2171 lines
140 KiB
Markdown
2171 lines
140 KiB
Markdown
*[English](README.md) ∙ [日本語](README-ja.md) ∙ [简体中文](README-zh-Hans.md) ∙ [繁體中文](README-zh-TW.md) | [العَرَبِيَّة](https://github.com/donnemartin/system-design-primer/issues/170) ∙ [বাংলা](https://github.com/donnemartin/system-design-primer/issues/220) ∙ [Português do Brasil](https://github.com/donnemartin/system-design-primer/issues/40) ∙ [Deutsch](https://github.com/donnemartin/system-design-primer/issues/186) ∙ [ελληνικά](https://github.com/donnemartin/system-design-primer/issues/130) ∙ [עברית](https://github.com/donnemartin/system-design-primer/issues/272) ∙ [Italiano](https://github.com/donnemartin/system-design-primer/issues/104) ∙ [한국어](https://github.com/donnemartin/system-design-primer/issues/102) ∙ [فارسی](https://github.com/donnemartin/system-design-primer/issues/110) ∙ [Polski](https://github.com/donnemartin/system-design-primer/issues/68) ∙ [русский язык](https://github.com/donnemartin/system-design-primer/issues/87) ∙ [Español](https://github.com/donnemartin/system-design-primer/issues/136) ∙ [ภาษาไทย](https://github.com/donnemartin/system-design-primer/issues/187) ∙ [Türkçe](https://github.com/donnemartin/system-design-primer/issues/39) ∙ [tiếng Việt](https://github.com/donnemartin/system-design-primer/issues/127) ∙ [Français](https://github.com/donnemartin/system-design-primer/issues/250) | [Add Translation](https://github.com/donnemartin/system-design-primer/issues/28)*
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**Help [translate](TRANSLATIONS.md) this guide!**
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# Thiết Kế Hệ Thống Cơ Bản
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<p align="center">
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<img src="images/jj3A5N8.png">
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<br/>
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</p>
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## Động Lực
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> Học được cách thiết kế những hệ thống lớn.
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>
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> Chuẩn bị trước khi phỏng vấn thiết kế hệ thống.
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### Học cách thiết kế các hệ thống lớn
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Học cách thiết kế hệ thống scalable giúp bạn trở thành một kỹ sư tốt hơn.
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Thiết kế hệ thống là một chủ đề rộng. Có rất nhiều tài liệu rải rác trên web về các nguyên tắc thiết kế hệ thống.
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Repo này là góp nhặt từ nhiều nguồn tài liệu một cách có **sắp xếp* để giúp bạn học được cách xây dựng nên những hệ thống cả khả năng scale được.
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### Học hỏi từ cộng đồng mã nguồn mở
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This is a continually updated, open source project.
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Đây là dự án nguồn mỡ và liên tục được cập nhật
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[Contributions](#contributing) are welcome!
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[Đóng góp](#contributing) được chào đón!
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### Prep for the system design interview
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### Chuẩn bị cho phỏng vấn thiết kế hệ thống
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In addition to coding interviews, system design is a **required component** of the **technical interview process** at many tech companies.
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Bên cạnh phỏng vấn coding, thiết kế hệ thống là một **cấu thành quan trọng** trong **quy trình phỏng vấn kỹ thuật** ở rất nhiều công ty công nghệ.
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**Practice common system design interview questions** and **compare** your results with **sample solutions**: discussions, code, and diagrams.
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Additional topics for interview prep:
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Các chủ đều bổ sung để chuẩn bị phỏng vấn:
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* [Hướng dẫn học](#study-guide)
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* [How to approach a system design interview question](#how-to-approach-a-system-design-interview-question)
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* [Cách tiếp cận câu hỏi phỏng vấn thiết kế hệ thống](#how-to-approach-a-system-design-interview-question)
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* [System design interview questions, **with solutions**](#system-design-interview-questions-with-solutions)
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* [Các câu hỏi phỏng vấn thiết kế hệ thống, **với lời giải**](#system-design-interview-questions-with-solutions)
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* [Object-oriented design interview questions, **with solutions**](#object-oriented-design-interview-questions-with-solutions)
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* [Các câu hỏi phỏng vấn thiết kế hướng đối tượng, **với lời giải**](#object-oriented-design-interview-questions-with-solutions)
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* [Additional system design interview questions](#additional-system-design-interview-questions)
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* [Câu hỏi bổ sung thiết kế hệ thống](#additional-system-design-interview-questions)
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## Anki flashcards
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<p align="center">
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<img src="images/zdCAkB3.png">
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<br/>
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</p>
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The provided [Anki flashcard decks](https://apps.ankiweb.net/) use spaced repetition to help you retain key system design concepts.
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[Bộ Anki flashcard] dùng cách ôn tập giãn cách (spaced repetition) để giúp bạn ghi nhớ sâu hơn các khái niệm thiết kế hệ thống cốt lỗi.
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* [System design deck](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/System%20Design.apkg)
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* [Bộ card thiết kế hệ thống](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/System%20Design.apkg)
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* [System design exercises deck](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/System%20Design%20Exercises.apkg)
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* [Bộ card bài tập thiết kế hệ thống](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/System%20Design%20Exercises.apkg)
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* [Object oriented design exercises deck](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/OO%20Design.apkg)
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* [Bộ card thiết kế hướng đối tượng](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/OO%20Design.apkg)
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Great for use while on-the-go.
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Phù hợp để ôn trên đường.
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### Coding Resource: Interactive Coding Challenges
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### Tài liệu code: Tương tác (literal translation would be odd here)
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Looking for resources to help you prep for the [**Coding Interview**](https://github.com/donnemartin/interactive-coding-challenges)?
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Bạn đang tìm tài liệu để hỗ trợ cho [**Phỏng vấn Coding**](https://github.com/donnemartin/interactive-coding-challenges)
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<p align="center">
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<img src="images/b4YtAEN.png">
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<br/>
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</p>
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Check out the sister repo [**Interactive Coding Challenges**](https://github.com/donnemartin/interactive-coding-challenges), which contains an additional Anki deck:
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Hãy xem qua repo "em" [**Coding Tương Tác**](https://github.com/donnemartin/interactive-coding-challenges), repo này chứa những bộ Anki bổ sung sau:
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* [Coding deck](https://github.com/donnemartin/interactive-coding-challenges/tree/master/anki_cards/Coding.apkg)
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## Đóng góp
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> Học hỏi từ cộng đồng.
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Feel free to submit pull requests to help:
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Xin cứ tự nhiên tạo pull requests cho:
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* Fix errors
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* Improve sections
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* Add new sections
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* [Translate](https://github.com/donnemartin/system-design-primer/issues/28)
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* Sửa lỗi
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* Cải thiện các mục
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* Tạo các mục mới
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* [Phiên dịch](https://github.com/donnemartin/system-design-primer/issues/28)
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Content that needs some polishing is placed [under development](#under-development).
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Nội dung cần cải thiện được đặt ở [đang phát triển](#under-development)
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Review the [Contributing Guidelines](CONTRIBUTING.md).
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## Chỉ mục các chủ đề thiết kế hệ thống
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> Summaries of various system design topics, including pros and cons. **Everything is a trade-off**.
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> Tóm tắt một loạt chủ đề thiết kế hệ thống, gồm cả mặt mạnh và yếu. **Tất cả đều là đánh đổi**
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>
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> Each section contains links to more in-depth resources.
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> Mỗi một phân đoạn đều chứa liên kết đều nhiều tài liệu chuyên sâu hơn.
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<p align="center">
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<img src="images/jrUBAF7.png">
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<br/>
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</p>
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* [System design topics: start here](#system-design-topics-start-here)
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* [Các chủ đề thiết kế hệ thống: bắt đầu ở đây](#system-design-topics-start-here)
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* [Step 1: Review the scalability video lecture](#step-1-review-the-scalability-video-lecture)
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* [Bước 1: Xem qua các video bài giảng về scalability](#step-1-review-the-scalability-video-lecture)
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* [Step 2: Review the scalability article](#step-2-review-the-scalability-article)
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* [Bước 2: Xem qua các bài viết về scalability](#step-2-review-the-scalability-article)
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* [Next steps](#next-steps)
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* [Bước tiếp theo](#next-steps)
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* [Performance vs scalability](#performance-vs-scalability)
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* [Hiệu suất và scalability](#performance-vs-scalability)
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* [Latency vs throughput](#latency-vs-throughput)
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* [Độ trễ và công suất](#latency-vs-throughput)
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* [Availability vs consistency](#availability-vs-consistency)
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* [Tính khả dụng và tính nhất quán](#availability-vs-consistency)
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* [CAP theorem](#cap-theorem)
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* [Định lý CAP](#cap-theorem)
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* [CP - consistency and partition tolerance](#cp---consistency-and-partition-tolerance)
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* [CP - tính nhất quán và dung sai phân vùng](#cp---consistency-and-partition-tolerance)
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* [AP - availability and partition tolerance](#ap---availability-and-partition-tolerance)
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* [AP - tính khả dụng và dung sai phân vùng](#ap---availability-and-partition-tolerance)
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* [Consistency patterns](#consistency-patterns)
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* [Các mẫu hình về nhất quán](#consistency-patterns) # FIXME: bad translation
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* [Weak consistency](#weak-consistency)
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* [Nhất quán yếu](#weak-consistency) # FIXME: bad translation
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* [Eventual consistency](#eventual-consistency)
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* [Hậu nhất quán](#eventual-consistency) # FIXME: bad translation
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* [Nhất quán mạnh](#strong-consistency) # FIXME: bad translation
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* [Availability patterns](#availability-patterns)
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* [Các mẫu hình của tính khả dụng](#availability-patterns)
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* [Fail-over](#fail-over)
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* [Replication](#replication)
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* [Availability in numbers](#availability-in-numbers)
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* [Domain name system](#domain-name-system)
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* [Hệ thống tên miền](#domain-name-system)
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* [Content delivery network](#content-delivery-network)
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* [Push CDNs](#push-cdns)
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* [Pull CDNs](#pull-cdns)
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* [Load balancer](#load-balancer)
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* [Cân bằng tải](#load-balancer)
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* [Active-passive](#active-passive)
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* [Chủ động - bị động](#active-passive)
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* [Active-active](#active-active)
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* [Chủ động - chủ động](#active-active)
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* [Layer 4 load balancing](#layer-4-load-balancing)
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* [Cân bằng tải ở lớp 4](#layer-4-load-balancing)
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* [Layer 7 load balancing](#layer-7-load-balancing)
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* [Cân bằng tải ở lớp 7](#layer-7-load-balancing)
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* [Horizontal scaling](#horizontal-scaling)
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* [Mở rộng bề ngang](#horizontal-scaling) # FIXME: bad translation
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* [Reverse proxy (web server)](#reverse-proxy-web-server)
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* [Load balancer vs reverse proxy](#load-balancer-vs-reverse-proxy)
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* [Application layer](#application-layer)
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* [Microservices](#microservices)
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* [Service discovery](#service-discovery)
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* [Database](#database)
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* [Cơ sở dữ liệu](#database)
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* [Relational database management system (RDBMS)](#relational-database-management-system-rdbms)
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* [Cơ sở dữ liệu quan hệ (RDBMS)](#relational-database-management-system-rdbms)
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* [Master-slave replication](#master-slave-replication)
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* [Tái tạo Master-slaver](#master-slave-replication) # FIXME: bad translation
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* [Master-master replication](#master-master-replication)
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* [Tái tạo master-master](#master-master-replication)
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* [Federation](#federation)
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* [Liên đoàn](#federation)
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* [Sharding](#sharding)
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* [Phân mảnh](#sharding)
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* [Denormalization](#denormalization)
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* [Phản tiêu chuẩn](#demonomalization) # FIXME: really bad translation
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* [SQL tuning](#sql-tuning)
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* [Tinh chỉnh SQL](#sql-tuning)
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* [NoSQL](#nosql)
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* [NoSQL](#nosql)
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* [Key-value store](#key-value-store)
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* [Kho khoá-trị](#key-value-store)
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* [Document store](#document-store)
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* [Kho tài liệu](#document-store) # FIXME: bad translation
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* [Wide column store](#wide-column-store)
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* [Kho cột rộng](#wide-column-store)
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* [Graph Database](#graph-database)
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* [Cơ sở dữ liệu đồ thị](#graph-database)
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* [SQL or NoSQL](#sql-or-nosql)
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* [SQL hay NoSQL](#sql-or-nosql)
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* [Cache](#cache)
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* [Cache](#cache)
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* [Client caching](#client-caching)
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* [CDN caching](#cdn-caching)
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* [Web server caching](#web-server-caching)
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* [Database caching](#database-caching)
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* [Application caching](#application-caching)
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* [Caching at the database query level](#caching-at-the-database-query-level)
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* [Caching at the object level](#caching-at-the-object-level)
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* [When to update the cache](#when-to-update-the-cache)
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* [Cache-aside](#cache-aside)
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* [Write-through](#write-through)
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* [Write-behind (write-back)](#write-behind-write-back)
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* [Refresh-ahead](#refresh-ahead)
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* [Asynchronism](#asynchronism)
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* [Bất đồng bộ]
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* [Message queues](#message-queues)
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* [Hàng đợi tin](#message-queues)
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* [Task queues](#task-queues)
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* [Hàng đợi việc](#task-queues)
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* [Back pressure](#back-pressure)
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* [Giảm tải nghịch](#back-pressure) # FIXME: probably bad translation
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* [Communication](#communication)
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* [Giao tiếp](#communication)
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* [Transmission control protocol (TCP)](#transmission-control-protocol-tcp)
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* [User datagram protocol (UDP)](#user-datagram-protocol-udp)
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* [Remote procedure call (RPC)](#remote-procedure-call-rpc)
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* [Representational state transfer (REST)](#representational-state-transfer-rest)
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* [Security](#security)
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* [An ninh](#security)
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* [Appendix](#appendix)
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* [Phụ lục](#security)
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* [Bảng bình phương](#powers-of-two-table)
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* [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know)
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* [Additional system design interview questions](#additional-system-design-interview-questions)
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* [Real world architectures](#real-world-architectures)
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* [Company architectures](#company-architectures)
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* [Company engineering blogs](#company-engineering-blogs)
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* [Under development](#under-development)
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* [Đang phát triển](#under-development)
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* [Credits](#credits)
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* [Contact info](#contact-info)
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* [License](#license)
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## Hướng dẫn học
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> Suggested topics to review based on your interview timeline (short, medium, long).
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> Các chủ đề được đề xuất để ôn tập dựa vào lịch trình phỏng vấn (ngắn, vừa, dài)
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**Hỏi: Để cho việc phỏng vấn thì tôi có cần biết tất cả mọi thứ ở đây?**
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**Đáp: Không, bạn không cần biết tất cả mọi thứ ở đây để chuẩn bị cho phỏng vấn.**
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What you are asked in an interview depends on variables such as:
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Những gì bạn được hỏi trong buổi phỏng vấn sẽ tuỳ thuộc vào những yếu tố sau:
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* How much experience you have
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* What your technical background is
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* What positions you are interviewing for
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* Which companies you are interviewing with
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* Luck
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* Bạn có bao lâu kinh nghiệm
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* Nền kiến thức kỹ thuật của bạn là gì
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* Vị trí mà bạn phỏng vấn vào
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* Bạn phỏng vấn với công ty nào
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* May mắn
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More experienced candidates are generally expected to know more about system design. Architects or team leads might be expected to know more than individual contributors. Top tech companies are likely to have one or more design interview rounds.
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Ứng cử viên càng có bề dày kinh nghiệm thì càng được mong dợi là sẽ biết nhiều hơn về thiết kế hệ thống. Architects hoặc team leads có được mong là biết nhiều hơn những cá nhân khác. Các công ty hàng đầu thì khả năng cao là có nhiều phỏng vấn về thiết kế hơn.
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Start broad and go deeper in a few areas. It helps to know a little about various key system design topics. Adjust the following guide based on your timeline, experience, what positions you are interviewing for, and which companies you are interviewing with.
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Bắt đầu ở bề rộng và đi sâu hơn vào từng vùng. Biết một chút cho nhiều chủ đề khác nhau thì thường sẽ giúp ít bạn hơn. Điều chỉnh những hướng dẫn trong đây tùy vào thời gian, kinh nghiệm, và vị trí nào mà bạn đang phỏng vấn vào, và công ty nào mà bạn phỏng vấn với.
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* **Short timeline** - Aim for **breadth** with system design topics. Practice by solving **some** interview questions.
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* **Medium timeline** - Aim for **breadth** and **some depth** with system design topics. Practice by solving **many** interview questions.
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* **Long timeline** - Aim for **breadth** and **more depth** with system design topics. Practice by solving **most** interview questions.
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* **Thời gian ngắn** - Nhắm vào bề rộng của các chủ đề thiết kế hệ thống. Tập luyện bằng cách giải quyết **một số** câu hỏi phỏng vấn.
