mirrors
/
system-design-primer
mirror of https://github.com/donnemartin/system-design-primer.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
system-design-primer/README-zh-Hant.md

123 KiB
Raw Blame History

The System Design Primer


Motivation

Learn how to design large scale systems.

Prep for the system design interview.

Learn how to design large scale systems

Learning how to design scalable systems will help you become a better engineer.

System design is a broad topic. There is a vast amount of resources scattered throughout the web on system design principles.

This repo is an organized collection of resources to help you learn how to build systems at scale.

Learn from the open source community

This is an early draft of a continually updated, open source project.

Contributions are welcome!

Prep for the system design interview

In addition to coding interviews, system design is a required component of the technical interview process at many tech companies.

Practice common system design interview questions and compare your results with sample solutions: discussions, code, and diagrams.

Additional topics for interview prep:

Anki flashcards


The provided Anki flashcard decks use spaced repetition to help you retain key system design concepts.

Great for use while on-the-go.

Contributing

Learn from the community.

Feel free to submit pull requests to help:

  • Fix errors
  • Improve sections
  • Add new sections

Content that needs some polishing is placed under development.

Review the Contributing Guidelines.

Translations

Interested in translating? Please see the following ticket.

Index of system design topics

Summaries of various system design topics, including pros and cons. Everything is a trade-off.

Each section contains links to more in-depth resources.


Study guide

Suggested topics to review based on your interview timeline (short, medium, long).

Imgur

Q: For interviews, do I need to know everything here?

A: No, you don't need to know everything here to prepare for the interview.

What you are asked in an interview depends on variables such as:

  • How much experience you have
  • What your technical background is
  • What positions you are interviewing for
  • Which companies you are interviewing with
  • Luck

More experienced candidates are generally expected to know more about system design. Architects or team leads might be expected to know more than individual contributors. Top tech companies are likely to have one or more design interview rounds.

Start broad and go deeper in a few areas. It helps to know a little about various key system design topics. Adjust the following guide based on your timeline, experience, what positions you are interviewing for, and which companies you are interviewing with.

  • Short timeline - Aim for breadth with system design topics. Practice by solving some interview questions.
  • Medium timeline - Aim for breadth and some depth with system design topics. Practice by solving many interview questions.
  • Long timeline - Aim for breadth and more depth with system design topics. Practice by solving most interview questions.
Short Medium Long
Read through the System design topics to get a broad understanding of how systems work 👍 👍 👍
Read through a few articles in the Company engineering blogs for the companies you are interviewing with 👍 👍 👍
Read through a few Real world architectures 👍 👍 👍
Review How to approach a system design interview question 👍 👍 👍
Work through System design interview questions with solutions Some Many Most
Work through Object-oriented design interview questions with solutions Some Many Most
Review Additional system design interview questions Some Many Most

How to approach a system design interview question

How to tackle a system design interview question.

The system design interview is an open-ended conversation. You are expected to lead it.

You can use the following steps to guide the discussion. To help solidify this process, work through the System design interview questions with solutions section using the following steps.

Step 1: Outline use cases, constraints, and assumptions

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

  • Who is going to use it?
  • How are they going to use it?
  • How many users are there?
  • What does the system do?
  • What are the inputs and outputs of the system?
  • How much data do we expect to handle?
  • How many requests per second do we expect?
  • What is the expected read to write ratio?

Step 2: Create a high level design

Outline a high level design with all important components.

  • Sketch the main components and connections
  • Justify your ideas

Step 3: Design core components

Dive into details for each core component. For example, if you were asked to design a url shortening service, discuss:

  • Generating and storing a hash of the full url
    • MD5 and Base62
    • Hash collisions
    • SQL or NoSQL
    • Database schema
  • Translating a hashed url to the full url
    • Database lookup
  • API and object-oriented design

Step 4: Scale the design

Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues?

  • Load balancer
  • Horizontal scaling
  • Caching
  • Database sharding

Discuss potential solutions and trade-offs. Everything is a trade-off. Address bottlenecks using principles of scalable system design.

Back-of-the-envelope calculations

You might be asked to do some estimates by hand. Refer to the Appendix for the following resources:

Source(s) and further reading

Check out the following links to get a better idea of what to expect:

System design interview questions with solutions

Common system design interview questions with sample discussions, code, and diagrams.

Solutions linked to content in the solutions/ folder.

Question
Design Pastebin.com (or Bit.ly) Solution
Design the Twitter timeline (or Facebook feed)
Design Twitter search (or Facebook search)		 Solution
Design a web crawler Solution
Design Mint.com Solution
Design the data structures for a social network Solution
Design a key-value store for a search engine Solution
Design Amazon's sales ranking by category feature Solution
Design a system that scales to millions of users on AWS Solution
Add a system design question Contribute

Design Pastebin.com (or Bit.ly)

View exercise and solution

Imgur

Design the Twitter timeline and search (or Facebook feed and search)

View exercise and solution

Imgur

Design a web crawler

View exercise and solution

Imgur

Design Mint.com

View exercise and solution

Imgur

Design the data structures for a social network

View exercise and solution

Imgur

Design a key-value store for a search engine

View exercise and solution

Imgur

Design Amazon's sales ranking by category feature

View exercise and solution

Imgur

Design a system that scales to millions of users on AWS

View exercise and solution

Imgur

Object-oriented design interview questions with solutions

Common object-oriented design interview questions with sample discussions, code, and diagrams.

Solutions linked to content in the solutions/ folder.

Note: This section is under development

Question
Design a hash map Solution
Design a least recently used cache Solution
Design a call center Solution
Design a deck of cards Solution
Design a parking lot Solution
Design a chat server Solution
Design a circular array Contribute
Add an object-oriented design question Contribute

System design topics: start here

New to system design?

First, you'll need a basic understanding of common principles, learning about what they are, how they are used, and their pros and cons.

