System Design Interview Preparation: Mastering the Art of System Design

The fact is, designing a scalable system is a challenging task, especially when your are asked to do it in an interview.

In this guide I will share my learnings about system design interviews and what process I followed to clear FAANG system design interviews.

Let me start with listing the two most important aspects of learning system design:

Develop a solid understanding of system design basics like Load Balancing, Caching, Partitioning, Replication, CAP Theorem, SQL vs. NoSQL, etc.
Researching common system design interview questions and practicing your answers.

One thing you should keep in mind, just understanding the basic system design concepts isn’t enough. In a system design interview, the interviewer wants to see that you can apply your knowledge to real-world scenarios.

So, my advice to you is to learn system design basics and then practice solving common system design problems and familiarize yourself with the technologies and design patterns that are commonly used. That’s the best way to show them you’re ready for the job.

What are the goals of a system design interview?

System design interviews are an important step in the hiring process for many software engineering roles, particularly for positions that involve building and scaling complex systems.

The goal of these interviews is to see if you’ve got what it takes to design and implement a scalable system that can handle a ton of traffic, data, and users.

Another important goal of these interviews is to provide a way for the interviewer to evaluate your problem-solving skills; how you make design decisions, and if you can effectively communicate your thought process.

1. Understanding the Basics of System Design

The first step in preparing for a system design interview is to have a solid understanding of the basics of system design. This includes key building blocks and principles such as scalability, fault tolerance, and load balancing. It’s important to understand the different types of databases and distributed systems, such as relational databases, NoSQL databases, and distributed key-value stores. Additionally, it’s helpful to be familiar with common design patterns such as microservices, event sourcing, and sharding.

a. Key concepts and principles

Here are a few basic concepts you should understand:

Scalability: The ability of a system to handle increasing amounts of load or traffic. It’s important to understand different scalability approaches such as horizontal scaling (adding more machines to a system) and vertical scaling (adding more resources to a single machine).

Fault tolerance: The ability of a system to continue functioning despite the failure of one or more of its components. Techniques such as redundancy and load balancing can help increase a system’s fault tolerance.
Load balancing: Load balancers distribute workloads across multiple machines in order to optimize resource usage and ensure that no single machine is overwhelmed.
Caching: Storing frequently accessed data in a high-speed storage layer to reduce the load on the underlying data store and improve system performance.
Availability: The ability of a system to respond to requests in a timely manner. This is closely related to fault tolerance and is typically measured as a percentage of time that the system is operational.
Consistency: The degree to which all nodes in a distributed system see the same data at the same time. Consistency can be divided into different levels, such as strong consistency, eventual consistency, and no consistency.
Latency: The time it takes for a request to be processed and a response to be returned. Latency is an important factor in system design, particularly for systems that handle real-time data.
Throughput: The number of requests a system can handle per unit of time. Throughput is closely related to scalability and is often used as a measure of a system’s performance.
Partition Tolerance: The ability of a system to continue functioning when network partitions occur. In distributed systems, it’s impossible to have both consistency and partition tolerance at the same time, so a designer must decide which one is more important for the use case.
CAP Theorem: The theorem states that it is impossible for a distributed system to simultaneously provide all three of the following guarantees: Consistency, Availability, and Partition Tolerance.
ACID Properties: A set of properties that guarantee that database transactions are processed reliably. The acronym stands for Atomicity, Consistency, Isolation, and Durability.

It’s important to be familiar with these concepts and understand how they apply to different types of systems. For example, a real-time financial trading system would need to have a high level of consistency and low latency, while a social media platform might prioritize high availability and partition tolerance over consistency.