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* **Thời gian trung bình** - Nhắm vào bề rộng và một chút phần sâu với các chủ đều thiết kế hệ thống. Tập luyện giải quyết **nhiều** câu hỏi phỏng vấn.
|
||
* ** Thời gian dài** - Nhắm vào bề rộng và nhiều phần sâu với các chủ đề thiết ké hệ thống. Tập luyện giải quyết **hầu hết** các câu hỏi phỏng vấn.
|
||
|
||
| | Short | Medium | Long |
|
||
|---|---|---|---|
|
||
| Read through the [System design topics](#index-of-system-design-topics) to get a broad understanding of how systems work | :+1: | :+1: | :+1: |
|
||
| Read through a few articles in the [Company engineering blogs](#company-engineering-blogs) for the companies you are interviewing with | :+1: | :+1: | :+1: |
|
||
| Read through a few [Real world architectures](#real-world-architectures) | :+1: | :+1: | :+1: |
|
||
| Review [How to approach a system design interview question](#how-to-approach-a-system-design-interview-question) | :+1: | :+1: | :+1: |
|
||
| Work through [System design interview questions with solutions](#system-design-interview-questions-with-solutions) | Some | Many | Most |
|
||
| Work through [Object-oriented design interview questions with solutions](#object-oriented-design-interview-questions-with-solutions) | Some | Many | Most |
|
||
| Review [Additional system design interview questions](#additional-system-design-interview-questions) | Some | Many | Most |
|
||
|
||
## How to approach a system design interview question
|
||
## Cách tiếp cận câu hỏi phỏng vấn thiết kế hệ thống
|
||
|
||
> How to tackle a system design interview question.
|
||
> Cách xử trí một câu hỏi thiết kế hệ thống.
|
||
|
||
The system design interview is an **open-ended conversation**. You are expected to lead it.
|
||
Phỏng vấn thiết kế hệ thống là đối thoại mở. Bạn được trông đợi là người dẫn dắt.
|
||
|
||
You can use the following steps to guide the discussion. To help solidify this process, work through the [System design interview questions with solutions](#system-design-interview-questions-with-solutions) section using the following steps.
|
||
Bạn có thể dùng những bước sao để lái cuộc thảo luận. Để củng cố, hãy đi qua phần [Các câu hỏi phỏng vấn thiết kế hệ thống kèm lời giải](#system-design-interview-questions-with-solutions) sử dụng các bước sau.
|
||
|
||
### Step 1: Outline use cases, constraints, and assumptions
|
||
### Bước 1: Phác thảo các trường hợp sử dụng, ràng buộc, và các giả định
|
||
|
||
Gather requirements and scope the problem. Ask questions to clarify use cases and constraints. Discuss assumptions.
|
||
Thu thập nhu cầu và phạm vi của vấn đề. Đặt các câu hỏi làm rõ các nhu cầu sử dụng và ràng buộc. Bàn bạc các giả định.
|
||
|
||
* Who is going to use it?
|
||
* How are they going to use it?
|
||
* How many users are there?
|
||
* What does the system do?
|
||
* What are the inputs and outputs of the system?
|
||
* How much data do we expect to handle?
|
||
* How many requests per second do we expect?
|
||
* What is the expected read to write ratio?
|
||
|
||
* Ai là người sử dụng?
|
||
* Họ sử dụng với cách nào?
|
||
* Có bao nhiêu người dùng?
|
||
* Hệ thống này làm gì?
|
||
* Đầu vào và đ
|
||
|
||
### Step 2: Create a high level design
|
||
|
||
Outline a high level design with all important components.
|
||
|
||
* Sketch the main components and connections
|
||
* Justify your ideas
|
||
|
||
### Step 3: Design core components
|
||
### Bước 3: Thiết kế các thành phần lõi
|
||
|
||
Dive into details for each core component. For example, if you were asked to [design a url shortening service](solutions/system_design/pastebin/README.md), discuss:
|
||
|
||
|
||
* Generating and storing a hash of the full url
|
||
* [MD5](solutions/system_design/pastebin/README.md) and [Base62](solutions/system_design/pastebin/README.md)
|
||
* Hash collisions
|
||
* SQL or NoSQL
|
||
* Database schema
|
||
* Translating a hashed url to the full url
|
||
* Database lookup
|
||
* API and object-oriented design
|
||
|
||
### Step 4: Scale the design
|
||
### Bước 4: "Scale" thiết kế
|
||
|
||
Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues?
|
||
Nhận diện và xử lý cổ chai, với các ràng buộc được đưa ra. Ví du, bạn có cần những thứ sau để giải quyết vấn đề về scalability?
|
||
|
||
* Load balancer
|
||
* Horizontal scaling
|
||
* Caching
|
||
* Database sharding
|
||
|
||
* Cân bằng tải
|
||
* Scale ngang
|
||
* Caching
|
||
* Database sharding # FIXME: I don't know how to translate this
|
||
|
||
Discuss potential solutions and trade-offs. Everything is a trade-off. Address bottlenecks using [principles of scalable system design](#index-of-system-design-topics).
|
||
Thảo luận các phương án khả thi và đánh đổi. Mọi thứ đều là đánh đổi. Giải quyết cổ chai bằng cách dùng [các nguyên tắc của thiết kế hệ thống scalable](#index-of-system-design-topics)
|
||
|
||
### Back-of-the-envelope calculations
|
||
### Tính toán nháp
|
||
|
||
You might be asked to do some estimates by hand. Refer to the [Appendix](#appendix) for the following resources:
|
||
Bạn có thể được yêu cầu làm một số ước lượng bằng tay. Tham khảo phần [Phụ lục](#appendix) cho một số tài liệu sau:
|
||
|
||
* [Use back of the envelope calculations](http://highscalability.com/blog/2011/1/26/google-pro-tip-use-back-of-the-envelope-calculations-to-choo.html)
|
||
* [Bảng bình phương](#powers-of-two-table)
|
||
* [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know)
|
||
|
||
* [Tính toán nháp](http://highscalability.com/blog/2011/1/26/google-pro-tip-use-back-of-the-envelope-calculations-to-choo.html)
|
||
* [Bảng bình phương](#powers-of-two-table)
|
||
* [Những độ trễ mà mọi lập trình viên nên biết](#latency-numbers-every-programmer-should-know)
|
||
|
||
### Source(s) and further reading
|
||
|
||
Check out the following links to get a better idea of what to expect:
|
||
|
||
* [How to ace a systems design interview](https://www.palantir.com/2011/10/how-to-rock-a-systems-design-interview/)
|
||
* [The system design interview](http://www.hiredintech.com/system-design)
|
||
* [Intro to Architecture and Systems Design Interviews](https://www.youtube.com/watch?v=ZgdS0EUmn70)
|
||
* [System design template](https://leetcode.com/discuss/career/229177/My-System-Design-Template)
|
||
|
||
## Các câu hỏi phỏng vấn thiết kế hệ thống kèm lời giải
|
||
|
||
> Common system design interview questions with sample discussions, code, and diagrams.
|
||
> Các câu hỏi phỏng vấn thiết kế hệ thống phổ biến kèm với các đối thoại mẫu, mã nguồn, và biểu đồ.
|
||
>
|
||
> Solutions linked to content in the `solutions/` folder.
|
||
> Đường dẫn bài giải nằm trong thư mục `solutions/`.
|
||
|
||
| Question | |
|
||
|---|---|
|
||
| Design Pastebin.com (or Bit.ly) | [Solution](solutions/system_design/pastebin/README.md) |
|
||
| Design the Twitter timeline and search (or Facebook feed and search) | [Solution](solutions/system_design/twitter/README.md) |
|
||
| Design a web crawler | [Solution](solutions/system_design/web_crawler/README.md) |
|
||
| Design Mint.com | [Solution](solutions/system_design/mint/README.md) |
|
||
| Design the data structures for a social network | [Solution](solutions/system_design/social_graph/README.md) |
|
||
| Design a key-value store for a search engine | [Solution](solutions/system_design/query_cache/README.md) |
|
||
| Design Amazon's sales ranking by category feature | [Solution](solutions/system_design/sales_rank/README.md) |
|
||
| Design a system that scales to millions of users on AWS | [Solution](solutions/system_design/scaling_aws/README.md) |
|
||
| Add a system design question | [Contribute](#contributing) |
|
||
|
||
|
||
| Câu hỏi | |
|
||
|---|---|
|
||
| Thiết kế Pastebin.com (or Bit.ly) | [Lời giải](solutions/system_design/pastebin/README.md) |
|
||
| Thiết kế Twitter timeline và tìm kiếm (or Facebook feed and tìm kiếm) | [Lời giải](solutions/system_design/twitter/README.md) |
|
||
| Thiết kế một web crawler | [Lời giải](solutions/system_design/web_crawler/README.md) |
|
||
| Thiết kế Mint.com | [Lời giải](solutions/system_design/mint/README.md) |
|
||
| Thiết kế the data structures for a social network | [Lời giải](solutions/system_design/social_graph/README.md) |
|
||
| Thiết kế a key-value store for a tìm kiếm engine | [Lời giải](solutions/system_design/query_cache/README.md) |
|
||
| Thiết kế Amazon's sales ranking by category feature | [Lời giải](solutions/system_design/sales_rank/README.md) |
|
||
| Thiết kế a system that scales to millions of users on AWS | [Lời giải](solutions/system_design/scaling_aws/README.md) |
|
||
| Add a system design question | [Contribute](#contributing) |
|
||
|
||
|
||
### Design Pastebin.com (or Bit.ly)
|
||
### Thiết kế Pastebin.com (hoặc Bit.ly)
|
||
|
||
[View exercise and solution](solutions/system_design/pastebin/README.md)
|
||
[Xem bài tập và bài giải](solutions/system_design/pastebin/README.md)
|
||
|
||
|
||
|
||
### Design the Twitter timeline and search (or Facebook feed and search)
|
||
### Thiết kế Twitter timeline và tìm kiếm (hoặc Facebook feed và tìm kiếm)
|
||
|
||
[Xem bài tập và bài giải](solutions/system_design/twitter/README.md)
|
||
|
||
|
||
|
||
### Thiết kế một web crawler
|
||
|
||
[View exercise and solution](solutions/system_design/web_crawler/README.md)
|
||
[](solutions/system_design/web_crawler/README.md)
|
||
|
||
|
||
|
||
### Thiết kế Mint.com
|
||
|
||
[View exercise and solution](solutions/system_design/mint/README.md)
|
||
[Xem bài tập và bài giải](solutions/system_design/mint/README.md)
|
||
|
||
|
||
|
||
### Thiết kế các cấu trúc dữ liệu cho một mạng xã hội
|
||
|
||
[Xem bài tập và bài giải](solutions/system_design/social_graph/README.md)
|
||
|
||
|
||
|
||
### Design a key-value store for a search engine
|
||
### Thiết kế một kho khoá-giá trị (key-value store) phục vụ một máy tìm kiếm (search engine)
|
||
|
||
[Xem bài tập và bài giải](solutions/system_design/query_cache/README.md)
|
||
|
||
|
||
|
||
### Design Amazon's sales ranking by category feature
|
||
### Thiết kế bảng xếp hạng bán hàng theo loại cho Amazon.
|
||
|
||
[Xem bài tập và bài giải](solutions/system_design/sales_rank/README.md
|
||
|
||
|
||
|
||
### Design a system that scales to millions of users on AWS
|
||
### Thiết kế một hệ thống có thể scale đến hàng triệu người dùng trên AWS
|
||
|
||
[Xem bài tập và bài giải](solutions/system_design/scaling_aws/README.md)
|
||
|
||
|
||
|
||
## Object-oriented design interview questions with solutions
|
||
## Các câu hỏi về thiết kế hướng đối với bài giải
|
||
|
||
> Common object-oriented design interview questions with sample discussions, code, and diagrams.
|
||
>
|
||
> Solutions linked to content in the `solutions/` folder.
|
||
|
||
> Các câu hỏi phỏng vấn phổ biến về thiết kế hướng đối tượng với các thảo luận mẫu, code, và biểu đồ.
|
||
>
|
||
> Bài giải với các nội dung liên kết nằm trong thư mục `solutions/`
|
||
|
||
>**Note: This section is under development**
|
||
>**Chú thích: Phần này vẫn đang được phát triển**
|
||
|
||
| Question | |
|
||
|---|---|
|
||
| Design a hash map | [Solution](solutions/object_oriented_design/hash_table/hash_map.ipynb) |
|
||
| Design a least recently used cache | [Solution](solutions/object_oriented_design/lru_cache/lru_cache.ipynb) |
|
||
| Design a call center | [Solution](solutions/object_oriented_design/call_center/call_center.ipynb) |
|
||
| Design a deck of cards | [Solution](solutions/object_oriented_design/deck_of_cards/deck_of_cards.ipynb) |
|
||
| Design a parking lot | [Solution](solutions/object_oriented_design/parking_lot/parking_lot.ipynb) |
|
||
| Design a chat server | [Solution](solutions/object_oriented_design/online_chat/online_chat.ipynb) |
|
||
| Design a circular array | [Contribute](#contributing) |
|
||
| Add an object-oriented design question | [Contribute](#contributing) |
|
||
|
||
|
||
| Câu hỏi | |
|
||
|---|---|
|
||
| Thiết kế một hash map | [Solution](solutions/object_oriented_design/hash_table/hash_map.ipynb) |
|
||
| Thiết kế một LRU cache | [Solution](solutions/object_oriented_design/lru_cache/lru_cache.ipynb) |
|
||
| Thiết kế một tổng đài | [Solution](solutions/object_oriented_design/call_center/call_center.ipynb) |
|
||
| Thiết kế một bộ bài | [Solution](solutions/object_oriented_design/deck_of_cards/deck_of_cards.ipynb) |
|
||
| Thiết kế một bãi đổ xe | [Solution](solutions/object_oriented_design/parking_lot/parking_lot.ipynb) |
|
||
| Thiết kế một server cho chat | [Solution](solutions/object_oriented_design/online_chat/online_chat.ipynb) |
|
||
| Thiết kế một mảng vòng | [Contribute](#contributing) |
|
||
| Bổ sung một câu hỏi về thiết kế hướng đối tượng | [Contribute](#contributing) |
|
||
|
||
## System design topics: start here
|
||
|
||
New to system design?
|
||
|
||
First, you'll need a basic understanding of common principles, learning about what they are, how they are used, and their pros and cons.
|
||
|
||
### Step 1: Review the scalability video lecture
|
||
|
||
[Scalability Lecture at Harvard](https://www.youtube.com/watch?v=-W9F__D3oY4)
|
||
|
||
* Topics covered:
|
||
* Vertical scaling
|
||
* Horizontal scaling
|
||
* Caching
|
||
* Load balancing
|
||
* Database replication
|
||
* Database partitioning
|
||
* Các chủ đề bao gồm:
|
||
* Vertical scaling
|
||
* Horizontal scaling
|
||
* Caching
|
||
* Cân bằng tải
|
||
* Bản sao cơ sở dữ liệu
|
||
* Phân vùng cơ sở dữ liệu
|
||
|
||
### Step 2: Review the scalability article
|
||
### Bước 2: Xem qua các bài báo về scalability
|
||
|
||
[Scalability](http://www.lecloud.net/tagged/scalability/chrono)
|
||
|
||
* Topics covered:
|
||
* [Clones](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
|
||
* [Databases](http://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database)
|
||
* [Caches](http://www.lecloud.net/post/9246290032/scalability-for-dummies-part-3-cache)
|
||
* [Asynchronism](http://www.lecloud.net/post/9699762917/scalability-for-dummies-part-4-asynchronism)
|
||
|
||
### Next steps
|
||
|
||
Next, we'll look at high-level trade-offs:
|
||
|
||
* **Performance** vs **scalability**
|
||
* **Latency** vs **throughput**
|
||
* **Availability** vs **consistency**
|
||
|
||
Keep in mind that **everything is a trade-off**.
|
||
Nên nhớ rằng **mọi thứ đều là đánh đổi**
|
||
|
||
Then we'll dive into more specific topics such as DNS, CDNs, and load balancers.
|
||
Sau đó chung ta sẽ đi sâu vào từng chủ đề cụ thể như là DNS, CDNs và cân bằng tải.
|
||
|
||
## Performance vs scalability
|
||
## Hiệu năng và tính mở rộng
|
||
|
||
A service is **scalable** if it results in increased **performance** in a manner proportional to resources added. Generally, increasing performance means serving more units of work, but it can also be to handle larger units of work, such as when datasets grow.<sup><a href=http://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html>1</a></sup>
|
||
Một dịch vụ có tính mở rộng nếu đạt tăng được hiệu năng tỷ lệ thuận với tài nguyên được thêm vào. Tăng hiệu năng đồng nghĩa với phục vụ được nhiều đơn vị công việc hơn, nhưng cũng có nghĩa là xử lý được công việc lớn hơn, chẳng hạn như dataset tăng.<sup><a href=http://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html>1</a></sup>
|
||
|
||
Another way to look at performance vs scalability:
|
||
Một cách nhìn khác về hiệu năng và tính mở rộng:
|
||
|
||
* If you have a **performance** problem, your system is slow for a single user.
|
||
* Nếu bạn có vấn đề về **hiệu năng**, hệ thống của bạn chậm cho một người dùng.
|
||
* If you have a **scalability** problem, your system is fast for a single user but slow under heavy load.
|
||
* Nếu bạn có vấn đề về *tính mở rộng*, hệ thống của bạn nhanh cho một người dùng nhưng chậm khi chịu tải nhiều.
|
||
|
||
### Source(s) and further reading
|
||
|
||
* [A word on scalability](http://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html)
|
||
* [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
|
||
|
||
## Latency vs throughput
|
||
## Độ trễ và lưu lượng
|
||
|
||
**Latency** is the time to perform some action or to produce some result.
|
||
**Độ trễ** là thời gian thực hiện một tác vụ và cho ra kết quả.
|
||
|
||
**Throughput** is the number of such actions or results per unit of time.
|
||
**Lưu lượng** là số lượng tác vụ và kết quả trong một đơn vị thời gian.
|
||
|
||
Generally, you should aim for **maximal throughput** with **acceptable latency**.
