I ACED my Technical Interviews knowing these System Design Basics

Summary

This video provides a comprehensive overview of how to design a scalable and reliable social media platform. It covers essential topics such as distributed systems, horizontal and vertical scaling, load balancing, caching strategies, and choosing between SQL and NoSQL databases. The video emphasizes key concepts like system scalability, reliability, and efficiency, and introduces challenges like data consistency, indexing, and database partitioning. It also highlights strategies for improving performance and handling the growing data needs of large applications, making it an invaluable guide for system design.

Takeaways

• 😀 Distributed systems are essential for scaling applications and involve a network of independent computers working as one cohesive unit.
• 😀 Scalability can be achieved through horizontal scaling (adding more servers) or vertical scaling (upgrading existing servers).
• 😀 Reliability ensures the system continues to function correctly even when components fail, while availability measures the system's operational time.
• 😀 Load balancing helps manage traffic by distributing requests across multiple servers, preventing any single server from becoming overwhelmed.
• 😀 There are various load balancing algorithms like least connections, round robin, and IP hashing, each suitable for different use cases.
• 😀 Caching improves system performance by storing frequently accessed data closer to the user, reducing load on the database and lowering latency.
• 😀 The CAP theorem outlines a trade-off in distributed systems, where you can only guarantee two out of the three properties: consistency, availability, and partition tolerance.
• 😀 Choosing between SQL and NoSQL databases depends on your needs: SQL is ideal for structured data, while NoSQL is better for large, unstructured data and scalability.
• 😀 Indexing speeds up database queries by creating separate data structures, but it can slow down write operations, so indexes should be used strategically.
• 😀 Data partitioning helps manage large datasets by dividing them into smaller, more manageable parts, improving performance and scalability. Common techniques include horizontal and vertical partitioning.
• 😀 Consistent hashing allows for easy scaling in distributed systems by minimizing the data rebalancing required when adding or removing servers.

Q & A

What is a distributed system, and why is it important for scalability?

A distributed system is a network of independent computers working together as a single system. It is crucial for scalability because it enables handling growing user demands by adding more servers (horizontal scaling) or upgrading existing ones (vertical scaling).

What is the CAP Theorem, and what does it explain about distributed systems?

The CAP Theorem states that a distributed system can only guarantee two out of three properties: Consistency, Availability, and Partition Tolerance. This explains the trade-offs in system design, where achieving all three is impossible. Depending on the needs, systems prioritize two properties, like consistency and availability, over partition tolerance, or vice versa.

How does horizontal scaling differ from vertical scaling?

Horizontal scaling involves adding more servers to the system to distribute the load, whereas vertical scaling involves upgrading the hardware of existing servers (e.g., adding more RAM or CPU power) to handle more traffic.

What role do load balancers play in distributed systems?

Load balancers distribute incoming user requests across multiple servers to prevent any single server from becoming overwhelmed. This ensures efficient use of resources and helps in handling high traffic by maintaining optimal performance.

What is caching, and why is it important in large-scale systems?

Caching stores frequently accessed data in faster storage (e.g., RAM or Redis) to reduce latency and improve performance. It is crucial in large-scale systems because it prevents repeated queries to the primary database, thus speeding up data retrieval.

What are the challenges of caching, and how can data consistency be maintained?

The main challenge of caching is maintaining data consistency, ensuring the cached data is up-to-date with the original database. Strategies to maintain consistency include write-through (updating both the cache and the database simultaneously) and write-around (bypassing the cache to directly update the database).

What is the difference between SQL and NoSQL databases?

SQL databases (e.g., MySQL, PostgreSQL) use a structured schema and are best for transactional applications requiring strong consistency. NoSQL databases (e.g., MongoDB, Cassandra) offer more flexibility, handle unstructured data, and are designed for scalability and high-performance applications.

Why are indexes important in databases, and what types of indexes exist?

Indexes speed up data retrieval by providing a quick reference to the actual data, reducing search time. Common types of indexes include primary keys (unique identifiers), secondary indexes (on non-primary columns), and composite indexes (on multiple columns for more complex queries).

What is data partitioning, and why is it used in scalable systems?

Data partitioning splits large databases into smaller, more manageable chunks, improving performance, load balancing, and availability. It can be done using horizontal (dividing rows across databases), vertical (separating columns), or directory-based partitioning (using a lookup service to distribute data).

What is consistent hashing, and how does it benefit distributed systems?

Consistent hashing is a technique used in distributed systems to minimize data redistribution when adding or removing servers. It ensures that only a small fraction of data needs to be remapped, making it easier to scale the system dynamically without disrupting data availability.