the Beehive platform, builds an end‑to‑end content chain—from creator tools and health scoring to personalized distribution and commerce mechanisms—enabling massive, efficient content production and monetization during the Double 11 shopping festival.
This article details a comprehensive e‑commerce data architecture, covering product module design, SKU handling, packaging strategies, order schema, promotion tracking, visit analytics, and reporting best practices, offering practical insights for building robust backend systems.
This article details Dangdang's 15‑year evolution from a monolithic system to a distributed, SOA‑based architecture, outlining the challenges of high‑traffic e‑commerce events and the strategies—system grading, decoupling, asynchronous processing, batching, and rate limiting—used to achieve reliable, scalable operations.
This article shares practical strategies for designing a cost‑effective, highly available infrastructure for a fresh‑food e‑commerce business, covering business model impact, virtual‑physical hybrid data centers, cloud‑on‑premise balance, open‑source tools, load balancing, monitoring, security, and internal service systems.
This article details Alibaba's eight‑year journey of scaling its Double 11 shopping festival by evolving high‑availability middleware, capacity planning, unit‑based architecture, hybrid cloud and automated traffic control to achieve reliable, cost‑effective performance at massive peak loads.
At Alibaba's 2016 Double 11 Tech Forum, researcher Xu Yinghui presented how online AI technologies enhance search and recommendation on the e‑commerce platform, turning massive user behavior data into actionable insights that improve traffic allocation and maximize welfare for consumers, sellers, and the platform.
MIT Technology Review reports that Alibaba’s 2022 Double 11 shopping festival set new e‑commerce records while showcasing the company’s advanced big‑data, AI, and cloud‑computing technologies, highlighting massive transaction volumes, high‑quality data processing, robust security measures, and the strategic push toward global digital infrastructure.
The discussion outlines essential service‑monitoring techniques—including health checks, JVM metrics, traffic and payment ring‑ratio analysis, client‑side exception tracking, third‑party CDN monitoring, alert thresholds, instrumentation via AOP or SDKs, and tooling such as Datadog, Zabbix, and the Elastic stack—to reliably detect and respond to incidents in e‑commerce environments.
The article explores how AI, cloud computing, big data, and advanced logistics power China’s Double 11 shopping festival, turning it into a showcase for cutting‑edge technologies that drive personalization, real‑time analysis, and industry‑wide digital transformation.
The Double 11 shopping surge set new sales and user‑experience records, prompting the China Software Developer Conference to host a comprehensive e‑commerce architecture track from November 18‑20 that covered international infrastructure, global regional deployment, large‑scale product systems, startup‑scale platforms, cloud migration, smart pharma solutions, and platform governance.
This article details JD.com senior frontend architect Liu Wei's presentation on the challenges of a high‑traffic e‑commerce homepage and explains the static page generation pipeline, overall JS/CSS architecture, floor‑by‑floor loading strategy, disaster‑recovery measures, performance optimizations, and the JDF engineering toolchain that supports modular development and rapid deployment.
Although written in 2014, this article remains relevant as it compares the distinct technology strategies of three major Chinese e‑commerce players—Taobao’s open‑source approach, JD.com’s .NET‑based architecture, and Suning’s adoption of IBM WCS—highlighting their development demands, scaling challenges, and staffing efforts.
From its 2009 launch with a simple WAP site to handling over 100 million daily active users, Mobile Taobao’s architecture evolved through four stages—introducing API gateways, HTML5/WebApp integration, Bundle deployment, and the PackageApp system—while building comprehensive R&D, testing, operations, and release support.
This article outlines comprehensive e‑commerce platform architecture practices—including caching strategies, indexing, parallel and distributed computing, load balancing, sharding, high availability, monitoring, resource optimization, and messaging—to improve system performance, scalability, and reliability under high concurrency.
The article recounts how Xiaomi’s engineering team built, tested, and iteratively improved a high‑concurrency flash‑sale platform—first in PHP with Redis and later in Go—to reliably handle millions of users during the 2014 Mi Fan Festival, detailing architectural choices, performance optimizations, and lessons learned.
This article explains how Alibaba combines big‑data analytics, machine learning, and mechanism‑design game theory to create a recommendation system that removes incentives for merchants to generate fake orders, improving fairness and user experience on e‑commerce platforms.
