Merge remote-tracking branch 'origin' into bn-translation
farhadyasir
commit
afad8f0c6a
|
|
@ -0,0 +1,11 @@
|
|||
## Review the Contributing Guidelines
|
||||
|
||||
Before submitting a pull request, verify it meets all requirements in the [Contributing Guidelines](https://github.com/donnemartin/system-design-primer/blob/master/CONTRIBUTING.md).
|
||||
|
||||
### Translations
|
||||
|
||||
See the [Contributing Guidelines](https://github.com/donnemartin/system-design-primer/blob/master/CONTRIBUTING.md). Verify you've:
|
||||
|
||||
* Tagged the [language maintainer](https://github.com/donnemartin/system-design-primer/blob/master/TRANSLATIONS.md)
|
||||
* Prefixed the title with a language code
|
||||
* Example: "ja: Fix ..."
|
||||
|
|
@ -1,4 +1,5 @@
|
|||
# Byte-compiled / optimized / DLL files
|
||||
*.epub
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
|
||||
|
|
|
|||
|
|
@ -45,27 +45,33 @@ If you are not familiar with pull requests, review the [pull request docs](https
|
|||
We'd like for the guide to be available in many languages. Here is the process for maintaining translations:
|
||||
|
||||
* This original version and content of the guide is maintained in English.
|
||||
* Translations follow the content of the original. Unfortunately, contributors must speak at least some English, so that translations do not diverge.
|
||||
* Each translation has a maintainer to update the translation as the original evolves and to review others' changes. This doesn't require a lot of time, but review by the maintainer is important to maintain quality.
|
||||
* Translations follow the content of the original. Contributors must speak at least some English, so that translations do not diverge.
|
||||
* Each translation has a maintainer to update the translation as the original evolves and to review others' changes. This doesn't require a lot of time, but a review by the maintainer is important to maintain quality.
|
||||
|
||||
See [Translations](TRANSLATIONS.md).
|
||||
|
||||
### Changes to translations
|
||||
|
||||
* Changes to content should be made to the English version first, and then translated to each other language.
|
||||
* Changes that improve translations should be made directly on the file for that language. PRs should only modify one language at a time.
|
||||
* Submit a PR with changes to the file in that language. Each language has a maintainer, who reviews changes in that language. Then the primary maintainer @donnemartin merges it in.
|
||||
* Prefix PRs and issues with language codes if they are for that translation only, e.g. "es: Improve grammar", so maintainers can find them easily.
|
||||
* Changes that improve translations should be made directly on the file for that language. Pull requests should only modify one language at a time.
|
||||
* Submit a pull request with changes to the file in that language. Each language has a maintainer, who reviews changes in that language. Then the primary maintainer [@donnemartin](https://github.com/donnemartin) merges it in.
|
||||
* Prefix pull requests and issues with language codes if they are for that translation only, e.g. "es: Improve grammar", so maintainers can find them easily.
|
||||
* Tag the translation maintainer for a code review, see the list of [translation maintainers](TRANSLATIONS.md).
|
||||
* You will need to get a review from a native speaker (preferably the language maintainer) before your pull request is merged.
|
||||
|
||||
### Adding translations to new languages
|
||||
|
||||
Translations to new languages are always welcome, especially if you can maintain the translation!
|
||||
Translations to new languages are always welcome! Keep in mind a transation must be maintained.
|
||||
|
||||
* Check existing issues to see if a translation is in progress or stalled. If so, offer to help.
|
||||
* If it is not in progress, file an issue for your language so people know you are working on it and we can arrange. Confirm you are native level in the language and are willing to maintain the translation, so it's not orphaned.
|
||||
* To get it started, fork the repo, then submit a PR with the single file README-xx.md added, where xx is the language code. Use standard [IETF language tags](https://www.w3.org/International/articles/language-tags/), i.e. the same as is used by Wikipedia, *not* the code for a single country. These are usually just the two-letter lowercase code, for example, `fr` for French and `uk` for Ukrainian (not `ua`, which is for the country). For languages that have variations, use the shortest tag, such as `zh-Hant`.
|
||||
* Invite friends to review if possible. If desired, feel free to invite friends to help your original translation by letting them fork your repo, then merging their PRs.
|
||||
* Add links to your translation at the top of every README*.md file. (For consistency, the link should be added in alphabetical order by ISO code, and the anchor text should be in the native language.)
|
||||
* When done, indicate on the PR that it's ready to be merged into the main repo.
|
||||
* Once accepted, your PR will be squashed into a single commit into the `master` branch.
|
||||
* Do you have time to be a maintainer for a new language? Please see the list of [translations](TRANSLATIONS.md) and tell us so we know we can count on you in the future.
|
||||
* Check the [translations](TRANSLATIONS.md), issues, and pull requests to see if a translation is in progress or stalled. If it's in progress, offer to help. If it's stalled, consider becoming the maintainer if you can commit to it.
|
||||
* If a translation has not yet been started, file an issue for your language so people know you are working on it and we'll coordinate. Confirm you are native level in the language and are willing to maintain the translation, so it's not orphaned.
|
||||
* To get started, fork the repo, then submit a pull request to the main repo with the single file README-xx.md added, where xx is the language code. Use standard [IETF language tags](https://www.w3.org/International/articles/language-tags/), i.e. the same as is used by Wikipedia, *not* the code for a single country. These are usually just the two-letter lowercase code, for example, `fr` for French and `uk` for Ukrainian (not `ua`, which is for the country). For languages that have variations, use the shortest tag, such as `zh-Hant`.
|
||||
* Feel free to invite friends to help your original translation by having them fork your repo, then merging their pull requests to your forked repo. Translations are difficult and usually have errors that others need to find.
|
||||
* Add links to your translation at the top of every README-XX.md file. For consistency, the link should be added in alphabetical order by ISO code, and the anchor text should be in the native language.
|
||||
* When you've fully translated the English README.md, comment on the pull request in the main repo that it's ready to be merged.
|
||||
* You'll need to have a complete and reviewed translation of the English README.md before your translation will be merged into the `master` branch.
|
||||
* Once accepted, your pull request will be squashed into a single commit into the `master` branch.
|
||||
|
||||
### Translation template credits
|
||||
|
||||
|
|
|
|||
144
README-ja.md
144
README-ja.md
|
|
@ -1,9 +1,9 @@
|
|||
*[English](README.md) ∙ [简体中文](README-zh-Hans.md) | [Brazilian Portuguese](https://github.com/donnemartin/system-design-primer/issues/40) ∙ [Polish](https://github.com/donnemartin/system-design-primer/issues/68) ∙ [Russian](https://github.com/donnemartin/system-design-primer/issues/87) ∙ [Turkish](https://github.com/donnemartin/system-design-primer/issues/39) | [Add Translation](https://github.com/donnemartin/system-design-primer/issues/28)*
|
||||
*[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)*
|
||||
|
||||
# システム設計入門
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/jj3A5N8.png">
|
||||
<img src="images/jj3A5N8.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -38,21 +38,21 @@
|
|||
* [学習指針](#学習指針)
|
||||
* [システム設計面接課題にどのように準備するか](#システム設計面接にどのようにして臨めばいいか)
|
||||
* [システム設計課題例 **とその解答**](#システム設計課題例とその解答)
|
||||
* [オブジェクト思考設計課題例、 **とその解答**](#オブジェクト志向設計問題と解答)
|
||||
* [オブジェクト指向設計課題例、 **とその解答**](#オブジェクト指向設計問題と解答)
|
||||
* [その他のシステム設計面接課題例](#他のシステム設計面接例題)
|
||||
|
||||
## 暗記カード
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/zdCAkB3.png">
|
||||
<img src="images/zdCAkB3.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
この[暗記カードアプリケーション](https://apps.ankiweb.net/) は、システム設計の主要な概念を学ぶのに役立つアプリケーションです。程よい間隔で同じ問題を繰り返し出題してくれます。
|
||||
この[Anki用フラッシュカードデッキ](https://apps.ankiweb.net/) は、間隔反復を活用して、システム設計のキーコンセプトの学習を支援します。
|
||||
|
||||
* [システム設計デッキ](resources/flash_cards/System%20Design.apkg)
|
||||
* [システム設計練習課題デッキ](resources/flash_cards/System%20Design%20Exercises.apkg)
|
||||
* [オブジェクト思考練習課題デッキ](resources/flash_cards/OO%20Design.apkg)
|
||||
* [オブジェクト指向練習課題デッキ](resources/flash_cards/OO%20Design.apkg)
|
||||
|
||||
外出先や移動中の勉強に役立つでしょう。
|
||||
|
||||
|
|
@ -61,7 +61,7 @@
|
|||
コード技術面接用の問題を探している場合は[**こちら**](https://github.com/donnemartin/interactive-coding-challenges)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/b4YtAEN.png">
|
||||
<img src="images/b4YtAEN.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -91,7 +91,7 @@
|
|||
> それぞれのセクションはより学びを深めるような他の文献へのリンクが貼られています。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/jrUBAF7.png">
|
||||
<img src="images/jrUBAF7.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -180,7 +180,7 @@
|
|||
|
||||
> 学習スパンに応じてみるべきトピックス (short, medium, long)
|
||||
|
||||
|
||||
|
||||
|
||||
**Q: 面接のためには、ここにあるものすべてをやらないといけないのでしょうか？**
|
||||
|
||||
|
|
@ -209,7 +209,7 @@
|
|||
| 次のリンク先のいくつかのページを読む [実世界でのアーキテクチャ](#実世界のアーキテクチャ) | :+1: | :+1: | :+1: |
|
||||
| 復習する [システム設計面接課題にどのように準備するか](#システム設計面接にどのようにして臨めばいいか) | :+1: | :+1: | :+1: |
|
||||
| とりあえず一周する [システム設計課題例](#システム設計課題例とその解答) | Some | Many | Most |
|
||||
| とりあえず一周する [オブジェクト志向設計問題と解答](#オブジェクト志向設計問題と解答) | Some | Many | Most |
|
||||
| とりあえず一周する [オブジェクト指向設計問題と解答](#オブジェクト指向設計問題と解答) | Some | Many | Most |
|
||||
| 復習する [その他システム設計面接での質問例](#他のシステム設計面接例題) | Some | Many | Most |
|
||||
|
||||
## システム設計面接にどのようにして臨めばいいか
|
||||
|
|
@ -302,53 +302,53 @@
|
|||
|
||||
[問題と解答を見る](solutions/system_design/pastebin/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Twitterタイムライン&検索 (もしくはFacebookフィード&検索)を設計する
|
||||
|
||||
[問題と解答を見る](solutions/system_design/twitter/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### ウェブクローラーの設計
|
||||
|
||||
[問題と解答を見る](solutions/system_design/web_crawler/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Mint.comの設計
|
||||
|
||||
[問題と解答を見る](solutions/system_design/mint/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### SNSサービスのデータ構造を設計する
|
||||
|
||||
[問題と解答を見る](solutions/system_design/social_graph/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 検索エンジンのキー/バリュー構造を設計する
|
||||
|
||||
[問題と解答を見る](solutions/system_design/query_cache/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Amazonのカテゴリ毎の売り上げランキングを設計する
|
||||
|
||||
[問題と解答を見る](solutions/system_design/sales_rank/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### AWS上で100万人規模のユーザーを捌くサービスを設計する
|
||||
|
||||
[問題と解答を見る](solutions/system_design/scaling_aws/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
## オブジェクト志向設計問題と解答
|
||||
## オブジェクト指向設計問題と解答
|
||||
|
||||
> 頻出のオブジェクト志向システム設計面接課題と参考解答、コード及びダイアグラム
|
||||
> 頻出のオブジェクト指向システム設計面接課題と参考解答、コード及びダイアグラム
|
||||
>
|
||||
> 解答は `solutions/` フォルダ以下にリンクが貼られている
|
||||
|
||||
|
|
@ -363,7 +363,7 @@
|
|||
| 駐車場の設計 | [解答](solutions/object_oriented_design/parking_lot/parking_lot.ipynb) |
|
||||
| チャットサーバーの設計 | [解答](solutions/object_oriented_design/online_chat/online_chat.ipynb) |
|
||||
| 円形配列の設計 | [Contribute](#contributing) |
|
||||
| オブジェクト志向システム設計問題を追加する | [Contribute](#contributing) |
|
||||
| オブジェクト指向システム設計問題を追加する | [Contribute](#contributing) |
|
||||
|
||||
## システム設計トピックス: まずはここから
|
||||
|
||||
|
|
@ -385,7 +385,7 @@
|
|||
|
||||
### ステップ 2: スケーラビリティに関する資料を読んで復習する
|
||||
|
||||
[スケーラビリティ](http://www.lecloud.net/tagged/scalability)
|
||||
[スケーラビリティ](http://www.lecloud.net/tagged/scalability/chrono)
|
||||
|
||||
* ここで触れられているトピックス:
|
||||
* [クローン](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
|
||||
|
|
@ -436,7 +436,7 @@
|
|||
### CAP 理論
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/bgLMI2u.png">
|
||||
<img src="images/bgLMI2u.png">
|
||||
<br/>
|
||||
<i><a href=http://robertgreiner.com/2014/08/cap-theorem-revisited>Source: CAP theorem revisited</a></i>
|
||||
</p>
|
||||
|
|
@ -471,9 +471,9 @@
|
|||
|
||||
### 弱い一貫性
|
||||
|
||||
書き込み後の読み取りでは、その最新の書き込みを読めたり読めなかったりする。一番良いアプローチが選択される。
|
||||
書き込み後の読み取りでは、その最新の書き込みを読めたり読めなかったりする。ベストエフォート型のアプローチに基づく。
|
||||
|
||||
メムキャッシュなどのシステムにおいてこのアプローチは取られる。弱い一貫性はリアルタイム性が必要な使用例、例えばVoIP、ビデオチャット、リアルタイムマルチプレイヤーゲームなどと相性がいいでしょう。例えば、電話に出ていて、受信を数秒受け取れなかったとして、その後に接続回復してもその接続が切断されていた間に話されていたことは聞き取れないというような感じです。
|
||||
このアプローチはmemcachedなどのシステムに見られます。弱い一貫性はリアルタイム性が必要なユースケース、例えばVoIP、ビデオチャット、リアルタイムマルチプレイヤーゲームなどと相性がいいでしょう。例えば、電話に出ているときに数秒間音声が受け取れなくなったとしたら、その後に接続が回復してもその接続が切断されていた間に話されていたことは聞き取れないというような感じです。
|
||||
|
||||
### 結果整合性
|
||||
|
||||
|
|
@ -530,7 +530,7 @@
|
|||
## ドメインネームシステム
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/IOyLj4i.jpg">
|
||||
<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>
|
||||
|
|
@ -568,7 +568,7 @@ DNSは少数のオーソライズされたサーバーが上位に位置する
|
|||
## コンテンツデリバリーネットワーク(Content delivery network)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/h9TAuGI.jpg">
|
||||
<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>
|
||||
|
|
@ -582,7 +582,7 @@ CDNを用いてコンテンツを配信することで以下の二つの理由
|
|||
|
||||
### プッシュCDN
|
||||
|
||||
プッシュCDNではサーバーデータに更新があった時には必ず、新しいコンテンツを受け取る方式です。コンテンツを配信し、CDNに直接アップロードし、URLをCDNを指すように指定するところまで全ての責任を負う形です。コンテンツがいつ期限切れになるのか更新されるのかを設定することができます。コンテンツは新規作成時、更新時のみアップロードされることでトラフィックは最小化される一方、ストレージは最大限消費されてしまいます。
|
||||
プッシュCDNではサーバーデータに更新があった時には必ず、新しいコンテンツを受け取る方式です。コンテンツを用意し、CDNに直接アップロードし、URLをCDNを指すように指定するところまで、全て自分で責任を負う形です。コンテンツがいつ期限切れになるのか更新されるのかを設定することができます。コンテンツは新規作成時、更新時のみアップロードされることでトラフィックは最小化される一方、ストレージは最大限消費されてしまいます。
|
||||
|
||||
トラフィックの少ない、もしくは頻繁にはコンテンツが更新されないサイトの場合にはプッシュCDNと相性がいいでしょう。コンテンツは定期的に再びプルされるのではなく、CDNに一度のみ配置されます。
|
||||
|
||||
|
|
@ -609,7 +609,7 @@ CDNを用いてコンテンツを配信することで以下の二つの理由
|
|||
## ロードバランサー
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/h81n9iK.png">
|
||||
<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>
|
||||
|
|
@ -651,7 +651,7 @@ Layer 7 ロードバランサーは [アプリケーションレイヤー](#通
|
|||
|
||||
### 水平スケーリング
|
||||
|
||||
ロードバランサーでは水平スケーリングによってパフォーマンスと可用性を向上させることができます。手頃な汎用マシンを追加することによってスケーリングさせる方が、 **垂直スケーリング** と言って、サーバーをよりハイパフォーマンスなマシンに載せ替えることよりもずっと費用対効果も可用性も高いでしょう。また、汎用ハードウェアを扱える人材を雇う方が、特化型の商用ハードウェアを扱える人材を雇うよりも簡単でしょう。
|
||||
ロードバランサーでは水平スケーリングによってパフォーマンスと可用性を向上させることができます。手頃な汎用マシンを追加することによってスケールアウトさせる方が、一つのサーバーをより高価なマシンにスケールアップする（**垂直スケーリング**）より費用対効果も高くなり、結果的に可用性も高くなります。また、汎用ハードウェアを扱える人材を雇う方が、特化型の商用ハードウェアを扱える人材を雇うよりも簡単でしょう。
|
||||
|
||||
#### 欠点: 水平スケーリング
|
||||
|
||||
|
|
@ -664,7 +664,7 @@ Layer 7 ロードバランサーは [アプリケーションレイヤー](#通
|
|||
|
||||
* ロードバランサーはリソースが不足していたり、設定が適切でない場合、システム全体のボトルネックになる可能性があります。
|
||||
* 単一障害点を除こうとしてロードバランサーを導入した結果、複雑さが増してしまうことになります。
|
||||
* 単一ロードバランサーでは単一障害点が除かれたことにはなりませんが、複数のロードバランサーはそれすなわち複雑化です。
|
||||
* ロードバランサーが一つだけだとそこが単一障害点になってしまいます。一方で、ロードバランサーを複数にすると、さらに複雑さが増してしまいます。
|
||||
|
||||
### その他の参考資料、ページ
|
||||
|
||||
|
|
@ -679,7 +679,7 @@ Layer 7 ロードバランサーは [アプリケーションレイヤー](#通
|
|||
## リバースプロキシ(webサーバー)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/n41Azff.png">
|
||||
<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/>
|
||||
|
|
@ -689,7 +689,7 @@ Layer 7 ロードバランサーは [アプリケーションレイヤー](#通
|
|||
|
||||
他には以下のような利点があります:
|
||||
|
||||
* **より堅牢なセキュリティ** - バックエンドサーバーの情報、ブラックリストIP、クライアントごとの接続数などの情報を隠すことができます。
|
||||
* **より堅牢なセキュリティ** - バックエンドサーバーの情報を隠したり、IPアドレスをブラックリスト化したり、クライアントごとの接続数を制限したりできます。
|
||||
* **スケーラビリティや柔軟性が増します** - クライアントはリバースプロキシのIPしか見ないので、裏でサーバーをスケールしたり、設定を変えやすくなります。
|
||||
* **SSL termination** - 入力されるリクエストを解読し、サーバーのレスポンスを暗号化することでサーバーがこのコストのかかりうる処理をしなくて済むようになります。
|
||||
* [X.509 証明書](https://en.wikipedia.org/wiki/X.509) を各サーバーにインストールする必要がなくなります。
|
||||
|
|
@ -722,7 +722,7 @@ Layer 7 ロードバランサーは [アプリケーションレイヤー](#通
|
|||
## アプリケーション層
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/yB5SYwm.png">
|
||||
<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>
|
||||
|
|
@ -741,7 +741,7 @@ Layer 7 ロードバランサーは [アプリケーションレイヤー](#通
|
|||
|
||||
### サービスディスカバリー
|
||||
|
||||
[Consul](https://www.consul.io/docs/index.html)、 [Etcd](https://coreos.com/etcd/docs/latest)、 そして [Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) などのシステムはそれぞれを見つけやすいように、登録された名前、アドレス、そしてポート番号などを監視しています。[Health checks](https://www.consul.io/intro/getting-started/checks.html) はサービスの統一性を証明するのに有用ですが、しばしば[HTTP](#hypertext-transfer-protocol-http) エンドポイントを用いています。 Consul と Etcd のいずれも組み込みの [key-value store](#キーバリューストア) を持っており、設定データや共有データなどのデータを保存しておくことに使われます。
|
||||
[Consul](https://www.consul.io/docs/index.html)、 [Etcd](https://coreos.com/etcd/docs/latest)、 [Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) などのシステムでは、登録されているサービスの名前、アドレス、ポートの情報を監視することで、サービス同士が互いを見つけやすくしています。サービスの完全性の確認には [Health checks](https://www.consul.io/intro/getting-started/checks.html) が便利で、これには [HTTP](#hypertext-transfer-protocol-http) エンドポイントがよく使われます。 Consul と Etcd のいずれも組み込みの [key-value store](#キーバリューストア) を持っており、設定データや共有データなどのデータを保存しておくことに使われます。
|
||||
|
||||
### 欠点: アプリケーション層
|
||||
|
||||
|
|
@ -759,7 +759,7 @@ Layer 7 ロードバランサーは [アプリケーションレイヤー](#通
|
|||
## データベース
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/Xkm5CXz.png">
|
||||
<img src="images/Xkm5CXz.png">
|
||||
<br/>
|
||||
<i><a href=https://www.youtube.com/watch?v=w95murBkYmU>Source: Scaling up to your first 10 million users</a></i>
|
||||
</p>
|
||||
|
|
@ -782,7 +782,7 @@ SQLなどのリレーショナルデータベースはテーブルに整理さ
|
|||
マスターデータベースが読み取りと書き込みを処理し、書き込みを一つ以上のスレーブデータベースに複製します。スレーブデータベースは読み取りのみを処理します。スレーブデータベースは木構造のように追加のスレーブにデータを複製することもできます。マスターデータベースがオフラインになった場合には、いずれかのスレーブがマスターに昇格するか、新しいマスターデータベースが追加されるまでは読み取り専用モードで稼働します。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/C9ioGtn.png">
|
||||
<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>
|
||||
|
|
@ -797,7 +797,7 @@ SQLなどのリレーショナルデータベースはテーブルに整理さ
|
|||
いずれのマスターも読み取り書き込みの両方に対応する。書き込みに関してはそれぞれ協調する。いずれかのマスターが落ちても、システム全体としては読み書き両方に対応したまま運用できる。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/krAHLGg.png">
|
||||
<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>
|
||||
|
|
@ -825,12 +825,12 @@ SQLなどのリレーショナルデータベースはテーブルに整理さ
|
|||
#### Federation
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/U3qV33e.png">
|
||||
<img src="images/U3qV33e.png">
|
||||
<br/>
|
||||
<i><a href=https://www.youtube.com/watch?v=w95murBkYmU>Source: Scaling up to your first 10 million users</a></i>
|
||||
</p>
|
||||
|
||||
フェデレーション (もしくは機能分割化とも言う) はデータベースを機能ごとに分割する。例えば、モノリシックな単一データベースの代わりに三つのデータベースを持つことができます: **フォーラム**、 **ユーザー** そして **プロダクト**です。各データベースへの書き込み読み取りのトラフィックが減ることで複製ラグも短くなります。より小さなデータベースを用いることで、メモリーに収まるデータが増えます。ローカルキャッシュに保存できる量が増えることで、キャッシュヒット率も上がります。単一の中央マスターが書き込みの処理をしなくても、並列で書き込みを処理することができ、スループットの向上が期待できます。
|
||||
フェデレーション (もしくは機能分割化とも言う) はデータベースを機能ごとに分割する。例えば、モノリシックな単一データベースの代わりに、データベースを **フォーラム**、 **ユーザー**、 **プロダクト** のように三つにすることで、データベース一つあたりの書き込み・読み取りのトラフィックが減り、その結果レプリケーションのラグも短くなります。データベースが小さくなることで、メモリーに収まるデータが増えます。キャッシュの局所性が高まるため、キャッシュヒット率も上がります。単一の中央マスターで書き込みを直列化したりしないため、並列で書き込みを処理することができ、スループットの向上が期待できます。
|
||||
|
||||
##### 欠点: federation
|
||||
|
||||
|
|
@ -846,7 +846,7 @@ SQLなどのリレーショナルデータベースはテーブルに整理さ
|
|||
#### シャーディング
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/wU8x5Id.png">
|
||||
<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>
|
||||
|
|
@ -902,31 +902,31 @@ SQLチューニングは広範な知識を必要とする分野で多くの [本
|
|||
|
||||
##### スキーマを絞る
|
||||
|
||||
* より早い接続を得るために、連続したブロックの中のディスクにMySQLをダンプする。
|
||||
* MySQLはアクセス速度向上のため、ディスク上の連続したブロックへデータを格納しています。
|
||||
* 長さの決まったフィールドに対しては `VARCHAR` よりも `CHAR` を使うようにしましょう。
|
||||
* `CHAR` の方が効率的に速くランダムにデータにアクセスできます。 一方、 `VARCHAR` では次のデータに移る前にデータの末尾を検知しなければならないために速度が犠牲になります。
|
||||
* ブログ投稿などの大きなテキスト `TEXT` を使いましょう。 `TEXT` ではブーリアン型の検索も可能です。 `TEXT` フィールドを使うことは、テキストブロックを配置するのに用いたポインターをディスク上に保存することになります。
|
||||
* 2の32乗や40億を超えてくる数に関しては `INT` を使いましょう
|
||||
* ブログの投稿など、大きなテキストには TEXT を使いましょう。 TEXT ではブーリアン型の検索も可能です。 TEXT フィールドには、テキストブロックが配置されている、ディスク上の場所へのポインターが保存されます。
|
||||
* 2の32乗や40億以下を超えない程度の大きな数には INT を使いましょう。
|
||||
* 通貨に関しては小数点表示上のエラーを避けるために `DECIMAL` を使いましょう。
|
||||
* 大きな `BLOBS` を保存するのは避けましょう。どこからそのオブジェクトを取ってくることができるかの情報を保存しましょう。
|
||||
* `VARCHAR(255)` は8ビットで数えることができる中で最大の文字数ですが、このフィールドがしばしばRDBMSの中で大きな容量を食います。
|
||||
* [検索性能を向上させる](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search) ことが可能な箇所については `NOT NULL` 制約を設定しましょう
|
||||
* `VARCHAR(255)` は8ビットで数えられる最大の文字数です。一部のDBMSでは、1バイトの利用効率を最大化するためにこの文字数がよく使われます。
|
||||
* [検索性能向上のため](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search) 、可能であれば `NOT NULL` 制約を設定しましょう。
|
||||
|
||||
##### インデックスを効果的に用いる
|
||||
|
||||
* クエリ(`SELECT`、 `GROUP BY`、 `ORDER BY`、 `JOIN`) を用いて取得する列はインデックスを用いると速度を向上できる。
|
||||
* インデックスは通常、対数的にデータを検索、挿入、削除する際に用いる[B-tree](https://en.wikipedia.org/wiki/B-tree)として表現されています。
|
||||
* クエリ(`SELECT`、 `GROUP BY`、 `ORDER BY`、 `JOIN`) の対象となる列にインデックスを使うことで速度を向上できるかもしれません。
|
||||
* インデックスは通常、平衡探索木である[B木](https://en.wikipedia.org/wiki/B-tree)の形で表されます。B木によりデータは常にソートされた状態になります。また検索、順次アクセス、挿入、削除を対数時間で行えます。
|
||||
* インデックスを配置することはデータをメモリーに残すことにつながりより容量を必要とします。
|
||||
* インデックスの更新も必要になるため書き込みも遅くなります。
|
||||
* 大きなデータを読み込む際には、インデックスを切ってからデータをロードして再びインデックスをビルドした方が速いことがあります。
|
||||
* 大量のデータをロードする際には、インデックスを切ってからデータをロードして再びインデックスをビルドした方が速いことがあります。
|
||||
|
||||
##### 高負荷なジョインを避ける
|
||||
|
||||
* パフォーマンスが必要なところには[非正規化](#非正規化)を適用する
|
||||
* パフォーマンス上必要なところには[非正規化](#非正規化)を適用する
|
||||
|
||||
##### テーブルのパーティション
|
||||
|
||||
* メモリー内に保つために、分離されたテーブルを分割してそれぞれにホットスポットを設定する。
|
||||
* テーブルを分割し、ホットスポットを独立したテーブルに分離してメモリーに乗せられるようにする。
|
||||
|
||||
##### クエリキャッシュを調整する
|
||||
|
||||
|
|
@ -935,7 +935,7 @@ SQLチューニングは広範な知識を必要とする分野で多くの [本
|
|||
##### その他の参考資料、ページ: SQLチューニング
|
||||
|
||||
* [MySQLクエリを最適化するためのTips](http://20bits.com/article/10-tips-for-optimizing-mysql-queries-that-dont-suck)
|
||||
* [VARCHAR(255)をそんなにたくさん使う必要ある？](http://stackoverflow.com/questions/1217466/is-there-a-good-reason-i-see-varchar255-used-so-often-as-opposed-to-another-l)
|
||||
* [VARCHAR(255)をやたらよく見かけるのはなんで？](http://stackoverflow.com/questions/1217466/is-there-a-good-reason-i-see-varchar255-used-so-often-as-opposed-to-another-l)
|
||||
* [null値はどのようにパフォーマンスに影響するのか？](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)
|
||||
|
||||
|
|
@ -955,7 +955,7 @@ NoSQL は **key-value store**、 **document-store**、 **wide column store**、
|
|||
|
||||
> 概要: ハッシュテーブル
|
||||
|
||||
キーバリューストアでは一般的に0、1の読み、書きができ、それらはメモリないしSSDで裏付けられています。データストアはキーを [辞書的順序](https://en.wikipedia.org/wiki/Lexicographical_order) で保持することでキーの効率的な取得を可能にしています。キーバリューストアではメタデータを値とともに保持することが可能です。
|
||||
キーバリューストアでは一般的にO(1)の読み書きができ、それらはメモリないしSSDで裏付けられています。データストアはキーを [辞書的順序](https://en.wikipedia.org/wiki/Lexicographical_order) で保持することでキーの効率的な取得を可能にしています。キーバリューストアではメタデータを値とともに保持することが可能です。
|
||||
|
||||
キーバリューストアはハイパフォーマンスな挙動が可能で、単純なデータモデルやインメモリーキャッシュレイヤーなどのデータが急速に変わる場合などに使われます。単純な処理のみに機能が制限されているので、追加の処理機能が必要な場合にはその複雑性はアプリケーション層に載せることになります。
|
||||
|
||||
|
|
@ -982,7 +982,7 @@ NoSQL は **key-value store**、 **document-store**、 **wide column store**、
|
|||
|
||||
##### その他の参考資料、ページ: ドキュメントストア
|
||||
|
||||
* [ドキュメント志向 データベース](https://en.wikipedia.org/wiki/Document-oriented_database)
|
||||
* [ドキュメント指向 データベース](https://en.wikipedia.org/wiki/Document-oriented_database)
|
||||
* [MongoDB アーキテクチャ](https://www.mongodb.com/mongodb-architecture)
|
||||
* [CouchDB アーキテクチャ](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/)
|
||||
* [Elasticsearch アーキテクチャ](https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up)
|
||||
|
|
@ -990,7 +990,7 @@ NoSQL は **key-value store**、 **document-store**、 **wide column store**、
|
|||
#### ワイドカラムストア
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/n16iOGk.png">
|
||||
<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>
|
||||
|
|
@ -1013,7 +1013,7 @@ Googleは[Bigtable](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/cha
|
|||
#### グラフデータベース
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/fNcl65g.png">
|
||||
<img src="images/fNcl65g.png">
|
||||
<br/>
|
||||
<i><a href=https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png>Source: Graph database</a></i>
|
||||
</p>
|
||||
|
|
@ -1041,7 +1041,7 @@ Googleは[Bigtable](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/cha
|
|||
### SQLか？NoSQLか？
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/wXGqG5f.png">
|
||||
<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>
|
||||
|
|
@ -1083,7 +1083,7 @@ NoSQLに適するサンプルデータ:
|
|||
## キャッシュ
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/Q6z24La.png">
|
||||
<img src="images/Q6z24La.png">
|
||||
<br/>
|
||||
<i><a href=http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html>Source: Scalable system design patterns</a></i>
|
||||
</p>
|
||||
|
|
@ -1154,7 +1154,7 @@ Redisはさらに以下のような機能を備えています:
|
|||
#### キャッシュアサイド
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/ONjORqk.png">
|
||||
<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>
|
||||
|
|
@ -1166,7 +1166,7 @@ Redisはさらに以下のような機能を備えています:
|
|||
* エントリをキャッシュに追加します
|
||||
* エントリを返します
|
||||
|
||||
```
|
||||
```python
|
||||
def get_user(self, user_id):
|
||||
user = cache.get("user.{0}", user_id)
|
||||
if user is None:
|
||||
|
|
@ -1190,7 +1190,7 @@ def get_user(self, user_id):
|
|||
#### ライトスルー
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/0vBc0hN.png">
|
||||
<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>
|
||||
|
|
@ -1209,7 +1209,7 @@ set_user(12345, {"foo":"bar"})
|
|||
|
||||
キャッシュコード:
|
||||
|
||||
```
|
||||
```python
|
||||
def set_user(user_id, values):
|
||||
user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
|
||||
cache.set(user_id, user)
|
||||
|
|
@ -1225,7 +1225,7 @@ def set_user(user_id, values):
|
|||
#### ライトビハインド (ライトバック)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/rgSrvjG.png">
|
||||
<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>
|
||||
|
|
@ -1243,7 +1243,7 @@ def set_user(user_id, values):
|
|||
#### リフレッシュアヘッド
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/kxtjqgE.png">
|
||||
<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>
|
||||
|
|
@ -1275,7 +1275,7 @@ def set_user(user_id, values):
|
|||
## 非同期処理
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/54GYsSx.png">
|
||||
<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>
|
||||
|
|
@ -1321,7 +1321,7 @@ def set_user(user_id, values):
|
|||
## 通信
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/5KeocQs.jpg">
|
||||
<img src="images/5KeocQs.jpg">
|
||||
<br/>
|
||||
