设计Pastebin.com(或者Bit.ly)

文档摘要

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。

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

Imgur

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。

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。我们需要讨论选择 SQL 或 NoSQL 之间的权衡。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：


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，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。¹

为了生成唯一的 url，我们可以：

使用 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 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 伪代码执行的时间复杂度是 O(k)，k 是数字的数量 = 7：


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：


url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH]

我们将会用一个公开的 REST 风格接口：


$ curl -X POST --data '{"expiration_length_in_minutes":"60", \"paste_contents":"Hello World!"}' https://pastebin.com/api/v1/paste

Response:


{
    "shortlink": "foobar"
}

用于内部通信，我们可以用 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：


curl https://pastebin.com/api/v1/paste?shortlink=foobar

Response:


{
    "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。


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。

Imgur

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获取主要讨论要点、权衡和替代方案：

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:

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

缓存

异步和微服务

通信

讨论权衡:
- 跟客户端之间的外部通信 - HTTP APIs following REST
- 内部通信 - RPC
服务发现

安全

参考安全。

延迟数字

见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

Design Pastebin.com (or Bit.ly)

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Design Bit.ly - is a similar question, except pastebin requires storing the paste contents instead of the original unshortened url.

Step 1: Outline use cases and constraints

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases

We'll scope the problem to handle only the following use cases

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

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

Constraints and assumptions

State assumptions

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

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

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

Handy conversion guide:

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

Step 2: Create a high level design

Outline a high level design with all important components.

Imgur

Step 3: Design core components

Dive into details for each core component.

Use case: User enters a block of text and gets a randomly generated link

We could use a relational database as a large hash table, mapping the generated url to a file server and path containing the paste file.

Instead of managing a file server, we could use a managed Object Store such as Amazon S3 or a NoSQL document store.

An alternative to a relational database acting as a large hash table, we could use a NoSQL key-value store. We should discuss the tradeoffs between choosing SQL or NoSQL. The following discussion uses the relational database approach.

The Client sends a create paste request to the Web Server, running as a reverse proxy
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

Clarify with your interviewer how much code you are expected to write.

The pastes table could have the following structure:


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)

Setting the primary key to be based on the shortlink column creates an index 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.¹

To generate the unique url, we could:

Take the 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 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 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 runs in O(k) time where k is the number of digits = 7:


def base_encode(num, base=62):
    digits = []
    while num > 0
      remainder = modulo(num, base)
      digits.push(remainder)
      num = divide(num, base)
    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:


url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH]

We'll use a public REST API:


$ curl -X POST --data '{ "expiration_length_in_minutes": "60", \
    "paste_contents": "Hello World!" }' https://pastebin.com/api/v1/paste

Response:


{
    "shortlink": "foobar"
}

For internal communications, we could use Remote Procedure Calls.

Use case: User enters a paste's url and views the contents

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

REST API:


$ curl https://pastebin.com/api/v1/paste?shortlink=foobar

Response:


{
    "paste_contents": "Hello World"
    "created_at": "YYYY-MM-DD HH:MM:SS"
    "expiration_length_in_minutes": "60"
}

Use case: Service tracks analytics of pages

Since realtime analytics are not a requirement, we could simply MapReduce the Web Server logs to generate hit counts.

Clarify with your interviewer how much code you are expected to write.


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)

Use case: Service deletes expired pastes

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.

Step 4: Scale the design

Identify and address bottlenecks, given the constraints.

Imgur

Important: Do not simply jump right into the final design from the initial design!

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 as a sample on how to iteratively scale the initial design.

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?

We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.

To avoid repeating discussions, refer to the following system design topics for main talking points, tradeoffs, and alternatives:

The Analytics Database could use a data warehousing solution such as Amazon Redshift or Google BigQuery.

An Object Store such as Amazon S3 can comfortably handle the constraint of 12.7 GB of new content per month.

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.

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:

We should also consider moving some data to a NoSQL Database.

Additional talking points

Additional topics to dive into, depending on the problem scope and time remaining.

NoSQL

Caching

Where to cache
What to cache
- Caching at the database query level
- Caching at the object level
When to update the cache

Asynchronism and microservices

Communications

Discuss tradeoffs:
- External communication with clients - HTTP APIs following REST
- Internal communications - RPC
Service discovery

Security

Refer to the security section.

Latency numbers

See Latency numbers every programmer should know.

Ongoing

Continue benchmarking and monitoring your system to address bottlenecks as they come up
Scaling is an iterative process

设计Pastebin.com(或者Bit.ly)

文档摘要

设计 Pastebin.com (或者 Bit.ly)

第一步：概述用例和约束

用例

我们将问题的范畴限定在如下用例

超出范畴的用例

约束和假设

状态假设

计算使用

第二步：创建一个高层次设计

第三步：设计核心组件

用例：用户输入一段文本，然后得到一个随机生成的链接

用例：用户输入一个 paste 的 url 后可以看到它存储的内容

用例： 服务跟踪分析页面

用例： 服务删除过期的 pastes

第四步：扩展这个设计

额外的话题

NoSQL

缓存

异步和微服务

通信

安全

延迟数字

持续进行

Design Pastebin.com (or Bit.ly)

Step 1: Outline use cases and constraints

Use cases

We'll scope the problem to handle only the following use cases

Out of scope

Constraints and assumptions

State assumptions

Calculate usage

Step 2: Create a high level design

Step 3: Design core components

Use case: User enters a block of text and gets a randomly generated link

Use case: User enters a paste's url and views the contents

Use case: Service tracks analytics of pages

Use case: Service deletes expired pastes

Step 4: Scale the design

Additional talking points

NoSQL

Caching

Asynchronism and microservices

Communications

Security

Latency numbers

Ongoing

用例：服务跟踪分析页面

用例：服务删除过期的 pastes