System Design Fundamentals for Interviews

This lecture explains Facebook News Feed system design with a deep focus on database table structure, data modeling, indexing, and scalable backend architecture for system design interviews.

This lecture explains SQL queries used in Facebook News Feed system design, including feed generation, joins, indexing, pagination, and query optimization for large-scale systems.

This lecture discusses the real-world system design issues Facebook faced when scaling to 1 billion users, including database bottlenecks, feed generation challenges, caching limits, and latency problems.

In this lecture, we discuss how Facebook addressed the data size problem when scaling its system to 1 billion users.

We introduce architectural and database-level solutions used to manage massive storage growth in the Facebook News Feed system, including data retention strategies, table design decisions, and storage optimization techniques.

This session focuses only on solving the data size challenge. The read and write scalability issues, along with performance bottlenecks, are intentionally deferred and will be discussed in the next lecture to maintain a clear problem-by-problem system design flow.

In this lecture, we analyze one of the most critical challenges in large-scale systems — balancing read and write workloads — using Facebook’s system design as a case study.

After addressing the data size problem in the previous lecture, we now focus on how Facebook handles massive write traffic from user actions and heavy read traffic generated by news feed consumption. You will learn why reads and writes scale differently, how write-heavy systems are designed, and why optimizing for fast reads often introduces new architectural trade-offs.

We discuss common bottlenecks related to database writes, indexing overhead, replication lag, caching limits, and fan-out strategies, and explain how these issues emerge at scale when serving millions of concurrent users.

By the end of this lecture, you will understand:

• Why read and write traffic behave differently at large scale

• Key bottlenecks in write-heavy systems like Facebook News Feed

• Trade-offs between read optimization and write performance

• How these challenges are evaluated in system design interviews

This lecture builds directly on earlier discussions and prepares you for deeper optimizations covered in upcoming sessions.

In this final lecture, we bring together all the concepts discussed throughout the Facebook News Feed system design series and consolidate the key learnings into a clear, structured understanding.

We recap how Facebook’s system evolved as it scaled to hundreds of millions and eventually billions of users, starting from data size challenges, moving through storage optimization, and then addressing critical read and write bottlenecks at scale. This session highlights how each design decision introduced trade-offs and why solving system design problems requires addressing constraints one step at a time.

Rather than introducing new components, this lecture focuses on connecting the dots—helping you understand how table design, data lifecycle management, read optimization, write scalability, and caching strategies work together in a real-world large-scale system.

By the end of this lecture, you will be able to:

• Explain Facebook News Feed system design end-to-end

• Identify key scalability challenges and their trade-offs

• Structure system design answers clearly in interviews

• Apply the same thinking framework to other large-scale systems

This conclusion also reinforces a problem-first, solution-driven approach that is essential for system design interviews and real-world backend engineering.