System Design Interview Questions and Answers

Introduction

Big tech companies use system design interviews to assess how engineers approach designing scalable, reliable, and maintainable systems. Rather than testing your coding skills, these interviews evaluate you on system designs that can handle millions of users, high traffic levels, and distributed infrastructure.

In this guide, we go over key system design interview questions.

1. Design a URL Shortener (like TinyURL)

A URL shortening service takes long URLs and creates short, unique links that direct to their original destination.

Important Components: – An API service for generating the shortened URL– A Hashing or an id generation service to obtain just a random unique key (usually UUID) when post-processing is completed, check next request for uniqueness and find what was previously out into cache layer / in db store which links are memorized on frequently accessed 수준.

Design: When a user enters the long URL, we create a unique key through some techniques, such as Base62 encoding or hashing. A key is stored alongside the original URL in a database. When a user visits the short link, the service fetches the original URL and forwards their request.

Challenges:- Avoiding collisions in the generated Key- Handling billions of URLs- Fast Redirection using caching

2. What is Load Balancing?

Load balancing is the method of spreading incoming network traffic over several servers. It will ensure that no server is overloaded, thereby ensuring the performance and availability of the service.

So, a load balancer. It handles incoming requests and forwards them to the servers either in round robin fashion, least connections or weighted routing.

Advantages: - Prevents server overloads - Provides better system availability - Enables horizontal scaling of servers as demand increases - Allows for failover if one or more of the servers go down

3. Design a Ride-Hailing System (Uber/Ola)

A ride-hailing system pairs riders with nearby drivers in real time.

To begin our design, we will establish some core components: 1- Rider service, 2- Driver service, 3- Matching or dispatch service, 4- GPS location tracking system, 5- Payment system

Design Choice: Driver positions get updated continually through Geolocation Services. Spatial indexing techniques, such as geohashing, allow the dispatch services to find nearby drivers quickly when a user requests a ride.

Challenges: – Real-time driver monitoring – Matching driver to passenger effectively – Balancing surge demand

4. What is Caching?

Caching is a strategy that stores data to improve subsequent access speed: caches are high-performance storage layers (like memory) that hold commonly accessed data.

The system provides caching, so instead of running the database query every time a page is accessed, it fetches cached data for faster load times and reduced queries on the database.

Some Common Caching Strategies: ∙ Cache Aside ∙ Write Through ∙ Write Back ∙ Refresh Ahead

Examples of such tools are Redis and Memcached.

5. Design a Chat Application (WhatsApp)

The chat system allows real-time message exchange between users.

Architecture:– · Messaging servers · WebSocket connection · Message storage database up to date.

Real-time communication is achieved by utilizing a persistent connection like WebSockets for message transmission.

These challenges can include message ordering, delivery guarantees, and offline message storage.

6. What is the CAP Theorem?

CAP theorem states that distributed systems cannot have Consistency, Availability and Partition Tolerance at the same time.

Consistency means that all nodes see the same data. Availability guarantees that each request is responded to. The partition tolerance means the system can keep working as long as there is no failure in the network.

In the real world, most distributed systems trade off one property or another, based on their use case.

7. How to design a Video Streaming Platform (Netflix)

Video streaming platforms serve large video files to millions of users with low buffers.

With CDNs, the video files are spread out on edge servers that are distributed all around the world, meaning users can request their streams from nearby locations.

8. Latency, Throughput, and Availability Explained

Latency, throughput, and Availability are the parameters to measure system performance. Latency is the time a system takes to process a request and return a response. Latency is typically measured in milliseconds, and falls directly under the user experience umbrella — lower latency equals faster responses. Throughput is the amount of work a system can perform during a specific period (e.g., number of requests processed per second). On top of this, high throughput means that the system can efficiently support tons of users or operations.

Availability describes the percentage of time a system is running and available for use to users (ex: 99.9% → availability). A highly available system remains operational despite multiple point failures and is often achieved through redundancy, hardware, or application-level load balancing as well as fault-tolerant design. Latency, throughput, and availability together enable engineers to gauge how fast, scalable, and reliable the system is under different types of workloads.

9. What is Sharding?

Sharding is a method for scaling databases and helps us to divide large datasets into smaller, more manageable pieces called shards, where each shard can be stored on a different database server. This approach allows the system to scale horizontally -- instead of having a single machine that stores all the data, we spread the data on multiple computers. Queries can be done in parallel on a number of servers, which enhances performance.

Most times, each shard will contain a subset of data at the granularity of a defined shard-key, such as user ID, geographic region, or other logical partition. For example, users with IDs from 1 to 1M may be stored in one shard, and the ones with IDs between 1M and 2M in another. This method of distributing data is called sharding, and it directly enhances the scalability, storage capacity, and availability of a system. These characteristics are ideal for large applications that need to support millions of users.

10. What is Leader Election?

Leader Election: What is Leader Election in distributed systems? The leader will coordinate vital tasks, such as managing updates, handling write operations or communicating with external services. Well, having a single leader avoids contention between nodes for performing the same operation at the same time.

If the leader node fails or goes down, all other nodes within the system perform a leader election algorithm and automatically select another leader. This allows the system to keep working seamlessly. Datanode leader election is popularly achieved via consensus algorithms such as Raft, Paxos, or ZooKeeper-based coordination and is being used broadly in distributed databases, cluster management systems, and microservices architectures.

11. Horizontal vs Vertical Scaling

Horizontal scaling and vertical scaling are two common approaches to increase the capacity and performance of a system. Scale-out (horizontal scaling)– Here, a larger number of machines or servers are added to the system, and the load is distributed across these systems. We do not run an application on a single server, but add additional servers and use a load balancer that distributes requests to those servers. Handling components individually helps parcel out the functionality so we can scale these independently.

Vertical scaling (scale-up) means adding more resources to a single machine, like CPU, RAM or storage. This increases the machine’s processing power without altering the system architecture. But vertical scaling has hardware limitations and generates a single point of failure, while horizontal scaling enables systems to scale more consistently and dependably by using additional servers.

12. What are Consistency Models?

“Consistency model,” as it’s known, defines the rules on when and how updates made to a given piece of data become visible to users in a distributed system. Consistency models guarantee that users of systems with replicated data across multiple servers get the correct and predictable results when reading data. They also describe how quickly all the nodes in the system reflect a new write operation, helping balance trade-offs between performance, availability, and accuracy of data.

Examples of common consistency models are strong consistency, eventual consistency, and weak consistency. In the case of strong consistency, after writing some data, every later read will return the most up-to-date value. In practice, it means that after performing a write, the update will eventually be propagated to all nodes in the system -- but some reads might return stale data during that time. Weak consistency guarantees that there is no guarantee that a read will return the most recent write immediately, but it might update eventually. Which models are selected varies according to systems for their performance and reliability demands.

13. What is a NoSQL Database? How is it Different from SQL Databases?

SQL vs NoSQL databases differ primarily in the structure of their data, schema flexibility, and ability to scale. SQL databases (Relational Databases) are labeled with aria-labels, data in the form of its predefined schemas known as tables, which are made up of rows and columns. These adhere to ACID properties (Atomicity, Consistency, Isolation, Durability) that provide strong consistency and durable transactions. MySQL, PostgreSQL, and Oracle are examples of this type. SQL bases are usually needed for applications needing crane-cage data and intricate demands, like banking systems or trading business plans.

Unlike SQL databases, which are relational and suited to the processing of structured data, NoSQL databases are non-relational and built for large quantities of unstructured or semi-structured data. Unlike SQL databases, they employ flexible schemas and save information in key-value pairs, documents, graphs, or wide columns. These are some of the notable differences between NoSQL and SQL: While SQL follows ACID properties (Atomicity, Consistency, Isolation, Durability), NoSQL systems are more BASE in nature (Basically Available, Soft State, Eventual Consistency), thus establishing them as more versatile for distributed systems or large-scale applications. For example, MongoDB, Cassandra, or Redis. NoSQL databases are widely used in big data applications, real-time web, and analytic processing..

Do visit our channel to know more: SevenMentor