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Posted on Dec 10

15 System design tradeoffs for Software Developer Interviews

#programming #development #systemdesign #softwaredevelopment

Hello devs, if you've designed distributed systems and software, you may know that System design is about making crucial decisions to balance various trade-offs, which determine a system's functionality, performance, and maintainability.

Understanding these trade-offs when building or scaling a system helps engineers and architects make informed choices that align with business and technical requirements.

Earlier, I have shared many popular system design questions like API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy as well as 50 system design problems and today, we will delve into 15 critical system design trade-offs that every tech professional should know.

Whether you are learning System design or preparing for a System design interview, these tradeoffs will greatly help you articulate your answer and justify your selection and design for the interview.

Though, if you are learning these concepts as part of interview prep then I also suggest you check out sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, InterviewReddy.io and Udemy which have many great System design courses

15 System Design Tradeoffs for Experienced Developers and Software Architects

Without any further ado, here is a list of 15 system design tradeoffs that I think play an important role in designing real-world systems. Knowing these will help you to better architect a system that can withstand the test of time in production.

1. Horizontal vs Vertical Scaling

Vertical scaling, or scaling up, involves enhancing the power of existing servers by adding more CPU, RAM, or storage.

It is simple but has a limit. Horizontal scaling, or scaling out, means adding more servers to distribute the load.

While this approach can theoretically handle unlimited growth, it requires careful management of distributed systems and can introduce complexities such as data consistency and load balancing.

2. Stateful vs. Stateless Architecture

Stateful systems maintain session data, providing context for users, but they can be harder to scale and manage.

Stateless systems, in contrast, treat each request as independent, making them easier to scale but requiring all necessary information to be included in each request.

3. Latency vs. Throughput

Latency is the time taken to process a single task, while throughput is the total number of tasks processed in a given time frame.

Optimizing for latency means prioritizing quick responses, suitable for systems like online gaming or high-frequency trading.

Conversely, optimizing for throughput is ideal for batch processing systems, where the goal is to maximize the volume of processed data over time.

4. SQL vs. NoSQL Databases

SQL databases, like MySQL and PostgreSQL, are structured and enforce strict schemas, making them ideal for complex queries and ACID compliance.

NoSQL databases, such as MongoDB and Cassandra, offer more flexibility, scalability, and speed for unstructured or semi-structured data. The choice depends on the data model, consistency requirements, and scalability needs of the application.

5. Consistency vs. Availability (CAP Theorem)

This is another important System design concept and tradeoff a senior engineer should remember.

According to the CAP theorem, a distributed system can only guarantee two out of three properties: Consistency, Availability, and Partition Tolerance.

Consistency ensures that all nodes see the same data at the same time, while availability means the system is always operational.

Choosing consistency over availability is crucial for financial transactions, while for social media feeds, availability may take precedence.

6. Strong vs. Eventual Consistency

Strong consistency guarantees immediate data synchronization across all nodes, which is essential for financial systems.

Eventual consistency allows data to be updated across nodes with a slight delay, improving performance and availability.

This approach suits applications where temporary inconsistency is acceptable, like social media updates.

7. Read-Through vs. Write-Through Caching

In a read-through cache, data is loaded into the cache on a read miss. This approach reduces cache pollution but might increase latency for uncached data.

In a write-through cache, data is simultaneously written to the cache and the database, ensuring consistency but at the cost of write performance.

8. Synchronous vs. Asynchronous Processing

Synchronous processing requires each task to be completed before the next begins, which can lead to inefficiencies and latency.

Asynchronous processing allows multiple tasks to be executed concurrently, reducing wait times and enhancing system responsiveness, suitable for non-blocking I/O operations or parallel processing.

9. Batch vs. Stream Processing

Batch processing handles data in chunks, ideal for tasks like payroll processing, where the entire dataset is processed at once.

Stream processing deals with continuous data flow, suitable for real-time analytics, where immediate data processing is crucial.

10. Scalability vs. Performance

Scalability refers to the system's ability to handle growing amounts of work by adding resources. Performance, on the other hand, is about how fast a system can process tasks.