Step 1: Review the scalability video lecture

Scalability Lecture at Harvard

  • Topics covered:
    • Vertical scaling
    • Horizontal scaling
    • Caching
    • Load balancing
    • Database replication
    • Database partitioning

Step 2: Review the scalability article

Scalability

Next steps

Next, we'll look at high-level trade-offs:

  • Performance vs scalability
  • Latency vs throughput
  • Availability vs consistency

Keep in mind that everything is a trade-off.

Then we'll dive into more specific topics such as DNS, CDNs, and load balancers.

Performance vs scalability

A service is scalable if it results in increased performance in a manner proportional to resources added. Generally, increasing performance means serving more units of work, but it can also be to handle larger units of work, such as when datasets grow.1

Another way to look at performance vs scalability:

  • If you have a performance problem, your system is slow for a single user.
  • If you have a scalability problem, your system is fast for a single user but slow under heavy load.

Source(s) and further reading

Latency vs throughput

Latency is the time to perform some action or to produce some result.

Throughput is the number of such actions or results per unit of time.

Generally, you should aim for maximal throughput with acceptable latency.

Source(s) and further reading

Availability vs consistency

CAP theorem


Source: CAP theorem revisited

In a distributed computer system, you can only support two of the following guarantees:

  • Consistency - Every read receives the most recent write or an error
  • Availability - Every request receives a response, without guarantee that it contains the most recent version of the information
  • Partition Tolerance - The system continues to operate despite arbitrary partitioning due to network failures

Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability.

CP - consistency and partition tolerance

Waiting for a response from the partitioned node might result in a timeout error. CP is a good choice if your business needs require atomic reads and writes.

AP - availability and partition tolerance

Responses return the most recent version of the data, which might not be the latest. Writes might take some time to propagate when the partition is resolved.

AP is a good choice if the business needs allow for eventual consistency or when the system needs to continue working despite external errors.

Source(s) and further reading

Consistency patterns

With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the CAP theorem - Every read receives the most recent write or an error.

Weak consistency

After a write, reads may or may not see it. A best effort approach is taken.

This approach is seen in systems such as memcached. Weak consistency works well in real time use cases such as VoIP, video chat, and realtime multiplayer games. For example, if you are on a phone call and lose reception for a few seconds, when you regain connection you do not hear what was spoken during connection loss.

Eventual consistency

After a write, reads will eventually see it (typically within milliseconds). Data is replicated asynchronously.

This approach is seen in systems such as DNS and email. Eventual consistency works well in highly available systems.

Strong consistency

After a write, reads will see it. Data is replicated synchronously.

This approach is seen in file systems and RDBMSes. Strong consistency works well in systems that need transactions.

Source(s) and further reading

Availability patterns

There are two main patterns to support high availability: fail-over and replication.

Fail-over

Active-passive

With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active's IP address and resumes service.

The length of downtime is determined by whether the passive server is already running in 'hot' standby or whether it needs to start up from 'cold' standby. Only the active server handles traffic.

Active-passive failover can also be referred to as master-slave failover.

Active-active

In active-active, both servers are managing traffic, spreading the load between them.

If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers.

Active-active failover can also be referred to as master-master failover.

Disadvantage(s): failover

  • Fail-over adds more hardware and additional complexity.
  • There is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive.

Replication

Master-slave and master-master

This topic is further discussed in the Database section:

Domain name system


Source: DNS security presentation

A Domain Name System (DNS) translates a domain name such as www.example.com to an IP address.

DNS is hierarchical, with a few authoritative servers at the top level. Your router or ISP provides information about which DNS server(s) to contact when doing a lookup. Lower level DNS servers cache mappings, which could become stale due to DNS propagation delays. DNS results can also be cached by your browser or OS for a certain period of time, determined by the time to live (TTL).

  • NS record (name server) - Specifies the DNS servers for your domain/subdomain.
  • MX record (mail exchange) - Specifies the mail servers for accepting messages.
  • A record (address) - Points a name to an IP address.
  • CNAME (canonical) - Points a name to another name or CNAME (example.com to www.example.com) or to an A record.

Services such as CloudFlare and Route 53 provide managed DNS services. Some DNS services can route traffic through various methods:

  • Weighted round robin
    • Prevent traffic from going to servers under maintenance
    • Balance between varying cluster sizes
    • A/B testing
  • Latency-based
  • Geolocation-based

Disadvantage(s): DNS

  • Accessing a DNS server introduces a slight delay, although mitigated by caching described above.
  • DNS server management could be complex, although they are generally managed by governments, ISPs, and large companies.
  • DNS services have recently come under DDoS attack, preventing users from accessing websites such as Twitter without knowing Twitter's IP address(es).

Source(s) and further reading

Content delivery network


Source: Why use a CDN

A content delivery network (CDN) is a globally distributed network of proxy servers, serving content from locations closer to the user. Generally, static files such as HTML/CSS/JS, photos, and videos are served from CDN, although some CDNs such as Amazon's CloudFront support dynamic content. The site's DNS resolution will tell clients which server to contact.

Serving content from CDNs can significantly improve performance in two ways:

  • Users receive content at data centers close to them
  • Your servers do not have to serve requests that the CDN fulfills

Push CDNs

Push CDNs receive new content whenever changes occur on your server. You take full responsibility for providing content, uploading directly to the CDN and rewriting URLs to point to the CDN. You can configure when content expires and when it is updated. Content is uploaded only when it is new or changed, minimizing traffic, but maximizing storage.

Sites with a small amount of traffic or sites with content that isn't often updated work well with push CDNs. Content is placed on the CDNs once, instead of being re-pulled at regular intervals.

Pull CDNs

Pull CDNs grab new content from your server when the first user requests the content. You leave the content on your server and rewrite URLs to point to the CDN. This results in a slower request until the content is cached on the server.

A time-to-live (TTL) determines how long content is cached. Pull CDNs minimize storage space on the CDN, but can create redundant traffic if files expire and are pulled before they have actually changed.