<i><a href=http://www.escotal.com/osilayer.html>Source: OSI 7 layer model</a></i>
|
||||
</p>
|
||||
|
|
@ -1353,7 +1353,7 @@ HTTPは**TCP** や **UDP** などの低級プロトコルに依存している
|
|||
### 伝送制御プロトコル (TCP)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/JdAsdvG.jpg">
|
||||
<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>
|
||||
|
|
@ -1377,7 +1377,7 @@ TCPは高い依存性を要し、時間制約が厳しくないものに適し
|
|||
### ユーザデータグラムプロトコル (UDP)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/yzDrJtA.jpg">
|
||||
<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>
|
||||
|
|
@ -1406,7 +1406,7 @@ TCPよりもUDPを使うのは:
|
|||
### 遠隔手続呼出 (RPC)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/iF4Mkb5.png">
|
||||
<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>
|
||||
|
|
@ -1555,7 +1555,7 @@ Latency Comparison Numbers
|
|||
L1 cache reference 0.5 ns
|
||||
Branch mispredict 5 ns
|
||||
L2 cache reference 7 ns 14x L1 cache
|
||||
Mutex lock/unlock 100 ns
|
||||
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
|
||||
|
|
@ -1602,7 +1602,7 @@ Notes
|
|||
| 質問 | 解答 |
|
||||
|---|---|
|
||||
| Dropboxのようなファイル同期サービスを設計する | [youtube.com](https://www.youtube.com/watch?v=PE4gwstWhmc) |
|
||||
| Googleのような検索エンジンの設計 | [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) |
|
||||
| Googleのような検索エンジンの設計 | [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) |
|
||||
| Googleのようなスケーラブルなwebクローラーの設計 | [quora.com](https://www.quora.com/How-can-I-build-a-web-crawler-from-scratch) |
|
||||
| Google docsの設計 | [code.google.com](https://code.google.com/p/google-mobwrite/)<br/>[neil.fraser.name](https://neil.fraser.name/writing/sync/) |
|
||||
| Redisのようなキーバリューストアの設計 | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
|
||||
|
|
@ -1629,7 +1629,7 @@ Notes
|
|||
> 世の中のシステムがどのように設計されているかについての記事
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/TcUo2fw.png">
|
||||
<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>
|
||||
|
|
|
|||
|
|
@ -1,23 +1,17 @@
|
|||
> * 原文地址：[github.com/donnemartin/system-design-primer](https://github.com/donnemartin/system-design-primer)
|
||||
> * 译文出自：[掘金翻译计划](https://github.com/xitu/gold-miner)
|
||||
> * 译者：[XatMassacrE](https://github.com/XatMassacrE)、[L9m](https://github.com/L9m)、[Airmacho](https://github.com/Airmacho)、[xiaoyusilen](https://github.com/xiaoyusilen)、[jifaxu](https://github.com/jifaxu)
|
||||
> * 译者：[XatMassacrE](https://github.com/XatMassacrE)、[L9m](https://github.com/L9m)、[Airmacho](https://github.com/Airmacho)、[xiaoyusilen](https://github.com/xiaoyusilen)、[jifaxu](https://github.com/jifaxu)、[根号三](https://github.com/sqrthree)
|
||||
> * 这个 [链接](https://github.com/xitu/system-design-primer/compare/master...donnemartin:master) 用来查看本翻译与英文版是否有差别（如果你没有看到 README.md 发生变化，那就意味着这份翻译文档是最新的）。
|
||||
|
||||
*[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)*
|
||||
|
||||
# 系统设计入门
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/jj3A5N8.png">
|
||||
<img src="images/jj3A5N8.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
## 翻译
|
||||
|
||||
有兴趣参与[翻译](https://github.com/donnemartin/system-design-primer/issues/28)? 以下是正在进行中的翻译:
|
||||
|
||||
* [巴西葡萄牙语](https://github.com/donnemartin/system-design-primer/issues/40)
|
||||
* [简体中文](https://github.com/donnemartin/system-design-primer/issues/38)
|
||||
* [土耳其语](https://github.com/donnemartin/system-design-primer/issues/39)
|
||||
|
||||
## 目的
|
||||
|
||||
> 学习如何设计大型系统。
|
||||
|
|
@ -55,7 +49,7 @@
|
|||
## 抽认卡
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/zdCAkB3.png">
|
||||
<img src="images/zdCAkB3.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -72,7 +66,7 @@
|
|||
你正在寻找资源以准备[**编程面试**](https://github.com/donnemartin/interactive-coding-challenges)吗？
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/b4YtAEN.png">
|
||||
<img src="images/b4YtAEN.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -89,6 +83,7 @@
|
|||
* 修复错误
|
||||
* 完善章节
|
||||
* 添加章节
|
||||
* [帮助翻译](https://github.com/donnemartin/system-design-primer/issues/28)
|
||||
|
||||
一些还需要完善的内容放在了[正在完善中](#正在完善中)。
|
||||
|
||||
|
|
@ -102,7 +97,7 @@
|
|||
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/jrUBAF7.png">
|
||||
<img src="images/jrUBAF7.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -191,7 +186,7 @@
|
|||
|
||||
> 基于你面试的时间线（短、中、长）去复习那些推荐的主题。
|
||||
|
||||
|
||||
|
||||
|
||||
**问：对于面试来说，我需要知道这里的所有知识点吗？**
|
||||
|
||||
|
|
@ -298,7 +293,7 @@
|
|||
|
||||
| 问题 | |
|
||||
| ---------------------------------------- | ---------------------------------------- |
|
||||
| 设计 Pastebin.com (或者 Bit.ly) | [解答](solutions/system_design/pastebin/README.md) |
|
||||
| 设计 Pastebin.com (或者 Bit.ly) | [解答](solutions/system_design/pastebin/README-zh-Hans.md) |
|
||||
| 设计 Twitter 时间线和搜索 (或者 Facebook feed 和搜索) | [解答](solutions/system_design/twitter/README.md) |
|
||||
| 设计一个网页爬虫 | [解答](solutions/system_design/web_crawler/README.md) |
|
||||
| 设计 Mint.com | [解答](solutions/system_design/mint/README.md) |
|
||||
|
|
@ -312,49 +307,49 @@
|
|||
|
||||
[查看实践与解答](solutions/system_design/pastebin/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 设计 Twitter 时间线和搜索 (或者 Facebook feed 和搜索)
|
||||
|
||||
[查看实践与解答](solutions/system_design/twitter/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 设计一个网页爬虫
|
||||
|
||||
[查看实践与解答](solutions/system_design/web_crawler/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 设计 Mint.com
|
||||
|
||||
[查看实践与解答](solutions/system_design/mint/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 为一个社交网络设计数据结构
|
||||
|
||||
[查看实践与解答](solutions/system_design/social_graph/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 为搜索引擎设计一个 key-value 储存
|
||||
|
||||
[查看实践与解答](solutions/system_design/query_cache/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 设计按类别分类的 Amazon 销售排名
|
||||
|
||||
[查看实践与解答](solutions/system_design/sales_rank/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 在 AWS 上设计一个百万用户级别的系统
|
||||
|
||||
[查看实践与解答](solutions/system_design/scaling_aws/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
## 面向对象设计的面试问题及解答
|
||||
|
||||
|
|
@ -395,7 +390,7 @@
|
|||
|
||||
### 第二步：回顾可扩展性文章
|
||||
|
||||
[可扩展性](http://www.lecloud.net/tagged/scalability)
|
||||
[可扩展性](http://www.lecloud.net/tagged/scalability/chrono)
|
||||
|
||||
* 主题涵盖：
|
||||
* [Clones](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
|
||||
|
|
@ -446,7 +441,7 @@
|
|||
### CAP 理论
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/bgLMI2u.png">
|
||||
<img src="images/bgLMI2u.png">
|
||||
<br/>
|
||||
<strong><a href="http://robertgreiner.com/2014/08/cap-theorem-revisited">来源：再看 CAP 理论</a></strong>
|
||||
</p>
|
||||
|
|
@ -541,7 +536,7 @@ DNS 和 email 等系统使用的是此种方式。最终一致性在高可用性
|
|||
## 域名系统
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/IOyLj4i.jpg">
|
||||
<img src="images/IOyLj4i.jpg">
|
||||
<br/>
|
||||
<strong><a href="http://www.slideshare.net/srikrupa5/dns-security-presentation-issa">来源：DNS 安全介绍</a></strong>
|
||||
</p>
|
||||
|
|
@ -579,7 +574,7 @@ DNS 和 email 等系统使用的是此种方式。最终一致性在高可用性
|
|||
## 内容分发网络（CDN）
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/h9TAuGI.jpg">
|
||||
<img src="images/h9TAuGI.jpg">
|
||||
<br/>
|
||||
<strong><a href="https://www.creative-artworks.eu/why-use-a-content-delivery-network-cdn/">来源：为什么使用 CDN</a></strong>
|
||||
</p>
|
||||
|
|
@ -618,7 +613,7 @@ CDN 拉取是当第一个用户请求该资源时，从服务器上拉取资源
|
|||
## 负载均衡器
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/h81n9iK.png">
|
||||
<img src="images/h81n9iK.png">
|
||||
<br/>
|
||||
<strong><a href="http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html">来源：可扩展的系统设计模式</a></strong>
|
||||
</p>
|
||||
|
|
@ -687,7 +682,7 @@ CDN 拉取是当第一个用户请求该资源时，从服务器上拉取资源
|
|||
## 反向代理（web 服务器）
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/n41Azff.png">
|
||||
<img src="images/n41Azff.png">
|
||||
<br/>
|
||||
<strong><a href="https://upload.wikimedia.org/wikipedia/commons/6/67/Reverse_proxy_h2g2bob.svg">资料来源：维基百科</a></strong>
|
||||
<br/>
|
||||
|
|
@ -731,7 +726,7 @@ CDN 拉取是当第一个用户请求该资源时，从服务器上拉取资源
|
|||
## 应用层
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/yB5SYwm.png">
|
||||
<img src="images/yB5SYwm.png">
|
||||
<br/>
|
||||
<strong><a href="http://lethain.com/introduction-to-architecting-systems-for-scale/#platform_layer">资料来源：可缩放系统构架介绍</a></strong>
|
||||
</p>
|
||||
|
|
@ -769,7 +764,7 @@ CDN 拉取是当第一个用户请求该资源时，从服务器上拉取资源
|
|||
## 数据库
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/Xkm5CXz.png">
|
||||
<img src="images/Xkm5CXz.png">
|
||||
<br/>
|
||||
<strong><a href="https://www.youtube.com/watch?v=w95murBkYmU">资料来源：扩展你的用户数到第一个一千万</a></strong>
|
||||
</p>
|
||||
|
|
@ -790,7 +785,7 @@ CDN 拉取是当第一个用户请求该资源时，从服务器上拉取资源
|
|||
关系型数据库扩展包括许多技术：**主从复制**、**主主复制**、**联合**、**分片**、**非规范化**和 **SQL调优**。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/C9ioGtn.png">
|
||||
<img src="images/C9ioGtn.png">
|
||||
<br/>
|
||||
<strong><a href="http://www.slideshare.net/jboner/scalability-availability-stability-patterns/">资料来源：可扩展性、可用性、稳定性、模式</a></strong>
|
||||
</p>
|
||||
|
|
@ -805,7 +800,7 @@ CDN 拉取是当第一个用户请求该资源时，从服务器上拉取资源
|
|||
- 参考[不利之处：复制](#不利之处复制)中，主从复制和主主复制**共同**的问题。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/krAHLGg.png">
|
||||
<img src="images/krAHLGg.png">
|
||||
<br/>
|
||||
<strong><a href="http://www.slideshare.net/jboner/scalability-availability-stability-patterns/">资料来源：可扩展性、可用性、稳定性、模式</a></strong>
|
||||
</p>
|
||||
|
|
@ -840,7 +835,7 @@ CDN 拉取是当第一个用户请求该资源时，从服务器上拉取资源
|
|||
#### 联合
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/U3qV33e.png">
|
||||
<img src="images/U3qV33e.png">
|
||||
<br/>
|
||||
<strong><a href="https://www.youtube.com/watch?v=w95murBkYmU">资料来源：扩展你的用户数到第一个一千万</a></strong>
|
||||
</p>
|
||||
|
|
@ -862,7 +857,7 @@ CDN 拉取是当第一个用户请求该资源时，从服务器上拉取资源
|
|||
#### 分片
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/wU8x5Id.png">
|
||||
<img src="images/wU8x5Id.png">
|
||||
<br/>
|
||||
<strong><a href="http://www.slideshare.net/jboner/scalability-availability-stability-patterns/">资料来源：可扩展性、可用性、稳定性、模式</a></strong>
|
||||
</p>
|
||||
|
|
@ -924,7 +919,7 @@ SQL 调优是一个范围很广的话题，有很多相关的[书](https://www.a
|
|||
- 使用 `TEXT` 类型存储大块的文本，例如博客正文。`TEXT` 还允许布尔搜索。使用 `TEXT` 字段需要在磁盘上存储一个用于定位文本块的指针。
|
||||
- 使用 `INT` 类型存储高达 2^32 或 40 亿的较大数字。
|
||||
- 使用 `DECIMAL` 类型存储货币可以避免浮点数表示错误。
|
||||
- 避免使用 `BLOBS` 存储对象，存储存放对象的位置。
|
||||
- 避免使用 `BLOBS` 存储实际对象，而是用来存储存放对象的位置。
|
||||
- `VARCHAR(255)` 是以 8 位数字存储的最大字符数，在某些关系型数据库中，最大限度地利用字节。
|
||||
- 在适用场景中设置 `NOT NULL` 约束来[提高搜索性能](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search)。
|
||||
|
||||
|
|
@ -1006,7 +1001,7 @@ MongoDB 和 CouchDB 等一些文档类型存储还提供了类似 SQL 语言的
|
|||
#### 列型存储
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/n16iOGk.png">
|
||||
<img src="images/n16iOGk.png">
|
||||
<br/>
|
||||
<strong><a href="http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html">资料来源: SQL 和 NoSQL，一个简短的历史</a></strong>
|
||||
</p>
|
||||
|
|
@ -1029,9 +1024,9 @@ Google 发布了第一个列型存储数据库 [Bigtable](http://www.read.seas.h
|
|||
#### 图数据库
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/fNcl65g.png">
|
||||
<img src="images/fNcl65g.png">
|
||||
<br/>
|
||||
<strong><a href="https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png">资料来源：图数据库</a></strong>
|
||||
<strong><a href="https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png"/>资料来源：图数据库</a></strong>
|
||||
</p>
|
||||
|
||||
> 抽象模型： 图
|
||||
|
|
@ -1056,7 +1051,7 @@ Google 发布了第一个列型存储数据库 [Bigtable](http://www.read.seas.h
|
|||
### SQL 还是 NoSQL
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/wXGqG5f.png">
|
||||
<img src="images/wXGqG5f.png">
|
||||
<br/>
|
||||
<strong><a href="https://www.infoq.com/articles/Transition-RDBMS-NoSQL/">资料来源：从 RDBMS 转换到 NoSQL</a></strong>
|
||||
</p>
|
||||
|
|
@ -1097,7 +1092,7 @@ Google 发布了第一个列型存储数据库 [Bigtable](http://www.read.seas.h
|
|||
## 缓存
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/Q6z24La.png">
|
||||
<img src="images/Q6z24La.png">
|
||||
<br/>
|
||||
<strong><a href="http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html">资料来源：可扩展的系统设计模式</a></strong>
|
||||
</p>
|
||||
|
|
@ -1168,7 +1163,7 @@ Redis 有下列附加功能：
|
|||
#### 缓存模式
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/ONjORqk.png">
|
||||
<img src="images/ONjORqk.png">
|
||||
<br/>
|
||||
<strong><a href="http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast">资料来源：从缓存到内存数据网格</a></strong>
|
||||
</p>
|
||||
|
|
@ -1180,7 +1175,7 @@ Redis 有下列附加功能：
|
|||
- 将查找到的结果存储到缓存中
|
||||
- 返回所需内容
|
||||
|
||||
```
|
||||
```python
|
||||
def get_user(self, user_id):
|
||||
user = cache.get("user.{0}", user_id)
|
||||
if user is None:
|
||||
|
|
@ -1204,7 +1199,7 @@ def get_user(self, user_id):
|
|||
#### 直写模式
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/0vBc0hN.png">
|
||||
<img src="images/0vBc0hN.png">
|
||||
<br/>
|
||||
<strong><a href="http://www.slideshare.net/jboner/scalability-availability-stability-patterns/">资料来源：可扩展性、可用性、稳定性、模式</a></strong>
|
||||
</p>
|
||||
|
|
@ -1223,7 +1218,7 @@ set_user(12345, {"foo":"bar"})
|
|||
|
||||
缓存代码：
|
||||
|
||||
```
|
||||
```python
|
||||
def set_user(user_id, values):
|
||||
user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
|
||||
cache.set(user_id, user)
|
||||
|
|
@ -1239,7 +1234,7 @@ def set_user(user_id, values):
|
|||
#### 回写模式
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/rgSrvjG.png">
|
||||
<img src="images/rgSrvjG.png">
|
||||
<br/>
|
||||
<strong><a href="http://www.slideshare.net/jboner/scalability-availability-stability-patterns/">资料来源：可扩展性、可用性、稳定性、模式</a></strong>
|
||||
</p>
|
||||
|
|
@ -1257,7 +1252,7 @@ def set_user(user_id, values):
|
|||
#### 刷新
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/kxtjqgE.png">
|
||||
<img src="images/kxtjqgE.png">
|
||||
<br/>
|
||||
<strong><a href=http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast>资料来源：从缓存到内存数据网格</a></strong>
|
||||
</p>
|
||||
|
|
@ -1289,7 +1284,7 @@ def set_user(user_id, values):
|
|||
## 异步
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/54GYsSx.png">
|
||||
<img src="images/54GYsSx.png">
|
||||
<br/>
|
||||
<strong><a href=http://lethain.com/introduction-to-architecting-systems-for-scale/#platform_layer>资料来源：可缩放系统构架介绍</a></strong>
|
||||
</p>
|
||||
|
|
@ -1335,7 +1330,7 @@ def set_user(user_id, values):
|
|||
## 通讯
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/5KeocQs.jpg">
|
||||
<img src="images/5KeocQs.jpg">
|
||||
<br/>
|
||||
<strong><a href=http://www.escotal.com/osilayer.html>资料来源：OSI 7层模型</a></strong>
|
||||
</p>
|
||||
|
|
@ -1370,7 +1365,7 @@ HTTP 是依赖于较低级协议（如 **TCP** 和 **UDP**）的应用层协议
|
|||
### 传输控制协议（TCP）
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/JdAsdvG.jpg">
|
||||
<img src="images/JdAsdvG.jpg">
|
||||
<br/>
|
||||
<strong><a href="http://www.wildbunny.co.uk/blog/2012/10/09/how-to-make-a-multi-player-game-part-1/">资料来源：如何制作多人游戏</a></strong>
|
||||
</p>
|
||||
|
|
@ -1394,7 +1389,7 @@ TCP 对于需要高可靠性但时间紧迫的应用程序很有用。比如包
|
|||
### 用户数据报协议（UDP）
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/yzDrJtA.jpg">
|
||||
<img src="images/yzDrJtA.jpg">
|
||||
<br/>
|
||||
<strong><a href="http://www.wildbunny.co.uk/blog/2012/10/09/how-to-make-a-multi-player-game-part-1">资料来源：如何制作多人游戏</a></strong>
|
||||
</p>
|
||||
|
|
@ -1423,7 +1418,7 @@ UDP 可靠性更低但适合用在网络电话、视频聊天，流媒体和实
|
|||
### 远程过程调用协议（RPC）
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/iF4Mkb5.png">
|
||||
<img src="images/iF4Mkb5.png">
|
||||
<br/>
|
||||
<strong><a href="http://www.puncsky.com/blog/2016/02/14/crack-the-system-design-interview">Source: Crack the system design interview</a></strong>
|
||||
</p>
|
||||
|
|
@ -1572,7 +1567,7 @@ Latency Comparison Numbers
|
|||
L1 cache reference 0.5 ns
|
||||
Branch mispredict 5 ns
|
||||
L2 cache reference 7 ns 14x L1 cache
|
||||
Mutex lock/unlock 100 ns
|
||||
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
|
||||
|
|
@ -1618,10 +1613,10 @@ Notes
|
|||
| 问题 | 引用 |
|
||||
| ----------------------- | ---------------------------------------- |
|
||||
| 设计类似于 Dropbox 的文件同步服务 | [youtube.com](https://www.youtube.com/watch?v=PE4gwstWhmc) |
|
||||
| 设计类似于 Google 的搜索引擎 | [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) |
|
||||
| 设计类似于 Google 的搜索引擎 | [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) |
|
||||
| 设计类似于 Google 的可扩展网络爬虫 | [quora.com](https://www.quora.com/How-can-I-build-a-web-crawler-from-scratch) |
|
||||
| 设计 Google 文档 | [code.google.com](https://code.google.com/p/google-mobwrite/)<br/>[neil.fraser.name](https://neil.fraser.name/writing/sync/) |
|
||||
| 设计类似 Redis 的建值存储 | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
|
||||
| 设计类似 Redis 的键值存储 | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
|
||||
| 设计类似 Memcached 的缓存系统 | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) |
|
||||
| 设计类似亚马逊的推荐系统 | [hulu.com](http://tech.hulu.com/blog/2011/09/19/recommendation-system.html)<br/>[ijcai13.org](http://ijcai13.org/files/tutorial_slides/td3.pdf) |
|
||||
| 设计类似 Bitly 的短链接系统 | [n00tc0d3r.blogspot.com](http://n00tc0d3r.blogspot.com/) |
|
||||
|
|
@ -1645,7 +1640,7 @@ Notes
|
|||
> 关于现实中真实的系统是怎么设计的文章。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/TcUo2fw.png">
|
||||
<img src="images/TcUo2fw.png">
|
||||
<br/>
|
||||
<strong><a href="https://www.infoq.com/presentations/Twitter-Timeline-Scalability">Source: Twitter timelines at scale</a></strong>
|
||||
</p>
|
||||
|
|
|
|||
126
README-zh-TW.md
126
README-zh-TW.md
|
|
@ -1,9 +1,9 @@
|
|||
*[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) | [Brazilian Portuguese](https://github.com/donnemartin/system-design-primer/issues/40) ∙ [Italian](https://github.com/donnemartin/system-design-primer/issues/104) ∙ [Korean](https://github.com/donnemartin/system-design-primer/issues/102) ∙ [Persian](https://github.com/donnemartin/system-design-primer/issues/110) ∙ [Polish](https://github.com/donnemartin/system-design-primer/issues/68) ∙ [Russian](https://github.com/donnemartin/system-design-primer/issues/87) ∙ [Turkish](https://github.com/donnemartin/system-design-primer/issues/39) ∙ [Vietnamese](https://github.com/donnemartin/system-design-primer/issues/127) | [Add Translation](https://github.com/donnemartin/system-design-primer/issues/28)*
|
||||
*[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)*
|
||||
|
||||
# 系統設計入門
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/jj3A5N8.png">
|
||||
<img src="images/jj3A5N8.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -44,7 +44,7 @@
|
|||
## 學習單字卡
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/zdCAkB3.png">
|
||||
<img src="images/zdCAkB3.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -61,7 +61,7 @@
|
|||
你正在尋找資源來面對[**程式語言面試**](https://github.com/donnemartin/interactive-coding-challenges)嗎？
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/b4YtAEN.png">
|
||||
<img src="images/b4YtAEN.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -91,20 +91,20 @@
|
|||
> 每一章節都包含更深入資源的連結。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/jrUBAF7.png">
|
||||
<img src="images/jrUBAF7.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
* [系統設計主題：從這裡開始](#系統設計主題：從這裡開始)
|
||||
* [第一步：複習關於可擴展性的影片講座](#第一步：複習關於可擴展性的影片講座)
|
||||
* [第二步：複習關於可擴展性的文章](#第二步：複習關於可擴展性的文章)
|
||||
* [系統設計主題：從這裡開始](#系統設計主題從這裡開始)
|
||||
* [第一步：複習關於可擴展性的影片講座](#第一步複習關於可擴展性的影片講座)
|
||||
* [第二步：複習關於可擴展性的文章](#第二步複習關於可擴展性的文章)
|
||||
* [下一步](#下一步)
|
||||
* [效能與可擴展性](#效能與可擴展性)
|
||||
* [延遲與吞吐量](#延遲與吞吐量)
|
||||
* [可用性與一致性](#可用性與一致性)
|
||||
* [CAP 理論](#CAP-理論)
|
||||
* [CP - 一致性與部分容錯性](#CP-一致性與部分容錯性)
|
||||
* [AP - 可用性與部分容錯性](#AP-可用性與部分容錯性)
|
||||
* [CAP 理論](#cap-理論)
|
||||
* [CP-一致性與部分容錯性](#cp-一致性與部分容錯性)
|
||||
* [AP-可用性與部分容錯性](#ap-可用性與部分容錯性)
|
||||
* [一致性模式](#一致性模式)
|
||||
* [弱一致性](#弱一致性)
|
||||
* [最終一致性](#最終一致性)
|
||||
|
|
@ -113,37 +113,37 @@
|
|||
* [容錯轉移](#容錯轉移)
|
||||
* [複寫機制](#複寫機制)
|
||||
* [域名系統](#域名系統)
|
||||
* [內容傳遞網路(CDN)](#內容傳遞網路(CDN))
|
||||
* [推送式 CDNs](#推送式-CDNs)
|
||||
* [拉取式 CDNs](#拉取式-CDNs)
|
||||
* [內容傳遞網路(CDN)](#內容傳遞網路cdn)
|
||||
* [推送式 CDNs](#推送式-cdns)
|
||||
* [拉取式 CDNs](#拉取式-cdns)
|
||||
* [負載平衡器](#負載平衡器)
|
||||
* [主動到備用切換模式(AP Mode)](#主動到備用切換模式-(AP-Mode)-)
|
||||
* [雙主動切換模式(AA Mode)](#雙主動切換模式-(AA-Mode)-)
|
||||
* [主動到備用切換模式(AP Mode)](#主動到備用切換模式ap-mode)
|
||||
* [雙主動切換模式(AA Mode)](#雙主動切換模式aa-mode)
|
||||
* [第四層負載平衡](#第四層負載平衡)
|
||||
* [第七層負載平衡](#第七層負載平衡)
|
||||
* [水平擴展](#水平擴展)
|
||||
* [反向代理(網頁伺服器)](#反向代理(網頁伺服器))
|
||||
* [反向代理(網頁伺服器)](#反向代理網頁伺服器)
|
||||
* [負載平衡器與反向代理伺服器](#負載平衡器與反向代理伺服器)
|
||||
* [應用層](#應用層)
|
||||
* [微服務](#微服務)
|
||||
* [服務發現](#服務發現)
|
||||
* [資料庫](#資料庫)
|
||||
* [關連式資料庫管理系統(RDBMS)](#關連式資料庫管理系統(RDBMS))
|
||||
* [關連式資料庫管理系統(RDBMS)](#關連式資料庫管理系統rdbms)
|
||||
* [主從複寫](#主從複寫)
|
||||
* [主動模式複寫](#主動模式複寫)
|
||||
* [聯邦式資料庫](#聯邦式資料庫)
|
||||
* [分片](#分片)
|
||||
* [反正規化](#反正規化)
|
||||
* [SQL 優化](#SQL-優化)
|
||||
* [NoSQL](#NoSQL)
|
||||
* [SQL 優化](#sql-優化)
|
||||
* [NoSQL](#nosql)
|
||||
* [鍵-值對的資料庫](#鍵-值對的資料庫)
|
||||
* [文件類型資料庫](#文件類型資料庫)
|
||||
* [列儲存型資料庫](#列儲存型資料庫)
|
||||
* [圖形資料庫](#圖形資料庫)
|
||||
* [SQL 或 NoSQL](#SQL-或-NoSQL)
|
||||
* [SQL 或 NoSQL](#sql-或-nosql)
|
||||
* [快取](#快取)
|
||||
* [客戶端快取](#客戶端快取)
|
||||
* [CDN 快取](#CDN-快取)
|
||||
* [CDN 快取](#cdn-快取)
|
||||
* [網站伺服器快取](#網站伺服器快取)
|
||||
* [資料庫快取](#資料庫快取)
|
||||
* [應用程式快取](#應用程式快取)
|
||||
|
|
@ -152,21 +152,21 @@
|
|||
* [什麼時候要更新快取](#什麼時候要更新快取)
|
||||
* [快取模式](#快取模式)
|
||||
* [寫入模式](#寫入模式)
|
||||
* [事後寫入(回寫)](#事後寫入(回寫))
|
||||
* [事後寫入(回寫)](#事後寫入回寫)
|
||||
* [更新式快取](#更新式快取)
|
||||
* [非同步機制](#非同步機制)
|
||||
* [訊息佇列](#訊息佇列)
|
||||
* [工作佇列](#工作佇列)
|
||||
* [背壓機制](#背壓機制)
|
||||
* [通訊](#通訊)
|
||||
* [傳輸控制通訊協定(TCP)](#傳輸控制通訊協定(TCP))
|
||||
* [傳輸控制通訊協定(TCP)](#傳輸控制通訊協定tcp)
|
||||
* [使用者資料流通訊協定 (UDP)](#使用者資料流通訊協定-udp)
|
||||
* [遠端程式呼叫 (RPC)](#遠端程式呼叫-rpc)
|
||||
* [具象狀態轉移 (REST)](#具象狀態轉移-rest)
|
||||
* [資訊安全](#資訊安全)
|
||||
* [附錄](#附錄)
|
||||
* [2 的次方表](#2-的次方表)
|
||||
* [每個開發者都應該知道的延遲數量](#每個開發者都應該知道的延遲數量)
|
||||
* [每個開發者都應該知道的延遲數量級](#每個開發者都應該知道的延遲數量級)
|
||||
* [其他的系統設計面試問題](#其他的系統設計面試問題)
|
||||
* [真實世界的架構](#真實世界的架構)
|
||||
* [公司的系統架構](#公司的系統架構)
|
||||
|
|
@ -180,7 +180,7 @@
|
|||
|
||||
> 基於你面試的時間 (短、中、長) 來複習這些建議的主題。
|
||||
|
||||
|
||||
|
||||
|
||||
**Q: 對於面試者來說，我需要知道這裡所有的知識嗎？**
|
||||
|
||||
|
|
@ -302,49 +302,49 @@
|
|||
|
||||
[閱讀練習與解答](solutions/system_design/pastebin/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 設計一個像是 Twitter 的 timeline (或 Facebook feed)設計一個 Twitter 搜尋功能 (or Facebook 搜尋功能)
|
||||
|
||||
[閱讀練習與解答](solutions/system_design/twitter/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 設計一個爬蟲系統
|
||||
|
||||
[閱讀練習與解答](solutions/system_design/web_crawler/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 設計 Mint.com 網站
|
||||
|
||||
[閱讀練習與解答](solutions/system_design/mint/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 設計一個社交網站的資料結構
|
||||
|
||||
[閱讀練習與解答](solutions/system_design/social_graph/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 設計一個搜尋引擎使用的鍵值儲存資料結構
|
||||
|
||||
[閱讀練習與解答](solutions/system_design/query_cache/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 設計一個根據產品分類的亞馬遜銷售排名
|
||||
|
||||
[閱讀練習與解答](solutions/system_design/sales_rank/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### 在 AWS 上設計一個百萬用戶等級的系統
|
||||
|
||||
[閱讀練習與解答](solutions/system_design/scaling_aws/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
## 物件導向設計面試問題與解答
|
||||
|
||||
|
|
@ -384,7 +384,7 @@
|
|||
|
||||
### 第二步：複習關於可擴展性的文章
|
||||
|
||||
[可擴展性](http://www.lecloud.net/tagged/scalability)
|
||||
[可擴展性](http://www.lecloud.net/tagged/scalability/chrono)
|
||||
|
||||
* 包含以下主題：
|
||||
* [複製](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
|
||||
|
|
@ -435,7 +435,7 @@
|
|||
### CAP 理論
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/bgLMI2u.png">
|
||||
<img src="images/bgLMI2u.png">
|
||||
<br/>
|
||||
<i><a href=http://robertgreiner.com/2014/08/cap-theorem-revisited>來源：再看 CAP 理論</a></i>
|
||||
</p>
|
||||
|
|
@ -529,7 +529,7 @@ DNS 或是電子郵件系統使用的就是這種方式，最終一致性在高
|
|||
## 域名系統
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/IOyLj4i.jpg">
|
||||
<img src="images/IOyLj4i.jpg">
|
||||
<br/>
|
||||
<i><a href=http://www.slideshare.net/srikrupa5/dns-security-presentation-issa>資料來源：DNS 安全介紹</a></i>
|
||||
</p>
|
||||
|
|
@ -567,7 +567,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
## 內容傳遞網路(CDN)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/h9TAuGI.jpg">
|
||||
<img src="images/h9TAuGI.jpg">
|
||||
<br/>
|
||||
<i><a href=https://www.creative-artworks.eu/why-use-a-content-delivery-network-cdn/>來源：為什麼要使用 CDN</a></i>
|
||||
</p>
|
||||
|
|
@ -608,7 +608,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
## 負載平衡器
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/h81n9iK.png">
|
||||
<img src="images/h81n9iK.png">
|
||||
<br/>
|
||||
<i><a href=http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html>來源：可擴展的系統設計模式</a></i>
|
||||
</p>
|
||||
|
|
@ -678,7 +678,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
## 反向代理(網頁伺服器)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/n41Azff.png">
|
||||
<img src="images/n41Azff.png">
|
||||
<br/>
|
||||
<i><a href=https://upload.wikimedia.org/wikipedia/commons/6/67/Reverse_proxy_h2g2bob.svg>來源：維基百科</a></i>
|
||||
<br/>
|
||||
|
|
@ -721,7 +721,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
## 應用層
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/yB5SYwm.png">
|
||||
<img src="images/yB5SYwm.png">
|
||||
<br/>
|
||||
<i><a href=http://lethain.com/introduction-to-architecting-systems-for-scale/#platform_layer>資料來源：可縮放式系統架構介紹</a></i>
|
||||
</p>
|
||||
|
|
@ -758,7 +758,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
## 資料庫
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/Xkm5CXz.png">
|
||||
<img src="images/Xkm5CXz.png">
|
||||
<br/>
|
||||
<i><a href=https://www.youtube.com/watch?v=vg5onp8TU6Q>來源：擴展你的使用者數量到第一個一千萬量級</a></i>
|
||||
</p>
|
||||
|
|
@ -781,7 +781,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
主資料庫負責讀和寫，並且將寫入的資料複寫至一或多個從屬資料庫中，從屬資料庫只負責讀取。而從屬資料庫可以再將寫入複製到更多以樹狀結構的其他資料庫中。如果主資料庫離線了，系統可以以只讀模式運行，直到某個從屬資料庫被提升為主資料庫，或有新的主資料庫出現。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/C9ioGtn.png">
|
||||
<img src="images/C9ioGtn.png">
|
||||
<br/>
|
||||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>來源： 可擴展性、可用性、穩定性及其模式</a></i>
|
||||
</p>
|
||||
|
|
@ -796,7 +796,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
兩個主要的資料庫都負責讀取和寫入，並且兩者互相協調。如果其中一個主要資料庫離線，系統可以繼續運作。
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/krAHLGg.png">
|
||||
<img src="images/krAHLGg.png">
|
||||
<br/>
|
||||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>來源： 可擴展性、可用性、穩定性及其模式</a></i>
|
||||
</p>
|
||||
|
|
@ -824,7 +824,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
#### 聯邦式資料庫
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/U3qV33e.png">
|
||||
<img src="images/U3qV33e.png">
|
||||
<br/>
|
||||
<i><a href=https://www.youtube.com/watch?v=vg5onp8TU6Q>來源：擴展你的使用者數量到第一個一千萬量級</a></i>
|
||||
</p>
|
||||
|
|
@ -845,7 +845,7 @@ DNS 是階層式的架構，一部分的 DNS 伺服器位於頂層，當查詢
|
