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Merge pull request #4 from Airmacho/part5 

第三部分初稿完成
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@ -278,6 +278,7 @@
> 普通的系统设计面试题和相关事例的论述，代码和图表。 > 普通的系统设计面试题和相关事例的论述，代码和图表。
> >
> 与内容有关的解答在 `solutions/` 文件夹中。 > 与内容有关的解答在 `solutions/` 文件夹中。
| 问题 | | | 问题 | |
| ---------------------------------------- | ---------------------------------------- | | ---------------------------------------- | ---------------------------------------- |
@ -347,16 +348,16 @@
>**注：此节还在完善中** >**注：此节还在完善中**
| 问题 | | | 问题 | |
|---|---| | ------------ | ---------------------------------------- |
| 设计 hash map | [解决方案](solutions/object_oriented_design/hash_table/hash_map.ipynb) | | 设计 hash map | [解决方案](solutions/object_oriented_design/hash_table/hash_map.ipynb) |
| 设计 LRU 缓存 | [解决方案](solutions/object_oriented_design/lru_cache/lru_cache.ipynb) | | 设计 LRU 缓存 | [解决方案](solutions/object_oriented_design/lru_cache/lru_cache.ipynb) |
| 设计一个呼叫中心 | [解决方案](solutions/object_oriented_design/call_center/call_center.ipynb) | | 设计一个呼叫中心 | [解决方案](solutions/object_oriented_design/call_center/call_center.ipynb) |
| 设计一副牌 | [解决方案](solutions/object_oriented_design/deck_of_cards/deck_of_cards.ipynb) | | 设计一副牌 | [解决方案](solutions/object_oriented_design/deck_of_cards/deck_of_cards.ipynb) |
| 设计一个停车场 | [解决方案](solutions/object_oriented_design/parking_lot/parking_lot.ipynb) | | 设计一个停车场 | [解决方案](solutions/object_oriented_design/parking_lot/parking_lot.ipynb) |
| 设计一个聊天服务 | [解决方案](solutions/object_oriented_design/online_chat/online_chat.ipynb) | | 设计一个聊天服务 | [解决方案](solutions/object_oriented_design/online_chat/online_chat.ipynb) |
| 设计一个环形数组 | [待解决](#contributing) | | 设计一个环形数组 | [待解决](#contributing) |
| 添加一个面向对象设计问题 | [待解决](#contributing) | | 添加一个面向对象设计问题 | [待解决](#contributing) |
## 系统设计主题：从这里开始 ## 系统设计主题：从这里开始
@ -670,7 +671,7 @@ 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
* [七层负载平衡](https://www.nginx.com/resources/glossary/layer-7-load-balancing/) * [七层负载平衡](https://www.nginx.com/resources/glossary/layer-7-load-balancing/)
* [ELB 监听器配置](http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/elb-listener-config.html) * [ELB 监听器配置](http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/elb-listener-config.html)
## Reverse proxy (web server) ## 反向代理（web 服务器）
<p align="center"> <p align="center">
<img src="http://i.imgur.com/n41Azff.png"> <img src="http://i.imgur.com/n41Azff.png">
@ -679,41 +680,42 @@ 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
<br/> <br/>
</p> </p>
A reverse proxy is a web server that centralizes internal services and provides unified interfaces to the public. Requests from clients are forwarded to a server that can fulfill it before the reverse proxy returns the server's response to the client. 反向代理是一种可以集中地调用内部服务，并提供统一接口给公共客户的 web 服务器。来自客户端的请求先被反向代理服务器转发到可响应请求的服务器，然后代理再把服务器的响应结果返回给客户端。
Additional benefits include: 带来的好处包括：
* **Increased security** - Hide information about backend servers, blacklist IPs, limit number of connections per client - **增加安全性** - 隐藏后端服务器的信息，屏蔽黑名单中的 IP，限制每个客户端的连接数。
* **Increased scalability and flexibility** - Clients only see the reverse proxy's IP, allowing you to scale servers or change their configuration - **提高可扩展性和灵活性** - 客户端只能看到反向代理服务器的 IP，这使你可以增减服务器或者修改它们的配置。
* **SSL termination** - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations - **本地终结 SSL 会话** - 解密传入请求，加密服务器响应，这样后端服务器就不必完成这些潜在的高成本的操作。
* Removes the need to install [X.509 certificates](https://en.wikipedia.org/wiki/X.509) on each server - 免除了在每个服务器上安装 [X.509](https://en.wikipedia.org/wiki/X.509) 证书的需要
* **Compression** - Compress server responses - **压缩** - 压缩服务器响应
* **Caching** - Return the response for cached requests - **缓存** - 直接返回命中的缓存结果
* **Static content** - Serve static content directly - **静态内容** - 直接提供静态内容
* HTML/CSS/JS - HTML/CSS/JS
* Photos - 图片
* Videos - 视频
* Etc - 等等
### Load balancer vs reverse proxy ### 负载均衡器 VS 反向代理
* Deploying a load balancer is useful when you have multiple servers. Often, load balancers route traffic to a set of servers serving the same function. - 当你有多个服务器时，部署负载均衡器非常有用。通常，负载均衡器将流量路由给一组功能相同的服务器上。
* Reverse proxies can be useful even with just one web server or application server, opening up the benefits described in the previous section. - 即使只有一台 web 服务器或者应用服务器时，反向代理也有用，可以参考上一节介绍的好处。
* Solutions such as NGINX and HAProxy can support both layer 7 reverse proxying and load balancing. - NGINX 和 HAProxy 等解决方案可以同时支持第 7 层反向代理和负载均衡。
### Disadvantage(s): reverse proxy ### 不利之处：反向代理
* Introducing a reverse proxy results in increased complexity. - 引入反向代理会增加系统的复杂度。
* A single reverse proxy is a single point of failure, configuring multiple reverse proxies (ie a [failover](https://en.wikipedia.org/wiki/Failover)) further increases complexity. - 单独一个反向代理服务器仍可能发生单点故障，配置多台反向代理服务器（如[故障转移](https://en.wikipedia.org/wiki/Failover)）会进一步增加复杂度。
### Source(s) and further reading ### 来源及延伸阅读
* [Reverse proxy vs load balancer](https://www.nginx.com/resources/glossary/reverse-proxy-vs-load-balancer/)
* [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/)
* [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt)
* [Wikipedia](https://en.wikipedia.org/wiki/Reverse_proxy)
## Application layer - [反向代理 VS 负载均衡](https://www.nginx.com/resources/glossary/reverse-proxy-vs-load-balancer/)
- [NGINX 架构](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/)
- [HAProxy 架构指南](http://www.haproxy.org/download/1.2/doc/architecture.txt)
- [Wikipedia](https://en.wikipedia.org/wiki/Reverse_proxy)
## 应用层
<p align="center"> <p align="center">
<img src="http://i.imgur.com/yB5SYwm.png"> <img src="http://i.imgur.com/yB5SYwm.png">
@ -721,36 +723,37 @@ Additional benefits include:
<i><a href=http://lethain.com/introduction-to-architecting-systems-for-scale/#platform_layer>Source: Intro to architecting systems for scale</a></i> <i><a href=http://lethain.com/introduction-to-architecting-systems-for-scale/#platform_layer>Source: Intro to architecting systems for scale</a></i>
</p> </p>
Separating out the web layer from the application layer (also known as platform layer) allows you to scale and configure both layers independently. Adding a new API results in adding application servers without necessarily adding additional web servers. 将 Web 服务层与应用层（也被称作平台层）分离，可以独立缩放和配置这两层。添加新的 API 只需要添加应用服务器，而不必添加额外的 web 服务器。
The **single responsibility principle** advocates for small and autonomous services that work together. Small teams with small services can plan more aggressively for rapid growth. **单一职责原则**提倡小型的，自治的服务共同合作。小团队通过提供小型的服务，可以更激进地计划增长。
Workers in the application layer also help enable [asynchronism](#asynchronism). 应用层中的工作进程也有可以实现[异步化](#asynchronism)。
### Microservices ### 微服务
Related to this discussion are [microservices](https://en.wikipedia.org/wiki/Microservices), which can be described as a suite of independently deployable, small, modular services. Each service runs a unique process and communicates through a well-defined, lightweight mechanism to serve a business goal. <sup><a href=https://smartbear.com/learn/api-design/what-are-microservices>1</a></sup> 与此讨论相关的话题是 [微服务](https://en.wikipedia.org/wiki/Microservices)，可以被描述为一系列可以独立部署的小型的，模块化服务。每个服务运行在一个独立的线程中，通过明确定义的轻量级机制通讯，共同实现业务目标。<sup><a href=https://smartbear.com/learn/api-design/what-are-microservices>1</a></sup>
Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc. 例如，Pinterest 可能有这些微服务： 用户资料，关注者，Feed 流，搜索，照片上传等。
### Service Discovery ### 服务发现
Systems such as [Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) can help services find each other by keeping track of registered names, addresses, ports, etc. 诸如 Zookeeper 这类系统可以通过追踪注册名、地址、端口等来帮助服务互相发现对方。
### Disadvantage(s): application layer ### 不利之处：应用层
* Adding an application layer with loosely coupled services requires a different approach from an architectural, operations, and process viewpoint (vs a monolithic system). - 添加由多个松耦合服务组成的应用层，从架构、运营、流程等层面来讲将非常不同（相对于单体系统）。
* Microservices can add complexity in terms of deployments and operations. - 微服务会增加部署和运营的复杂度。
### Source(s) and further reading
* [Intro to architecting systems for scale](http://lethain.com/introduction-to-architecting-systems-for-scale) ### 来源及延伸阅读
* [Crack the system design interview](http://www.puncsky.com/blog/2016/02/14/crack-the-system-design-interview/)
* [Service oriented architecture](https://en.wikipedia.org/wiki/Service-oriented_architecture)
* [Introduction to Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper)
* [Here's what you need to know about building microservices](https://cloudncode.wordpress.com/2016/07/22/msa-getting-started/)
## Database - [可缩放系统构架介绍](http://lethain.com/introduction-to-architecting-systems-for-scale)
- [破解系统设计面试](http://www.puncsky.com/blog/2016/02/14/crack-the-system-design-interview/)
- [面向服务架构](https://en.wikipedia.org/wiki/Service-oriented_architecture)
- [Zookeeper 介绍](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper)
- [构建微服务，你所需要知道的一切](https://cloudncode.wordpress.com/2016/07/22/msa-getting-started/)
## 数据库
<p align="center"> <p align="center">
<img src="http://i.imgur.com/Xkm5CXz.png"> <img src="http://i.imgur.com/Xkm5CXz.png">
@ -758,22 +761,20 @@ Systems such as [Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-t
<i><a href=https://www.youtube.com/watch?v=vg5onp8TU6Q>Source: Scaling up to your first 10 million users</a></i> <i><a href=https://www.youtube.com/watch?v=vg5onp8TU6Q>Source: Scaling up to your first 10 million users</a></i>
</p> </p>
### Relational database management system (RDBMS) ### 关系型数据库管理系统（RDBMS）
A relational database like SQL is a collection of data items organized in tables. 像 SQL 这样的关系型数据库是一系列以表的形式组织的数据项集合。
**ACID** is a set of properties of relational database [transactions](https://en.wikipedia.org/wiki/Database_transaction). > 校对注：这里作者 SQL 可能指的是 MySQL
* **Atomicity** - Each transaction is all or nothing **ACID** 用来描述关系型数据库[事务](https://en.wikipedia.org/wiki/Database_transaction)的特性。
* **Consistency** - Any transaction will bring the database from one valid state to another
* **Isolation** - Executing transactions concurrently has the same results as if the transactions were executed serially
* **Durability** - Once a transaction has been committed, it will remain so
There are many techniques to scale a relational database: **master-slave replication**, **master-master replication**, **federation**, **sharding**, **denormalization**, and **SQL tuning**. - **原子性** - 每个事务内部所有操作要么全部完成，要么全部不完成。
- **一致性** - 任何事务都使数据库从一个有效的状态转换到另一个有效状态。
- **隔离性** - 并发执行事务的结果与顺序执行事务的结果相同。
- **持久性** - 事务提交后，对系统的影响是永久的。
#### Master-slave replication 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**。
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"> <p align="center">
<img src="http://i.imgur.com/C9ioGtn.png"> <img src="http://i.imgur.com/C9ioGtn.png">
@ -781,14 +782,14 @@ The master serves reads and writes, replicating writes to one or more slaves, wh
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i> <i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i>
</p> </p>
##### Disadvantage(s): master-slave replication #### 主从复制
* Additional logic is needed to promote a slave to a master. 主库同时负责读取和写入操作，并复制写入到一个或多个从库中，从库只负责读操作。树状形式的从库再将写入复制到更多的从库中去。如果主库离线，系统可以以只读模式运行，直到某个从库被提升为主库或有新的主库出现。
* See [Disadvantage(s): replication](#disadvantages-replication) for points related to **both** master-slave and master-master.
