introduction about system design

A Comprehensive Guide to System Design

Introduction to system design: a comprehensive guide.

In the world of software development, system design plays a critical role in creating scalable, efficient, and reliable applications. Whether you are building a simple web application or a complex distributed system, having a solid understanding of system design principles is essential.

In this comprehensive guide, we will delve into the fundamentals of system design, exploring key concepts and methodologies that can help you design robust and scalable solutions. We will cover various aspects of system design, including architecture, scalability, fault tolerance, and performance optimization.

Table of Contents

• 1. Architecture
• 2. Scalability
• 3. Fault Tolerance
• 4. Performance Optimization
• 5. Conclusion

1. Architecture <a name="architecture"> </a>

The architecture of a system defines its overall structure and organization. It encompasses the components, their interactions, and the flow of data within the system. Let's explore some commonly used architectural patterns:

1.1 Monolithic Architecture

In a monolithic architecture, all components of an application are tightly coupled and deployed as a single unit. While this approach simplifies development, it lacks scalability and flexibility. Here's a code snippet showcasing a basic monolithic architecture:

1.2 Microservices Architecture

Microservices architecture decomposes an application into smaller, loosely coupled services that can be developed and deployed independently. Each microservice focuses on a specific business capability and communicates with others through well-defined APIs. Here's a code snippet illustrating a basic microservices architecture:

2. Scalability <a name="scalability"> </a>

Scalability refers to the ability of a system to handle increased workload without compromising performance. Achieving scalability involves distributing the workload across multiple resources, such as servers or containers. Let's discuss two popular scalability patterns:

2.1 Horizontal Scalability

Horizontal scalability involves adding more resources to the system, such as additional servers or instances. By distributing the load across multiple resources, horizontal scalability enables the system to handle increased traffic. Here's an example demonstrating horizontal scalability using a load balancer:

2.2 Vertical Scalability

Vertical scalability involves upgrading the resources of a single machine, such as increasing CPU power or memory capacity. While vertical scalability is limited by the capacity of a single machine, it can still improve the system's performance. Here's an example showcasing vertical scalability through resource scaling:

3. Fault Tolerance <a name="fault-tolerance"> </a>

Building fault-tolerant systems ensures that the application remains operational in the face of failures. Here are two common strategies for achieving fault tolerance:

3.1 Replication

Replication involves creating redundant copies of data or services across multiple servers. If one server fails, the replicated instances can take over, ensuring uninterrupted service. Here's a code snippet demonstrating a basic replication strategy:

3.2 Monitoring and Recovery

Monitoring the health of a system and recovering from failures is crucial for maintaining uptime. By proactively monitoring key metrics and implementing automated recovery mechanisms, a system can quickly identify and mitigate failures. Consider this code snippet that exemplifies a monitoring and recovery approach:

4. Performance Optimization <a name="performance-optimization"> </a>

Optimizing the performance of a system is crucial for providing a responsive and efficient user experience. Let's explore two techniques for performance optimization:

4.1 Caching

Caching involves storing frequently accessed data in memory to reduce the response time. By caching the results of expensive computations or database queries, a system can significantly improve its performance. Here's an example showcasing a basic caching mechanism:

4.2 Load Testing

Load testing helps evaluate the performance of a system under high loads to identify bottlenecks and optimize resource allocation. By simulating realistic workloads, load testing enables you to fine-tune the system's configuration parameters. Consider this code snippet for load testing:

5. Conclusion <a name="conclusion"> </a>

System design is a crucial aspect of software development, and understanding its principles is paramount for building scalable and reliable applications. In this comprehensive guide, we explored the fundamentals of system design, spanning architecture, scalability, fault tolerance, and performance optimization. Armed with this knowledge, you can confidently design and develop robust and efficient systems that meet the requirements of your project.

Remember, system design is a continuous learning process, and it's essential to stay updated with the latest technologies and best practices. Keep experimenting, explore real-world examples, and never stop improving your system design skills.

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A Comprehensive Guide to System Design

In the dynamic landscape of software engineering, system design stands as a pivotal phase that lays the foundation for robust and scalable applications. Mastering the art of system design involves understanding intricate design concepts that fuel innovation and ensure seamless functionality. In this comprehensive guide, we'll delve into the world of system design, explore its core concepts, and equip you with the knowledge to excel in this crucial aspect of software engineering.

Table of Contents

What is system design, key concepts in system design, understanding the components of system design, the process of system design, best practices for effective system design, case studies of successful system designs.

At its essence, system design is the blueprint that transforms concepts and requirements into tangible software structures. It is the strategic process of defining the architecture, components, and interactions of a software system to fulfill specific functional and non-functional requirements. It involves translating high-level concepts into concrete designs that can be implemented and executed efficiently.

Modularity and Abstraction: Breaking down a complex system into modular components promotes reusability, ease of maintenance, and efficient collaboration among developers.

Coupling and Cohesion: Striking the right balance between component interdependence (coupling) and the functional relatedness within a component (cohesion) is crucial for a well-structured system.

Architectural Patterns: Different architectural patterns, such as client-server, microservices , and monolithic, offer guidelines for organizing components and handling interactions.

Data Flow and Communication: Designing effective data flow and communication mechanisms ensures seamless information exchange between system components.

Trade-offs and Constraints: System designers often face trade-offs between factors like performance, cost, and development time. Understanding these trade-offs is vital to making informed decisions.

A well-crafted software system is a composition of carefully crafted components, each serving a specific purpose and collaborating seamlessly to bring a digital vision to life. Every component is designed and precisely placed to ensure smooth operation and optimal performance. These components encompass a spectrum of functionalities, from managing data and user interactions to securing sensitive information and facilitating communication between different parts of the system.

Let's take a closer look at these fundamental components that form the backbone of a well-structured system design:

Components and Modules: Each component encapsulates a specific functionality, allowing for modularity and reusability. This means that developers can focus on refining and enhancing individual components without disrupting the entire system.

Data Management: Data management involves designing databases , defining schemas, and implementing strategies for data storage, retrieval, and maintenance. A well-designed data management component ensures that information flows seamlessly through the system, enabling accurate and timely decision-making.

User Interfaces: A user interface component involves creating intuitive, aesthetically pleasing, and user-friendly interfaces that facilitate smooth interactions. From buttons and menus to forms and visual elements, a well-designed user interface component enhances user experience, making it easy for users to navigate and engage with the application.

Communication Protocols: In a world where systems often need to talk to each other, communication protocols act as the language that enables effective dialogue. These protocols define the rules and conventions for data exchange between different components or even between separate systems. Whether it's a web service requesting information from a server or two modules sharing critical data, a robust communication protocol ensures that information is transmitted accurately and efficiently.

Security Measures: With the increasing importance of data privacy and cybersecurity, a dedicated security component is essential. This component encompasses encryption, authentication, access control, and other measures to safeguard sensitive information and prevent unauthorized access. A well-designed security component shields the software system from potential threats and vulnerabilities.

System design is a structured process that involves several stages. It involves a methodical process of conceptualization, refinement, and construction. This process serves as the blueprint for transforming abstract ideas into tangible and functional software systems.

Now, let's delve into the phases that constitute the process of system design:

Requirements Analysis : It is the foundation upon which the entire structure rests. This involves understanding user needs, functional specifications, and potential challenges to ensure that the design aligns with the project's goals.

Architectural Design : The architectural design phase shapes the high-level structure of the software. Here, designers determine the major components, their relationships, and the overall flow of information. This stage lays the groundwork for the subsequent design steps.

Component Design : The component design phase adds depth to the narrative. Designers delve into the specifics of each module, determining how they function and interact. This involves defining interfaces, algorithms, and internal mechanisms that contribute to the overall narrative of the software.

Database Design : The database design phase sculpts the data storage and retrieval mechanisms. Designers create a blueprint for organizing data, defining tables, relationships, and access methods. This step ensures that data is managed efficiently and can be accessed when needed.

User Interface Design : Designers craft user interfaces that are visually appealing and user-friendly. They create layouts, select fonts, and design interactive elements that enhance the user experience.

Integration and Testing : The integration and testing phase brings all the components together. System designers integrate different modules, test their interactions, and identify and rectify any issues. This step ensures that the entire system functions seamlessly as a unified entity.

• Thoroughly understand user needs and requirements before diving into design.
• Create modular components to enhance reusability and maintainability.
• Maintain comprehensive documentation to aid understanding and future development.
• Develop prototypes to validate design concepts and gather user feedback.
• Keep It Simple. Strive for simplicity in design to reduce complexity and improve system clarity.

Let's dive into some fascinating real-world examples that showcase how the principles of system design have been applied to create impressive and impactful digital experiences. These case studies provide a glimpse into how thoughtful design choices can lead to revolutionary solutions and redefine entire industries.

Netflix Streaming Service

Netflix, the world's leading streaming platform, is a prime example of successful system design. Behind the scenes, Netflix uses a microservices-based architecture . Think of this as a city with different neighborhoods, each serving a unique purpose. These microservices allow Netflix to deliver content seamlessly to millions of users worldwide. Just like each neighborhood has its own shops and services, each microservice handles a specific task, like user authentication or video streaming. This modular approach ensures scalability, easy maintenance, and quick updates, contributing to the smooth streaming experience that millions enjoy daily.

Uber's Real-time Matching

Uber's success is built on a robust system design that enables real-time matching of drivers and riders. Imagine a magic matchmaking spell that instantly connects people needing a ride with available drivers. Uber's system uses advanced algorithms and location data to make this happen. It's like having a digital GPS guiding drivers to their passengers in real-time. This efficient system design ensures that users get reliable rides quickly, making urban transportation more convenient and accessible.

Amazon Web Services (AWS)

Amazon Web Services (AWS) is a game-changing example of system design in the cloud computing world. Imagine having a supercharged toolbox of digital tools and resources that you can access from anywhere. AWS provides this by offering a wide range of services, like storage, computing power, and databases , through the cloud. It's like having a virtual workshop where developers can build and deploy applications without worrying about the hardware. AWS's scalable and flexible system design has transformed how businesses manage their IT infrastructure, enabling startups and large enterprises alike to innovate and grow rapidly.

System design is the backbone of software engineering, shaping the way applications function, scale, and deliver value to users. By understanding the core concepts, following a systematic design process, and considering key factors, developers can create software systems that meet user expectations and adapt to changing technological landscapes. Learning system design empowers software engineers to craft innovative, reliable, and efficient solutions that drive the digital world forward.

Written by Amani Undru

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Introduction to Modern System Design

Get an overview of the topics we’ll cover in this course.

Modern system design using building blocks

About this course, who should take this course, prerequisites for this course.

System design is the process of defining components and their integration, APIs, and data models to build large-scale systems that meet a specified set of functional and non-functional requirements.

System design uses the concepts of computer networking, parallel computing, and distributed systems to craft systems that scale well and are performant. Distributed systems scale well by nature. However, distributed systems are inherently complex. The discipline of system design helps us tame this complexity and get the work done.

System Design is an important topic to understand if you want to advance further in your career as a software engineer. Even if you are just beginning your coding journey, it's a good idea to get a head start on learning about system design.

Early in your career you will mostly just be tested on your coding ability. In higher level interviews, however, there will often be a greater focus on testing your ability and experience at designing applications.

The biggest struggle engineers have with system design interviews is that they are more open-ended and there isn't any single correct answer. This lack of structure can be intimidating, so my goal with this article is to give you a roadmap for navigating these types of interviews with confidence.

What this article will cover:

• What is a system design interview and why they are used
• The main stages of a system design interview
• Example interview problem – Design YouTube

Video Tutorial

You can also watch this tutorial on YouTube if you like:

And I've created a playlist of videos on specific topics related to system design and web architecture:

System Design Interview Overview

At first glance it seems silly to ask somebody to design a huge app like Twitter or YouTube in 45-60 minutes. These apps were designed over a period of years by hundreds of engineers working together, so it's clearly an impossible task to do in a short interview.

There are two main reasons why companies use these types of interviews. The first is, of course, to test your knowledge about the technologies being discussed. They want you to go deep enough to make sure you aren't just throwing buzzwords around without understanding how things actually work.

The second reason might be more important, though. The system design interview is a way to simulate a realistic scenario where you are working together with the interviewer to determine the best design decision.

Getting the perfect answer isn't necessarily the most important thing here – it's some of the other things you can show, like:

• How do you handle being challenged? Do you get defensive or take feedback with a positive attitude? Are you stubborn or narrow-minded?
• Do you show knowledge of the various tradeoffs certain design decisions involve? There's a big difference between blindly making a decision and not realizing the consequences, and knowing the pros/cons and accepting the tradeoffs.
• Are you able to effectively communicate and if necessary explain complex technical concepts in an easy to understand way?
• Are you candidate somebody the interviewer would want to work with long term? Even if somebody is a genius, if they are miserable to work with they might not be a good hire.

Stages of a System Design Interview

In this section you'll learn a general framework for structuring how to handle a problem during a system design interview.

Clarify the problem and establish design scope

The first thing you'll want to do after your interviewer gives you the problem is to take a few minutes to ask some clarifying questions and figure out what exactly they are looking for.

The worst thing you could do here is just start off in the completely wrong direction because you didn't take the time to ask a few questions. You have a limited amount of time during the interview, so you want to make sure you focus on what's important.

Here are some examples of questions you might ask:

What are the use cases / features of the app?

In this article we will be using YouTube as an example. There are hundreds of different features you could design like ad delivery, authentication, recommendation algorithms, comments, video upload, video processing, and many others.

During an interview you only have time to cover a few of those, so make sure to ask the interviewer questions to figure out what they want you to focus on designing.

How many users are expected / what is the likely traffic volume?

The complexity of the system will depend on the amount of traffic it needs to handle, so make sure to gather this information.

You don't want to over-engineer things if the traffic is relatively low and you also don't want to get stuck with an app that can't scale because you didn't design it properly.

Ask questions like how many users the app will have, the average amount of data per request, how long data needs to be stored, and how reliable and available does the system need to be?

This step is going to help you beyond just getting more information to work with. You're also showing the interviewer that you understand how to gather information about a vague problem.

Determine Rough Capacity Estimates

Using the information you gathered during the first step, you can begin to make some rough estimates and generalizations for things like storage and bandwidth requirements.

This process will involve some basic math like multiplying the number of users by the average request size and the amount of requests each user is expected to make daily.

