Why scalability handles growing traffic in HLD - Design It to Understand It

Design: Scalable System for Growing Traffic

Focus on explaining how scalability techniques help manage growing traffic. Does not include detailed implementation of specific business logic.

Functional Requirements

FR1: Handle increasing number of users and requests smoothly

FR2: Maintain low response time under heavy load

FR3: Ensure system availability during traffic spikes

FR4: Allow easy addition of resources without downtime

Non-Functional Requirements

NFR1: Support up to 100,000 concurrent users

NFR2: API response time p99 under 300ms

NFR3: Availability target of 99.9% uptime

NFR4: System should scale horizontally

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

Key Components

Load balancers to distribute traffic

Application servers that can be added or removed

Caching layers to reduce database load

Databases with read replicas or sharding

Message queues for asynchronous processing

Design Patterns

Horizontal scaling (adding more machines)

Vertical scaling (upgrading existing machines)

Caching to reduce repeated work

Load balancing to spread requests evenly

Auto-scaling to adjust resources dynamically

Reference Architecture

Client
  |
Load Balancer
  |
Multiple Application Servers
  |
Cache Layer (e.g., Redis)
  |
Database Cluster (Master + Read Replicas)
  |
Message Queue (for async tasks)

Components

Load Balancer

Nginx, AWS ELB

Distributes incoming traffic evenly across application servers

Application Servers

Docker containers, Kubernetes pods

Process user requests and business logic

Cache Layer

Redis or Memcached

Store frequently accessed data to reduce database load

Database Cluster

PostgreSQL with read replicas

Store persistent data with read scalability

Message Queue

RabbitMQ, Kafka

Handle asynchronous tasks to smooth peak loads

Request Flow

1. Client sends request to Load Balancer

2. Load Balancer forwards request to one of the Application Servers

3. Application Server checks Cache for data

4. If cache miss, Application Server queries Database

5. Application Server processes request and returns response

6. For heavy or delayed tasks, Application Server sends message to Message Queue

7. Background workers consume messages and update Database or Cache

Database Schema

Entities: User, Session, RequestLog Relationships: User has many Sessions; Requests logged with timestamps Supports read replicas for scaling read queries

Scaling Discussion

Bottlenecks

Single application server CPU or memory limits

Database write throughput limits

Cache size and eviction policies

Load balancer capacity

Message queue throughput

Solutions

Add more application servers (horizontal scaling)

Use database sharding or partitioning for writes

Increase cache size or use distributed cache

Use multiple load balancers or DNS-based load balancing

Scale message queue cluster or partition topics

Interview Tips

Time: 10 minutes to clarify requirements and constraints, 20 minutes to explain architecture and data flow, 10 minutes to discuss scaling and trade-offs, 5 minutes for questions

Explain difference between horizontal and vertical scaling

Describe how load balancers help distribute traffic

Discuss caching benefits and cache invalidation challenges

Highlight database scaling techniques like read replicas

Mention asynchronous processing to handle spikes

Address trade-offs between complexity and scalability