[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-system-design-interviews-5-core-ideas-zh":3,"article-related-system-design-interviews-5-core-ideas-zh":35,"series-industry-3bc90ce2-80ef-4233-a9bb-a0476f2c606a":78},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":27,"views":31,"created_at":32,"published_at":33,"topic_cluster_id":34},"3bc90ce2-80ef-4233-a9bb-a0476f2c606a","system-design-interviews-5-core-ideas-zh","系統設計面試先懂這 5 個核心觀念","\u003Cp>系統設計面試到底先看哪 5 個觀念？\u003C\u002Fp>\u003Cp data-speakable=\"summary\">這篇把系統設計拆成 5 個核心觀念，幫你判斷擴充性、可靠性、效能與取捨。\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>你要先看什麼\u003C\u002Fth>\u003Cth>常見設計選擇\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>擴充性\u003C\u002Ftd>\u003Ctd>流量與資料成長\u003C\u002Ftd>\u003Ctd>垂直擴充、水平擴充\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>可靠性與可用性\u003C\u002Ftd>\u003Ctd>故障後能否持續服務\u003C\u002Ftd>\u003Ctd>備援、複寫、自動切換\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>一致性與分區容忍\u003C\u002Ftd>\u003Ctd>分散式環境下的資料正確性\u003C\u002Ftd>\u003Ctd>偏一致、偏可用\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>延遲與吞吐量\u003C\u002Ftd>\u003Ctd>單次回應時間與整體處理量\u003C\u002Ftd>\u003Ctd>p95、p99、QPS\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>快取、分片與通訊\u003C\u002Ftd>\u003Ctd>資料怎麼存、怎麼拆、怎麼傳\u003C\u002Ftd>\u003Ctd>Redis、Shard、REST、gRPC\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>1. 擴充性：先問系統能撐多大\u003C\u002Fh2>\u003Cp>擴充性是系統設計最先要回答的問題：使用者、資料或\u003Ca href=\"\u002Fnews\u002Famd-helios-lets-cerebras-widen-inference-throughput-zh\">流量\u003C\u002Fa>變多時，系統能不能跟上？這通常會拆成兩條路，\u003Ccode>vertical scaling\u003C\u002Fcode> 是把單台機器加強，\u003Ccode>horizontal scaling\u003C\u002Fcode> 是增加機器數量。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785007970878-aujo.png\" alt=\"系統設計面試先懂這 5 個核心觀念\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>面試時講到擴充性，不只是丟名詞，而是要說出容量規劃與成本取捨。單機方案比較直覺，但很快會碰到硬體上限；多機方案比較能長大，卻會引入分散式管理的複雜度。\u003C\u002Fp>\u003Cul>\u003Cli>垂直擴充：加 CPU、RAM、儲存空間\u003C\u002Fli>\u003Cli>水平擴充：在負載平衡器後面加更多伺服器\u003C\u002Fli>\u003Cli>彈性擴充：依需求自動增減資源\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>2. 可靠性與可用性：出問題時還能不能用\u003C\u002Fh2>\u003Cp>可靠性看的是系統能不能持續完成\u003Ca href=\"\u002Fnews\u002Fclaude-voice-mode-more-work-in-chat-zh\">工作\u003C\u002Fa>，可用性看的是使用者有多常連得上服務。這一項通常會帶出 \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fwhat-is\u002Fsla\u002F\">SLA\u003C\u002Fa>、SLO 和 SLI，因為團隊需要同一套語言來描述服務健康狀態。\u003C\u002Fp>\u003Cp>真正重要的是恢復能力。當單一節點、可用區或服務掛掉時，備援、複寫與自動切換能讓系統維持可用，而不是整片停擺。這也是為什麼架構圖不能只畫正常路徑，還要畫故障路徑。\u003C\u002Fp>\u003Cul>\u003Cli>SLI：你實際量測的指標，例如延遲\u003C\u002Fli>\u003Cli>SLO：你希望達到的目標，例如 99% 請求在 200ms 內\u003C\u002Fli>\u003Cli>SLA：對客戶承諾的服務條件\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>3. 一致性與分區容忍：分散式系統最難的取捨\u003C\u002Fh2>\u003Cp>只要系統分散到多台機器，就要面對網路分割、訊息延遲和過期讀取。這時候 \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCAP_theorem\">CAP theorem\u003C\u002Fa> 會變成最常被問的框架：在分區發生時，你通常得在一致性和可用性之間做選擇。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785007966300-yc9u.png\" alt=\"系統設計面試先懂這 5 個核心觀念\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>這一題很適合拿來看你是否真的懂業務需求。銀行帳本和社群動態牆不能用同一種答案，因為前者偏向資料正確，後者偏向服務不中斷。像 Cassandra、MongoDB 這類資料庫，選擇不同，就是因為工作負載不同。