Create a High Level Design

Here you want to create a rough architecture for the system. Draw out things like load balancers, web servers, app servers, task queues, database, caching, file storage, and so on. You should include all the core components you need to create the system.

Make sure to communicate with the interviewer during this stage and check to ensure that you aren't missing anything. While they probably won't tell you directly, they will give you a nudge in the right direction if you forgot about some crucial feature.

This part is almost cheating because you are using the structure of the interview to your advantage to confirm that you are on the right path.

The interviewer is never going to deliberately lead you down the wrong path, so once you've created your high level design you can start sketching out some rough API endpoints for each component.

For the YouTube example they might look something like this, depending on which features you are building:

• uploadVideo (userID, video, description, title)
• comment (userID, videoID, comment)
• viewVideo (videoID)
• videoSearch (query)

In some cases you might not need to drill down to this level. If the interview question is very high level like "design Youtube", you can probably skip this part. On the other hand if you get a more focused question like "design YouTube's comment system", it would make sense to go more in depth.

Create a Data Schema

At this point you should have a good idea of all the requirements and data needed for the application to work, so now you can plan out how your data is structured.

Depending on what you are building and the requirements, you'll need to weigh the costs and benefits of things like using a relational vs non-relational database. When modeling your data you'll also want to account for things like potential data partitioning and replication.

Take a Detailed Look at the Components

What happens during this section will mainly depend on the feedback of the interviewer. They will probably pick out a few specific components to focus on and ask why you made certain decisions.

The most important part here isn't necessarily being 100% right. Instead, it's to show that you didn't just blindly make decisions and understand exactly what tradeoffs you were making.

You should be able to propose alternate design decisions that could have been used and explain why you didn't use them.

How to Design YouTube

Now that you have a general idea of how a system design interview works and a framework for handling a system design problem, I'm going to show you how to put it all into practice using YouTube as an example.

Step 1 – Define Problem Scope and Requirements

This will be a high level problem where we implement a few of YouTube's major features without diving too in-depth on any of them. The features to focus on will be:

• Users can upload videos
• Users can view videos
• Users can comment on videos

Step 2 – Determine Capacity estimates

The two biggest capacity factors in an app handling large amounts of video like YouTube will be storing all that content and bandwidth requirements to deliver the content to users. In this section you'll learn how to make rough estimates for capacity requirements.

The main focus here is not on being highly accurate, but showing a logical thought process for calculating these numbers based on the information available to you.

In an interview you would be given the data, but in this case I'm using two key pieces of data that YouTube has made public:

• YouTube creators upload 500 hours of video every minute
• YouTube users watch 1 billion hours of video per day

You can use these numbers to calculate storage and bandwidth requirements with a few assumptions.

Bandwidth Calculation

To calculate an estimate for bandwidth, we start with the amount of video watched daily. The key assumption here is how much bandwidth is used per hour watched, as this would depend on the quality of video most users choose to watch.

The 3 Gigabyte estimate is based on a rough percentage of users watching in standard definition and others choosing HD or 4K, which consume much more bandwidth per hour watched.

The math here is fairly simple: multiply 1 billion hours by the average bandwidth of an hour of video, then divide that by 1000 to convert to terabytes, then divide by 1000 again to get to Petabytes. The final bandwidth estimate is 3,000 PB used daily.

Storage Calculation

Based on a few assumptions we can calculate that YouTube will need to store around 2.16 Petabytes of new video every day. Here's how we get that number:

• Convert 500 hours to 30,000 minutes of video uploaded per minute
• Each minute of HD video is roughly 50 Megabytes due to having copies of each video in multiple formats. We multiply that by 30,000 minutes and then divide by 1000 to convert to Gigabytes.
• We then take the 1,500GB uploaded per minute and multiply by 60 then 24 to calculate the daily amount of video uploaded. We divide by 1000 again to convert Gigabytes to Terabytes
• Our final total is 2,160 Terabytes uploaded daily or 2.16 Petabytes

Step 3 – Database Design

For our database we will use a standard relational database like MySQL. The schema will look something like this:

This design is very simple but has the essentials that you'd need for a basic implementation. It would be a good idea to do some research into the differences between relational and non-relational databases so you understand what kind of situations each excel at and when to use them.

For certain apps with different requirements a NoSQL database might make sense. Often large systems will have many different services that use different types of databases depending on their needs.

Step 4 – High Level Design

That's a pretty complex diagram, so let me break down what's happening:

• Client – This could be a user on a mobile app or their computer trying to upload a video, make a comment, or watch a video
• CDN – A content distribution network is used to reduce latency and improve reliability when it comes to delivering static content like videos or images. A CDN works by storing content in data centers all around the world so that the content is closer to users. This results in reduced latency because requests travel a shorter distance. There's also an added benefit of content being stored in multiple locations so even if one location can't serve traffic for some reason, another location can.
• Load Balancers – A load balancer accepts requests and routes them to servers depending on a number of factors. At YouTube's scale, a single server can't handle all the traffic and you want replication to prevent a single point of failure. The load balancer can check the status of servers and verify they can handle traffic or choose another server that can handle the request.
• Services – You can think of this as the app layer of the system. Instead of using a single monolith to handle traffic, we'll use several microservices to handle specific tasks. The second box for each of these services in the diagram represents multiple servers running for each of them to increase reliability. If one replica of the service goes down, there's always another to step in and handle traffic.
• Data Stores – When using microservices it is generally best practice for each microservice to own its own data. If one service needs data from another they can access it through an API.
• Video Upload Process – Handling the video uploads will involve multiple steps, as trying to handle it synchronously with the app server would be fragile and reduce performance. I'll cover this more in depth in the next section

I don't want to go too in-depth on these individual components because I could write entire articles on any of them if I wanted to explain them fully.

If you are interested in a more detailed explanation you can check out the system design playlist I linked to above which has videos covering most of these concepts.

Step 5 – Go Over Specific Components and Details

At this stage you have a working design. Now let's look at some of the specific components in detail.

Video Upload

Video content is the lifeblood of YouTube, and it doesn't exist without it. This means that making it quick and easy for users to upload videos is probably the most important feature.

Imagine uploading a multi-gigabyte video to YouTube and then seeing the upload fail after 30 minutes when it's 95% done. To prevent this you'll want to support the ability for resuming uploads if the client's connection is lost temporarily. The uploaded video can then be stored with a distributed file system like HDFS.

Once the upload is complete there's still a lot more to do before the video is ready for users to access. The video needs to be encoded into multiple different quality formats, you need to generate thumbnails, and push copies of the video to the global CDN.

Again, at any stage one of these processes could fail. To prevent this you'll have a task queue to manage this process and retry the processing attempt if it fails at any stage.

Database Scaling

The database is often the bottleneck of an application. You will probably be tested on whether you understand some of the fundamental concepts around database scaling. This could include caching to handle read requests, sharding, and replication.

Hopefully this article has given you a better understanding of what to expect during a system design interview.

This article mainly focused on the structure of the interview itself rather than the concepts you need to understand to answer the questions given during the interview.

Two great resources for beginning to learn about that are:

A great article posted here on Free Code Camp News: https://www.freecodecamp.org/news/systems-design-for-interviews/

The system design primer repo on GitHub: https://github.com/donnemartin/system-design-primer

Both cover just about every major concept you need to know for your system design interview and should put you in a great position for success.

