How to Scale a System from 0 to 10 million+ Users
【原文摘要】
Core Scaling Principles
This article outlines 7 incremental stages of scaling a system from 0 to 10 million+ users, emphasizing that over-engineering early wastes resources. The key rule is to start simple, identify bottlenecks, and scale incrementally. User ranges and metrics are approximate, as actual thresholds vary by product, workload, and traffic patterns. Understanding this progression benefits app builders, system design interview candidates, and anyone curious about large-scale systems.
Stage 1: 0-100 Users (Single Server)
The priority is to ship a product and validate the idea, not optimize. A single server hosts the web app, database, and background jobs—this was Instagram's initial setup. Benefits include fast deployment, low cost ($20-50/month for a VPS), easy debugging, and full-stack visibility. Trade-offs and scaling triggers: Database slowdowns during peak traffic, consistent 70-80%+ CPU/memory usage, deployment downtime, background job crashes affecting the web server, and inability to tolerate even brief outages.
Stage 2: 100-10,000 Users (Separate Database Server)
The first architectural split separates the database from the application server, addressing resource competition between the two. Benefits include resource isolation, independent scaling, better security (database in a private network), specialized optimization, and non-disruptive backups. Most teams use managed databases (Amazon RDS, Supabase) to save maintenance time, though self-hosting may be preferred for large-scale cost optimization, custom configurations, compliance, or database product development. Connection pooling (e.g., PgBouncer) reduces connection overhead, improving efficiency by 3-5x for most apps. The trade-off is minor network latency between app and database, which can be mitigated by optimizing query patterns.
【单词表】
- incremental /ˌɪŋkrəˈmentl/ 渐进的，递增的
- scaling /ˈskeɪlɪŋ/ 扩容，扩展
- over-engineering /ˌəʊvərendʒɪˈnɪərɪŋ/ 过度设计
- bottlenecks /ˈbɒtlneks/ 瓶颈
- metrics /ˈmetrɪks/ 指标，度量标准
- approximate /əˈprɒksɪmət/ 近似的，大概的
- thresholds /ˈθreʃhəʊldz/ 阈值，临界点
- workload /ˈwɜːkləʊd/ 工作负载
- progression /prəˈɡreʃn/ 发展进程，演变
- validate /ˈvælɪdeɪt/ 验证，证实
- deployment /dɪˈplɔɪmənt/ 部署，上线
- debugging /ˌdiːˈbʌɡɪŋ/ 调试，排错
- full-stack /ˈfʊl stæk/ 全栈的
- trade-offs /ˈtreɪd ɒfs/ 权衡取舍，利弊交换
- triggers /ˈtrɪɡəz/ 触发因素，诱因
- outage /ˈaʊtɪdʒ/ 故障中断，停机
- architectural /ˌɑːkɪˈtektʃərəl/ 架构的，体系结构的
- isolation /ˌaɪsəˈleɪʃn/ 隔离，独立
- non-disruptive /ˌnɒndɪsˈrʌptɪv/ 无中断的，不造成干扰的
- managed /ˈmænɪd/ 托管的，受管理的
- compliance /kəmˈplaɪəns/ 合规性，符合规定
- pooling /ˈpuːlɪŋ/ 池化，集中管理
- overhead /ˈəʊvəhed/ 开销，额外成本
- latency /ˈleɪtnsi/ 延迟，滞后
- mitigate /ˈmɪtɪɡeɪt/ 缓解，减轻
- query /ˈkwɪəri/ 查询，检索
【句子翻译】