|
||
Nhìn chung là bận cần nhắm vào **tối đa hoá lưu lượng** với **độ trễ ở mức chấp nhận**
|
||
|
||
### Source(s) and further reading
|
||
### Nguồn đọc thêm
|
||
|
||
* [Hiểu về độ trễ và lưu lượng](https://community.cadence.com/cadence_blogs_8/b/sd/archive/2010/09/13/understanding-latency-vs-throughput)
|
||
|
||
## Tính khả dụng so với tính nhất quán
|
||
|
||
### Định lý CAP
|
||
|
||
<p align="center">
|
||
<img src="images/bgLMI2u.png">
|
||
<br/>
|
||
<i><a href=http://robertgreiner.com/2014/08/cap-theorem-revisited>Nguồn: CAP theorem revisited</a></i>
|
||
</p>
|
||
|
||
In a distributed computer system, you can only support two of the following guarantees:
|
||
Trong một hệ thống máy tính phân tán, ta chỉ có thể hỗ trợ hai trong số những bảo toàn sau đây:
|
||
|
||
* **Consistency** - Every read receives the most recent write or an error
|
||
* **Availability** - Every request receives a response, without guarantee that it contains the most recent version of the information
|
||
* **Partition Tolerance** - The system continues to operate despite arbitrary partitioning due to network failures
|
||
|
||
* **Tính nhất quán** - Mọi thao tác đọc nhận được kết quả ghi gần nhất hoặc lỗi
|
||
* **Tính khả dụng** - Mọi yêu cầu đều nhận được hồi đáp, nhưng không bảo đảm là chứa phiên bản gần nhất.
|
||
* **Dung sai phân vùng** - Hệ thống tiếp tục hoạt động mặc dù bị phân vùng do lỗi mạng lưới.
|
||
|
||
*Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability.*
|
||
|
||
#### CP - tính nhất quán và dung sai phân vùng
|
||
|
||
Waiting for a response from the partitioned node might result in a timeout error. CP is a good choice if your business needs require atomic reads and writes.
|
||
Việc đợi phản hồi từ một node bị phân vùng (mạng) có thể mang lại kết quả lỗi timeout. CP là một lựa chọn tốt nếu nhu cầu đòi hỏi đọc và ghi phải atomic.
|
||
|
||
#### AP - availability and partition tolerance
|
||
#### AP - availability và partition tolerance
|
||
|
||
Responses return the most readily available version of the data available on any node, which might not be the latest. Writes might take some time to propagate when the partition is resolved.
|
||
Các câu trả lời trả về phiên bản đang có của dữ liệu trên node, có thể không phải là bản mới nhất. Việc ghi có thể mất thời gian để lan truyền khi các partition được phân giải. # FIXME: "phân giải" doesn't sound right.
|
||
|
||
AP is a good choice if the business needs allow for [eventual consistency](#eventual-consistency) or when the system needs to continue working despite external errors.
|
||
AP là một lựa chọn tốt cho các nhu cầu cần [eventual consistency](#eventual-consistency) hoặc các hệ thống cần tiếp tục vận hành khi đối mặt với các lỗi hỏng bên ngoài.
|
||
|
||
### Source(s) and further reading
|
||
### Các nguồn tài liệu đọc thêm
|
||
|
||
* [CAP theorem revisited](http://robertgreiner.com/2014/08/cap-theorem-revisited/)
|
||
* [A plain english introduction to CAP theorem](http://ksat.me/a-plain-english-introduction-to-cap-theorem)
|
||
* [CAP FAQ](https://github.com/henryr/cap-faq)
|
||
* [The CAP theorem](https://www.youtube.com/watch?v=k-Yaq8AHlFA)
|
||
|
||
## Consistency patterns
|
||
## Các khuôn mẫu về tính nhất quán
|
||
|
||
With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the [CAP theorem](#cap-theorem) - Every read receives the most recent write or an error.
|
||
Với nhiều phiên bản của cùng một dữ liệu, chúng ta đối mặt với những cách thức đồng bộ để các client nhìn dữ liệu một cách nhất quán. Gợi nhớ lại định nghĩa của tính nhất quán [Định lý CAP](#cap-theorem) - Mọi hành vi đọc nhận được phiên bản ghi gần nhất hoặc là một lỗi.
|
||
|
||
### Weak consistency
|
||
### Nhất quán kém
|
||
|
||
After a write, reads may or may not see it. A best effort approach is taken.
|
||
Sau một sự kiện ghi, các lần đọc sau có thể hoặc không thấy được sự thay đổi. Phương cách "tốt nhất có thể" được áp dụng. # FIXME: bad translation
|
||
|
||
This approach is seen in systems such as memcached. Weak consistency works well in real time use cases such as VoIP, video chat, and realtime multiplayer games. For example, if you are on a phone call and lose reception for a few seconds, when you regain connection you do not hear what was spoken during connection loss.
|
||
Phương cách này thấy áp dụng ở nhiều hệ thống như memcached. Nhất quán kém làm việc tốt trong cách nhu cầu cần thời gian thực như VoIP, video chat, và game thời gian thực nhiều người chơi. Ví dụ nếu trong một cuộc gọi, ta bị mất tín hiệu một vài giây, khi lấy lại được kết nối thì bạn không nghe lại cuộc nói chuyện trong quá trình đứt kết nối.
|
||
|
||
### Eventual consistency
|
||
|
||
After a write, reads will eventually see it (typically within milliseconds). Data is replicated asynchronously.
|
||
Sau một sự kiện ghi, các lần đọc sau sẽ dần thấy được (thường thì trong một vài milli giây). Dự liệu được tái tạo một cách bất đồng bộ.
|
||
|
||
This approach is seen in systems such as DNS and email. Eventual consistency works well in highly available systems.
|
||
Cách tiếp cận này được thấy trong các hệ thống như là DNS hay email. Eventual consistency làm việc tốt trong các hệ thống với availability cao.
|
||
|
||
### Strong consistency
|
||
|
||
After a write, reads will see it. Data is replicated synchronously.
|
||
Sau một sự kiện ghi, các lần đọc sau sẽ (chắc chắn) thấy. Dữ liệu được tái tạo một cách đồng bộ.
|
||
|
||
This approach is seen in file systems and RDBMSes. Strong consistency works well in systems that need transactions.
|
||
Cách tiếp cận này được thấy trong file systems và RDBMS. Strong consistency làm việc tốt trong các hệ thống cần transactions.
|
||
|
||
### Source(s) and further reading
|
||
|
||
* [Transactions across data centers](http://snarfed.org/transactions_across_datacenters_io.html)
|
||
|
||
## Availability patterns
|
||
## Các mẫu hình về tính khả dụng
|
||
|
||
There are two complementary patterns to support high availability: **fail-over** and **replication**.
|
||
Có hai mẫu hình bổ sung lẫn nhau hỗ trợ khả năng khả dụng cao: **fail-over** và **bản tái tạo*** # FIXME: bad translation
|
||
|
||
### Fail-over
|
||
|
||
#### Active-passive
|
||
#### Chủ động - bị động
|
||
|
||
With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active's IP address and resumes service.
|
||
Với fail-over chủ động - bị động, nhịp tim (heartbeat) sẽ được gửi giữa máy chủ chủ động và máy chủ bị động trực chờ. Nếu nhịp tim bị ngắt quãng, máy chủ bị động sẽ tiếp quản địa chỉ IP của máy chủ động và tiếp tục dịch vụ.
|
||
|
||
The length of downtime is determined by whether the passive server is already running in 'hot' standby or whether it needs to start up from 'cold' standby. Only the active server handles traffic.
|
||
Độ dài của thời gian dừng sẽ được quyết định bởi việc máy chủ bị động có đang chạy ở chế độ trực chờ sẵn sàng ('hot' standby) hay cần phải được khởi động ('cold' standby). Chỉ duy nhất máy chủ chủ động xử lí liên lạc. # FIXME: dull translation
|
||
|
||
Active-passive failover can also be referred to as master-slave failover.
|
||
Failover chủ động - bị động còn được biết dưới tên failover master-slave.
|
||
|
||
#### Active-active
|
||
|
||
In active-active, both servers are managing traffic, spreading the load between them.
|
||
|
||
If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers.
|
||
|
||
Active-active failover can also be referred to as master-master failover.
|
||
|
||
### Disadvantage(s): failover
|
||
|
||
* Fail-over adds more hardware and additional complexity.
|
||
* There is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive.
|
||
|
||
### Replication
|
||
|
||
#### Master-slave and master-master
|
||
|
||
This topic is further discussed in the [Database](#database) section:
|
||
|
||
* [Master-slave replication](#master-slave-replication)
|
||
* [Master-master replication](#master-master-replication)
|
||
|
||
### Availability in numbers
|
||
|
||
Availability is often quantified by uptime (or downtime) as a percentage of time the service is available. Availability is generally measured in number of 9s--a service with 99.99% availability is described as having four 9s.
|
||
|
||
#### 99.9% availability - three 9s
|
||
|
||
| Duration | Acceptable downtime|
|
||
|---------------------|--------------------|
|
||
| Downtime per year | 8h 45min 57s |
|
||
| Downtime per month | 43m 49.7s |
|
||
| Downtime per week | 10m 4.8s |
|
||
| Downtime per day | 1m 26.4s |
|
||
|
||
#### 99.99% availability - four 9s
|
||
|
||
| Duration | Acceptable downtime|
|
||
|---------------------|--------------------|
|
||
| Downtime per year | 52min 35.7s |
|
||
| Downtime per month | 4m 23s |
|
||
| Downtime per week | 1m 5s |
|
||
| Downtime per day | 8.6s |
|
||
|
||
#### Availability in parallel vs in sequence
|
||
#### Tính khả dụng trong thực thi song song so với trình tự
|
||
|
||
If a service consists of multiple components prone to failure, the service's overall availability depends on whether the components are in sequence or in parallel.
|
||
Nếu một dịch vụ bao gồm nhiều thành phần dễ mắc lỗi, thì tính khả dụng toàn phần của dịch vụ này phụ thuộc vào các thành phần nằm trong một trình tự hay nằm song song.
|
||
|
||
###### In sequence
|
||
###### Trong trình tự
|
||
|
||
Overall availability decreases when two components with availability < 100% are in sequence:
|
||
Tính khả dụng toàn phần giảm đi khi có 2 thành phần với tính khả dụng < 100% nằm theo trình tự:
|
||
|
||
|
||
```
|
||
Availability (Total) = Availability (Foo) * Availability (Bar)
|
||
```
|
||
```
|
||
Tính khả dụng (Tổng) = Tính khả dụng (A) * Tính khả dụng (B)
|
||
```
|
||
|
||
If both `Foo` and `Bar` each had 99.9% availability, their total availability in sequence would be 99.8%.
|
||
Nếu cả hai `A` và `B`, có 99,9% "khả dụng", tổng khả dụng trong trình tự sẽ là 99,8%.
|
||
|
||
###### In parallel
|
||
###### Trong song song # FIXME: bad translation
|
||
|
||
Overall availability increases when two components with availability < 100% are in parallel:
|
||
|
||
```
|
||
Availability (Total) = 1 - (1 - Availability (Foo)) * (1 - Availability (Bar))
|
||
```
|
||
|
||
If both `Foo` and `Bar` each had 99.9% availability, their total availability in parallel would be 99.9999%.
|
||
|
||
## Hệ thống tên miền
|
||
|
||
<p align="center">
|
||
<img src="images/IOyLj4i.jpg">
|
||
<br/>
|
||
<i><a href=http://www.slideshare.net/srikrupa5/dns-security-presentation-issa>Source: DNS security presentation</a></i>
|
||
</p>
|
||
|
||
A Domain Name System (DNS) translates a domain name such as www.example.com to an IP address.
|
||
Hệ thống tên miền (DNS) dịch một tên miền như là www.example.com sang một địa chỉ IP.
|
||
|
||
DNS is hierarchical, with a few authoritative servers at the top level. Your router or ISP provides information about which DNS server(s) to contact when doing a lookup. Lower level DNS servers cache mappings, which could become stale due to DNS propagation delays. DNS results can also be cached by your browser or OS for a certain period of time, determined by the [time to live (TTL)](https://en.wikipedia.org/wiki/Time_to_live).
|
||
DNS được phân cấp, với một vài máy chủ ở cấp cao nhất. Router hoặc ISP cung cấp thông tin về máy chủ DNS để liên lạc khi cần tra cứu tên miền. Máy chủ DNS cấp thấp sẽ lưu nhớ kết quả, kết quả này có thể trở nên lạc hậu do độ trễ lan truyền của DNS. Kết quả DNS cũng có thể được lưu nhớ ở trình duyệt hoặc hệ điều hành trong một khoảng thời gian xác định, được quyết định bởi ["thời gian tồn tại" (TTL)](https://en.wikipedia.org/wiki/Time_to_live) # FIXME: incomplete and bad translation
|
||
|
||
* **NS record (name server)** - Specifies the DNS servers for your domain/subdomain.
|
||
* **MX record (mail exchange)** - Specifies the mail servers for accepting messages.
|
||
* **A record (address)** - Points a name to an IP address.
|
||
* **CNAME (canonical)** - Points a name to another name or `CNAME` (example.com to www.example.com) or to an `A` record.
|
||
|
||
* **Record NS (name server)** - Chỉ định máy chủ DNS cho tên miền chính và tên miền con của bạn.
|
||
* **Record MX (mail exchange)** - Chỉ định máy chủ mai để tiếp nhận thư.
|
||
* **Record A (address)** - Chỉ từ tên miền sang một địa chỉ IP.
|
||
* **CNAME (canonical)** - Chỉ từ một tên miền sang một tên miền khác hoặc một `CNAME` khác (example.com đến www.example.com) hoặc đến một record `A`.
|
||
|
||
|
||
Services such as [CloudFlare](https://www.cloudflare.com/dns/) and [Route 53](https://aws.amazon.com/route53/) provide managed DNS services. Some DNS services can route traffic through various methods:
|
||
|
||
* [Weighted round robin](https://www.g33kinfo.com/info/round-robin-vs-weighted-round-robin-lb)
|
||
* Prevent traffic from going to servers under maintenance
|
||
* Balance between varying cluster sizes
|
||
* A/B testing
|
||
* [Latency-based](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html#routing-policy-latency)
|
||
* [Geolocation-based](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html#routing-policy-geo)
|
||
|
||
### Disadvantage(s): DNS
|
||
|
||
* Accessing a DNS server introduces a slight delay, although mitigated by caching described above.
|
||
* DNS server management could be complex and is generally managed by [governments, ISPs, and large companies](http://superuser.com/questions/472695/who-controls-the-dns-servers/472729).
|
||
* DNS services have recently come under [DDoS attack](http://dyn.com/blog/dyn-analysis-summary-of-friday-october-21-attack/), preventing users from accessing websites such as Twitter without knowing Twitter's IP address(es).
|
||
|
||
### Source(s) and further reading
|
||
|
||
* [DNS architecture](https://technet.microsoft.com/en-us/library/dd197427(v=ws.10).aspx)
|
||
* [Wikipedia](https://en.wikipedia.org/wiki/Domain_Name_System)
|
||
* [DNS articles](https://support.dnsimple.com/categories/dns/)
|
||
|
||
## Content delivery network
|
||
|
||
<p align="center">
|
||
<img src="images/h9TAuGI.jpg">
|
||
<br/>
|
||
<i><a href=https://www.creative-artworks.eu/why-use-a-content-delivery-network-cdn/>Source: Why use a CDN</a></i>
|
||
</p>
|
||
|
||
A content delivery network (CDN) is a globally distributed network of proxy servers, serving content from locations closer to the user. Generally, static files such as HTML/CSS/JS, photos, and videos are served from CDN, although some CDNs such as Amazon's CloudFront support dynamic content. The site's DNS resolution will tell clients which server to contact.
|
||
|
||
Serving content from CDNs can significantly improve performance in two ways:
|
||
|
||
* Users receive content from data centers close to them
|
||
* Your servers do not have to serve requests that the CDN fulfills
|
||
|
||
### Push CDNs
|
||
|
||
Push CDNs receive new content whenever changes occur on your server. You take full responsibility for providing content, uploading directly to the CDN and rewriting URLs to point to the CDN. You can configure when content expires and when it is updated. Content is uploaded only when it is new or changed, minimizing traffic, but maximizing storage.
|
||
Push CDN nhận nội dung mới khi thay đổi xày ra phía máy chủ của bạn. Bạn nhận hoàn toàn trách nhiệm của việc cung cấp nội dung, tải trực tiếp lên CDN và viết lại URL trỏ vvào CDN. Bạn có thể cấu hình khi nội dung hết hạn và khi nào được cập nhật. Nội dung được tải lên chỉ khi có nội dung mới hoặc được thay đổi, tối thiểu hoá lưu thông mạng, nhưng lại tối đa việc lưu trữ.
|
||
|
||
Sites with a small amount of traffic or sites with content that isn't often updated work well with push CDNs. Content is placed on the CDNs once, instead of being re-pulled at regular intervals.
|
||
Các trang với với lưu thông nhỏ hoặc với nội dung không được cập nhật thường xuyên thì thường làm việc tốt với push CDN. Nội dung được đặt lên CDN chỉ một lần, thay vì phải được kéo về đều đặn.
|
||
|
||
### Pull CDNs
|
||
|
||
Pull CDNs grab new content from your server when the first user requests the content. You leave the content on your server and rewrite URLs to point to the CDN. This results in a slower request until the content is cached on the CDN.
|
||
Pull CDN kéo nội dung mới từ máy chủ của bạn khi có yêu cầu đầu tiên từ người dùng cho nội dung này. Bạn để nội dung trên máy chủ và viết lại URL để trỏ đến CDN. Việc này dẫn đến thời gian thực thi yêu cầu sẽ chậm, cho đến hki nội dung được cache trên CDN.
