System design interviews evaluate your ability to design scalable, reliable, and efficient systems. Unlike coding interviews, they focus on architecture, problem-solving, communication skills, and the ability to make design trade-offs.
- We should first understand the requirements, estimate the system scale, and design a high-level architecture before discussing implementation details.
- Interviewers assess your thought process, scalability considerations, reliability, database choices, and how well you justify design decisions.
Example: If asked to Design a URL Shortener, first clarify requirements (create short URLs, redirect users), estimate traffic, design components such as load balancer, application servers, database, and cache, then discuss scalability and reliability improvements.
Step 1: Clarify Requirements
Before starting the design, understand exactly what the system needs to do. Never jump directly into architecture.
Functional Requirements: These define the core features of the system.
Questions to ask:
- What features should the system support?
- Who are the users?
- What are the main use cases?
Non-Functional Requirements: These define the quality attributes of the system.
Questions to ask:
- What should be the availability?
- What latency is acceptable?
- How scalable should the system be?
- Are there any security requirements?
Example: Design a URL Shortener
Functional Requirements:
- Generate short URLs.
- Redirect users to original URLs.
- Track click counts.
Non-Functional Requirements:
- High availability.
- Low latency.
- Support millions of URLs.
Step 2: Capacity Estimation
Estimating scale helps choose the right architecture and technologies.
Traffic Estimation
- Estimate the expected system traffic to understand scalability requirements.
- Includes: Daily Active Users (DAU) and Requests Per Second (RPS).
Storage Estimation
- Estimate how much data the system needs to store over time.
- Includes: Daily data generation and yearly storage requirements.
Network Bandwidth
- Determine the amount of data transferred through the system.
- Includes: Expected incoming and outgoing traffic between users and servers.
Example
Assume:
- 10 million users
- Each user creates 2 URLs/day
Total URLs/day:
10M × 2 = 20M URLsIf each URL record takes 500 bytes:
20M × 500 bytes = 10 GB/dayThis estimation helps decide storage and database requirements.
Step 3: High-Level Design
Design the overall architecture and major components.
Common Components
Client (Web/Mobile), Load Balancer, Application Server, Database, Cache, Message Queue, and CDN.
Goals
Scalability, Reliability, and Maintainability.
Example: URL Shortener
User submits a URL -> Application Server generates a short code -> Data is stored in the Database -> Frequently accessed URLs are cached in Redis for faster retrieval.
Step 4: Database Design
Database design is one of the most important parts of the interview.
Identify Entities
- Determine the main entities and the data that needs to be stored in the system.
- Includes: Users, Products, Orders, Posts, URLs, etc.
Choose Database Type
- Select a database based on the system's requirements.
- SQL Database: Structured data, strong consistency, and predefined schema. Examples: MySQL, PostgreSQL.
- NoSQL Database: High scalability, flexible schema, and large-scale data handling.
Examples: MongoDB, Cassandra.
Indexing
- Create indexes on frequently searched or queried fields to improve performance.
- Example: Create an index on ShortURL for faster URL lookups in a URL Shortener system.
Step 5: API Design
- Endpoints: Estimate the required public API endpoints for each significant functionality. Enforce RESTful design constraints (or other architectural ones).
- Request and Response Formats: Select media types: Request and Response formats (e.g. JSON, XML). Include sample payloads.
- Error Handling: Determine the error policies such as how the errors are going to be handled or communicated. g. , standard error codes).
Define APIs that clients will use.
Example APIs: Create Short URL
http -> POST /shortenRequest:
JSON ->
{
"url": "https://example.com"
}
Response:
JSON ->
{
"shortUrl": "abc123"
}
Redirect URL
GET /abc123Response: Redirect to original URL.
Get Analytics
GET /analytics/abc123Returns click statistics.
Step 6: Deep Dive into Key Components
After presenting the architecture, interviewers usually ask for details about important components. Explain the internal working of the major components used in the system.
Backend Service
- Handles the core functionality of the application.
- Includes: Business Logic, Request Validation, API Processing.
Cache
- Stores frequently accessed data to improve performance.
- Benefits: Faster Response Time, Reduced Database Load. Examples: Redis, Memcached.
Message Queue
- Used for asynchronous communication and background processing.
- Examples: Kafka, RabbitMQ.
- Use Cases: Notifications, Logging, Analytics Processing.
Example
User Request -> Backend Service -> Cache Check -> Database -> Response
Background Tasks -> Message Queue -> Notification/Analytics Service.
Step 7: Scalability Design
A scalable system should be able to handle increasing users, traffic, and data efficiently.
Horizontal Scaling
- Increase system capacity by adding more servers.
- Advantages: Easy Scaling, High Availability.
- Example: Server 1, Server 2, Server 3 handling requests together.
Vertical Scaling
- Increase the resources of an existing server.
- Advantages: Simple Implementation.
- Disadvantages: Hardware Limitations and Higher Cost.
Load Balancing
- Distributes incoming traffic across multiple servers.
- Benefits: Prevents Server Overload, Improves Reliability and Availability.
Database Sharding
- Divide large datasets across multiple database servers.
- Benefits: Better Performance, Improved Scalability.
Example:
Shard 1 -> Users A-F
Shard 2 -> Users G-M
Shard 3 -> Users N-Z
Step 8: Reliability and Fault Tolerance
A reliable system should continue working even when some components fail.
Replication
- Store multiple copies of data on different servers.
- Benefits: High Availability, Disaster Recovery.
Failover
- Automatically switch to a backup server or system when the primary one fails.
- Benefit: Minimizes downtime and ensures continuous service.
Monitoring
- Continuously track system performance, health, and failures.
- Tools: Prometheus, Grafana, Datadog.
Example
Primary Database Fails -> Automatic Failover -> Replica Database Takes Over -> Users Continue Using the Application Without Interruption.
Step 9: Identify Bottlenecks and Optimizations
Analyze potential system weaknesses and discuss ways to improve performance and scalability.
Common Bottlenecks
- Areas that may reduce system performance under heavy load.
- Examples: Database Overload, Cache Misses, Network Congestion, Large File Uploads.
Optimizations
- Techniques used to improve system performance and efficiency.
- Examples: Caching, CDN, Database Replication, Data Partitioning (Sharding), Asynchronous Processing.
Example
- Problem: Database receives millions of read requests causing high load.
- Solution: Use Redis Cache to store frequently accessed data in memory, reducing database queries and improving response time.
Practice Problems for System Design
Below are some of the practice problems for system design:
