
Understand design fundamentals as essential knowledge for system design, including data partitioning and charting data, data redundancy, client load balancing, physical databases, and deployment strategies.
Explore data partitioning to distribute data across multiple servers with vertical and horizontal strategies, including hashing, range, round-robin, and geo partitioning.
Explore how data redundancy and replication boost availability and failover, scale throughput, and apply full dump and load, incremental, or logged application methods across replication strategies.
Explore caching and content delivery networks to reduce latency by serving data from fast storage, with a focus on top 20 percent of items and distributed caching across multiple servers.
Balance requests across servers, monitor health, and remove unhealthy nodes to prevent downtime. Compare software and hardware load balancers, and algorithms like round-robin, least connections, least response time, and hashing.
Learn how SQL and NoSQL databases compare, including key-value stores, document databases, column families, and graph databases, and choose based on use case, schema flexibility, and agile development.
Discover how big data solutions guide system design using open source platforms and cloud services to accelerate development and enable scalable data processing with Hadoop and MapReduce.
Explore hashing and consistent hashing for distributing data across multiple servers. See how a hash function maps keys to a circular space and uses labels and angles to balance load.
Explore the application deployment strategy with blue-green deployment (blue and green production versions and traffic switching for zero downtime) and partial traffic rollouts, addressing data compatibility, cost, and rollback considerations.
Explore the seven steps of system design interview, from defining scope and requirements to data modeling, high-level design, capacity estimation, and handling distributed, scalable architectures.
Design a youtube-like video streaming app by outlining functional and non-functional requirements, capacity estimation, prototype data models, high and detailed design, encoding, storage, caching, and fault-tolerant architecture.
Designing an application without knowing design fundamentals is like you are playing a game without knowing the rules. So You can consider design fundamentals as foundational knowledge for system design. It is as important as data structures for Coding interviews.
One thing is very important that there is nothing like wrong design, It always may be good or bad design. When interviewers ask you about system limitations or design problems then fundamental knowledge can be the key player to answer such questions because all the fundamentals are solutions for some specific problems.
The goal of such a question is to check your problem-solving ability on a complex problem. It is quite understandable that you might not have the experience to design such big applications but proposing the best possible solutions would show your understanding of design fundamentals.
The interviewer wants to see if you consider common distributed system challenges in your design like distributing the data to multiple machines and aggregating them together later and equally distributing the load on multiple servers. It is also quite interesting to see your design approach when user throughput increases.
System design questions are quite open-ended and there is no standard answer for such questions. They want to see that you think about operational scenarios, edge cases, limitations, and assumptions.
Interviewers can decide whatever direction the discussion will go in. With that in mind, even for the same question, you may have a completely different discussion with a different interviewer.
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