|
||
|
||
A [time-to-live (TTL)](https://en.wikipedia.org/wiki/Time_to_live) determines how long content is cached. Pull CDNs minimize storage space on the CDN, but can create redundant traffic if files expire and are pulled before they have actually changed.
|
||
|
||
Sites with heavy traffic work well with pull CDNs, as traffic is spread out more evenly with only recently-requested content remaining on the CDN.
|
||
|
||
### Disadvantage(s): CDN
|
||
|
||
* CDN costs could be significant depending on traffic, although this should be weighed with additional costs you would incur not using a CDN.
|
||
* Content might be stale if it is updated before the TTL expires it.
|
||
* CDNs require changing URLs for static content to point to the CDN.
|
||
|
||
### Source(s) and further reading
|
||
|
||
* [Globally distributed content delivery](https://figshare.com/articles/Globally_distributed_content_delivery/6605972)
|
||
* [The differences between push and pull CDNs](http://www.travelblogadvice.com/technical/the-differences-between-push-and-pull-cdns/)
|
||
* [Wikipedia](https://en.wikipedia.org/wiki/Content_delivery_network)
|
||
|
||
## Load balancer
|
||
## Cân bằng tải
|
||
|
||
<p align="center">
|
||
<img src="images/h81n9iK.png">
|
||
<br/>
|
||
<i><a href=http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html>Source: Scalable system design patterns</a></i>
|
||
</p>
|
||
|
||
Load balancers distribute incoming client requests to computing resources such as application servers and databases. In each case, the load balancer returns the response from the computing resource to the appropriate client. Load balancers are effective at:
|
||
Cân bằng tải phân phối các yêu cầu từ máy khách đến các tài nguyên tính toán như là máy chủ ứng dụng và cơ sở dữ liệu. Trong mỗi trường hợp, các cân bằng tải trả hồi đáp từ các tài nguyên tính toán về cho máy khách thích hợp. Cân bằng tải hiệu quả cho:
|
||
|
||
|
||
* Preventing requests from going to unhealthy servers
|
||
* Tránh gửi yêu cầu đến các máy chủ không khoẻ mạnh # FIXME: although unhealthy is literally "không khoẻ**, but the translation in this context sounds unusual.
|
||
* Preventing overloading resources
|
||
* Tránh làm quá tải tài nguyên
|
||
* Helping to eliminate a single point of failure
|
||
* Giúp triệt tiêu "single point of failure" # FIXME: any parlance in Vietnamese?
|
||
|
||
Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.
|
||
Cân bằng tải có thể được hiện thực hoá với phân cứng (đắt đỏ) hoặc với phần mềm như là HAProxy.
|
||
|
||
Additional benefits include:
|
||
Các lợi ích thêm: # FIXME: bad translation
|
||
|
||
* **SSL termination** - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
|
||
* **Triệt tiêu SSL** - Giải mã các requests đi đến và mã hoá responses từ máy chủ, để các máy chủ không phải thực hiện các thao tác chi phí (tính toán) cao này.
|
||
* Removes the need to install [X.509 certificates](https://en.wikipedia.org/wiki/X.509) on each server
|
||
* Bớt đi nhu cầu phải cài đặt [chứng thực X.509](https://en.wikipedia.org/wiki/X.509) trên mỗi máy chủ
|
||
* **Session persistence** - Issue cookies and route a specific client's requests to same instance if the web apps do not keep track of sessions
|
||
* **Lưu phiên** - Phát hành cookie và điều hướng yêu cầu từ máy khách đến đúng nơi (máy chủ, ứng dụng) mà máy khách đang làm việc nếu các ứng dụng web không theo dõi phiên.
|
||
|
||
To protect against failures, it's common to set up multiple load balancers, either in [active-passive](#active-passive) or [active-active](#active-active) mode.
|
||
Để bảo vệ failure, thường sẽ nhiều cân bằng tải sẽ được dựng, hoặc dùng mô hình [active-passive](#active-passive) hoặc [active-active](#active-active).
|
||
|
||
Load balancers can route traffic based on various metrics, including:
|
||
|
||
* Random
|
||
* Least loaded
|
||
* Session/cookies
|
||
* [Round robin or weighted round robin](https://www.g33kinfo.com/info/round-robin-vs-weighted-round-robin-lb)
|
||
* [Layer 4](#layer-4-load-balancing)
|
||
* [Layer 7](#layer-7-load-balancing)
|
||
|
||
### Layer 4 load balancing
|
||
### Cân bằng tải lớp 4
|
||
|
||
Layer 4 load balancers look at info at the [transport layer](#communication) to decide how to distribute requests. Generally, this involves the source, destination IP addresses, and ports in the header, but not the contents of the packet. Layer 4 load balancers forward network packets to and from the upstream server, performing [Network Address Translation (NAT)](https://www.nginx.com/resources/glossary/layer-4-load-balancing/).
|
||
Cân bằng tải ở lớp 4 nhìn vào thông tin ở [lớp truyền tải (transport)](#communication) để quyết định cách phân phối yêu cầu. Nhìn chung thì điều này sẽ liên quan đến nguồn, đích của địa chỉ IP, và cổng (port) trong header, nhưng không dựa vào nội dung của gói tin (packet). Cân bằng tải lớp 4 truyền tải các gói tin mạng đi đến và từ máy trạm đầu nguồn, thực hiện [biên dịch địa chỉ mạng (Network Address Translation - NAT)](https://www.nginx.com/resources/glossary/layer-4-load-balancing/)
|
||
|
||
### Layer 7 load balancing
|
||
### Cân bằng tải lớp 7
|
||
|
||
Layer 7 load balancers look at the [application layer](#communication) to decide how to distribute requests. This can involve contents of the header, message, and cookies. Layer 7 load balancers terminate network traffic, reads the message, makes a load-balancing decision, then opens a connection to the selected server. For example, a layer 7 load balancer can direct video traffic to servers that host videos while directing more sensitive user billing traffic to security-hardened servers.
|
||
|
||
Cân bằng tải lớp 7 nhìn vào [lớp ứng dụng (application)](#communication) quyết định cách phân phối yêu cầu. Việc này có thể liên quan đến nội dung của header, gói tin, và cookie. Cân bằng tải lớp 7 kết thúc liên lạc, đọc tin, thực hiện quyết định về cân bằng tải, sau đó mở một kết nối đến máy trạm được lựa chọn. Ví dụ một cân bằng tải lớp 7 có thể điều hướng liên lạc hình ảnh đến các máy trạm chứa dữ liệu hình ảnh, trong khi đó, các liên lạc nhạy cảm hơn về thông tin thanh toán của người dùng có thể được điều hướng đến các máy chủ đã được củng cố về an ninh. # FIXME: bad translation
|
||
|
||
|
||
At the cost of flexibility, layer 4 load balancing requires less time and computing resources than Layer 7, although the performance impact can be minimal on modern commodity hardware.
|
||
|
||
### Horizontal scaling
|
||
|
||
Load balancers can also help with horizontal scaling, improving performance and availability. Scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware, called **Vertical Scaling**. It is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems.
|
||
|
||
#### Disadvantage(s): horizontal scaling
|
||
|
||
* Scaling horizontally introduces complexity and involves cloning servers
|
||
* Servers should be stateless: they should not contain any user-related data like sessions or profile pictures
|
||
* Sessions can be stored in a centralized data store such as a [database](#database) (SQL, NoSQL) or a persistent [cache](#cache) (Redis, Memcached)
|
||
* Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out
|
||
|
||
### Disadvantage(s): load balancer
|
||
### Bất lợi của cân bằng tải
|
||
|
||
* The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly.
|
||
* Cân bằng tải có thể trở thành một điểm nghẽn hiệu suất nếu nó không có đủ tài nguyên hoặc nếu không được cấu hình phù hợp.
|
||
* Introducing a load balancer to help eliminate a single point of failure results in increased complexity.
|
||
* Việc đưa vào một cân bằng tải sẽ giúp triệt tiêu "single point of failure" sẽ làm tăng thêm tính phức tạp. # FIXME: incomplete translation
|
||
* A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.
|
||
* Bản thân một cân bằng tải là một "single point of failure", cấu hình thêm nhiều cân bằng tải càng tăng thêm tính phức tạp.
|
||
|
||
### Source(s) and further reading
|
||
### Nguồn đọc thêm
|
||
|
||
* [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/)
|
||
* [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt)
|
||
* [Scalability](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
|
||
* [Wikipedia](https://en.wikipedia.org/wiki/Load_balancing_(computing))
|
||
* [Layer 4 load balancing](https://www.nginx.com/resources/glossary/layer-4-load-balancing/)
|
||
* [Layer 7 load balancing](https://www.nginx.com/resources/glossary/layer-7-load-balancing/)
|
||
* [ELB listener config](http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/elb-listener-config.html)
|
||
|
||
## Reverse proxy (web server)
|
||
|
||
<p align="center">
|
||
<img src="images/n41Azff.png">
|
||
<br/>
|
||
<i><a href=https://upload.wikimedia.org/wikipedia/commons/6/67/Reverse_proxy_h2g2bob.svg>Source: Wikipedia</a></i>
|
||
<br/>
|
||
</p>
|
||
|
||
A reverse proxy is a web server that centralizes internal services and provides unified interfaces to the public. Requests from clients are forwarded to a server that can fulfill it before the reverse proxy returns the server's response to the client.
|
||
|
||
Additional benefits include:
|
||
|
||
* **Increased security** - Hide information about backend servers, blacklist IPs, limit number of connections per client
|
||
* **Increased scalability and flexibility** - Clients only see the reverse proxy's IP, allowing you to scale servers or change their configuration
|
||
* **SSL termination** - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
|
||
* Removes the need to install [X.509 certificates](https://en.wikipedia.org/wiki/X.509) on each server
|
||
* **Compression** - Compress server responses
|
||
* **Caching** - Return the response for cached requests
|
||
* **Static content** - Serve static content directly
|
||
* HTML/CSS/JS
|
||
* Photos
|
||
* Videos
|
||
* Etc
|
||
|
||
### Load balancer vs reverse proxy
|
||
### Cân bằng tải và proxy đảo # FIXME: need to check if "proxy đảo** is a word.
|
||
|
||
* Deploying a load balancer is useful when you have multiple servers. Often, load balancers route traffic to a set of servers serving the same function.
|
||
* Reverse proxies can be useful even with just one web server or application server, opening up the benefits described in the previous section.
|
||
* Solutions such as NGINX and HAProxy can support both layer 7 reverse proxying and load balancing.
|
||
|
||
* Việc triển khai một cân bằng tải là hữu dụng khi bạn có nhiều máy trạm. Thông thường thì cân bằng tải điều hướng lưu thông đến một tập máy trạm phục vụ chung một chức năng.
|
||
* Proxy đảo có thể hữu dụng ngay cả với chỉ một máy trạm web, hoặc một máy trạm ứng dung, mở ra những ưu thế được mô tả ở phần trước.
|
||
* Các giải pháp như là NGINX và HAProxy có thể hỗ trợ cả proxy đảo ở lớp 7 và cân bằng tải.
|
||
|
||
### Disadvantage(s): reverse proxy
|
||
|
||
* Introducing a reverse proxy results in increased complexity.
|
||
* A single reverse proxy is a single point of failure, configuring multiple reverse proxies (ie a [failover](https://en.wikipedia.org/wiki/Failover)) further increases complexity.
|
||
|
||
### Source(s) and further reading
|
||
|
||
* [Reverse proxy vs load balancer](https://www.nginx.com/resources/glossary/reverse-proxy-vs-load-balancer/)
|
||
* [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/)
|
||
* [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt)
|
||
* [Wikipedia](https://en.wikipedia.org/wiki/Reverse_proxy)
|
||
|
||
## Application layer
|
||
|
||
<p align="center">
|
||
<img src="images/yB5SYwm.png">
|
||
<br/>
|
||
<i><a href=http://lethain.com/introduction-to-architecting-systems-for-scale/#platform_layer>Source: Intro to architecting systems for scale</a></i>
|
||
</p>
|
||
|
||
Separating out the web layer from the application layer (also known as platform layer) allows you to scale and configure both layers independently. Adding a new API results in adding application servers without necessarily adding additional web servers. The **single responsibility principle** advocates for small and autonomous services that work together. Small teams with small services can plan more aggressively for rapid growth.
|
||
|
||
Workers in the application layer also help enable [asynchronism](#asynchronism).
|
||
|
||
### Microservices
|
||
|
||
Related to this discussion are [microservices](https://en.wikipedia.org/wiki/Microservices), which can be described as a suite of independently deployable, small, modular services. Each service runs a unique process and communicates through a well-defined, lightweight mechanism to serve a business goal. <sup><a href=https://smartbear.com/learn/api-design/what-are-microservices>1</a></sup>
|
||
Liên quan đến chủ đề này là [microservices](https://en.wikipedia.org/wiki/Microservices), có thể được diễn đạt là một bộ của những service được deploy độc lập, nhỏ, tách rời. Mỗi service chạy một process duy nhất và liên lạc thông qua một cơ chế đơn giản, được định nghĩa rõ ràng. <sup><a href=https://smartbear.com/learn/api-design/what-are-microservices>1</a></sup> # FIXME: incomplete translation
|
||
|
||
Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc.
|
||
Ví dụ, Pinterest, có thể có những microservices sau: thông tin người dùng, theo dõi, feed, tìm kiếm, tải hình, vâng vâng.
|
||
|
||
### Service Discovery
|
||
|
||
Systems such as [Consul](https://www.consul.io/docs/index.html), [Etcd](https://coreos.com/etcd/docs/latest), and [Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) can help services find each other by keeping track of registered names, addresses, and ports. [Health checks](https://www.consul.io/intro/getting-started/checks.html) help verify service integrity and are often done using an [HTTP](#hypertext-transfer-protocol-http) endpoint. Both Consul and Etcd have a built in [key-value store](#key-value-store) that can be useful for storing config values and other shared data.
|
||
|
||
### Disadvantage(s): application layer
|
||
|
||
* Adding an application layer with loosely coupled services requires a different approach from an architectural, operations, and process viewpoint (vs a monolithic system).
|
||
* Microservices can add complexity in terms of deployments and operations.
|
||
|
||
### Source(s) and further reading
|
||
### Nguồn đọc thêm
|
||
|
||
* [Intro to architecting systems for scale](http://lethain.com/introduction-to-architecting-systems-for-scale)
|
||
* [Crack the system design interview](http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview)
|
||
* [Service oriented architecture](https://en.wikipedia.org/wiki/Service-oriented_architecture)
|
||
* [Introduction to Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper)
|
||
* [Here's what you need to know about building microservices](https://cloudncode.wordpress.com/2016/07/22/msa-getting-started/)
|
||
|
||
## Database
|
||
## Cơ Sở Dữ Liệu
|
||
|
||
<p align="center">
|
||
<img src="images/Xkm5CXz.png">
|
||
<br/>
|
||
<i><a href=https://www.youtube.com/watch?v=kKjm4ehYiMs>Source: Scaling up to your first 10 million users</a></i>
|
||
</p>
|
||
|
||
### Relational database management system (RDBMS)
|
||
### Hệ thống quản lí cơ sở dữ liệu quan hệ (RDBMS)
|
||
|
||
Cơ sử dữ liệu quan hệ như SQL là một tập các mục dữ liệu được tổ chức vào trong các bảng.
|
||
|
||
**ACID** là một tập hợp những tính chất của [giao dịch](https://en.wikipedia.org/wiki/Database_transaction) trong cơ sở dữ liệu quan hệ.
|
||
|
||
* **Atomicity** - Each transaction is all or nothing
|
||
* **Consistency** - Any transaction will bring the database from one valid state to another
|
||
* **Isolation** - Executing transactions concurrently has the same results as if the transactions were executed serially
|
||
* **Durability** - Once a transaction has been committed, it will remain so
|
||
|
||
* **Atomicity** - Mỗi giao dịch hoặc là hoàn thành tất cả, hoặc là không gì cả
|
||
* **Consistency** - Bất kỳ giao dịch nào sẽ chuyển cơ sở dữ liệu từ một trạng thái đúng đắn này sang một trạng thái đúng đắn khác # FIXME: bad translation
|
||
* **Isolation** - Thực thi các giao dịch sẽ mang lại kết quả y như là nếu các giao dịch được thực thi một cách tuần tự
|
||
* **Durability** - Một khi giao dịch đã được cam kết (commit), nó sẽ tồn tại như nguyên
|
||
|
||
There are many techniques to scale a relational database: **master-slave replication**, **master-master replication**, **federation**, **sharding**, **denormalization**, and **SQL tuning**.
|
||
|
||
#### Master-slave replication
|
||
|
||
The master serves reads and writes, replicating writes to one or more slaves, which serve only reads. Slaves can also replicate to additional slaves in a tree-like fashion. If the master goes offline, the system can continue to operate in read-only mode until a slave is promoted to a master or a new master is provisioned.
|
||
|
||
<p align="center">
|
||
<img src="images/C9ioGtn.png">
|
||
<br/>
|
||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i>
|
||
</p>
|
||
|
||
##### Disadvantage(s): master-slave replication
|
||
|
||
* Additional logic is needed to promote a slave to a master.
|
||
* See [Disadvantage(s): replication](#disadvantages-replication) for points related to **both** master-slave and master-master.
|
||
|
||
#### Master-master replication
|
||
|
||
Both masters serve reads and writes and coordinate with each other on writes. If either master goes down, the system can continue to operate with both reads and writes.