#### Master-master replication ##### 不利之处：主从复制
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. - 将从库提升为主库需要额外的逻辑。
- 参考[不利之处：复制](#disadvantages-replication)中，主从复制和主主复制**共同**的问题。
<p align="center"> <p align="center">
<img src="http://i.imgur.com/krAHLGg.png"> <img src="http://i.imgur.com/krAHLGg.png">
@ -796,27 +797,34 @@ Both masters serve reads and writes and coordinate with each other on writes. I
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i> <i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i>
</p> </p>
##### Disadvantage(s): master-master replication #### 主主复制
* You'll need a load balancer or you'll need to make changes to your application logic to determine where to write. 两个主库都负责读操作和写操作，写入操作时互相协调。如果其中一个主库挂机，系统可以继续读取和写入。
* Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization.
* Conflict resolution comes more into play as more write nodes are added and as latency increases.
* See [Disadvantage(s): replication](#disadvantages-replication) for points related to **both** master-slave and master-master.
##### Disadvantage(s): replication ##### 不利之处： 主主复制
* There is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes. - 你需要添加负载均衡器或者在应用逻辑中做改动，来确定写入哪一个数据库。
* Writes are replayed to the read replicas. If there are a lot of writes, the read replicas can get bogged down with replaying writes and can't do as many reads. - 多数主-主系统要么不能保证一致性（违反 ACID），要么因为同步产生了写入延迟。
* The more read slaves, the more you have to replicate, which leads to greater replication lag. - 随着更多写入节点的加入和延迟的提高，如何解决冲突显得越发重要。
* On some systems, writing to the master can spawn multiple threads to write in parallel, whereas read replicas only support writing sequentially with a single thread. - 参考[不利之处：复制](#disadvantages-replication)中，主从复制和主主复制**共同**的问题。
* Replication adds more hardware and additional complexity.
##### Source(s) and further reading: replication ##### 不利之处：复制
* [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
* [Multi-master replication](https://en.wikipedia.org/wiki/Multi-master_replication)
#### Federation - 如果主库在将新写入的数据复制到其他节点前挂掉，则有数据丢失的可能。
- 写入会被重放到负责读取操作的副本。副本可能因为过多写操作阻塞住，导致读取功能异常。
- 读取从库越多，需要复制的写入数据就越多，导致更严重的复制延迟。
- 在某些数据库系统中，写入主库的操作可以用多个线程并行写入，但读取副本只支持单线程顺序地写入。
- 复制意味着更多的硬件和额外的复杂度。
##### 来源及延伸阅读
- [扩展性，可用性，稳定性模式](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
- [多主复制](https://en.wikipedia.org/wiki/Multi-master_replication)
#### 联合
<p align="center"> <p align="center">
<img src="http://i.imgur.com/U3qV33e.png"> <img src="http://i.imgur.com/U3qV33e.png">
@ -824,20 +832,21 @@ Both masters serve reads and writes and coordinate with each other on writes. I
<i><a href=https://www.youtube.com/watch?v=vg5onp8TU6Q>Source: Scaling up to your first 10 million users</a></i> <i><a href=https://www.youtube.com/watch?v=vg5onp8TU6Q>Source: Scaling up to your first 10 million users</a></i>
</p> </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. 联合（或按功能划分）将数据库按对应功能分割。例如，你可以有三个数据库：**论坛**、**用户**和**产品**，而不仅是一个单体数据库，从而减少每个数据库的读取和写入流量，减少复制延迟。较小的数据库意味着更多适合放入内存的数据，进而意味着更高的缓存命中几率。没有只能串行写入的中心化主库，你可以并行写入，提高负载能力。
##### Disadvantage(s): federation ##### 不利之处：联合
* Federation is not effective if your schema requires huge functions or tables.
* You'll need to update your application logic to determine which database to read and write.
* Joining data from two databases is more complex with a [server link](http://stackoverflow.com/questions/5145637/querying-data-by-joining-two-tables-in-two-database-on-different-servers).
* Federation adds more hardware and additional complexity.
##### Source(s) and further reading: federation - 如果你的数据库模式需要大量的功能和数据表，联合的效率并不好。
- 你需要更新应用程序的逻辑来确定要读取和写入哪个数据库。
- 用 [server link](http://stackoverflow.com/questions/5145637/querying-data-by-joining-two-tables-in-two-database-on-different-servers) 从两个库联结数据更复杂。
- 联合需要更多的硬件和额外的复杂度。
* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=vg5onp8TU6Q) ##### 来源及延伸阅读：联合
#### Sharding - [扩展你的用户数到第一个千万]((https://www.youtube.com/watch?v=vg5onp8TU6Q))
#### 分片
<p align="center"> <p align="center">
<img src="http://i.imgur.com/wU8x5Id.png"> <img src="http://i.imgur.com/wU8x5Id.png">
@ -845,143 +854,143 @@ Federation (or functional partitioning) splits up databases by function. For ex
<i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i> <i><a href=http://www.slideshare.net/jboner/scalability-availability-stability-patterns/>Source: Scalability, availability, stability, patterns</a></i>
</p> </p>
Sharding distributes data across different databases such that each database can only manage a subset of the data. Taking a users database as an example, as the number of users increases, more shards are added to the cluster. 分片将数据分配在不同的数据库上，使得每个数据库仅管理整个数据集的一个子集。以用户数据库为例，随着用户数量的增加，越来越多的分片会被添加到集群中。
Similar to the advantages of [federation](#federation), sharding results in less read and write traffic, less replication, and more cache hits. Index size is also reduced, which generally improves performance with faster queries. If one shard goes down, the other shards are still operational, although you'll want to add some form of replication to avoid data loss. Like federation, there is no single central master serializing writes, allowing you to write in parallel with increased throughput. 类似[联合](#federation)的优点，分片可以减少读取和写入流量，减少复制并提高缓存命中率。也减少了索引，通常意味着查询更快，性能更好。如果一个分片出问题，其他的仍能运行，你可以使用某种形式的冗余来防止数据丢失。类似联合，没有只能串行写入的中心化主库，你可以并行写入，提高负载能力。
Common ways to shard a table of users is either through the user's last name initial or the user's geographic location. 常见的做法是用户姓氏的首字母或者用户的地理位置来分隔用户表。
##### Disadvantage(s): sharding ##### 不利之处：分片
* You'll need to update your application logic to work with shards, which could result in complex SQL queries. - 你需要修改应用程序的逻辑来实现分片，这会带来复杂的 SQL 查询。
* Data distribution can become lopsided in a shard. For example, a set of power users on a shard could result in increased load to that shard compared to others. - 分片不合理可能导致数据负载不均衡。例如，被频繁访问的用户数据会导致其所在分片的负载相对其他分片高。
* Rebalancing adds additional complexity. A sharding function based on [consistent hashing](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html) can reduce the amount of transferred data. - 再平衡会引入额外的复杂度。基于[一致性哈希](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html)的分片算法可以减少这种情况。
* Joining data from multiple shards is more complex. - 联结多个分片的数据操作更复杂。
* Sharding adds more hardware and additional complexity. - 分片需要更多的硬件和额外的复杂度。
##### Source(s) and further reading: sharding #### 来源及延伸阅读：分片
* [The coming of the shard](http://highscalability.com/blog/2009/8/6/an-unorthodox-approach-to-database-design-the-coming-of-the.html) - [分片时代来临](http://highscalability.com/blog/2009/8/6/an-unorthodox-approach-to-database-design-the-coming-of-the.html)
* [Shard database architecture](https://en.wikipedia.org/wiki/Shard_(database_architecture)) - [数据库分片架构](https://en.wikipedia.org/wiki/Shard_(database_architecture))
* [Consistent hashing](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html) - [一致性哈希](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html)
#### Denormalization #### 非规范化
Denormalization attempts to improve read performance at the expense of some write performance. Redundant copies of the data are written in multiple tables to avoid expensive joins. Some RDBMS such as [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) and Oracle support [materialized views](https://en.wikipedia.org/wiki/Materialized_view) which handle the work of storing redundant information and keeping redundant copies consistent. 非规范化试图以写入性能为代价来换取读取性能。在多个表中冗余数据副本，以避免高成本的联结操作。一些关系型数据库，比如 [PostgreSQl](https://en.wikipedia.org/wiki/PostgreSQL) 和 Oracle 支持[物化视图](https://en.wikipedia.org/wiki/Materialized_view)，可以处理冗余信息存储和保证冗余副本一致。
Once data becomes distributed with techniques such as [federation](#federation) and [sharding](#sharding), managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins. 当数据使用诸如[联合](#federation)和[分片](#sharding)等技术被分割，进一步提高了处理跨数据中心的联结操作复杂度。非规范化可以规避这种复杂的联结操作。
In most systems, reads can heavily number writes 100:1 or even 1000:1. A read resulting in a complex database join can be very expensive, spending a significant amount of time on disk operations. 在多数系统中，读取操作的频率远高于写入操作，比例可达到 100:1，甚至 1000:1。需要复杂的数据库联结的读取操作成本非常高，在磁盘操作上消耗了大量时间。
##### Disadvantage(s): denormalization ##### 不利之处：非规范化
* Data is duplicated. - 数据会冗余。
* Constraints can help redundant copies of information stay in sync, which increases complexity of the database design. - 约束可以帮助冗余的信息副本保持同步，但这样会增加数据库设计的复杂度。
* A denormalized database under heavy write load might perform worse than its normalized counterpart. - 非规范化的数据库在高写入负载下性能可能比规范化的数据库差。
###### Source(s) and further reading: denormalization ##### 来源及延伸阅读：非规范化
* [Denormalization](https://en.wikipedia.org/wiki/Denormalization) - [非规范化](https://en.wikipedia.org/wiki/Denormalization)
#### SQL tuning #### SQL 调优
SQL tuning is a broad topic and many [books](https://www.amazon.com/s/ref=nb_sb_noss_2?url=search-alias%3Daps&field-keywords=sql+tuning) have been written as reference. SQL 调优是一个范围很广的话题，有很多相关的[书](https://www.amazon.com/s/ref=nb_sb_noss_2?url=search-alias%3Daps&field-keywords=sql+tuning)可以作为参考。
It's important to **benchmark** and **profile** to simulate and uncover bottlenecks. 利用**基准测试**和**性能分析**来模拟和发现系统瓶颈很重要。
* **Benchmark** - Simulate high-load situations with tools such as [ab](http://httpd.apache.org/docs/2.2/programs/ab.html). - **基准测试** - 用 [ab](http://httpd.apache.org/docs/2.2/programs/ab.html) 等工具模拟高负载情况。
* **Profile** - Enable tools such as the [slow query log](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html) to help track performance issues. - **性能分析** - 通过启用如[慢查询日志](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html)等工具来辅助追踪性能问题。
Benchmarking and profiling might point you to the following optimizations. 基准测试和性能分析可能会指引你到以下优化方案。
##### Tighten up the schema ##### 改进模式
* MySQL dumps to disk in contiguous blocks for fast access. - 为了实现快速访问，MySQL 在磁盘上用连续的块存储数据。
* Use `CHAR` instead of `VARCHAR` for fixed-length fields. - 使用 `CHAR` 类型存储固定长度的字段，不要用 `VARCHAR`。
* `CHAR` effectively allows for fast, random access, whereas with `VARCHAR`, you must find the end of a string before moving onto the next one. - `CHAR` 在快速、随机访问时效率很高。如果使用 `VARCHAR`，如果你想读取下一个字符串，不得不先读取到当前字符串的末尾。
* Use `TEXT` for large blocks of text such as blog posts. `TEXT` also allows for boolean searches. Using a `TEXT` field results in storing a pointer on disk that is used to locate the text block. - 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。
* Use `INT` for larger numbers up to 2^32 or 4 billion. - 使用 `INT` 类型存储高达 2^32 或 40 亿的较大数字。
* Use `DECIMAL` for currency to avoid floating point representation errors. - 使用 `DECIMAL` 类型存储货币可以避免浮点数表示错误。
* Avoid storing large `BLOBS`, store the location of where to get the object instead. - 避免使用 `BLOBS` 存储对象，存储存放对象的位置。
* `VARCHAR(255)` is the largest number of characters that can be counted in an 8 bit number, often maximizing the use of a byte in some RDBMS. - `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。
* Set the `NOT NULL` constraint where applicable to [improve search performance](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search). - 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)。
##### Use good indices ##### 使用正确的索引
* Columns that you are querying (`SELECT`, `GROUP BY`, `ORDER BY`, `JOIN`) could be faster with indices. - 你正查询（`SELECT`、`GROUP BY`、`ORDER BY`、`JOIN`）的列如果用了索引会更快。
* Indices are usually represented as self-balancing [B-tree](https://en.wikipedia.org/wiki/B-tree) that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. - 索引通常表示为自平衡的 [B 树](https://en.wikipedia.org/wiki/B-tree)，可以保持数据有序，并允许在对数时间内进行搜索，顺序访问，插入，删除操作。
* Placing an index can keep the data in memory, requiring more space. - 设置索引，会将数据存在内存中，占用了更多内存空间。
* Writes could also be slower since the index also needs to be updated. - 写入操作会变慢，因为索引需要被更新。
* When loading large amounts of data, it might be faster to disable indices, load the data, then rebuild the indices. - 加载大量数据时，禁用索引再加载数据，然后重建索引，这样也许会更快。
##### Avoid expensive joins ##### 避免高成本的联结操作
* [Denormalize](#denormalization) where performance demands it. - 有性能需要，可以进行非规范化。
##### Partition tables ##### 分割数据表
* Break up a table by putting hot spots in a separate table to help keep it in memory. - 将热点数据拆分到单独的数据表中，可以有助于缓存。
##### Tune the query cache ##### 调优查询缓存
* In some cases, the [query cache](http://dev.mysql.com/doc/refman/5.7/en/query-cache) could lead to [performance issues](https://www.percona.com/blog/2014/01/28/10-mysql-performance-tuning-settings-after-installation/). - 在某些情况下，[查询缓存](http://dev.mysql.com/doc/refman/5.7/en/query-cache)可能会导致[性能问题](https://www.percona.com/blog/2014/01/28/10-mysql-performance-tuning-settings-after-installation/)。
##### Source(s) and further reading: SQL tuning ##### 来源及延伸阅读
* [Tips for optimizing MySQL queries](http://20bits.com/article/10-tips-for-optimizing-mysql-queries-that-dont-suck) - [MySQL 查询优化小贴士](http://20bits.com/article/10-tips-for-optimizing-mysql-queries-that-dont-suck)
* [Is there a good reason i see VARCHAR(255) used so often?](http://stackoverflow.com/questions/1217466/is-there-a-good-reason-i-see-varchar255-used-so-often-as-opposed-to-another-l) - [为什么 VARCHAR(255) 很常见？](http://stackoverflow.com/questions/1217466/is-there-a-good-reason-i-see-varchar255-used-so-often-as-opposed-to-another-l)
* [How do null values affect performance?](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search) - [Null 值是如何影响数据库性能的？](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) - [慢查询日志](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html)
### NoSQL ### 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 是**键-值数据库**、**文档型数据库**、**列型数据库**或**图数据库**的统称。数据库是非规范化的，表联结大多在应用程序代码中完成。大多数 NoSQL 无法实现真正符合 ACID 的事务，支持[最终一致](#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. **BASE** 通常被用于描述 NoSQL 数据库的特性。相比 [CAP 定理](#cap-theorem)，BASE 强调可用性超过一致性。
* **Basically available** - the system guarantees availability. - **基本可用** - 系统保证可用性。
* **Soft state** - the state of the system may change over time, even without input. - **软状态** - 即使没有输入，系统状态也可能随着时间变化。
* **Eventual consistency** - the system will become consistent over a period of time, given that the system doesn't receive input during that period. - **最终一致性** - 经过一段时间之后，系统最终会变一致，因为系统在此期间没有收到任何输入。
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. 除了在 [SQL 还是 NoSQL](#sql-or-nosql) 之间做选择，了解哪种类型的 NoSQL 数据库最适合你的用例也是非常有帮助的。我们将在下一节中快速了解下 **键-值存储**、**文档型存储**、**列型存储**和**图存储**数据库。
#### Key-value store #### 键-值存储
> Abstraction: hash table > 抽象模型：哈希表
A key-value store generally allows for O(1) reads and writes and is often backed by memory or SSD. Data stores can maintain keys in [lexicographic order](https://en.wikipedia.org/wiki/Lexicographical_order), allowing efficient retrieval of key ranges. Key-value stores can allow for storing of metadata with a value. 键-值存储通常可以实现 O(1) 时间读写，用内存或 SSD 存储数据。数据存储可以按[字典顺序](https://en.wikipedia.org/wiki/Lexicographical_order)维护键，从而实现键的高效检索。键-值存储可以用于存储元数据。
Key-value stores provide high performance and are often used for simple data models or for rapidly-changing data, such as an in-memory cache layer. Since they offer only a limited set of operations, complexity is shifted to the application layer if additional operations are needed. 键-值存储性能很高，通常用于存储简单数据模型或频繁修改的数据，如存放在内存中的缓存。键-值存储提供的操作有限，如果需要更多操作，复杂度将转嫁到应用程序层面。
A key-value store is the basis for more complex systems such as a document store, and in some cases, a graph database. 键-值存储是如文档存储，在某些情况下，甚至是图存储等更复杂的存储系统的基础。
##### Source(s) and further reading: key-value store #### 来源及延伸阅读
* [Key-value database](https://en.wikipedia.org/wiki/Key-value_database) - [键-值数据库](https://en.wikipedia.org/wiki/Key-value_database)
* [Disadvantages of key-value stores](http://stackoverflow.com/questions/4056093/what-are-the-disadvantages-of-using-a-key-value-table-over-nullable-columns-or) - [键-值存储的劣势](http://stackoverflow.com/questions/4056093/what-are-the-disadvantages-of-using-a-key-value-table-over-nullable-columns-or)
* [Redis architecture](http://qnimate.com/overview-of-redis-architecture/) - [Redis 架构](http://qnimate.com/overview-of-redis-architecture/)
* [Memcached architecture](https://www.adayinthelifeof.nl/2011/02/06/memcache-internals/) - [Memcached 架构](https://www.adayinthelifeof.nl/2011/02/06/memcache-internals/)
#### Document store #### 文档类型存储
> Abstraction: key-value store with documents stored as values > 抽象模型：将文档作为值的键-值存储
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.* 文档类型存储以文档（XML、JSON、二进制文件等）为中心，文档存储了指定对象的全部信息。文档存储根据文档自身的内部结构提供 API 或查询语句来实现查询。请注意，许多键-值存储数据库有用值存储元数据的特性，这也模糊了这两种存储类型的界限。
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. 基于底层实现，文档可以根据集合、标签、元数据或者文件夹组织。尽管不同文档可以被组织在一起或者分成一组，但相互之间可能具有完全不同的字段。
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. MongoDB 和 CouchDB 等一些文档类型存储还提供了类似 SQL 语言的查询语句来实现复杂查询。DynamoDB 同时支持键-值存储和文档类型存储。
Document stores provide high flexibility and are often used for working with occasionally changing data. 文档类型存储具备高度的灵活性，常用于处理偶尔变化的数据。
##### Source(s) and further reading: document store #### 来源及延伸阅读：文档类型存储
* [Document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database) - [面向文档的数据库](https://en.wikipedia.org/wiki/Document-oriented_database)
* [MongoDB architecture](https://www.mongodb.com/mongodb-architecture) - [MongoDB 架构](https://www.mongodb.com/mongodb-architecture)
* [CouchDB architecture](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/) - [CouchDB 架构](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/)
* [Elasticsearch architecture](https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up) - [Elasticsearch 架构](https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up)
#### Wide column store #### 列型存储
<p align="center"> <p align="center">
<img src="http://i.imgur.com/n16iOGk.png"> <img src="http://i.imgur.com/n16iOGk.png">
