BestBlogs.dev Highlights Issue #6

This article explores how Netflix constructs highly reliable online stateful services through a multi-layered approach. It emphasizes that reliability goes beyond simply reducing failure rates; it also involves minimizing the impact of failures and optimizing recovery time. Netflix achieves high availability and strong consistency for microservices by replicating data across multiple regions and availability zones. Furthermore, Netflix invests significantly in stateful services to handle regional failures and enable rapid recovery, ensuring system stability and resilience. The article delves into how Netflix enhances system reliability and performance by leveraging caching, stateful clients, and server signals. Through the implementation of retry mechanisms and load balancing techniques, Netflix effectively addresses system failures and load reduction, further enhancing system reliability. Netflix also utilizes weighted n-choose-1 algorithms, concurrency control, and idempotency tokens (which ensure that a request, even if repeated, has the same effect) to build a highly reliable online stateful system capable of automatically mitigating impacts and recovering quickly under high loads without human intervention. Finally, Netflix combines server, client, and API designs to create large-scale, scalable, and SLO-compliant stateful services, guaranteeing high availability and high utilization. By handling high-frequency writes asynchronously, Netflix ensures the system operates at nearly 100% uptime.

This article explains the transition from First Input Delay (FID) to Interaction to Next Paint (INP) as a Core Web Vital metric. It details how FID measures the delay between a user's first interaction and the browser's response, while INP expands this to include processing and presentational delays, focusing on the worst interaction throughout a page's lifespan. Strategies to optimize INP include efficient event handling, reducing processing and presentational delays, and using web workers for non-urgent tasks.

This article discusses the importance of building a frontend observability system to monitor and optimize the performance of frontend applications. It covers the background, design implementation, and the results of implementing such a system. The system focuses on unifying data reporting, automating dashboard creation, and providing real-time monitoring of critical performance metrics to enhance user experience and business decision-making.

The article first discusses the performance and stability issues caused by frequent communication between services in a microservices architecture and proposes solutions such as deploying frequently communicating service Pods on the same machine and using local communication methods (IPC) instead of network communication. It then introduces a mathematical scheduling model for Pods called RASA, which considers service affinity, and implements a multi-stage service traffic graph segmentation technique to manage large-scale clusters. To optimize the choice of scheduling algorithms, the article also introduces the use of machine learning to select the most suitable algorithms, including the column generation algorithm (CG) and the mixed integer programming solver algorithm (MIP). With these technologies, significant optimization of end-to-end latency and request failure rates can be achieved, enhancing service performance and reliability. Experimental assessments indicate that the RASA algorithm outperforms existing scheduling algorithms in both solution efficiency and quality. Since its deployment at ByteDance in 2023, the algorithm has achieved a reduction in latency of 10%-70%.

The article explores the role of a Technical PM, detailing their responsibilities and challenges in complex projects while proposing specific practical methods based on real-world experience. It clarifies that core responsibilities include requirement engineering, technical solution design, resource coordination, and risk management. The article analyzes four major challenges: risk identification and management, cross-team collaboration, handling requirement changes, and balancing quality with progress, emphasizing the importance of managing requirement changes. It focuses on practical risk management strategies such as 'tight at first, loose later' and 'divide and conquer,' illustrating through case studies how these approaches reduce project delays. Finally, it provides reference guidelines for each project stage, highlighting the need for Technical PMs to continuously improve their comprehensive skills. The article also highlights the performance benefits and enhanced personal influence that Technical PMs can achieve, motivating readers to embrace this role. Overall, the content is highly instructive for technical professionals.

Google Cloud's new Hierarchical Namespace (HNS) for Cloud Storage is an optimized bucket option for data-intensive and file-oriented applications. HNS improves performance and consistency by providing a file system-like "tree" structure and new folder operation APIs.

Compared to traditional flat namespaces, HNS supports rapid and atomic folder operations such as renaming and deletion, which are crucial for workloads that require frequent large file movements. HNS also offers higher initial bucket query rates per second (QPS), supports new APIs such as CreateFolder, DeleteFolder, GetFolder, RenameFolder, and ListFolders, and integrates with Hadoop/Spark and Cloud Storage FUSE.

