diff --git a/solutions/system_design/pastebin/README.md b/solutions/system_design/pastebin/README.md index 19a3587e..aba38a18 100644 --- a/solutions/system_design/pastebin/README.md +++ b/solutions/system_design/pastebin/README.md @@ -1,112 +1,113 @@ -# Design Pastebin.com (or Bit.ly) +# 设计 Pastebin.com（或 Bit.ly） -*Note: This document links directly to relevant areas found in the [system design topics](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.* +**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。** -**Design Bit.ly** - is a similar question, except pastebin requires storing the paste contents instead of the original unshortened url. +除了粘贴板需要存储的是完整的内容而不是短链接之外，**设计 Bit.ly**是与本文类似的一个问题。 -## Step 1: Outline use cases and constraints +## 第一步：简述用例与约束条件 -> Gather requirements and scope the problem. -> Ask questions to clarify use cases and constraints. -> Discuss assumptions. +> 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。 -Without an interviewer to address clarifying questions, we'll define some use cases and constraints. +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。 -### Use cases +### 用例 -#### We'll scope the problem to handle only the following use cases +#### 我们将把问题限定在仅处理以下用例的范围中 -* **User** enters a block of text and gets a randomly generated link - * Expiration - * Default setting does not expire - * Can optionally set a timed expiration -* **User** enters a paste's url and views the contents -* **User** is anonymous -* **Service** tracks analytics of pages - * Monthly visit stats -* **Service** deletes expired pastes -* **Service** has high availability -#### Out of scope +* **用户**输入一些文本，然后得到一个随机生成的链接 + * 过期时间 + * 默认为永不过期 + * 可选设置为一定时间过期 +* **用户**输入粘贴板中的 url，查看内容 +* **用户**是匿名访问的 +* **服务**需要能够对页面进行跟踪分析 + * 月访问量统计 +* **服务**将过期的内容删除 +* **服务**有着高可用性 -* **User** registers for an account - * **User** verifies email -* **User** logs into a registered account - * **User** edits the document -* **User** can set visibility -* **User** can set the shortlink +#### 不在用例范围内的有 -### Constraints and assumptions +* **用户**注册了账号 + * **用户**通过了邮箱验证 +* **用户**登录已注册的账号 + * **用户**编辑他们的文档 +* **用户**能设置他们的内容是否可见 +* **用户**是否能自行设置短链接 -#### State assumptions +### 限制条件与假设 -* Traffic is not evenly distributed -* Following a short link should be fast -* Pastes are text only -* Page view analytics do not need to be realtime -* 10 million users -* 10 million paste writes per month -* 100 million paste reads per month -* 10:1 read to write ratio +#### 提出假设 -#### Calculate usage +* 网络流量不是均匀分布的 +* 生成短链接的速度必须要快 +* 只允许粘贴文本 +* 不需要对页面预览做实时分析 +* 1000 万用户 +* 每个月 1000 万次粘贴 +* 每个月 1 亿次读取请求 +* 10:1 的读写比例 -**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.** +#### 计算用量 -* Size per paste - * 1 KB content per paste - * `shortlink` - 7 bytes - * `expiration_length_in_minutes` - 4 bytes - * `created_at` - 5 bytes - * `paste_path` - 255 bytes - * total = ~1.27 KB -* 12.7 GB of new paste content per month - * 1.27 KB per paste * 10 million pastes per month - * ~450 GB of new paste content in 3 years - * 360 million shortlinks in 3 years - * Assume most are new pastes instead of updates to existing ones -* 4 paste writes per second on average -* 40 read requests per second on average +**如果你需要进行粗略的用量计算，请向你的面试官说明。** -Handy conversion guide: +* 每次粘贴的用量 + * 1 KB 的内容 + * `shortlink` - 7 字节 + * `expiration_length_in_minutes` - 4 字节 + * `created_at` - 5 字节 + * `paste_path` - 255 字节 + * 总计：大约 1.27 KB +* 每个月的粘贴造作将会产生 12.7 GB 的记录 + * 每次粘贴 1.27 KB * 1000 万次粘贴 + * 3年内大约产生了 450 GB 的新内容记录 + * 3年内生成了 36000 万个短链接 + * 假设大多数的粘贴操作都是新的粘贴而不是更新以前的粘贴内容 +* 平均每秒 4 次读取粘贴 +* 平均每秒 40 次读取粘贴请求 -* 2.5 million seconds per month -* 1 request per second = 2.5 million requests per month -* 40 requests per second = 100 million requests per month -* 400 requests per second = 1 billion requests per month +便利换算指南： -## Step 2: Create a high level design +* 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 -> Outline a high level design with all important components. +## 第二步：概要设计 + +> 列出所有重要组件以规划概要设计。 ![Imgur](http://i.imgur.com/BKsBnmG.png) -## Step 3: Design core components +## 第三步：设计核心组件 -> Dive into details for each core component. +> 深入每个核心组件的细节。 -### Use case: User enters a block of text and gets a randomly generated link +### 用例：用户输入一些文本，然后得到一个随机生成的链接 -We could use a [relational database](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms) as a large hash table, mapping the generated url to a file server and path containing the paste file. +我们将使用[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)，将其作为一个超大哈希表，将生成的 url 和文件服务器上对应文件的路径一一对应。 -Instead of managing a file server, we could use a managed **Object Store** such as Amazon S3 or a [NoSQL document store](https://github.com/donnemartin/system-design-primer#document-store). +我们可以使用诸如 Amazon S3 之类的**对象存储服务**或者[NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#nosql)来代替自建文件服务器。 -An alternative to a relational database acting as a large hash table, we could use a [NoSQL key-value store](https://github.com/donnemartin/system-design-primer#key-value-store). We should discuss the [tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql). The following discussion uses the relational database approach. +除了使用关系型数据库来作为一个超大哈希表之外，我们也可以使用[NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#nosql)来代替它。[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)。不过在下面的讨论中，我们默认选择了使用关系型数据库的方案。 -* The **Client** sends a create paste request to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server) -* The **Web Server** forwards the request to the **Write API** server -* The **Write API** server does the following: - * Generates a unique url - * Checks if the url is unique by looking at the **SQL Database** for a duplicate - * If the url is not unique, it generates another url - * If we supported a custom url, we could use the user-supplied (also check for a duplicate) - * Saves to the **SQL Database** `pastes` table - * Saves the paste data to the **Object Store** - * Returns the url +* **客户端**向向运行[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**发送一个粘贴请求 +* **Web 服务器** 将请求转发给**Write API** 服务 +* **Write API**服务将会： + * 生成一个独一无二的 url + * 通过在 **SQL 数据库**中查重来确认这个 url 是否的确独一无二 + * 如果这个 url 已经存在了，重新生成一个 url + * 如果支持自定义 url，我们也可以使用用户提供的 url（也需要进行查重） + * 将 url 存入 **SQL 数据库**的 `pastes` 表中 + * 将粘贴的数据存入**对象存储**系统中 + * 返回 url -**Clarify with your interviewer how much code you are expected to write**. +**向你的面试官告知你准备写多少代码**。 -The `pastes` table could have the following structure: +`pastes` 表的数据结构如下： ``` shortlink char(7) NOT NULL @@ -116,19 +117,19 @@ 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.1 +我们会以`shortlink` 与 `created_at` 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。1 -To generate the unique url, we could: +为了生成独一无二的 url，我们需要： -* Take the [**MD5**](https://en.wikipedia.org/wiki/MD5) hash of the user's ip_address + timestamp - * MD5 is a widely used hashing function that produces a 128-bit hash value - * MD5 is uniformly distributed - * Alternatively, we could also take the MD5 hash of randomly-generated data -* [**Base 62**](https://www.kerstner.at/2012/07/shortening-strings-using-base-62-encoding/) encode the MD5 hash - * Base 62 encodes to `[a-zA-Z0-9]` which works well for urls, eliminating the need for escaping special characters - * There is only one hash result for the original input and and Base 62 is deterministic (no randomness involved) - * 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: +* 对用户的 IP 地址 + 时间戳进行 [**MD5**](https://en.wikipedia.org/wiki/MD5) 哈希编码 + * MD5 是一种非常常用的哈希化函数，它能生成 128 字节的哈希值 + * MD5 是均匀分布的 + * 另外，我们可以使用 MD5 哈希算法来生成随机数据 +* 对 MD5 哈希值进行 [**Base 62**](https://www.kerstner.at/2012/07/shortening-strings-using-base-62-encoding/) 编码 + * Base 62 编码后的值由 `[a-zA-Z0-9]` 组成，它们可以直接作为 url 的字符，不需要再次转义 + * 在这儿仅仅只对原始输入进行过一次哈希处理，Base 62 编码步骤是确定性的（不涉及随机性） + * Base 64 是另一种很流行的编码形式，但是它生成的字符串作为 url 存在一些问题：Base 64m字符串内包含 `+` 和 `/` 符号 + * 下面的 [Base 62 pseudocode](http://stackoverflow.com/questions/742013/how-to-code-a-url-shortener) 算法时间复杂度为 O(k)，本例中取 num =7，即 k 值为 7： ``` def base_encode(num, base=62): @@ -140,20 +141,19 @@ def base_encode(num, base=62): digits = digits.reverse ``` -* 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: +* 输出前 7 个字符，其结果将有 62^7 种可能的值，作为短链接来说足够了。因为我们限制了 3 年内最多产生 36000 万个短链接： ``` url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH] ``` - -We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest): +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 '{ "expiration_length_in_minutes": "60", \ "paste_contents": "Hello World!" }' https://pastebin.com/api/v1/paste ``` -Response: +返回： ``` { @@ -161,16 +161,16 @@ Response: } ``` -For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc). +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)。 -### Use case: User enters a paste's url and views the contents +### 用例：用户输入了一个之前粘贴得到的 url，希望浏览其存储的内容 -* The **Client** sends a get paste request to the **Web Server** -* The **Web Server** forwards the request to the **Read API** server -* The **Read API** server does the following: - * Checks the **SQL Database** for the generated url - * If the url is in the **SQL Database**, fetch the paste contents from the **Object Store** - * Else, return an error message for the user +* **客户端**向**Web 服务器**发起读取内容请求 +* **Web 服务器**将请求转发给**Read API**服务 +* **Read API**服务将会： + * 在**SQL 数据库**中检查生成的 url + * 如果查询的 url 存在于 **SQL 数据库**中，从**对象存储**服务将对应的粘贴内容取出 + * 否则，给用户返回报错 REST API: @@ -178,7 +178,7 @@ REST API: $ curl https://pastebin.com/api/v1/paste?shortlink=foobar ``` -Response: +返回： ``` { @@ -188,27 +188,27 @@ Response: } ``` -### Use case: Service tracks analytics of pages +### 用例：对页面进行跟踪分析 -Since realtime analytics are not a requirement, we could simply **MapReduce** the **Web Server** logs to generate hit counts. +由于不需要进行实时分析，因此我们可以简单地对 **Web 服务**产生的日志用 **MapReduce** 来统计 hit 计数（命中数）。 -**Clarify with your interviewer how much code you are expected to write**. +**向你的面试官告知你准备写多少代码**。 ``` class HitCounts(MRJob): def extract_url(self, line): - """Extract the generated url from the log line.""" + """从 log 中取出生成的 url。""" ... def extract_year_month(self, line): - """Return the year and month portions of the timestamp.""" + """返回时间戳中表示年份与月份的一部分""" ... def mapper(self, _, line): - """Parse each log line, extract and transform relevant lines. + """解析日志的每一行，提取并转换相关行， - Emit key value pairs of the form: + 将键值对设定为如下形式： (2016-01, url0), 1 (2016-01, url0), 1 @@ -219,7 +219,7 @@ class HitCounts(MRJob): yield (period, url), 1 def reducer(self, key, value): - """Sum values for each key. + """将所有的 key 加起来 (2016-01, url0), 2 (2016-01, url1), 1 @@ -227,106 +227,105 @@ class HitCounts(MRJob): yield key, sum(values) ``` -### Use case: Service deletes expired pastes +### 用例：服务删除过期的粘贴内容 -To delete expired pastes, we could just scan the **SQL Database** for all entries whose expiration timestamp are older than the current timestamp. All expired entries would then be deleted (or marked as expired) from the table. +我们可以通过扫描 **SQL 数据库**，查找出那些过期时间戳小于当前时间戳的条目，然后在表中删除（或者将其标记为过期）这些过期的粘贴内容。 -## Step 4: Scale the design +## 第四步：架构扩展 -> Identify and address bottlenecks, given the constraints. +> 根据限制条件，找到并解决瓶颈。 ![Imgur](http://i.imgur.com/4edXG0T.png) -**Important: Do not simply jump right into the final design from the initial design!** +**重要提示：不要从最初设计直接跳到最终设计中！** -State you would do this iteratively: 1) **Benchmark/Load Test**, 2) **Profile** for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See [Design a system that scales to millions of users on AWS](../scaling_aws/README.md) as a sample on how to iteratively scale the initial design. +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。 -It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a **Load Balancer** with multiple **Web Servers**? **CDN**? **Master-Slave Replicas**? What are the alternatives and **Trade-Offs** for each? +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？ -We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter. +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。 -*To avoid repeating discussions*, refer to the following [system design topics](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) for main talking points, tradeoffs, and alternatives: +**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#domain-name-system) -* [CDN](https://github.com/donnemartin/system-design-primer#content-delivery-network) -* [Load balancer](https://github.com/donnemartin/system-design-primer#load-balancer) -* [Horizontal scaling](https://github.com/donnemartin/system-design-primer#horizontal-scaling) -* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server) -* [API server (application layer)](https://github.com/donnemartin/system-design-primer#application-layer) -* [Cache](https://github.com/donnemartin/system-design-primer#cache) -* [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms) -* [SQL write master-slave failover](https://github.com/donnemartin/system-design-primer#fail-over) -* [Master-slave replication](https://github.com/donnemartin/system-design-primer#master-slave-replication) -* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns) -* [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns) +* [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) -The **Analytics Database** could use a data warehousing solution such as Amazon Redshift or Google BigQuery. +**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。 -An **Object Store** such as Amazon S3 can comfortably handle the constraint of 12.7 GB of new content per month. +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 12.7 GB 的存储内容。 -To address the 40 *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. +平均每秒 40 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。只要 SQL 副本不陷入复制-写入困境中，**SQL Read 副本** 基本能够处理缓存命中问题。 -4 *average* paste writes per second (with higher at peak) should be do-able for a single **SQL Write Master-Slave**. Otherwise, we'll need to employ additional SQL scaling patterns: +平均每秒 4 次的粘贴写入操作（峰值将会更高）对于单个**SQL 写主-从** 模式来说是可行的。不过，我们也需要考虑其它的 SQL 性能拓展技术： -* [Federation](https://github.com/donnemartin/system-design-primer#federation) -* [Sharding](https://github.com/donnemartin/system-design-primer#sharding) -* [Denormalization](https://github.com/donnemartin/system-design-primer#denormalization) -* [SQL Tuning](https://github.com/donnemartin/system-design-primer#sql-tuning) +* [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) -We should also consider moving some data to a **NoSQL Database**. +我们也可以考虑将一些数据移至 **NoSQL 数据库**。 -## Additional talking points +## 其它要点 -> Additional topics to dive into, depending on the problem scope and time remaining. +> 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 -* [Key-value store](https://github.com/donnemartin/system-design-primer#key-value-store) -* [Document store](https://github.com/donnemartin/system-design-primer#document-store) -* [Wide column store](https://github.com/donnemartin/system-design-primer#wide-column-store) -* [Graph database](https://github.com/donnemartin/system-design-primer#graph-database) -* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-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) -### Caching +### 缓存 -* Where to cache - * [Client caching](https://github.com/donnemartin/system-design-primer#client-caching) - * [CDN caching](https://github.com/donnemartin/system-design-primer#cdn-caching) - * [Web server caching](https://github.com/donnemartin/system-design-primer#web-server-caching) - * [Database caching](https://github.com/donnemartin/system-design-primer#database-caching) - * [Application caching](https://github.com/donnemartin/system-design-primer#application-caching) -* What to cache - * [Caching at the database query level](https://github.com/donnemartin/system-design-primer#caching-at-the-database-query-level) - * [Caching at the object level](https://github.com/donnemartin/system-design-primer#caching-at-the-object-level) -* When to update the cache - * [Cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside) - * [Write-through](https://github.com/donnemartin/system-design-primer#write-through) - * [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer#write-behind-write-back) - * [Refresh ahead](https://github.com/donnemartin/system-design-primer#refresh-ahead) +* 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) -### Asynchronism and microservices +### 异步与微服务 -* [Message queues](https://github.com/donnemartin/system-design-primer#message-queues) -* [Task queues](https://github.com/donnemartin/system-design-primer#task-queues) -* [Back pressure](https://github.com/donnemartin/system-design-primer#back-pressure) -* [Microservices](https://github.com/donnemartin/system-design-primer#microservices) +* [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) -### Communications +### 通信 -* Discuss tradeoffs: - * External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest) - * Internal communications - [RPC](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc) -* [Service discovery](https://github.com/donnemartin/system-design-primer#service-discovery) +* 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) -### Security +### 安全性 -Refer to the [security section](https://github.com/donnemartin/system-design-primer#security). +请参阅[「安全」](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#安全)一章。 -### Latency numbers +### 延迟数值 -See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know). +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)。 -### Ongoing +### 持续探讨 -* Continue benchmarking and monitoring your system to address bottlenecks as they come up -* Scaling is an iterative process +* 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。