It’s also important to note that these concepts and principles are not mutually exclusive and that they can be balanced and traded off based on the specific requirements of the system. A system designer must consider the trade-offs and make a design decision that balances the requirements with the constraints and limitations of the system.

b. Common design patterns:

Here are 7 most famous design patterns:

Microservices: It is a software architecture pattern in which an application is broken down into a collection of small, independent services that communicate with each other over a network. Each service is responsible for a specific functionality and is developed, deployed, and scaled independently. Microservices offer several benefits, such as increased scalability, improved fault tolerance, and faster deployment cycles. However, they also introduce additional complexity, such as the need for service discovery and inter-service communication.
Event sourcing: Event sourcing is a pattern in which the state of an application is represented as a stream of events, rather than a snapshot of its current state. This pattern is often used in systems that need to handle a large number of concurrent updates, such as financial systems and gaming platforms. Event sourcing allows for easy replay of events, which can be useful for debugging and auditing. However, it also requires additional storage and computational resources to maintain the event stream.
Sharding: Sharding is a technique for horizontally partitioning data across multiple machines in order to improve scalability and performance. In a sharded system, each machine is responsible for a specific subset of the data, and queries are routed to the appropriate machine based on the data’s partition key. Sharding can be used to distribute the load on a system, improve read and write performance, and increase the overall capacity of a system. However, it also introduces additional complexity, such as the need for consistent hashing, data replication, and partition-aware clients; follow this to read more about the disadvantages of sharding.

CQRS (Command Query Responsibility Segregation): CQRS is a pattern that separates the read and write operations of a system into separate models, allowing for optimized performance and scalability. This pattern can be useful in systems that handle a high volume of read and write operations, such as e-commerce websites. CQRS allows for different data stores and caching strategies to be used for read and write operations, improving the performance of both. However, it also requires more complex design and more effort to maintain two separate models of the data.
Reverse proxy: A reverse proxy is a server that sits in front of one or more web servers and forwards client requests to the appropriate server. It can be used to improve security, performance, and scalability of a system. It can also be used to provide additional functionality such as SSL termination, caching, and compression.

Circuit Breaker: A Circuit breaker is a design pattern that can be used to prevent cascading failures in a distributed system. It works by monitoring the health of a service and, when it detects an issue, it “trips” and prevents further requests from being sent to that service. This helps to prevent a single point of failure from bringing down the entire system.
Backpressure: Backpressure is a technique used to control the rate at which data is processed in a system, preventing it from being overwhelmed. This can be done by buffering incoming data and only processing it at a specific rate, or by rejecting incoming data if the system is unable to handle it.
Object Pool: An object pool is a design pattern that is used to improve the performance of a system by reusing objects, rather than creating new ones. Object pools are often used to manage the lifecycle of expensive resources, such as database connections or threads.

It’s important to be familiar with these design patterns and understand how they can be used to solve common problems in system design. However, it’s also important to remember that there’s no one-size-fits-all solution and that the choice of a design pattern should be based on the specific requirements of the system and the trade-offs that are willing to be made.

c. Familiarity with different types of databases

Here are the most famous database types:

Relational databases: Relational databases are the most common type of database and store data in tables, using SQL (Structured Query Language) for querying and manipulating that data. They are based on the relational model, which organizes data into one or more tables, with each table consisting of rows and columns. Popular examples of relational databases include MySQL, PostgreSQL, and Oracle.
NoSQL databases: NoSQL databases, also known as “not only SQL” databases, do not use a fixed schema and are optimized for handling large amounts of unstructured data. They are designed to handle the scale and performance requirements of modern web and mobile applications. NoSQL databases can be classified into different types, such as document databases, key-value stores, graph databases, and column-family stores. Popular examples of NoSQL databases include MongoDB, Cassandra, and Redis.
Distributed key-value stores: Distributed key-value stores are a type of NoSQL database that stores data as key-value pairs and is designed for horizontal scalability. They are often used as a caching layer or to store session data. Popular examples of distributed key-value stores include Riak and Redis.
Document databases: Document databases store data as semi-structured documents, such as JSON or XML, and are optimized for storing and querying large amounts of data. They are often used for applications that require flexible data modeling and rich querying capabilities. Popular examples of document databases include MongoDB and Couchbase.
Graph databases: Graph databases are optimized for storing and querying data with complex relationships. They store data as nodes and edges, rather than tables and rows, and are often used for applications that involve social networking, recommendation systems, and fraud detection. Popular examples of graph databases include Neo4j and JanusGraph.
Time-series databases: Time-series databases are optimized for storing and querying time-stamped data. They are often used for applications that involve monitoring, IoT, and financial data. Popular examples of time-series databases include InfluxDB, OpenTSDB, and Prometheus.