|
||
|
||
<p align="center">
|
||
<img src="images/krAHLGg.png">
|
||
<br/>
|
||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i>
|
||
</p>
|
||
|
||
##### Disadvantage(s): master-master replication
|
||
|
||
* You'll need a load balancer or you'll need to make changes to your application logic to determine where to write.
|
||
* Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization.
|
||
* Conflict resolution comes more into play as more write nodes are added and as latency increases.
|
||
* See [Disadvantage(s): replication](#disadvantages-replication) for points related to **both** master-slave and master-master.
|
||
|
||
##### Disadvantage(s): replication
|
||
|
||
* There is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes.
|
||
* Writes are replayed to the read replicas. If there are a lot of writes, the read replicas can get bogged down with replaying writes and can't do as many reads.
|
||
* The more read slaves, the more you have to replicate, which leads to greater replication lag.
|
||
* On some systems, writing to the master can spawn multiple threads to write in parallel, whereas read replicas only support writing sequentially with a single thread.
|
||
* Replication adds more hardware and additional complexity.
|
||
|
||
##### Source(s) and further reading: replication
|
||
|
||
* [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
|
||
* [Multi-master replication](https://en.wikipedia.org/wiki/Multi-master_replication)
|
||
|
||
#### Federation
|
||
|
||
<p align="center">
|
||
<img src="images/U3qV33e.png">
|
||
<br/>
|
||
<i><a href=https://www.youtube.com/watch?v=kKjm4ehYiMs>Source: Scaling up to your first 10 million users</a></i>
|
||
</p>
|
||
|
||
Federation (or functional partitioning) splits up databases by function. For example, instead of a single, monolithic database, you could have three databases: **forums**, **users**, and **products**, resulting in less read and write traffic to each database and therefore less replication lag. Smaller databases result in more data that can fit in memory, which in turn results in more cache hits due to improved cache locality. With no single central master serializing writes you can write in parallel, increasing throughput.
|
||
|
||
##### Disadvantage(s): federation
|
||
|
||
* Federation is not effective if your schema requires huge functions or tables.
|
||
* You'll need to update your application logic to determine which database to read and write.
|
||
* Joining data from two databases is more complex with a [server link](http://stackoverflow.com/questions/5145637/querying-data-by-joining-two-tables-in-two-database-on-different-servers).
|
||
* Federation adds more hardware and additional complexity.
|
||
|
||
##### Source(s) and further reading: federation
|
||
|
||
* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs)
|
||
|
||
#### Sharding
|
||
|
||
<p align="center">
|
||
<img src="images/wU8x5Id.png">
|
||
<br/>
|
||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i>
|
||
</p>
|
||
|
||
Sharding distributes data across different databases such that each database can only manage a subset of the data. Taking a users database as an example, as the number of users increases, more shards are added to the cluster.
|
||
|
||
Similar to the advantages of [federation](#federation), sharding results in less read and write traffic, less replication, and more cache hits. Index size is also reduced, which generally improves performance with faster queries. If one shard goes down, the other shards are still operational, although you'll want to add some form of replication to avoid data loss. Like federation, there is no single central master serializing writes, allowing you to write in parallel with increased throughput.
|
||
|
||
Common ways to shard a table of users is either through the user's last name initial or the user's geographic location.
|
||
|
||
##### Disadvantage(s): sharding
|
||
|
||
* You'll need to update your application logic to work with shards, which could result in complex SQL queries.
|
||
* Data distribution can become lopsided in a shard. For example, a set of power users on a shard could result in increased load to that shard compared to others.
|
||
* Rebalancing adds additional complexity. A sharding function based on [consistent hashing](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html) can reduce the amount of transferred data.
|
||
* Joining data from multiple shards is more complex.
|
||
* Sharding adds more hardware and additional complexity.
|
||
|
||
##### Source(s) and further reading: sharding
|
||
|
||
* [The coming of the shard](http://highscalability.com/blog/2009/8/6/an-unorthodox-approach-to-database-design-the-coming-of-the.html)
|
||
* [Shard database architecture](https://en.wikipedia.org/wiki/Shard_(database_architecture))
|
||
* [Consistent hashing](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html)
|
||
|
||
#### Denormalization
|
||
|
||
Denormalization attempts to improve read performance at the expense of some write performance. Redundant copies of the data are written in multiple tables to avoid expensive joins. Some RDBMS such as [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) and Oracle support [materialized views](https://en.wikipedia.org/wiki/Materialized_view) which handle the work of storing redundant information and keeping redundant copies consistent.
|
||
|
||
Once data becomes distributed with techniques such as [federation](#federation) and [sharding](#sharding), managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins.
|
||
|
||
In most systems, reads can heavily outnumber writes 100:1 or even 1000:1. A read resulting in a complex database join can be very expensive, spending a significant amount of time on disk operations.
|
||
|
||
##### Disadvantage(s): denormalization
|
||
|
||
* Data is duplicated.
|
||
* Constraints can help redundant copies of information stay in sync, which increases complexity of the database design.
|
||
* A denormalized database under heavy write load might perform worse than its normalized counterpart.
|
||
|
||
###### Source(s) and further reading: denormalization
|
||
|
||
* [Denormalization](https://en.wikipedia.org/wiki/Denormalization)
|
||
|
||
#### SQL tuning
|
||
|
||
SQL tuning is a broad topic and many [books](https://www.amazon.com/s/ref=nb_sb_noss_2?url=search-alias%3Daps&field-keywords=sql+tuning) have been written as reference.
|
||
|
||
It's important to **benchmark** and **profile** to simulate and uncover bottlenecks.
|
||
|
||
* **Benchmark** - Simulate high-load situations with tools such as [ab](http://httpd.apache.org/docs/2.2/programs/ab.html).
|
||
* **Profile** - Enable tools such as the [slow query log](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html) to help track performance issues.
|
||
|
||
Benchmarking and profiling might point you to the following optimizations.
|
||
|
||
##### Tighten up the schema
|
||
|
||
* MySQL dumps to disk in contiguous blocks for fast access.
|
||
* Use `CHAR` instead of `VARCHAR` for fixed-length fields.
|
||
* `CHAR` effectively allows for fast, random access, whereas with `VARCHAR`, you must find the end of a string before moving onto the next one.
|
||
* Use `TEXT` for large blocks of text such as blog posts. `TEXT` also allows for boolean searches. Using a `TEXT` field results in storing a pointer on disk that is used to locate the text block.
|
||
* Use `INT` for larger numbers up to 2^32 or 4 billion.
|
||
* Use `DECIMAL` for currency to avoid floating point representation errors.
|
||
* Avoid storing large `BLOBS`, store the location of where to get the object instead.
|
||
* `VARCHAR(255)` is the largest number of characters that can be counted in an 8 bit number, often maximizing the use of a byte in some RDBMS.
|
||
* Set the `NOT NULL` constraint where applicable to [improve search performance](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search).
|
||
|
||
##### Use good indices
|
||
|
||
* Columns that you are querying (`SELECT`, `GROUP BY`, `ORDER BY`, `JOIN`) could be faster with indices.
|
||
* Indices are usually represented as self-balancing [B-tree](https://en.wikipedia.org/wiki/B-tree) that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time.
|
||
* Placing an index can keep the data in memory, requiring more space.
|
||
* Writes could also be slower since the index also needs to be updated.
|
||
* When loading large amounts of data, it might be faster to disable indices, load the data, then rebuild the indices.
|
||
|
||
##### Avoid expensive joins
|
||
|
||
* [Denormalize](#denormalization) where performance demands it.
|
||
|
||
##### Partition tables
|
||
|
||
* Break up a table by putting hot spots in a separate table to help keep it in memory.
|
||
|
||
##### Tune the query cache
|
||
|
||
* In some cases, the [query cache](https://dev.mysql.com/doc/refman/5.7/en/query-cache.html) could lead to [performance issues](https://www.percona.com/blog/2016/10/12/mysql-5-7-performance-tuning-immediately-after-installation/).
|
||
|
||
##### Source(s) and further reading: SQL tuning
|
||
|
||
* [Tips for optimizing MySQL queries](http://aiddroid.com/10-tips-optimizing-mysql-queries-dont-suck/)
|
||
* [Is there a good reason i see VARCHAR(255) used so often?](http://stackoverflow.com/questions/1217466/is-there-a-good-reason-i-see-varchar255-used-so-often-as-opposed-to-another-l)
|
||
* [How do null values affect performance?](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search)
|
||
* [Slow query log](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html)
|
||
|
||
### NoSQL
|
||
|
||
NoSQL is a collection of data items represented in a **key-value store**, **document store**, **wide column store**, or a **graph database**. Data is denormalized, and joins are generally done in the application code. Most NoSQL stores lack true ACID transactions and favor [eventual consistency](#eventual-consistency).
|
||
|
||
**BASE** is often used to describe the properties of NoSQL databases. In comparison with the [CAP Theorem](#cap-theorem), BASE chooses availability over consistency.
|
||
|
||
* **Basically available** - the system guarantees availability.
|
||
* **Soft state** - the state of the system may change over time, even without input.
|
||
* **Eventual consistency** - the system will become consistent over a period of time, given that the system doesn't receive input during that period.
|
||
|
||
In addition to choosing between [SQL or NoSQL](#sql-or-nosql), it is helpful to understand which type of NoSQL database best fits your use case(s). We'll review **key-value stores**, **document stores**, **wide column stores**, and **graph databases** in the next section.
|
||
|
||
#### Key-value store
|
||
|
||
> Abstraction: hash table
|
||
|
||
A key-value store generally allows for O(1) reads and writes and is often backed by memory or SSD. Data stores can maintain keys in [lexicographic order](https://en.wikipedia.org/wiki/Lexicographical_order), allowing efficient retrieval of key ranges. Key-value stores can allow for storing of metadata with a value.
|
||
|
||
Key-value stores provide high performance and are often used for simple data models or for rapidly-changing data, such as an in-memory cache layer. Since they offer only a limited set of operations, complexity is shifted to the application layer if additional operations are needed.
|
||
|
||
A key-value store is the basis for more complex systems such as a document store, and in some cases, a graph database.
|
||
|
||
##### Source(s) and further reading: key-value store
|
||
|
||
* [Key-value database](https://en.wikipedia.org/wiki/Key-value_database)
|
||
* [Disadvantages of key-value stores](http://stackoverflow.com/questions/4056093/what-are-the-disadvantages-of-using-a-key-value-table-over-nullable-columns-or)
|
||
* [Redis architecture](http://qnimate.com/overview-of-redis-architecture/)
|
||
* [Memcached architecture](https://www.adayinthelifeof.nl/2011/02/06/memcache-internals/)
|
||
|
||
#### Document store
|
||
|
||
> Abstraction: key-value store with documents stored as values
|
||
|
||
A document store is centered around documents (XML, JSON, binary, etc), where a document stores all information for a given object. Document stores provide APIs or a query language to query based on the internal structure of the document itself. *Note, many key-value stores include features for working with a value's metadata, blurring the lines between these two storage types.*
|
||
|
||
Based on the underlying implementation, documents are organized by collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other.
|
||
|
||
Some document stores like [MongoDB](https://www.mongodb.com/mongodb-architecture) and [CouchDB](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/) also provide a SQL-like language to perform complex queries. [DynamoDB](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf) supports both key-values and documents.
|
||
|
||
Document stores provide high flexibility and are often used for working with occasionally changing data.
|
||
|
||
##### Source(s) and further reading: document store
|
||
|
||
* [Document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database)
|
||
* [MongoDB architecture](https://www.mongodb.com/mongodb-architecture)
|
||
* [CouchDB architecture](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/)
|
||
* [Elasticsearch architecture](https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up)
|
||
|
||
#### Wide column store
|
||
|
||
<p align="center">
|
||
<img src="images/n16iOGk.png">
|
||
<br/>
|
||
<i><a href=http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html>Source: SQL & NoSQL, a brief history</a></i>
|
||
</p>
|
||
|
||
> Abstraction: nested map `ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>>`
|
||
|
||
A wide column store's basic unit of data is a column (name/value pair). A column can be grouped in column families (analogous to a SQL table). Super column families further group column families. You can access each column independently with a row key, and columns with the same row key form a row. Each value contains a timestamp for versioning and for conflict resolution.
|
||
|
||
Google introduced [Bigtable](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) as the first wide column store, which influenced the open-source [HBase](https://www.edureka.co/blog/hbase-architecture/) often-used in the Hadoop ecosystem, and [Cassandra](http://docs.datastax.com/en/cassandra/3.0/cassandra/architecture/archIntro.html) from Facebook. Stores such as BigTable, HBase, and Cassandra maintain keys in lexicographic order, allowing efficient retrieval of selective key ranges.
|
||
|
||
Wide column stores offer high availability and high scalability. They are often used for very large data sets.
|
||
|
||
##### Source(s) and further reading: wide column store
|
||
|
||
* [SQL & NoSQL, a brief history](http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html)
|
||
* [Bigtable architecture](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf)
|
||
* [HBase architecture](https://www.edureka.co/blog/hbase-architecture/)
|
||
* [Cassandra architecture](http://docs.datastax.com/en/cassandra/3.0/cassandra/architecture/archIntro.html)
|
||
|
||
#### Graph database
|
||
|
||
<p align="center">
|
||
<img src="images/fNcl65g.png">
|
||
<br/>
|
||
<i><a href=https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png>Source: Graph database</a></i>
|
||
</p>
|
||
|
||
> Abstraction: graph
|
||
|
||
In a graph database, each node is a record and each arc is a relationship between two nodes. Graph databases are optimized to represent complex relationships with many foreign keys or many-to-many relationships.
|
||
|
||
Graphs databases offer high performance for data models with complex relationships, such as a social network. They are relatively new and are not yet widely-used; it might be more difficult to find development tools and resources. Many graphs can only be accessed with [REST APIs](#representational-state-transfer-rest).
|
||
|
||
##### Source(s) and further reading: graph
|
||
|
||
* [Graph database](https://en.wikipedia.org/wiki/Graph_database)
|
||
* [Neo4j](https://neo4j.com/)
|
||
* [FlockDB](https://blog.twitter.com/2010/introducing-flockdb)
|
||
|
||
#### Source(s) and further reading: NoSQL
|
||
|
||
* [Explanation of base terminology](http://stackoverflow.com/questions/3342497/explanation-of-base-terminology)
|
||
* [NoSQL databases a survey and decision guidance](https://medium.com/baqend-blog/nosql-databases-a-survey-and-decision-guidance-ea7823a822d#.wskogqenq)
|
||
* [Scalability](http://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database)
|
||
* [Introduction to NoSQL](https://www.youtube.com/watch?v=qI_g07C_Q5I)
|
||
* [NoSQL patterns](http://horicky.blogspot.com/2009/11/nosql-patterns.html)
|
||
|
||
### SQL or NoSQL
|
||
|
||
<p align="center">
|
||
<img src="images/wXGqG5f.png">
|
||
<br/>
|
||
<i><a href=https://www.infoq.com/articles/Transition-RDBMS-NoSQL/>Source: Transitioning from RDBMS to NoSQL</a></i>
|
||
</p>
|
||
|
||
Reasons for **SQL**:
|
||
|
||
* Structured data
|
||
* Strict schema
|
||
* Relational data
|
||
* Need for complex joins
|
||
* Transactions
|
||
* Clear patterns for scaling
|
||
* More established: developers, community, code, tools, etc
|
||
* Lookups by index are very fast
|
||
|
||
Reasons for **NoSQL**:
|
||
|
||
* Semi-structured data
|
||
* Dynamic or flexible schema
|
||
* Non-relational data
|
||
* No need for complex joins
|
||
* Store many TB (or PB) of data
|
||
* Very data intensive workload
|
||
* Very high throughput for IOPS
|
||
|
||
Sample data well-suited for NoSQL:
|
||
|
||
* Rapid ingest of clickstream and log data
|
||
* Leaderboard or scoring data
|
||
* Temporary data, such as a shopping cart
|
||
* Frequently accessed ('hot') tables
|
||
* Metadata/lookup tables
|
||
|
||
##### Source(s) and further reading: SQL or NoSQL
|
||
|
||
* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs)
|
||
* [SQL vs NoSQL differences](https://www.sitepoint.com/sql-vs-nosql-differences/)
|
||
|
||
## Cache
|
||
|
||
<p align="center">
|
||
<img src="images/Q6z24La.png">
|
||
<br/>
|
||
<i><a href=http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html>Nguồn: Scalable system design patterns</a></i>
|
||
</p>
|
||
|
||
Caching improves page load times and can reduce the load on your servers and databases. In this model, the dispatcher will first lookup if the request has been made before and try to find the previous result to return, in order to save the actual execution.
|
||
Cache cải thiện thời gian tải trang và giảm tải lên máy chủ và cơ sở dữ liệu. Trong mô hình này, bộ điều phối sẽ tra cứu nếu yêu cầu đã được làm trước đây và thử tìm kết quả trước đó để trả về, nhằm mục đích tiết kiệm thời gian thực thi.
|
||
|
||
Databases often benefit from a uniform distribution of reads and writes across its partitions. Popular items can skew the distribution, causing bottlenecks. Putting a cache in front of a database can help absorb uneven loads and spikes in traffic.
|
||
Cơ sở dữ liệu thường được lợi từ sự phân phối đồng đều giữa đọc và ghi giữa các phân vùng. Các mục phổ biến có thể xiên khả năng phân phối, gây nên tắc nghẽn. Đặt cache trước cơ sở dữ liệu có thể giúp hấp thu mất cân bằng tải và "gai" liên lạc thông tin. # FIXME: bad translation, I'm not sure if whether "gai" as "spike" is an acceptable analogy in Vietnamese.
|
||
|
||
### Client caching
|
||
### Cache ở máy khách
|
||
|
||
Caches can be located on the client side (OS or browser), [server side](#reverse-proxy-web-server), or in a distinct cache layer.
|
||
Cache có thể được đặt ở phía máy khách (hệ điều hành hoặc trình duyệt), [máy chủ](#reverse-proxy-web-server), hoặc ở một lớp cache riêng biệt
|
||
|
||
### CDN caching
|
||
### Cache ở CDN
|
||
|
||
[CDNs](#content-delivery-network) are considered a type of cache.
|
||
[CDNs](#content-delivery-network) được coi là một dạng cache.
|
||
|
||
### Web server caching
|
||
### Cache ở máy chủ web
|
||
|
||
[Reverse proxies](#reverse-proxy-web-server) and caches such as [Varnish](https://www.varnish-cache.org/) can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers.
|
||
[Reverse proxy](#reverse-proxy-web-server) và cache như là [Varnish](https://www.varnish-cache.org/) có thể trực tiếp cung cấp nội dung tĩnh hoặc động. Máy chủ web có thể cache request, trả về response mà không cần liên lạc với máy chủ ứng dụng.
|
||
|
||
### Database caching
|
||
### Cache ở cơ sở dữ liệu
|
||
|
||
Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance.
|
||
Cơ sở dữ liệu có thể bao gồm một vài mức độ cache ở cấu hình mặc định, tối ưu hoá cho những mục chung. Chỉnh sửa những cấu hình này cho những kiểu sử dụng đặc thù có thể làm tăng được hiệu suất. # FIXME: bad translation
|
||
|
||
|
||
### Application caching
|
||
|
||
In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so [cache invalidation](https://en.wikipedia.org/wiki/Cache_algorithms) algorithms such as [least recently used (LRU)](https://en.wikipedia.org/wiki/Cache_replacement_policies#Least_recently_used_(LRU)) can help invalidate 'cold' entries and keep 'hot' data in RAM.
|
||
|
||
Redis has the following additional features:
|
||
|
||
* Persistence option
|
||
* Built-in data structures such as sorted sets and lists
|
||
|
||
There are multiple levels you can cache that fall into two general categories: **database queries** and **objects**:
|
||
|
||
* Row level
|
||
* Query-level
|
||
* Fully-formed serializable objects
|
||
* Fully-rendered HTML
|
||
|
||
Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult.
|
||
|
||
### Caching at the database query level
|
||
|
||
Whenever you query the database, hash the query as a key and store the result to the cache. This approach suffers from expiration issues:
|
||
|
||
* Hard to delete a cached result with complex queries
|
||
* If one piece of data changes such as a table cell, you need to delete all cached queries that might include the changed cell
|
||
|
||
### Caching at the object level
|
||
|
||
See your data as an object, similar to what you do with your application code. Have your application assemble the dataset from the database into a class instance or a data structure(s):
|
||
|
||
* Remove the object from cache if its underlying data has changed
|
||
* Allows for asynchronous processing: workers assemble objects by consuming the latest cached object
|
||
|
||
Suggestions of what to cache:
|
||
|
||
* User sessions
|
||
* Fully rendered web pages
|
||
* Activity streams
|
||
* User graph data
|
||
|
||
### When to update the cache
|
||
|
||
Since you can only store a limited amount of data in cache, you'll need to determine which cache update strategy works best for your use case.
|
||
|
||
#### Cache-aside
|
||
|
||
<p align="center">
|
||
<img src="images/ONjORqk.png">
|
||
<br/>
|
||
<i><a href=http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast>Source: From cache to in-memory data grid</a></i>
|
||
</p>
|
||
|
||
The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following:
|
||
|
||
* Look for entry in cache, resulting in a cache miss
|
||
* Load entry from the database
|
||
* Add entry to cache
|
||
* Return entry
|
||
|
||
```python
|
||
def get_user(self, user_id):
|
||
user = cache.get("user.{0}", user_id)
|
||
if user is None:
|
||
user = db.query("SELECT * FROM users WHERE user_id = {0}", user_id)
|
||
if user is not None:
|
||
key = "user.{0}".format(user_id)
|
||
cache.set(key, json.dumps(user))
|
||
return user
|
||
```
|
||
|
||
[Memcached](https://memcached.org/) is generally used in this manner.
|
||
|
||
Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested.
|
||
|
||
##### Disadvantage(s): cache-aside
|
||
|
||
* Each cache miss results in three trips, which can cause a noticeable delay.
|
||
* Data can become stale if it is updated in the database. This issue is mitigated by setting a time-to-live (TTL) which forces an update of the cache entry, or by using write-through.
|
||
* When a node fails, it is replaced by a new, empty node, increasing latency.
|
||
|
||
#### Write-through
|
||
|
||
<p align="center">
|
||
<img src="images/0vBc0hN.png">
|
||
<br/>
|
||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i>
|
||
</p>
|
||
|
||
The application uses the cache as the main data store, reading and writing data to it, while the cache is responsible for reading and writing to the database:
|
||
|
||
* Application adds/updates entry in cache
|
||
* Cache synchronously writes entry to data store
|
||
* Return
|
||
|
||
Application code:
|
||
|
||
```python
|
||
set_user(12345, {"foo":"bar"})
|
||
```
|
||
|
||
Cache code:
|
||
|
||
```python
|
||
def set_user(user_id, values):
|
||
user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
|
||
cache.set(user_id, user)
|
||
```
|
||
|
||
Write-through is a slow overall operation due to the write operation, but subsequent reads of just written data are fast. Users are generally more tolerant of latency when updating data than reading data. Data in the cache is not stale.
|
||
|
||
##### Disadvantage(s): write through
|
||
|
||
* When a new node is created due to failure or scaling, the new node will not cache entries until the entry is updated in the database. Cache-aside in conjunction with write through can mitigate this issue.
|
||
* Most data written might never be read, which can be minimized with a TTL.
|
||
|
||
#### Write-behind (write-back)
|
||
|
||
<p align="center">
|
||
<img src="images/rgSrvjG.png">
|
||
<br/>
|
||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i>
|
||
</p>
|
||
|
||
In write-behind, the application does the following:
|
||
|
||
* Add/update entry in cache
|
||
* Asynchronously write entry to the data store, improving write performance
|
||
|
||
##### Disadvantage(s): write-behind
|
||
|
||
* There could be data loss if the cache goes down prior to its contents hitting the data store.
|
||
* It is more complex to implement write-behind than it is to implement cache-aside or write-through.
|
||
|
||
#### Refresh-ahead
|
||
|
||
<p align="center">
|
||
<img src="images/kxtjqgE.png">
|
||
<br/>
|
||
<i><a href=http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast>Source: From cache to in-memory data grid</a></i>
|
||
</p>
|
||
|
||
You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration.
|
||
|
||
Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future.
|
||
|
||
##### Disadvantage(s): refresh-ahead
|
||
|
||
* Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead.
|
||
|
||
### Disadvantage(s): cache
|
||
|
||
* Need to maintain consistency between caches and the source of truth such as the database through [cache invalidation](https://en.wikipedia.org/wiki/Cache_algorithms).
|
||
* Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.
|
||
* Need to make application changes such as adding Redis or memcached.
|
||
|
||
### Source(s) and further reading
|
||
|
||
* [From cache to in-memory data grid](http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast)
|
||
* [Scalable system design patterns](http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html)
|
||
* [Introduction to architecting systems for scale](http://lethain.com/introduction-to-architecting-systems-for-scale/)
|
||
* [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
|
||
* [Scalability](http://www.lecloud.net/post/9246290032/scalability-for-dummies-part-3-cache)
|
||
* [AWS ElastiCache strategies](http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/Strategies.html)
|
||
* [Wikipedia](https://en.wikipedia.org/wiki/Cache_(computing))
|
||
|
||
## Asynchronism
|
||
|
||
<p align="center">
|
||
<img src="images/54GYsSx.png">
|
||
<br/>
|
||
<i><a href=http://lethain.com/introduction-to-architecting-systems-for-scale/#platform_layer>Source: Intro to architecting systems for scale</a></i>
|
||
</p>
|
||
|
||
Asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in-line. They can also help by doing time-consuming work in advance, such as periodic aggregation of data.
|
||
|
||
### Message queues
|
||
### Hàng đợi tin
|
||
|
||
Message queues receive, hold, and deliver messages. If an operation is too slow to perform inline, you can use a message queue with the following workflow:
|
||
Hàng đợi tin nhận, giữ và giao tin nhắn. Nếu một thao tác quá chậm để thực hiện tại tại chỗ, bạn có thể dùng một hàng đợi với các tiến trình sau:
|
||
|
||
* An application publishes a job to the queue, then notifies the user of job status
|
||
* Một ứng dụng xuất bản một tác vụ đến hàng đợi, rồi thông báo cho các người dùng về trạng thái của công việc
|
||
* A worker picks up the job from the queue, processes it, then signals the job is complete
|
||
* Worker lấy một tác vụ từng hàng đợi, xử lí, rồi báo hiệu là tác vụ đã hoàn thành
|
||
|
||
The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers.
|
||
|
||
**[Redis](https://redis.io/)** is useful as a simple message broker but messages can be lost.
|
||
|
||
**[RabbitMQ](https://www.rabbitmq.com/)** is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.
|
||
|
||
**[Amazon SQS](https://aws.amazon.com/sqs/)** is hosted but can have high latency and has the possibility of messages being delivered twice.
|
||
|
||
### Task queues
|
||
### Hàng đợi tác vụ
|
||
|
||
Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background.
|
||
Hàng đợi nhận tác vụ và các dữ liệu liên quan, thực thi và giao kết quả. Hàng đợi có thể hỗ trợ lên thời gian biểu và có thể được dùng cho các công việc ngầm.
|
||
|
||
**[Celery](https://docs.celeryproject.org/en/stable/)** has support for scheduling and primarily has python support.
|
||
|
||
### Back pressure
|
||
|
||
If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance. [Back pressure](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html) can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue. Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later. Clients can retry the request at a later time, perhaps with [exponential backoff](https://en.wikipedia.org/wiki/Exponential_backoff).
|
||
|
||
### Disadvantage(s): asynchronism
|
||
|
||
* Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.
|
||
|
||
### Source(s) and further reading
|
||
### Tài liệu bổ sung
|
||
|
||
* [It's all a numbers game](https://www.youtube.com/watch?v=1KRYH75wgy4)
|
||
* [Applying back pressure when overloaded](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html)
|
||
* [Little's law](https://en.wikipedia.org/wiki/Little%27s_law)
|
||
* [What is the difference between a message queue and a task queue?](https://www.quora.com/What-is-the-difference-between-a-message-queue-and-a-task-queue-Why-would-a-task-queue-require-a-message-broker-like-RabbitMQ-Redis-Celery-or-IronMQ-to-function)
|
||
|
||
## Communication
|
||
## Giao tiếp
|
||
|
||
<p align="center">
|
||
<img src="images/5KeocQs.jpg">
|
||
<br/>
|
||
<i><a href=http://www.escotal.com/osilayer.html>Source: OSI 7 layer model</a></i>
|
||
</p>
|
||
<p align="center">
|
||
<img src="images/5KeocQs.jpg">
|
||
<br/>
|
||
<i><a href=http://www.escotal.com/osilayer.html>Nguồn: OSI 7 layer model</a></i>
|
||
</p>
|
||
|
||
### Hypertext transfer protocol (HTTP)
|
||
### Giao thức truyền tải siêu văn bản (HTTP) # FIXME: @huy: this is one of those things that sounds really weird if literally translated
|
||
|
||
HTTP is a method for encoding and transporting data between a client and a server. It is a request/response protocol: clients issue requests and servers issue responses with relevant content and completion status info about the request. HTTP is self-contained, allowing requests and responses to flow through many intermediate routers and servers that perform load balancing, caching, encryption, and compression.
|
||
HTTP là một phương thức mã hoá và truyền tải dữ liệu giữa một client và một server. Là một giao thức yêu cầu/hồi đáp (request/response): client phát yêu cầu và server phát hồi đáp với nội dung tương ứng kèm theo thông tin về trạng thái hoàn tất của yêu cầu. HTTP là độc lập (self-contained), chấp nhận yêu cầu và hồi đáp đi qua nhiều router và server trung gian, cân bằng tải; cache, mã hoá, và nén có thể được thực hiện ở các chặng giữa này.
|
||
|
||
A basic HTTP request consists of a verb (method) and a resource (endpoint). Below are common HTTP verbs:
|
||
Một yêu cầu HTTP cơ bản bao gồm một động từ (phương thức) và một tài nguyên (điểm cuối). Dưới đây là các động từ HTTP phổ biến:
|
||
|
||
| Verb | Description | Idempotent* | Safe | Cacheable |
|
||
|--------|-----------------------------------------------------------|-------------|------|-----------------------------------------|
|
||
| GET | Reads a resource | Yes | Yes | Yes |
|
||
| POST | Creates a resource or trigger a process that handles data | No | No | Yes if response contains freshness info |
|
||
| PUT | Creates or replace a resource | Yes | No | No |
|
||
| PATCH | Partially updates a resource | No | No | Yes if response contains freshness info |
|
||
| DELETE | Deletes a resource | Yes | No | No |
|
||
|
||
*Can be called many times without different outcomes.
|
||
|
||
HTTP is an application layer protocol relying on lower-level protocols such as **TCP** and **UDP**.
|
||
|
||
#### Source(s) and further reading: HTTP
|
||
|
||
* [What is HTTP?](https://www.nginx.com/resources/glossary/http/)
|
||
* [Difference between HTTP and TCP](https://www.quora.com/What-is-the-difference-between-HTTP-protocol-and-TCP-protocol)
|
||
* [Difference between PUT and PATCH](https://laracasts.com/discuss/channels/general-discussion/whats-the-differences-between-put-and-patch?page=1)
|
||
|
||
### Transmission control protocol (TCP)
|
||
|
||
<p align="center">
|
||
<img src="images/JdAsdvG.jpg">
|
||
<br/>
|
||
<i><a href=http://www.wildbunny.co.uk/blog/2012/10/09/how-to-make-a-multi-player-game-part-1/>Source: How to make a multiplayer game</a></i>
|
||
</p>
|
||
|
||
TCP is a connection-oriented protocol over an [IP network](https://en.wikipedia.org/wiki/Internet_Protocol). Connection is established and terminated using a [handshake](https://en.wikipedia.org/wiki/Handshaking). All packets sent are guaranteed to reach the destination in the original order and without corruption through:
|
||
|
||
* Sequence numbers and [checksum fields](https://en.wikipedia.org/wiki/Transmission_Control_Protocol#Checksum_computation) for each packet
|
||
* [Acknowledgement](https://en.wikipedia.org/wiki/Acknowledgement_(data_networks)) packets and automatic retransmission
|
||
|
||
If the sender does not receive a correct response, it will resend the packets. If there are multiple timeouts, the connection is dropped. TCP also implements [flow control](https://en.wikipedia.org/wiki/Flow_control_(data)) and [congestion control](https://en.wikipedia.org/wiki/Network_congestion#Congestion_control). These guarantees cause delays and generally result in less efficient transmission than UDP.
|
||
|
||
To ensure high throughput, web servers can keep a large number of TCP connections open, resulting in high memory usage. It can be expensive to have a large number of open connections between web server threads and say, a [memcached](https://memcached.org/) server. [Connection pooling](https://en.wikipedia.org/wiki/Connection_pool) can help in addition to switching to UDP where applicable.
|
||
|
||
TCP is useful for applications that require high reliability but are less time critical. Some examples include web servers, database info, SMTP, FTP, and SSH.
|
||
|
||
Use TCP over UDP when:
|
||
Sử dụng TCP thay vì UDP khi:
|
||
|
||
* You need all of the data to arrive intact
|
||
* Bạn cần tất cả dữ liệu nguyện vẹn khi nhận
|
||
* You want to automatically make a best estimate use of the network throughput
|
||
* Bạn muốn ước lượng tốt nhất có thể về công suất đường truyền
|
||
|
||
### User datagram protocol (UDP)
|
||
|
||
<p align="center">
|
||
<img src="images/yzDrJtA.jpg">
|
||
<br/>
|
||
<i><a href=http://www.wildbunny.co.uk/blog/2012/10/09/how-to-make-a-multi-player-game-part-1/>Source: How to make a multiplayer game</a></i>
|
||
</p>
|
||
|
||
UDP is connectionless. Datagrams (analogous to packets) are guaranteed only at the datagram level. Datagrams might reach their destination out of order or not at all. UDP does not support congestion control. Without the guarantees that TCP support, UDP is generally more efficient.
|
||
|
||
UDP can broadcast, sending datagrams to all devices on the subnet. This is useful with [DHCP](https://en.wikipedia.org/wiki/Dynamic_Host_Configuration_Protocol) because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address.
|
||
|
||
UDP is less reliable but works well in real time use cases such as VoIP, video chat, streaming, and realtime multiplayer games.
|
||
|
||
Use UDP over TCP when:
|
||
|
||
* You need the lowest latency
|
||
* Late data is worse than loss of data
|
||
* You want to implement your own error correction
|
||
|
||
#### Source(s) and further reading: TCP and UDP
|
||
|
||
* [Networking for game programming](http://gafferongames.com/networking-for-game-programmers/udp-vs-tcp/)
|
||
* [Key differences between TCP and UDP protocols](http://www.cyberciti.biz/faq/key-differences-between-tcp-and-udp-protocols/)
|
||
* [Difference between TCP and UDP](http://stackoverflow.com/questions/5970383/difference-between-tcp-and-udp)
|
||
* [Transmission control protocol](https://en.wikipedia.org/wiki/Transmission_Control_Protocol)
|
||
* [User datagram protocol](https://en.wikipedia.org/wiki/User_Datagram_Protocol)
|
||
* [Scaling memcache at Facebook](http://www.cs.bu.edu/~jappavoo/jappavoo.github.com/451/papers/memcache-fb.pdf)
|
||
|
||
### Remote procedure call (RPC)
|
||
|
||
<p align="center">
|
||
<img src="images/iF4Mkb5.png">
|
||
<br/>
|
||
<i><a href=http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview>Source: Crack the system design interview</a></i>
|
||
</p>
|
||
|
||
In an RPC, a client causes a procedure to execute on a different address space, usually a remote server. The procedure is coded as if it were a local procedure call, abstracting away the details of how to communicate with the server from the client program. Remote calls are usually slower and less reliable than local calls so it is helpful to distinguish RPC calls from local calls. Popular RPC frameworks include [Protobuf](https://developers.google.com/protocol-buffers/), [Thrift](https://thrift.apache.org/), and [Avro](https://avro.apache.org/docs/current/).
|
||
|
||
RPC is a request-response protocol:
|
||
|
||
* **Client program** - Calls the client stub procedure. The parameters are pushed onto the stack like a local procedure call.
|
||
* **Client stub procedure** - Marshals (packs) procedure id and arguments into a request message.
|
||
* **Client communication module** - OS sends the message from the client to the server.
|
||
* **Server communication module** - OS passes the incoming packets to the server stub procedure.
|
||
* **Server stub procedure** - Unmarshalls the results, calls the server procedure matching the procedure id and passes the given arguments.
|
||
* The server response repeats the steps above in reverse order.
|
||
|
||
Sample RPC calls:
|
||
|
||
```
|
||
GET /someoperation?data=anId
|
||
|
||
POST /anotheroperation
|
||
{
|
||
"data":"anId";
|
||
"anotherdata": "another value"
|
||
}
|
||
```
|
||
|
||
RPC is focused on exposing behaviors. RPCs are often used for performance reasons with internal communications, as you can hand-craft native calls to better fit your use cases.
|
||
|
||
Choose a native library (aka SDK) when:
|
||
|
||
* You know your target platform.
|
||
* You want to control how your "logic" is accessed.
|
||
* You want to control how error control happens off your library.
|
||
* Performance and end user experience is your primary concern.
|
||
|
||
HTTP APIs following **REST** tend to be used more often for public APIs.
|
||
|
||
#### Disadvantage(s): RPC
|
||
|
||
* RPC clients become tightly coupled to the service implementation.
|
||
* A new API must be defined for every new operation or use case.
|
||
* It can be difficult to debug RPC.
|
||
* You might not be able to leverage existing technologies out of the box. For example, it might require additional effort to ensure [RPC calls are properly cached](http://etherealbits.com/2012/12/debunking-the-myths-of-rpc-rest/) on caching servers such as [Squid](http://www.squid-cache.org/).
|
||
|
||
### Representational state transfer (REST)
|
||
|
||
REST is an architectural style enforcing a client/server model where the client acts on a set of resources managed by the server. The server provides a representation of resources and actions that can either manipulate or get a new representation of resources. All communication must be stateless and cacheable.
|
||
|
||
There are four qualities of a RESTful interface:
|
||
|
||
* **Identify resources (URI in HTTP)** - use the same URI regardless of any operation.
|
||
* **Change with representations (Verbs in HTTP)** - use verbs, headers, and body.
|
||
* **Self-descriptive error message (status response in HTTP)** - Use status codes, don't reinvent the wheel.
|
||
* **[HATEOAS](http://restcookbook.com/Basics/hateoas/) (HTML interface for HTTP)** - your web service should be fully accessible in a browser.
|
||
|
||
Sample REST calls:
|
||
|
||
```
|
||
GET /someresources/anId
|
||
|
||
PUT /someresources/anId
|
||
{"anotherdata": "another value"}
|
||
```
|
||
|
||
REST is focused on exposing data. It minimizes the coupling between client/server and is often used for public HTTP APIs. REST uses a more generic and uniform method of exposing resources through URIs, [representation through headers](https://github.com/for-GET/know-your-http-well/blob/master/headers.md), and actions through verbs such as GET, POST, PUT, DELETE, and PATCH. Being stateless, REST is great for horizontal scaling and partitioning.
|
||
|
||
#### Disadvantage(s): REST
|
||
|
||
* With REST being focused on exposing data, it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy. For example, returning all updated records from the past hour matching a particular set of events is not easily expressed as a path. With REST, it is likely to be implemented with a combination of URI path, query parameters, and possibly the request body.
|
||
* REST typically relies on a few verbs (GET, POST, PUT, DELETE, and PATCH) which sometimes doesn't fit your use case. For example, moving expired documents to the archive folder might not cleanly fit within these verbs.
|
||
* Fetching complicated resources with nested hierarchies requires multiple round trips between the client and server to render single views, e.g. fetching content of a blog entry and the comments on that entry. For mobile applications operating in variable network conditions, these multiple roundtrips are highly undesirable.
|
||
* Over time, more fields might be added to an API response and older clients will receive all new data fields, even those that they do not need, as a result, it bloats the payload size and leads to larger latencies.
|
||
|
||
### RPC and REST calls comparison
|
||
|
||
| Operation | RPC | REST |
|
||
|---|---|---|
|
||
| Signup | **POST** /signup | **POST** /persons |
|
||
| Resign | **POST** /resign<br/>{<br/>"personid": "1234"<br/>} | **DELETE** /persons/1234 |
|
||
| Read a person | **GET** /readPerson?personid=1234 | **GET** /persons/1234 |
|
||
| Read a person’s items list | **GET** /readUsersItemsList?personid=1234 | **GET** /persons/1234/items |
|
||
| Add an item to a person’s items | **POST** /addItemToUsersItemsList<br/>{<br/>"personid": "1234";<br/>"itemid": "456"<br/>} | **POST** /persons/1234/items<br/>{<br/>"itemid": "456"<br/>} |
|
||
| Update an item | **POST** /modifyItem<br/>{<br/>"itemid": "456";<br/>"key": "value"<br/>} | **PUT** /items/456<br/>{<br/>"key": "value"<br/>} |
|
||
| Delete an item | **POST** /removeItem<br/>{<br/>"itemid": "456"<br/>} | **DELETE** /items/456 |
|
||
|
||
<p align="center">
|
||
<i><a href=https://apihandyman.io/do-you-really-know-why-you-prefer-rest-over-rpc/>Source: Do you really know why you prefer REST over RPC</a></i>
|
||
</p>
|
||
|
||
#### Source(s) and further reading: REST and RPC
|
||
|
||
* [Do you really know why you prefer REST over RPC](https://apihandyman.io/do-you-really-know-why-you-prefer-rest-over-rpc/)
|
||
* [When are RPC-ish approaches more appropriate than REST?](http://programmers.stackexchange.com/a/181186)
|
||
* [REST vs JSON-RPC](http://stackoverflow.com/questions/15056878/rest-vs-json-rpc)
|
||
* [Debunking the myths of RPC and REST](http://etherealbits.com/2012/12/debunking-the-myths-of-rpc-rest/)
|
||
* [What are the drawbacks of using REST](https://www.quora.com/What-are-the-drawbacks-of-using-RESTful-APIs)
|
||
* [Crack the system design interview](http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview)
|
||
* [Thrift](https://code.facebook.com/posts/1468950976659943/)
|
||
* [Why REST for internal use and not RPC](http://arstechnica.com/civis/viewtopic.php?t=1190508)
|
||
|
||
## Security
|
||
## An ninh
|
||
|
||
This section could use some updates. Consider [contributing](#contributing)!
|
||
Phần cần được bổ sung thêm. Hãy [đóng góp](#contributing)!
|
||
|
||
Security is a broad topic. Unless you have considerable experience, a security background, or are applying for a position that requires knowledge of security, you probably won't need to know more than the basics:
|
||
An ninh là một chủ đề rộng. Trừ khi bạn có kinh nghiệm đáng kể, một nền tảng về an ninh, hoặc đang ứng tuyển vào một ví trí đòi hỏi kiến thức về an ninh, hầu như bạn sẽ không cần biết nhiều hơn ở mức cơ bản này:
|
||
|
||
* Encrypt in transit and at rest.
|
||
* Sanitize all user inputs or any input parameters exposed to user to prevent [XSS](https://en.wikipedia.org/wiki/Cross-site_scripting) and [SQL injection](https://en.wikipedia.org/wiki/SQL_injection).
|
||
* Use parameterized queries to prevent SQL injection.
|
||
* Use the principle of [least privilege](https://en.wikipedia.org/wiki/Principle_of_least_privilege).
|
||
|
||
### Source(s) and further reading
|
||
|
||
* [API security checklist](https://github.com/shieldfy/API-Security-Checklist)
|
||
* [Security guide for developers](https://github.com/FallibleInc/security-guide-for-developers)
|
||
* [OWASP top ten](https://www.owasp.org/index.php/OWASP_Top_Ten_Cheat_Sheet)
|
||
|
||
## Appendix
|
||
|
||
You'll sometimes be asked to do 'back-of-the-envelope' estimates. For example, you might need to determine how long it will take to generate 100 image thumbnails from disk or how much memory a data structure will take. The **Powers of two table** and **Latency numbers every programmer should know** are handy references.
|
||
|
||
### Bảng bình phương
|
||
|
||
```
|
||
Power Exact Value Approx Value Bytes
|
||
---------------------------------------------------------------
|
||
7 128
|
||
8 256
|
||
10 1024 1 thousand 1 KB
|
||
16 65,536 64 KB
|
||
20 1,048,576 1 million 1 MB
|
||
30 1,073,741,824 1 billion 1 GB
|
||
32 4,294,967,296 4 GB
|
||
40 1,099,511,627,776 1 trillion 1 TB
|
||
```
|
||
|
||
#### Source(s) and further reading
|
||
|
||
* [Bảng bình phương](https://en.wikipedia.org/wiki/Power_of_two)
|
||
|
||
### Latency numbers every programmer should know
|
||
### Các chỉ số độ trễ mọi LTV nên biết
|
||
|
||
```
|
||
Latency Comparison Numbers
|
||
--------------------------
|
||
L1 cache reference 0.5 ns
|
||
Branch mispredict 5 ns
|
||
L2 cache reference 7 ns 14x L1 cache
|
||
Mutex lock/unlock 25 ns
|
||
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
|
||
Compress 1K bytes with Zippy 10,000 ns 10 us
|
||
Send 1 KB bytes over 1 Gbps network 10,000 ns 10 us
|
||
Read 4 KB randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
|
||
Read 1 MB sequentially from memory 250,000 ns 250 us
|
||
Round trip within same datacenter 500,000 ns 500 us
|
||
Read 1 MB sequentially from SSD* 1,000,000 ns 1,000 us 1 ms ~1GB/sec SSD, 4X memory
|
||
HDD seek 10,000,000 ns 10,000 us 10 ms 20x datacenter roundtrip
|
||
Read 1 MB sequentially from 1 Gbps 10,000,000 ns 10,000 us 10 ms 40x memory, 10X SSD
|
||
Read 1 MB sequentially from HDD 30,000,000 ns 30,000 us 30 ms 120x memory, 30X SSD
|
||
Send packet CA->Netherlands->CA 150,000,000 ns 150,000 us 150 ms
|
||
|
||
Notes
|
||
-----
|
||
1 ns = 10^-9 seconds
|
||
1 us = 10^-6 seconds = 1,000 ns
|
||
1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns
|
||
``**
|
||
|
||
Handy metrics based on numbers above:
|
||
Một số tham số hữu dụng từ các con số trên:
|
||
|
||
* Read sequentially from HDD at 30 MB/s
|
||
* Read sequentially from 1 Gbps Ethernet at 100 MB/s
|
||
* Read sequentially from SSD at 1 GB/s
|
||
* Read sequentially from main memory at 4 GB/s
|
||
* 6-7 world-wide round trips per second
|
||
* 2,000 round trips per second within a data center
|
||
|
||
* Tốc độ đọc liên tiếp trên ổ cứng từ (HDD) là 30 MB/s
|
||
* Tốc độ đọc liên tiếp trên Ethernet 1Gbps là 100 MB/s
|
||
* Tốc độ đọc liên tiếp trên ổ đĩa bán dẫn (SSD) là 1 GB/s
|
||
* Tốc độ đọc liên tiếp trên bộ nhớ chính (main memory) là 4 GB/s
|
||
* Tốc độ truyền tải mỗi giây có thể đi 6-7 vòng trái đất.
|
||
* Tốc độ truyền tải có thể đạt 2.0000 hành trình xuyên suốt một trung tâm dữ liệu.
|
||
|
||
#### Latency numbers visualized
|
||
#### Hình tượng hoá các độ trễ khác nhau # FIXME: bad translation
|
||
|
||
|
||
|
||
#### Source(s) and further reading
|
||
#### Các tài liệu đọc thêm
|
||
|
||
* [Latency numbers every programmer should know - 1](https://gist.github.com/jboner/2841832)
|
||
* [Latency numbers every programmer should know - 2](https://gist.github.com/hellerbarde/2843375)
|
||
* [Designs, lessons, and advice from building large distributed systems](http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf)
|
||
* [Software Engineering Advice from Building Large-Scale Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//people/jeff/stanford-295-talk.pdf)
|
||
|
||
### Additional system design interview questions
|
||
### Các câu hỏi phỏng vấn thiết kế hệ thống bổ sung
|
||
|
||
> Common system design interview questions, with links to resources on how to solve each.
|
||
> Các câu hỏi phỏng vấn thiết kế hệ thống phổ biến, với đường dẫn đến tài liệu hướng dẫn giải.
|
||
|
||
| Question | Reference(s) |
|
||
|---|---|
|
||
| Design a file sync service like Dropbox | [youtube.com](https://www.youtube.com/watch?v=PE4gwstWhmc) |
|
||
| Design a search engine like Google | [queue.acm.org](http://queue.acm.org/detail.cfm?id=988407)<br/>[stackexchange.com](http://programmers.stackexchange.com/questions/38324/interview-question-how-would-you-implement-google-search)<br/>[ardendertat.com](http://www.ardendertat.com/2012/01/11/implementing-search-engines/)<br/>[stanford.edu](http://infolab.stanford.edu/~backrub/google.html) |
|
||
| Design a scalable web crawler like Google | [quora.com](https://www.quora.com/How-can-I-build-a-web-crawler-from-scratch) |
|
||
| Design Google docs | [code.google.com](https://code.google.com/p/google-mobwrite/)<br/>[neil.fraser.name](https://neil.fraser.name/writing/sync/) |
|
||
| Design a key-value store like Redis | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
|
||
| Design a cache system like Memcached | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) |
|
||
| Design a recommendation system like Amazon's | [hulu.com](https://web.archive.org/web/20170406065247/http://tech.hulu.com/blog/2011/09/19/recommendation-system.html)<br/>[ijcai13.org](http://ijcai13.org/files/tutorial_slides/td3.pdf) |
|
||
| Design a tinyurl system like Bitly | [n00tc0d3r.blogspot.com](http://n00tc0d3r.blogspot.com/) |
|
||
| Design a chat app like WhatsApp | [highscalability.com](http://highscalability.com/blog/2014/2/26/the-whatsapp-architecture-facebook-bought-for-19-billion.html)
|
||
| Design a picture sharing system like Instagram | [highscalability.com](http://highscalability.com/flickr-architecture)<br/>[highscalability.com](http://highscalability.com/blog/2011/12/6/instagram-architecture-14-million-users-terabytes-of-photos.html) |
|
||
| Design the Facebook news feed function | [quora.com](http://www.quora.com/What-are-best-practices-for-building-something-like-a-News-Feed)<br/>[quora.com](http://www.quora.com/Activity-Streams/What-are-the-scaling-issues-to-keep-in-mind-while-developing-a-social-network-feed)<br/>[slideshare.net](http://www.slideshare.net/danmckinley/etsy-activity-feeds-architecture) |
|
||
| Design the Facebook timeline function | [facebook.com](https://www.facebook.com/note.php?note_id=10150468255628920)<br/>[highscalability.com](http://highscalability.com/blog/2012/1/23/facebook-timeline-brought-to-you-by-the-power-of-denormaliza.html) |
|
||
| Design the Facebook chat function | [erlang-factory.com](http://www.erlang-factory.com/upload/presentations/31/EugeneLetuchy-ErlangatFacebook.pdf)<br/>[facebook.com](https://www.facebook.com/note.php?note_id=14218138919&id=9445547199&index=0) |
|
||
| Design a graph search function like Facebook's | [facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-building-out-the-infrastructure-for-graph-search/10151347573598920)<br/>[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-indexing-and-ranking-in-graph-search/10151361720763920)<br/>[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-the-natural-language-interface-of-graph-search/10151432733048920) |
|
||
| Design a content delivery network like CloudFlare | [figshare.com](https://figshare.com/articles/Globally_distributed_content_delivery/6605972) |
|
||
| Design a trending topic system like Twitter's | [michael-noll.com](http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/)<br/>[snikolov .wordpress.com](http://snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/) |
|
||
| Design a random ID generation system | [blog.twitter.com](https://blog.twitter.com/2010/announcing-snowflake)<br/>[github.com](https://github.com/twitter/snowflake/) |
|
||
| Return the top k requests during a time interval | [cs.ucsb.edu](https://www.cs.ucsb.edu/sites/cs.ucsb.edu/files/docs/reports/2005-23.pdf)<br/>[wpi.edu](http://davis.wpi.edu/xmdv/docs/EDBT11-diyang.pdf) |
|
||
| Design a system that serves data from multiple data centers | [highscalability.com](http://highscalability.com/blog/2009/8/24/how-google-serves-data-from-multiple-datacenters.html) |
|
||
| Design an online multiplayer card game | [indieflashblog.com](https://web.archive.org/web/20180929181117/http://www.indieflashblog.com/how-to-create-an-asynchronous-multiplayer-game.html)<br/>[buildnewgames.com](http://buildnewgames.com/real-time-multiplayer/) |
|
||
| Design a garbage collection system | [stuffwithstuff.com](http://journal.stuffwithstuff.com/2013/12/08/babys-first-garbage-collector/)<br/>[washington.edu](http://courses.cs.washington.edu/courses/csep521/07wi/prj/rick.pdf) |
|
||
| Design an API rate limiter | [https://stripe.com/blog/](https://stripe.com/blog/rate-limiters) |
|
||
| Design a Stock Exchange (like NASDAQ or Binance) | [Jane Street](https://youtu.be/b1e4t2k2KJY)<br/>[Golang Implementation](https://around25.com/blog/building-a-trading-engine-for-a-crypto-exchange/)<br/>[Go Implemenation](http://bhomnick.net/building-a-simple-limit-order-in-go/) |
|
||
| Add a system design question | [Contribute](#contributing) |
|
||
|
||
### Real world architectures
|
||
### Kiến trúc ở đời thực
|
||
|
||
> Articles on how real world systems are designed.
|
||
> Các bài viết về cách các hệ thống thực tế được thiết kế như thế nào
|
||
|
||
<p align="center">
|
||
<img src="images/TcUo2fw.png">
|
||
<br/>
|
||
<i><a href=https://www.infoq.com/presentations/Twitter-Timeline-Scalability>Source: Twitter timelines at scale</a></i>
|
||
</p>
|
||
|
||
**Don't focus on nitty gritty details for the following articles, instead:**
|
||
**Đừng tập trung quá và các tiểu tiết của những bài dưới đây, thay vì đó:**
|
||
|
||
* Identify shared principles, common technologies, and patterns within these articles
|
||
* Nhận dạng cách nguyên tắc chung, các công nghệ phổ biến, và những mẫu hình trong các bài viết này
|
||
* Study what problems are solved by each component, where it works, where it doesn't
|
||
* Học các vấn đề được giải quyết ở từng thành phần, chỗ nào tốt, chỗ nào chưa
|
||
* Review the lessons learned
|
||
* Duyệt lại những bài học trước
|
||
|
||
|Type | System | Reference(s) |
|
||
|---|---|---|
|
||
| Data processing | **MapReduce** - Distributed data processing from Google | [research.google.com](http://static.googleusercontent.com/media/research.google.com/zh-CN/us/archive/mapreduce-osdi04.pdf) |
|
||
| Data processing | **Spark** - Distributed data processing from Databricks | [slideshare.net](http://www.slideshare.net/AGrishchenko/apache-spark-architecture) |
|
||
| Data processing | **Storm** - Distributed data processing from Twitter | [slideshare.net](http://www.slideshare.net/previa/storm-16094009) |
|
||
| | | |
|
||
| Data store | **Bigtable** - Distributed column-oriented database from Google | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) |
|
||
| Data store | **HBase** - Open source implementation of Bigtable | [slideshare.net](http://www.slideshare.net/alexbaranau/intro-to-hbase) |
|
||
| Data store | **Cassandra** - Distributed column-oriented database from Facebook | [slideshare.net](http://www.slideshare.net/planetcassandra/cassandra-introduction-features-30103666)
|
||
| Data store | **DynamoDB** - Document-oriented database from Amazon | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf) |
|
||
| Data store | **MongoDB** - Document-oriented database | [slideshare.net](http://www.slideshare.net/mdirolf/introduction-to-mongodb) |
|
||
| Data store | **Spanner** - Globally-distributed database from Google | [research.google.com](http://research.google.com/archive/spanner-osdi2012.pdf) |
|
||
| Data store | **Memcached** - Distributed memory caching system | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) |
|
||
| Data store | **Redis** - Distributed memory caching system with persistence and value types | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
|
||
| | | |
|
||
| File system | **Google File System (GFS)** - Distributed file system | [research.google.com](http://static.googleusercontent.com/media/research.google.com/zh-CN/us/archive/gfs-sosp2003.pdf) |
|
||
| File system | **Hadoop File System (HDFS)** - Open source implementation of GFS | [apache.org](http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html) |
|
||
| | | |
|
||
| Misc | **Chubby** - Lock service for loosely-coupled distributed systems from Google | [research.google.com](http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/chubby-osdi06.pdf) |
|
||
| Misc | **Dapper** - Distributed systems tracing infrastructure | [research.google.com](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36356.pdf)
|
||
| Misc | **Kafka** - Pub/sub message queue from LinkedIn | [slideshare.net](http://www.slideshare.net/mumrah/kafka-talk-tri-hug) |
|
||
| Misc | **Zookeeper** - Centralized infrastructure and services enabling synchronization | [slideshare.net](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) |
|
||
| | Add an architecture | [Contribute](#contributing) |
|
||
|
||
### Company architectures
|
||
|
||
| Company | Reference(s) |
|
||
|---|---|
|
||
| Amazon | [Amazon architecture](http://highscalability.com/amazon-architecture) |
|
||
| Cinchcast | [Producing 1,500 hours of audio every day](http://highscalability.com/blog/2012/7/16/cinchcast-architecture-producing-1500-hours-of-audio-every-d.html) |
|
||
| DataSift | [Realtime datamining At 120,000 tweets per second](http://highscalability.com/blog/2011/11/29/datasift-architecture-realtime-datamining-at-120000-tweets-p.html) |
|
||
| Dropbox | [How we've scaled Dropbox](https://www.youtube.com/watch?v=PE4gwstWhmc) |
|
||
| ESPN | [Operating At 100,000 duh nuh nuhs per second](http://highscalability.com/blog/2013/11/4/espns-architecture-at-scale-operating-at-100000-duh-nuh-nuhs.html) |
|
||
| Google | [Google architecture](http://highscalability.com/google-architecture) |
|
||
| Instagram | [14 million users, terabytes of photos](http://highscalability.com/blog/2011/12/6/instagram-architecture-14-million-users-terabytes-of-photos.html)<br/>[What powers Instagram](http://instagram-engineering.tumblr.com/post/13649370142/what-powers-instagram-hundreds-of-instances) |
|
||
| Justin.tv | [Justin.Tv's live video broadcasting architecture](http://highscalability.com/blog/2010/3/16/justintvs-live-video-broadcasting-architecture.html) |
|
||
| Facebook | [Scaling memcached at Facebook](https://cs.uwaterloo.ca/~brecht/courses/854-Emerging-2014/readings/key-value/fb-memcached-nsdi-2013.pdf)<br/>[TAO: Facebook’s distributed data store for the social graph](https://cs.uwaterloo.ca/~brecht/courses/854-Emerging-2014/readings/data-store/tao-facebook-distributed-datastore-atc-2013.pdf)<br/>[Facebook’s photo storage](https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf)<br/>[How Facebook Live Streams To 800,000 Simultaneous Viewers](http://highscalability.com/blog/2016/6/27/how-facebook-live-streams-to-800000-simultaneous-viewers.html) |
|
||
| Flickr | [Flickr architecture](http://highscalability.com/flickr-architecture) |
|
||
| Mailbox | [From 0 to one million users in 6 weeks](http://highscalability.com/blog/2013/6/18/scaling-mailbox-from-0-to-one-million-users-in-6-weeks-and-1.html) |
|
||
| Netflix | [A 360 Degree View Of The Entire Netflix Stack](http://highscalability.com/blog/2015/11/9/a-360-degree-view-of-the-entire-netflix-stack.html)<br/>[Netflix: What Happens When You Press Play?](http://highscalability.com/blog/2017/12/11/netflix-what-happens-when-you-press-play.html) |
|
||
| Pinterest | [From 0 To 10s of billions of page views a month](http://highscalability.com/blog/2013/4/15/scaling-pinterest-from-0-to-10s-of-billions-of-page-views-a.html)<br/>[18 million visitors, 10x growth, 12 employees](http://highscalability.com/blog/2012/5/21/pinterest-architecture-update-18-million-visitors-10x-growth.html) |
|
||
| Playfish | [50 million monthly users and growing](http://highscalability.com/blog/2010/9/21/playfishs-social-gaming-architecture-50-million-monthly-user.html) |
|
||
| PlentyOfFish | [PlentyOfFish architecture](http://highscalability.com/plentyoffish-architecture) |
|
||
| Salesforce | [How they handle 1.3 billion transactions a day](http://highscalability.com/blog/2013/9/23/salesforce-architecture-how-they-handle-13-billion-transacti.html) |
|
||
| Stack Overflow | [Stack Overflow architecture](http://highscalability.com/blog/2009/8/5/stack-overflow-architecture.html) |
|
||
| TripAdvisor | [40M visitors, 200M dynamic page views, 30TB data](http://highscalability.com/blog/2011/6/27/tripadvisor-architecture-40m-visitors-200m-dynamic-page-view.html) |
|
||
| Tumblr | [15 billion page views a month](http://highscalability.com/blog/2012/2/13/tumblr-architecture-15-billion-page-views-a-month-and-harder.html) |
|
||
| Twitter | [Making Twitter 10000 percent faster](http://highscalability.com/scaling-twitter-making-twitter-10000-percent-faster)<br/>[Storing 250 million tweets a day using MySQL](http://highscalability.com/blog/2011/12/19/how-twitter-stores-250-million-tweets-a-day-using-mysql.html)<br/>[150M active users, 300K QPS, a 22 MB/S firehose](http://highscalability.com/blog/2013/7/8/the-architecture-twitter-uses-to-deal-with-150m-active-users.html)<br/>[Timelines at scale](https://www.infoq.com/presentations/Twitter-Timeline-Scalability)<br/>[Big and small data at Twitter](https://www.youtube.com/watch?v=5cKTP36HVgI)<br/>[Operations at Twitter: scaling beyond 100 million users](https://www.youtube.com/watch?v=z8LU0Cj6BOU)<br/>[How Twitter Handles 3,000 Images Per Second](http://highscalability.com/blog/2016/4/20/how-twitter-handles-3000-images-per-second.html) |
|
||
| Uber | [How Uber scales their real-time market platform](http://highscalability.com/blog/2015/9/14/how-uber-scales-their-real-time-market-platform.html)<br/>[Lessons Learned From Scaling Uber To 2000 Engineers, 1000 Services, And 8000 Git Repositories](http://highscalability.com/blog/2016/10/12/lessons-learned-from-scaling-uber-to-2000-engineers-1000-ser.html) |
|
||
| WhatsApp | [The WhatsApp architecture Facebook bought for $19 billion](http://highscalability.com/blog/2014/2/26/the-whatsapp-architecture-facebook-bought-for-19-billion.html) |
|
||
| YouTube | [YouTube scalability](https://www.youtube.com/watch?v=w5WVu624fY8)<br/>[YouTube architecture](http://highscalability.com/youtube-architecture) |
|
||
|
||
### Company engineering blogs
|
||
|
||
> Architectures for companies you are interviewing with.
|
||
>
|
||
> Questions you encounter might be from the same domain.
|
||
|
||
* [Airbnb Engineering](http://nerds.airbnb.com/)
|
||
* [Atlassian Developers](https://developer.atlassian.com/blog/)
|
||
* [AWS Blog](https://aws.amazon.com/blogs/aws/)
|
||
* [Bitly Engineering Blog](http://word.bitly.com/)
|
||
* [Box Blogs](https://blog.box.com/blog/category/engineering)
|
||
* [Cloudera Developer Blog](http://blog.cloudera.com/)
|
||
* [Dropbox Tech Blog](https://tech.dropbox.com/)
|
||
* [Engineering at Quora](https://www.quora.com/q/quoraengineering)
|
||
* [Ebay Tech Blog](http://www.ebaytechblog.com/)
|
||
* [Evernote Tech Blog](https://blog.evernote.com/tech/)
|
||
* [Etsy Code as Craft](http://codeascraft.com/)
|
||
* [Facebook Engineering](https://www.facebook.com/Engineering)
|
||
* [Flickr Code](http://code.flickr.net/)
|
||
* [Foursquare Engineering Blog](http://engineering.foursquare.com/)
|
||
* [GitHub Engineering Blog](http://githubengineering.com/)
|
||
* [Google Research Blog](http://googleresearch.blogspot.com/)
|
||
* [Groupon Engineering Blog](https://engineering.groupon.com/)
|
||
* [Heroku Engineering Blog](https://engineering.heroku.com/)
|
||
* [Hubspot Engineering Blog](http://product.hubspot.com/blog/topic/engineering)
|
||
* [High Scalability](http://highscalability.com/)
|
||
* [Instagram Engineering](http://instagram-engineering.tumblr.com/)
|
||
* [Intel Software Blog](https://software.intel.com/en-us/blogs/)
|
||
* [Jane Street Tech Blog](https://blogs.janestreet.com/category/ocaml/)
|
||
* [LinkedIn Engineering](http://engineering.linkedin.com/blog)
|
||
* [Microsoft Engineering](https://engineering.microsoft.com/)
|
||
* [Microsoft Python Engineering](https://blogs.msdn.microsoft.com/pythonengineering/)
|
||
* [Netflix Tech Blog](http://techblog.netflix.com/)
|
||
* [Paypal Developer Blog](https://medium.com/paypal-engineering)
|
||
* [Pinterest Engineering Blog](https://medium.com/@Pinterest_Engineering)
|
||
* [Reddit Blog](http://www.redditblog.com/)
|
||
* [Salesforce Engineering Blog](https://developer.salesforce.com/blogs/engineering/)
|
||
* [Slack Engineering Blog](https://slack.engineering/)
|
||
* [Spotify Labs](https://labs.spotify.com/)
|
||
* [Twilio Engineering Blog](http://www.twilio.com/engineering)
|
||
* [Twitter Engineering](https://blog.twitter.com/engineering/)
|
||
* [Uber Engineering Blog](http://eng.uber.com/)
|
||
* [Yahoo Engineering Blog](http://yahooeng.tumblr.com/)
|
||
* [Yelp Engineering Blog](http://engineeringblog.yelp.com/)
|
||
* [Zynga Engineering Blog](https://www.zynga.com/blogs/engineering)
|
||
|
||
#### Source(s) and further reading
|
||
|
||
Looking to add a blog? To avoid duplicating work, consider adding your company blog to the following repo:
|
||
|
||
* [kilimchoi/engineering-blogs](https://github.com/kilimchoi/engineering-blogs)
|
||
|
||
## Under development
|
||
|
||
Interested in adding a section or helping complete one in-progress? [Contribute](#contributing)!
|
||
|
||
* Distributed computing with MapReduce
|
||
* Consistent hashing
|
||
* Scatter gather
|
||
* [Contribute](#contributing)
|
||
|
||
## Credits
|
||
|
||
Credits and sources are provided throughout this repo.
|
||
|
||
Special thanks to:
|
||
|
||
* [Hired in tech](http://www.hiredintech.com/system-design/the-system-design-process/)
|
||
* [Cracking the coding interview](https://www.amazon.com/dp/0984782850/)
|
||
* [High scalability](http://highscalability.com/)
|
||
* [checkcheckzz/system-design-interview](https://github.com/checkcheckzz/system-design-interview)
|
||
* [shashank88/system_design](https://github.com/shashank88/system_design)
|
||
* [mmcgrana/services-engineering](https://github.com/mmcgrana/services-engineering)
|
||
* [System design cheat sheet](https://gist.github.com/vasanthk/485d1c25737e8e72759f)
|
||
* [A distributed systems reading list](http://dancres.github.io/Pages/)
|
||
* [Cracking the system design interview](http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview)
|
||
|
||
## Contact info
|
||
|
||
Feel free to contact me to discuss any issues, questions, or comments.
|
||
|
||
My contact info can be found on my [GitHub page](https://github.com/donnemartin).
|
||
|
||
## License
|
||
|
||
*I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).*
|
||
|
||
Copyright 2017 Donne Martin
|
||
|
||
Creative Commons Attribution 4.0 International License (CC BY 4.0)
|
||
|
||
http://creativecommons.org/licenses/by/4.0/
|
||
|
||
|
||
## Vietnamese Meta Notes (will remove after the translation is done)
|
||
|
||
### Words that I haven't figured out how to translate clearly yet
|
||
|
||
- Scale / scalable / scalability: "mở rộng được"?
|
||
- Large-scale system: "hệ thống lớn"?
|
||
- Partition tolerance: "dung sai phân vùng" (Typically, "dung sai" in Vietnamese is a scalar value, and often accompanied with an unit. So I'm not sure this is the right one.)
|
||
- Client/server
|
||
- Request/response
|
||
- Service: "dịch vụ"?
|
||
- Worker: "công nhân" sounds off.
|
||
- Tasks vs. jobs vs. operations: should be translated consistently (tác vụ, công việc, thao tác?).
|
||
- Cache: "tiền bộ nhớ"?
|
||
- Item: highly contextual, it doesn't really have a parlance on the Vietnamese side.
|
||
- Client
|
||
- Replication/replica
|
||
- Availability: tính sẵn có / hiện có / khả dụng
|
||
|
||
### Words that does have a translation but the English version is widely accepted among the Vietnamese-speakers
|
||
|
||
- Database: "cơ sở dữ liệu", but virtually no one use them. |