|||
#### 分片
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/wU8x5Id.png">
|
||||
<img src="images/wU8x5Id.png">
|
||||
<br/>
|
||||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>來源： 可擴展性、可用性、穩定性及其模式</a></i>
|
||||
</p>
|
||||
|
|
@ -991,7 +991,7 @@ NoSQL 指的是 **鍵-值對的資料庫**、**文件類型資料庫**、**列
|
|||
#### 列儲存型資料庫
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/n16iOGk.png">
|
||||
<img src="images/n16iOGk.png">
|
||||
<br/>
|
||||
<i><a href=http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html>來源：SQL 和 NoSQL，簡短的歷史介紹</a></i>
|
||||
</p>
|
||||
|
|
@ -1014,7 +1014,7 @@ Google 發表了第一個列儲存型資料庫 [Bigtable](http://www.read.seas.h
|
|||
#### 圖形資料庫
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/fNcl65g.png">
|
||||
<img src="images/fNcl65g.png">
|
||||
<br/>
|
||||
<i><a href=https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png>來源： 圖形化資料庫</a></i>
|
||||
</p>
|
||||
|
|
@ -1042,7 +1042,7 @@ Google 發表了第一個列儲存型資料庫 [Bigtable](http://www.read.seas.h
|
|||
### SQL 或 NoSQL
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/wXGqG5f.png">
|
||||
<img src="images/wXGqG5f.png">
|
||||
<br/>
|
||||
<i><a href=https://www.infoq.com/articles/Transition-RDBMS-NoSQL/>來源：從 RDBMS 轉換到 NoSQL</a></i>
|
||||
</p>
|
||||
|
|
@ -1084,7 +1084,7 @@ Google 發表了第一個列儲存型資料庫 [Bigtable](http://www.read.seas.h
|
|||
## 快取
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/Q6z24La.png">
|
||||
<img src="images/Q6z24La.png">
|
||||
<br/>
|
||||
<i><a href=http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html>來源：可擴展的系統設計模式</a></i>
|
||||
</p>
|
||||
|
|
@ -1155,7 +1155,7 @@ Redis 還有以下額外的功能：
|
|||
#### 快取模式
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/ONjORqk.png">
|
||||
<img src="images/ONjORqk.png">
|
||||
<br/>
|
||||
<i><a href=http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast>資料來源：從快取到記憶體資料網格</a></i>
|
||||
</p>
|
||||
|
|
@ -1167,7 +1167,7 @@ Redis 還有以下額外的功能：
|
|||
* 將該筆記錄儲存到快取
|
||||
* 將資料返回
|
||||
|
||||
```
|
||||
```python
|
||||
def get_user(self, user_id):
|
||||
user = cache.get("user.{0}", user_id)
|
||||
if user is None:
|
||||
|
|
@ -1191,7 +1191,7 @@ def get_user(self, user_id):
|
|||
#### 寫入模式
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/0vBc0hN.png">
|
||||
<img src="images/0vBc0hN.png">
|
||||
<br/>
|
||||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>資料來源：可獲展性、可用性、穩定性與模式</a></i>
|
||||
</p>
|
||||
|
|
@ -1210,7 +1210,7 @@ set_user(12345, {"foo":"bar"})
|
|||
|
||||
快取程式碼：
|
||||
|
||||
```
|
||||
```python
|
||||
def set_user(user_id, values):
|
||||
user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
|
||||
cache.set(user_id, user)
|
||||
|
|
@ -1226,7 +1226,7 @@ def set_user(user_id, values):
|
|||
#### 事後寫入(回寫)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/rgSrvjG.png">
|
||||
<img src="images/rgSrvjG.png">
|
||||
<br/>
|
||||
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>資料來源：可獲展性、可用性、穩定性與模式</a></i>
|
||||
</p>
|
||||
|
|
@ -1244,7 +1244,7 @@ def set_user(user_id, values):
|
|||
#### 更新式快取
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/kxtjqgE.png">
|
||||
<img src="images/kxtjqgE.png">
|
||||
<br/>
|
||||
<i><a href=http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast>來源：從快取到記憶體資料網格技術</a></i>
|
||||
</p>
|
||||
|
|
@ -1276,7 +1276,7 @@ def set_user(user_id, values):
|
|||
## 非同步機制
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/54GYsSx.png">
|
||||
<img src="images/54GYsSx.png">
|
||||
<br/>
|
||||
<i><a href=http://lethain.com/introduction-to-architecting-systems-for-scale/#platform_layer>資料來源：可縮放性系統架構介紹</a></i>
|
||||
</p>
|
||||
|
|
@ -1322,7 +1322,7 @@ def set_user(user_id, values):
|
|||
## 通訊
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/5KeocQs.jpg">
|
||||
<img src="images/5KeocQs.jpg">
|
||||
<br/>
|
||||
<i><a href=http://www.escotal.com/osilayer.html>來源：OSI 七層模型</a></i>
|
||||
</p>
|
||||
|
|
@ -1354,7 +1354,7 @@ HTTP 是依賴於較底層的協議(例如：**TCP** 和 **UDP**) 的應用層
|
|||
### 傳輸控制通訊協定(TCP)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/JdAsdvG.jpg">
|
||||
<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/>來源：如何開發多人遊戲</a></i>
|
||||
</p>
|
||||
|
|
@ -1378,7 +1378,7 @@ TCP 對於需要高可靠、低時間急迫性的應用來說很有用，比如
|
|||
### 使用者資料流通訊協定 (UDP)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/yzDrJtA.jpg">
|
||||
<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/>資料來源：如何製作多人遊戲</a></i>
|
||||
</p>
|
||||
|
|
@ -1407,7 +1407,7 @@ UDP 的可靠性較低，但適合用在像是網路電話、視訊聊天、串
|
|||
### 遠端程式呼叫 (RPC)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/iF4Mkb5.png">
|
||||
<img src="images/iF4Mkb5.png">
|
||||
<br/>
|
||||
<i><a href=http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview>資料來源：破解系統設計面試</a></i>
|
||||
</p>
|
||||
|
|
@ -1556,7 +1556,7 @@ REST 關注於揭露資料，減少客戶端/伺服器之間耦合的程度，
|
|||
L1 快取參考數量級 0.5 ns
|
||||
Branch mispredict 5 ns
|
||||
L2 快取參考數量級 7 ns 14x L1 cache
|
||||
Mutex lock/unlock 100 ns
|
||||
Mutex lock/unlock 25 ns
|
||||
主記憶體參考數量級 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
|
||||
|
|
@ -1630,7 +1630,7 @@ Notes
|
|||
> 底下是關於真實世界的系統架構是如何設計的文章
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/TcUo2fw.png">
|
||||
<img src="images/TcUo2fw.png">
|
||||
<br/>
|
||||
<i><a href=https://www.infoq.com/presentations/Twitter-Timeline-Scalability>資料來源：可擴展式的 Twitter 時間軸設計</a></i>
|
||||
</p>
|
||||
|
|
|
|||
198
README.md
198
README.md
|
|
@ -1,9 +1,11 @@
|
|||
*[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) | [Arabic](https://github.com/donnemartin/system-design-primer/issues/170) ∙ [Brazilian Portuguese](https://github.com/donnemartin/system-design-primer/issues/40) ∙ [German](https://github.com/donnemartin/system-design-primer/issues/186) ∙ [Greek](https://github.com/donnemartin/system-design-primer/issues/130) ∙ [Italian](https://github.com/donnemartin/system-design-primer/issues/104) ∙ [Korean](https://github.com/donnemartin/system-design-primer/issues/102) ∙ [Persian](https://github.com/donnemartin/system-design-primer/issues/110) ∙ [Polish](https://github.com/donnemartin/system-design-primer/issues/68) ∙ [Russian](https://github.com/donnemartin/system-design-primer/issues/87) ∙ [Spanish](https://github.com/donnemartin/system-design-primer/issues/136) ∙ [Thai](https://github.com/donnemartin/system-design-primer/issues/187) ∙ [Turkish](https://github.com/donnemartin/system-design-primer/issues/39) ∙ [Vietnamese](https://github.com/donnemartin/system-design-primer/issues/127) | [Add Translation](https://github.com/donnemartin/system-design-primer/issues/28)*
|
||||
*[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)*
|
||||
|
||||
**Help [translate](TRANSLATIONS.md) this guide!**
|
||||
|
||||
# The System Design Primer
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/jj3A5N8.png">
|
||||
<img src="images/jj3A5N8.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -44,7 +46,7 @@ Additional topics for interview prep:
|
|||
## Anki flashcards
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/zdCAkB3.png">
|
||||
<img src="images/zdCAkB3.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -61,7 +63,7 @@ Great for use while on-the-go.
|
|||
Looking for resources to help you prep for the [**Coding Interview**](https://github.com/donnemartin/interactive-coding-challenges)?
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/b4YtAEN.png">
|
||||
<img src="images/b4YtAEN.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -91,7 +93,7 @@ Review the [Contributing Guidelines](CONTRIBUTING.md).
|
|||
> Each section contains links to more in-depth resources.
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/jrUBAF7.png">
|
||||
<img src="images/jrUBAF7.png">
|
||||
<br/>
|
||||
</p>
|
||||
|
||||
|
|
@ -112,6 +114,7 @@ Review the [Contributing Guidelines](CONTRIBUTING.md).
|
|||
* [Availability patterns](#availability-patterns)
|
||||
* [Fail-over](#fail-over)
|
||||
* [Replication](#replication)
|
||||
* [Availability in numbers](#availability-in-numbers)
|
||||
* [Domain name system](#domain-name-system)
|
||||
* [Content delivery network](#content-delivery-network)
|
||||
* [Push CDNs](#push-cdns)
|
||||
|
|
@ -180,7 +183,7 @@ Review the [Contributing Guidelines](CONTRIBUTING.md).
|
|||
|
||||
> Suggested topics to review based on your interview timeline (short, medium, long).
|
||||
|
||||
|
||||
|
||||
|
||||
**Q: For interviews, do I need to know everything here?**
|
||||
|
||||
|
|
@ -279,6 +282,7 @@ 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)
|
||||
|
||||
## System design interview questions with solutions
|
||||
|
||||
|
|
@ -302,49 +306,49 @@ Check out the following links to get a better idea of what to expect:
|
|||
|
||||
[View exercise and solution](solutions/system_design/pastebin/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Design the Twitter timeline and search (or Facebook feed and search)
|
||||
|
||||
[View exercise and solution](solutions/system_design/twitter/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Design a web crawler
|
||||
|
||||
[View exercise and solution](solutions/system_design/web_crawler/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Design Mint.com
|
||||
|
||||
[View exercise and solution](solutions/system_design/mint/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Design the data structures for a social network
|
||||
|
||||
[View exercise and solution](solutions/system_design/social_graph/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Design a key-value store for a search engine
|
||||
|
||||
[View exercise and solution](solutions/system_design/query_cache/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Design Amazon's sales ranking by category feature
|
||||
|
||||
[View exercise and solution](solutions/system_design/sales_rank/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
### Design a system that scales to millions of users on AWS
|
||||
|
||||
[View exercise and solution](solutions/system_design/scaling_aws/README.md)
|
||||
|
||||
|
||||
|
||||
|
||||
## Object-oriented design interview questions with solutions
|
||||
|
||||
|
|
@ -385,7 +389,7 @@ First, you'll need a basic understanding of common principles, learning about wh
|
|||
|
||||
### Step 2: Review the scalability article
|
||||
|
||||
[Scalability](http://www.lecloud.net/tagged/scalability)
|
||||
[Scalability](http://www.lecloud.net/tagged/scalability/chrono)
|
||||
|
||||
* Topics covered:
|
||||
* [Clones](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
|
||||
|
|
@ -436,7 +440,7 @@ Generally, you should aim for **maximal throughput** with **acceptable latency**
|
|||
### CAP theorem
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/bgLMI2u.png">
|
||||
<img src="images/bgLMI2u.png">
|
||||
<br/>
|
||||
<i><a href=http://robertgreiner.com/2014/08/cap-theorem-revisited>Source: CAP theorem revisited</a></i>
|
||||
</p>
|
||||
|
|
@ -455,15 +459,16 @@ Waiting for a response from the partitioned node might result in a timeout error
|
|||
|
||||
#### AP - availability and partition tolerance
|
||||
|
||||
Responses return the most recent version of the data available on a node, which might not be the latest. Writes might take some time to propagate when the partition is resolved.
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
### Source(s) and further reading
|
||||
|
||||
* [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/)
|
||||
* [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
|
||||
|
||||
|
|
@ -493,7 +498,7 @@ This approach is seen in file systems and RDBMSes. Strong consistency works wel
|
|||
|
||||
## Availability patterns
|
||||
|
||||
There are two main patterns to support high availability: **fail-over** and **replication**.
|
||||
There are two complementary patterns to support high availability: **fail-over** and **replication**.
|
||||
|
||||
### Fail-over
|
||||
|
||||
|
|
@ -527,10 +532,56 @@ 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
|
||||
|
||||
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.
|
||||
|
||||
###### In sequence
|
||||
|
||||
Overall availability decreases when two components with availability < 100% are in sequence:
|
||||
|
||||
```
|
||||
Availability (Total) = Availability (Foo) * Availability (Bar)
|
||||
```
|
||||
|
||||
If both `Foo` and `Bar` each had 99.9% availability, their total availability in sequence would be 99.8%.
|
||||
|
||||
###### In parallel
|
||||
|
||||
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%.
|
||||
|
||||
## Domain name system
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/IOyLj4i.jpg">
|
||||
<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>
|
||||
|
|
@ -546,12 +597,12 @@ DNS is hierarchical, with a few authoritative servers at the top level. Your ro
|
|||
|
||||
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](http://g33kinfo.com/info/archives/2657)
|
||||
* [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
|
||||
* Geolocation-based
|
||||
* [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
|
||||
|
||||
|
|
@ -568,7 +619,7 @@ Services such as [CloudFlare](https://www.cloudflare.com/dns/) and [Route 53](ht
|
|||
## Content delivery network
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/h9TAuGI.jpg">
|
||||
<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>
|
||||
|
|
@ -577,7 +628,7 @@ A content delivery network (CDN) is a globally distributed network of proxy serv
|
|||
|
||||
Serving content from CDNs can significantly improve performance in two ways:
|
||||
|
||||
* Users receive content at data centers close to them
|
||||
* Users receive content from data centers close to them
|
||||
* Your servers do not have to serve requests that the CDN fulfills
|
||||
|
||||
### Push CDNs
|
||||
|
|
@ -609,7 +660,7 @@ Sites with heavy traffic work well with pull CDNs, as traffic is spread out more
|
|||
## Load balancer
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/h81n9iK.png">
|
||||
<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>
|
||||
|
|
@ -618,7 +669,7 @@ Load balancers distribute incoming client requests to computing resources such a
|
|||
|
||||
* Preventing requests from going to unhealthy servers
|
||||
* Preventing overloading resources
|
||||
* Helping eliminate single points of failure
|
||||
* Helping to eliminate a single point of failure
|
||||
|
||||
Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.
|
||||
|
||||
|
|
@ -635,7 +686,7 @@ Load balancers can route traffic based on various metrics, including:
|
|||
* Random
|
||||
* Least loaded
|
||||
* Session/cookies
|
||||
* [Round robin or weighted round robin](http://g33kinfo.com/info/archives/2657)
|
||||
* [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)
|
||||
|
||||
|
|
@ -645,7 +696,7 @@ Layer 4 load balancers look at info at the [transport layer](#communication) to
|
|||
|
||||
### Layer 7 load balancing
|
||||
|
||||
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 terminates 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.
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
|
|
@ -663,7 +714,7 @@ Load balancers can also help with horizontal scaling, improving performance and
|
|||
### Disadvantage(s): load balancer
|
||||
|
||||
* The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly.
|
||||
* Introducing a load balancer to help eliminate single points of failure results in increased complexity.
|
||||
* Introducing a load balancer to help eliminate a single point of failure results in increased complexity.
|
||||
* A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.
|
||||
|
||||
### Source(s) and further reading
|
||||
|
|
@ -679,7 +730,7 @@ Load balancers can also help with horizontal scaling, improving performance and
|
|||
## Reverse proxy (web server)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/n41Azff.png">
|
||||
<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/>
|
||||
|
|
@ -722,7 +773,7 @@ Additional benefits include:
|
|||
## Application layer
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/yB5SYwm.png">
|
||||
<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>
|
||||
|
|
@ -757,9 +808,9 @@ Systems such as [Consul](https://www.consul.io/docs/index.html), [Etcd](https://
|
|||
## Database
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/Xkm5CXz.png">
|
||||
<img src="images/Xkm5CXz.png">
|
||||
<br/>
|
||||
<i><a href=https://www.youtube.com/watch?v=w95murBkYmU>Source: Scaling up to your first 10 million users</a></i>
|
||||
<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)
|
||||
|
|
@ -780,7 +831,7 @@ There are many techniques to scale a relational database: **master-slave replica
|
|||
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="http://i.imgur.com/C9ioGtn.png">
|
||||
<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>
|
||||
|
|
@ -795,7 +846,7 @@ The master serves reads and writes, replicating writes to one or more slaves, wh
|
|||
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="http://i.imgur.com/krAHLGg.png">
|
||||
<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>
|
||||
|
|
@ -823,9 +874,9 @@ Both masters serve reads and writes and coordinate with each other on writes. I
|
|||
#### Federation
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/U3qV33e.png">
|
||||
<img src="images/U3qV33e.png">
|
||||
<br/>
|
||||
<i><a href=https://www.youtube.com/watch?v=w95murBkYmU>Source: Scaling up to your first 10 million users</a></i>
|
||||
<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.
|
||||
|
|
@ -839,12 +890,12 @@ Federation (or functional partitioning) splits up databases by function. For ex
|
|||
|
||||
##### Source(s) and further reading: federation
|
||||
|
||||
* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=w95murBkYmU)
|
||||
* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs)
|
||||
|
||||
#### Sharding
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/wU8x5Id.png">
|
||||
<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>
|
||||
|
|
@ -939,7 +990,7 @@ Benchmarking and profiling might point you to the following optimizations.
|
|||
|
||||
### 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).
|
||||
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.
|
||||
|
||||
|
|
@ -947,7 +998,7 @@ NoSQL is a collection of data items represented in a **key-value store**, **docu
|
|||
* **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.
|
||||
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
|
||||
|
||||
|
|
@ -972,7 +1023,7 @@ A key-value store is the basis for more complex systems such as a document store
|
|||
|
||||
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 in either collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other.
|
||||
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.
|
||||
|
||||
|
|
@ -988,7 +1039,7 @@ Document stores provide high flexibility and are often used for working with occ
|
|||
#### Wide column store
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/n16iOGk.png">
|
||||
<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>
|
||||
|
|
@ -1011,7 +1062,7 @@ Wide column stores offer high availability and high scalability. They are often
|
|||
#### Graph database
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/fNcl65g.png">
|
||||
<img src="images/fNcl65g.png">
|
||||
<br/>
|
||||
<i><a href=https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png>Source: Graph database</a></i>
|
||||
</p>
|
||||
|
|
@ -1039,7 +1090,7 @@ Graphs databases offer high performance for data models with complex relationshi
|
|||
### SQL or NoSQL
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/wXGqG5f.png">
|
||||
<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>
|
||||
|
|
@ -1075,13 +1126,13 @@ Sample data well-suited for NoSQL:
|
|||
|
||||
##### Source(s) and further reading: SQL or NoSQL
|
||||
|
||||
* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=w95murBkYmU)
|
||||
* [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="http://i.imgur.com/Q6z24La.png">
|
||||
<img src="images/Q6z24La.png">
|
||||
<br/>
|
||||
<i><a href=http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html>Source: Scalable system design patterns</a></i>
|
||||
</p>
|
||||
|
|
@ -1152,7 +1203,7 @@ Since you can only store a limited amount of data in cache, you'll need to deter
|
|||
#### Cache-aside
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/ONjORqk.png">
|
||||
<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>
|
||||
|
|
@ -1164,7 +1215,7 @@ The application is responsible for reading and writing from storage. The cache
|
|||
* Add entry to cache
|
||||
* Return entry
|
||||
|
||||
```
|
||||
```python
|
||||
def get_user(self, user_id):
|
||||
user = cache.get("user.{0}", user_id)
|
||||
if user is None:
|
||||
|
|
@ -1188,7 +1239,7 @@ Subsequent reads of data added to cache are fast. Cache-aside is also referred
|
|||
#### Write-through
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/0vBc0hN.png">
|
||||
<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>
|
||||
|
|
@ -1201,13 +1252,13 @@ The application uses the cache as the main data store, reading and writing data
|
|||
|
||||
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)
|
||||
|
|
@ -1218,12 +1269,12 @@ Write-through is a slow overall operation due to the write operation, but subseq
|
|||
##### 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 read, which can be minimized with a TTL.
|
||||
* Most data written might never be read, which can be minimized with a TTL.
|
||||
|
||||
#### Write-behind (write-back)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/rgSrvjG.png">
|
||||
<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>
|
||||
|
|
@ -1241,7 +1292,7 @@ In write-behind, the application does the following:
|
|||
#### Refresh-ahead
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/kxtjqgE.png">
|
||||
<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>
|
||||
|
|
@ -1273,7 +1324,7 @@ Refresh-ahead can result in reduced latency vs read-through if the cache can acc
|
|||
## Asynchronism
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/54GYsSx.png">
|
||||
<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>
|
||||
|
|
@ -1319,7 +1370,7 @@ If queues start to grow significantly, the queue size can become larger than mem
|
|||
## Communication
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/5KeocQs.jpg">
|
||||
<img src="images/5KeocQs.jpg">
|
||||
<br/>
|
||||
<i><a href=http://www.escotal.com/osilayer.html>Source: OSI 7 layer model</a></i>
|
||||
</p>
|
||||
|
|
@ -1351,7 +1402,7 @@ HTTP is an application layer protocol relying on lower-level protocols such as *
|
|||
### Transmission control protocol (TCP)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/JdAsdvG.jpg">
|
||||
<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>
|
||||
|
|
@ -1375,7 +1426,7 @@ Use TCP over UDP when:
|
|||
### User datagram protocol (UDP)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/yzDrJtA.jpg">
|
||||
<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>
|
||||
|
|
@ -1404,7 +1455,7 @@ Use UDP over TCP when:
|
|||
### Remote procedure call (RPC)
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/iF4Mkb5.png">
|
||||
<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>
|
||||
|
|
@ -1519,6 +1570,7 @@ Security is a broad topic. Unless you have considerable experience, a security
|
|||
|
||||
### 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)
|
||||
|
||||
|
|
@ -1553,7 +1605,7 @@ Latency Comparison Numbers
|
|||
L1 cache reference 0.5 ns
|
||||
Branch mispredict 5 ns
|
||||
L2 cache reference 7 ns 14x L1 cache
|
||||
Mutex lock/unlock 100 ns
|
||||
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
|
||||
|
|
@ -1561,9 +1613,9 @@ Read 4 KB randomly from SSD* 150,000 ns 150 us ~1GB/se
|
|||
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
|
||||
Disk seek 10,000,000 ns 10,000 us 10 ms 20x datacenter roundtrip
|
||||
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 disk 30,000,000 ns 30,000 us 30 ms 120x memory, 30X 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
|
||||
|
|
@ -1575,7 +1627,7 @@ Notes
|
|||
|
||||
Handy metrics based on numbers above:
|
||||
|
||||
* Read sequentially from disk at 30 MB/s
|
||||
* 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
|
||||
|
|
@ -1600,7 +1652,7 @@ Handy metrics based on numbers above:
|
|||
| 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 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) |
|
||||
|
|
@ -1621,6 +1673,7 @@ Handy metrics based on numbers above:
|
|||
| Design an online multiplayer card game | [indieflashblog.com](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
|
||||
|
|
@ -1628,7 +1681,7 @@ Handy metrics based on numbers above:
|
|||
> Articles on how real world systems are designed.
|
||||
|
||||
<p align="center">
|
||||
<img src="http://i.imgur.com/TcUo2fw.png">
|
||||
<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>
|
||||
|
|
@ -1655,7 +1708,7 @@ Handy metrics based on numbers above:
|
|||
| 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](https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html) |
|
||||
| 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)
|
||||
|
|
@ -1678,7 +1731,7 @@ Handy metrics based on numbers above:
|
|||
| 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 | [Netflix: What Happens When You Press Play?](http://highscalability.com/blog/2017/12/11/netflix-what-happens-when-you-press-play.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) |
|
||||
|
|
@ -1686,7 +1739,7 @@ Handy metrics based on numbers above:
|
|||
| 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) |
|
||||
| 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) |
|
||||
|
|
@ -1704,7 +1757,7 @@ Handy metrics based on numbers above:
|
|||
* [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](http://engineering.quora.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/)
|
||||
|
|
@ -1724,9 +1777,8 @@ Handy metrics based on numbers above:
|
|||
* [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://devblog.paypal.com/category/engineering/)
|
||||
* [Paypal Developer Blog](https://medium.com/paypal-engineering)
|
||||
* [Pinterest Engineering Blog](https://medium.com/@Pinterest_Engineering)
|
||||
* [Quora Engineering](https://engineering.quora.com/)
|
||||
* [Reddit Blog](http://www.redditblog.com/)
|
||||
* [Salesforce Engineering Blog](https://developer.salesforce.com/blogs/engineering/)
|
||||
* [Slack Engineering Blog](https://slack.engineering/)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,163 @@
|
|||
# Translations
|
||||
|
||||
**Thank you to our awesome translation maintainers!**
|
||||
|
||||
## Contributing
|
||||
|
||||
See the [Contributing Guidelines](CONTRIBUTING.md).