Often, enhancing scalability can lead to reduced performance due to increased overheads like network latency and data synchronization.

Therefore, a balance must be struck between accommodating more users and maintaining quick response times.

11. Long Polling vs. WebSockets

Long polling allows a client to wait for data from the server, which is resource-intensive and can cause delays.

WebSockets offer a persistent two-way connection, enabling real-time data transfer.

This is ideal for applications like chat systems or live dashboards, where continuous updates are necessary.

Another benefit of WebSockets it that they run on port 80 which is also used by HTTP and hence almost always open. You don't need to open another port on firewall which means less headache as you don't have to deal with network teams .

12. Normalization vs. Denormalization

Normalization reduces data redundancy and improves data integrity by dividing data into related tables.

However, this can lead to complex joins and slower reads.

Denormalization combines data into fewer tables, speeding up read operations but introducing redundancy, which can complicate updates and maintenance.

13. Monolithic vs. Microservices Architecture

This tradeoff is probably the most important one for system design interviews.

Monolithic architecture involves a single, unified codebase, simplifying development and deployment but making scaling and maintenance challenging.

Microservices break down the system into independent services, enabling scalability and flexibility at the cost of increased operational complexity.

14. TCP vs. UDP

The classic tradeoff for software developers.

TCP provides reliable, ordered, and error-checked delivery of data, making it suitable for applications like web browsing and file transfers.

UDP, in contrast, offers faster, connectionless communication but without guarantees of delivery or order, making it ideal for applications like online gaming or video streaming, where speed is crucial, and occasional data loss is acceptable.

15. REST vs. GraphQL

REST uses standard HTTP methods and endpoints, which are simple and scalable but can lead to over-fetching or under-fetching of data.

GraphQL, on the other hand, allows clients to query exactly what they need, reducing the amount of data transferred and improving performance, especially for complex or nested data requirements.

12 Best System Design Interview Resources

And, here are curated list of best system design books, online courses, and practice websites which you can check to better prepare for System design interviews. Most of these courses also answer questions I have shared here.

DesignGuru's Grokking System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.
Codemia.io: This is another great platform to practice System design problems for interviews. It has more than 120+ System design problems, many of which are free and it also has a proper structure to solve them.
ByteByteGo: A live book and course by Alex Xu for System design interview preparation. It contains all the content of System Design Interview book volumes 1 and 2 and will be updated with volume 3 which is coming soon.
Exponent: A specialized site for interview prep especially for FAANG companies like Amazon and Google, They also have a great system design course and many other materials that can help you crack FAAN interviews
"System Design Interview" by Alex Xu: This book provides an in-depth exploration of system design concepts, strategies, and interview preparation tips.
"Designing Data-Intensive Applications" by Martin Kleppmann: A comprehensive guide that covers the principles and practices for designing scalable and reliable systems.
LeetCode System Design Tag: LeetCode is a popular platform for technical interview preparation. The System Design tag on LeetCode includes a variety of questions to practice.
"System Design Primer" on GitHub: A curated list of resources, including articles, books, and videos, to help you prepare for system design interviews.
Educative's System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.
High Scalability Blog: A blog that features articles and case studies on the architecture of high-traffic websites and scalable systems.
YouTube Channels: Check out channels like "Gaurav Sen" and "Tech Dummies" for insightful videos on system design concepts and interview preparation.
InterviewReddy.io: This site has been created by Gaurav Sen, an ex-Google engineer, and popular YouTuber and creator of the System Design simplified course. If you are aiming for a FAANG interview, you can also check this website.

image_credit --- ByteByteGo

You should also remember to combine theoretical knowledge with practical application by working on real-world projects and participating in mock interviews. Continuous practice and learning will give you confidence for system design interviews.

That's all about 15 essential System Design tradeoffs that every developer needs to be aware of. Understanding these system design trade-offs is crucial for architects and developers as they enable the design of robust, scalable, and maintainable systems.

By weighing the pros and cons of each approach, one can make informed decisions that align with the technical and business requirements.

Whether you're optimizing for performance, scalability, or consistency, recognizing these trade-offs will help you build better systems that meet user expectations and handle future growth.

All the best with your interviews !!