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Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

The System Design Primer

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 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:

Study guide

How to approach a system design interview question.

• System design interview questions, with solutions
• Object-oriented design interview questions, with solutions

Additional system design interview questions

Anki flashcards.

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

• System design deck
• System design exercises deck
• Object oriented design exercises deck

Great for use while on-the-go.

Coding Resource: Interactive Coding Challenges

Looking for resources to help you prep for the Coding Interview ?

Check out the sister repo Interactive Coding Challenges , which contains an additional Anki deck:

• Coding deck

Contributing

Learn from the community.

Feel free to submit pull requests to help:

• Improve sections
• Add new sections

Content that needs some polishing is placed under development .

Review the Contributing Guidelines .

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.

Step 1: Review the scalability video lecture

Step 2: review the scalability article, performance vs scalability, latency vs throughput, cp - consistency and partition tolerance, ap - availability and partition tolerance, weak consistency, eventual consistency, strong consistency, replication, availability in numbers, domain name system, active-passive, active-active.

• Layer 4 load balancing
• Layer 7 load balancing

Horizontal scaling

Load balancer vs reverse proxy, microservices.

• Service discovery

Master-slave replication

Master-master replication.

• Denormalization

Key-value store

Document store, wide column store.

• Graph Database

SQL or NoSQL

Client caching, cdn caching, web server caching, database caching, application caching, caching at the database query level, caching at the object level, cache-aside, write-through, write-behind (write-back), refresh-ahead, message queues, task queues, back pressure, transmission control protocol (tcp), user datagram protocol (udp), remote procedure call (rpc), representational state transfer (rest), powers of two table, latency numbers every programmer should know, real world architectures, company architectures, company engineering blogs, under development, contact info.

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

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

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.

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:

• MD5 and Base62
• Hash collisions
• Database schema
• 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

• 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:

• Use back of the envelope calculations

Source(s) and further reading

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

• How to ace a systems design interview
• The system design interview
• Intro to Architecture and Systems Design Interviews
• System design template

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.

Design Pastebin.com (or Bit.ly)

View exercise and solution

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

Design a web crawler

Design Mint.com

Design the data structures for a social network

Design a key-value store for a search engine

Design Amazon's sales ranking by category feature

Design a system that scales to millions of users on AWS

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

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.

Scalability Lecture at Harvard

• Vertical scaling
• Load balancing
• Database replication
• Database partitioning
• Scalability

Asynchronism

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

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.

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.
• A word on scalability
• Scalability, availability, stability, patterns

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 .

• Understanding latency vs throughput

CAP theorem

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.

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.

Responses return the most readily available version of the data available on any node, 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 to allow for eventual consistency or when the system needs to continue working despite external errors.

• CAP theorem revisited
• A plain english introduction to CAP theorem
• The CAP theorem

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.

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.

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.

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.

• Transactions across data centers

Availability patterns

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

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.

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.

Master-slave and master-master

This topic is further discussed in the Database section:

Availability is often quantified by uptime (or downtime) as a percentage of time the service is available. Availability is generally measured in number of 9s--a service with 99.99% availability is described as having four 9s.

99.9% availability - three 9s

99.99% availability - four 9s, availability in parallel vs in sequence.

If a service consists of multiple components prone to failure, the service's overall availability depends on whether the components are in sequence or in parallel.

In sequence

Overall availability decreases when two components with availability < 100% are in sequence:

If both Foo and Bar each had 99.9% availability, their total availability in sequence would be 99.8%.

In parallel

Overall availability increases when two components with availability < 100% are in parallel:

If both Foo and Bar each had 99.9% availability, their total availability in parallel would be 99.9999%.

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:

• 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 and is 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).
• DNS architecture
• DNS articles

Content delivery network

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 from data centers close to them
• Your servers do not have to serve requests that the CDN fulfills

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 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 CDN.

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.
• Globally distributed content delivery
• The differences between push and pull CDNs

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 to eliminate a single point of failure

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

Additional benefits include:

• Removes the need to install X.509 certificates on each server
• 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:

• Least loaded
• Session/cookies
• Round robin or weighted round robin

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 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 terminate 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.

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

• 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 a single point of failure results in increased complexity.
• A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.
• NGINX architecture
• HAProxy architecture guide
• ELB listener config

Reverse proxy (web server)

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.

• 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
• Compression - Compress server responses
• Caching - Return the response for cached requests
• HTML/CSS/JS
• 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.
• Reverse proxy vs load balancer

Application layer

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 .

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 Consul , Etcd , and Zookeeper can help services find each other by keeping track of registered names, addresses, and ports. Health checks help verify service integrity and are often done using an HTTP endpoint. Both Consul and Etcd have a built in key-value store that can be useful for storing config values and other shared data.

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.
• Intro to architecting systems for scale
• Crack the system design interview
• Service oriented architecture
• Introduction to Zookeeper
• Here's what you need to know about building microservices

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 .

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.

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.

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.

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.

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

• Multi-master replication

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

• Scaling up to your first 10 million users

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.
• 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

• The coming of the shard
• Shard database architecture
• Consistent hashing

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 outnumber 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

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.
• 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

• Denormalize where performance demands it.

Partition tables

• Break up a table by putting hot spots in a separate table to help keep it in memory.

Tune the query cache

• In some cases, the query cache could lead to performance issues .

Source(s) and further reading: SQL tuning

• Tips for optimizing MySQL queries
• Is there a good reason i see VARCHAR(255) used so often?
• How do null values affect performance?
• Slow query log

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.

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

• Key-value database
• Disadvantages of key-value stores
• Redis architecture
• Memcached architecture

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 by 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

• Document-oriented database
• MongoDB architecture
• CouchDB architecture
• Elasticsearch architecture

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

• SQL & NoSQL, a brief history
• Bigtable architecture
• HBase architecture
• Cassandra architecture
• 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

Source(s) and further reading: nosql.

• Explanation of base terminology
• NoSQL databases a survey and decision guidance
• Introduction to NoSQL
• NoSQL patterns

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 vs NoSQL differences

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.

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

CDNs are considered a type of cache.

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.

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.

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 :

• 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.

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

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.

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

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.

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

Application code:

Cache code:

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 be read, which can be minimized with a TTL.

In write-behind, the 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.

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 .
• Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.
• Need to make application changes such as adding Redis or memcached.
• From cache to in-memory data grid
• Scalable system design patterns
• Introduction to architecting systems for scale
• AWS ElastiCache strategies

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 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.

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.

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.
• It's all a numbers game
• Applying back pressure when overloaded
• Little's law
• What is the difference between a message queue and a task queue?

Communication

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:

*Can be called many times without different outcomes.

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

Source(s) and further reading: HTTP

• What is HTTP?
• Difference between HTTP and TCP
• Difference between PUT and PATCH

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:

• Sequence numbers and checksum fields for each packet
• Acknowledgement packets and automatic retransmission

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 result 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

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

• Networking for game programming
• Key differences between TCP and UDP protocols
• Difference between TCP and UDP
• Transmission control protocol
• User datagram protocol
• Scaling memcache at Facebook

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:

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 .