The article details Dangdang's 15‑year evolution from a monolithic platform to a distributed, SOA‑based architecture, describing system tiering, front‑end and back‑end scaling techniques, asynchronous processing, data‑flow optimization, and operational practices that enable stable handling of ten‑fold traffic spikes during major sales events.
The article describes how Yihaodian built a low‑latency, highly available, and easily scalable streaming computation platform using Storm, Kafka, Linux containers and a custom CGroup management framework to handle massive e‑commerce traffic spikes and real‑time analytics.
The article examines how JD’s e‑commerce platform tackles massive traffic spikes and high concurrency by employing modular system decomposition, distributed architecture, API‑centric services, performance tuning, and virtual elasticity to ensure stability, scalability, and a smooth user experience during major promotional events.
This article presents a series of slides that explain the recommendation algorithms commonly used in e‑commerce, drawing on concepts taught by Xavier Amatriain at Carnegie Mellon University's Machine Learning summer school.
The article examines common pitfalls and best‑practice solutions for traditional enterprises building e‑commerce platforms that must handle traffic spikes, covering large‑scale query optimization, distributed architecture, database design, service degradation strategies, and comprehensive monitoring and operations.
The article analyzes the technical challenges of massive e‑commerce traffic spikes such as Double 11 and presents a three‑pillar architecture—fast front‑end delivery, stable back‑end services, and dazzling real‑time analytics—using CDN caching, database optimizations, load‑balancing, system decoupling, and cloud‑native components.
This article recounts Alibaba's 13‑year journey from a simple e‑commerce site to a sophisticated business middle‑platform, detailing the architectural evolution, technical challenges, and strategic solutions that enabled massive scale, agility, and global expansion.
Image search, once limited by early CBIR techniques, has surged back thanks to deep learning, offering improved relevance, coverage, scalability, and user experience across applications like e‑commerce, shopping, entertainment, and surveillance, while integrating data, users, models, and systems to bridge the semantic gap.
The article details how Mogujie's e‑commerce transaction system evolved from a monolithic PHP application to a service‑oriented architecture through DB vertical sharding, service decomposition, custom sharding component TSharding, and performance optimizations such as asynchronous processing, caching, and distributed transaction handling to support rapid growth and high‑availability requirements.
This article explains the theoretical basis of bounce‑rate and conversion‑rate calculations, describes a platform that visualizes and monitors full‑link performance, outlines concrete optimization techniques such as dynamic acceleration, static‑plus‑ESI, element request merging and CDN scheduling, and reports a 5.07% increase in orders after applying these measures.
The article examines JD.com's strategic focus on user experience—covering pricing, logistics, service, and product quality—and explains how its integrated systems and "no‑no" policy drive operational efficiency and competitive advantage in China's e‑commerce market.
This article walks through the functional and non‑functional requirements of a B2C e‑commerce platform, illustrates a progression from a three‑server starter architecture to a clustered high‑availability design, and details capacity‑planning calculations for supporting millions of users and peak traffic spikes.
This article outlines the design of both system-wide and personalized recommendation modules for e‑commerce platforms, explains three recommendation approaches (demographic, content‑based, collaborative filtering), details the implementation of Mahout’s collaborative‑filtering algorithm with Java code, discusses data sources, technology stack, algorithm choices, and solutions to the cold‑start problem.
The article explains the overall architecture of an e‑commerce search engine, covering indexing, static scoring, retrieval, title and store deduplication, query analysis and rewriting, and related big‑data and AI techniques used to improve relevance and diversity of search results.
During Alibaba’s Double‑11 shopping festivals, Tmall’s product‑detail and shop pages faced traffic spikes dozens of times higher than normal, prompting a multi‑stage architectural evolution—from server‑side staticization and distributed caching to a unified CDN‑based system that delivers high availability, low latency, and efficient cache invalidation.
The 2016 ATF Alibaba Technology Forum presented a comprehensive overview of Alibaba's mobile e‑commerce platform, detailing massive scale challenges, user‑experience driven innovations, container‑based client architecture, the Weex framework, and future directions such as live video, VR/AR, and high‑performance mobile networking.
This article explains a dynamic, template‑driven rendering architecture for e‑commerce single‑page applications, covering CMS configuration, multi‑version data storage in Redis, front‑end rendering flow, version rollback, gray‑release handling, and exception management.
This article examines VIPshop’s business model evolution and the technical transformation of its e‑commerce platform, detailing the shift from a single‑application LAMP monolith through vertical and distributed architectures to a cloud‑native, service‑oriented design, and outlines the key governance, scalability and operational challenges addressed along the way.