Sites with heavy traffic work well with pull CDNs, as traffic is spread out more evenly with only recently-requested content remaining on the CDN.

Disadvantage(s): CDN

  • CDN costs could be significant depending on traffic, although this should be weighed with additional costs you would incur not using a CDN.
  • Content might be stale if it is updated before the TTL expires it.
  • CDNs require changing URLs for static content to point to the CDN.

Source(s) and further reading

Load balancer


Source: Scalable system design patterns

Load balancers distribute incoming client requests to computing resources such as application servers and databases. In each case, the load balancer returns the response from the computing resource to the appropriate client. Load balancers are effective at:

  • Preventing requests from going to unhealthy servers
  • Preventing overloading resources
  • Helping eliminate single points of failure

Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.

Additional benefits include:

  • SSL termination - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
  • Session persistence - Issue cookies and route a specific client's requests to same instance if the web apps do not keep track of sessions

To protect against failures, it's common to set up multiple load balancers, either in active-passive or active-active mode.

Load balancers can route traffic based on various metrics, including:

Layer 4 load balancing

Layer 4 load balancers look at info at the transport layer to decide how to distribute requests. Generally, this involves the source, destination IP addresses, and ports in the header, but not the contents of the packet. Layer 4 load balancers forward network packets to and from the upstream server, performing Network Address Translation (NAT).

layer 7 load balancing

Layer 7 load balancers look at the application layer to decide how to distribute requests. This can involve contents of the header, message, and cookies. Layer 7 load balancers terminates network traffic, reads the message, makes a load-balancing decision, then opens a connection to the selected server. For example, a layer 7 load balancer can direct video traffic to servers that host videos while directing more sensitive user billing traffic to security-hardened servers.

At the cost of flexibility, layer 4 load balancing requires less time and computing resources than Layer 7, although the performance impact can be minimal on modern commodity hardware.

Horizontal scaling

Load balancers can also help with horizontal scaling, improving performance and availability. Scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware, called Vertical Scaling. It is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems.

Disadvantage(s): horizontal scaling

  • Scaling horizontally introduces complexity and involves cloning servers
    • Servers should be stateless: they should not contain any user-related data like sessions or profile pictures
    • Sessions can be stored in a centralized data store such as a database (SQL, NoSQL) or a persistent cache (Redis, Memcached)
  • Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out

Disadvantage(s): load balancer

  • The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly.
  • Introducing a load balancer to help eliminate single points of failure results in increased complexity.
  • A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.

Source(s) and further reading

Reverse proxy (web server)


Source: Wikipedia

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.

Additional benefits include:

  • Increased security - Hide information about backend servers, blacklist IPs, limit number of connections per client
  • Increased scalability and flexibility - Clients only see the reverse proxy's IP, allowing you to scale servers or change their configuration
  • SSL termination - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
  • Compression - Compress server responses
  • Caching - Return the response for cached requests
  • Static content - Serve static content directly
    • HTML/CSS/JS
    • Photos
    • Videos
    • Etc

反向代理（web 服务器）

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。

带来的好处包括：

  • 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。
  • 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。
  • 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。
    • 免除了在每个服务器上安装 X.509 证书的需要
  • 压缩 - 压缩服务器响应
  • 缓存 - 直接返回命中的缓存结果
  • 静态内容 - 直接提供静态内容
    • HTML/CSS/JS
    • 图片
    • 视频
    • 等等

Load balancer vs reverse proxy

  • 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.
  • Reverse proxies can be useful even with just one web server or application server, opening up the benefits described in the previous section.
  • Solutions such as NGINX and HAProxy can support both layer 7 reverse proxying and load balancing.

Disadvantage(s): reverse proxy

  • Introducing a reverse proxy results in increased complexity.
  • A single reverse proxy is a single point of failure, configuring multiple reverse proxies (ie a failover) further increases complexity.

Source(s) and further reading

负载均衡器 VS 反向代理

  • 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。
  • 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。
  • 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。

不利之处：反向代理

  • 引入反向代理会增加系统的复杂度。
  • 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）会进一步增加复杂度。

来源及延伸阅读

Application layer


Source: Intro to architecting systems for scale

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.

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.

Workers in the application layer also help enable asynchronism.

Microservices

Related to this discussion are 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. 1

Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc.

Service Discovery

Systems such as Zookeeper can help services find each other by keeping track of registered names, addresses, ports, etc.

Disadvantage(s): application layer

  • Adding an application layer with loosely coupled services requires a different approach from an architectural, operations, and process viewpoint (vs a monolithic system).
  • Microservices can add complexity in terms of deployments and operations.

Source(s) and further reading

应用层

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。

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。

应用层中的工作进程也有可以实现 异步化。

微服务

与此讨论相关的话题是 微服务，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进程中，通过明确定义的轻量级机制通讯，共同实现业务目标。1

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。

服务发现

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。

不利之处：应用层

  • 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）。
  • 微服务会增加部署和运营的复杂度。

来源及延伸阅读

Database


Source: Scaling up to your first 10 million users

Relational database management system (RDBMS)

A relational database like SQL is a collection of data items organized in tables.

ACID is a set of properties of relational database transactions.

  • Atomicity - Each transaction is all or nothing
  • 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.

数据库

关系型数据库管理系统（RDBMS）

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。

校对注：这里是否是作者笔误，SQL 并不是一种数据库？

ACID 用来描述关系型数据库事务的特性。

  • 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 - 事务使数据库从一个一致的状态转换到另一个一致状态。
  • 隔离性 - 并发执行事务的结果与顺序执行执行的结果相同。
  • 持久性 - 事务提交后，对系统的影响是永久的。

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。

Master-slave replication

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.


Source: Scalability, availability, stability, patterns

Disadvantage(s): master-slave replication
  • Additional logic is needed to promote a slave to a master.
  • See Disadvantage(s): replication for points related to both master-slave and master-master.

主从复制

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。

不利之处：主从复制
  • 将从库提升为主库需要额外的逻辑。
  • 参考不利之处：复制中，主从复制和主主复制共同的问题。

Master-master replication

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.


Source: Scalability, availability, stability, patterns

Disadvantage(s): master-master replication
  • You'll need a load balancer or you'll need to make changes to your application logic to determine where to write.
  • 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 for points related to both master-slave and master-master.
Disadvantage(s): replication
  • There is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes.
  • 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.
  • The more read slaves, the more you have to replicate, which leads to greater replication lag.
  • 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.
  • Replication adds more hardware and additional complexity.
Source(s) and further reading: replication

主主复制

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。

不利之处： 主主复制
  • 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。
  • 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。
  • 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。
  • 参考不利之处：复制中，主从复制和主主复制共同的问题。
不利之处：复制
  • 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进程并行写入，但读取副本只支持单进程顺序地写入。
  • 复制意味着更多的硬件和额外的复杂度。
来源及延伸阅读

Federation


Source: Scaling up to your first 10 million users

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.

Disadvantage(s): federation
  • 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.
  • Federation adds more hardware and additional complexity.
Source(s) and further reading: federation

联合

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。

不利之处：联合
  • 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 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。
来源及延伸阅读：联合
  • 扩展你的用户数到第一个千万

Sharding


Source: Scalability, availability, stability, patterns

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.

Similar to the advantages of 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.

Common ways to shard a table of users is either through the user's last name initial or the user's geographic location.

Disadvantage(s): sharding
  • You'll need to update your application logic to work with shards, which could result in complex SQL queries.
  • 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.
    • Rebalancing adds additional complexity. A sharding function based on consistent hashing can reduce the amount of transferred data.
  • Joining data from multiple shards is more complex.
  • Sharding adds more hardware and additional complexity.
Source(s) and further reading: sharding

分片

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。

类似联合的优点，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。

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。

不利之处：分片
  • 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。
  • 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。
    • 再平衡会引入额外的复杂度。基于一致性哈希的分片算法可以减少这种情况。
  • 联结多个分片的数据操作更复杂。
  • 分片需要更多的硬件和额外的复杂度。

来源及延伸阅读：分片

Denormalization

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 and Oracle support materialized views which handle the work of storing redundant information and keeping redundant copies consistent.

Once data becomes distributed with techniques such as federation and sharding, managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins.

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.

Disadvantage(s): denormalization
  • Data is duplicated.
  • Constraints can help redundant copies of information stay in sync, which increases complexity of the database design.
  • A denormalized database under heavy write load might perform worse than its normalized counterpart.
Source(s) and further reading: denormalization

非规范化

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 和 Oracle 支持物化视图，可以处理冗余信息存储和保证冗余副本一致。

当数据使用诸如联合和分片等技术被分割，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。

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。

不利之处：非规范化
  • 数据会冗余。
  • 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。
  • 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。
来源及延伸阅读：非规范化

SQL tuning

SQL tuning is a broad topic and many books have been written as reference.

It's important to benchmark and profile to simulate and uncover bottlenecks.

  • Benchmark - Simulate high-load situations with tools such as ab.
  • Profile - Enable tools such as the slow query log to help track performance issues.

Benchmarking and profiling might point you to the following optimizations.

Tighten up the schema
  • MySQL dumps to disk in contiguous blocks for fast access.
  • Use CHAR instead of VARCHAR for fixed-length fields.
    • CHAR effectively allows for fast, random access, whereas with VARCHAR, you must find the end of a string before moving onto the next one.
  • 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.
  • Use INT for larger numbers up to 2^32 or 4 billion.
  • Use DECIMAL for currency to avoid floating point representation errors.
  • Avoid storing large BLOBS, store the location of where to get the object instead.
  • 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.
  • Set the NOT NULL constraint where applicable to improve search performance
Use good indices
  • Columns that you are querying (SELECT, GROUP BY, ORDER BY, JOIN) could be faster with indices.
  • Indices are usually represented as self-balancing B-tree that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time.
  • Placing an index can keep the data in memory, requiring more space.
  • Writes could also be slower since the index also needs to be updated.
  • When loading large amounts of data, it might be faster to disable indices, load the data, then rebuild the indices.
Avoid expensive joins
Partition tables
  • Break up a table by putting hot spots in a separate table to help keep it in memory.
Tune the query cache
Source(s) and further reading: SQL tuning

SQL 调优

SQL 调优是一个范围很广的话题，有很多相关的书可以作为参考。

利用基准测试和性能分析来模拟和发现系统瓶颈很重要。

  • 基准测试 - 用 ab 等工具模拟高负载情况。
  • 性能分析 - 通过启用如慢查询日志等工具来辅助追踪性能问题。

基准测试和性能分析可能会指引你到以下优化方案。

改进模式
  • 为了实现快速访问，MySQL 在磁盘上用连续的块存储数据。
  • 使用 CHAR 类型存储固定长度的字段，不要用 VARCHAR。
    • 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。
  • 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。
  • 使用 INT 类型存储高达 2^32 或 40 亿的较大数字。
  • 使用 DECIMAL 类型存储货币可以避免浮点数表示错误。
  • 避免使用 BLOBS 存储对象，存储存放对象的位置。
  • 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。
  • 在适用场景中设置 NOT NULL 约束来提高搜索性能。
使用正确的索引
  • 你正查询（SELECT、GROUP BY、ORDER BY、JOIN）的列如果用了索引会更快。
  • 索引通常表示为自平衡的 B 树，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。
  • 设置索引，会将数据存在内存中，占用了更多内存空间。
  • 写入操作会变慢，因为索引需要被更新。
  • 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。
避免高成本的联结操作
  • 有性能需要，可以进行非规范化。
分割数据表
  • 将热点数据拆分到单独的数据表中，可以有助于缓存。
调优查询缓存
来源及延伸阅读

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.

BASE is often used to describe the properties of NoSQL databases. In comparison with the CAP Theorem, BASE chooses availability over consistency.

  • Basically available - the system guarantees availability.
  • Soft state - the state of the system may change over time, even without input.
  • Eventual consistency - the system will become consistent over a period of time, given that the system doesn't receive input during that period.

In addition to choosing between 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.

NoSQL

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。

BASE 通常被用于描述 NoSQL 数据库的特性。相比 CAP 定理，BASE 强调可用性超过一致性。

  • 基本可用 - 系统保证可用性。
  • 软状态 - 即使没有输入，系统状态也可能随着时间变化。
  • 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。

除了在 SQL 还是 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

Abstraction: hash table

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, allowing efficient retrieval of key ranges. Key-value stores can allow for storing of metadata with a value.

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.

A key-value store is the basis for more complex systems such as a document store, and in some cases, a graph database.

Source(s) and further reading: key-value store

键-值存储

抽象模型：哈希表

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。

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。

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。

来源及延伸阅读

Document store

Abstraction: key-value store with documents stored as values

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.

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.

Some document stores like MongoDB and CouchDB also provide a SQL-like language to perform complex queries. DynamoDB supports both key-values and documents.

Document stores provide high flexibility and are often used for working with occasionally changing data.

Source(s) and further reading: document store

文档类型存储

抽象模型：将文档作为值的键-值存储

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。

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。

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。

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。

来源及延伸阅读：文档类型存储

Wide column store


Source: SQL & NoSQL, a brief history

Abstraction: nested map ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>>

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.

Google introduced Bigtable as the first wide column store, which influenced the open-source HBase often-used in the Hadoop ecosystem, and Cassandra from Facebook. Stores such as BigTable, HBase, and Cassandra maintain keys in lexicographic order, allowing efficient retrieval of selective key ranges.

Wide column stores offer high availability and high scalability. They are often used for very large data sets.

Source(s) and further reading: wide column store

列型存储

抽象模型：嵌套的 ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>> 映射

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 发布了第一个列型存储数据库 Bigtable，它影响了 Hadoop 生态系统中活跃的开源数据库 HBase 和 Facebook 的 Cassandra。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。

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。

来源及延伸阅读：列型存储

Graph database


Source: Graph database

Abstraction: graph

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.

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.

Source(s) and further reading: graph

图数据库

抽象模型： 图

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。

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 访问。

来源及延伸阅读：图数据库

Source(s) and further reading: NoSQL

来源及延伸阅读：NoSQL

SQL or NoSQL


Source: Transitioning from RDBMS to NoSQL

Reasons for SQL:

  • Structured data
  • Strict schema
  • Relational data
  • Need for complex joins
  • Transactions
  • Clear patterns for scaling
  • More established: developers, community, code, tools, etc
  • Lookups by index are very fast

Reasons for NoSQL:

  • Semi-structured data
  • Dynamic or flexible schema
  • Non relational data
  • No need for complex joins
  • Store many TB (or PB) of data
  • Very data intensive workload
  • Very high throughput for IOPS

Sample data well-suited for NoSQL:

  • Rapid ingest of clickstream and log data
  • Leaderboard or scoring data
  • Temporary data, such as a shopping cart
  • Frequently accessed ('hot') tables
  • Metadata/lookup tables
Source(s) and further reading: SQL or NoSQL

SQL 还是 NoSQL

选取 SQL:

  • 结构化数据
  • 严格的模式
  • 关系型数据
  • 需要复杂的联结操作
  • 事务
  • 清晰的扩展模式
  • 既有资源更丰富：开发者、社区、代码库、工具等
  • 通过索引进行查询非常快

选取 NoSQL：

  • 半结构化数据
  • 动态或灵活的模式
  • 非关系数型据
  • 不需要复杂的联结操作
  • 存储 TB （甚至 PB）级别的数据
  • 高数据密集的工作负载
  • IOPS 吞吐量非常高

适合 NoSQL 的示例数据：

  • 快速获取点击流和日志数据
  • 排行榜或者得分数据
  • 临时数据，如购物车
  • 频繁访问的（“热”）表
  • 元数据／查找表

来源及延伸阅读：SQL 或 NoSQL

Cache


Source: Scalable system design patterns

Caching improves page load times and can reduce the load on your servers and databases. In this model, the dispatcher will first lookup if the request has been made before and try to find the previous result to return, in order to save the actual execution.

Databases often benefit from a uniform distribution of reads and writes across its partitions. Popular items can skew the distribution, causing bottlenecks. Putting a cache in front of a database can help absorb uneven loads and spikes in traffic.

Client caching

Caches can be located on the client side (OS or browser), server side, or in a distinct cache layer.

CDN caching

CDNs are considered a type of cache.

Web server caching

Reverse proxies and caches such as Varnish can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers.

Database caching

Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance.

Application caching

In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so cache invalidation algorithms such as least recently used (LRU) can help invalidate 'cold' entries and keep 'hot' data in RAM.

Redis has the following additional features:

  • Persistence option
  • Built-in data structures such as sorted sets and lists

There are multiple levels you can cache that fall into two general categories: database queries and objects:

  • Row level
  • Query-level
  • Fully-formed serializable objects
  • Fully-rendered HTML

Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult.

Caching at the database query level

Whenever you query the database, hash the query as a key and store the result to the cache. This approach suffers from expiration issues:

  • Hard to delete a cached result with complex queries
  • If one piece of data changes such as a table cell, you need to delete all cached queries that might include the changed cell

Caching at the object level

See your data as an object, similar to what you do with your application code. Have your application assemble the dataset from the database into a class instance or a data structure(s):

  • Remove the object from cache if its underlying data has changed
  • Allows for asynchronous processing: workers assemble objects by consuming the latest cached object

Suggestions of what to cache:

  • User sessions
  • Fully rendered web pages
  • Activity streams
  • User graph data

When to update the cache

Since you can only store a limited amount of data in cache, you'll need to determine which cache update strategy works best for your use case.

Cache-aside


Source: From cache to in-memory data grid

The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following:

  • Look for entry in cache, resulting in a cache miss
  • Load entry from the database
  • Add entry to cache
  • Return entry
def get_user(self, user_id):
    user = cache.get("user.{0}", user_id)
    if user is None:
        user = db.query("SELECT * FROM users WHERE user_id = {0}", user_id)
        if user is not None:
            key = "user.{0}".format(user_id)
            cache.set(key, json.dumps(user))
    return user

Memcached is generally used in this manner.

Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested.

Disadvantage(s): cache-aside
  • Each cache miss results in three trips, which can cause a noticeable delay.
  • Data can become stale if it is updated in the database. This issue is mitigated by setting a time-to-live (TTL) which forces an update of the cache entry, or by using write-through.
  • When a node fails, it is replaced by a new, empty node, increasing latency.

Write-through


Source: Scalability, availability, stability, patterns

The application uses the cache as the main data store, reading and writing data to it, while the cache is responsible for reading and writing to the database:

  • Application adds/updates entry in cache
  • Cache synchronously writes entry to data store
  • Return

Application code:

set_user(12345, {"foo":"bar"})

Cache code:

def set_user(user_id, values):
    user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
    cache.set(user_id, user)

Write-through is a slow overall operation due to the write operation, but subsequent reads of just written data are fast. Users are generally more tolerant of latency when updating data than reading data. Data in the cache is not stale.

Disadvantage(s): write through
  • When a new node is created due to failure or scaling, the new node will not cache entries until the entry is updated in the database. Cache-aside in conjunction with write through can mitigate this issue.
  • Most data written might never read, which can be minimized with a TTL.

Write-behind (write-back)


Source: Scalability, availability, stability, patterns

In write-behind, tha application does the following:

  • Add/update entry in cache
  • Asynchronously write entry to the data store, improving write performance
Disadvantage(s): write-behind
  • There could be data loss if the cache goes down prior to its contents hitting the data store.
  • It is more complex to implement write-behind than it is to implement cache-aside or write-through.

Refresh-ahead


Source: From cache to in-memory data grid

You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration.

Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future.

Disadvantage(s): refresh-ahead
  • Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead.

Disadvantage(s): cache

  • Need to maintain consistency between caches and the source of truth such as the database through cache invalidation.
  • Need to make application changes such as adding Redis or memcached.
  • Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.

Source(s) and further reading

Asynchronism


Source: Intro to architecting systems for scale

Asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in-line. They can also help by doing time-consuming work in advance, such as periodic aggregation of data.

Message queues

Message queues receive, hold, and deliver messages. If an operation is too slow to perform inline, you can use a message queue with the following workflow:

  • An application publishes a job to the queue, then notifies the user of job status
  • A worker picks up the job from the queue, processes it, then signals the job is complete

The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers.

Redis is useful as a simple message broker but messages can be lost.

RabbitMQ is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.

Amazon SQS, is hosted but can have high latency and has the possibility of messages being delivered twice.

Task queues

Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background.

Celery has support for scheduling and primarily has python support.

Back pressure

If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance. Back pressure can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue. Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later. Clients can retry the request at a later time, perhaps with exponential backoff.

Disadvantage(s): asynchronism

  • Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.

Source(s) and further reading

Communication


Source: OSI 7 layer model

Hypertext transfer protocol (HTTP)

HTTP is a method for encoding and transporting data between a client and a server. It is a request/response protocol: clients issue requests and servers issue responses with relevant content and completion status info about the request. HTTP is self-contained, allowing requests and responses to flow through many intermediate routers and servers that perform load balancing, caching, encryption, and compression.

A basic HTTP request consists of a verb (method) and a resource (endpoint). Below are common HTTP verbs:

Verb Description Idempotent* Safe Cacheable
GET Reads a resource Yes Yes Yes
POST Creates a resource or trigger a process that handles data No No Yes if response contains freshness info
PUT Creates or replace a resource Yes No No
PATCH Partially updates a resource No No Yes if response contains freshness info
DELETE Deletes a resource Yes No No

*Can be called many times without different outcomes.

HTTP is an application layer protocol relying on lower-level protocols such as TCP and UDP.

Transmission control protocol (TCP)


Source: How to make a multiplayer game

TCP is a connection-oriented protocol over an IP network. Connection is established and terminated using a handshake. All packets sent are guaranteed to reach the destination in the original order and without corruption through:

If the sender does not receive a correct response, it will resend the packets. If there are multiple timeouts, the connection is dropped. TCP also implements flow control and congestion control. These guarantees cause delays and generally results in less efficient transmission than UDP.

To ensure high throughput, web servers can keep a large number of TCP connections open, resulting in high memory usage. It can be expensive to have a large number of open connections between web server threads and say, a memcached server. Connection pooling can help in addition to switching to UDP where applicable.

TCP is useful for applications that require high reliability but are less time critical. Some examples include web servers, database info, SMTP, FTP, and SSH.

Use TCP over UDP when:

  • You need all of the data to arrive intact
  • You want to automatically make a best estimate use of the network throughput

User datagram protocol (UDP)


Source: How to make a multiplayer game

UDP is connectionless. Datagrams (analogous to packets) are guaranteed only at the datagram level. Datagrams might reach their destination out of order or not at all. UDP does not support congestion control. Without the guarantees that TCP support, UDP is generally more efficient.

UDP can broadcast, sending datagrams to all devices on the subnet. This is useful with DHCP because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address.

UDP is less reliable but works well in real time use cases such as VoIP, video chat, streaming, and realtime multiplayer games.

Use UDP over TCP when:

  • You need the lowest latency
  • Late data is worse than loss of data
  • You want to implement your own error correction

Source(s) and further reading: TCP and UDP

Remote procedure call (RPC)


Source: Crack the system design interview

In an RPC, a client causes a procedure to execute on a different address space, usually a remote server. The procedure is coded as if it were a local procedure call, abstracting away the details of how to communicate with the server from the client program. Remote calls are usually slower and less reliable than local calls so it is helpful to distinguish RPC calls from local calls. Popular RPC frameworks include Protobuf, Thrift, and Avro.

