Q: AbhiBus DevOps / SRE Interview: How would you architect and troubleshoot a high-concurrency bus booking platform during festival flash traffic spikes with zero double-booking and sub-second latency?
Real-world DevOps & SRE interview questions asked at AbhiBus: architecting high-concurrency bus ticketing platforms, handling Diwali/festival traffic spikes, distributed seat locking with Redis, and payment gateway latency.
#abhibus interview questions #AbhiBus #Ticketing Architecture #Flash Sales #Redis Caching #High Concurrency #AWS Scaling #SRE Interview #Seat Locking
🎙️ Candidate Opening & Architectural Context
"Online travel and ticketing platforms like AbhiBus experience intense traffic surges during holiday booking windows (Diwali, Eid, long weekends) where 100,000 users compete for limited bus inventory within seconds. SREs must solve distributed locking, payment timeouts, and inventory cache consistency."
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🛠️ Production Runbook & Step-by-Step Resolution
1️⃣
High-Concurrency Travel Platform Architecture
Multi-tier design for travel inventory platforms:
- Edge Layer (CloudFront + WAF): Rate limiting, bot mitigation, and static seat layout asset caching at edge locations.
- Search Layer (Read-Heavy 95%): Microservices querying Redis Cluster / Elasticache for real-time bus schedules, operator pricing, and seat availability.
- Booking Engine (Write-Heavy 5%): Distributed locking service guaranteeing that two concurrent users clicking the same sleeper berth do not double-book.
- Payment Webhooks: Asynchronous queuing via Amazon SQS to handle payment gateway callbacks gracefully without blocking client connections.
2️⃣
Distributed Seat Locking & Race Conditions
Solving the double-booking dilemma under heavy load:
- Use Redis Distributed Locks (Redlock or SET with NX and EX) with an atomic expiration (e.g., 10 minutes) while the user completes payment.
SET seat:bus_102:berth_4A user_789 NX EX 600guarantees that only the first request succeeds; concurrent requests receive an immediate 'Seat selected by another user' response without hitting the relational database.- If the payment completes within 10 minutes, the worker marks the seat as permanently booked in PostgreSQL. If the lock expires, Redis automatically frees the berth for other travelers.
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3️⃣
Flash Sale & Festival Spike Scaling Playbook
Autoscaling and load shedding tactics:
- Scheduled Auto Scaling: Pre-scale EKS worker nodes and RDS instances 30 minutes before expected festival ticket release windows (don't rely solely on reactive metric thresholds).
- Virtual Waiting Room / Queue-it: When ingress request rates exceed 30,000 req/sec, redirect excess traffic to an edge-hosted virtual queue with fair FIFO admission tokens.
- Circuit Breaking: Protect payment gateway integrations with Resilience4j/Envoy circuit breakers to prevent connection pool starvation when external banking APIs stall.
4️⃣
Incident Scenario: 100% CPU on Redis Cluster
Troubleshooting high-load caching bottlenecks:
- Split popular routes (e.g., Hyderabad → Bangalore) into multiple hashed sub-keys to distribute load evenly across Redis cluster shards.
💡 The Senior SRE Gold Nugget (Key Architectural Takeaway)
"Ticketing platforms like AbhiBus require distributed caching for high-speed read searches, atomic Redis distributed locks (SET NX EX) to prevent seat double-booking, and scheduled pre-scaling paired with edge virtual waiting rooms to survive holiday traffic surges."
⚡ 60-Second Elevator Pitch Talking Points
- In travel ticketing platforms like AbhiBus, 95% of traffic is route searching while 5% is write-heavy checkout and seat allocation.
- We handle concurrency by decoupling search into multi-region Redis caches, while securing seat reservation using atomic distributed locks with 10-minute TTLs to eliminate race conditions.
- To handle festival traffic spikes, we implement scheduled proactive autoscaling on Kubernetes, edge rate limiting on AWS WAF, and asynchronous message queues for third-party payment callbacks.
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