Project 01
Bangladesh Cricket Board — Ticketing System
A ticketing platform engineered to stay reliable when thousands of fans try to book at once.
Overview
The Problem
Ticket sales for national cricket events can't fail under load — the system has to stay reliable when large numbers of fans try to book at the same time. A ticketing platform that slows down or breaks during a high-demand on-sale directly costs the organization sales and trust.
The Solution
Built high-performance ticketing modules engineered to support thousands of concurrent users, covering the backend logic and the interfaces fans use to browse and book tickets.
My Role
Full-Stack Engineering
Impact
VerifiedThousands of concurrent users supported without the system buckling under load.
Inside the System
How the pieces work together.
Users
Fans hitting the platform simultaneously during an on-sale window.
Request Flow
Architecture ConceptDatabase Design
Conceptual Data ModelUsers
Relationships
Orders
Purpose
Registered fans who purchase tickets.
When data starts growing
The right strategy at each stage depends on the actual workload — this progression isn't applied wholesale to every project.
Database Performance
Illustrative ComparisonBefore
Query
↓ Full Scan
↓ Large Dataset
↓ Slow Response
Optimized
Query
↓ Index
↓ Relevant Rows
↓ Fast Response
Traffic Simulator
See how a system like this typically responds as demand grows.
Simulated traffic
Illustrative Simulation — Not Live TrafficActive app servers
Cache hit rate
40%
Queue activity
15% utilized
Illustrates how the pieces of a system like this would typically respond as demand grows — more application instances come online, caching absorbs a larger share of reads, and queue throughput increases to keep pace with background work.
Explore 100K+ Architecture
Reference architecture / simulation — not a benchmark of this project's actual infrastructure. It shows how each component keeps any single part of the system from becoming the bottleneck.
What happens when something fails?
What happens when something fails?
Architecture SimulationSelect a failure scenario to see how the architecture is designed to respond.
Security Layer
Recommended Production PatternFrom Code to Production
Typical PipelineWhy These Decisions?
Decision
Move non-critical work to a queue
Why?
Keep the booking request path fast during high-demand windows.
Trade-off
Adds infrastructure (queue + workers) and eventual — not instant — consistency for queued actions.
Decision
Cache high-read, low-change data
Why?
Reduce repeated database load from availability checks during traffic spikes.
Trade-off
Cache invalidation and staleness become additional concerns to manage.
Decision
Design for horizontal scaling of the application layer
Why?
Let the system absorb concurrent demand by adding instances rather than relying on one large server.
Trade-off
Requires the application to be stateless, which shapes how sessions and uploads are handled.
What I Actually Built
Verified- Built backend ticketing modules in Laravel handling booking logic and data
- Built React interfaces for browsing events and completing ticket purchases
- Worked across the application layer to keep the system stable under concurrent demand
Impact
Scale
Thousands of concurrent users
The Engineering Takeaway
Ticketing infrastructure has to be designed for its worst moment, not its average one — concurrency and reliability under simultaneous demand matter more than any single feature.