\u003C\u002Fp>\u003Ccode>CAP 取捨例子：\u003Cbr>- 銀行帳本：偏一致性\u003Cbr>- 社群動態牆：偏可用性\u003Cbr>- 網路分割：要能持續運作\u003C\u002Fcode>\u003Ch2>4. 延遲與吞吐量：快不快和撐不撐得住是兩件事\u003C\u002Fh2>\u003Cp>效能不是單一數字。延遲是單次請求花多久，吞吐量是系統每秒能處理多少請求。看效能時，平均值常常會騙人，所以更常看 p95、p99 這種尾端分位數。\u003C\u002Fp>\u003Cp>這一項會\u003Ca href=\"\u002Fnews\u002Fchatgpt-health-turns-chat-into-health-layer-zh\">直接\u003C\u002Fa>影響你怎麼評估架構。某個設計可能單次回應很快，但一旦流量上來就塞車；另一個設計能吃大量請求，卻因為每一跳都多了一點等待而變慢。真正的判斷方式，是同時看回應時間與總處理量。\u003C\u002Fp>\u003Cul>\u003Cli>延遲：單次請求的速度\u003C\u002Fli>\u003Cli>吞吐量：每秒請求數或查詢數\u003C\u002Fli>\u003Cli>分位數：p95、p99 看尾端表現\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>5. 快取、分片與服務通訊：把系統做大又做順\u003C\u002Fh2>\u003Cp>最後一組觀念，講的是實務上怎麼讓系統維持速度與可維護性。\u003Ca href=\"https:\u002F\u002Fredis.io\u002F\">Redis\u003C\u002Fa> 這類快取可以減少重複計算，分片把大資料拆到不同分區，負載平衡則避免單一主機變成瓶頸。\u003C\u002Fp>\u003Cp>服務之間怎麼溝通也很關鍵。\u003Ca href=\"https:\u002F\u002Frestfulapi.net\u002F\">REST\u003C\u002Fa> 適合公開 \u003Ca href=\"\u002Ftag\u002Fapi\">API\u003C\u002Fa>，\u003Ca href=\"https:\u002F\u002Fgrpc.io\u002F\">gRPC\u003C\u002Fa> 常用在內部服務呼叫，\u003Ca href=\"https:\u002F\u002Fgraphql.org\u002F\">GraphQL\u003C\u002Fa> 則讓前端只拿需要的資料。這一層決定資料怎麼流動，不只是資料放在哪裡。\u003C\u002Fp>\u003Cul>\u003Cli>快取模式：look-aside、write-through\u003C\u002Fli>\u003Cli>分片鍵：user ID、region、穩定的分區欄位\u003C\u002Fli>\u003Cli>通訊協定：REST、gRPC、GraphQL\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>哪種適合你\u003C\u002Fh2>\u003Cp>如果你剛開始學系統設計，先抓擴充性和可靠性，因為它們最能幫你建立架構的基本骨架。若你是在準備面試，就把重點放在 CAP 取捨與延遲、吞吐量，這兩類題目最能看出你是否真的理解限制。\u003C\u002Fp>\u003Cp>如果你是在做產品或後端系統，快取、分片與通訊方式會更值得花時間。這些選擇會直接影響成本、速度和長期維護，通常比換一個框架名稱更重要。\u003C\u002Fp>","5 個系統設計核心觀念，幫你在面試與實作中判斷擴充性、可靠性、效能與取捨。","www.systemdesignhandbook.com","https:\u002F\u002Fwww.systemdesignhandbook.com\u002Fguides\u002Fsystem-design\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785007970878-aujo.png","industry","zh","29a0b9c8-2b86-476f-8262-751d409c1928",[17,18,19,20,21,22,23,24,25,26],"系統設計","系統設計面試","擴充性","可靠性","CAP theorem","延遲","吞吐量","快取","分片","gRPC",[28,29,30],"先用擴充性與可靠性建立系統骨架，再談具體技術。","CAP、延遲與吞吐量是面試中最能看出取捨能力的題目。","快取、分片與通訊協定決定系統能否長期維持速度與成本。",0,"2026-07-25T19:32:26.369407+00:00","2026-07-25T19:32:26.358+00:00","01e2f7b5-5b52-46e7-9546-7e213d5f2654",{"tags":36,"relatedLang":37,"relatedPosts":41},[],{"id":15,"slug":38,"title":39,"language":40},"system-design-interviews-5-core-ideas-en","System design interviews get easier with 5 core ideas","en",[42,48,54,60,66,72],{"id":43,"slug":44,"title":45,"cover_image":46,"image_url":46,"created_at":47,"category":13},"811eab3e-0105-455c-b196-83d77bb29e52","google-q2-2026-results-ai-spend-story-zh","Google Q2 2026：AI支出已成估值主軸，不再只是搜尋故事","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785033163241-3n0s.png","2026-07-26T02:32:19.848016+00:00",{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"8a081a75-f194-4e1c-9289-a36ac221f048","ai-regulation-india-business-risk-2026-zh","印度 AI 監管已成商業風險","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785031373528-ak4r.png","2026-07-26T02:02:33.344117+00:00",{"id":55,"slug":56,"title":57,"cover_image":58,"image_url":58,"created_at":59,"category":13},"af446384-5a3b-4675-9df8-8797809f33fd","europe-should-standardise-ai-act-harmonised-rules-zh","歐洲該用統一技術標準落實 