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为 Amazon 设计分类售卖排行 (#32)

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# Design Amazon's sales rank by category feature
# 为 Amazon 设计分类售卖排行
*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。**
## 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 case
#### 我们将把问题限定在仅处理以下用例的范围中
* **Service** calculates the past week's most popular products by category
* **User** views the past week's most popular products by category
* **Service** has high availability
* **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
#### Out of scope
#### 不在用例范围内的有
* The general e-commerce site
* Design components only for calculating sales rank
* 一般的电商网站
* 只为售卖排行榜设计组件
### Constraints and assumptions
### 限制条件与假设
#### State assumptions
#### 提出假设
* Traffic is not evenly distributed
* Items can be in multiple categories
* Items cannot change categories
* There are no subcategories ie `foo/bar/baz`
* Results must be updated hourly
* More popular products might need to be updated more frequently
* 10 million products
* 1000 categories
* 1 billion transactions per month
* 100 billion read requests per month
* 100:1 read to write ratio
* 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
#### Calculate usage
#### 计算用量
**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.**
**如果你需要进行粗略的用量计算，请向你的面试官说明。**
* Size per transaction:
* `created_at` - 5 bytes
* `product_id` - 8 bytes
* `category_id` - 4 bytes
* `seller_id` - 8 bytes
* `buyer_id` - 8 bytes
* `quantity` - 4 bytes
* `total_price` - 5 bytes
* Total: ~40 bytes
* 40 GB of new transaction content per month
* 40 bytes per transaction * 1 billion transactions per month
* 1.44 TB of new transaction content in 3 years
* Assume most are new transactions instead of updates to existing ones
* 400 transactions per second on average
* 40,000 read requests per second on average
* 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
Handy conversion guide:
便利换算指南：
* 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
* 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
## Step 2: Create a high level design
## 第二步：概要设计
> Outline a high level design with all important components.
> 列出所有重要组件以规划概要设计。
![Imgur](http://i.imgur.com/vwMa1Qu.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: Service calculates the past week's most popular products by category
### 用例：服务需要根据分类计算上周最受欢迎的商品
We could store the raw **Sales API** server log files on a managed **Object Store** such as Amazon S3, rather than managing our own distributed file system.
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。
**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**。
We'll assume this is a sample log entry, tab delimited:
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
@ -95,24 +95,25 @@ t5 product4 category1 1 5.00 5 6
...
```
The **Sales Rank Service** could use **MapReduce**, using the **Sales API** server log files as input and writing the results to an aggregate table `sales_rank` in a **SQL Database**. We should discuss the [use cases and tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql).
**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)。
We'll use a multi-step **MapReduce**:
我们需要通过以下步骤使用 **MapReduce**：
* **Step 1** - Transform the data to `(category, product_id), sum(quantity)`
* **Step 2** - Perform a distributed sort
* **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
```
class SalesRanker(MRJob):
def within_past_week(self, timestamp):
"""Return True if timestamp is within past week, False otherwise."""
"""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):
"""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：
(category1, product1), 2
(category2, product1), 2
@ -127,7 +128,7 @@ class SalesRanker(MRJob):
yield (category_id, product_id), quantity
def reducer(self, key, value):
"""Sum values for each key.
"""将每个 key 的值加起来。
(category1, product1), 2
(category2, product1), 3
@ -138,9 +139,9 @@ class SalesRanker(MRJob):
yield key, sum(values)
def mapper_sort(self, key, value):
"""Construct key to ensure proper sorting.
"""构造 key 以确保正确的排序。
Transform key and value to the form:
将键值对转换成如下形式：
(category1, 2), product1
(category2, 3), product1
@ -148,8 +149,8 @@ class SalesRanker(MRJob):
(category2, 7), product3
(category1, 1), product4
The shuffle/sort step of MapReduce will then do a
distributed sort on the keys, resulting in:
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
@ -165,7 +166,7 @@ class SalesRanker(MRJob):
yield key, value
def steps(self):
"""Run the map and reduce steps."""
""" 此处为 map reduce 步骤"""
return [
self.mr(mapper=self.mapper,
reducer=self.reducer),
@ -174,7 +175,7 @@ class SalesRanker(MRJob):
]
```
The result would be the following sorted list, which we could insert into the `sales_rank` table:
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
@ -184,7 +185,7 @@ The result would be the following sorted list, which we could insert into the `s
(category2, 7), product3
```
The `sales_rank` table could have the following structure:
`sales_rank` 表的数据结构如下：
```
id int NOT NULL AUTO_INCREMENT
@ -196,21 +197,21 @@ FOREIGN KEY(category_id) REFERENCES Categories(id)
FOREIGN KEY(product_id) REFERENCES Products(id)
```
We'll create an [index](https://github.com/donnemartin/system-design-primer#use-good-indices) on `id `, `category_id`, and `product_id` 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>
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>
### Use case: User views the past week's most popular products by category
### 用例：用户需要根据分类浏览上周中最受欢迎的商品
* The **Client** sends a 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 **Read API** server
* The **Read API** server reads from the **SQL Database** `sales_rank` table
* **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
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 https://amazon.com/api/v1/popular?category_id=1234
```
Response:
返回：
```
{
@ -233,106 +234,105 @@ 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)。
## 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/MzExP06.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 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。
We might only want to store a limited time period of 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 40 GB of new content per month.
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。
To address the 40,000 *average* read requests per second (higher at peak), traffic for popular content (and their sales rank) 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. With the large volume of reads, the **SQL Read Replicas** might not be able to handle the cache misses. We'll probably need to employ additional SQL scaling patterns.
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。
400 *average* writes per second (higher at peak) might be tough for a single **SQL Write Master-Slave**, also pointing to a need for additional scaling techniques.
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 scaling patterns include:
SQL 缩放模式包括：
* [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。