|
||||
|
||||
## Translation Statuses
|
||||
|
||||
* 🎉 **Live**: Merged into `master` branch
|
||||
* ⏳ **In Progress**: Under active translation for eventual merge into `master` branch
|
||||
* ❗ **Stalled***: Needs an active maintainer ✋
|
||||
|
||||
**Within the past 2 months, there has been 1) No active work in the translation fork, and 2) No discussions from previous maintainer(s) in the discussion thread.*
|
||||
|
||||
Languages not listed here have not been started, [contribute](CONTRIBUTING.md)!
|
||||
|
||||
Languages are grouped by status and are listed in alphabetical order.
|
||||
|
||||
## Live
|
||||
|
||||
### 🎉 Japanese
|
||||
|
||||
* [README-ja.md](README-ja.md)
|
||||
* Maintainer(s): [@tsukukobaan](https://github.com/tsukukobaan) 👏
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/100
|
||||
|
||||
### 🎉 Simplified Chinese
|
||||
|
||||
* [zh-Hans.md](README-zh-Hans.md)
|
||||
* Maintainer(s): [@sqrthree](https://github.com/sqrthree) 👏
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/38
|
||||
|
||||
### 🎉 Traditional Chinese
|
||||
|
||||
* [README-zh-TW.md](README-zh-TW.md)
|
||||
* Maintainer(s): [@kevingo](https://github.com/kevingo) 👏
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/88
|
||||
|
||||
## In Progress
|
||||
|
||||
### ⏳ Korean
|
||||
|
||||
* Maintainer(s): [@bonomoon](https://github.com/bonomoon), [@mingrammer](https://github.com/mingrammer) 👏
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/102
|
||||
* Translation Fork: https://github.com/bonomoon/system-design-primer, https://github.com/donnemartin/system-design-primer/pull/103
|
||||
|
||||
### ⏳ Russian
|
||||
|
||||
* Maintainer(s): [@voitau](https://github.com/voitau), [@DmitryOlkhovoi](https://github.com/DmitryOlkhovoi) 👏
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/87
|
||||
* Translation Fork: https://github.com/voitau/system-design-primer/blob/master/README-ru.md
|
||||
|
||||
## Stalled
|
||||
|
||||
**Notes**:
|
||||
|
||||
* If you're able to commit to being an active maintainer for a language, let us know in the discussion thread for your language and update this file with a pull request.
|
||||
* If you're listed here as a "Previous Maintainer" but can commit to being an active maintainer, also let us know.
|
||||
* See the [Contributing Guidelines](CONTRIBUTING.md).
|
||||
|
||||
### ❗ Arabic
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@aymns](https://github.com/aymns)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/170
|
||||
* Translation Fork: https://github.com/aymns/system-design-primer/blob/develop/README-ar.md
|
||||
|
||||
### ❗ Bengali
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@nutboltu](https://github.com/nutboltu)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/220
|
||||
* Translation Fork: https://github.com/donnemartin/system-design-primer/pull/240
|
||||
|
||||
### ❗ Brazilian Portuguese
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@IuryAlves](https://github.com/IuryAlves)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/40
|
||||
* Translation Fork: https://github.com/IuryAlves/system-design-primer, https://github.com/donnemartin/system-design-primer/pull/67
|
||||
|
||||
### ❗ French
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@spuyet](https://github.com/spuyet)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/250
|
||||
* Translation Fork: https://github.com/spuyet/system-design-primer/blob/add-french-translation/README-fr.md
|
||||
|
||||
### ❗ German
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@Allaman](https://github.com/Allaman)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/186
|
||||
* Translation Fork: None
|
||||
|
||||
### ❗ Greek
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@Belonias](https://github.com/Belonias)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/130
|
||||
* Translation Fork: None
|
||||
|
||||
### ❗ Hebrew
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@EladLeev](https://github.com/EladLeev)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/272
|
||||
* Translation Fork: https://github.com/EladLeev/system-design-primer/tree/he-translate
|
||||
|
||||
### ❗ Italian
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@pgoodjohn](https://github.com/pgoodjohn)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/104
|
||||
* Translation Fork: https://github.com/pgoodjohn/system-design-primer
|
||||
|
||||
### ❗ Persian
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@hadisinaee](https://github.com/hadisinaee)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/pull/112
|
||||
* Translation Fork: https://github.com/donnemartin/system-design-primer/pull/112
|
||||
|
||||
### ❗ Spanish
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@eamanu](https://github.com/eamanu)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/136
|
||||
* Translation Fork: https://github.com/donnemartin/system-design-primer/pull/189
|
||||
|
||||
### ❗ Thai
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@iphayao](https://github.com/iphayao)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/187
|
||||
* Translation Fork: https://github.com/donnemartin/system-design-primer/pull/221
|
||||
|
||||
### ❗ Turkish
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@hwclass](https://github.com/hwclass), [@canerbaran](https://github.com/canerbaran), [@emrahtoy](https://github.com/emrahtoy)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/39
|
||||
* Translation Fork: https://github.com/donnemartin/system-design-primer/pull/239
|
||||
|
||||
### ❗ Ukrainian
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@Kietzmann](https://github.com/Kietzmann), [@Acarus](https://github.com/Acarus)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/248
|
||||
* Translation Fork: https://github.com/Acarus/system-design-primer
|
||||
|
||||
### ❗ Vietnamese
|
||||
|
||||
* Maintainer(s): **Help Wanted** ✋
|
||||
* Previous Maintainer(s): [@tranlyvu](https://github.com/tranlyvu), [@duynguyenhoang](https://github.com/duynguyenhoang)
|
||||
* Discussion Thread: https://github.com/donnemartin/system-design-primer/issues/127
|
||||
* Translation Fork: https://github.com/donnemartin/system-design-primer/pull/241, https://github.com/donnemartin/system-design-primer/pull/327
|
||||
|
||||
## Not Started
|
||||
|
||||
Languages not listed here have not been started, [contribute](CONTRIBUTING.md)!
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
title: System Design Primer
|
||||
creator: Donne Martin
|
||||
date: 2018
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
#! /usr/bin/env bash
|
||||
|
||||
generate_from_stdin() {
|
||||
outfile=$1
|
||||
language=$2
|
||||
|
||||
echo "Generating '$language' ..."
|
||||
|
||||
pandoc --metadata-file=epub-metadata.yaml --metadata=lang:$2 --from=markdown -o $1 <&0
|
||||
|
||||
echo "Done! You can find the '$language' book at ./$outfile"
|
||||
}
|
||||
|
||||
generate_with_solutions () {
|
||||
tmpfile=$(mktemp /tmp/sytem-design-primer-epub-generator.XXX)
|
||||
|
||||
cat ./README.md >> $tmpfile
|
||||
|
||||
for dir in ./solutions/system_design/*; do
|
||||
case $dir in *template*) continue;; esac
|
||||
case $dir in *__init__.py*) continue;; esac
|
||||
: [[ -d "$dir" ]] && ( cd "$dir" && cat ./README.md >> $tmpfile && echo "" >> $tmpfile )
|
||||
done
|
||||
|
||||
cat $tmpfile | generate_from_stdin 'README.epub' 'en'
|
||||
|
||||
rm "$tmpfile"
|
||||
}
|
||||
|
||||
generate () {
|
||||
name=$1
|
||||
language=$2
|
||||
|
||||
cat $name.md | generate_from_stdin $name.epub $language
|
||||
}
|
||||
|
||||
# Check if depencies exist
|
||||
check_dependencies () {
|
||||
for dependency in "${dependencies[@]}"
|
||||
do
|
||||
if ! [ -x "$(command -v $dependency)" ]; then
|
||||
echo "Error: $dependency is not installed." >&2
|
||||
exit 1
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
dependencies=("pandoc")
|
||||
|
||||
check_dependencies
|
||||
generate_with_solutions
|
||||
generate README-ja ja
|
||||
generate README-zh-Hans zh-Hans
|
||||
generate README-zh-TW zh-TW
|
||||
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|
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|
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|
|
@ -66,7 +66,7 @@ class Director(Employee):
|
|||
super(Operator, self).__init__(employee_id, name, Rank.DIRECTOR)
|
||||
|
||||
def escalate_call(self):
|
||||
raise NotImplemented('Directors must be able to handle any call')
|
||||
raise NotImplementedError('Directors must be able to handle any call')
|
||||
|
||||
|
||||
class CallState(Enum):
|
||||
|
|
|
|||
|
|
@ -122,7 +122,7 @@
|
|||
"\n",
|
||||
" def score(self):\n",
|
||||
" total_value = 0\n",
|
||||
" for card in card:\n",
|
||||
" for card in self.cards:\n",
|
||||
" total_value += card.value\n",
|
||||
" return total_value\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -0,0 +1,440 @@
|
|||
# 设计 Mint.com
|
||||
|
||||
**注意：这个文档中的链接会直接指向[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题索引)中的有关部分，以避免重复的内容。您可以参考链接的相关内容，来了解其总的要点、方案的权衡取舍以及可选的替代方案。**
|
||||
|
||||
## 第一步：简述用例与约束条件
|
||||
|
||||
> 搜集需求与问题的范围。
|
||||
> 提出问题来明确用例与约束条件。
|
||||
> 讨论假设。
|
||||
|
||||
我们将在没有面试官明确说明问题的情况下，自己定义一些用例以及限制条件。
|
||||
|
||||
### 用例
|
||||
|
||||
#### 我们将把问题限定在仅处理以下用例的范围中
|
||||
|
||||
* **用户** 连接到一个财务账户
|
||||
* **服务** 从账户中提取交易
|
||||
* 每日更新
|
||||
* 分类交易
|
||||
* 允许用户手动分类
|
||||
* 不自动重新分类
|
||||
* 按类别分析每月支出
|
||||
* **服务** 推荐预算
|
||||
* 允许用户手动设置预算
|
||||
* 当接近或者超出预算时，发送通知
|
||||
* **服务** 具有高可用性
|
||||
|
||||
#### 非用例范围
|
||||
|
||||
* **服务** 执行附加的日志记录和分析
|
||||
|
||||
### 限制条件与假设
|
||||
|
||||
#### 提出假设
|
||||
|
||||
* 网络流量非均匀分布
|
||||
* 自动账户日更新只适用于 30 天内活跃的用户
|
||||
* 添加或者移除财务账户相对较少
|
||||
* 预算通知不需要及时
|
||||
* 1000 万用户
|
||||
* 每个用户10个预算类别= 1亿个预算项
|
||||
* 示例类别:
|
||||
* Housing = $1,000
|
||||
* Food = $200
|
||||
* Gas = $100
|
||||
* 卖方确定交易类别
|
||||
* 50000 个卖方
|
||||
* 3000 万财务账户
|
||||
* 每月 50 亿交易
|
||||
* 每月 5 亿读请求
|
||||
* 10:1 读写比
|
||||
* Write-heavy，用户每天都进行交易，但是每天很少访问该网站
|
||||
|
||||
#### 计算用量
|
||||
|
||||
**如果你需要进行粗略的用量计算，请向你的面试官说明。**
|
||||
|
||||
* 每次交易的用量:
|
||||
* `user_id` - 8 字节
|
||||
* `created_at` - 5 字节
|
||||
* `seller` - 32 字节
|
||||
* `amount` - 5 字节
|
||||
* Total: ~50 字节
|
||||
* 每月产生 250 GB 新的交易内容
|
||||
* 每次交易 50 比特 * 50 亿交易每月
|
||||
* 3年内新的交易内容 9 TB
|
||||
* Assume most are new transactions instead of updates to existing ones
|
||||
* 平均每秒产生 2000 次交易
|
||||
* 平均每秒产生 200 读请求
|
||||
|
||||
便利换算指南：
|
||||
|
||||
* 每个月有 250 万秒
|
||||
* 每秒一个请求 = 每个月 250 万次请求
|
||||
* 每秒 40 个请求 = 每个月 1 亿次请求
|
||||
* 每秒 400 个请求 = 每个月 10 亿次请求
|
||||
|
||||
## 第二步：概要设计
|
||||
|
||||
> 列出所有重要组件以规划概要设计。
|
||||
|
||||
|
||||
|
||||
## 第三步：设计核心组件
|
||||
|
||||
> 深入每个核心组件的细节。
|
||||
|
||||
### 用例：用户连接到一个财务账户
|
||||
|
||||
我们可以将 1000 万用户的信息存储在一个[关系数据库](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms)中。我们应该讨论一下[选择SQL或NoSQL之间的用例和权衡](https://github.com/donnemartin/system-design-primer#sql-or-nosql)了。
|
||||
|
||||
* **客户端** 作为一个[反向代理](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)，发送请求到 **Web 服务器**
|
||||
* **Web 服务器** 转发请求到 **账户API** 服务器
|
||||
* **账户API** 服务器将新输入的账户信息更新到 **SQL数据库** 的`accounts`表
|
||||
|
||||
**告知你的面试官你准备写多少代码**。
|
||||
|
||||
`accounts`表应该具有如下结构：
|
||||
|
||||
```
|
||||
id int NOT NULL AUTO_INCREMENT
|
||||
created_at datetime NOT NULL
|
||||
last_update datetime NOT NULL
|
||||
account_url varchar(255) NOT NULL
|
||||
account_login varchar(32) NOT NULL
|
||||
account_password_hash char(64) NOT NULL
|
||||
user_id int NOT NULL
|
||||
PRIMARY KEY(id)
|
||||
FOREIGN KEY(user_id) REFERENCES users(id)
|
||||
```
|
||||
|
||||
我们将在`id`，`user_id`和`created_at`等字段上创建一个[索引](https://github.com/donnemartin/system-design-primer#use-good-indices)以加速查找（对数时间而不是扫描整个表）并保持数据在内存中。从内存中顺序读取 1 MB数据花费大约250毫秒，而从SSD读取是其4倍，从磁盘读取是其80倍。<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
|
||||
|
||||
我们将使用公开的[**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
|
||||
|
||||
```
|
||||
$ curl -X POST --data '{ "user_id": "foo", "account_url": "bar", \
|
||||
"account_login": "baz", "account_password": "qux" }' \
|
||||
https://mint.com/api/v1/account
|
||||
```
|
||||
|
||||
对于内部通信，我们可以使用[远程过程调用](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc)。
|
||||
|
||||
接下来，服务从账户中提取交易。
|
||||
|
||||
### 用例：服务从账户中提取交易
|
||||
|
||||
如下几种情况下，我们会想要从账户中提取信息：
|
||||
|
||||
* 用户首次链接账户
|
||||
* 用户手动更新账户
|
||||
* 为过去 30 天内活跃的用户自动日更新
|
||||
|
||||
数据流:
|
||||
|
||||
* **客户端**向 **Web服务器** 发送请求
|
||||
* **Web服务器** 将请求转发到 **帐户API** 服务器
|
||||
* **帐户API** 服务器将job放在 **队列** 中，如 [Amazon SQS](https://aws.amazon.com/sqs/) 或者 [RabbitMQ](https://www.rabbitmq.com/)
|
||||
* 提取交易可能需要一段时间，我们可能希望[与队列异步](https://github.com/donnemartin/system-design-primer#asynchronism)地来做，虽然这会引入额外的复杂度。
|
||||
* **交易提取服务** 执行如下操作：
|
||||
* 从 **Queue** 中拉取并从金融机构中提取给定用户的交易，将结果作为原始日志文件存储在 **对象存储区**。
|
||||
* 使用 **分类服务** 来分类每个交易
|
||||
* 使用 **预算服务** 来按类别计算每月总支出
|
||||
* **预算服务** 使用 **通知服务** 让用户知道他们是否接近或者已经超出预算
|
||||
* 更新具有分类交易的 **SQL数据库** 的`transactions`表
|
||||
* 按类别更新 **SQL数据库** `monthly_spending`表的每月总支出
|
||||
* 通过 **通知服务** 提醒用户交易完成
|
||||
* 使用一个 **队列** (没有画出来) 来异步发送通知
|
||||
|
||||
`transactions`表应该具有如下结构：
|
||||
|
||||
```
|
||||
id int NOT NULL AUTO_INCREMENT
|
||||
created_at datetime NOT NULL
|
||||
seller varchar(32) NOT NULL
|
||||
amount decimal NOT NULL
|
||||
user_id int NOT NULL
|
||||
PRIMARY KEY(id)
|
||||
FOREIGN KEY(user_id) REFERENCES users(id)
|
||||
```
|
||||
|
||||
我们将在 `id`，`user_id`，和 `created_at`字段上创建[索引](https://github.com/donnemartin/system-design-primer#use-good-indices)。
|
||||
|
||||
`monthly_spending`表应该具有如下结构：
|
||||
|
||||
```
|
||||
id int NOT NULL AUTO_INCREMENT
|
||||
month_year date NOT NULL
|
||||
category varchar(32)
|
||||
amount decimal NOT NULL
|
||||
user_id int NOT NULL
|
||||
PRIMARY KEY(id)
|
||||
FOREIGN KEY(user_id) REFERENCES users(id)
|
||||
```
|
||||
|
||||
我们将在`id`，`user_id`字段上创建[索引](https://github.com/donnemartin/system-design-primer#use-good-indices)。
|
||||
|
||||
#### 分类服务
|
||||
|
||||
对于 **分类服务**，我们可以生成一个带有最受欢迎卖家的卖家-类别字典。如果我们估计 50000 个卖家，并估计每个条目占用不少于 255 个字节，该字典只需要大约 12 MB内存。
|
||||
|
||||
**告知你的面试官你准备写多少代码**。
|
||||
|
||||
```python
|
||||
class DefaultCategories(Enum):
|
||||
|
||||
HOUSING = 0
|
||||
FOOD = 1
|
||||
GAS = 2
|
||||
SHOPPING = 3
|
||||
...
|
||||
|
||||
seller_category_map = {}
|
||||
seller_category_map['Exxon'] = DefaultCategories.GAS
|
||||
seller_category_map['Target'] = DefaultCategories.SHOPPING
|
||||
...
|
||||
```
|
||||
|
||||
对于一开始没有在映射中的卖家，我们可以通过评估用户提供的手动类别来进行众包。在 O(1) 时间内，我们可以用堆来快速查找每个卖家的顶端的手动覆盖。
|
||||
|
||||
```python
|
||||
class Categorizer(object):
|
||||
|
||||
def __init__(self, seller_category_map, self.seller_category_crowd_overrides_map):
|
||||
self.seller_category_map = seller_category_map
|
||||
self.seller_category_crowd_overrides_map = \
|
||||
seller_category_crowd_overrides_map
|
||||
|
||||
def categorize(self, transaction):
|
||||
if transaction.seller in self.seller_category_map:
|
||||
return self.seller_category_map[transaction.seller]
|
||||
elif transaction.seller in self.seller_category_crowd_overrides_map:
|
||||
self.seller_category_map[transaction.seller] = \
|
||||
self.seller_category_crowd_overrides_map[transaction.seller].peek_min()
|
||||
return self.seller_category_map[transaction.seller]
|
||||
return None
|
||||
```
|
||||
|
||||
交易实现：
|
||||
|
||||
```python
|
||||
class Transaction(object):
|
||||
|
||||
def __init__(self, created_at, seller, amount):
|
||||
self.timestamp = timestamp
|
||||
self.seller = seller
|
||||
self.amount = amount
|
||||
```
|
||||
|
||||
### 用例：服务推荐预算
|
||||
|
||||
首先，我们可以使用根据收入等级分配每类别金额的通用预算模板。使用这种方法，我们不必存储在约束中标识的 1 亿个预算项目，只需存储用户覆盖的预算项目。如果用户覆盖预算类别，我们可以在
|
||||
`TABLE budget_overrides`中存储此覆盖。
|
||||
|
||||
```python
|
||||
class Budget(object):
|
||||
|
||||
def __init__(self, income):
|
||||
self.income = income
|
||||
self.categories_to_budget_map = self.create_budget_template()
|
||||
|
||||
def create_budget_template(self):
|
||||
return {
|
||||
'DefaultCategories.HOUSING': income * .4,
|
||||
'DefaultCategories.FOOD': income * .2
|
||||
'DefaultCategories.GAS': income * .1,
|
||||
'DefaultCategories.SHOPPING': income * .2
|
||||
...
|
||||
}
|
||||
|
||||
def override_category_budget(self, category, amount):
|
||||
self.categories_to_budget_map[category] = amount
|
||||
```
|
||||
|
||||
对于 **预算服务** 而言，我们可以在`transactions`表上运行SQL查询以生成`monthly_spending`聚合表。由于用户通常每个月有很多交易，所以`monthly_spending`表的行数可能会少于总共50亿次交易的行数。
|
||||
|
||||
作为替代，我们可以在原始交易文件上运行 **MapReduce** 作业来：
|
||||
|
||||
* 分类每个交易
|
||||
* 按类别生成每月总支出
|
||||
|
||||
对交易文件的运行分析可以显著减少数据库的负载。
|
||||
|
||||
如果用户更新类别，我们可以调用 **预算服务** 重新运行分析。
|
||||
|
||||
**告知你的面试官你准备写多少代码**.
|
||||
|
||||
日志文件格式样例，以tab分割：
|
||||
|
||||
```
|
||||
user_id timestamp seller amount
|
||||
```
|
||||
|
||||
**MapReduce** 实现:
|
||||
|
||||
```python
|
||||
class SpendingByCategory(MRJob):
|
||||
|
||||
def __init__(self, categorizer):
|
||||
self.categorizer = categorizer
|
||||
self.current_year_month = calc_current_year_month()
|
||||
...
|
||||
|
||||
def calc_current_year_month(self):
|
||||
"""返回当前年月"""
|
||||
...
|
||||
|
||||
def extract_year_month(self, timestamp):
|
||||
"""返回时间戳的年，月部分"""
|
||||
...
|
||||
|
||||
def handle_budget_notifications(self, key, total):
|
||||
"""如果接近或超出预算，调用通知API"""
|
||||
...
|
||||
|
||||
def mapper(self, _, line):
|
||||
"""解析每个日志行，提取和转换相关行。
|
||||
|
||||
参数行应为如下形式：
|
||||
|
||||
user_id timestamp seller amount
|
||||
|
||||
使用分类器来将卖家转换成类别，生成如下形式的key-value对：
|
||||
|
||||
(user_id, 2016-01, shopping), 25
|
||||
(user_id, 2016-01, shopping), 100
|
||||
(user_id, 2016-01, gas), 50
|
||||
"""
|
||||
user_id, timestamp, seller, amount = line.split('\t')
|
||||
category = self.categorizer.categorize(seller)
|
||||
period = self.extract_year_month(timestamp)
|
||||
if period == self.current_year_month:
|
||||
yield (user_id, period, category), amount
|
||||
|
||||
def reducer(self, key, value):
|
||||
"""将每个key对应的值求和。
|
||||
|
||||
(user_id, 2016-01, shopping), 125
|
||||
(user_id, 2016-01, gas), 50
|
||||
"""
|
||||
total = sum(values)
|
||||
yield key, sum(values)
|
||||
```
|
||||
|
||||
## 第四步：设计扩展
|
||||
|
||||
> 根据限制条件，找到并解决瓶颈。
|
||||
|
||||
|
||||
|
||||
**重要提示：不要从最初设计直接跳到最终设计中！**
|
||||
|
||||
现在你要 1) **基准测试、负载测试**。2) **分析、描述**性能瓶颈。3) 在解决瓶颈问题的同时，评估替代方案、权衡利弊。4) 重复以上步骤。请阅读[「设计一个系统，并将其扩大到为数以百万计的 AWS 用户服务」](../scaling_aws/README.md) 来了解如何逐步扩大初始设计。
|
||||
|
||||
讨论初始设计可能遇到的瓶颈及相关解决方案是很重要的。例如加上一个配置多台 **Web 服务器**的**负载均衡器**是否能够解决问题？**CDN**呢？**主从复制**呢？它们各自的替代方案和需要**权衡**的利弊又有什么呢？
|
||||
|
||||
我们将会介绍一些组件来完成设计，并解决架构扩张问题。内置的负载均衡器将不做讨论以节省篇幅。
|
||||
|
||||
**为了避免重复讨论**，请参考[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)相关部分来了解其要点、方案的权衡取舍以及可选的替代方案。
|
||||
|
||||
* [DNS](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#域名系统)
|
||||
* [负载均衡器](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#负载均衡器)
|
||||
* [水平拓展](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#水平扩展)
|
||||
* [反向代理（web 服务器）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)
|
||||
* [API 服务（应用层）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用层)
|
||||
* [缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存)
|
||||
* [关系型数据库管理系统 (RDBMS)](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#关系型数据库管理系统rdbms)
|
||||
* [SQL 故障主从切换](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#故障切换)
|
||||
* [主从复制](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#主从复制)
|
||||
* [异步](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#异步)
|
||||
* [一致性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#一致性模式)
|
||||
* [可用性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#可用性模式)
|
||||
|
||||
我们将增加一个额外的用例：**用户** 访问摘要和交易数据。
|
||||
|
||||
用户会话，按类别统计的统计信息，以及最近的事务可以放在 **内存缓存**（如 Redis 或 Memcached ）中。
|
||||
|
||||
* **客户端** 发送读请求给 **Web 服务器**
|
||||
* **Web 服务器** 转发请求到 **读 API** 服务器
|
||||
* 静态内容可通过 **对象存储** 比如缓存在 **CDN** 上的 S3 来服务
|
||||
* **读 API** 服务器做如下动作:
|
||||
* 检查 **内存缓存** 的内容
|
||||
* 如果URL在 **内存缓存**中，返回缓存的内容
|
||||
* 否则
|
||||
* 如果URL在 **SQL 数据库**中，获取该内容
|
||||
* 以其内容更新 **内存缓存**
|
||||
|
||||
参考 [何时更新缓存](https://github.com/donnemartin/system-design-primer#when-to-update-the-cache) 中权衡和替代的内容。以上方法描述了 [cache-aside缓存模式](https://github.com/donnemartin/system-design-primer#cache-aside).
|
||||
|
||||
我们可以使用诸如 Amazon Redshift 或者 Google BigQuery 等数据仓库解决方案，而不是将`monthly_spending`聚合表保留在 **SQL 数据库** 中。
|
||||
|
||||
我们可能只想在数据库中存储一个月的`交易`数据，而将其余数据存储在数据仓库或者 **对象存储区** 中。**对象存储区** （如Amazon S3) 能够舒服地解决每月 250 GB新内容的限制。
|
||||
|
||||
为了解决每秒 *平均* 2000 次读请求数（峰值时更高），受欢迎的内容的流量应由 **内存缓存** 而不是数据库来处理。 **内存缓存** 也可用于处理不均匀分布的流量和流量尖峰。 只要副本不陷入重复写入的困境，**SQL 读副本** 应该能够处理高速缓存未命中。
|
||||
|
||||
*平均* 200 次交易写入每秒（峰值时更高）对于单个 **SQL 写入主-从服务** 来说可能是棘手的。我们可能需要考虑其它的 SQL 性能拓展技术：
|
||||
|
||||
* [联合](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#联合)
|
||||
* [分片](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#分片)
|
||||
* [非规范化](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#非规范化)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-调优)
|
||||
|
||||
我们也可以考虑将一些数据移至 **NoSQL 数据库**。
|
||||
|
||||
## 其它要点
|
||||
|
||||
> 是否深入这些额外的主题，取决于你的问题范围和剩下的时间。
|
||||
|
||||
#### NoSQL
|
||||
|
||||
* [键-值存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#键-值存储)
|
||||
* [文档类型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#文档类型存储)
|
||||
* [列型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#列型存储)
|
||||
* [图数据库](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#图数据库)
|
||||
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)
|
||||
|
||||
### 缓存
|
||||
|
||||
* 在哪缓存
|
||||
* [客户端缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#客户端缓存)
|
||||
* [CDN 缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#cdn-缓存)
|
||||
* [Web 服务器缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#web-服务器缓存)
|
||||
* [数据库缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库缓存)
|
||||
* [应用缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用缓存)
|
||||
* 什么需要缓存
|
||||
* [数据库查询级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库查询级别的缓存)
|
||||
* [对象级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#对象级别的缓存)
|
||||
* 何时更新缓存
|
||||
* [缓存模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存模式)
|
||||
* [直写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#直写模式)
|
||||
* [回写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#回写模式)
|
||||
* [刷新](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#刷新)
|
||||
|
||||
### 异步与微服务
|
||||
|
||||
* [消息队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#消息队列)
|
||||
* [任务队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#任务队列)
|
||||
* [背压](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#背压)
|
||||
* [微服务](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#微服务)
|
||||
|
||||
### 通信
|
||||
|
||||
* 可权衡选择的方案：
|
||||
* 与客户端的外部通信 - [使用 REST 作为 HTTP API](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)
|
||||
* 服务器内部通信 - [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)
|
||||
* [服务发现](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#服务发现)
|
||||
|
||||
### 安全性
|
||||
|
||||
请参阅[「安全」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#安全)一章。
|
||||
|
||||
### 延迟数值
|
||||
|
||||
请参阅[「每个程序员都应该知道的延迟数」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数)。
|
||||
|
||||
### 持续探讨
|
||||
|
||||
* 持续进行基准测试并监控你的系统，以解决他们提出的瓶颈问题。
|
||||
* 架构拓展是一个迭代的过程。
|
||||
|
|
@ -182,7 +182,7 @@ For the **Category Service**, we can seed a seller-to-category dictionary with t
|
|||
|
||||
**Clarify with your interviewer how much code you are expected to write**.
|
||||
|
||||
```
|
||||
```python
|
||||
class DefaultCategories(Enum):
|
||||
|
||||
HOUSING = 0
|
||||
|
|
@ -199,10 +199,10 @@ seller_category_map['Target'] = DefaultCategories.SHOPPING
|
|||
|
||||
For sellers not initially seeded in the map, we could use a crowdsourcing effort by evaluating the manual category overrides our users provide. We could use a heap to quickly lookup the top manual override per seller in O(1) time.
|
||||
|
||||
```
|
||||
```python
|
||||
class Categorizer(object):
|
||||
|
||||
def __init__(self, seller_category_map, self.seller_category_crowd_overrides_map):
|
||||
def __init__(self, seller_category_map, seller_category_crowd_overrides_map):
|
||||
self.seller_category_map = seller_category_map
|
||||
self.seller_category_crowd_overrides_map = \
|
||||
seller_category_crowd_overrides_map
|
||||
|
|
@ -219,11 +219,11 @@ class Categorizer(object):
|
|||
|
||||
Transaction implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class Transaction(object):
|
||||
|
||||
def __init__(self, created_at, seller, amount):
|
||||
self.timestamp = timestamp
|
||||
self.created_at = created_at
|
||||
self.seller = seller
|
||||
self.amount = amount
|
||||
```
|
||||
|
|
@ -232,7 +232,7 @@ class Transaction(object):
|
|||
|
||||
To start, we could use a generic budget template that allocates category amounts based on income tiers. Using this approach, we would not have to store the 100 million budget items identified in the constraints, only those that the user overrides. If a user overrides a budget category, which we could store the override in the `TABLE budget_overrides`.
|
||||
|
||||
```
|
||||
```python
|
||||
class Budget(object):
|
||||
|
||||
def __init__(self, income):
|
||||
|
|
@ -241,10 +241,10 @@ class Budget(object):
|
|||
|
||||
def create_budget_template(self):
|
||||
return {
|
||||
'DefaultCategories.HOUSING': income * .4,
|
||||
'DefaultCategories.FOOD': income * .2
|
||||
'DefaultCategories.GAS': income * .1,
|
||||
'DefaultCategories.SHOPPING': income * .2
|
||||
DefaultCategories.HOUSING: self.income * .4,
|
||||
DefaultCategories.FOOD: self.income * .2,
|
||||
DefaultCategories.GAS: self.income * .1,
|
||||
DefaultCategories.SHOPPING: self.income * .2,
|
||||
...