- This article outlines 7 incremental stages of scaling a system from 0 to 10 million+ users, emphasizing that over-engineering early wastes resources. 本文概述了将系统从0扩容到1000万+用户的7个渐进阶段，强调早期过度设计会浪费资源。
- The key rule is to start simple, identify bottlenecks, and scale incrementally. 核心原则是从简单入手，识别瓶颈，逐步扩容。
- User ranges and metrics are approximate, as actual thresholds vary by product, workload, and traffic patterns. 用户范围和指标均为近似值，因为实际阈值会因产品、工作负载和流量模式而异。
- Understanding this progression benefits app builders, system design interview candidates, and anyone curious about large-scale systems. 了解这一发展进程对应用程序构建者、系统设计面试候选人以及任何对大型系统感兴趣的人都有帮助。
- The priority is to ship a product and validate the idea, not optimize. 首要任务是推出产品并验证想法，而非进行优化。
- Benefits include fast deployment, low cost ($20-50/month for a VPS), easy debugging, and full-stack visibility. 优势包括部署速度快、成本低（虚拟专用服务器每月20-50美元）、调试简便以及全栈可见性。
- Trade-offs and scaling triggers: Database slowdowns during peak traffic, consistent 70-80%+ CPU/memory usage, deployment downtime, background job crashes affecting the web server, and inability to tolerate even brief outages. 权衡取舍与扩容触发因素：峰值流量期间数据库运行缓慢、CPU/内存持续处于70-80%以上的使用率、部署停机、后台任务崩溃影响Web服务器，以及无法承受哪怕短暂的故障中断。
- The first architectural split separates the database from the application server, addressing resource competition between the two. 首次架构拆分将数据库与应用服务器分离，解决两者之间的资源竞争问题。
- Benefits include resource isolation, independent scaling, better security (database in a private network), specialized optimization, and non-disruptive backups. 优势包括资源隔离、独立扩容、更高的安全性（数据库部署在专用网络中）、针对性优化以及无中断备份。
- Most teams use managed databases (Amazon RDS, Supabase) to save maintenance time, though self-hosting may be preferred for large-scale cost optimization, custom configurations, compliance, or database product development. 大多数团队使用托管数据库（如亚马逊RDS、Supabase）以节省维护时间，但对于大规模成本优化、自定义配置、合规性或数据库产品开发，自行托管可能更受青睐。
- Connection pooling (e.g., PgBouncer) reduces connection overhead, improving efficiency by 3-5x for most apps. 连接池（如PgBouncer）可减少连接开销，将大多数应用程序的效率提升3至5倍。
- The trade-off is minor network latency between app and database, which can be mitigated by optimizing query patterns. 其弊端是应用程序与数据库之间会产生轻微的网络延迟，这可以通过优化查询模式来缓解。
【译文】
核心扩容原则
本文概述了将系统从0扩容到1000万+用户的7个渐进阶段，强调早期过度设计会浪费资源。核心原则是从简单入手，识别瓶颈，逐步扩容。用户范围和指标均为近似值，因为实际阈值会因产品、工作负载和流量模式而异。了解这一发展进程对应用程序构建者、系统设计面试候选人以及任何对大型系统感兴趣的人都有帮助。
阶段1：0-100用户（单服务器）
首要任务是推出产品并验证想法，而非进行优化。单台服务器同时托管Web应用、数据库和后台任务——这也是Instagram最初的架构设置。该方案的优势包括部署速度快、成本低（虚拟专用服务器每月20-50美元）、调试简便以及全栈可见性。 权衡取舍与扩容触发因素：峰值流量期间数据库运行缓慢、CPU/内存持续处于70-80%以上的使用率、部署停机、后台任务崩溃影响Web服务器，以及无法承受哪怕短暂的故障中断。
阶段2：100-10000用户（独立数据库服务器）
首次架构拆分将数据库与应用服务器分离，解决两者之间的资源竞争问题。该方案的优势包括资源隔离、独立扩容、更高的安全性（数据库部署在专用网络中）、针对性优化以及无中断备份。 大多数团队使用托管数据库（如亚马逊RDS、Supabase）以节省维护时间，但对于大规模成本优化、自定义配置、合规性或数据库产品开发，自行托管可能更受青睐。连接池（如PgBouncer）可减少连接开销，将大多数应用程序的效率提升3至5倍。其弊端是应用程序与数据库之间会产生轻微的网络延迟，这可以通过优化查询模式来缓解。
文章来源：https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users