However, HNS has some limitations, including the lack of support for object versioning, bucket lock, object retention lock, and object access control lists (ACLs). During the public preview, HNS does not support soft delete and autoclass features, and additional charges will apply upon general availability (GA).

The article first outlines the author's career progression from a junior full-stack developer to a technical manager, including work experiences in an outsourcing company and a startup, as well as how the team has embraced project-driven learning and self-improvement in technical skills to tackle complex projects.

Subsequently, the article details the optimization process of the Webpack packaging solution, which includes the use of the webpack-bundle-analyzer plugin for visual analysis, the implementation of Gzip compression, and the configuration of Gzip compression for both Nginx and the Webpack frontend.

Additionally, the article discusses how the introduction of the WebP image format and HTTP/2 technology, in conjunction with OSS cloud storage and CDN acceleration, can optimize image loading performance and enhance website access speeds.

Finally, the article emphasizes the importance of OSS thumbnails, demonstrating through actual cases how leveraging the OSS thumbnail feature significantly reduces image loading times and space usage.

At the JS Nation 2024 conference held in Amsterdam, the much-anticipated JavaScript Project of the Year Awards were announced. The Breakthrough of the Year Award was won by Rspack, a high-performance web build tool developed based on Rust, aiming to solve the performance issues of building large-scale projects.

The Most Exciting Application Technology Award went to Effect-TS, a TypeScript library that assists developers in creating complex synchronous and asynchronous programs.

The Excellence in Productivity Award was given to Biome, a front-end toolchain that provides rapid code formatting and rich linting features.

The AI-Assisted Excellence in Programming Award was awarded to screenshot-to-code, a project that generates front-end code from screenshots.

Other nominated projects included solid-start, WinterJS, Mitosis, PartyKit, elysia, hono.js, Javy, Nitro, Typescript Eslint, Vanilla Extract, Node.js Test Runner, Draw-a-ui, Web LLM, LangChain.js, and Ollama.js, all of which are contributing to the advancement of the JavaScript ecosystem and enhancing development efficiency.

The GitHub Blog published an article discussing how to extract insights from unstructured data using Retrieval-Augmented Generation (RAG) technology. The article first outlined the importance and challenges of unstructured data in software development, such as the fact that information about code repositories, organizational best practices, and customer feedback typically exists in unstructured forms, which are difficult to organize and interpret. Subsequently, the article detailed how RAG technology and large language models (LLMs) can help identify patterns and extract insights, particularly when dealing with textual data.

GitHub data scientists Pam Moriarty and Jessica Guo emphasized the unique value of unstructured data in software development and discussed how RAG technology can improve the efficiency and quality of the development process. The article also demonstrated how RAG technology assists developers in quickly understanding existing codebases, including their conventions, functions, common issues, and bugs. Additionally, the article explored how RAG technology can enhance product decisions by revealing user pain points to improve services and products. Finally, the article described how RAG technology extracts value from unstructured data by utilizing vector databases, general text search, and external or internal search engines, and mentioned how GitHub Copilot Enterprise leverages RAG technology to assist developers and leaders.

The Hodor system is a comprehensive strategy to prevent performance degradation in the QQ mobile application. Developed over three years, it represents an industry-leading solution, focusing on early detection and prevention of performance issues. The system covers features such as doorkeeping main-path problems, pre-release check systems, real-time data reports, and custom alert rules. By leveraging the collection of detailed performance data and dynamic tracing techniques with xctrace, the system aims to automatically identify the source of problems and initiate proactive measures to maintain stability and quality throughout the development and operation phases.

This article mainly introduces the concept of Web AI, how to run large language models (LLM) in the browser, the use of Visual Blocks to accelerate prototype design, and methods for implementing Web AI on a large scale in Chrome through JavaScript API.