|
||||
}
|
||||
|
||||
|
|
@ -273,7 +273,7 @@ user_id timestamp seller amount
|
|||
|
||||
**MapReduce** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class SpendingByCategory(MRJob):
|
||||
|
||||
def __init__(self, categorizer):
|
||||
|
|
@ -373,9 +373,9 @@ Instead of keeping the `monthly_spending` aggregate table in the **SQL Database*
|
|||
|
||||
We might only want to store a month of `transactions` data in the database, while storing the rest in a data warehouse or in an **Object Store**. An **Object Store** such as Amazon S3 can comfortably handle the constraint of 250 GB of new content per month.
|
||||
|
||||
To address the 2,000 *average* read requests per second (higher at peak), traffic for popular content should be handled by the **Memory Cache** instead of the database. The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes. The **SQL Read Replicas** should be able to handle the cache misses, as long as the replicas are not bogged down with replicating writes.
|
||||
To address the 200 *average* read requests per second (higher at peak), traffic for popular content should be handled by the **Memory Cache** instead of the database. The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes. The **SQL Read Replicas** should be able to handle the cache misses, as long as the replicas are not bogged down with replicating writes.
|
||||
|
||||
200 *average* transaction writes per second (higher at peak) might be tough for a single **SQL Write Master-Slave**. We might need to employ additional SQL scaling patterns:
|
||||
2,000 *average* transaction writes per second (higher at peak) might be tough for a single **SQL Write Master-Slave**. We might need to employ additional SQL scaling patterns:
|
||||
|
||||
* [Federation](https://github.com/donnemartin/system-design-primer#federation)
|
||||
* [Sharding](https://github.com/donnemartin/system-design-primer#sharding)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,330 @@
|
|||
# 设计 Pastebin.com (或者 Bit.ly)
|
||||
|
||||
**注意: 为了避免重复，当前文档会直接链接到[系统设计主题](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)的相关区域，请参考链接内容以获得综合的讨论点、权衡和替代方案。**
|
||||
|
||||
**设计 Bit.ly** - 是一个类似的问题，区别是 pastebin 需要存储的是 paste 的内容，而不是原始的未短化的 url。
|
||||
|
||||
## 第一步：概述用例和约束
|
||||
|
||||
> 收集这个问题的需求和范畴。
|
||||
> 问相关问题来明确用例和约束。
|
||||
> 讨论一些假设。
|
||||
|
||||
因为没有面试官来明确这些问题，所以我们自己将定义一些用例和约束。
|
||||
|
||||
### 用例
|
||||
|
||||
#### 我们将问题的范畴限定在如下用例
|
||||
|
||||
* **用户** 输入一段文本，然后得到一个随机生成的链接
|
||||
* 过期设置
|
||||
* 默认的设置是不会过期的
|
||||
* 可以选择设置一个过期的时间
|
||||
* **用户** 输入一个 paste 的 url 后，可以看到它存储的内容
|
||||
* **用户** 是匿名的
|
||||
* **Service** 跟踪页面分析
|
||||
* 一个月的访问统计
|
||||
* **Service** 删除过期的 pastes
|
||||
* **Service** 需要高可用
|
||||
|
||||
#### 超出范畴的用例
|
||||
|
||||
* **用户** 可以注册一个账户
|
||||
* **用户** 通过验证邮箱
|
||||
* **用户** 可以用注册的账户登录
|
||||
* **用户** 可以编辑文档
|
||||
* **用户** 可以设置可见性
|
||||
* **用户** 可以设置短链接
|
||||
|
||||
### 约束和假设
|
||||
|
||||
#### 状态假设
|
||||
|
||||
* 访问流量不是均匀分布的
|
||||
* 打开一个短链接应该是很快的
|
||||
* pastes 只能是文本
|
||||
* 页面访问分析数据可以不用实时
|
||||
* 一千万的用户量
|
||||
* 每个月一千万的 paste 写入量
|
||||
* 每个月一亿的 paste 读取量
|
||||
* 读写比例在 10:1
|
||||
|
||||
#### 计算使用
|
||||
|
||||
**向面试官说明你是否应该粗略计算一下使用情况。**
|
||||
|
||||
* 每个 paste 的大小
|
||||
* 每一个 paste 1 KB
|
||||
* `shortlink` - 7 bytes
|
||||
* `expiration_length_in_minutes` - 4 bytes
|
||||
* `created_at` - 5 bytes
|
||||
* `paste_path` - 255 bytes
|
||||
* 总共 = ~1.27 KB
|
||||
* 每个月新的 paste 内容在 12.7GB
|
||||
* (1.27 * 10000000)KB / 月的 paste
|
||||
* 三年内将近 450GB 的新 paste 内容
|
||||
* 三年内 3.6 亿短链接
|
||||
* 假设大部分都是新的 paste，而不是需要更新已存在的 paste
|
||||
* 平均 4paste/s 的写入速度
|
||||
* 平均 40paste/s 的读取速度
|
||||
|
||||
简单的转换指南:
|
||||
|
||||
* 2.5 百万 req/s
|
||||
* 1 req/s = 2.5 百万 req/m
|
||||
* 40 req/s = 1 亿 req/m
|
||||
* 400 req/s = 10 亿 req/m
|
||||
|
||||
## 第二步：创建一个高层次设计
|
||||
|
||||
> 概述一个包括所有重要的组件的高层次设计
|
||||
|
||||
|
||||
|
||||
## 第三步：设计核心组件
|
||||
|
||||
> 深入每一个核心组件的细节
|
||||
|
||||
### 用例：用户输入一段文本，然后得到一个随机生成的链接
|
||||
|
||||
我们可以用一个 [关系型数据库](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#关系型数据库管理系统rdbms)作为一个大的哈希表，用来把生成的 url 映射到一个包含 paste 文件的文件服务器和路径上。
|
||||
|
||||
为了避免托管一个文件服务器，我们可以用一个托管的**对象存储**，比如 Amazon 的 S3 或者[NoSQL 文档类型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#文档类型存储)。
|
||||
|
||||
作为一个大的哈希表的关系型数据库的替代方案，我们可以用[NoSQL 键值存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#键-值存储)。我们需要讨论[选择 SQL 或 NoSQL 之间的权衡](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)。下面的讨论是使用关系型数据库方法。
|
||||
|
||||
* **客户端** 发送一个创建 paste 的请求到作为一个[反向代理](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)启动的 **Web 服务器**。
|
||||
* **Web 服务器** 转发请求给 **写接口** 服务器
|
||||
* **写接口** 服务器执行如下操作：
|
||||
* 生成一个唯一的 url
|
||||
* 检查这个 url 在 **SQL 数据库** 里面是否是唯一的
|
||||
* 如果这个 url 不是唯一的，生成另外一个 url
|
||||
* 如果我们支持自定义 url，我们可以使用用户提供的 url（也需要检查是否重复）
|
||||
* 把生成的 url 存储到 **SQL 数据库** 的 `pastes` 表里面
|
||||
* 存储 paste 的内容数据到 **对象存储** 里面
|
||||
* 返回生成的 url
|
||||
|
||||
**向面试官阐明你需要写多少代码**
|
||||
|
||||
`pastes` 表可以有如下结构：
|
||||
|
||||
```sql
|
||||
shortlink char(7) NOT NULL
|
||||
expiration_length_in_minutes int NOT NULL
|
||||
created_at datetime NOT NULL
|
||||
paste_path varchar(255) NOT NULL
|
||||
PRIMARY KEY(shortlink)
|
||||
```
|
||||
|
||||
我们将在 `shortlink` 字段和 `created_at` 字段上创建一个[数据库索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#使用正确的索引)，用来提高查询的速度（避免因为扫描全表导致的长时间查询）并将数据保存在内存中，从内存里面顺序读取 1MB 的数据需要大概 250 微秒，而从 SSD 上读取则需要花费 4 倍的时间，从硬盘上则需要花费 80 倍的时间。<sup><a href=https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数 > 1</a></sup>
|
||||
|
||||
为了生成唯一的 url，我们可以：
|
||||
|
||||
* 使用 [**MD5**](https://en.wikipedia.org/wiki/MD5) 来哈希用户的 IP 地址 + 时间戳
|
||||
* MD5 是一个普遍用来生成一个 128-bit 长度的哈希值的一种哈希方法
|
||||
* MD5 是一致分布的
|
||||
* 或者我们也可以用 MD5 哈希一个随机生成的数据
|
||||
* 用 [**Base 62**](https://www.kerstner.at/2012/07/shortening-strings-using-base-62-encoding/) 编码 MD5 哈希值
|
||||
* 对于 urls，使用 Base 62 编码 `[a-zA-Z0-9]` 是比较合适的
|
||||
* 对于每一个原始输入只会有一个 hash 结果，Base 62 是确定的（不涉及随机性）
|
||||
* Base 64 是另外一个流行的编码方案，但是对于 urls，会因为额外的 `+` 和 `-` 字符串而产生一些问题
|
||||
* 以下 [Base 62 伪代码](http://stackoverflow.com/questions/742013/how-to-code-a-url-shortener) 执行的时间复杂度是 O(k)，k 是数字的数量 = 7：
|
||||
|
||||
```python
|
||||
def base_encode(num, base=62):
|
||||
digits = []
|
||||
while num > 0
|
||||
remainder = modulo(num, base)
|
||||
digits.push(remainder)
|
||||
num = divide(num, base)
|
||||
digits = digits.reverse
|
||||
```
|
||||
|
||||
* 取输出的前 7 个字符，结果会有 62^7 个可能的值，应该足以满足在 3 年内处理 3.6 亿个短链接的约束：
|
||||
|
||||
```python
|
||||
url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH]
|
||||
```
|
||||
|
||||
我们将会用一个公开的 [**REST 风格接口**](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)：
|
||||
|
||||
```shell
|
||||
$ curl -X POST --data '{"expiration_length_in_minutes":"60", \"paste_contents":"Hello World!"}' https://pastebin.com/api/v1/paste
|
||||
```
|
||||
|
||||
Response:
|
||||
|
||||
```json
|
||||
{
|
||||
"shortlink": "foobar"
|
||||
}
|
||||
```
|
||||
|
||||
用于内部通信，我们可以用 [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)。
|
||||
|
||||
### 用例：用户输入一个 paste 的 url 后可以看到它存储的内容
|
||||
|
||||
* **客户端** 发送一个获取 paste 请求到 **Web Server**
|
||||
* **Web Server** 转发请求给 **读取接口** 服务器
|
||||
* **读取接口** 服务器执行如下操作：
|
||||
* 在 **SQL 数据库** 检查这个生成的 url
|
||||
* 如果这个 url 在 **SQL 数据库** 里面，则从 **对象存储** 获取这个 paste 的内容
|
||||
* 否则，返回一个错误页面给用户
|
||||
|
||||
REST API：
|
||||
|
||||
```shell
|
||||
curl https://pastebin.com/api/v1/paste?shortlink=foobar
|
||||
```
|
||||
|
||||
Response:
|
||||
|
||||
```json
|
||||
{
|
||||
"paste_contents": "Hello World",
|
||||
"created_at": "YYYY-MM-DD HH:MM:SS",
|
||||
"expiration_length_in_minutes": "60"
|
||||
}
|
||||
```
|
||||
|
||||
### 用例： 服务跟踪分析页面
|
||||
|
||||
因为实时分析不是必须的，所以我们可以简单的 **MapReduce** **Web Server** 的日志，用来生成点击次数。
|
||||
|
||||
```python
|
||||
class HitCounts(MRJob):
|
||||
|
||||
def extract_url(self, line):
|
||||
"""Extract the generated url from the log line."""
|
||||
...
|
||||
|
||||
def extract_year_month(self, line):
|
||||
"""Return the year and month portions of the timestamp."""
|
||||
...
|
||||
|
||||
def mapper(self, _, line):
|
||||
"""Parse each log line, extract and transform relevant lines.
|
||||
|
||||
Emit key value pairs of the form:
|
||||
|
||||
(2016-01, url0), 1
|
||||
(2016-01, url0), 1
|
||||
(2016-01, url1), 1
|
||||
"""
|
||||
url = self.extract_url(line)
|
||||
period = self.extract_year_month(line)
|
||||
yield (period, url), 1
|
||||
|
||||
def reducer(self, key, values):
|
||||
"""Sum values for each key.
|
||||
|
||||
(2016-01, url0), 2
|
||||
(2016-01, url1), 1
|
||||
"""
|
||||
yield key, sum(values)
|
||||
```
|
||||
|
||||
### 用例： 服务删除过期的 pastes
|
||||
|
||||
为了删除过期的 pastes，我们可以直接搜索 **SQL 数据库** 中所有的过期时间比当前时间更早的记录，
|
||||
所有过期的记录将从这张表里面删除（或者将其标记为过期）。
|
||||
|
||||
## 第四步：扩展这个设计
|
||||
|
||||
> 给定约束条件，识别和解决瓶颈。
|
||||
|
||||
|
||||
|
||||
**重要提示: 不要简单的从最初的设计直接跳到最终的设计**
|
||||
|
||||
说明您将迭代地执行这样的操作：1)**Benchmark/Load 测试**，2)**Profile** 出瓶颈，3)在评估替代方案和权衡时解决瓶颈，4)重复前面，可以参考[在 AWS 上设计一个可以支持百万用户的系统](../scaling_aws/README.md)这个用来解决如何迭代地扩展初始设计的例子。
|
||||
|
||||
重要的是讨论在初始设计中可能遇到的瓶颈，以及如何解决每个瓶颈。比如，在多个 **Web 服务器** 上添加 **负载平衡器** 可以解决哪些问题？ **CDN** 解决哪些问题？**Master-Slave Replicas** 解决哪些问题? 替代方案是什么和怎么对每一个替代方案进行权衡比较？
|
||||
|
||||
我们将介绍一些组件来完成设计，并解决可伸缩性问题。内部的负载平衡器并不能减少杂乱。
|
||||
|
||||
**为了避免重复的讨论**， 参考以下[系统设计主题](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)获取主要讨论要点、权衡和替代方案：
|
||||
|
||||
* [DNS](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#域名系统)
|
||||
* [CDN](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#内容分发网络cdn)
|
||||
* [负载均衡器](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#负载均衡器)
|
||||
* [水平扩展](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#水平扩展)
|
||||
* [反向代理（web 服务器）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)
|
||||
* [应用层](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用层)
|
||||
* [缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存)
|
||||
* [关系型数据库管理系统 (RDBMS)](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#关系型数据库管理系统rdbms)
|
||||
* [SQL write master-slave failover](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#故障切换)
|
||||
* [主从复制](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#主从复制)
|
||||
* [一致性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#一致性模式)
|
||||
* [可用性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#可用性模式)
|
||||
|
||||
**分析存储数据库** 可以用比如 Amazon Redshift 或者 Google BigQuery 这样的数据仓库解决方案。
|
||||
|
||||
一个像 Amazon S3 这样的 **对象存储**，可以轻松处理每月 12.7 GB 的新内容约束。
|
||||
|
||||
要处理 *平均* 每秒 40 读请求(峰值更高)，其中热点内容的流量应该由 **内存缓存** 处理，而不是数据库。**内存缓存** 对于处理分布不均匀的流量和流量峰值也很有用。只要副本没有陷入复制写的泥潭，**SQL Read Replicas** 应该能够处理缓存丢失。
|
||||
|
||||
对于单个 **SQL Write Master-Slave**，*平均* 每秒 4paste 写入 (峰值更高) 应该是可以做到的。否则，我们需要使用额外的 SQL 扩展模式:
|
||||
|
||||
* [联合](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#联合)
|
||||
* [分片](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#分片)
|
||||
* [非规范化](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#非规范化)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#SQL调优)
|
||||
|
||||
我们还应该考虑将一些数据移动到 **NoSQL 数据库**。
|
||||
|
||||
## 额外的话题
|
||||
|
||||
> 是否更深入探讨额外主题，取决于问题的范围和面试剩余的时间。
|
||||
|
||||
### NoSQL
|
||||
|
||||
* [键值存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#键-值存储)
|
||||
* [文档存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#文档类型存储)
|
||||
* [列型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#列型存储)
|
||||
* [图数据库](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#图数据库)
|
||||
* [sql 还是 nosql](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)
|
||||
|
||||
### 缓存
|
||||
|
||||
* 在哪缓存
|
||||
* [客户端缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#客户端缓存)
|
||||
* [CDN 缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#cdn-缓存)
|
||||
* [Web 服务器缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#web-服务器缓存)
|
||||
* [数据库缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库缓存)
|
||||
* [应用缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用缓存)
|
||||
* 缓存什么
|
||||
* [数据库查询级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库查询级别的缓存)
|
||||
* [对象级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#对象级别的缓存)
|
||||
* 何时更新缓存
|
||||
* [缓存模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存模式)
|
||||
* [直写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#直写模式)
|
||||
* [回写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#回写模式)
|
||||
* [刷新](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#刷新)
|
||||
|
||||
### 异步和微服务
|
||||
|
||||
* [消息队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#消息队列)
|
||||
* [任务队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#任务队列)
|
||||
* [背压](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#背压)
|
||||
* [微服务](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#微服务)
|
||||
|
||||
### 通信
|
||||
|
||||
* 讨论权衡:
|
||||
* 跟客户端之间的外部通信 - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)
|
||||
* 内部通信 - [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)
|
||||
* [服务发现](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#服务发现)
|
||||
|
||||
### 安全
|
||||
|
||||
参考[安全](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#安全)。
|
||||
|
||||
### 延迟数字
|
||||
|
||||
见[每个程序员都应该知道的延迟数](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数)。
|
||||
|
||||
### 持续进行
|
||||
|
||||
* 继续对系统进行基准测试和监控，以在瓶颈出现时解决它们
|
||||
* 扩展是一个迭代的过程
|
||||
|
|
@ -116,7 +116,7 @@ paste_path varchar(255) NOT NULL
|
|||
PRIMARY KEY(shortlink)
|
||||
```
|
||||
|
||||
We'll create an [index](https://github.com/donnemartin/system-design-primer#use-good-indices) on `shortlink ` and `created_at` to speed up lookups (log-time instead of scanning the entire table) and to keep the data in memory. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
|
||||
Setting the primary key to be based on the `shortlink` column creates an [index](https://github.com/donnemartin/system-design-primer#use-good-indices) that the database uses to enforce uniqueness. We'll create an additional index on `created_at` to speed up lookups (log-time instead of scanning the entire table) and to keep the data in memory. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
|
||||
|
||||
To generate the unique url, we could:
|
||||
|
||||
|
|
@ -130,7 +130,7 @@ To generate the unique url, we could:
|
|||
* Base 64 is another popular encoding but provides issues for urls because of the additional `+` and `/` characters
|
||||
* The following [Base 62 pseudocode](http://stackoverflow.com/questions/742013/how-to-code-a-url-shortener) runs in O(k) time where k is the number of digits = 7:
|
||||
|
||||
```
|
||||
```python
|
||||
def base_encode(num, base=62):
|
||||
digits = []
|
||||
while num > 0
|
||||
|
|
@ -142,7 +142,7 @@ def base_encode(num, base=62):
|
|||
|
||||
* Take the first 7 characters of the output, which results in 62^7 possible values and should be sufficient to handle our constraint of 360 million shortlinks in 3 years:
|
||||
|
||||
```
|
||||
```python
|
||||
url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH]
|
||||
```
|
||||
|
||||
|
|
@ -194,7 +194,7 @@ Since realtime analytics are not a requirement, we could simply **MapReduce** th
|
|||
|
||||
**Clarify with your interviewer how much code you are expected to write**.
|
||||
|
||||
```
|
||||
```python
|
||||
class HitCounts(MRJob):
|
||||
|
||||
def extract_url(self, line):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,306 @@
|
|||
# 设计一个键-值缓存来存储最近 web 服务查询的结果
|
||||
|
||||
**注意：这个文档中的链接会直接指向[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)中的有关部分，以避免重复的内容。你可以参考链接的相关内容，来了解其总的要点、方案的权衡取舍以及可选的替代方案。**
|
||||
|
||||
## 第一步：简述用例与约束条件
|
||||
|
||||
> 搜集需求与问题的范围。
|
||||
> 提出问题来明确用例与约束条件。
|
||||
> 讨论假设。
|
||||
|
||||
我们将在没有面试官明确说明问题的情况下，自己定义一些用例以及限制条件。
|
||||
|
||||
### 用例
|
||||
|
||||
#### 我们将把问题限定在仅处理以下用例的范围中
|
||||
|
||||
* **用户**发送一个搜索请求，命中缓存
|
||||
* **用户**发送一个搜索请求，未命中缓存
|
||||
* **服务**有着高可用性
|
||||
|
||||
### 限制条件与假设
|
||||
|
||||
#### 提出假设
|
||||
|
||||
* 网络流量不是均匀分布的
|
||||
* 经常被查询的内容应该一直存于缓存中
|
||||
* 需要确定如何规定缓存过期、缓存刷新规则
|
||||
* 缓存提供的服务查询速度要快
|
||||
* 机器间延迟较低
|
||||
* 缓存有内存限制
|
||||
* 需要决定缓存什么、移除什么
|
||||
* 需要缓存百万级的查询
|
||||
* 1000 万用户
|
||||
* 每个月 100 亿次查询
|
||||
|
||||
#### 计算用量
|
||||
|
||||
**如果你需要进行粗略的用量计算，请向你的面试官说明。**
|
||||
|
||||
* 缓存存储的是键值对有序表，键为 `query`（查询），值为 `results`（结果）。
|
||||
* `query` - 50 字节
|
||||
* `title` - 20 字节
|
||||
* `snippet` - 200 字节
|
||||
* 总计：270 字节
|
||||
* 假如 100 亿次查询都是不同的，且全部需要存储，那么每个月需要 2.7 TB 的缓存空间
|
||||
* 单次查询 270 字节 * 每月查询 100 亿次
|
||||
* 假设内存大小有限制，需要决定如何制定缓存过期规则
|
||||
* 每秒 4,000 次请求
|
||||
|
||||
便利换算指南：
|
||||
|
||||
* 每个月有 250 万秒
|
||||
* 每秒一个请求 = 每个月 250 万次请求
|
||||
* 每秒 40 个请求 = 每个月 1 亿次请求
|
||||
* 每秒 400 个请求 = 每个月 10 亿次请求
|
||||
|
||||
## 第二步：概要设计
|
||||
|
||||
> 列出所有重要组件以规划概要设计。
|
||||
|
||||
|
||||
|
||||
## 第三步：设计核心组件
|
||||
|
||||
> 深入每个核心组件的细节。
|
||||
|
||||
### 用例：用户发送了一次请求，命中了缓存
|
||||
|
||||
常用的查询可以由例如 Redis 或者 Memcached 之类的**内存缓存**提供支持，以减少数据读取延迟，并且避免**反向索引服务**以及**文档服务**的过载。从内存读取 1 MB 连续数据大约要花 250 微秒，而从 SSD 读取同样大小的数据要花费 4 倍的时间，从机械硬盘读取需要花费 80 倍以上的时间。<sup><a href=https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数>1</a></sup>
|
||||
|
||||
由于缓存容量有限，我们将使用 LRU（近期最少使用算法）来控制缓存的过期。
|
||||
|
||||
* **客户端**向运行[反向代理](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)的 **Web 服务器**发送一个请求
|
||||
* 这个 **Web 服务器**将请求转发给**查询 API** 服务
|
||||
* **查询 API** 服务将会做这些事情：
|
||||
* 分析查询
|
||||
* 移除多余的内容
|
||||
* 将文本分割成词组
|
||||
* 修正拼写错误
|
||||
* 规范化字母的大小写
|
||||
* 将查询转换为布尔运算
|
||||
* 检测**内存缓存**是否有匹配查询的内容
|
||||
* 如果命中**内存缓存**，**内存缓存**将会做以下事情：
|
||||
* 将缓存入口的位置指向 LRU 链表的头部
|
||||
* 返回缓存内容
|
||||
* 否则，**查询 API** 将会做以下事情：
|
||||
* 使用**反向索引服务**来查找匹配查询的文档
|
||||
* **反向索引服务**对匹配到的结果进行排名，然后返回最符合的结果
|
||||
* 使用**文档服务**返回文章标题与片段
|
||||
* 更新**内存缓存**，存入内容，将**内存缓存**入口位置指向 LRU 链表的头部
|
||||
|
||||
#### 缓存的实现
|
||||
|
||||
缓存可以使用双向链表实现：新元素将会在头结点加入，过期的元素将会在尾节点被删除。我们使用哈希表以便能够快速查找每个链表节点。
|
||||
|
||||
**向你的面试官告知你准备写多少代码**。
|
||||
|
||||
实现**查询 API 服务**：
|
||||
|
||||
```python
|
||||
class QueryApi(object):
|
||||
|
||||
def __init__(self, memory_cache, reverse_index_service):
|
||||
self.memory_cache = memory_cache
|
||||
self.reverse_index_service = reverse_index_service
|
||||
|
||||
def parse_query(self, query):
|
||||
"""移除多余内容，将文本分割成词组，修复拼写错误，
|
||||
规范化字母大小写，转换布尔运算。
|
||||
"""
|
||||
...
|
||||
|
||||
def process_query(self, query):
|
||||
query = self.parse_query(query)
|
||||
results = self.memory_cache.get(query)
|
||||
if results is None:
|
||||
results = self.reverse_index_service.process_search(query)
|
||||
self.memory_cache.set(query, results)
|
||||
return results
|
||||
```
|
||||
|
||||
实现**节点**：
|
||||
|
||||
```python
|
||||
class Node(object):
|
||||
|
||||
def __init__(self, query, results):
|
||||
self.query = query
|
||||
self.results = results
|
||||
```
|
||||
|
||||
实现**链表**：
|
||||
|
||||
```python
|
||||
class LinkedList(object):
|
||||
|
||||
def __init__(self):
|
||||
self.head = None
|
||||
self.tail = None
|
||||
|
||||
def move_to_front(self, node):
|
||||
...
|
||||
|
||||
def append_to_front(self, node):
|
||||
...
|
||||
|
||||
def remove_from_tail(self):
|
||||
...
