Cracking System Design Interview: Real Questions and Practical Answers

The majority of candidates enter into a system design interview expecting another coding round and soon find out that it is a totally different test.

You are not requested to write code. You are called on to design systems capable of supporting real users, real traffic and real failure. Problems such as designing a chat system, designing a payment platform, etc are not about perfect answers but rather how you think, how you structure problems and how you make practical decisions under constraints.

That’s where many people struggle. System design is not the sort of thing that you can memorise, it is a sort of thing that you learn to reason through.

In this blog, you will come across real system design interviews questions with practical, structured answers so you are able to know not only how to answer, but how to think like a good applicant in interviews.

Basic System Design Interview Questions

1. What is system design?

System design is the process of determining how various components of a system such as APIs, databases, and services work together to solve a problem.

Practically it comprises such things as:

• how requests flow through the system
• how data are stored and accessed.
• how the system recovers after errors and scaling.

It is not really about coding but a lot more about making architectural decisions which will continue to work even when the system gets larger.

2. What is scalability?

Scalability is the ability of a system to handle increasing traffic without performance degradation.

Scaling can be done in two ways:

• Vertical scaling, here we are adding resources to a particular machine.
• Horizontal scaling, in which we increase the number of machines.

In practical systems, we would choose to scale horizontally since this does not put a limit on hardware and it also has a better fault tolerance. As an example, we do not upgrade a single server, we upgrade a set of servers in front of a load balancer.

3. What is the CAP theorem?

According to CAP theorem, only two out of three of consistency, availability, and partition tolerance can be guaranteed in a distributed system.

Partition tolerance is a requirement since failure of the network cannot be avoided. So the trade-off is between consistency and availability.

For example:

• Consistency is a priority in the banking systems.
• Availability in social media is important to us, even when data is a little bit slow.

4. What is load balancing?

Load balancing is used to divide the incoming traffic among several servers such that no single server is overloaded.

A load balancer is usually in front of servers and directs traffic to servers using algorithms such as round-robin or least connections.

It improves:

• Loading by sharing load.
• Availability through rerouting traffic in case of a server failure.

In its absence, a single server will act as a bottleneck.

5. What is caching?

Caching stores will often put frequently accessed information in a fast layer such as memory so that we do not have to repeatedly query the database.

As an example, when requests are made to the same user profile many times, we will store the profile in cache and not go to DB each time.

The most common is cache-aside:

• Check cache first
• If miss: fetch it out of DB: store it in cache.

The trade-off is:

• Faster reads
• But danger of stale data unless invalidated, in some convenient way

6. What is sharding?

Sharding is the process of splitting a large dataset into many machines, such that each machine only handles a small part of the data.

For example, user data can be split based on user ID ranges.

This improves:

• Scalability
• Performance

But it brings complexity, such as:

• Cross-shard queries
• Data rebalancing

7. SQL vs NoSQL?

SQL databases are relational, and impose strict schemas, which is handy in transactional systems with strong consistency requirements.

NoSQL databases are more horizontally scalable and are schema-flexible, which makes them useful in large distributed systems.

So typically:

• Banking or payment SQL.
• NoSQL in social media or analytics system

Intermediate System Design Interview Questions

8. What is consistent hashing?

Data across servers is distributed using consistent hashing so that the movement of data is minimized when adding and removing servers.

Instead of reassigning all data, a very small fraction of the data is redistributed.

It is particularly handy with systems such as distributed caches or load balancing.

9. Latency vs Throughput?

Latency is the time it takes to process a single request where throughput is the number of requests processed each second.

For example:

• A system can be very responsive (low latency)
• But continue to serve fewer requests (low throughput)

In practice, we attempt to strike a balance between the two based on the use case.

10. What is a CDN?

A CDN is a web of servers spread worldwide which provide the fixed content such as images or videos which are served at locations much closer to the users.

This minimizes latency and minimizes load on the main server.

For example, when you open Instagram, you are loaded with images on a nearby server (CDN) rather than on a central server.

11. What is eventual consistency?

The theory of eventual consistency states that, following an update to a piece of data, all nodes will eventually be updated to reflect it, but not immediately.

It is applied in distributed systems where immediate consistency is not as important as availability.

As an example, likes or comments can take a few seconds of time to update all around the world.