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:

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

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

Source(s) and further reading: REST and RPC

• Do you really know why you prefer REST over RPC
• When are RPC-ish approaches more appropriate than REST?
• REST vs JSON-RPC
• Debunking the myths of RPC and REST
• What are the drawbacks of using REST
• Why REST for internal use and not RPC

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 .
• API security checklist
• Security guide for developers
• OWASP top ten

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

Handy metrics based on numbers above:

• Read sequentially from HDD 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

• Latency numbers every programmer should know - 1
• Latency numbers every programmer should know - 2
• Designs, lessons, and advice from building large distributed systems
• Software Engineering Advice from Building Large-Scale Distributed Systems

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

Articles on how real world systems are designed.

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

Architectures for companies you are interviewing with. Questions you encounter might be from the same domain.

• Airbnb Engineering
• Atlassian Developers
• Bitly Engineering Blog
• Cloudera Developer Blog
• Dropbox Tech Blog
• Engineering at Quora
• Ebay Tech Blog
• Evernote Tech Blog
• Etsy Code as Craft
• Facebook Engineering
• Flickr Code
• Foursquare Engineering Blog
• GitHub Engineering Blog
• Google Research Blog
• Groupon Engineering Blog
• Heroku Engineering Blog
• Hubspot Engineering Blog
• High Scalability
• Instagram Engineering
• Intel Software Blog
• Jane Street Tech Blog
• LinkedIn Engineering
• Microsoft Engineering
• Microsoft Python Engineering
• Netflix Tech Blog
• Paypal Developer Blog
• Pinterest Engineering Blog
• Reddit Blog
• Salesforce Engineering Blog
• Slack Engineering Blog
• Spotify Labs
• Twilio Engineering Blog
• Twitter Engineering
• Uber Engineering Blog
• Yahoo Engineering Blog
• Yelp Engineering Blog
• Zynga Engineering Blog

Looking to add a blog? To avoid duplicating work, consider adding your company blog to the following repo:

• kilimchoi/engineering-blogs

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

• Distributed computing with MapReduce
• Scatter gather

Credits and sources are provided throughout this repo.

Special thanks to:

• Hired in tech
• Cracking the coding interview
• High scalability
• checkcheckzz/system-design-interview
• shashank88/system_design
• mmcgrana/services-engineering
• System design cheat sheet
• A distributed systems reading list
• Cracking the system design interview

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

My contact info can be found on my GitHub page .

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).

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System Analysis | System Design

• System Design | Online Banking System
• Differences between System Analysis and System Design
• Designing Authentication System | System Design
• Advantages of System Design
• Design a Logistics System
• What is System Design - Learn System Design
• System Design vs. Software Design
• Design Process & Task Analysis | HCI
• System Design - Design a Sequencer
• System Bus Design
• System Analysis and Design Interview Topics for Freshers
• Types of System Design
• Analysis of Monolithic and Distributed Systems - Learn System Design
• Goals and Objectives of System Design
• System Design of Uber App | Uber System Architecture
• Stateless and Stateful Systems in System Design
• What is State Space Analysis ?
• Byzantine Failure in System Design
• Cost/Benefit Analysis

In the areas of science, information technology, and knowledge, the difficulty of systems is of much importance. As systems became more complicated, the traditional method of problem-solving became inefficient. System analysis is to examine a business problem, identify its objectives and requirements, and then design the most optimal solution to fulfill those needs.

Important Topics for System Analysis

System Analysis

• System Design

What is a System?

Constraints of a system, properties of a system, elements of a system, types of systems, system models, categories of information.

It is the very first step in any system development and the critical phase where developers come together to understand the problem, needs, and objectives of the project.

Some of the key aspects of system analysis are:

• Problem Identification: It involves identifying the issues that the system is aiming to address. Whether it is automating a business process, improving data management, or improving the user experience, understanding the problem is the first and most important step.
• Requirements Gathering: Once the problem is identified, the next step is to gather and write down the requirements. This involves communicating with the customer and developer to gather information about how the system is to be designed.
• Feasibility study: Before going into development, it is important to check the feasibility of the project. This includes the evaluation of technical, operational, and financial aspects to determine the feasibility of the proposed solution.
• Analysis and modeling: To get a deep insight into the system, analysts develop various models, such as Data Flow Diagrams(DFD), Use Cases, and Entity-Relationship(ER) diagrams. These models help the customer to visualize the system and its interactions.
• Scope Definition: Defining the scope of the system is important to prevent adding excessive features to the system and ensure that the project stays within its limits. It identifies what is part of the system and what is not.

Fraud Detection Systems: Studying transaction patterns and inconsistencies in financial data to develop algorithms for detecting and preventing fraudulent activities.

System design is where the project’s blueprint is created. It involves transforming the requirements identified in the analysis phase into a visual solution. The main components of system design are as follows:

• Architecture design: This phase describes the high level structure of the system. This includes deciding software and hardware components, their connectivity with each other and the overall design of the system. Architects make critical designs ensuring scalability, performance, and security.
• Database configuration : The design phase includes defining the database schema, data storage, and access methods. A database programmer ensures that data is organized correctly, and that the system can retrieve and process data efficiently.
• Communication system: Communication controls are important components of most systems. In this phase, designers create the system’s visual elements and interactions.
• Algorithm Design: Complex algorithms are designed in this phase. Algorithms are the logic or program that makes systems work, and their efficiency and accuracy are critical.
• Security: Data security is a major concern in today’s digital world. Developers must plan for security measures to protect the system and its data, such as encryption, access control, and threat measures.
• Test and Maintenance: System plans should also include plans for testing and validation. The designer must specify how the system will be tested to ensure that it meets specified requirements and performs as planned.
• Documentation : Suitable documentation is necessary to maintain the system and enable future use. During the design phase, documentation should be created or updated to ensure that the development team and end users can access the necessary information.

Educational Management System:

• Components: Student database, course details, management module, grading system.
• Modules: Enrollment, attendance tracking, assignment submission, grades.
• Interfaces: Student portals, teacher interfaces, parents portal.

A system is a set of things that work together as an interconnecting network to achieve a particular goal. The set of things can be hardware, software, employees and much more. Systems are everywhere around us such as computer systems which have both hardware and software to execute certain functions.

Example: Biological system, Educational system, Physical system, etc.

Every system works within certain boundaries called constraints. These constraints define the limits within which the system can operate. Typical constraints include financial constraints, technical constraints, and time constraints, which are important in guiding program development and operation.

Systems exhibit several key homes:

• Interconnectedness: Components inside a device are interconnected, change in one system might cause change in the another system.
• Environment: Systems exist within an surroundings, interacting with it and being influenced through it.
• Boundary: Systems have a described boundary that separates them from the external environment. This is essential for studying how the system interact with external environment.
• Purpose: Systems are designed with clear purpose and specific objectives. The components of a system are organized in such a way to perform intended tasks.
• Input and Output: Systems need input which leads to give the desired output.
• Feedback: Feedbacks are most important part of the system as it helps the developers to upgrade it with the user requirements.
• Input: The data that the device gets from external source.
• Process : The activities that occur within the system.
• Output : The result after processing the input.
• Feedback : It is given by the customers end to improve the system.

• Open Systems: An open system is the one that interacts freely with the external factors. These systems are capable of adapting the changes made within the system. Example: business organizations.
• Closed Systems : A closed system is one which is contained within itself. It does not have any interaction with the environment. Example: A computer system.
• Adaptive Systems : Adaptive systems are those that change their behavior with the changing environment. Example: constantly changing market.
• Dynamic Systems : Dynamic systems are those that change and evolve over a period of time. Example: ecological system change with factors like climate change.

