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

Practice

1. What does scalability in system design primarily help with?

easy

A. Handling more users and traffic smoothly

B. Reducing the number of servers

C. Making the system slower

D. Limiting the system to one user

Solution

1. Step 1: Understand the meaning of scalability

   Scalability means the system can grow to handle more users or traffic without problems.

2. Step 2: Identify the main goal of scalability

   The main goal is to manage increasing traffic smoothly, not to reduce servers or slow down.

3. Final Answer:

   Handling more users and traffic smoothly -> Option A

4. Quick Check:

   Scalability = Handling more traffic [OK]

Hint: Scalability means growing capacity to handle more users [OK]

Common Mistakes:

• Thinking scalability reduces servers
• Confusing scalability with slowing system
• Believing scalability limits users

2. Which option correctly describes vertical scaling?

easy

A. Upgrading the hardware of a single machine

B. Distributing traffic using load balancers

C. Adding more machines to the system

D. Reducing the number of users

Solution

1. Step 1: Define vertical scaling

   Vertical scaling means improving one machine's hardware, like adding CPU or memory.

2. Step 2: Differentiate from other options

   Adding machines is horizontal scaling; load balancers distribute traffic; reducing users is unrelated.

3. Final Answer:

   Upgrading the hardware of a single machine -> Option A

4. Quick Check:

   Vertical scaling = Upgrade one machine [OK]

Hint: Vertical scaling upgrades one machine's hardware [OK]

Common Mistakes:

• Confusing vertical with horizontal scaling
• Thinking vertical scaling adds machines
• Mixing load balancers with scaling types

3. Consider a system with 3 servers behind a load balancer. If traffic doubles, what happens when horizontal scaling is applied?

medium

A. Remove servers to reduce complexity

B. Upgrade the CPU of one server only

C. Add more servers to share the load

D. Ignore traffic increase and keep same setup

Solution

1. Step 1: Understand horizontal scaling

   Horizontal scaling means adding more servers to handle increased traffic.

2. Step 2: Apply to the scenario

   With doubled traffic, adding servers helps share load; upgrading one server is vertical scaling.

3. Final Answer:

   Add more servers to share the load -> Option C

4. Quick Check:

   Horizontal scaling = Add servers [OK]

Hint: Horizontal scaling means adding servers [OK]

Common Mistakes:

• Choosing to upgrade one server (vertical scaling)
• Removing servers when traffic grows
• Ignoring traffic increase

4. A system uses a load balancer but still crashes under high traffic. What is the likely issue?

medium

A. Too many servers are added unnecessarily

B. Load balancer is not distributing traffic evenly

C. Vertical scaling was applied correctly

D. Traffic is decreasing, causing overload

Solution

1. Step 1: Analyze load balancer role

   Load balancers distribute traffic evenly to servers to prevent overload.

2. Step 2: Identify cause of crash

   If system crashes despite load balancer, it may not be distributing traffic properly, causing some servers to overload.

3. Final Answer:

   Load balancer is not distributing traffic evenly -> Option B

4. Quick Check:

   Uneven load distribution causes crashes [OK]

Hint: Check if load balancer spreads traffic evenly [OK]

Common Mistakes:

• Assuming too many servers cause crash
• Thinking vertical scaling always fixes crashes
• Believing traffic decrease causes overload

5. A web app faces sudden traffic spikes. Which combination best ensures smooth scaling?

hard

A. Only upgrade one server's CPU and RAM

B. Limit user access to reduce traffic

C. Remove load balancers and rely on a single powerful server

D. Use horizontal scaling with load balancers and auto-scaling groups

Solution

1. Step 1: Identify best scaling approach for spikes

   Horizontal scaling with load balancers allows adding/removing servers dynamically to handle spikes.

2. Step 2: Evaluate other options

   Upgrading one server limits capacity; removing load balancers causes bottlenecks; limiting users reduces usability.

3. Final Answer:

   Use horizontal scaling with load balancers and auto-scaling groups -> Option D

4. Quick Check:

   Horizontal scaling + load balancers = smooth spikes [OK]

Hint: Combine horizontal scaling and load balancers for spikes [OK]

Common Mistakes:

• Relying only on vertical scaling
• Removing load balancers causing bottlenecks
• Limiting users instead of scaling