The most common system design interview questions ask you to design a URL shortener, a chat app like WhatsApp, an Instagram-style news feed, a rate limiter, a ride-sharing app like Uber, a notification system, and a video streaming platform like Netflix. For each one, interviewers expect you to clarify requirements, estimate scale, propose a high-level architecture, and then go deep into database choices, caching, and bottlenecks. Practising 10 to 15 of these designs end to end covers most variations you will actually face.

Build fundamentals first: scaling, load balancing, caching, SQL vs NoSQL, sharding, replication, and message queues. Then study a few standard designs and practise 2 to 3 designs a week by solving them out loud in 35 to 45 minutes. Focus on real production trade-offs like cache invalidation, hot keys, and failure handling instead of textbook definitions, and finish with at least one or two mock interviews, because explaining your design clearly matters as much as the design itself.

Follow a clear framework: (1) clarify functional and non-functional requirements, (2) estimate scale in terms of users, requests per second, and storage, (3) draw the high-level design with the API, database, cache, and load balancer, (4) deep-dive into the most critical component, and (5) wrap up with bottlenecks, failure modes, and trade-offs. Keep talking throughout the interview, since interviewers evaluate how you reason about trade-offs rather than whether you recite a single correct architecture.

Grokking the System Design Interview is a popular online course that teaches system design through reusable patterns such as load balancing, caching, consistent hashing, and the CAP theorem, and then applies them to classic problems like designing a URL shortener, WhatsApp, Netflix, and Dropbox. It is a good starting point if you have never seen structured system design content before. Pair it with hands-on practice and mock interviews, since the course alone will not make you interview-ready.

The System Design Interview by Alex Xu is the most widely recommended system design interview book: Volume 1 covers core building blocks like scaling, caching, and consistent hashing, while Volume 2 walks through real designs such as YouTube, Zoom, and Google Maps. If you want deeper fundamentals, read Designing Data-Intensive Applications alongside it. Whichever you pick, apply each chapter by designing a system on your own instead of only reading.

Distributed systems in computer science are groups of independent computers that cooperate over a network and behave as a single system for the user. Apps you use daily, from UPI payments to Swiggy orders to WhatsApp messages, all run on distributed systems because no single machine can handle that scale or survive failures on its own. This field is known as distributed systems engineering, and a distributed systems engineer focuses on keeping these systems consistent, fault-tolerant, and fast even when individual machines go down.

Get your prerequisites solid first: operating systems, computer networks, databases, and basic backend development. Then learn the core concepts one by one, including the CAP theorem, consistency models, replication, partitioning, consensus, and message queues. Theory only sticks when you apply it, so build small systems, add load, and deliberately break them; a project-based distributed systems course works far better than passively watching videos. Designing Data-Intensive Applications is the standard book to go deeper.

Good beginner-friendly distributed systems projects include a URL shortener with caching and database replication, a Redis-based rate limiter, a Kafka-powered notification or chat service, a key-value store with leader-follower replication, and a task queue with retries and dead-letter handling. If you are unsure how to build distributed systems without working at a big tech company, recreate simplified versions of tools you already use and then stress-test them with high load and deliberate failures. These projects double as portfolio work and interview stories.

Distributed systems is the broad concept of multiple machines working together over a network. Microservices is an architectural style where one application is split into small, independently deployable services. Every microservices architecture is a distributed system, but not every distributed system uses microservices, since even a monolith replicated across data centres is a distributed system. Knowing this distinction clearly helps in interviews, where the two terms are often used interchangeably.

The most common distributed systems interview questions cover the CAP theorem, strong vs eventual consistency, replication strategies, sharding and partitioning, leader election, consensus algorithms like Raft and Paxos, idempotency, distributed transactions and sagas, and handling network partitions. Interviewers usually expect you to relate your answers to real systems, such as how Redis, Kafka, or a database handles these problems, rather than giving only definitions.

Redis is an open-source, in-memory key-value store used as a cache. Because data lives in RAM instead of on disk, it reads and writes in sub-millisecond time, which makes slow database-backed pages feel instant. Your application checks Redis first; on a cache hit it serves data from memory, and on a miss it fetches from the database and stores the result in Redis with an optional expiry time (TTL). Redis also supports rich data structures such as strings, hashes, lists, sets, and sorted sets, which is why it is used for far more than simple caching.

Redis caching is used for storing frequently accessed data in memory, including database query results, user sessions, API responses, and configuration. Beyond plain caching, Redis powers rate limiters, leaderboards through sorted sets, pub/sub messaging, and distributed locks, all of which need extremely fast reads and writes. If your product pages, feeds, or dashboards hit the database repeatedly with the same data, Redis caching is usually the first fix to reach for.

Add the spring-boot-starter-data-redis dependency, configure the Redis host, port, and password in your application properties, and enable caching with @EnableCaching. Then annotate service methods with @Cacheable to cache results, @CachePut to update the cache, and @CacheEvict to invalidate it, and set sensible TTLs through RedisCacheConfiguration. Watch out for serialization issues with complex objects, and start by caching read-heavy, rarely-changing data instead of trying to cache everything.

The main Redis caching strategies are cache-aside, where the application manages the cache itself and it is the most widely used pattern, along with read-through, write-through, and write-behind, where writes are queued asynchronously for speed. Combine any of these with TTLs and eviction policies like LRU so memory does not fill up. Also prepare for the classic failure cases: stale data after invalidation, cache stampede when a hot key expires, and hot keys that overload a single Redis node.

Interviewers commonly ask how Redis achieves such low latency, how persistence works (RDB vs AOF), which eviction policies it uses, the trade-offs between cache-aside and write-through, and how to handle cache penetration, cache breakdown, and cache avalanche. You may also get scenario questions on implementing a distributed lock, a rate limiter, or a leaderboard with Redis, and how Redis compares with Memcached. Having built even one small project with Redis makes these answers far more convincing.