d. Familiarity with different types of distributed systems and algorithms

Here is a list of distributed system algorithms that can be used to solve design problems:

2. Practical Tips for the Interview

a. Communicating your thought process

Communicating your thought process during a system design interview is the most important aspect that can demonstrate your problem-solving skills and ability to think through a design problem. Here are some practical tips to help you communicate your thought process effectively during the interview:

Start with the problem statement: Clearly explain the problem you are trying to solve and the requirements for the system. This will help the interviewer understand your approach and how you plan to tackle the problem.
Break down the problem: Divide the problem into smaller parts and explain how you will solve each part individually. This will make it easier for the interviewer to understand your approach and how you are thinking through the problem.
Use diagrams and sketches: Draw diagrams and sketches to help explain your design. This will make it easier for the interviewer to visualize your design and understand how the different components of the system interact with each other.
Discuss trade-offs and constraints: Explain the trade-offs you made and the constraints you considered during the design process. This will demonstrate your understanding of the problem and your ability to make informed decisions.
Explain your reasoning: Clearly explain why you made certain design decisions and how they address the problem and requirements. This will help the interviewer understand your thought process and how you arrived at your solution.
Be prepared to answer questions and provide alternatives: The interviewer may ask follow-up questions about your design, be prepared to answer them and also be ready to provide alternatives and explain the pros and cons of each one.
Be open to feedback: Be open to feedback and be prepared to revise your design based on feedback from the interviewer. This will demonstrate your ability to iterate and improve your design based on feedback.

Here are some additional details on what can distinguish you from others in a system design interview.

b. Handling edge cases and constraints

Here are some practical tips to help you handle edge cases and constraints during the interview:

Anticipate edge cases: Think through potential edge cases and how they may impact your design. Examples of edge cases include high traffic, low memory, and high user concurrency.
Plan for failure: Consider how your design will handle failures and how it will maintain availability and consistency in the event of a failure.
Consider scalability: Think through how your design will scale as the number of users or amount of data increases.
Consider security: Think through how your design will protect sensitive data and ensure security.
Be prepared to explain your reasoning: Be prepared to explain why you made certain design decisions and how they address edge cases and constraints.

By handling edge cases and constraints effectively during the interview, you can demonstrate your ability to anticipate and address potential issues in your design, which can help to build trust and credibility with the interviewer.

c. How to answer a system design question in an interview

Here is a 7–step process to answer any system design interview question:

7–step process to answer any system design question

Step 1: Requirements clarification

Step 2: Back-of-the-envelope estimation

Step 3: System interface definition

Step 4: Defining the data model

Step 5: High-level design

Step 6: Detailed design

Step 7: Identifying and resolving bottlenecks

For further details on each of these steps check Step By Step Guide.

3. Follow the System Design Master Template

Take a look at the following system design master template that should guide you in answering any system design interview question.

See more details at System Design Master Template.

4. Top System Design Interview Questions

Here are the top 12 system design interview questions asked at top tech companies, including FAANG (Facebook, Apple, Amazon, Netflix, and Google).

Design Facebook Messenger
Design Youtube
Design Facebook’s Newsfeed
Design an API Rate Limiter
Design Twitter
Design Dropbox or Google Drive
Design a Web Crawler
Design Twitter Search
Design a URL Shortening service like TinyURL
Design Instagram
Designing Yelp or Nearby Friends
Design Ticketmaster

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System Design Interview Guide (2023) | Preparation Strategies and Practical Tips