diff --git a/solutions/system_design/sales_rank/README.md b/solutions/system_design/sales_rank/README.md index 3ee50985..513b3208 100644 --- a/solutions/system_design/sales_rank/README.md +++ b/solutions/system_design/sales_rank/README.md @@ -1,88 +1,88 @@ -# 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.1 +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。1 -### 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。