System models are simplified representations of real-world systems that help us to understand, analyze, and design complex systems. These models are important tools used in various fields such as engineering, computer science, economics, and biology to study and predict behavior of the system. System models can be visual, mathematical or conceptual. They provide insights into program design, communication, and development. Here are a few types of system models commonly used: Mathematical, Simulation, Graphical, Physical, Conceptual.

In the context of system, records may be categorized as follows:

• Operational Information : Information used to perform each day operations.
• Management Information : Information utilized by managers for decision-making.
• Strategic Information : Information related to long-term making plans and approach formula.

In conclusion, system analysis and design form the cornerstone of successful software development and problem-solving in different domains. System analysis and design are fundamental processes that help us navigate the complexities of modern systems and to make innovation in a rapidly changing world.

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Getting Started Introduction

Our goal is to build the most comprehensive, concise, and free system design library on the internet.

This growing library is meant to be a resource for you to leverage as you learn about system design and prepare for your interview.

We want to tailor the topics and scope of the resources here to what you can expect to encounter during a typical system design interview.

If you have any questions, want to add an article or have feedback, let us know in the discord (linked in the footer)!

• System Analysis and Design Tutorial
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• System Analysis & Design - Overview
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System Analysis and Design - Overview

Systems development is systematic process which includes phases such as planning, analysis, design, deployment, and maintenance. Here, in this tutorial, we will primarily focus on −

• Systems analysis
• Systems design

Systems Analysis

It is a process of collecting and interpreting facts, identifying the problems, and decomposition of a system into its components.

System analysis is conducted for the purpose of studying a system or its parts in order to identify its objectives. It is a problem solving technique that improves the system and ensures that all the components of the system work efficiently to accomplish their purpose.

Analysis specifies what the system should do .

Systems Design

It is a process of planning a new business system or replacing an existing system by defining its components or modules to satisfy the specific requirements. Before planning, you need to understand the old system thoroughly and determine how computers can best be used in order to operate efficiently.

System Design focuses on how to accomplish the objective of the system .

System Analysis and Design (SAD) mainly focuses on −

What is a System?

The word System is derived from Greek word Systema, which means an organized relationship between any set of components to achieve some common cause or objective.

A system is “an orderly grouping of interdependent components linked together according to a plan to achieve a specific goal.”

Constraints of a System

A system must have three basic constraints −

A system must have some structure and behavior which is designed to achieve a predefined objective.

Interconnectivity and interdependence must exist among the system components.

The objectives of the organization have a higher priority than the objectives of its subsystems.

For example, traffic management system, payroll system, automatic library system, human resources information system.

Properties of a System

A system has the following properties −

Organization

Organization implies structure and order. It is the arrangement of components that helps to achieve predetermined objectives.

Interaction

It is defined by the manner in which the components operate with each other.

For example, in an organization, purchasing department must interact with production department and payroll with personnel department.

Interdependence

Interdependence means how the components of a system depend on one another. For proper functioning, the components are coordinated and linked together according to a specified plan. The output of one subsystem is the required by other subsystem as input.

Integration

Integration is concerned with how a system components are connected together. It means that the parts of the system work together within the system even if each part performs a unique function.

Central Objective

The objective of system must be central. It may be real or stated. It is not uncommon for an organization to state an objective and operate to achieve another.

The users must know the main objective of a computer application early in the analysis for a successful design and conversion.

Elements of a System

The following diagram shows the elements of a system −

Outputs and Inputs

The main aim of a system is to produce an output which is useful for its user.

Inputs are the information that enters into the system for processing.

Output is the outcome of processing.

Processor(s)

The processor is the element of a system that involves the actual transformation of input into output.

It is the operational component of a system. Processors may modify the input either totally or partially, depending on the output specification.

As the output specifications change, so does the processing. In some cases, input is also modified to enable the processor for handling the transformation.

The control element guides the system.

It is the decision–making subsystem that controls the pattern of activities governing input, processing, and output.

The behavior of a computer System is controlled by the Operating System and software. In order to keep system in balance, what and how much input is needed is determined by Output Specifications.

Feedback provides the control in a dynamic system.

Positive feedback is routine in nature that encourages the performance of the system.

Negative feedback is informational in nature that provides the controller with information for action.

Environment

The environment is the “supersystem” within which an organization operates.

It is the source of external elements that strike on the system.

It determines how a system must function. For example, vendors and competitors of organization’s environment, may provide constraints that affect the actual performance of the business.

Boundaries and Interface

A system should be defined by its boundaries. Boundaries are the limits that identify its components, processes, and interrelationship when it interfaces with another system.

Each system has boundaries that determine its sphere of influence and control.

The knowledge of the boundaries of a given system is crucial in determining the nature of its interface with other systems for successful design.

Types of Systems

The systems can be divided into the following types −

Physical or Abstract Systems

Physical systems are tangible entities. We can touch and feel them.

Physical System may be static or dynamic in nature. For example, desks and chairs are the physical parts of computer center which are static. A programmed computer is a dynamic system in which programs, data, and applications can change according to the user's needs.

Abstract systems are non-physical entities or conceptual that may be formulas, representation or model of a real system.

Open or Closed Systems

An open system must interact with its environment. It receives inputs from and delivers outputs to the outside of the system. For example, an information system which must adapt to the changing environmental conditions.

A closed system does not interact with its environment. It is isolated from environmental influences. A completely closed system is rare in reality.

Adaptive and Non Adaptive System

Adaptive System responds to the change in the environment in a way to improve their performance and to survive. For example, human beings, animals.

Non Adaptive System is the system which does not respond to the environment. For example, machines.

Permanent or Temporary System

Permanent System persists for long time. For example, business policies.

Temporary System is made for specified time and after that they are demolished. For example, A DJ system is set up for a program and it is dissembled after the program.

Natural and Manufactured System

Natural systems are created by the nature. For example, Solar system, seasonal system.

Manufactured System is the man-made system. For example, Rockets, dams, trains.

Deterministic or Probabilistic System

Deterministic system operates in a predictable manner and the interaction between system components is known with certainty. For example, two molecules of hydrogen and one molecule of oxygen makes water.

Probabilistic System shows uncertain behavior. The exact output is not known. For example, Weather forecasting, mail delivery.

Social, Human-Machine, Machine System

Social System is made up of people. For example, social clubs, societies.

In Human-Machine System, both human and machines are involved to perform a particular task. For example, Computer programming.

Machine System is where human interference is neglected. All the tasks are performed by the machine. For example, an autonomous robot.

Man–Made Information Systems

It is an interconnected set of information resources to manage data for particular organization, under Direct Management Control (DMC).

This system includes hardware, software, communication, data, and application for producing information according to the need of an organization.

Man-made information systems are divided into three types −

Formal Information System − It is based on the flow of information in the form of memos, instructions, etc., from top level to lower levels of management.

Informal Information System − This is employee based system which solves the day to day work related problems.

Computer Based System − This system is directly dependent on the computer for managing business applications. For example, automatic library system, railway reservation system, banking system, etc.

Systems Models

Schematic models.