12. What is a message queue?

A message queue is used to enable services to communicate asynchronously.

We do not directly call another service and instead send a message to a queue which will be processed later by the consumer.

This helps:

• Decouple services
• Cope with traffic jams.
• Improve reliability

13. What is a single point of failure?

Any type of component whose failure causes the whole system to fail is a single point-of-failure.

For example, having only one database server.

To prevent this we have:

• Replication
• Load balancing
• Failover systems

14. What is rate limiting?

Rate limiting is the number of requests that a user is allowed to make within a specified time frame.

It makes it difficult to abuse the system and safeguard system resources.

Common implementations include:

• Token bucket
• Sliding window

It’s usually implemented using fast stores like Redis.

Advanced System Design Interview Question

15. What would you do to create a URL shortening service (such as bit.ly)?

What are the necessary features?

• Convert long URLs into short, unique links
• Instantly redirect short URL to original URL.
• Support custom aliases (user-defined short links).
• Monitor statistics such as number of clicks.
• Support high read traffic (millions of redirects)

What are the general issues?

• What if two users request the same custom alias?
• What do you do to make sure that short URLs are original?
• What will the system do when the read traffic is very high?
• What is the result of failure of a database node?
• What to do with expired or unused links?

Possible approach / considerations:

• Short URLs should be encoded using Base62 and a random ID generator.
• Store mapping in a NoSQL database for fast lookups
• Install Redis cache to provide the frequently visited URLs in a short time.
• Apply load balancers so as to distribute traffic.
• Apply rate limiting to prevent abuse
• Implement TTL (expiry) for temporary URLs

16. What would you implement to create a global chat system (such as WhatsApp?

What are the necessary features?

• Live chatting among users.
• Support for group chats
• Persistence of messages (chat history)
• Offline message delivery
• Security: End-to-end encryption.

What are the general issues?

• What is the behavior when a user is offline?
• What do you do to guarantee the delivery of messages and order?
• Will encryption slow down the system?
• What do you do to serve millions of concurrent users?

Possible approach / considerations:

• Real-time communication should be done using WebSockets.
• Store messages in a distributed NoSQL database
• Use message queues (Kafka/RabbitMQ) for reliable delivery
• Order by giving a message ID, or sequence number.
• Push notifications should be used by offline users.
• Evenly distribute users among servers based on a consistent hashing.

17. How would you design a rate limiter?

What are the necessary features?

• Restrict the amount of requests that a user can make within a certain time frame.
• Avoid overloading and misuse of systems.
• Give a quick response to approved/ blocked requests.

What are the general issues?

• What to do with distributed systems involving a number of servers?
• What to do to be accurate and not to slow down performance?
• What can be done to prevent bursts at time boundaries?

Possible approach / considerations:

• Apply the Token Bucket algorithm to control the flow of traffic smoothly.
• Store request counters in Redis for fast access
• Use sliding window approach to avoid burst spikes
• Use per-user or per-IP restrictions.
• Atomic operations should be ensured to prevent race conditions.

18. How would you design a notification system?

What are the necessary features?

• Notification through email, SMS or push.
• Process large amounts of events.
• Allow user preference control (opt-in/out)

What are the general issues?

• What would occur in case of failure in delivering notification?
• What to do to prevent spamming users?
• What to do when there are unexpected jumps in traffic?

Possible approach / considerations:

• Decouple services using message queues.
• Add worker services to receive notifications.
• Implement retry logic and dead-letter queues
• Add rate limiting to avoid spam
• Use batching for non-critical notifications

19. How would you create a ride-sharing system (such as Uber?

What are the necessary features?

• The users should be able to book rides.
• Match riders with nearby drivers
• Offer live tracking of location.
• Determine approximate arrival time (ETA)

What are the general issues?

• How to efficiently match drivers and riders?
• What to do with real-time updates on location?
• How is it when demand is at its peak?

Possible approach / considerations:

• Use geospatial indexing (geohashing) to find nearby drivers
• Tracking in real-time using WebSockets.
• Store live locations in in-memory databases (Redis)
• Partition system by regions or cities
• Introduce queueing of situations with high demand.

20. How would you design a news feed system (like Instagram)?

What are the necessary features?