@ -989,22 +998,22 @@ Document stores provide high flexibility and are often used for working with occ
<i><a href=http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html>Source: SQL & NoSQL, a brief history</a></i> <i><a href=http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html>Source: SQL & NoSQL, a brief history</a></i>
</p> </p>
> Abstraction: nested map `ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>>` > 抽象模型：嵌套的 `ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>>` 映射
A wide column store's basic unit of data is a column (name/value pair). A column can be grouped in column families (analogous to a SQL table). Super column families further group column families. You can access each column independently with a row key, and columns with the same row key form a row. Each value contains a timestamp for versioning and for conflict resolution. 类型存储的基本数据单元是列（名／值对）。列可以在列族（类似于 SQL 的数据表）中被分组。超级列族再分组普通列族。你可以使用行键独立访问每一列，具有相同行键值的列组成一行。每个值都包含版本的时间戳用于解决版本冲突。
Google introduced [Bigtable](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) as the first wide column store, which influenced the open-source [HBase](https://www.mapr.com/blog/in-depth-look-hbase-architecture) often-used in the Hadoop ecosystem, and [Cassandra](http://docs.datastax.com/en/archived/cassandra/2.0/cassandra/architecture/architectureIntro_c.html) from Facebook. Stores such as BigTable, HBase, and Cassandra maintain keys in lexicographic order, allowing efficient retrieval of selective key ranges. Google 发布了第一个列型存储数据库 [Bigtable](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf)，它影响了 Hadoop 生态系统中活跃的开源数据库 [HBase](https://www.mapr.com/blog/in-depth-look-hbase-architecture) 和 Facebook 的 [Cassandra](http://docs.datastax.com/en/archived/cassandra/2.0/cassandra/architecture/architectureIntro_c.html)。像 BigTable，HBase 和 Cassandra 这样的存储系统将键以字母顺序存储，可以高效地读取键列。
Wide column stores offer high availability and high scalability. They are often used for very large data sets. 列型存储具备高可用性和高可扩展性。通常被用于大数据相关存储。
##### Source(s) and further reading: wide column store ##### 来源及延伸阅读：列型存储
* [SQL & NoSQL, a brief history](http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html) - [SQL 与 NoSQL 简史](http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html)
* [Bigtable architecture](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) - [BigTable 架构](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf)
* [HBase architecture](https://www.mapr.com/blog/in-depth-look-hbase-architecture) - [Hbase 架构](https://www.mapr.com/blog/in-depth-look-hbase-architecture)
* [Cassandra architecture](http://docs.datastax.com/en/archived/cassandra/2.0/cassandra/architecture/architectureIntro_c.html) - [Cassandra 架构](http://docs.datastax.com/en/archived/cassandra/2.0/cassandra/architecture/architectureIntro_c.html)
#### Graph database #### 图数据库
<p align="center"> <p align="center">
<img src="http://i.imgur.com/fNcl65g.png"> <img src="http://i.imgur.com/fNcl65g.png">
@ -1012,24 +1021,25 @@ Wide column stores offer high availability and high scalability. They are often
<i><a href=https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png>Source: Graph database</a></i> <i><a href=https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png>Source: Graph database</a></i>
</p> </p>
> Abstraction: graph > 抽象模型： 图
In a graph database, each node is a record and each arc is a relationship between two nodes. Graph databases are optimized to represent complex relationships with many foreign keys or many-to-many relationships. 在图数据库中，一个节点对应一条记录，一个弧对应两个节点之间的关系。图数据库被优化用于表示外键繁多的复杂关系或多对多关系。
Graphs databases offer high performance for data models with complex relationships, such as a social network. They are relatively new and are not yet widely-used; it might be more difficult to find development tools and resources. Many graphs can only be accessed with [REST APIs](#representational-state-transfer-rest). 图数据库为存储复杂关系的数据模型，如社交网络，提供了很高的性能。它们相对较新，尚未广泛应用，查找开发工具或者资源相对较难。许多图只能通过 [REST API](#representational-state-transfer-restE) 访问。
##### 相关资源和延伸阅读：图 ##### 相关资源和延伸阅读：图
- [图数据库](https://en.wikipedia.org/wiki/Graph_database) - [图数据库](https://en.wikipedia.org/wiki/Graph_database)
- [Neo4j](https://neo4j.com/) - [Neo4j](https://neo4j.com/)
- [FlockDB](https://blog.twitter.com/2010/introducing-flockdb) - [FlockDB](https://blog.twitter.com/2010/introducing-flockdb)
#### 相关资源和延伸阅读：NoSQL #### 来源及延伸阅读：NoSQL
- [基础术语解释](http://stackoverflow.com/questions/3342497/explanation-of-base-terminology) - [数据库术语解释](http://stackoverflow.com/questions/3342497/explanation-of-base-terminology)
- [NoSQL 数据库 — 调查与决策指导](https://medium.com/baqend-blog/nosql-databases-a-survey-and-decision-guidance-ea7823a822d#.wskogqenq) - [NoSQL 数据库 - 调查及决策指南](https://medium.com/baqend-blog/nosql-databases-a-survey-and-decision-guidance-ea7823a822d#.wskogqenq)
- [可扩展性](http://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database) - [可扩展性](http://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database)
- [NoSQL 的介绍](https://www.youtube.com/watch?v=qI_g07C_Q5I) - [NoSQL 介绍](https://www.youtube.com/watch?v=qI_g07C_Q5I)
- [NoSQL 模式](http://horicky.blogspot.com/2009/11/nosql-patterns.html) - [NoSQL 模式](http://horicky.blogspot.com/2009/11/nosql-patterns.html)
### SQL 还是 NoSQL ### SQL 还是 NoSQL
@ -1040,40 +1050,41 @@ Graphs databases offer high performance for data models with complex relationshi
<i><a href=https://www.infoq.com/articles/Transition-RDBMS-NoSQL/>Source: Transitioning from RDBMS to NoSQL</a></i> <i><a href=https://www.infoq.com/articles/Transition-RDBMS-NoSQL/>Source: Transitioning from RDBMS to NoSQL</a></i>
</p> </p>
选择 **SQL** 的原因： 选取 **SQL** 的原因:
- 结构化数据 - 结构化数据
- 严格的架构 - 严格的模式
- 关系型数据 - 关系型数据
- 需要复杂的 joins - 需要复杂的联结操作
- 事务 - 事务
- 清晰的缩放模式 - 清晰的扩展模式
- 更成熟的开发人员，社区，代码，工具等等 - 既有资源更丰富：开发者、社区、代码库、工具等
- 通过索引查找非常快 - 通过索引进行查询非常快
选择 **NoSQL** 的原因： 选取 **NoSQL** 的原因：
- 半结构化数据 - 半结构化数据
- 动态/灵活的模式 - 动态或灵活的模式
- 非关系型数据 - 非关系型数据
- 不需要复杂的 joins 操作 - 不需要复杂的联结操作
- 可以存储大量 TB/PB 数据 - 存储 TB （甚至 PB）级别的数据