AI Act","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785029576170-tlby.png","2026-07-26T01:32:30.58516+00:00",{"id":61,"slug":62,"title":63,"cover_image":64,"image_url":64,"created_at":65,"category":13},"57d808f5-6a9c-4577-96c9-31ea907879ea","amd-anthropic-2gw-ai-capacity-deal-zh","AMD 與 Anthropic 的 2GW 交易，重寫 AI 供應","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785027761252-4vbd.png","2026-07-26T01:02:21.669601+00:00",{"id":67,"slug":68,"title":69,"cover_image":70,"image_url":70,"created_at":71,"category":13},"53d12771-e48a-42dd-a6ef-897634324360","openai-anthropic-dual-dominance-google-falls-behind-zh","OpenAI與Anthropic已進入雙雄時代，谷歌跌出第一梯隊","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785025977106-44rp.png","2026-07-26T00:32:30.875959+00:00",{"id":73,"slug":74,"title":75,"cover_image":76,"image_url":76,"created_at":77,"category":13},"aa7b45bd-f148-4b99-b960-ec0a3c30a88f","eu-ai-act-2026-omnibus-more-time-zh","EU AI Act 2026 Omnibus：4 個期限變動與新禁令","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785006169227-svpp.png","2026-07-25T19:02:25.666605+00:00",[79,84,89,94,99,104,109,114,119,124],{"id":80,"slug":81,"title":82,"created_at":83},"ee073da7-28b3-4752-a319-5a501459fb87","ai-in-2026-what-actually-matters-now-zh","2026 AI 真正重要的事","2026-03-26T07:09:12.008134+00:00",{"id":85,"slug":86,"title":87,"created_at":88},"83bd1795-8548-44c9-9a7e-de50a0923f71","trump-ai-framework-power-speech-state-preemption-zh","川普 AI 框架瞄準電力、言論與州權","2026-03-26T07:12:18.695466+00:00",{"id":90,"slug":91,"title":92,"created_at":93},"ea6be18b-c903-4e54-97b7-5f7447a612e0","nvidia-gtc-2026-big-ai-announcements-zh","NVIDIA GTC 2026 重點拆解","2026-03-26T07:14:26.62638+00:00",{"id":95,"slug":96,"title":97,"created_at":98},"4bcec76f-4c36-4daa-909f-54cd702f7c93","claude-users-spreading-out-and-getting-better-zh","Claude 用戶更分散，也更會用","2026-03-26T07:22:52.325888+00:00",{"id":100,"slug":101,"title":102,"created_at":103},"bd903b15-2473-4178-9789-b7557816e535","openclaw-raises-hard-question-for-ai-models-zh","OpenClaw 逼問 AI 模型價值","2026-03-26T07:24:54.707486+00:00",{"id":105,"slug":106,"title":107,"created_at":108},"eeac6b9e-ad9d-4831-8eec-8bba3f9bca6a","gap-google-gemini-checkout-fashion-search-zh","Gap 把結帳搬進 Gemini","2026-03-26T07:28:23.937768+00:00",{"id":110,"slug":111,"title":112,"created_at":113},"0740e53f-605d-4d57-8601-c10beb126f3c","google-pushes-gemini-transition-to-march-2026-zh","Google 把 Gemini 轉換延到 2026 年 3…","2026-03-26T07:30:12.825269+00:00",{"id":115,"slug":116,"title":117,"created_at":118},"e660d801-2421-4529-8fa9-86b82b066990","metas-llama-4-benchmark-scandal-gets-worse-zh","Meta Llama 4 分數風波又擴大","2026-03-26T07:34:21.156421+00:00",{"id":120,"slug":121,"title":122,"created_at":123},"183f9e7c-e143-40bb-a6d5-67ba84a3a8bc","accenture-mistral-ai-sovereign-enterprise-deal-zh","Accenture 攜手 Mistral AI 賣主權 AI","2026-03-26T07:38:14.818906+00:00",{"id":125,"slug":126,"title":127,"created_at":128},"191d9b1b-768a-478c-978c-dd7431a38149","mistral-ai-faces-its-hardest-year-yet-zh","Mistral AI 迎來最硬的一年","2026-03-26T07:40:23.716374+00:00"]