Super Bowl Campaign Success: Scaling a Hybrid Architecture Under Extreme Traffic

Just as NFL teams are frantically preparing their draft boards for tomorrow, we're reflecting on how our team tackled a different kind of high-stakes football challenge.

Our client, a major grocery retailer with a strong digital presence, was knee-deep in rewiring their entire online platform. Imagine trying to replace the engine of a car while driving down the highway – that's essentially what they were doing with their tech stack.

Then came the curveball (excuse the mixed sports metaphor): they decided to run a Super Bowl ad. With traffic expected to jump to 5 times normal levels during the ad and stay high for days after, our team rolled up our sleeves and found creative ways to keep everything stable without derailing their ongoing tech migration.

The Challenge

The client faced a tough technical puzzle:

1. Half-Old, Half-New Architecture: They were midway through moving from their aging monolithic e-commerce platform to shiny new microservices on AWS. This meant traffic would bounce between both systems like a football on a trick play.

2. Old System Limitations: The legacy system had some hard constraints:

   • Fixed computing power that couldn't easily bulk up

   • Database that couldn't stretch much further

   • Complicated interconnections that made simple scaling impossible

3. Traffic Game Plan:

   • Expected 5x normal traffic within minutes of their Super Bowl ad

   • High traffic continuing for 48+ hours after the event

   • Potential for millions of hungry shoppers hitting the site at once

4. No Time-Outs Available: Pausing the ongoing migration wasn't an option due to business commitments

Strategic Approach

We tackled this challenge with three main plays:

1. Traffic Redirection: Send as much traffic as possible to the newer, scalable parts and away from the old system

2. Prepare for the Rush: Scale up AWS resources before the big game, not during it

3. Tune-Up Time: Find and fix inefficiencies wherever we could spot them

Implementation

Load Deflection and Optimization

GraphQL Optimization Campaign

• Conducted comprehensive analysis of all GraphQL queries between microservices and the monolith

• Identified redundant data requests and inefficient query patterns

• Implemented data batching and caching strategies

• Result: Eliminated 15,000 requests per minute to the monolith

Profile Microservice Launch

• Accelerated the development and deployment of the Profile microservice

• Moved user profile data and authentication workflows from the monolith to the new service

• Implemented aggressive caching with Redis to minimize database queries

• Added circuit breakers to gracefully handle potential failures

• Result: Diverted approximately 40% of user-related traffic away from the monolith

Static Content Delivery

• Migrated product images and static assets to CloudFront CDN

• Implemented browser caching strategies

• Result: Reduced origin requests by 65% for product detail pages

Proactive AWS Resource Scaling

ECS Fargate Pre-scaling

• Based on previous experience with Fargate's scaling limitations during sudden traffic bursts, we implemented a pre-scaling strategy

• Increased minimum task counts for critical microservices to 300% of normal capacity

• Implemented custom scaling policies based on CloudWatch metrics

• Deployed redundant services across multiple availability zones

• Result: Ensured immediate capacity for the traffic surge without waiting for auto-scaling

DynamoDB Capacity Planning

• Analyzed historical traffic patterns and calculated projected throughput requirements

• Temporarily increased provisioned capacity for critical tables to 5x normal levels

• Implemented on-demand scaling for secondary tables

• Result: Eliminated potential throttling issues during peak load

Ingress Proxy and Load Balancer Configuration

• Increased connection limits on load balancers

• Implemented request rate limiting to protect backend services

• Added custom throttling rules to prioritize critical transactions

• Result: Created a controlled "pressure release valve" to maintain overall system stability

Testing and Validation

Load Testing Campaign

• Designed test scenarios to simulate the expected Super Bowl traffic patterns

• Conducted multiple rounds of testing, gradually increasing load

• Identified and resolved bottlenecks in database connections and API endpoints

• Result: Validated system could handle 5x normal traffic with acceptable response times

Real-time Monitoring Enhancement

• Deployed additional CloudWatch dashboards specific to the campaign

• Implemented custom alerts with appropriate thresholds

• Created a dedicated war room setup with clear escalation procedures

• Result: Ensured immediate visibility into system performance during the event

Results

Super Bowl Performance

• Successfully handled a traffic spike that reached 6.2x normal volume just minutes after the commercial aired

• Kept page loads snappy at under 350ms throughout the event

• Zero downtime or system hiccups during the entire campaign

• 99.99% availability (that's just 8.6 seconds of issues per day)

Business Impact

• The client saw almost 3x more conversions during the first 24 hours after their ad

• Shopping cart abandonment stayed at normal levels despite the traffic tsunami

• Their Super Bowl investment paid off better than expected, with 32% higher returns than projected

System Performance

• The old monolith's CPU usage peaked at 78% (we projected it would have been over 110% without our changes)

• Database performance stayed comfortably below capacity

• AWS microservices scaled up smoothly with no throttling issues

• Error rates stayed flat compared to normal days

Key Lessons Learned

1. AWS Services Don't Always Play by the Rulebook: We learned that AWS Fargate and DynamoDB don't scale up instantly when traffic surges – they need a head start. Understanding these quirks helped us develop effective pre-game strategies.

2. Fix Before You Add: The most cost-effective approach was cleaning up inefficient code before throwing more computing power at the problem. It's like fixing your form before buying expensive new running shoes.

3. Test Like It's Game Day: Load testing at Super Bowl-level traffic was essential to spot problems that never show up during regular season traffic.

4. Old Meets New Requires Special Care: Managing traffic between microservices and a monolith is like conducting an orchestra where half the musicians are reading different sheet music. It takes careful planning around data consistency, caching strategies, and fallback plans.

5. You Can't Fix What You Can't See: Having detailed, real-time metrics was crucial for making quick decisions during the event.

Conclusion

As NFL teams prepare for the draft, we're reminded that success in tech, like football, comes down to preparation, adaptability, and knowing your strengths and limitations.

With smart planning and targeted fixes, you can handle massive traffic spikes even while juggling a complex system migration. By understanding the specific quirks of each system component and applying the right solutions, we kept everything running smoothly while the client scored big with their Super Bowl investment.

The best part? The techniques we put in place aren't just one-game wonders. The client now uses these same approaches for all their big promotional events, turning what could have been a nail-biting fourth quarter into a repeatable winning strategy.