|
||||
```
|
||||
|
||||
实现**缓存**：
|
||||
|
||||
```python
|
||||
class Cache(object):
|
||||
|
||||
def __init__(self, MAX_SIZE):
|
||||
self.MAX_SIZE = MAX_SIZE
|
||||
self.size = 0
|
||||
self.lookup = {} # key: query, value: node
|
||||
self.linked_list = LinkedList()
|
||||
|
||||
def get(self, query)
|
||||
"""从缓存取得存储的内容
|
||||
|
||||
将入口节点位置更新为 LRU 链表的头部。
|
||||
"""
|
||||
node = self.lookup[query]
|
||||
if node is None:
|
||||
return None
|
||||
self.linked_list.move_to_front(node)
|
||||
return node.results
|
||||
|
||||
def set(self, results, query):
|
||||
"""将所给查询键的结果存在缓存中。
|
||||
|
||||
当更新缓存记录的时候，将它的位置指向 LRU 链表的头部。
|
||||
如果这个记录是新的记录，并且缓存空间已满，应该在加入新记录前
|
||||
删除最老的记录。
|
||||
"""
|
||||
node = self.lookup[query]
|
||||
if node is not None:
|
||||
# 键存在于缓存中，更新它对应的值
|
||||
node.results = results
|
||||
self.linked_list.move_to_front(node)
|
||||
else:
|
||||
# 键不存在于缓存中
|
||||
if self.size == self.MAX_SIZE:
|
||||
# 在链表中查找并删除最老的记录
|
||||
self.lookup.pop(self.linked_list.tail.query, None)
|
||||
self.linked_list.remove_from_tail()
|
||||
else:
|
||||
self.size += 1
|
||||
# 添加新的键值对
|
||||
new_node = Node(query, results)
|
||||
self.linked_list.append_to_front(new_node)
|
||||
self.lookup[query] = new_node
|
||||
```
|
||||
|
||||
#### 何时更新缓存
|
||||
|
||||
缓存将会在以下几种情况更新：
|
||||
|
||||
* 页面内容发生变化
|
||||
* 页面被移除或者加入了新页面
|
||||
* 页面的权值发生变动
|
||||
|
||||
解决这些问题的最直接的方法，就是为缓存记录设置一个它在被更新前能留在缓存中的最长时间，这个时间简称为存活时间（TTL）。
|
||||
|
||||
参考 [「何时更新缓存」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#何时更新缓存)来了解其权衡取舍及替代方案。以上方法在[缓存模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存模式)一章中详细地进行了描述。
|
||||
|
||||
## 第四步：架构扩展
|
||||
|
||||
> 根据限制条件，找到并解决瓶颈。
|
||||
|
||||
|
||||
|
||||
**重要提示：不要从最初设计直接跳到最终设计中！**
|
||||
|
||||
现在你要 1) **基准测试、负载测试**。2) **分析、描述**性能瓶颈。3) 在解决瓶颈问题的同时，评估替代方案、权衡利弊。4) 重复以上步骤。请阅读[「设计一个系统，并将其扩大到为数以百万计的 AWS 用户服务」](../scaling_aws/README.md) 来了解如何逐步扩大初始设计。
|
||||
|
||||
讨论初始设计可能遇到的瓶颈及相关解决方案是很重要的。例如加上一个配置多台 **Web 服务器**的**负载均衡器**是否能够解决问题？**CDN**呢？**主从复制**呢？它们各自的替代方案和需要**权衡**的利弊又有什么呢？
|
||||
|
||||
我们将会介绍一些组件来完成设计，并解决架构扩张问题。内置的负载均衡器将不做讨论以节省篇幅。
|
||||
|
||||
**为了避免重复讨论**，请参考[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)相关部分来了解其要点、方案的权衡取舍以及可选的替代方案。
|
||||
|
||||
* [DNS](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#域名系统)
|
||||
* [负载均衡器](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#负载均衡器)
|
||||
* [水平拓展](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#水平扩展)
|
||||
* [反向代理（web 服务器）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)
|
||||
* [API 服务（应用层）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用层)
|
||||
* [缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存)
|
||||
* [一致性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#一致性模式)
|
||||
* [可用性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#可用性模式)
|
||||
|
||||
### 将内存缓存扩大到多台机器
|
||||
|
||||
为了解决庞大的请求负载以及巨大的内存需求，我们将要对架构进行水平拓展。如何在我们的**内存缓存**集群中存储数据呢？我们有以下三个主要可选方案：
|
||||
|
||||
* **缓存集群中的每一台机器都有自己的缓存** - 简单，但是它会降低缓存命中率。
|
||||
* **缓存集群中的每一台机器都有缓存的拷贝** - 简单，但是它的内存使用效率太低了。
|
||||
* **对缓存进行[分片](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#分片)，分别部署在缓存集群中的所有机器中** - 更加复杂，但是它是最佳的选择。我们可以使用哈希，用查询语句 `machine = hash(query)` 来确定哪台机器有需要缓存。当然我们也可以使用[一致性哈希](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#正在完善中)。
|
||||
|
||||
## 其它要点
|
||||
|
||||
> 是否深入这些额外的主题，取决于你的问题范围和剩下的时间。
|
||||
|
||||
### SQL 缩放模式
|
||||
|
||||
* [读取复制](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#主从复制)
|
||||
* [联合](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#联合)
|
||||
* [分片](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#分片)
|
||||
* [非规范化](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#非规范化)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-调优)
|
||||
|
||||
#### NoSQL
|
||||
|
||||
* [键-值存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#键-值存储)
|
||||
* [文档类型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#文档类型存储)
|
||||
* [列型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#列型存储)
|
||||
* [图数据库](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#图数据库)
|
||||
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)
|
||||
|
||||
### 缓存
|
||||
|
||||
* 在哪缓存
|
||||
* [客户端缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#客户端缓存)
|
||||
* [CDN 缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#cdn-缓存)
|
||||
* [Web 服务器缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#web-服务器缓存)
|
||||
* [数据库缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库缓存)
|
||||
* [应用缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用缓存)
|
||||
* 什么需要缓存
|
||||
* [数据库查询级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库查询级别的缓存)
|
||||
* [对象级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#对象级别的缓存)
|
||||
* 何时更新缓存
|
||||
* [缓存模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存模式)
|
||||
* [直写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#直写模式)
|
||||
* [回写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#回写模式)
|
||||
* [刷新](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#刷新)
|
||||
|
||||
### 异步与微服务
|
||||
|
||||
* [消息队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#消息队列)
|
||||
* [任务队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#任务队列)
|
||||
* [背压](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#背压)
|
||||
* [微服务](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#微服务)
|
||||
|
||||
### 通信
|
||||
|
||||
* 可权衡选择的方案：
|
||||
* 与客户端的外部通信 - [使用 REST 作为 HTTP API](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)
|
||||
* 服务器内部通信 - [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)
|
||||
* [服务发现](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#服务发现)
|
||||
|
||||
### 安全性
|
||||
|
||||
请参阅[「安全」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#安全)一章。
|
||||
|
||||
### 延迟数值
|
||||
|
||||
请参阅[「每个程序员都应该知道的延迟数」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数)。
|
||||
|
||||
### 持续探讨
|
||||
|
||||
* 持续进行基准测试并监控你的系统，以解决他们提出的瓶颈问题。
|
||||
* 架构拓展是一个迭代的过程。
|
||||
|
|
@ -97,7 +97,7 @@ The cache can use a doubly-linked list: new items will be added to the head whil
|
|||
|
||||
**Query API Server** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class QueryApi(object):
|
||||
|
||||
def __init__(self, memory_cache, reverse_index_service):
|
||||
|
|
@ -121,7 +121,7 @@ class QueryApi(object):
|
|||
|
||||
**Node** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class Node(object):
|
||||
|
||||
def __init__(self, query, results):
|
||||
|
|
@ -131,7 +131,7 @@ class Node(object):
|
|||
|
||||
**LinkedList** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class LinkedList(object):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -150,7 +150,7 @@ class LinkedList(object):
|
|||
|
||||
**Cache** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class Cache(object):
|
||||
|
||||
def __init__(self, MAX_SIZE):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,338 @@
|
|||
# 为 Amazon 设计分类售卖排行
|
||||
|
||||
**注意：这个文档中的链接会直接指向[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)中的有关部分，以避免重复的内容。你可以参考链接的相关内容，来了解其总的要点、方案的权衡取舍以及可选的替代方案。**
|
||||
|
||||
## 第一步：简述用例与约束条件
|
||||
|
||||
> 搜集需求与问题的范围。
|
||||
> 提出问题来明确用例与约束条件。
|
||||
> 讨论假设。
|
||||
|
||||
我们将在没有面试官明确说明问题的情况下，自己定义一些用例以及限制条件。
|
||||
|
||||
### 用例
|
||||
|
||||
#### 我们将把问题限定在仅处理以下用例的范围中
|
||||
|
||||
* **服务**根据分类计算过去一周中最受欢迎的商品
|
||||
* **用户**通过分类浏览过去一周中最受欢迎的商品
|
||||
* **服务**有着高可用性
|
||||
|
||||
#### 不在用例范围内的有
|
||||
|
||||
* 一般的电商网站
|
||||
* 只为售卖排行榜设计组件
|
||||
|
||||
### 限制条件与假设
|
||||
|
||||
#### 提出假设
|
||||
|
||||
* 网络流量不是均匀分布的
|
||||
* 一个商品可能存在于多个分类中
|
||||
* 商品不能够更改分类
|
||||
* 不会存在如 `foo/bar/baz` 之类的子分类
|
||||
* 每小时更新一次结果
|
||||
* 受欢迎的商品越多，就需要更频繁地更新
|
||||
* 1000 万个商品
|
||||
* 1000 个分类
|
||||
* 每个月 10 亿次交易
|
||||
* 每个月 1000 亿次读取请求
|
||||
* 100:1 的读写比例
|
||||
|
||||
#### 计算用量
|
||||
|
||||
**如果你需要进行粗略的用量计算，请向你的面试官说明。**
|
||||
|
||||
* 每笔交易的用量：
|
||||
* `created_at` - 5 字节
|
||||
* `product_id` - 8 字节
|
||||
* `category_id` - 4 字节
|
||||
* `seller_id` - 8 字节
|
||||
* `buyer_id` - 8 字节
|
||||
* `quantity` - 4 字节
|
||||
* `total_price` - 5 字节
|
||||
* 总计：大约 40 字节
|
||||
* 每个月的交易内容会产生 40 GB 的记录
|
||||
* 每次交易 40 字节 * 每个月 10 亿次交易
|
||||
* 3年内产生了 1.44 TB 的新交易内容记录
|
||||
* 假定大多数的交易都是新交易而不是更改以前进行完的交易
|
||||
* 平均每秒 400 次交易次数
|
||||
* 平均每秒 40,000 次读取请求
|
||||
|
||||
便利换算指南：
|
||||
|
||||
* 每个月有 250 万秒
|
||||
* 每秒一个请求 = 每个月 250 万次请求
|
||||
* 每秒 40 个请求 = 每个月 1 亿次请求
|
||||
* 每秒 400 个请求 = 每个月 10 亿次请求
|
||||
|
||||
## 第二步：概要设计
|
||||
|
||||
> 列出所有重要组件以规划概要设计。
|
||||
|
||||
|
||||
|
||||
## 第三步：设计核心组件
|
||||
|
||||
> 深入每个核心组件的细节。
|
||||
|
||||
### 用例：服务需要根据分类计算上周最受欢迎的商品
|
||||
|
||||
我们可以在现成的**对象存储**系统（例如 Amazon S3 服务）中存储 **售卖 API** 服务产生的日志文本， 因此不需要我们自己搭建分布式文件系统了。
|
||||
|
||||
**向你的面试官告知你准备写多少代码**。
|
||||
|
||||
假设下面是一个用 tab 分割的简易的日志记录：
|
||||
|
||||
```
|
||||
timestamp product_id category_id qty total_price seller_id buyer_id
|
||||
t1 product1 category1 2 20.00 1 1
|
||||
t2 product1 category2 2 20.00 2 2
|
||||
t2 product1 category2 1 10.00 2 3
|
||||
t3 product2 category1 3 7.00 3 4
|
||||
t4 product3 category2 7 2.00 4 5
|
||||
t5 product4 category1 1 5.00 5 6
|
||||
...
|
||||
```
|
||||
|
||||
**售卖排行服务** 需要用到 **MapReduce**，并使用 **售卖 API** 服务进行日志记录，同时将结果写入 **SQL 数据库**中的总表 `sales_rank` 中。我们也可以讨论一下[究竟是用 SQL 还是用 NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)。
|
||||
|
||||
我们需要通过以下步骤使用 **MapReduce**：
|
||||
|
||||
* **第 1 步** - 将数据转换为 `(category, product_id), sum(quantity)` 的形式
|
||||
* **第 2 步** - 执行分布式排序
|
||||
|
||||
```python
|
||||
class SalesRanker(MRJob):
|
||||
|
||||
def within_past_week(self, timestamp):
|
||||
"""如果时间戳属于过去的一周则返回 True，
|
||||
否则返回 False。"""
|
||||
...
|
||||
|
||||
def mapper(self, _ line):
|
||||
"""解析日志的每一行，提取并转换相关行，
|
||||
|
||||
将键值对设定为如下形式：
|
||||
|
||||
(category1, product1), 2
|
||||
(category2, product1), 2
|
||||
(category2, product1), 1
|
||||
(category1, product2), 3
|
||||
(category2, product3), 7
|
||||
(category1, product4), 1
|
||||
"""
|
||||
timestamp, product_id, category_id, quantity, total_price, seller_id, \
|
||||
buyer_id = line.split('\t')
|
||||
if self.within_past_week(timestamp):
|
||||
yield (category_id, product_id), quantity
|
||||
|
||||
def reducer(self, key, value):
|
||||
"""将每个 key 的值加起来。
|
||||
|
||||
(category1, product1), 2
|
||||
(category2, product1), 3
|
||||
(category1, product2), 3
|
||||
(category2, product3), 7
|
||||
(category1, product4), 1
|
||||
"""
|
||||
yield key, sum(values)
|
||||
|
||||
def mapper_sort(self, key, value):
|
||||
"""构造 key 以确保正确的排序。
|
||||
|
||||
将键值对转换成如下形式：
|
||||
|
||||
(category1, 2), product1
|
||||
(category2, 3), product1
|
||||
(category1, 3), product2
|
||||
(category2, 7), product3
|
||||
(category1, 1), product4
|
||||
|
||||
MapReduce 的随机排序步骤会将键
|
||||
值的排序打乱，变成下面这样：
|
||||
|
||||
(category1, 1), product4
|
||||
(category1, 2), product1
|
||||
(category1, 3), product2
|
||||
(category2, 3), product1
|
||||
(category2, 7), product3
|
||||
"""
|
||||
category_id, product_id = key
|
||||
quantity = value
|
||||
yield (category_id, quantity), product_id
|
||||
|
||||
def reducer_identity(self, key, value):
|
||||
yield key, value
|
||||
|
||||
def steps(self):
|
||||
""" 此处为 map reduce 步骤"""
|
||||
return [
|
||||
self.mr(mapper=self.mapper,
|
||||
reducer=self.reducer),
|
||||
self.mr(mapper=self.mapper_sort,
|
||||
reducer=self.reducer_identity),
|
||||
]
|
||||
```
|
||||
|
||||
得到的结果将会是如下的排序列，我们将其插入 `sales_rank` 表中：
|
||||
|
||||
```
|
||||
(category1, 1), product4
|
||||
(category1, 2), product1
|
||||
(category1, 3), product2
|
||||
(category2, 3), product1
|
||||
(category2, 7), product3
|
||||
```
|
||||
|
||||
`sales_rank` 表的数据结构如下：
|
||||
|
||||
```
|
||||
id int NOT NULL AUTO_INCREMENT
|
||||
category_id int NOT NULL
|
||||
total_sold int NOT NULL
|
||||
product_id int NOT NULL
|
||||
PRIMARY KEY(id)
|
||||
FOREIGN KEY(category_id) REFERENCES Categories(id)
|
||||
FOREIGN KEY(product_id) REFERENCES Products(id)
|
||||
```
|
||||
|
||||
我们会以 `id`、`category_id` 与 `product_id` 创建一个 [索引](https://github.com/donnemartin/system-design-primer#use-good-indices)以加快查询速度（只需要使用读取日志的时间，不再需要每次都扫描整个数据表）并让数据常驻内存。从内存读取 1 MB 连续数据大约要花 250 微秒，而从 SSD 读取同样大小的数据要花费 4 倍的时间，从机械硬盘读取需要花费 80 倍以上的时间。<sup><a href=https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数>1</a></sup>
|
||||
|
||||
### 用例：用户需要根据分类浏览上周中最受欢迎的商品
|
||||
|
||||
* **客户端**向运行[反向代理](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)的 **Web 服务器**发送一个请求
|
||||
* 这个 **Web 服务器**将请求转发给**查询 API** 服务
|
||||
* The **查询 API** 服务将从 **SQL 数据库**的 `sales_rank` 表中读取数据
|
||||
|
||||
我们可以调用一个公共的 [REST API](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)：
|
||||
|
||||
```
|
||||
$ curl https://amazon.com/api/v1/popular?category_id=1234
|
||||
```
|
||||
|
||||
返回：
|
||||
|
||||
```
|
||||
{
|
||||
"id": "100",
|
||||
"category_id": "1234",
|
||||
"total_sold": "100000",
|
||||
"product_id": "50",
|
||||
},
|
||||
{
|
||||
"id": "53",
|
||||
"category_id": "1234",
|
||||
"total_sold": "90000",
|
||||
"product_id": "200",
|
||||
},
|
||||
{
|
||||
"id": "75",
|
||||
"category_id": "1234",
|
||||
"total_sold": "80000",
|
||||
"product_id": "3",
|
||||
},
|
||||
```
|
||||
|
||||
而对于服务器内部的通信，我们可以使用 [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)。
|
||||
|
||||
## 第四步：架构扩展
|
||||
|
||||
> 根据限制条件，找到并解决瓶颈。
|
||||
|
||||
|
||||
|
||||
**重要提示：不要从最初设计直接跳到最终设计中！**
|
||||
|
||||
现在你要 1) **基准测试、负载测试**。2) **分析、描述**性能瓶颈。3) 在解决瓶颈问题的同时，评估替代方案、权衡利弊。4) 重复以上步骤。请阅读[「设计一个系统，并将其扩大到为数以百万计的 AWS 用户服务」](../scaling_aws/README.md) 来了解如何逐步扩大初始设计。
|
||||
|
||||
讨论初始设计可能遇到的瓶颈及相关解决方案是很重要的。例如加上一个配置多台 **Web 服务器**的**负载均衡器**是否能够解决问题？**CDN**呢？**主从复制**呢？它们各自的替代方案和需要**权衡**的利弊又有什么呢？
|
||||
|
||||
我们将会介绍一些组件来完成设计，并解决架构扩张问题。内置的负载均衡器将不做讨论以节省篇幅。
|
||||
|
||||
**为了避免重复讨论**，请参考[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)相关部分来了解其要点、方案的权衡取舍以及可选的替代方案。
|
||||
|
||||
* [DNS](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#域名系统)
|
||||
* [负载均衡器](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#负载均衡器)
|
||||
* [水平拓展](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#水平扩展)
|
||||
* [反向代理（web 服务器）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)
|
||||
* [API 服务（应用层）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用层)
|
||||
* [缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存)
|
||||
* [关系型数据库管理系统 (RDBMS)](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#关系型数据库管理系统rdbms)
|
||||
* [SQL 故障主从切换](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#故障切换)
|
||||
* [主从复制](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#主从复制)
|
||||
* [一致性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#一致性模式)
|
||||
* [可用性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#可用性模式)
|
||||
|
||||
**分析数据库** 可以用现成的数据仓储系统，例如使用 Amazon Redshift 或者 Google BigQuery 的解决方案。
|
||||
|
||||
当使用数据仓储技术或者**对象存储**系统时，我们只想在数据库中存储有限时间段的数据。Amazon S3 的**对象存储**系统可以很方便地设置每个月限制只允许新增 40 GB 的存储内容。
|
||||
|
||||
平均每秒 40,000 次的读取请求（峰值将会更高）, 可以通过扩展 **内存缓存** 来处理热点内容的读取流量，这对于处理不均匀分布的流量和流量峰值也很有用。由于读取量非常大，**SQL Read 副本** 可能会遇到处理缓存未命中的问题，我们可能需要使用额外的 SQL 扩展模式。
|
||||
|
||||
平均每秒 400 次写操作（峰值将会更高）可能对于单个 **SQL 写主-从** 模式来说比较很困难，因此同时还需要更多的扩展技术
|
||||
|
||||
SQL 缩放模式包括：
|
||||
|
||||
* [联合](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#联合)
|
||||
* [分片](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#分片)
|
||||
* [非规范化](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#非规范化)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-调优)
|
||||
|
||||
我们也可以考虑将一些数据移至 **NoSQL 数据库**。
|
||||
|
||||
## 其它要点
|
||||
|
||||
> 是否深入这些额外的主题，取决于你的问题范围和剩下的时间。
|
||||
|
||||
#### NoSQL
|
||||
|
||||
* [键-值存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#键-值存储)
|
||||
* [文档类型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#文档类型存储)
|
||||
* [列型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#列型存储)
|
||||
* [图数据库](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#图数据库)
|
||||
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)
|
||||
|
||||
### 缓存
|
||||
|
||||
* 在哪缓存
|
||||
* [客户端缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#客户端缓存)
|
||||
* [CDN 缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#cdn-缓存)
|
||||
* [Web 服务器缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#web-服务器缓存)
|
||||
* [数据库缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库缓存)
|
||||
* [应用缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用缓存)
|
||||
* 什么需要缓存
|
||||
* [数据库查询级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库查询级别的缓存)
|
||||
* [对象级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#对象级别的缓存)
|
||||
* 何时更新缓存
|
||||
* [缓存模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存模式)
|
||||
* [直写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#直写模式)
|
||||
* [回写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#回写模式)
|
||||
* [刷新](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#刷新)
|
||||
|
||||
### 异步与微服务
|
||||
|
||||
* [消息队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#消息队列)
|
||||
* [任务队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#任务队列)
|
||||
* [背压](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#背压)
|
||||
* [微服务](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#微服务)
|
||||
|
||||
### 通信
|
||||
|
||||
* 可权衡选择的方案：
|
||||
* 与客户端的外部通信 - [使用 REST 作为 HTTP API](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)
|
||||
* 服务器内部通信 - [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)
|
||||
* [服务发现](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#服务发现)
|
||||
|
||||
### 安全性
|
||||
|
||||
请参阅[「安全」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#安全)一章。
|
||||
|
||||
### 延迟数值
|
||||
|
||||
请参阅[「每个程序员都应该知道的延迟数」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数)。
|
||||
|
||||
### 持续探讨
|
||||
|
||||
* 持续进行基准测试并监控你的系统，以解决他们提出的瓶颈问题。
|
||||
* 架构拓展是一个迭代的过程。
|
||||
|
|
@ -102,7 +102,7 @@ We'll use a multi-step **MapReduce**:
|
|||
* **Step 1** - Transform the data to `(category, product_id), sum(quantity)`
|
||||
* **Step 2** - Perform a distributed sort
|
||||
|
||||
```
|
||||
```python
|
||||
class SalesRanker(MRJob):
|
||||
|
||||
def within_past_week(self, timestamp):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,403 @@
|
|||
# 在 AWS 上设计支持百万级到千万级用户的系统
|
||||
|
||||
**注释：为了避免重复，这篇文章的链接直接关联到 [系统设计主题](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) 的相关章节。为一讨论要点、折中方案和可选方案做参考。**
|
||||
|
||||
## 第 1 步：用例和约束概要
|
||||
|
||||
> 收集需求并调查问题。
|
||||
> 通过提问清晰用例和约束。
|
||||
> 讨论假设。
|
||||
|
||||
如果没有面试官提出明确的问题，我们将自己定义一些用例和约束条件。
|
||||
|
||||
### 用例
|
||||
|
||||
解决这个问题是一个循序渐进的过程：1) **基准/负载 测试**， 2) 瓶颈 **概述**， 3) 当评估可选和折中方案时定位瓶颈，4) 重复，这是向可扩展的设计发展基础设计的好模式。
|
||||
|
||||
除非你有 AWS 的背景或者正在申请需要 AWS 知识的相关职位，否则不要求了解 AWS 的相关细节。并且，这个练习中讨论的许多原则可以更广泛地应用于AWS生态系统之外。
|
||||
|
||||
#### 我们就处理以下用例讨论这一问题
|
||||
|
||||
* **用户** 进行读或写请求
|
||||
* **服务** 进行处理，存储用户数据，然后返回结果
|
||||
* **服务** 需要从支持小规模用户开始到百万用户
|
||||
* 在我们演化架构来处理大量的用户和请求时，讨论一般的扩展模式
|
||||
* **服务** 高可用
|
||||
|
||||
### 约束和假设
|
||||
|
||||
#### 状态假设
|
||||
|
||||
* 流量不均匀分布
|
||||
* 需要关系数据
|
||||
* 从一个用户扩展到千万用户
|
||||
* 表示用户量的增长
|
||||
* 用户量+
|
||||
* 用户量++
|
||||
* 用户量+++
|
||||
* ...
|
||||
* 1000 万用户
|
||||
* 每月 10 亿次写入
|
||||
* 每月 1000 亿次读出
|
||||
* 100:1 读写比率
|
||||
* 每次写入 1 KB 内容
|
||||
|
||||
#### 计算使用
|
||||
|
||||
**向你的面试官厘清你是否应该做粗略的使用计算**
|
||||
|
||||
* 1 TB 新内容 / 月
|
||||
* 1 KB 每次写入 * 10 亿 写入 / 月
|
||||
* 36 TB 新内容 / 3 年
|
||||
* 假设大多数写入都是新内容而不是更新已有内容
|
||||
* 平均每秒 400 次写入
|
||||
* 平均每秒 40,000 次读取
|
||||
|
||||
便捷的转换指南：
|
||||
|
||||
* 250 万秒 / 月
|
||||
* 1 次请求 / 秒 = 250 万次请求 / 月
|
||||
* 40 次请求 / 秒 = 1 亿次请求 / 月
|
||||
* 400 次请求 / 秒 = 10 亿请求 / 月
|
||||
|
||||
## 第 2 步：创建高级设计方案
|
||||
|
||||
> 用所有重要组件概述高水平设计
|
||||
|
||||
|
||||
|
||||
## 第 3 步：设计核心组件
|
||||
|
||||
> 深入每个核心组件的细节。
|
||||
|
||||
### 用例：用户进行读写请求
|
||||
|
||||
#### 目标
|
||||
|
||||
* 只有 1-2 个用户时，你只需要基础配置
|
||||
* 为简单起见，只需要一台服务器
|
||||
* 必要时进行纵向扩展
|
||||
* 监控以确定瓶颈
|
||||
|
||||
#### 以单台服务器开始
|
||||
|
||||
* **Web 服务器** 在 EC2 上
|
||||
* 存储用户数据
|
||||
* [**MySQL 数据库**](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms)
|
||||
|
||||
运用 **纵向扩展**：
|
||||
|
||||
* 选择一台更大容量的服务器
|
||||
* 密切关注指标，确定如何扩大规模
|
||||
* 使用基本监控来确定瓶颈:CPU、内存、IO、网络等
|
||||
* CloudWatch, top, nagios, statsd, graphite等
|
||||
* 纵向扩展的代价将变得更昂贵
|
||||
* 无冗余/容错
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* **纵向扩展** 的可选方案是 [**横向扩展**](https://github.com/donnemartin/system-design-primer#horizontal-scaling)
|
||||
|
||||
#### 自 SQL 开始，但认真考虑 NoSQL
|
||||
|
||||
约束条件假设需要关系型数据。我们可以开始时在单台服务器上使用 **MySQL 数据库**。
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* 查阅 [关系型数据库管理系统 (RDBMS)](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms) 章节
|
||||
* 讨论使用 [SQL 或 NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql) 的原因
|
||||
|
||||
#### 分配公共静态 IP
|
||||
|
||||
* 弹性 IP 提供了一个公共端点，不会在重启时改变 IP。
|
||||
* 故障转移时只需要把域名指向新 IP。
|
||||
|
||||
#### 使用 DNS 服务
|
||||
|
||||
添加 **DNS** 服务，比如 Route 53（[Amazon Route 53](https://aws.amazon.com/cn/route53/) - 译者注），将域映射到实例的公共 IP 中。
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* 查阅 [域名系统](https://github.com/donnemartin/system-design-primer#domain-name-system) 章节
|
||||
|
||||
#### 安全的 Web 服务器
|
||||
|
||||
* 只开放必要的端口
|
||||
* 允许 Web 服务器响应来自以下端口的请求
|
||||
* HTTP 80
|
||||
* HTTPS 443
|
||||
* SSH IP 白名单 22
|
||||
* 防止 Web 服务器启动外链
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* 查阅 [安全](https://github.com/donnemartin/system-design-primer#security) 章节
|
||||
|
||||
## 第 4 步：扩展设计
|
||||
|
||||
> 在给定约束条件下，定义和确认瓶颈。
|
||||
|
||||
### 用户+
|
||||
|
||||
|
||||
|
||||
#### 假设
|
||||
|
||||
我们的用户数量开始上升，并且单台服务器的负载上升。**基准/负载测试** 和 **分析** 指出 **MySQL 数据库** 占用越来越多的内存和 CPU 资源，同时用户数据将填满硬盘空间。
|
||||
|
||||
目前，我们尚能在纵向扩展时解决这些问题。不幸的是，解决这些问题的代价变得相当昂贵，并且原来的系统并不能允许在 **MySQL 数据库** 和 **Web 服务器** 的基础上进行独立扩展。
|
||||
|
||||
#### 目标
|
||||
|
||||
* 减轻单台服务器负载并且允许独立扩展
|
||||
* 在 **对象存储** 中单独存储静态内容
|
||||
* 将 **MySQL 数据库** 迁移到单独的服务器上
|
||||
* 缺点
|
||||
* 这些变化会增加复杂性，并要求对 **Web服务器** 进行更改，以指向 **对象存储** 和 **MySQL 数据库**
|
||||
* 必须采取额外的安全措施来确保新组件的安全
|
||||
* AWS 的成本也会增加，但应该与自身管理类似系统的成本做比较
|
||||
|
||||
#### 独立保存静态内容
|
||||
|
||||
* 考虑使用像 S3 这样可管理的 **对象存储** 服务来存储静态内容
|
||||
* 高扩展性和可靠性
|
||||
* 服务器端加密
|
||||
* 迁移静态内容到 S3
|
||||
* 用户文件
|
||||
* JS
|
||||
* CSS
|
||||
* 图片
|
||||
* 视频
|
||||
|
||||
#### 迁移 MySQL 数据库到独立机器上
|
||||
|
||||
* 考虑使用类似 RDS 的服务来管理 **MySQL 数据库**
|
||||
* 简单的管理，扩展
|
||||
* 多个可用区域
|
||||
* 空闲时加密
|
||||
|
||||
#### 系统安全
|
||||
|
||||
* 在传输和空闲时对数据进行加密
|
||||
* 使用虚拟私有云
|
||||
* 为单个 **Web 服务器** 创建一个公共子网，这样就可以发送和接收来自 internet 的流量
|
||||
* 为其他内容创建一个私有子网，禁止外部访问
|
||||
* 在每个组件上只为白名单 IP 打开端口
|
||||
* 这些相同的模式应当在新的组件的实现中实践
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* 查阅 [安全](https://github.com/donnemartin/system-design-primer#security) 章节
|
||||
|
||||
### 用户+++
|
||||
|
||||
|
||||
|
||||
#### 假设
|
||||
|
||||
我们的 **基准/负载测试** 和 **性能测试** 显示，在高峰时段，我们的单一 **Web服务器** 存在瓶颈，导致响应缓慢，在某些情况下还会宕机。随着服务的成熟，我们也希望朝着更高的可用性和冗余发展。
|
||||
|
||||
#### 目标
|
||||
|
||||
* 下面的目标试图用 **Web服务器** 解决扩展问题
|
||||
* 基于 **基准/负载测试** 和 **分析**，你可能只需要实现其中的一两个技术
|
||||
* 使用 [**横向扩展**](https://github.com/donnemartin/system-design-primer#horizontal-scaling) 来处理增加的负载和单点故障
|
||||
* 添加 [**负载均衡器**](https://github.com/donnemartin/system-design-primer#load-balancer) 例如 Amazon 的 ELB 或 HAProxy
|
||||
* ELB 是高可用的
|
||||
* 如果你正在配置自己的 **负载均衡器**, 在多个可用区域中设置多台服务器用于 [双活](https://github.com/donnemartin/system-design-primer#active-active) 或 [主被](https://github.com/donnemartin/system-design-primer#active-passive) 将提高可用性
|
||||
* 终止在 **负载平衡器** 上的SSL，以减少后端服务器上的计算负载，并简化证书管理
|
||||
* 在多个可用区域中使用多台 **Web服务器**
|
||||
* 在多个可用区域的 [**主-从 故障转移**](https://github.com/donnemartin/system-design-primer#master-slave-replication) 模式中使用多个 **MySQL** 实例来改进冗余
|
||||
* 分离 **Web 服务器** 和 [**应用服务器**](https://github.com/donnemartin/system-design-primer#application-layer)
|
||||
* 独立扩展和配置每一层
|
||||
* **Web 服务器** 可以作为 [**反向代理**](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
|
||||
* 例如, 你可以添加 **应用服务器** 处理 **读 API** 而另外一些处理 **写 API**
|
||||
* 将静态（和一些动态）内容转移到 [**内容分发网络 (CDN)**](https://github.com/donnemartin/system-design-primer#content-delivery-network) 例如 CloudFront 以减少负载和延迟
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* 查阅以上链接获得更多细节
|
||||
|
||||
### 用户+++
|
||||
|
||||
|
||||
|
||||
**注意：** **内部负载均衡** 不显示以减少混乱
|
||||
|
||||
#### 假设
|
||||
|
||||
我们的 **性能/负载测试** 和 **性能测试** 显示我们读操作频繁（100:1 的读写比率），并且数据库在高读请求时表现很糟糕。