This article, based on the Efficient Operations Community Talk, outlines the evolution of e‑commerce systems, the challenges faced during rapid growth, and how LeTV’s e‑commerce cloud leverages micro‑service architecture, container technology, and hybrid cloud solutions to address scalability, security, and operational efficiency.
This article details JD.com's front‑end engineering practices for its 2015 PC homepage, covering challenges, static page generation, overall architecture, modular widget system, build tools, performance optimizations, disaster‑recovery strategies, and a Q&A session that reveals practical implementation insights.
This article presents a comprehensive e‑commerce website architecture case study, detailing functional and non‑functional requirements, initial single‑server design, capacity estimation, and progressive evolution toward a distributed, high‑availability architecture with clustering, caching, load balancing, service‑orientation, and database sharding.
This article explains the unique characteristics of mother‑baby e‑commerce, describes a comprehensive big‑data platform architecture—including data collection, offline and real‑time computing, and recommendation algorithms—and shows how user profiling and personalized ranking dramatically improve conversion and user experience.
The article chronicles Xiaomi Web's architectural journey from a simple three‑engineer monolith in 2011 through systematic service decomposition, asynchronous messaging, database sharding with Cobar, cloud‑native scaling, advanced caching, virtual inventory allocation, and sophisticated monitoring, illustrating practical lessons for building high‑performance e‑commerce platforms.
This article chronicles JD.com's POP platform evolution from a simple monolithic system in 2010 to a micro‑service‑based, highly scalable architecture by V3.0, highlighting the technical challenges, solutions, and the introduction of service‑oriented APIs, diversified data stores, and unified operational tools.
This article explains JD.com’s search update cycles, indexing rules for SKUs and product attributes, common reasons products disappear or drop in rank, and practical optimization tips covering high‑relevance categories, text relevance, pricing models, title strategies, and traffic entry points.
The article explains why service degradation is essential for large‑scale shopping events, outlines its different dimensions such as page, business module, and remote service downgrade, and describes both manual and automatic implementation methods to maintain system availability under heavy load.
This article explores how Meilishuo, China’s leading fast‑fashion discovery platform, tackles fragmented mobile attention by using AI‑powered personalization techniques—including user modeling, real‑time feedback, and tailored push notifications—to deliver highly relevant fashion recommendations and boost user engagement.
A seasoned operations leader discusses how e‑commerce operational support differs from general internet applications, covering longer support chains, consistency models, seasonal traffic spikes, team role separation, mobile‑internet challenges, future planning, and the rise of enterprise‑level ops services.
The article explains how JD.com leverages big‑data and personalized recommendation algorithms across PC and mobile platforms, detailing its recall and ranking models, efficiency analysis, weekly algorithm iterations, and future AI‑driven optimizations that together contribute about 10% of its orders.
This article explains how JD.com’s Order Fulfillment Center (OFC) was built, re‑engineered, and continuously optimized to handle massive order volumes during major sales events, covering its architecture, migration from .Net to Java, distributed task queues, flow control, and operational practices that ensure reliability and scalability.
This article explains the fundamentals and business value of personalized recommendation systems for e‑commerce, outlines practical implementations on homepages, list pages, and search result pages, and provides case studies showing how tailored product suggestions improve conversion rates, user experience, and sales performance.
An in‑depth interview with 1hao Store’s senior tech director reveals the evolution of its e‑commerce architecture, hybrid‑cloud strategy, performance‑driven culture, and practical steps for achieving high availability, elasticity, and cost efficiency during massive traffic spikes.
During the 2015 Double 11 shopping festival, Suning’s IT team combined big‑data‑driven capacity planning, system tiering, stress testing, architecture review, comprehensive monitoring, and emergency response mechanisms to achieve a 358% increase in total orders, with mobile sales up 67% and offline growth of 153%.
This article details Baiba's evolution from a simple flash‑sale site to a mobile‑centric e‑commerce platform, describing its backend flow through CDN, caching layers, PHP‑FPM, memcached, Redis, MySQL, search engines, monitoring tools, deployment pipelines, and future plans for service‑orientation and hybrid apps.
This article details Alibaba's mobile infrastructure evolution that powered a record‑breaking 2015 Double‑11 sale, covering client container design, network pipeline challenges, dynamic rendering, cross‑platform solutions like Weex, and the open‑source plans that enable massive scalability.