The article 'How Auto Scaling and Load Balancing Work in Software Architecture' published by freeCodeCamp.org delves into the intricacies of two critical components in modern software architecture: auto scaling and load balancing. These techniques are often implemented together to manage unpredictable fluctuations in demand, ensuring that systems remain both resilient and cost-effective. The article begins by explaining the concept of auto scaling, which involves automatically adjusting the number of compute instances based on predefined metrics such as CPU utilization or incoming requests. It distinguishes between dynamic scaling, suitable for unpredictable demand fluctuations, and scheduled scaling, which is used for known demand patterns. The article uses a restaurant analogy to illustrate how dynamic scaling can be likened to adding chefs on the fly to handle sudden surges in orders. The importance of auto scaling is highlighted through its ability to improve resilience by automatically increasing instances during high demand and reducing them during low demand, thereby preventing overloading and ensuring high availability. Additionally, auto scaling helps in cost reduction by avoiding the need for over-provisioning resources. Next, the article moves on to explain load balancing, which involves distributing incoming network traffic across multiple instances to prevent any single instance from becoming a bottleneck. It discusses the OSI model and how load balancers operate at either layer 7 (application layer) or layer 4 (transport layer). The article emphasizes the benefits of load balancing, including load distribution, high availability, and loose coupling, which allows for independent scaling of different tiers in a multi-tier architecture. The article concludes by integrating the concepts of auto scaling and load balancing in a three-tiered web application architecture, demonstrating how these techniques work together to ensure resilience, cost control, and efficient load distribution.

Google Cloud offers six deployment archetypes to assist in meeting cloud workloads' needs for availability, latency, performance, and cost. This includes zonal, regional, multi-regional, global, hybrid, and multicloud deployment models, each tailored to specific application requirements.

Cloud SQL for MySQL recently got support for vector embedding search and AI-assistive tools powered by Gemini. The new features include integrated support for vector embedding search, enabling the building of generative AI applications, and AI-assistive tools like Index Advisor to optimize database performance. Key points include: 1. Vector Search for generative AI applications; 2. LangChain integration for generating vector embeddings; 3. Gemini for optimizing and managing MySQL databases; 4. Index Advisor for query efficiency; 5. Active Queries for real-time performance analysis.

The article discusses the application of A/B testing in the context of AI, emphasizing its role in making every decision more accurate and its amplified effects in the era of large AI models. It introduces DataTester, a core product under the VeDI platform of Volcano Engine, which has served hundreds of companies in scientific decision-making for business growth, user conversion, product iteration, strategy optimization, and operational efficiency. The article highlights the importance of A/B testing in the rapid iteration and optimization of AI-related products, enabling companies to quickly respond to market changes and user needs. It presents specific scenarios such as faster product launches, user experience improvements, and rapid rollback in case of issues, demonstrating how A/B testing accelerates the product iteration cycle and ensures continuous quality and experience optimization.

This article analyzes how Duolingo achieved user growth and stock price improvement in a competitive market through gamified elements, data-driven product iteration, and personalized push notifications. The article reviews Duolingo's early failed attempts at gamification and recommendation systems and summarizes key factors to consider when learning from other products' experiences. It emphasizes the importance of data analysis in determining the North Star Metric (a key metric for growth) and optimizing user retention. The article also introduces Duolingo's innovative strategies in push notifications and streak features, where the Multi-Armed Bandit Algorithm plays a crucial role in personalized push notifications. Additionally, the article mentions Duolingo's experimental strategies across different platforms, and the driving role of maintaining a sense of urgency and rapid iteration in compound growth. Finally, the article emphasizes the importance of identifying a strategic advantage (users' desire to form habits) and learning from existing solutions before developing original innovations.

At WWDC 2024, Apple unveiled a revolutionary AI operating system — Apple Intelligence, which is more than just the integration of large language models (LLMs) into devices. The core of Apple Intelligence lies in providing a personalized, intuitive, and secure AI experience, encompassing text processing, image generation, and enhancements to Siri, as well as reinforced privacy protection.

Apple demonstrates its unique path of innovation in the AI field by leveraging its proprietary local models and private cloud computing, while also supporting third-party LLMs such as GPT-4. The launch of this system marks Apple's redefinition of how AI should be used, emphasizing that AI products should be human-centric, enhancing life efficiency while ensuring data security.

Furthermore, through the App Intents framework, Apple encourages developers to integrate AI capabilities into their applications, driving the deep integration and widespread application of AI technology within the ecosystem. This article delves into the functions of Apple Intelligence and the design philosophy behind it, showcasing the potential future of AI OS and Apple's leading position in the AI domain.