A schematic model is a 2-D chart that shows system elements and their linkages.

Different arrows are used to show information flow, material flow, and information feedback.

Flow System Models

A flow system model shows the orderly flow of the material, energy, and information that hold the system together.

Program Evaluation and Review Technique (PERT), for example, is used to abstract a real world system in model form.

Static System Models

They represent one pair of relationships such as activity–time or cost–quantity .

The Gantt chart, for example, gives a static picture of an activity-time relationship.

Dynamic System Models

Business organizations are dynamic systems. A dynamic model approximates the type of organization or application that analysts deal with.

It shows an ongoing, constantly changing status of the system. It consists of −

Inputs that enter the system

The processor through which transformation takes place

The program(s) required for processing

The output(s) that result from processing.

Categories of Information

There are three categories of information related to managerial levels and the decision managers make.

Strategic Information

This information is required by topmost management for long range planning policies for next few years. For example, trends in revenues, financial investment, and human resources, and population growth.

This type of information is achieved with the aid of Decision Support System (DSS).

Managerial Information

This type of Information is required by middle management for short and intermediate range planning which is in terms of months. For example, sales analysis, cash flow projection, and annual financial statements.

It is achieved with the aid of Management Information Systems (MIS).

Operational information

This type of information is required by low management for daily and short term planning to enforce day-to-day operational activities. For example, keeping employee attendance records, overdue purchase orders, and current stocks available.

It is achieved with the aid of Data Processing Systems (DPS).

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How do you design services like Instagram or Messenger, which have over 1 Billion users? This book will teach you how to answer these questions and more. While this book is oriented towards Software Developers and Architects, this book can be helpful for any working in the High-Tech industry. In this, we will understand how popular products such as Instagram, Facebook Messenger, Dropbox, TinyURL, and Pastebin are designed. No prior technical knowledge is required before picking up this book.

Systems design is the process of defining the architecture, product design, modules, interfaces, and data for a system to satisfy specified requirements. Systems design could be seen as the application of systems theory to product development. As a result, there is some overlap with the disciplines of systems analysis, systems architecture, and systems engineering. Designing software systems is a vast topic, and even a software engineer having years of experience at a top software company may not claim to be an expert on system design. Companies spend not weeks but months and hire a big team of software engineers to build such systems in real life.

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Introduction to Intelligent Robot System Design

Application Development with ROS

• © 2023
• Gang Peng   ORCID: https://orcid.org/0000-0001-8801-0972 0 ,
• Tin Lun LAM 1 ,
• Chunxu Hu 2 ,
• Jintao Liu 4 ,

School of AI and Automation, Huazhong University of Science and Technology, Wuhan, China

You can also search for this author in PubMed   Google Scholar

Lab of Robot and AI, Chinese University of Hong Kong, Shenzhen, China

Research and development department, wuhan jingfeng micro control technology co., limited, wuhan, china, school of computer science, wuhan university, wuhan, china, research and development department, yike robot lab, nanjing, china, research and development department, shenzhen nxrobo co., limited, shenzhen, china.

• Offers step-by-step guidelines on the Intelligent robot system design
• Presents easy-to-follow and practically applicable content on robot operating system
• Helps readers to master essential development methods for Intelligent robot system applications

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Table of contents (11 chapters)

Front matter, composition of robot system.

• Gang Peng, Tin Lun Lam, Chunxu Hu, Yu Yao, Jintao Liu, Fan Yang

Connecting the Robot to ROS

Robot system modeling, lidar slam for mobile robot, autonomous navigation for mobile robot, slam based on multi-sensor, manipulator motion control, computer vision, vision-based manipulator grasping, visual slam for mobile robot, introduction to ros 2 and programming foundation, back matter.

• manipulator
• mobile robot
• sensor fusion
• pattern recognition
• intelligent control

About this book

This book introduces readers to the principles and practical applications of intelligent robot system with robot operating system (ROS), pursuing a task-oriented and hands-on approach. Taking the conception, design, implementation, and operation of robot application systems as a typical project, and through “learning-by-doing, practicing-while-learning” approach, it familiarizes readers with ROS-based intelligent robot system design and development step by step.

The topics covered include ROS principles, mobile robot control, Lidar, simultaneous localization and mapping (SLAM), navigation, manipulator control, image recognition, vision calibration, object grasping, vision SALM, etc., with typical practical application tasks throughout the book, which are essential to mastering development methods for intelligent robot system.

Easy to follow and rich in content, the book can be used at colleges and universities as learning material and a teaching reference book for “intelligent robot,” “autonomous intelligent system,” “robotics principles,” and “robot system application development with ROS” in connection with automation, robotics engineering, artificial intelligence (AI), mechatronics, and other related majors. The book can assist in mastering the development and design of robot systems and provide the necessary theoretical and practical references to cultivate robot system development capabilities and can be used as teaching material for engineering training and competitions, or for reference, self-study, and training by engineering and technical personnel, teachers, and anyone who wants to engage in intelligent robot system development and design.

Authors and Affiliations

Tin Lun LAM

About the authors

Gang Peng, PhD, has research interests in intelligent robotics and intelligent manufacturing systems, intelligent sensing and control based on sensor fusion, intelligent analysis of industrial big data, AI, and machine learning algorithms. He has long been engaged in teaching, research, and development of intelligent robot control, multi-sensor integration and information fusion, intelligent driving, and human-robot collaborative systems. He has edited and published three Chinese monographs and one Springer English monograph. He has co-edited two Chinese monographs and one English translation. Additionally, as the first author or corresponding author, he has published papers in IEEE Transactions and other international journals in the field of robotics and automation, and has been granted more than 30 patents. Furthermore, he has presided over and completed the product transfer of scientific and technological achievements. He has been awarded the Outstanding Instructor Award of Huazhong University of Science and Technology for national major competitions and science and technology innovation many times and has supervised graduate students who have won provincial and municipal innovation and entrepreneurial talent awards.

Tin Lun LAM, PhD, Senior member of IEEE, serves as an assistant professor at Chinese University of Hong Kong (Shenzhen), Executive Deputy Director of the National-local Joint Engineering Laboratory of Robotics and Intelligent Manufacturing, and Director of Research at the Center on Intelligent Robots of Shenzhen Institute of Artificial Intelligence and Robotics for Society. His research focus includes new mobile robots, multi-robot systems, field robotics, soft robotics, and human-robot interaction. He has published two monographs and over 50 papers in top international journals and conferences in the field of robotics and automation and has been granted more than 60 patents. He has received the IEEE/ASME T-MECH Best Paper Award andIROS Best Paper Award on Robot Mechanisms and Design.

Chunxu Hu, the founder of the robotics community “Guyuehome,” obtained his master’s degree from the School of Artificial Intelligence and Automation of Huazhong University of Science and Technology. He focuses on the promotion and application of robot operating system and is an evangelist of the China ROS Foundation. He has been awarded the honorary title of one of the 10 most influential people in ROS in 2019.

Yu Yao, PhD, has been teaching, researching, and developing computer operating systems, AI technologies, and machine learning algorithms for many years.

Jintao Liu, PhD, the founder of ExBot Robotics Lab, has translated and published more than ten ROS books and is dedicated to the promotion and application of robot operating system.