|
||||
|
||||
#### 目标
|
||||
|
||||
* 下面的目标试图解决 **MySQL数据库** 的伸缩性问题
|
||||
* * 基于 **基准/负载测试** 和 **分析**，你可能只需要实现其中的一两个技术
|
||||
* 将下列数据移动到一个 [**内存缓存**](https://github.com/donnemartin/system-design-primer#cache)，例如弹性缓存，以减少负载和延迟：
|
||||
* **MySQL** 中频繁访问的内容
|
||||
* 首先, 尝试配置 **MySQL 数据库** 缓存以查看是否足以在实现 **内存缓存** 之前缓解瓶颈
|
||||
* 来自 **Web 服务器** 的会话数据
|
||||
* **Web 服务器** 变成无状态的, 允许 **自动伸缩**
|
||||
* 从内存中读取 1 MB 内存需要大约 250 微秒，而从SSD中读取时间要长 4 倍，从磁盘读取的时间要长 80 倍。<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
|
||||
* 添加 [**MySQL 读取副本**](https://github.com/donnemartin/system-design-primer#master-slave-replication) 来减少写主线程的负载
|
||||
* 添加更多 **Web 服务器** and **应用服务器** 来提高响应
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* 查阅以上链接获得更多细节
|
||||
|
||||
#### 添加 MySQL 读取副本
|
||||
|
||||
* 除了添加和扩展 **内存缓存**，**MySQL 读副本服务器** 也能够帮助缓解在 **MySQL 写主服务器** 的负载。
|
||||
* 添加逻辑到 **Web 服务器** 来区分读和写操作
|
||||
* 在 **MySQL 读副本服务器** 之上添加 **负载均衡器** （不是为了减少混乱）
|
||||
* 大多数服务都是读取负载大于写入负载
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* 查阅 [关系型数据库管理系统 (RDBMS)](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms) 章节
|
||||
|
||||
### 用户++++
|
||||
|
||||
|
||||
|
||||
#### 假设
|
||||
|
||||
**基准/负载测试** 和 **分析** 显示，在美国，正常工作时间存在流量峰值，当用户离开办公室时，流量骤降。我们认为，可以通过真实负载自动转换服务器数量来降低成本。我们是一家小商店，所以我们希望 DevOps 尽量自动化地进行 **自动伸缩** 和通用操作。
|
||||
|
||||
#### 目标
|
||||
|
||||
* 根据需要添加 **自动扩展**
|
||||
* 跟踪流量高峰
|
||||
* 通过关闭未使用的实例来降低成本
|
||||
* 自动化 DevOps
|
||||
* Chef, Puppet, Ansible 工具等
|
||||
* 继续监控指标以解决瓶颈
|
||||
* **主机水平** - 检查一个 EC2 实例
|
||||
* **总水平** - 检查负载均衡器统计数据
|
||||
* **日志分析** - CloudWatch, CloudTrail, Loggly, Splunk, Sumo
|
||||
* **外部站点的性能** - Pingdom or New Relic
|
||||
* **处理通知和事件** - PagerDuty
|
||||
* **错误报告** - Sentry
|
||||
|
||||
#### 添加自动扩展
|
||||
|
||||
* 考虑使用一个托管服务，比如AWS **自动扩展**
|
||||
* 为每个 **Web 服务器** 创建一个组，并为每个 **应用服务器** 类型创建一个组，将每个组放置在多个可用区域中
|
||||
* 设置最小和最大实例数
|
||||
* 通过 CloudWatch 来扩展或收缩
|
||||
* 可预测负载的简单时间度量
|
||||
* 一段时间内的指标：
|
||||
* CPU 负载
|
||||
* 延迟
|
||||
* 网络流量
|
||||
* 自定义指标
|
||||
* 缺点
|
||||
* 自动扩展会引入复杂性
|
||||
* 可能需要一段时间才能适当扩大规模，以满足增加的需求，或者在需求下降时缩减规模
|
||||
|
||||
### 用户+++++
|
||||
|
||||
|
||||
|
||||
**注释：** **自动伸缩** 组不显示以减少混乱
|
||||
|
||||
#### 假设
|
||||
|
||||
当服务继续向着限制条件概述的方向发展，我们反复地运行 **基准/负载测试** 和 **分析** 来进一步发现和定位新的瓶颈。
|
||||
|
||||
#### 目标
|
||||
|
||||
由于问题的约束，我们将继续提出扩展性的问题：
|
||||
|
||||
* 如果我们的 **MySQL 数据库** 开始变得过于庞大, 我们可能只考虑把数据在数据库中存储一段有限的时间, 同时在例如 Redshift 这样的数据仓库中存储其余的数据
|
||||
* 像 Redshift 这样的数据仓库能够轻松处理每月 1TB 的新内容
|
||||
* 平均每秒 40,000 次的读取请求, 可以通过扩展 **内存缓存** 来处理热点内容的读取流量，这对于处理不均匀分布的流量和流量峰值也很有用
|
||||
* **SQL读取副本** 可能会遇到处理缓存未命中的问题, 我们可能需要使用额外的 SQL 扩展模式
|
||||
* 对于单个 **SQL 写主-从** 模式来说，平均每秒 400 次写操作（明显更高）可能会很困难，同时还需要更多的扩展技术
|
||||
|
||||
SQL 扩展模型包括：
|
||||
|
||||
* [集合](https://github.com/donnemartin/system-design-primer#federation)
|
||||
* [分片](https://github.com/donnemartin/system-design-primer#sharding)
|
||||
* [反范式](https://github.com/donnemartin/system-design-primer#denormalization)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer#sql-tuning)
|
||||
|
||||
为了进一步处理高读和写请求，我们还应该考虑将适当的数据移动到一个 [**NoSQL数据库**](https://github.com/donnemartin/system-design-primer#nosql) ，例如 DynamoDB。
|
||||
|
||||
我们可以进一步分离我们的 [**应用服务器**](https://github.com/donnemartin/system-design-primer#application-layer) 以允许独立扩展。不需要实时完成的批处理任务和计算可以通过 Queues 和 Workers 异步完成：
|
||||
|
||||
* 以照片服务为例，照片上传和缩略图的创建可以分开进行
|
||||
* **客户端** 上传图片
|
||||
* **应用服务器** 推送一个任务到 **队列** 例如 SQS
|
||||
* EC2 上的 **Worker 服务** 或者 Lambda 从 **队列** 拉取 work，然后：
|
||||
* 创建缩略图
|
||||
* 更新 **数据库**
|
||||
* 在 **对象存储** 中存储缩略图
|
||||
|
||||
**折中方案, 可选方案, 和其他细节:**
|
||||
|
||||
* 查阅以上链接获得更多细节
|
||||
|
||||
## 额外的话题
|
||||
|
||||
> 根据问题的范围和剩余时间，还需要深入讨论其他问题。
|
||||
|
||||
### SQL 扩展模式
|
||||
|
||||
* [读取副本](https://github.com/donnemartin/system-design-primer#master-slave-replication)
|
||||
* [集合](https://github.com/donnemartin/system-design-primer#federation)
|
||||
* [分区](https://github.com/donnemartin/system-design-primer#sharding)
|
||||
* [反规范化](https://github.com/donnemartin/system-design-primer#denormalization)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer#sql-tuning)
|
||||
|
||||
#### NoSQL
|
||||
|
||||
* [键值存储](https://github.com/donnemartin/system-design-primer#key-value-store)
|
||||
* [文档存储](https://github.com/donnemartin/system-design-primer#document-store)
|
||||
* [宽表存储](https://github.com/donnemartin/system-design-primer#wide-column-store)
|
||||
* [图数据库](https://github.com/donnemartin/system-design-primer#graph-database)
|
||||
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql)
|
||||
|
||||
### 缓存
|
||||
|
||||
* 缓存到哪里
|
||||
* [客户端缓存](https://github.com/donnemartin/system-design-primer#client-caching)
|
||||
* [CDN 缓存](https://github.com/donnemartin/system-design-primer#cdn-caching)
|
||||
* [Web 服务缓存](https://github.com/donnemartin/system-design-primer#web-server-caching)
|
||||
* [数据库缓存](https://github.com/donnemartin/system-design-primer#database-caching)
|
||||
* [应用缓存](https://github.com/donnemartin/system-design-primer#application-caching)
|
||||
* 缓存什么
|
||||
* [数据库请求层缓存](https://github.com/donnemartin/system-design-primer#caching-at-the-database-query-level)
|
||||
* [对象层缓存](https://github.com/donnemartin/system-design-primer#caching-at-the-object-level)
|
||||
* 何时更新缓存
|
||||
* [预留缓存](https://github.com/donnemartin/system-design-primer#cache-aside)
|
||||
* [完全写入](https://github.com/donnemartin/system-design-primer#write-through)
|
||||
* [延迟写 (写回)](https://github.com/donnemartin/system-design-primer#write-behind-write-back)
|
||||
* [事先更新](https://github.com/donnemartin/system-design-primer#refresh-ahead)
|
||||
|
||||
### 异步性和微服务
|
||||
|
||||
* [消息队列](https://github.com/donnemartin/system-design-primer#message-queues)
|
||||
* [任务队列](https://github.com/donnemartin/system-design-primer#task-queues)
|
||||
* [回退压力](https://github.com/donnemartin/system-design-primer#back-pressure)
|
||||
* [微服务](https://github.com/donnemartin/system-design-primer#microservices)
|
||||
|
||||
### 沟通
|
||||
|
||||
* 关于折中方案的讨论:
|
||||
* 客户端的外部通讯 - [遵循 REST 的 HTTP APIs](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest)
|
||||
* 内部通讯 - [RPC](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc)
|
||||
* [服务探索](https://github.com/donnemartin/system-design-primer#service-discovery)
|
||||
|
||||
### 安全性
|
||||
|
||||
参考 [安全章节](https://github.com/donnemartin/system-design-primer#security)
|
||||
|
||||
### 延迟数字指标
|
||||
|
||||
查阅 [每个程序员必懂的延迟数字](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know)
|
||||
|
||||
### 正在进行
|
||||
|
||||
* 继续基准测试并监控你的系统以解决出现的瓶颈问题
|
||||
* 扩展是一个迭代的过程
|
||||
|
|
@ -83,7 +83,7 @@ Handy conversion guide:
|
|||
|
||||
* **Web server** on EC2
|
||||
* Storage for user data
|
||||
* [**MySQL Database**](https://github.com/donnemartin/system-design-primer#sql)
|
||||
* [**MySQL Database**](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms)
|
||||
|
||||
Use **Vertical Scaling**:
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,348 @@
|
|||
# 为社交网络设计数据结构
|
||||
|
||||
**注释：为了避免重复，这篇文章的链接直接关联到 [系统设计主题](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) 的相关章节。为一讨论要点、折中方案和可选方案做参考。**
|
||||
|
||||
## 第 1 步：用例和约束概要
|
||||
|
||||
> 收集需求并调查问题。
|
||||
> 通过提问清晰用例和约束。
|
||||
> 讨论假设。
|
||||
|
||||
如果没有面试官提出明确的问题，我们将自己定义一些用例和约束条件。
|
||||
|
||||
### 用例
|
||||
|
||||
#### 我们就处理以下用例审视这一问题
|
||||
|
||||
* **用户** 寻找某人并显示与被寻人之间的最短路径
|
||||
* **服务** 高可用
|
||||
|
||||
### 约束和假设
|
||||
|
||||
#### 状态假设
|
||||
|
||||
* 流量分布不均
|
||||
* 某些搜索比别的更热门，同时某些搜索仅执行一次
|
||||
* 图数据不适用单一机器
|
||||
* 图的边没有权重
|
||||
* 1 千万用户
|
||||
* 每个用户平均有 50 个朋友
|
||||
* 每月 10 亿次朋友搜索
|
||||
|
||||
训练使用更传统的系统 - 别用图特有的解决方案例如 [GraphQL](http://graphql.org/) 或图数据库如 [Neo4j](https://neo4j.com/)。
|
||||
|
||||
#### 计算使用
|
||||
|
||||
**向你的面试官厘清你是否应该做粗略的使用计算**
|
||||
|
||||
* 50 亿朋友关系
|
||||
* 1 亿用户 * 平均每人 50 个朋友
|
||||
* 每秒 400 次搜索请求
|
||||
|
||||
便捷的转换指南：
|
||||
|
||||
* 每月 250 万秒
|
||||
* 每秒 1 个请求 = 每月 250 万次请求
|
||||
* 每秒 40 个请求 = 每月 1 亿次请求
|
||||
* 每秒 400 个请求 = 每月 10 亿次请求
|
||||
|
||||
## 第 2 步：创建高级设计方案
|
||||
|
||||
> 用所有重要组件概述高水平设计
|
||||
|
||||
|
||||
|
||||
## 第 3 步：设计核心组件
|
||||
|
||||
> 深入每个核心组件的细节。
|
||||
|
||||
### 用例： 用户搜索某人并查看到被搜人的最短路径
|
||||
|
||||
**和你的面试官说清你期望的代码量**
|
||||
|
||||
没有百万用户（点）的和十亿朋友关系（边）的限制，我们能够用一般 BFS 方法解决无权重最短路径任务：
|
||||
|
||||
```python
|
||||
class Graph(Graph):
|
||||
|
||||
def shortest_path(self, source, dest):
|
||||
if source is None or dest is None:
|
||||
return None
|
||||
if source is dest:
|
||||
return [source.key]
|
||||
prev_node_keys = self._shortest_path(source, dest)
|
||||
if prev_node_keys is None:
|
||||
return None
|
||||
else:
|
||||
path_ids = [dest.key]
|
||||
prev_node_key = prev_node_keys[dest.key]
|
||||
while prev_node_key is not None:
|
||||
path_ids.append(prev_node_key)
|
||||
prev_node_key = prev_node_keys[prev_node_key]
|
||||
return path_ids[::-1]
|
||||
|
||||
def _shortest_path(self, source, dest):
|
||||
queue = deque()
|
||||
queue.append(source)
|
||||
prev_node_keys = {source.key: None}
|
||||
source.visit_state = State.visited
|
||||
while queue:
|
||||
node = queue.popleft()
|
||||
if node is dest:
|
||||
return prev_node_keys
|
||||
prev_node = node
|
||||
for adj_node in node.adj_nodes.values():
|
||||
if adj_node.visit_state == State.unvisited:
|
||||
queue.append(adj_node)
|
||||
prev_node_keys[adj_node.key] = prev_node.key
|
||||
adj_node.visit_state = State.visited
|
||||
return None
|
||||
```
|
||||
|
||||
我们不能在同一台机器上满足所有用户，我们需要通过 **人员服务器** [拆分](https://github.com/donnemartin/system-design-primer#sharding) 用户并且通过 **查询服务** 访问。
|
||||
|
||||
* **客户端** 向 **服务器** 发送请求，**服务器** 作为 [反向代理](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
|
||||
* **搜索 API** 服务器向 **用户图服务** 转发请求
|
||||
* **用户图服务** 有以下功能：
|
||||
* 使用 **查询服务** 找到当前用户信息存储的 **人员服务器**
|
||||
* 找到适当的 **人员服务器** 检索当前用户的 `friend_ids` 列表
|
||||
* 把当前用户作为 `source` 运行 BFS 搜索算法同时 当前用户的 `friend_ids` 作为每个 `adjacent_node` 的 ids
|
||||
* 给定 id 获取 `adjacent_node`:
|
||||
* **用户图服务** 将 **再次** 和 **查询服务** 通讯，最后判断出和给定 id 相匹配的存储 `adjacent_node` 的 **人员服务器**（有待优化）
|
||||
|
||||
**和你的面试官说清你应该写的代码量**
|
||||
|
||||
**注释**：简易版错误处理执行如下。询问你是否需要编写适当的错误处理方法。
|
||||
|
||||
**查询服务** 实现：
|
||||
|
||||
```python
|
||||
class LookupService(object):
|
||||
|
||||
def __init__(self):
|
||||
self.lookup = self._init_lookup() # key: person_id, value: person_server
|
||||
|
||||
def _init_lookup(self):
|
||||
...
|
||||
|
||||
def lookup_person_server(self, person_id):
|
||||
return self.lookup[person_id]
|
||||
```
|
||||
|
||||
**人员服务器** 实现：
|
||||
|
||||
```python
|
||||
class PersonServer(object):
|
||||
|
||||
def __init__(self):
|
||||
self.people = {} # key: person_id, value: person
|
||||
|
||||
def add_person(self, person):
|
||||
...
|
||||
|
||||
def people(self, ids):
|
||||
results = []
|
||||
for id in ids:
|
||||
if id in self.people:
|
||||
results.append(self.people[id])
|
||||
return results
|
||||
```
|
||||
|
||||
**用户** 实现：
|
||||
|
||||
```python
|
||||
class Person(object):
|
||||
|
||||
def __init__(self, id, name, friend_ids):
|
||||
self.id = id
|
||||
self.name = name
|
||||
self.friend_ids = friend_ids
|
||||
```
|
||||
|
||||
**用户图服务** 实现：
|
||||
|
||||
```python
|
||||
class UserGraphService(object):
|
||||
|
||||
def __init__(self, lookup_service):
|
||||
self.lookup_service = lookup_service
|
||||
|
||||
def person(self, person_id):
|
||||
person_server = self.lookup_service.lookup_person_server(person_id)
|
||||
return person_server.people([person_id])
|
||||
|
||||
def shortest_path(self, source_key, dest_key):
|
||||
if source_key is None or dest_key is None:
|
||||
return None
|
||||
if source_key is dest_key:
|
||||
return [source_key]
|
||||
prev_node_keys = self._shortest_path(source_key, dest_key)
|
||||
if prev_node_keys is None:
|
||||
return None
|
||||
else:
|
||||
# Iterate through the path_ids backwards, starting at dest_key
|
||||
path_ids = [dest_key]
|
||||
prev_node_key = prev_node_keys[dest_key]
|
||||
while prev_node_key is not None:
|
||||
path_ids.append(prev_node_key)
|
||||
prev_node_key = prev_node_keys[prev_node_key]
|
||||
# Reverse the list since we iterated backwards
|
||||
return path_ids[::-1]
|
||||
|
||||
def _shortest_path(self, source_key, dest_key, path):
|
||||
# Use the id to get the Person
|
||||
source = self.person(source_key)
|
||||
# Update our bfs queue
|
||||
queue = deque()
|
||||
queue.append(source)
|
||||
# prev_node_keys keeps track of each hop from
|
||||
# the source_key to the dest_key
|
||||
prev_node_keys = {source_key: None}
|
||||
# We'll use visited_ids to keep track of which nodes we've
|
||||
# visited, which can be different from a typical bfs where
|
||||
# this can be stored in the node itself
|
||||
visited_ids = set()
|
||||
visited_ids.add(source.id)
|
||||
while queue:
|
||||
node = queue.popleft()
|
||||
if node.key is dest_key:
|
||||
return prev_node_keys
|
||||
prev_node = node
|
||||
for friend_id in node.friend_ids:
|
||||
if friend_id not in visited_ids:
|
||||
friend_node = self.person(friend_id)
|
||||
queue.append(friend_node)
|
||||
prev_node_keys[friend_id] = prev_node.key
|
||||
visited_ids.add(friend_id)
|
||||
return None
|
||||
```
|
||||
|
||||
我们用的是公共的 [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest)：
|
||||
|
||||
```
|
||||
$ curl https://social.com/api/v1/friend_search?person_id=1234
|
||||
```
|
||||
|
||||
响应：
|
||||
|
||||
```
|
||||
{
|
||||
"person_id": "100",
|
||||
"name": "foo",
|
||||
"link": "https://social.com/foo",
|
||||
},
|
||||
{
|
||||
"person_id": "53",
|
||||
"name": "bar",
|
||||
"link": "https://social.com/bar",
|
||||
},
|
||||
{
|
||||
"person_id": "1234",
|
||||
"name": "baz",
|
||||
"link": "https://social.com/baz",
|
||||
},
|
||||
```
|
||||
|
||||
内部通信使用 [远端过程调用](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc)。
|
||||
|
||||
## 第 4 步：扩展设计
|
||||
|
||||
> 在给定约束条件下，定义和确认瓶颈。
|
||||
|
||||
|
||||
|
||||
**重要：别简化从最初设计到最终设计的过程！**
|
||||
|
||||
你将要做的是：1) **基准/负载 测试**， 2) 瓶颈 **概述**， 3) 当评估可选和折中方案时定位瓶颈，4) 重复。以 [在 AWS 上设计支持百万级到千万级用户的系统](../scaling_aws/README.md) 为参考迭代地扩展最初设计。
|
||||
|
||||
讨论最初设计可能遇到的瓶颈和处理方法十分重要。例如，什么问题可以通过添加多台 **Web 服务器** 作为 **负载均衡** 解决？**CDN**？**主从副本**？每个问题都有哪些替代和 **折中** 方案？
|
||||
|
||||
我们即将介绍一些组件来完成设计和解决扩展性问题。内部负载均衡不显示以减少混乱。
|
||||
|
||||
**避免重复讨论**，以下网址链接到 [系统设计主题](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) 相关的主流方案、折中方案和替代方案。
|
||||
|
||||
* [DNS](https://github.com/donnemartin/system-design-primer#domain-name-system)
|
||||
* [负载均衡](https://github.com/donnemartin/system-design-primer#load-balancer)
|
||||
* [横向扩展](https://github.com/donnemartin/system-design-primer#horizontal-scaling)
|
||||
* [Web 服务器（反向代理）](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
|
||||
* [API 服务器（应用层）](https://github.com/donnemartin/system-design-primer#application-layer)
|
||||
* [缓存](https://github.com/donnemartin/system-design-primer#cache)
|
||||
* [一致性模式](https://github.com/donnemartin/system-design-primer#consistency-patterns)
|
||||
* [可用性模式](https://github.com/donnemartin/system-design-primer#availability-patterns)
|
||||
|
||||
解决 **平均** 每秒 400 次请求的限制（峰值），人员数据可以存在例如 Redis 或 Memcached 这样的 **内存** 中以减少响应次数和下游流量通信服务。这尤其在用户执行多次连续查询和查询哪些广泛连接的人时十分有用。从内存中读取 1MB 数据大约要 250 微秒，从 SSD 中读取同样大小的数据时间要长 4 倍，从硬盘要长 80 倍。<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
|
||||
|
||||
以下是进一步优化方案：
|
||||
|
||||
* 在 **内存** 中存储完整的或部分的BFS遍历加快后续查找
|
||||
* 在 **NoSQL 数据库** 中批量离线计算并存储完整的或部分的BFS遍历加快后续查找
|
||||
* 在同一台 **人员服务器** 上托管批处理同一批朋友查找减少机器跳转
|
||||
* 通过地理位置 [拆分](https://github.com/donnemartin/system-design-primer#sharding) **人员服务器** 来进一步优化，因为朋友通常住得都比较近
|
||||
* 同时进行两个 BFS 查找，一个从 source 开始，一个从 destination 开始，然后合并两个路径
|
||||
* 从有庞大朋友圈的人开始找起，这样更有可能减小当前用户和搜索目标之间的 [离散度数](https://en.wikipedia.org/wiki/Six_degrees_of_separation)
|
||||
* 在询问用户是否继续查询之前设置基于时间或跳跃数阈值，当在某些案例中搜索耗费时间过长时。
|
||||
* 使用类似 [Neo4j](https://neo4j.com/) 的 **图数据库** 或图特定查询语法，例如 [GraphQL](http://graphql.org/)（如果没有禁止使用 **图数据库** 的限制的话）
|
||||
|
||||
## 额外的话题
|
||||
|
||||
> 根据问题的范围和剩余时间，还需要深入讨论其他问题。
|
||||
|
||||
### SQL 扩展模式
|
||||
|
||||
* [读取副本](https://github.com/donnemartin/system-design-primer#master-slave-replication)
|
||||
* [集合](https://github.com/donnemartin/system-design-primer#federation)
|
||||
* [分区](https://github.com/donnemartin/system-design-primer#sharding)
|
||||
* [反规范化](https://github.com/donnemartin/system-design-primer#denormalization)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer#sql-tuning)
|
||||
|
||||
#### NoSQL
|
||||
|
||||
* [键值存储](https://github.com/donnemartin/system-design-primer#key-value-store)
|
||||
* [文档存储](https://github.com/donnemartin/system-design-primer#document-store)
|
||||
* [宽表存储](https://github.com/donnemartin/system-design-primer#wide-column-store)
|
||||
* [图数据库](https://github.com/donnemartin/system-design-primer#graph-database)
|
||||
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql)
|
||||
|
||||
### 缓存
|
||||
|
||||
* 缓存到哪里
|
||||
* [客户端缓存](https://github.com/donnemartin/system-design-primer#client-caching)
|
||||
* [CDN 缓存](https://github.com/donnemartin/system-design-primer#cdn-caching)
|
||||
* [Web 服务缓存](https://github.com/donnemartin/system-design-primer#web-server-caching)
|
||||
* [数据库缓存](https://github.com/donnemartin/system-design-primer#database-caching)
|
||||
* [应用缓存](https://github.com/donnemartin/system-design-primer#application-caching)
|
||||
* 缓存什么
|
||||
* [数据库请求层缓存](https://github.com/donnemartin/system-design-primer#caching-at-the-database-query-level)
|
||||
* [对象层缓存](https://github.com/donnemartin/system-design-primer#caching-at-the-object-level)
|
||||
* 何时更新缓存
|
||||
* [预留缓存](https://github.com/donnemartin/system-design-primer#cache-aside)
|
||||
* [完全写入](https://github.com/donnemartin/system-design-primer#write-through)
|
||||
* [延迟写 (写回)](https://github.com/donnemartin/system-design-primer#write-behind-write-back)
|
||||
* [事先更新](https://github.com/donnemartin/system-design-primer#refresh-ahead)
|
||||
|
||||
### 异步性和微服务
|
||||
|
||||
* [消息队列](https://github.com/donnemartin/system-design-primer#message-queues)
|
||||
* [任务队列](https://github.com/donnemartin/system-design-primer#task-queues)
|
||||
* [回退压力](https://github.com/donnemartin/system-design-primer#back-pressure)
|
||||
* [微服务](https://github.com/donnemartin/system-design-primer#microservices)
|
||||
|
||||
### 沟通
|
||||
|
||||
* 关于折中方案的讨论:
|
||||
* 客户端的外部通讯 - [遵循 REST 的 HTTP APIs](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest)
|
||||
* 内部通讯 - [RPC](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc)
|
||||
* [服务探索](https://github.com/donnemartin/system-design-primer#service-discovery)
|
||||
|
||||
### 安全性
|
||||
|
||||
参考 [安全章节](https://github.com/donnemartin/system-design-primer#security)
|
||||
|
||||
### 延迟数字指标
|
||||
|
||||
查阅 [每个程序员必懂的延迟数字](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know)
|
||||
|
||||
### 正在进行
|
||||
|
||||
* 继续基准测试并监控你的系统以解决出现的瓶颈问题
|
||||
* 扩展是一个迭代的过程
|
||||
|
|
@ -62,7 +62,7 @@ Handy conversion guide:
|
|||
|
||||
Without the constraint of millions of users (vertices) and billions of friend relationships (edges), we could solve this unweighted shortest path task with a general BFS approach:
|
||||
|
||||
```
|
||||
```python
|
||||
class Graph(Graph):
|
||||
|
||||
def shortest_path(self, source, dest):
|
||||
|
|
@ -117,7 +117,7 @@ We won't be able to fit all users on the same machine, we'll need to [shard](htt
|
|||
|
||||
**Lookup Service** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class LookupService(object):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -132,7 +132,7 @@ class LookupService(object):
|
|||
|
||||
**Person Server** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class PersonServer(object):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -151,7 +151,7 @@ class PersonServer(object):
|
|||
|
||||
**Person** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class Person(object):
|
||||
|
||||
def __init__(self, id, name, friend_ids):
|
||||
|
|
@ -162,7 +162,7 @@ class Person(object):
|
|||
|
||||
**User Graph Service** implementation:
|
||||
|
||||
```
|
||||
```python
|
||||
class UserGraphService(object):
|
||||
|
||||
def __init__(self, lookup_service):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,331 @@
|
|||
# 设计推特时间轴与搜索功能
|
||||
|
||||
**注意：这个文档中的链接会直接指向[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)中的有关部分，以避免重复的内容。你可以参考链接的相关内容，来了解其总的要点、方案的权衡取舍以及可选的替代方案。**
|
||||
|
||||
**设计 Facebook 的 feed** 与**设计 Facebook 搜索**与此为同一类型问题。
|
||||
|
||||
## 第一步：简述用例与约束条件
|
||||
|
||||
> 搜集需求与问题的范围。
|
||||
> 提出问题来明确用例与约束条件。
|
||||
> 讨论假设。
|
||||
|
||||
我们将在没有面试官明确说明问题的情况下，自己定义一些用例以及限制条件。
|
||||
|
||||
### 用例
|
||||
|
||||
#### 我们将把问题限定在仅处理以下用例的范围中
|
||||
|
||||
* **用户**发布了一篇推特
|
||||
* **服务**将推特推送给关注者，给他们发送消息通知与邮件
|
||||
* **用户**浏览用户时间轴（用户最近的活动）
|
||||
* **用户**浏览主页时间轴（用户关注的人最近的活动）
|
||||
* **用户**搜索关键词
|
||||
* **服务**需要有高可用性
|
||||
|
||||
#### 不在用例范围内的有
|
||||
|
||||
* **服务**向 Firehose 与其它流数据接口推送推特
|
||||
* **服务**根据用户的”是否可见“选项排除推特
|
||||
* 隐藏未关注者的 @回复
|
||||
* 关心”隐藏转发“设置
|
||||
* 数据分析
|
||||
|
||||
### 限制条件与假设
|
||||
|
||||
#### 提出假设
|
||||
|
||||
普遍情况
|
||||
|
||||
* 网络流量不是均匀分布的
|
||||
* 发布推特的速度需要足够快速
|
||||
* 除非有上百万的关注者，否则将推特推送给粉丝的速度要足够快
|
||||
* 1 亿个活跃用户
|
||||
* 每天新发布 5 亿条推特，每月新发布 150 亿条推特
|
||||
* 平均每条推特需要推送给 5 个人
|
||||
* 每天需要进行 50 亿次推送
|
||||
* 每月需要进行 1500 亿次推送
|
||||
* 每月需要处理 2500 亿次读取请求
|
||||
* 每月需要处理 100 亿次搜索
|
||||
|
||||
时间轴功能
|
||||
|
||||
* 浏览时间轴需要足够快
|
||||
* 推特的读取负载要大于写入负载
|
||||
* 需要为推特的快速读取进行优化
|
||||
* 存入推特是高写入负载功能
|
||||
|
||||
搜索功能
|
||||
|
||||
* 搜索速度需要足够快
|
||||
* 搜索是高负载读取功能
|
||||
|
||||
#### 计算用量
|
||||
|
||||
**如果你需要进行粗略的用量计算，请向你的面试官说明。**
|
||||
|
||||
* 每条推特的大小：
|
||||
* `tweet_id` - 8 字节
|
||||
* `user_id` - 32 字节
|
||||
* `text` - 140 字节
|
||||
* `media` - 平均 10 KB
|
||||
* 总计： 大约 10 KB
|
||||
* 每月产生新推特的内容为 150 TB
|
||||
* 每条推特 10 KB * 每天 5 亿条推特 * 每月 30 天
|
||||
* 3 年产生新推特的内容为 5.4 PB
|
||||
* 每秒需要处理 10 万次读取请求
|
||||
* 每个月需要处理 2500 亿次请求 * (每秒 400 次请求 / 每月 10 亿次请求)
|
||||
* 每秒发布 6000 条推特
|
||||
* 每月发布 150 亿条推特 * (每秒 400 次请求 / 每月 10 次请求)
|
||||
* 每秒推送 6 万条推特
|
||||
* 每月推送 1500 亿条推特 * (每秒 400 次请求 / 每月 10 亿次请求)
|
||||
* 每秒 4000 次搜索请求
|
||||
|
||||
便利换算指南：
|
||||
|
||||
* 每个月有 250 万秒
|
||||
* 每秒一个请求 = 每个月 250 万次请求
|
||||
* 每秒 40 个请求 = 每个月 1 亿次请求
|
||||
* 每秒 400 个请求 = 每个月 10 亿次请求
|
||||
|
||||
## 第二步：概要设计
|
||||
|
||||
> 列出所有重要组件以规划概要设计。
|
||||
|
||||
|
||||
|
||||
## 第三步：设计核心组件
|
||||
|
||||
> 深入每个核心组件的细节。
|
||||
|
||||
### 用例：用户发表了一篇推特
|
||||
|
||||
我们可以将用户自己发表的推特存储在[关系数据库](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms)中。我们也可以讨论一下[究竟是用 SQL 还是用 NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)。
|
||||
|
||||
构建用户主页时间轴（查看关注用户的活动）以及推送推特是件麻烦事。将特推传播给所有关注者（每秒约递送 6 万条推特）这一操作有可能会使传统的[关系数据库](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms)超负载。因此，我们可以使用 **NoSQL 数据库**或**内存数据库**之类的更快的数据存储方式。从内存读取 1 MB 连续数据大约要花 250 微秒，而从 SSD 读取同样大小的数据要花费 4 倍的时间，从机械硬盘读取需要花费 80 倍以上的时间。<sup><a href=https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数>1</a></sup>
|
||||
|
||||
我们可以将照片、视频之类的媒体存储于**对象存储**中。
|
||||
|
||||
* **客户端**向应用[反向代理](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)的**Web 服务器**发送一条推特
|
||||
* **Web 服务器**将请求转发给**写 API**服务器
|
||||
* **写 API**服务器将推特使用 **SQL 数据库**存储于用户时间轴中
|
||||
* **写 API**调用**消息输出服务**，进行以下操作：
|
||||
* 查询**用户 图 服务**找到存储于**内存缓存**中的此用户的粉丝
|
||||
* 将推特存储于**内存缓存**中的**此用户的粉丝的主页时间轴**中
|
||||
* O(n) 复杂度操作： 1000 名粉丝 = 1000 次查找与插入
|
||||
* 将特推存储在**搜索索引服务**中，以加快搜索
|
||||
* 将媒体存储于**对象存储**中
|
||||
* 使用**通知服务**向粉丝发送推送：
|
||||
* 使用**队列**异步推送通知
|
||||
|
||||
**向你的面试官告知你准备写多少代码**。
|
||||
|
||||
如果我们用 Redis 作为**内存缓存**，那可以用 Redis 原生的 list 作为其数据结构。结构如下：
|
||||
|
||||
```
|
||||
tweet n+2 tweet n+1 tweet n
|
||||
| 8 bytes 8 bytes 1 byte | 8 bytes 8 bytes 1 byte | 8 bytes 8 bytes 1 byte |
|
||||
| tweet_id user_id meta | tweet_id user_id meta | tweet_id user_id meta |
|
||||
```
|
||||
|
||||
新发布的推特将被存储在对应用户（关注且活跃的用户）的主页时间轴的**内存缓存**中。
|
||||
|
||||
我们可以调用一个公共的 [REST API](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)：
|
||||
|
||||
```
|
||||
$ curl -X POST --data '{ "user_id": "123", "auth_token": "ABC123", \
|
||||
"status": "hello world!", "media_ids": "ABC987" }' \
|
||||
https://twitter.com/api/v1/tweet
|
||||
```
|
||||
|
||||
返回：
|
||||
|
||||
```
|
||||
{
|
||||
