Introduction
High-traffic events, such as ticket bookings for ICC tournaments, Black Friday sales, or government portal registrations, often lead to massive surges in user requests. When thousands of users attempt to access a system simultaneously, it can lead to server crashes, database failures, and poor user experience.
One effective way to handle such high-load scenarios is by implementing a queuing system. This article explores how queuing can help manage high-traffic spikes, with a focus on a Queue-It-like system and how to design a similar solution using system architecture principles.
Challenges in High-Traffic Scenarios
Handling a surge of users in a short timeframe presents several challenges:
Server Overload: Sudden traffic spikes can overwhelm web servers, causing slowdowns or downtime.
Database Bottlenecks: Concurrent reads/writes can lead to performance issues, increasing response times.
Unfair Access: Without queuing, first-come-first-serve may not work efficiently, as bots or high-speed connections may get an unfair advantage.
Poor User Experience: Users facing infinite loading screens or crashes may lose trust in the system.
Queuing as a Solution
A queue-based system helps control the incoming traffic load by holding users in a virtual queue instead of allowing unrestricted access. This ensures that only a limited number of users reach the main system at any given time, reducing the risk of failures.
How Queueing Works
User Request Handling: When users visit the application, their requests go through a load balancer.
Traffic Threshold Check: If traffic exceeds a predefined threshold, users are redirected to a queue system.
Queue Placement: Users are assigned a position in a queue with an estimated wait time.
Queue Expiry Notification: Users can opt for an email notification 2 minutes before their queue slot opens.
Progressive Access: As slots become available, users are gradually redirected to the main application.
Session Timer: After redirection, users have 10 minutes to complete their transaction. If time expires, they are sent back to the queue.
Session Tracking & Prevention of Repeated DDoS Requests: Identifying repeated session misuse and blocking abusive traffic.
Session Tracking & Database Design
Each user's session must be tracked to ensure fair access. The following session details should be recorded:
Session ID: Unique identifier for the user session
User IP Address: Helps detect abuse
User Agent (Browser & Device Info): Helps identify repeated bot patterns
Queue Position: Helps assign priority in the queue
Entry Time: Tracks when the user entered the queue
Expiry Time: Timestamp to track session validity (e.g., 10 minutes post-queue completion)
Alert Subscription: Whether the user opted for a queue alert email
Database Schema Example
CREATE TABLE queue_sessions (
session_id VARCHAR(255) PRIMARY KEY,
user_ip VARCHAR(50),
user_agent TEXT,
queue_position INT,
entry_time TIMESTAMP,
expiry_time TIMESTAMP,
alert_email VARCHAR(255),
status ENUM('waiting', 'active', 'expired')
);
Preventing Repeated DDoS Attacks
To prevent bots or repeated queue abuses:
IP Rate Limiting: Use Redis or Cloudflare rate-limiting to prevent excessive requests from the same IP.
User Agent Verification: Track user agents and detect anomalies indicating bot activity.
Session Token Validation: Ensure that a valid session token is required to re-enter the queue.
CAPTCHA Challenge: Before entering the queue, users must solve a CAPTCHA to filter bots.
Web Application Firewall (WAF): Deploy WAF rules to detect and block malicious traffic.
Architecture Diagram
Below is a structured diagram representation of a queue-based traffic management system:
┌──────────────────────────┐
│ Users │
└──────────┬───────────────┘
│ Requests
▼
┌────────────────────────────┐
│ Load Balancer (Nginx, ALB) │
└──────────┬─────────────────┘
│
▼
┌──────────────────────────┐
│ Queue Management System │
│ (Redis, Kafka, SQS) │
└──────────┬───────────────┘
│ Assigns Queue Position
▼
┌──────────────────────────┐
│ Queue Server (Web App) │
│ (Node.js, Spring Boot) │
└──────────┬───────────────┘
│ Grants Access Based on Slots
▼
┌──────────────────────────┐
│ Main Application │
│ (Booking System, etc.) │
└──────────┬───────────────┘
│
▼
┌──────────────────────────┐
│ Session Expiry Handler │
│ (Tracks 10-Minute Limit) │
└──────────────────────────┘
Key Technologies
Queueing Services: AWS SQS, Kafka, Redis Queue
Load Balancing: Nginx, AWS ALB, Cloudflare
Rate Limiting: Redis Rate-Limiting, API Gateway Policies
Session Tracking: Redis, PostgreSQL
DDoS Prevention: CAPTCHA, WAF, IP Throttling
Conclusion
By implementing a queueing system with session expiration and abuse prevention mechanisms, applications can efficiently manage high-load scenarios, ensuring system stability and fair access.
The ICC Champions Trophy ticket booking experience highlights the importance of such an approach.
Using technologies like Redis, Kafka, and cloud-based queue management solutions, we can build scalable and resilient architectures to handle traffic spikes without system failures.