Fan Yang, extramural advisor of graduate student at Tsinghua University, has been dedicated to the research of intelligent robots and intelligent hardware innovation and entrepreneurship. He has completed the marketing of several products, achieving good economic and social benefits. He is one of the editors of China’s White Paper on Artificial Intelligence Education, an evangelist of the China ROS Foundation, and a co-founder of the “Spark Project” ROS public classes.

Bibliographic Information

Book Title : Introduction to Intelligent Robot System Design

Book Subtitle : Application Development with ROS

Authors : Gang Peng, Tin Lun LAM, Chunxu Hu, Yu Yao, Jintao Liu, Fan Yang

DOI : https://doi.org/10.1007/978-981-99-1814-0

Publisher : Springer Singapore

eBook Packages : Intelligent Technologies and Robotics , Intelligent Technologies and Robotics (R0)

Copyright Information : Publishing House of Electronics Industry 2023

Hardcover ISBN : 978-981-99-1813-3 Published: 05 September 2023

Softcover ISBN : 978-981-99-1816-4 Due: 06 October 2023

eBook ISBN : 978-981-99-1814-0 Published: 04 September 2023

Edition Number : 1

Number of Pages : XX, 569

Number of Illustrations : 1 b/w illustrations

Topics : Control, Robotics, Mechatronics , Robotics , Robotics and Automation

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Apple introduces M4 chip

New Technologies Enabling the New iPad Pro

New 10-core CPU

GPU Brings New Capabilities to iPad Pro

The Most Powerful Neural Engine Ever

Advanced Media Engine for Smooth, Efficient Streaming

Better for the Environment

Text of this article

May 7, 2024

PRESS RELEASE

M4 enables the breakthrough design and stunning display of the new iPad Pro, while delivering a giant leap in performance

CUPERTINO, CALIFORNIA Apple today announced M4, the latest chip delivering phenomenal performance to the all-new iPad Pro . Built using second-generation 3-nanometer technology, M4 is a system on a chip (SoC) that advances the industry-leading power efficiency of Apple silicon and enables the incredibly thin design of iPad Pro. It also features an entirely new display engine to drive the stunning precision, color, and brightness of the breakthrough Ultra Retina XDR display on iPad Pro. A new CPU has up to 10 cores, while the new 10-core GPU builds on the next-generation GPU architecture introduced in M3, and brings Dynamic Caching, hardware-accelerated ray tracing, and hardware-accelerated mesh shading to iPad for the first time. M4 has Apple’s fastest Neural Engine ever, capable of up to 38 trillion operations per second, which is faster than the neural processing unit of any AI PC today. Combined with faster memory bandwidth, along with next-generation machine learning (ML) accelerators in the CPU, and a high-performance GPU, M4 makes the new iPad Pro an outrageously powerful device for artificial intelligence.

“The new iPad Pro with M4 is a great example of how building best-in-class custom silicon enables breakthrough products,” said Johny Srouji, Apple’s senior vice president of Hardware Technologies. “The power-efficient performance of M4, along with its new display engine, makes the thin design and game-changing display of iPad Pro possible, while fundamental improvements to the CPU, GPU, Neural Engine, and memory system make M4 extremely well suited for the latest applications leveraging AI. Altogether, this new chip makes iPad Pro the most powerful device of its kind.”

Delivering a giant leap in performance over the previous iPad Pro with M2, M4 consists of 28 billion transistors built using a second-generation 3-nanometer technology that further advances the power efficiency of Apple silicon. M4 also features an entirely new display engine designed with pioneering technologies, enabling the stunning precision, color accuracy, and brightness uniformity of the Ultra Retina XDR display, a state-of-the-art display created by combining the light of two OLED panels.

M4 has a new up-to-10-core CPU consisting of up to four performance cores and now six efficiency cores. The next-generation cores feature improved branch prediction, with wider decode and execution engines for the performance cores, and a deeper execution engine for the efficiency cores. And both types of cores also feature enhanced, next-generation ML accelerators.

M4 delivers up to 1.5x faster CPU performance over the powerful M2 in the previous iPad Pro. 1 Whether working with complex orchestral music files in Logic Pro or adding highly demanding effects to 4K video in LumaFusion, M4 boosts performance across pro workflows.

The new 10-core GPU of M4 builds upon the next-generation graphics architecture of the M3 family of chips. It features Dynamic Caching, an Apple innovation that allocates local memory dynamically in hardware and in real time to dramatically increase the average utilization of the GPU. This significantly increases performance for the most demanding pro apps and games.

Hardware-accelerated ray tracing comes to iPad for the first time, and enables even more realistic shadows and reflections in games and other graphically rich experiences. Hardware-accelerated mesh shading is also built into the GPU, and delivers greater capability and efficiency in geometry processing, enabling more visually complex scenes in games and graphics-intensive apps. Pro rendering performance in apps like Octane gets a huge boost with M4, and is now up to four times faster than on M2. 1 With these improvements to the CPU and GPU, M4 maintains Apple silicon’s industry-leading performance per watt. M4 can deliver the same performance as M2 using just half the power. And compared with the latest PC chip in a thin and light laptop, M4 can deliver the same performance using just a fourth of the power. 2

M4 has a blazing-fast Neural Engine — an IP block in the chip dedicated to the acceleration of AI workloads. This is Apple’s most powerful Neural Engine ever, capable of an astounding 38 trillion operations per second — a breathtaking 60x faster than the first Neural Engine in A11 Bionic. Together with next-generation ML accelerators in the CPU, the high-performance GPU, and higher-bandwidth unified memory, the Neural Engine makes M4 an outrageously powerful chip for AI. And with AI features in iPadOS like Live Captions for real-time audio captions, and Visual Look Up, which identifies objects in video and photos, the new iPad Pro allows users to accomplish amazing AI tasks quickly and on device.

iPad Pro with M4 can easily isolate a subject from its background throughout a 4K video in Final Cut Pro with just a tap, and can automatically create musical notation in real time in StaffPad by simply listening to someone play the piano. And inference workloads can be done efficiently and privately while minimizing the impact on app memory, app responsiveness, and battery life. The Neural Engine in M4 is Apple’s most capable yet, and is more powerful than any neural processing unit in any AI PC today.

The Media Engine of M4 is the most advanced to come to iPad. In addition to supporting the most popular video codecs, like H.264, HEVC, and ProRes, it brings hardware acceleration for AV1 to iPad for the first time. This provides more power-efficient playback of high-resolution video experiences from streaming services.

The power-efficient performance of M4 helps the all-new iPad Pro meet Apple’s high standards for energy efficiency and deliver all-day battery life. This results in less time needing to be plugged in and less energy consumed over its lifetime.

Today, Apple is carbon neutral for global corporate operations, and by 2030, plans to be carbon neutral across the entire manufacturing supply chain and life cycle of every product.

• Testing was conducted by Apple in March and April 2024. See apple.com/ipad-pro for more information.
• Testing was conducted by Apple in March and April 2024 using preproduction 13-inch iPad Pro (M4) units with a 10-core CPU and 16GB of RAM. Performance was measured using select industry‑standard benchmarks. PC laptop chip performance data is from testing ASUS Zenbook 14 OLED (UX3405MA) with Core Ultra 7 155H and 32GB of RAM. Performance tests are conducted using specific computer systems and reflect the approximate performance of iPad Pro.

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Todd Wilder

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