"created_at": "Wed Sep 05 00:37:15 +0000 2012",
|
||||
"status": "hello world!",
|
||||
"tweet_id": "987",
|
||||
"user_id": "123",
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
而对于服务器内部的通信，我们可以使用 [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)。
|
||||
|
||||
### 用例：用户浏览主页时间轴
|
||||
|
||||
* **客户端**向 **Web 服务器**发起一次读取主页时间轴的请求
|
||||
* **Web 服务器**将请求转发给**读取 API**服务器
|
||||
* **读取 API**服务器调用**时间轴服务**进行以下操作：
|
||||
* 从**内存缓存**读取时间轴数据，其中包括推特 id 与用户 id - O(1)
|
||||
* 通过 [multiget](http://redis.io/commands/mget) 向**推特信息服务**进行查询，以获取相关 id 推特的额外信息 - O(n)
|
||||
* 通过 muiltiget 向**用户信息服务**进行查询，以获取相关 id 用户的额外信息 - O(n)
|
||||
|
||||
REST API：
|
||||
|
||||
```
|
||||
$ curl https://twitter.com/api/v1/home_timeline?user_id=123
|
||||
```
|
||||
|
||||
返回：
|
||||
|
||||
```
|
||||
{
|
||||
"user_id": "456",
|
||||
"tweet_id": "123",
|
||||
"status": "foo"
|
||||
},
|
||||
{
|
||||
"user_id": "789",
|
||||
"tweet_id": "456",
|
||||
"status": "bar"
|
||||
},
|
||||
{
|
||||
"user_id": "789",
|
||||
"tweet_id": "579",
|
||||
"status": "baz"
|
||||
},
|
||||
```
|
||||
|
||||
### 用例：用户浏览用户时间轴
|
||||
|
||||
* **客户端**向**Web 服务器**发起获得用户时间线的请求
|
||||
* **Web 服务器**将请求转发给**读取 API**服务器
|
||||
* **读取 API**从 **SQL 数据库**中取出用户的时间轴
|
||||
|
||||
REST API 与前面的主页时间轴类似，区别只在于取出的推特是由用户自己发送而不是关注人发送。
|
||||
|
||||
### 用例：用户搜索关键词
|
||||
|
||||
* **客户端**将搜索请求发给**Web 服务器**
|
||||
* **Web 服务器**将请求转发给**搜索 API**服务器
|
||||
* **搜索 API**调用**搜索服务**进行以下操作：
|
||||
* 对输入进行转换与分词，弄明白需要搜索什么东西
|
||||
* 移除标点等额外内容
|
||||
* 将文本打散为词组
|
||||
* 修正拼写错误
|
||||
* 规范字母大小写
|
||||
* 将查询转换为布尔操作
|
||||
* 查询**搜索集群**（例如[Lucene](https://lucene.apache.org/)）检索结果：
|
||||
* 对集群内的所有服务器进行查询，将有结果的查询进行[发散聚合（Scatter gathers）](https://github.com/donnemartin/system-design-primer#under-development)
|
||||
* 合并取到的条目，进行评分与排序，最终返回结果
|
||||
|
||||
REST API：
|
||||
|
||||
```
|
||||
$ curl https://twitter.com/api/v1/search?query=hello+world
|
||||
```
|
||||
|
||||
返回结果与前面的主页时间轴类似，只不过返回的是符合查询条件的推特。
|
||||
|
||||
## 第四步：架构扩展
|
||||
|
||||
> 根据限制条件，找到并解决瓶颈。
|
||||
|
||||
|
||||
|
||||
**重要提示：不要从最初设计直接跳到最终设计中！**
|
||||
|
||||
现在你要 1) **基准测试、负载测试**。2) **分析、描述**性能瓶颈。3) 在解决瓶颈问题的同时，评估替代方案、权衡利弊。4) 重复以上步骤。请阅读[「设计一个系统，并将其扩大到为数以百万计的 AWS 用户服务」](../scaling_aws/README.md) 来了解如何逐步扩大初始设计。
|
||||
|
||||
讨论初始设计可能遇到的瓶颈及相关解决方案是很重要的。例如加上一个配置多台 **Web 服务器**的**负载均衡器**是否能够解决问题？**CDN**呢？**主从复制**呢？它们各自的替代方案和需要**权衡**的利弊又有什么呢？
|
||||
|
||||
我们将会介绍一些组件来完成设计，并解决架构扩张问题。内置的负载均衡器将不做讨论以节省篇幅。
|
||||
|
||||
**为了避免重复讨论**，请参考[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)相关部分来了解其要点、方案的权衡取舍以及可选的替代方案。
|
||||
|
||||
* [DNS](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#域名系统)
|
||||
* [负载均衡器](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#负载均衡器)
|
||||
* [水平拓展](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#水平扩展)
|
||||
* [反向代理（web 服务器）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)
|
||||
* [API 服务（应用层）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用层)
|
||||
* [缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存)
|
||||
* [关系型数据库管理系统 (RDBMS)](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#关系型数据库管理系统rdbms)
|
||||
* [SQL 故障主从切换](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#故障切换)
|
||||
* [主从复制](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#主从复制)
|
||||
* [一致性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#一致性模式)
|
||||
* [可用性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#可用性模式)
|
||||
|
||||
**消息输出服务**有可能成为性能瓶颈。那些有着百万数量关注着的用户可能发一条推特就需要好几分钟才能完成消息输出进程。这有可能使 @回复 这种推特时出现竞争条件，因此需要根据服务时间对此推特进行重排序来降低影响。
|
||||
|
||||
我们还可以避免从高关注量的用户输出推特。相反，我们可以通过搜索来找到高关注量用户的推特，并将搜索结果与用户的主页时间轴合并，再根据时间对其进行排序。
|
||||
|
||||
此外，还可以通过以下内容进行优化：
|
||||
|
||||
* 仅为每个主页时间轴在**内存缓存**中存储数百条推特
|
||||
* 仅在**内存缓存**中存储活动用户的主页时间轴
|
||||
* 如果某个用户在过去 30 天都没有产生活动，那我们可以使用 **SQL 数据库**重新构建他的时间轴
|
||||
* 使用**用户 图 服务**来查询并确定用户关注的人
|
||||
* 从 **SQL 数据库**中取出推特，并将它们存入**内存缓存**
|
||||
* 仅在**推特信息服务**中存储一个月的推特
|
||||
* 仅在**用户信息服务**中存储活动用户的信息
|
||||
* **搜索集群**需要将推特保留在内存中，以降低延迟
|
||||
|
||||
我们还可以考虑优化 **SQL 数据库** 来解决一些瓶颈问题。
|
||||
|
||||
**内存缓存**能减小一些数据库的负载，靠 **SQL Read 副本**已经足够处理缓存未命中情况。我们还可以考虑使用一些额外的 SQL 性能拓展技术。
|
||||
|
||||
高容量的写入将淹没单个的 **SQL 写主从**模式，因此需要更多的拓展技术。
|
||||
|
||||
* [联合](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#联合)
|
||||
* [分片](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#分片)
|
||||
* [非规范化](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#非规范化)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-调优)
|
||||
|
||||
我们也可以考虑将一些数据移至 **NoSQL 数据库**。
|
||||
|
||||
## 其它要点
|
||||
|
||||
> 是否深入这些额外的主题，取决于你的问题范围和剩下的时间。
|
||||
|
||||
#### NoSQL
|
||||
|
||||
* [键-值存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#键-值存储)
|
||||
* [文档类型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#文档类型存储)
|
||||
* [列型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#列型存储)
|
||||
* [图数据库](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#图数据库)
|
||||
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)
|
||||
|
||||
### 缓存
|
||||
|
||||
* 在哪缓存
|
||||
* [客户端缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#客户端缓存)
|
||||
* [CDN 缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#cdn-缓存)
|
||||
* [Web 服务器缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#web-服务器缓存)
|
||||
* [数据库缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库缓存)
|
||||
* [应用缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用缓存)
|
||||
* 什么需要缓存
|
||||
* [数据库查询级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库查询级别的缓存)
|
||||
* [对象级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#对象级别的缓存)
|
||||
* 何时更新缓存
|
||||
* [缓存模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存模式)
|
||||
* [直写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#直写模式)
|
||||
* [回写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#回写模式)
|
||||
* [刷新](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#刷新)
|
||||
|
||||
### 异步与微服务
|
||||
|
||||
* [消息队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#消息队列)
|
||||
* [任务队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#任务队列)
|
||||
* [背压](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#背压)
|
||||
* [微服务](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#微服务)
|
||||
|
||||
### 通信
|
||||
|
||||
* 可权衡选择的方案：
|
||||
* 与客户端的外部通信 - [使用 REST 作为 HTTP API](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)
|
||||
* 服务器内部通信 - [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)
|
||||
* [服务发现](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#服务发现)
|
||||
|
||||
### 安全性
|
||||
|
||||
请参阅[「安全」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#安全)一章。
|
||||
|
||||
### 延迟数值
|
||||
|
||||
请参阅[「每个程序员都应该知道的延迟数」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数)。
|
||||
|
||||
### 持续探讨
|
||||
|
||||
* 持续进行基准测试并监控你的系统，以解决他们提出的瓶颈问题。
|
||||
* 架构拓展是一个迭代的过程。
|
||||
|
|
@ -26,7 +26,7 @@ Without an interviewer to address clarifying questions, we'll define some use ca
|
|||
#### Out of scope
|
||||
|
||||
* **Service** pushes tweets to the Twitter Firehose and other streams
|
||||
* **Service** strips out tweets based on user's visibility settings
|
||||
* **Service** strips out tweets based on users' visibility settings
|
||||
* Hide @reply if the user is not also following the person being replied to
|
||||
* Respect 'hide retweets' setting
|
||||
* Analytics
|
||||
|
|
@ -80,6 +80,7 @@ Search
|
|||
* 60 thousand tweets delivered on fanout per second
|
||||
* 150 billion tweets delivered on fanout per month * (400 requests per second / 1 billion requests per month)
|
||||
* 4,000 search requests per second
|
||||
* 10 billion searches per month * (400 requests per second / 1 billion requests per month)
|
||||
|
||||
Handy conversion guide:
|
||||
|
||||
|
|
@ -128,7 +129,7 @@ If our **Memory Cache** is Redis, we could use a native Redis list with the foll
|
|||
| tweet_id user_id meta | tweet_id user_id meta | tweet_id user_id meta |
|
||||
```
|
||||
|
||||
The new tweet would be placed in the **Memory Cache**, which populates user's home timeline (activity from people the user is following).
|
||||
The new tweet would be placed in the **Memory Cache**, which populates the user's home timeline (activity from people the user is following).
|
||||
|
||||
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
|
||||
|
||||
|
|
@ -249,7 +250,7 @@ We'll introduce some components to complete the design and to address scalabilit
|
|||
|
||||
The **Fanout Service** is a potential bottleneck. Twitter users with millions of followers could take several minutes to have their tweets go through the fanout process. This could lead to race conditions with @replies to the tweet, which we could mitigate by re-ordering the tweets at serve time.
|
||||
|
||||
We could also avoid fanning out tweets from highly-followed users. Instead, we could search to find tweets for high-followed users, merge the search results with the user's home timeline results, then re-order the tweets at serve time.
|
||||
We could also avoid fanning out tweets from highly-followed users. Instead, we could search to find tweets for highly-followed users, merge the search results with the user's home timeline results, then re-order the tweets at serve time.
|
||||
|
||||
Additional optimizations include:
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,356 @@
|
|||
# 设计一个网页爬虫
|
||||
|
||||
**注意：这个文档中的链接会直接指向[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)中的有关部分，以避免重复的内容。你可以参考链接的相关内容，来了解其总的要点、方案的权衡取舍以及可选的替代方案。**
|
||||
|
||||
## 第一步：简述用例与约束条件
|
||||
|
||||
> 把所有需要的东西聚集在一起，审视问题。不停的提问，以至于我们可以明确使用场景和约束。讨论假设。
|
||||
|
||||
我们将在没有面试官明确说明问题的情况下，自己定义一些用例以及限制条件。
|
||||
|
||||
### 用例
|
||||
|
||||
#### 我们把问题限定在仅处理以下用例的范围中
|
||||
|
||||
* **服务** 抓取一系列链接：
|
||||
* 生成包含搜索词的网页倒排索引
|
||||
* 生成页面的标题和摘要信息
|
||||
* 页面标题和摘要都是静态的，它们不会根据搜索词改变
|
||||
* **用户** 输入搜索词后，可以看到相关的搜索结果列表，列表每一项都包含由网页爬虫生成的页面标题及摘要
|
||||
* 只给该用例绘制出概要组件和交互说明，无需讨论细节
|
||||
* **服务** 具有高可用性
|
||||
|
||||
#### 无需考虑
|
||||
|
||||
* 搜索分析
|
||||
* 个性化搜索结果
|
||||
* 页面排名
|
||||
|
||||
### 限制条件与假设
|
||||
|
||||
#### 提出假设
|
||||
|
||||
* 搜索流量分布不均
|
||||
* 有些搜索词非常热门，有些则非常冷门
|
||||
* 只支持匿名用户
|
||||
* 用户很快就能看到搜索结果
|
||||
* 网页爬虫不应该陷入死循环
|
||||
* 当爬虫路径包含环的时候，将会陷入死循环
|
||||
* 抓取 10 亿个链接
|
||||
* 要定期重新抓取页面以确保新鲜度
|
||||
* 平均每周重新抓取一次，网站越热门，那么重新抓取的频率越高
|
||||
* 每月抓取 40 亿个链接
|
||||
* 每个页面的平均存储大小：500 KB
|
||||
* 简单起见，重新抓取的页面算作新页面
|
||||
* 每月搜索量 1000 亿次
|
||||
|
||||
用更传统的系统来练习 —— 不要使用 [solr](http://lucene.apache.org/solr/) 、[nutch](http://nutch.apache.org/) 之类的现成系统。
|
||||
|
||||
#### 计算用量
|
||||
|
||||
**如果你需要进行粗略的用量计算，请向你的面试官说明。**
|
||||
|
||||
* 每月存储 2 PB 页面
|
||||
* 每月抓取 40 亿个页面，每个页面 500 KB
|
||||
* 三年存储 72 PB 页面
|
||||
* 每秒 1600 次写请求
|
||||
* 每秒 40000 次搜索请求
|
||||
|
||||
简便换算指南：
|
||||
|
||||
* 一个月有 250 万秒
|
||||
* 每秒 1 个请求，即每月 250 万个请求
|
||||
* 每秒 40 个请求，即每月 1 亿个请求
|
||||
* 每秒 400 个请求，即每月 10 亿个请求
|
||||
|
||||
## 第二步： 概要设计
|
||||
|
||||
> 列出所有重要组件以规划概要设计。
|
||||
|
||||
|
||||
|
||||
## 第三步：设计核心组件
|
||||
|
||||
> 对每一个核心组件进行详细深入的分析。
|
||||
|
||||
### 用例：爬虫服务抓取一系列网页
|
||||
|
||||
假设我们有一个初始列表 `links_to_crawl`（待抓取链接），它最初基于网站整体的知名度来排序。当然如果这个假设不合理，我们可以使用 [Yahoo](https://www.yahoo.com/)、[DMOZ](http://www.dmoz.org/) 等知名门户网站作为种子链接来进行扩散 。
|
||||
|
||||
我们将用表 `crawled_links` （已抓取链接 ）来记录已经处理过的链接以及相应的页面签名。
|
||||
|
||||
我们可以将 `links_to_crawl` 和 `crawled_links` 记录在键-值型 **NoSQL 数据库**中。对于 `crawled_links` 中已排序的链接，我们可以使用 [Redis](https://redis.io/) 的有序集合来维护网页链接的排名。我们应当在 [选择 SQL 还是 NoSQL 的问题上，讨论有关使用场景以及利弊 ](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)。
|
||||
|
||||
* **爬虫服务**按照以下流程循环处理每一个页面链接：
|
||||
* 选取排名最靠前的待抓取链接
|
||||
* 在 **NoSQL 数据库**的 `crawled_links` 中，检查待抓取页面的签名是否与某个已抓取页面的签名相似
|
||||
* 若存在，则降低该页面链接的优先级
|
||||
* 这样做可以避免陷入死循环
|
||||
* 继续（进入下一次循环）
|
||||
* 若不存在，则抓取该链接
|
||||
* 在**倒排索引服务**任务队列中，新增一个生成[倒排索引](https://en.wikipedia.org/wiki/Search_engine_indexing)任务。
|
||||
* 在**文档服务**任务队列中，新增一个生成静态标题和摘要的任务。
|
||||
* 生成页面签名
|
||||
* 在 **NoSQL 数据库**的 `links_to_crawl` 中删除该链接
|
||||
* 在 **NoSQL 数据库**的 `crawled_links` 中插入该链接以及页面签名
|
||||
|
||||
**向面试官了解你需要写多少代码**。
|
||||
|
||||
`PagesDataStore` 是**爬虫服务**中的一个抽象类，它使用 **NoSQL 数据库**进行存储。
|
||||
|
||||
```python
|
||||
class PagesDataStore(object):
|
||||
|
||||
def __init__(self, db);
|
||||
self.db = db
|
||||
...
|
||||
|
||||
def add_link_to_crawl(self, url):
|
||||
"""将指定链接加入 `links_to_crawl`。"""
|
||||
...
|
||||
|
||||
def remove_link_to_crawl(self, url):
|
||||
"""从 `links_to_crawl` 中删除指定链接。"""
|
||||
...
|
||||
|
||||
def reduce_priority_link_to_crawl(self, url)
|
||||
"""在 `links_to_crawl` 中降低一个链接的优先级以避免死循环。"""
|
||||
...
|
||||
|
||||
def extract_max_priority_page(self):
|
||||
"""返回 `links_to_crawl` 中优先级最高的链接。"""
|
||||
...
|
||||
|
||||
def insert_crawled_link(self, url, signature):
|
||||
"""将指定链接加入 `crawled_links`。"""
|
||||
...
|
||||
|
||||
def crawled_similar(self, signature):
|
||||
"""判断待抓取页面的签名是否与某个已抓取页面的签名相似。"""
|
||||
...
|
||||
```
|
||||
|
||||
`Page` 是**爬虫服务**的一个抽象类，它封装了网页对象，由页面链接、页面内容、子链接和页面签名构成。
|
||||
|
||||
```python
|
||||
class Page(object):
|
||||
|
||||
def __init__(self, url, contents, child_urls, signature):
|
||||
self.url = url
|
||||
self.contents = contents
|
||||
self.child_urls = child_urls
|
||||
self.signature = signature
|
||||
```
|
||||
|
||||
`Crawler` 是**爬虫服务**的主类，由`Page` 和 `PagesDataStore` 组成。
|
||||
|
||||
```python
|
||||
class Crawler(object):
|
||||
|
||||
def __init__(self, data_store, reverse_index_queue, doc_index_queue):
|
||||
self.data_store = data_store
|
||||
self.reverse_index_queue = reverse_index_queue
|
||||
self.doc_index_queue = doc_index_queue
|
||||
|
||||
def create_signature(self, page):
|
||||
"""基于页面链接与内容生成签名。"""
|
||||
...
|
||||
|
||||
def crawl_page(self, page):
|
||||
for url in page.child_urls:
|
||||
self.data_store.add_link_to_crawl(url)
|
||||
page.signature = self.create_signature(page)
|
||||
self.data_store.remove_link_to_crawl(page.url)
|
||||
self.data_store.insert_crawled_link(page.url, page.signature)
|
||||
|
||||
def crawl(self):
|
||||
while True:
|
||||
page = self.data_store.extract_max_priority_page()
|
||||
if page is None:
|
||||
break
|
||||
if self.data_store.crawled_similar(page.signature):
|
||||
self.data_store.reduce_priority_link_to_crawl(page.url)
|
||||
else:
|
||||
self.crawl_page(page)
|
||||
```
|
||||
|
||||
### 处理重复内容
|
||||
|
||||
我们要谨防网页爬虫陷入死循环，这通常会发生在爬虫路径中存在环的情况。
|
||||
|
||||
**向面试官了解你需要写多少代码**.
|
||||
|
||||
删除重复链接：
|
||||
|
||||
* 假设数据量较小，我们可以用类似于 `sort | unique` 的方法。（译注： 先排序，后去重）
|
||||
* 假设有 10 亿条数据，我们应该使用 **MapReduce** 来输出只出现 1 次的记录。
|
||||
|
||||
```python
|
||||
class RemoveDuplicateUrls(MRJob):
|
||||
|
||||
def mapper(self, _, line):
|
||||
yield line, 1
|
||||
|
||||
def reducer(self, key, values):
|
||||
total = sum(values)
|
||||
if total == 1:
|
||||
yield key, total
|
||||
```
|
||||
|
||||
比起处理重复内容，检测重复内容更为复杂。我们可以基于网页内容生成签名，然后对比两者签名的相似度。可能会用到的算法有 [Jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) 以及 [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity)。
|
||||
|
||||
### 抓取结果更新策略
|
||||
|
||||
要定期重新抓取页面以确保新鲜度。抓取结果应该有个 `timestamp` 字段记录上一次页面抓取时间。每隔一段时间，比如说 1 周，所有页面都需要更新一次。对于热门网站或是内容频繁更新的网站，爬虫抓取间隔可以缩短。
|
||||
|
||||
尽管我们不会深入网页数据分析的细节，我们仍然要做一些数据挖掘工作来确定一个页面的平均更新时间，并且根据相关的统计数据来决定爬虫的重新抓取频率。
|
||||
|
||||
当然我们也应该根据站长提供的 `Robots.txt` 来控制爬虫的抓取频率。
|
||||
|
||||
### 用例：用户输入搜索词后，可以看到相关的搜索结果列表，列表每一项都包含由网页爬虫生成的页面标题及摘要
|
||||
|
||||
* **客户端**向运行[反向代理](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)的 **Web 服务器**发送一个请求
|
||||
* **Web 服务器** 发送请求到 **Query API** 服务器
|
||||
* **查询 API** 服务将会做这些事情：
|
||||
* 解析查询参数
|
||||
* 删除 HTML 标记
|
||||
* 将文本分割成词组 （译注： 分词处理）
|
||||
* 修正错别字
|
||||
* 规范化大小写
|
||||
* 将搜索词转换为布尔运算
|
||||
* 使用**倒排索引服务**来查找匹配查询的文档
|
||||
* **倒排索引服务**对匹配到的结果进行排名，然后返回最符合的结果
|
||||
* 使用**文档服务**返回文章标题与摘要
|
||||
|
||||
我们使用 [**REST API**](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest) 与客户端通信：
|
||||
|
||||
```
|
||||
$ curl https://search.com/api/v1/search?query=hello+world
|
||||
```
|
||||
|
||||
响应内容:
|
||||
|
||||
```
|
||||
{
|
||||
"title": "foo's title",
|
||||
"snippet": "foo's snippet",
|
||||
"link": "https://foo.com",
|
||||
},
|
||||
{
|
||||
"title": "bar's title",
|
||||
"snippet": "bar's snippet",
|
||||
"link": "https://bar.com",
|
||||
},
|
||||
{
|
||||
"title": "baz's title",
|
||||
"snippet": "baz's snippet",
|
||||
"link": "https://baz.com",
|
||||
},
|
||||
```
|
||||
|
||||
对于服务器内部通信，我们可以使用 [远程过程调用协议（RPC）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)
|
||||
|
||||
|
||||
## 第四步：架构扩展
|
||||
|
||||
> 根据限制条件，找到并解决瓶颈。
|
||||
|
||||
|
||||
|
||||
**重要提示：不要直接从最初设计跳到最终设计！**
|
||||
|
||||
现在你要 1) **基准测试、负载测试**。2) **分析、描述**性能瓶颈。3) 在解决瓶颈问题的同时，评估替代方案、权衡利弊。4) 重复以上步骤。请阅读[设计一个系统，并将其扩大到为数以百万计的 AWS 用户服务](../scaling_aws/README.md) 来了解如何逐步扩大初始设计。
|
||||
|
||||
讨论初始设计可能遇到的瓶颈及相关解决方案是很重要的。例如加上一套配备多台 **Web 服务器**的**负载均衡器**是否能够解决问题？**CDN**呢？**主从复制**呢？它们各自的替代方案和需要**权衡**的利弊又有哪些呢？
|
||||
|
||||
我们将会介绍一些组件来完成设计，并解决架构规模扩张问题。内置的负载均衡器将不做讨论以节省篇幅。
|
||||
|
||||
**为了避免重复讨论**，请参考[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)相关部分来了解其要点、方案的权衡取舍以及替代方案。
|
||||
|
||||
* [DNS](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#域名系统)
|
||||
* [负载均衡器](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#负载均衡器)
|
||||
* [水平扩展](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#水平扩展)
|
||||
* [Web 服务器（反向代理）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)
|
||||
* [API 服务器（应用层）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用层)
|
||||
* [缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存)
|
||||
* [NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#nosql)
|
||||
* [一致性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#一致性模式)
|
||||
* [可用性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#可用性模式)
|
||||
|
||||
有些搜索词非常热门，有些则非常冷门。热门的搜索词可以通过诸如 Redis 或者 Memcached 之类的**内存缓存**来缩短响应时间，避免**倒排索引服务**以及**文档服务**过载。**内存缓存**同样适用于流量分布不均匀以及流量短时高峰问题。从内存中读取 1 MB 连续数据大约需要 250 微秒，而从 SSD 读取同样大小的数据要花费 4 倍的时间，从机械硬盘读取需要花费 80 倍以上的时间。<sup><a href="https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数">1</a></sup>
|
||||
|
||||
|
||||
以下是优化**爬虫服务**的其他建议：
|
||||
|
||||
* 为了处理数据大小问题以及网络请求负载，**倒排索引服务**和**文档服务**可能需要大量应用数据分片和数据复制。
|
||||
* DNS 查询可能会成为瓶颈，**爬虫服务**最好专门维护一套定期更新的 DNS 查询服务。
|
||||
* 借助于[连接池](https://en.wikipedia.org/wiki/Connection_pool)，即同时维持多个开放网络连接，可以提升**爬虫服务**的性能并减少内存使用量。
|
||||
* 改用 [UDP](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#用户数据报协议udp) 协议同样可以提升性能
|
||||
* 网络爬虫受带宽影响较大，请确保带宽足够维持高吞吐量。
|
||||
|
||||
## 其它要点
|
||||
|
||||
> 是否深入这些额外的主题，取决于你的问题范围和剩下的时间。
|
||||
|
||||
### SQL 扩展模式
|
||||
|
||||
* [读取复制](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#主从复制)
|
||||
* [联合](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#联合)
|
||||
* [分片](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#分片)
|
||||
* [非规范化](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#非规范化)
|
||||
* [SQL 调优](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-调优)
|
||||
|
||||
#### NoSQL
|
||||
|
||||
* [键-值存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#键-值存储)
|
||||
* [文档类型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#文档类型存储)
|
||||
* [列型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#列型存储)
|
||||
* [图数据库](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#图数据库)
|
||||
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)
|
||||
|
||||
|
||||
### 缓存
|
||||
|
||||
* 在哪缓存
|
||||
* [客户端缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#客户端缓存)
|
||||
* [CDN 缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#cdn-缓存)
|
||||
* [Web 服务器缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#web-服务器缓存)
|
||||
* [数据库缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库缓存)
|
||||
* [应用缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用缓存)
|
||||
* 什么需要缓存
|
||||
* [数据库查询级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库查询级别的缓存)
|
||||
* [对象级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#对象级别的缓存)
|
||||
* 何时更新缓存
|
||||
* [缓存模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存模式)
|
||||
* [直写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#直写模式)
|
||||
* [回写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#回写模式)
|
||||
* [刷新](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#刷新)
|
||||
|
||||
### 异步与微服务
|
||||
|
||||
* [消息队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#消息队列)
|
||||
* [任务队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#任务队列)
|
||||
* [背压](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#背压)
|
||||
* [微服务](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#微服务)
|
||||
|
||||
### 通信
|
||||
|
||||
* 可权衡选择的方案：
|
||||
* 与客户端的外部通信 - [使用 REST 作为 HTTP API](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest)
|
||||
* 内部通信 - [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc)
|
||||
* [服务发现](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#服务发现)
|
||||
|
||||
|
||||
### 安全性
|
||||
|
||||
请参阅[安全](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#安全)。
|
||||
|
||||
|
||||
### 延迟数值
|
||||
|
||||
请参阅[每个程序员都应该知道的延迟数](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数)。
|
||||
|
||||
### 持续探讨
|
||||
|
||||
* 持续进行基准测试并监控你的系统，以解决他们提出的瓶颈问题。
|
||||
* 架构扩展是一个迭代的过程。
|
||||
|
|
@ -77,7 +77,7 @@ Handy conversion guide:
|
|||
|
||||
### Use case: Service crawls a list of urls
|
||||
|
||||
We'll assume we have an initial list of `links_to_crawl` ranked initially based on overall site popularity. If this is not a reasonable assumption, we can seed the crawler with popular sites that link to outside content such as [Yahoo](https://www.yahoo.com/), [DMOZ](http://www.dmoz.org/), etc
|
||||
We'll assume we have an initial list of `links_to_crawl` ranked initially based on overall site popularity. If this is not a reasonable assumption, we can seed the crawler with popular sites that link to outside content such as [Yahoo](https://www.yahoo.com/), [DMOZ](http://www.dmoz.org/), etc.
|
||||
|
||||
We'll use a table `crawled_links` to store processed links and their page signatures.
|
||||
|
||||
|
|
@ -100,7 +100,7 @@ We could store `links_to_crawl` and `crawled_links` in a key-value **NoSQL Datab
|
|||
|
||||
`PagesDataStore` is an abstraction within the **Crawler Service** that uses the **NoSQL Database**:
|
||||
|
||||
```
|
||||
```python
|
||||
class PagesDataStore(object):
|
||||
|
||||
def __init__(self, db);
|
||||
|
|
@ -134,7 +134,7 @@ class PagesDataStore(object):
|
|||
|
||||
`Page` is an abstraction within the **Crawler Service** that encapsulates a page, its contents, child urls, and signature:
|
||||
|
||||
```
|
||||
```python
|
||||
class Page(object):
|
||||
|
||||
def __init__(self, url, contents, child_urls, signature):
|
||||
|
|
@ -146,7 +146,7 @@ class Page(object):
|
|||
|
||||
`Crawler` is the main class within **Crawler Service**, composed of `Page` and `PagesDataStore`.
|
||||
|
||||
```
|
||||
```python
|
||||
class Crawler(object):
|
||||
|
||||
def __init__(self, data_store, reverse_index_queue, doc_index_queue):
|
||||
|
|
@ -187,7 +187,7 @@ We'll want to remove duplicate urls:
|
|||
* For smaller lists we could use something like `sort | unique`
|
||||
* With 1 billion links to crawl, we could use **MapReduce** to output only entries that have a frequency of 1
|
||||
|
||||
```
|
||||
```python
|
||||
class RemoveDuplicateUrls(MRJob):
|
||||
|
||||
def mapper(self, _, line):
|
||||
|
|
@ -282,7 +282,7 @@ Some searches are very popular, while others are only executed once. Popular qu
|
|||
|
||||
Below are a few other optimizations to the **Crawling Service**:
|
||||
|
||||
* To handle the data size and request load, the **Reverse Index Service** and **Document Service** will likely need to make heavy use sharding and replication.
|
||||
* To handle the data size and request load, the **Reverse Index Service** and **Document Service** will likely need to make heavy use sharding and federation.
|
||||
* DNS lookup can be a bottleneck, the **Crawler Service** can keep its own DNS lookup that is refreshed periodically
|
||||
* The **Crawler Service** can improve performance and reduce memory usage by keeping many open connections at a time, referred to as [connection pooling](https://en.wikipedia.org/wiki/Connection_pool)
|
||||
* Switching to [UDP](https://github.com/donnemartin/system-design-primer#user-datagram-protocol-udp) could also boost performance
|
||||
|
|
|
|||
Loading…
Reference in New Issue