RPC is a request-response protocol:

  • Client program - Calls the client stub procedure. The parameters are pushed onto the stack like a local procedure call.
  • Client stub procedure - Marshals (packs) procedure id and arguments into a request message.
  • Client communication module - OS sends the message from the client to the server.
  • Server communication module - OS passes the incoming packets to the server stub procedure.
  • Server stub procedure - Unmarshalls the results, calls the server procedure matching the procedure id and passes the given arguments.
  • The server response repeats the steps above in reverse order.

Sample RPC calls:

GET /someoperation?data=anId

POST /anotheroperation
{
  "data":"anId";
  "anotherdata": "another value"
}

RPC is focused on exposing behaviors. RPCs are often used for performance reasons with internal communications, as you can hand-craft native calls to better fit your use cases.

Choose a Native Library aka SDK when:

  • You know your target platform.
  • You want to control how your "logic" is accessed
  • You want to control how error control happens off your library
  • Performance and end user experience is your primary concern

HTTP APIs following REST tend to be used more often for public APIs.

Disadvantage(s): RPC

  • RPC clients become tightly coupled to the service implementation.
  • A new API must be defined for every new operation or use case.
  • It can be difficult to debug RPC.
  • You might not be able to leverage existing technologies out of the box. For example, it might require additional effort to ensure RPC calls are properly cached on caching servers such as Squid.

Representational state transfer (REST)

REST is an architectural style enforcing a client/server model where the client acts on a set of resources managed by the server. The server provides a representation of resources and actions that can either manipulate or get a new representation of resources. All communication must be stateless and cacheable.

There are four qualities of a RESTful interface:

  • Identify resources (URI in HTTP) - use the same URI regardless of any operation.
  • Change with representations (Verbs in HTTP) - use verbs, headers, and body.
  • Self-descriptive error message (status response in HTTP) - Use status codes, don't reinvent the wheel.
  • HATEOAS (HTML interface for HTTP) - your web service should be fully accessible in a browser.

Sample REST calls:

GET /someresources/anId

PUT /someresources/anId
{"anotherdata": "another value"}

REST is focused on exposing data. It minimizes the coupling between client/server and is often used for public HTTP APIs. REST uses a more generic and uniform method of exposing resources through URIs, representation through headers, and actions through verbs such as GET, POST, PUT, DELETE, and PATCH. Being stateless, REST is great for horizontal scaling and partitioning.

Disadvantage(s): REST

  • With REST being focused on exposing data, it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy. For example, returning all updated records from the past hour matching a particular set of events is not easily expressed as a path. With REST, it is likely to be implemented with a combination of URI path, query parameters, and possibly the request body.
  • REST typically relies on a few verbs (GET, POST, PUT, DELETE, and PATCH) which sometimes doesn't fit your use case. For example, moving expired documents to the archive folder might not cleanly fit within these verbs.
  • Fetching complicated resources with nested hierarchies requires multiple round trips between the client and server to render single views, e.g. fetching content of a blog entry and the comments on that entry. For mobile applications operating in variable network conditions, these multiple roundtrips are highly undesirable.
  • Over time, more fields might be added to an API response and older clients will receive all new data fields, even those that they do not need, as a result, it bloats the payload size and leads to larger latencies.

RPC and REST calls comparison

Operation RPC REST
Signup POST /signup POST /persons
Resign POST /resign
{
"personid": "1234"
}		 DELETE /persons/1234
Read a person GET /readPerson?personid=1234 GET /persons/1234
Read a person’s items list GET /readUsersItemsList?personid=1234 GET /persons/1234/items
Add an item to a person’s items POST /addItemToUsersItemsList
{
"personid": "1234";
"itemid": "456"
}		 POST /persons/1234/items
{
"itemid": "456"
}
Update an item POST /modifyItem
{
"itemid": "456";
"key": "value"
}		 PUT /items/456
{
"key": "value"
}
Delete an item POST /removeItem
{
"itemid": "456"
}		 DELETE /items/456

Source: Do you really know why you prefer REST over RPC

Source(s) and further reading: REST and RPC

Security

This section could use some updates. Consider contributing!

Security is a broad topic. Unless you have considerable experience, a security background, or are applying for a position that requires knowledge of security, you probably won't need to know more than the basics:

  • Encrypt in transit and at rest.
  • Sanitize all user inputs or any input parameters exposed to user to prevent XSS and SQL injection.
  • Use parameterized queries to prevent SQL injection.
  • Use the principle of least privilege.

Source(s) and further reading

Appendix

You'll sometimes be asked to do 'back-of-the-envelope' estimates. For example, you might need to determine how long it will take to generate 100 image thumbnails from disk or how much memory a data structure will take. The Powers of two table and Latency numbers every programmer should know are handy references.

Powers of two table

Power           Exact Value         Approx Value        Bytes
---------------------------------------------------------------
7                             128
8                             256
10                           1024   1 thousand           1 KB
16                         65,536                       64 KB
20                      1,048,576   1 million            1 MB
30                  1,073,741,824   1 billion            1 GB
32                  4,294,967,296                        4 GB
40              1,099,511,627,776   1 trillion           1 TB

Source(s) and further reading

Latency numbers every programmer should know

Latency Comparison Numbers
--------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                          100   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy            10,000   ns       10 us
Send 1 KB bytes over 1 Gbps network     10,000   ns       10 us
Read 4 KB randomly from SSD*           150,000   ns      150 us          ~1GB/sec SSD
Read 1 MB sequentially from memory     250,000   ns      250 us
Round trip within same datacenter      500,000   ns      500 us
Read 1 MB sequentially from SSD*     1,000,000   ns    1,000 us    1 ms  ~1GB/sec SSD, 4X memory
Disk seek                           10,000,000   ns   10,000 us   10 ms  20x datacenter roundtrip
Read 1 MB sequentially from 1 Gbps  10,000,000   ns   10,000 us   10 ms  40x memory, 10X SSD
Read 1 MB sequentially from disk    30,000,000   ns   30,000 us   30 ms 120x memory, 30X SSD
Send packet CA->Netherlands->CA    150,000,000   ns  150,000 us  150 ms

Notes
-----
1 ns = 10^-9 seconds
1 us = 10^-6 seconds = 1,000 ns
1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns

Handy metrics based on numbers above:

  • Read sequentially from disk at 30 MB/s
  • Read sequentially from 1 Gbps Ethernet at 100 MB/s
  • Read sequentially from SSD at 1 GB/s
  • Read sequentially from main memory at 4 GB/s
  • 6-7 world-wide round trips per second
  • 2,000 round trips per second within a data center

Latency numbers visualized

Source(s) and further reading

Additional system design interview questions

Common system design interview questions, with links to resources on how to solve each.

Question Reference(s)
Design a file sync service like Dropbox youtube.com
Design a search engine like Google queue.acm.org
stackexchange.com
ardendertat.com
stanford.edu
Design a scalable web crawler like Google quora.com
Design Google docs code.google.com
neil.fraser.name
Design a key-value store like Redis slideshare.net
Design a cache system like Memcached slideshare.net
Design a recommendation system like Amazon's hulu.com
ijcai13.org
Design a tinyurl system like Bitly n00tc0d3r.blogspot.com
Design a chat app like WhatsApp highscalability.com
Design a picture sharing system like Instagram highscalability.com
highscalability.com
Design the Facebook news feed function quora.com
quora.com
slideshare.net
Design the Facebook timeline function facebook.com
highscalability.com
Design the Facebook chat function erlang-factory.com
facebook.com
Design a graph search function like Facebook's facebook.com
facebook.com
facebook.com
Design a content delivery network like CloudFlare cmu.edu
Design a trending topic system like Twitter's michael-noll.com
snikolov .wordpress.com
Design a random ID generation system blog.twitter.com
github.com
Return the top k requests during a time interval ucsb.edu
wpi.edu
Design a system that serves data from multiple data centers highscalability.com
Design an online multiplayer card game indieflashblog.com
buildnewgames.com
Design a garbage collection system stuffwithstuff.com
washington.edu
Add a system design question Contribute

Real world architectures

Articles on how real world systems are designed.


Source: Twitter timelines at scale

Don't focus on nitty gritty details for the following articles, instead:

  • Identify shared principles, common technologies, and patterns within these articles
  • Study what problems are solved by each component, where it works, where it doesn't
  • Review the lessons learned
Type System Reference(s)
Data processing MapReduce - Distributed data processing from Google research.google.com
Data processing Spark - Distributed data processing from Databricks slideshare.net
Data processing Storm - Distributed data processing from Twitter slideshare.net
Data store Bigtable - Distributed column-oriented database from Google harvard.edu
Data store HBase - Open source implementation of Bigtable slideshare.net
Data store Cassandra - Distributed column-oriented database from Facebook slideshare.net
Data store DynamoDB - Document-oriented database from Amazon harvard.edu
Data store MongoDB - Document-oriented database slideshare.net
Data store Spanner - Globally-distributed database from Google research.google.com
Data store Memcached - Distributed memory caching system slideshare.net
Data store Redis - Distributed memory caching system with persistence and value types slideshare.net
File system Google File System (GFS) - Distributed file system research.google.com
File system Hadoop File System (HDFS) - Open source implementation of GFS apache.org
Misc Chubby - Lock service for loosely-coupled distributed systems from Google research.google.com
Misc Dapper - Distributed systems tracing infrastructure research.google.com
Misc Kafka - Pub/sub message queue from LinkedIn slideshare.net
Misc Zookeeper - Centralized infrastructure and services enabling synchronization slideshare.net
Add an architecture Contribute

Company architectures

Company Reference(s)
Amazon Amazon architecture
Cinchcast Producing 1,500 hours of audio every day
DataSift Realtime datamining At 120,000 tweets per second
DropBox How we've scaled Dropbox
ESPN Operating At 100,000 duh nuh nuhs per second
Google Google architecture
Instagram 14 million users, terabytes of photos
What powers Instagram
Justin.tv Justin.Tv's live video broadcasting architecture
Facebook Scaling memcached at Facebook
TAO: Facebook’s distributed data store for the social graph
Facebook’s photo storage
Flickr Flickr architecture
Mailbox From 0 to one million users in 6 weeks
Pinterest From 0 To 10s of billions of page views a month
18 million visitors, 10x growth, 12 employees
Playfish 50 million monthly users and growing
PlentyOfFish PlentyOfFish architecture
Salesforce How they handle 1.3 billion transactions a day
Stack Overflow Stack Overflow architecture
TripAdvisor 40M visitors, 200M dynamic page views, 30TB data
Tumblr 15 billion page views a month
Twitter Making Twitter 10000 percent faster
Storing 250 million tweets a day using MySQL
150M active users, 300K QPS, a 22 MB/S firehose
Timelines at scale
Big and small data at Twitter
Operations at Twitter: scaling beyond 100 million users
Uber How Uber scales their real-time market platform
WhatsApp The WhatsApp architecture Facebook bought for $19 billion
YouTube YouTube scalability
YouTube architecture

Company engineering blogs

Architectures for companies you are interviewing with.

Questions you encounter might be from the same domain.

Source(s) and further reading

Under development

Interested in adding a section or helping complete one in-progress? Contribute!

  • Distributed computing with MapReduce
  • Consistent hashing
  • Scatter gather
  • Contribute

Credits

Credits and sources are provided throughout this repo.

Special thanks to:

Contact info

Feel free to contact me to discuss any issues, questions, or comments.

My contact info can be found on my GitHub page.

License

Creative Commons Attribution 4.0 International License (CC BY 4.0)

http://creativecommons.org/licenses/by/4.0/