• Display posts of the users you follow.
• Update feed in near real-time
• Rank the posts according to relevance.

What are the general issues?

• What do you do to produce feeds in large volumes to serve millions of end users?
• What happens with users having millions of followers?
• What do you do to keep feed fresh and prevent overloading?

Possible approach / considerations:

• Apply fan-out to write on active users.
• Use fan-out on read for less active users
• Store posts in NoSQL databases
• Cache feeds using Redis
• Rank posts using engagement and recency

21. What would you create to create a video streaming/system (e.g., Netflix?)

What are the necessary features?

• Post and broadcast videos.
• Support multiple video qualities
• Provide smooth playback

What are the general issues?

• What can be done to minimize buffering?
• What to do with millions of simultaneous users?
• What to do in order to deliver content on the global front?

Possible approach / considerations:

• Store videos on distributed object storage.
• Encode videos into multiple resolutions
• Use CDN to deliver content closer to users
• Use adaptive bit rate streaming.
• Store popular data in edge servers.

22. How would you create a file-storage system (such as Google Drive)?

What are the necessary features?

• Share, download and upload files.
• Synchronize files between devices.
• Work with large file sizes.

What are the general issues?

• What is the best way to deal with file updates?
• What in case more than one user is working on the same file?
• What to do with big files that are reliable?

Possible approach / considerations:

• Split files into chunks for upload
• Store data in distributed storage systems
• Use versioning for conflict resolution
• Only modified chunks are synchronized rather than complete file.
• CDN will provide faster downloads.

23. What would be your design of a search autocomplete system?

What are the necessary features?

• Suggest search queries as user types
• Display best related suggestions.
• Real time update recommendations.

What are the general issues?

• How to return results with low latency?
• What is the best way of ranking suggestions?
• What to do with huge query data?

Possible approach / considerations:

• Use Trie (prefix tree) for fast lookup
• Cache popular queries
• Ranking according to frequency and recency.
• Scale using sharding.

24. What would you do to design a logging system?

What are the necessary features?

• Collect logs from multiple services
• Efficiently store and query logs.
• Support real-time monitoring

What are the general issues?

• What to do with large volumes of logs?
• What to do in order to make sure that logs are not lost?
• How to search logs quickly?

Possible approach / considerations:

• Implement log gatherers + message lines.
• Store logs in distributed storage (like Elasticsearch)
• Fast querying: index logs.
• Apply retention policies

25. How would you design a payment system (like UPI / Stripe)?

What are the necessary features?

• Enable users to transfer and receive money safely.
• Support transaction history and status tracking
• Make it very reliable (there should not be money loss).
• Prevent duplicate transactions
• Process a large volume of transactions.

What are the general issues?

• What occurs when some transaction is not complete (money is debited and not credited)?
• What do you do to avoid a second payment when a user re-tries?
• How to maintain consistency of data across systems?
• What to do during rush times (such as sale days)?
• What to do to ensure that fraud or suspicious transactions are detected?

Possible approach / considerations:

• Avoid duplication of transactions with idempotency keys.
• Keep a transaction ledger system to accurately track.
• Financial consistency Use ACID-compliant databases (SQL).
• Reliability: Have a two-phase commit or other mechanisms in place.
• Implement a retry logic with adequate status checks.
• Asynchronous processing should be done using message queues.
• Apply rate limiting and fraud detection systems
• Ensure encryption and secure APIs for transactions

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Wrapping it Up:

The more you practice breaking down systems and explaining your approach clearly, the more confident you’ll become.

Hope you found this blog helpful and that it gives you a clear direction to approach your next system design interview with confidence.

FAQs

1. Are system design interviews asked for freshers?

Yes, they are at an extremely basic level. You will have to answer basic system design concepts such as APIs, databases, and other basics related to system design.

2. How should I answer system design questions in an interview?

You have to first clarify the requirements, then explain the high-level design you are going to create, give a clear explanation of each major component, explain how you would scale your design, and finally provide a brief description of any trade-offs your designs may have.

3. Do I need to memorize system design answers?

No, interviewers want to know how you think, not what you can memorise. The most important aspect of system design is understanding how a system works.

4. What are the most common system design topics?

The most common topics for system design questions are caching, load balancers, databases, scaling, and distributed systems.