- 非常数据密集的工作量 - 高数据密集的工作负载
- 非常高的 IOPS 吞吐量 - IOPS 高吞吐量
适合 NoSQL 操作的数据： 适合 NoSQL 的示例数据：
- 埋点数据以及日志数据 - 埋点数据和日志数据
- 排行榜或者得分数据 - 排行榜或者得分数据
- 临时数据，比如购物车 - 临时数据，如购物车
- 需要频繁访问的表 - 频繁访问的（“热”）表
- 元数据/查找表 - 元数据／查找表
##### 来源及延伸阅读：SQL 或 NoSQL
- [扩展你的用户数到第一个千万](https://www.youtube.com/watch?v=vg5onp8TU6Q)
##### 相关资源和延伸阅读：SQL 还是 NoSQL
- [扩大您的用户到第一个1000万](https://www.youtube.com/watch?v=vg5onp8TU6Q)
- [SQL 和 NoSQL 的不同](https://www.sitepoint.com/sql-vs-nosql-differences/) - [SQL 和 NoSQL 的不同](https://www.sitepoint.com/sql-vs-nosql-differences/)
## 缓存 ## 缓存
<p align="center"> <p align="center">
@ -1473,15 +1484,15 @@ REST 关注于暴露数据。它减少了客户端／服务端的耦合程度，
### RPC 与 REST 比较 ### RPC 与 REST 比较
| 操作 | RPC | REST | | 操作 | RPC | REST |
| ------------------------------- | ---------------------------------------- | ---------------------------------------- | | ----------- | ---------------------------------------- | ---------------------------------------- |
| 注册 | **POST** /signup | **POST** /persons | | 注册 | **POST** /signup | **POST** /persons |
| 注销 | **POST** /resign<br/>{<br/>"personid": "1234"<br/>} | **DELETE** /persons/1234 | | 注销 | **POST** /resign<br/>{<br/>"personid": "1234"<br/>} | **DELETE** /persons/1234 |
| 读取用户信息 | **GET** /readPerson?personid=1234 | **GET** /persons/1234 | | 读取用户信息 | **GET** /readPerson?personid=1234 | **GET** /persons/1234 |
| 读取用户物品列表 | **GET** /readUsersItemsList?personid=1234 | **GET** /persons/1234/items | | 读取用户物品列表 | **GET** /readUsersItemsList?personid=1234 | **GET** /persons/1234/items |
| 向用户物品列表添加一项 | **POST** /addItemToUsersItemsList<br/>{<br/>"personid": "1234";<br/>"itemid": "456"<br/>} | **POST** /persons/1234/items<br/>{<br/>"itemid": "456"<br/>} | | 向用户物品列表添加一项 | **POST** /addItemToUsersItemsList<br/>{<br/>"personid": "1234";<br/>"itemid": "456"<br/>} | **POST** /persons/1234/items<br/>{<br/>"itemid": "456"<br/>} |
| 更新一个物品 | **POST** /modifyItem<br/>{<br/>"itemid": "456";<br/>"key": "value"<br/>} | **PUT** /items/456<br/>{<br/>"key": "value"<br/>} | | 更新一个物品 | **POST** /modifyItem<br/>{<br/>"itemid": "456";<br/>"key": "value"<br/>} | **PUT** /items/456<br/>{<br/>"key": "value"<br/>} |
| 删除一个物品 | **POST** /removeItem<br/>{<br/>"itemid": "456"<br/>} | **DELETE** /items/456 | | 删除一个物品 | **POST** /removeItem<br/>{<br/>"itemid": "456"<br/>} | **DELETE** /items/456 |
<p align="center"> <p align="center">
<i><a href=https://apihandyman.io/do-you-really-know-why-you-prefer-rest-over-rpc/>Source: Do you really know why you prefer REST over RPC</a></i> <i><a href=https://apihandyman.io/do-you-really-know-why-you-prefer-rest-over-rpc/>Source: Do you really know why you prefer REST over RPC</a></i>
@ -1588,30 +1599,30 @@ Notes
> 常见的系统设计面试问题，给出了如何解决的方案链接 > 常见的系统设计面试问题，给出了如何解决的方案链接
| 问题 | 引用 | | 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 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 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 的建值存储 | [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 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/) | | 设计类似 Bitly 的短链接系统 | [n00tc0d3r.blogspot.com](http://n00tc0d3r.blogspot.com/) |
| 设计类似 WhatsApp 的聊天应用 | [highscalability.com](http://highscalability.com/blog/2014/2/26/the-whatsapp-architecture-facebook-bought-for-19-billion.html) | | 设计类似 WhatsApp 的聊天应用 | [highscalability.com](http://highscalability.com/blog/2014/2/26/the-whatsapp-architecture-facebook-bought-for-19-billion.html) |
| 设计类似 Instagram 的图片分享系统| [highscalability.com](http://highscalability.com/flickr-architecture)<br/>[highscalability.com](http://highscalability.com/blog/2011/12/6/instagram-architecture-14-million-users-terabytes-of-photos.html) | | 设计类似 Instagram 的图片分享系统 | [highscalability.com](http://highscalability.com/flickr-architecture)<br/>[highscalability.com](http://highscalability.com/blog/2011/12/6/instagram-architecture-14-million-users-terabytes-of-photos.html) |
| 设计 Facebook 的新闻推荐方法 | [quora.com](http://www.quora.com/What-are-best-practices-for-building-something-like-a-News-Feed)<br/>[quora.com](http://www.quora.com/Activity-Streams/What-are-the-scaling-issues-to-keep-in-mind-while-developing-a-social-network-feed)<br/>[slideshare.net](http://www.slideshare.net/danmckinley/etsy-activity-feeds-architecture) | | 设计 Facebook 的新闻推荐方法 | [quora.com](http://www.quora.com/What-are-best-practices-for-building-something-like-a-News-Feed)<br/>[quora.com](http://www.quora.com/Activity-Streams/What-are-the-scaling-issues-to-keep-in-mind-while-developing-a-social-network-feed)<br/>[slideshare.net](http://www.slideshare.net/danmckinley/etsy-activity-feeds-architecture) |
| 设计 Facebook 的时间线系统 | [facebook.com](https://www.facebook.com/note.php?note_id=10150468255628920)<br/>[highscalability.com](http://highscalability.com/blog/2012/1/23/facebook-timeline-brought-to-you-by-the-power-of-denormaliza.html) | | 设计 Facebook 的时间线系统 | [facebook.com](https://www.facebook.com/note.php?note_id=10150468255628920)<br/>[highscalability.com](http://highscalability.com/blog/2012/1/23/facebook-timeline-brought-to-you-by-the-power-of-denormaliza.html) |
| 设计 Facebook 的聊天系统 | [erlang-factory.com](http://www.erlang-factory.com/upload/presentations/31/EugeneLetuchy-ErlangatFacebook.pdf)<br/>[facebook.com](https://www.facebook.com/note.php?note_id=14218138919&id=9445547199&index=0) | | 设计 Facebook 的聊天系统 | [erlang-factory.com](http://www.erlang-factory.com/upload/presentations/31/EugeneLetuchy-ErlangatFacebook.pdf)<br/>[facebook.com](https://www.facebook.com/note.php?note_id=14218138919&id=9445547199&index=0) |
| 设计类似 Facebook 的图表搜索系统 | [facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-building-out-the-infrastructure-for-graph-search/10151347573598920)<br/>[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-indexing-and-ranking-in-graph-search/10151361720763920)<br/>[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-the-natural-language-interface-of-graph-search/10151432733048920) | | 设计类似 Facebook 的图表搜索系统 | [facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-building-out-the-infrastructure-for-graph-search/10151347573598920)<br/>[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-indexing-and-ranking-in-graph-search/10151361720763920)<br/>[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-the-natural-language-interface-of-graph-search/10151432733048920) |
| 设计类似 CloudFlare 的内容传递网络 | [cmu.edu](http://repository.cmu.edu/cgi/viewcontent.cgi?article=2112&context=compsci) | | 设计类似 CloudFlare 的内容传递网络 | [cmu.edu](http://repository.cmu.edu/cgi/viewcontent.cgi?article=2112&context=compsci) |
| 设计类似 Twitter 的热门话题系统 | [michael-noll.com](http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/)<br/>[snikolov .wordpress.com](http://snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/) | | 设计类似 Twitter 的热门话题系统 | [michael-noll.com](http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/)<br/>[snikolov .wordpress.com](http://snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/) |
| 设计一个随机 ID 生成系统 | [blog.twitter.com](https://blog.twitter.com/2010/announcing-snowflake)<br/>[github.com](https://github.com/twitter/snowflake/) | | 设计一个随机 ID 生成系统 | [blog.twitter.com](https://blog.twitter.com/2010/announcing-snowflake)<br/>[github.com](https://github.com/twitter/snowflake/) |
| 返回一定时间段内次数前 k 高的请求 | [ucsb.edu](https://icmi.cs.ucsb.edu/research/tech_reports/reports/2005-23.pdf)<br/>[wpi.edu](http://davis.wpi.edu/xmdv/docs/EDBT11-diyang.pdf) | | 返回一定时间段内次数前 k 高的请求 | [ucsb.edu](https://icmi.cs.ucsb.edu/research/tech_reports/reports/2005-23.pdf)<br/>[wpi.edu](http://davis.wpi.edu/xmdv/docs/EDBT11-diyang.pdf) |
| 设计一个数据源于多个数据中心的服务系统 | [highscalability.com](http://highscalability.com/blog/2009/8/24/how-google-serves-data-from-multiple-datacenters.html) | | 设计一个数据源于多个数据中心的服务系统 | [highscalability.com](http://highscalability.com/blog/2009/8/24/how-google-serves-data-from-multiple-datacenters.html) |
| 设计一个多人网络卡牌游戏 | [indieflashblog.com](http://www.indieflashblog.com/how-to-create-an-asynchronous-multiplayer-game.html)<br/>[buildnewgames.com](http://buildnewgames.com/real-time-multiplayer/) | | 设计一个多人网络卡牌游戏 | [indieflashblog.com](http://www.indieflashblog.com/how-to-create-an-asynchronous-multiplayer-game.html)<br/>[buildnewgames.com](http://buildnewgames.com/real-time-multiplayer/) |
| 设计一个垃圾回收系统 | [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) | | 设计一个垃圾回收系统 | [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) |
| 添加更多的系统设计问题 | [Contribute](#contributing) | | 添加更多的系统设计问题 | [Contribute](#contributing) |
### 真实的设计架构 ### 真实的设计架构
@ -1637,21 +1648,21 @@ Notes
| | | | | | | |
| Data store | **Bigtable** - Distributed column-oriented database from Google | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) | | Data store | **Bigtable** - Distributed column-oriented database from Google | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) |
| Data store | **HBase** - Open source implementation of Bigtable | [slideshare.net](http://www.slideshare.net/alexbaranau/intro-to-hbase) | | Data store | **HBase** - Open source implementation of Bigtable | [slideshare.net](http://www.slideshare.net/alexbaranau/intro-to-hbase) |
| Data store | **Cassandra** - Distributed column-oriented database from Facebook | [slideshare.net](http://www.slideshare.net/planetcassandra/cassandra-introduction-features-30103666) | | Data store | **Cassandra** - Distributed column-oriented database from Facebook | [slideshare.net](http://www.slideshare.net/planetcassandra/cassandra-introduction-features-30103666) |
| Data store | **DynamoDB** - Document-oriented database from Amazon | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf) | | Data store | **DynamoDB** - Document-oriented database from Amazon | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf) |
| Data store | **MongoDB** - Document-oriented database | [slideshare.net](http://www.slideshare.net/mdirolf/introduction-to-mongodb) | | Data store | **MongoDB** - Document-oriented database | [slideshare.net](http://www.slideshare.net/mdirolf/introduction-to-mongodb) |
| Data store | **Spanner** - Globally-distributed database from Google | [research.google.com](http://research.google.com/archive/spanner-osdi2012.pdf) | | Data store | **Spanner** - Globally-distributed database from Google | [research.google.com](http://research.google.com/archive/spanner-osdi2012.pdf) |
| Data store | **Memcached** - Distributed memory caching system | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) | | Data store | **Memcached** - Distributed memory caching system | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) |
| Data store | **Redis** - Distributed memory caching system with persistence and value types | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) | | 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 | **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](https://hadoop.apache.org/docs/r1.2.1/hdfs_design.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 | **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) | | Misc | **Dapper** - Distributed systems tracing infrastructure | [research.google.com](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36356.pdf) |
| Misc | **Kafka** - Pub/sub message queue from LinkedIn | [slideshare.net](http://www.slideshare.net/mumrah/kafka-talk-tri-hug) | | Misc | **Kafka** - Pub/sub message queue from LinkedIn | [slideshare.net](http://www.slideshare.net/mumrah/kafka-talk-tri-hug) |
| Misc | **Zookeeper** - Centralized infrastructure and services enabling synchronization | [slideshare.net](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) | | Misc | **Zookeeper** - Centralized infrastructure and services enabling synchronization | [slideshare.net](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) |
| | Add an architecture | [Contribute](#contributing) | | | Add an architecture | [Contribute](#contributing) |
### 公司的系统架构 ### 公司的系统架构