The article first introduces the common classifications of B-end popup design, including modal and non-modal popups, as well as informative, operative, and display popups, and small, medium, and large-sized popups. Subsequently, the article demonstrates through a practical case study how to determine the most suitable popup size based on varying user requirements and information volumes. The author, through communication with reviewers, identified the scale of the number of reasons customers currently have for customizing configurations and examined the impact of different popup widths (600px, 800px, 1200px) on operational efficiency and adaptability. Through analysis, the author concluded that an 800px wide popup performs best in terms of operational efficiency and adaptability, and proposed height standards for popups, suggesting that the height should be self-adaptive, not exceeding 700px, and support an internal scrollbar when necessary. Additionally, the author has also put forward browser adaptation standards to ensure a good user experience on displays with different resolutions.

Devv AI, an AI-powered search engine for programmers, delivers fast, accurate results for coding queries. Founder Forrest Zhang details his journey, emphasizing that solving real-world problems is key to entrepreneurial success. The article covers the process from initial idea to MVP launch, including user research, solution development, differentiation strategies for market dominance, and monetization tactics. Key insights include the importance of rapid validation, product differentiation, word-of-mouth marketing, and navigating challenges.

In the ever-evolving design industry, the rise of AI is fundamentally transforming the way designers work. This article delves into three modes of collaboration between design and AI: Embedding, Copilot, and Agents. Initially, the Embedding mode enhances the intelligence of tools by integrating AI features but remains constrained by traditional frameworks. Subsequently, the Copilot mode demonstrates AI's comprehensive support throughout the design process, from analyzing needs to generating solutions, making AI an indispensable partner to designers. Ultimately, in the Agents mode, AI not only participates in creative ideation but also autonomously completes complex tasks, while designers transition into roles focused on aesthetics and decision-making.

The article suggests that with the maturation of Agent technology, the role of designers is shifting from executors to thinkers and decision-makers, ushering in a new era of intelligent design work. AI is no longer just a tool for efficiency; it has become a source of creativity and the executor of design. This undoubtedly raises new considerations for the professional development of designers and the future of the design industry. How designers adapt to these changes and balance creativity with technology will be pivotal questions for the design world to ponder in the future.

The article discusses the importance and methodology of 'review' (复盘) in the workplace to accelerate personal growth. The author shares her experiences and provides practical templates for effective reviews. Key points include: 1. The significance of keeping a work log. 2. Different review models like KISS, PDCA, and 4Fs. 3. Step-by-step guide on how to conduct a review. 4. Tips for maintaining a consistent review habit. 5. Recommended tools for conducting reviews.

In today's commercial world, membership economy has permeated every corner of life, becoming a powerful tool for brands to build deep connections with users. Meanwhile, the 'pricing system' for memberships has become a critical variable for success or failure. A suitable and reasonable membership pricing system can stabilize and strengthen the relationship between the brand and its members. This article explores how to develop a reasonable membership pricing system from four dimensions: potential revenue sources, optimal timing for price adjustments, differences between freemium and free gifts, and common pricing mistakes.

Starting from the information usefulness model, the article elaborates on the impact of information content quality and source credibility on the usefulness of user reviews. It then delves into the analysis of Amazon's review system features, including review policy rules, C-end linkage functions, and B-end review invitation tools. In terms of review policy rules, Amazon prevents sellers from manipulating reviews and harassing users through strict policies and punitive measures, ensuring the authenticity of reviews. The C-end linkage functions provide various tools and methods to help users efficiently obtain useful, comprehensive, and trustworthy review information, while also enhancing the perceived authenticity of reviewers. The B-end review invitation tools include the request review function, the Vine Voice program, and the Transparency review card, which offer legitimate ways for sellers to accumulate reviews and provide additional avenues for users to obtain information. Ultimately, the article concludes that Amazon's review system, through its platform rules and supporting system functions, effectively meets users' demands for the authenticity and usefulness of reviews.

This article explores the concept of KOS (Key Opinion Sales) and its significance in the transition from public to private domains in marketing. It delves into how KOS, unlike KOLs and KOCs, offers a more personalized and professional approach to sales, leveraging platforms like Xiaohongshu to enhance consumer engagement and sales conversion. The article also discusses the strategic implementation of KOS in various industries and the benefits of integrating KOS into a brand's overall marketing strategy.