World Congress 2026 Europe Jul 10, 2026 Session details

Super scaling for the Super Bowl: How to survive 30 million users hitting your backend in 30 minutes

Irina Branovic

A Super Bowl ad triggered 100,000 requests per second. Auto-scaling was too slow. Discover how aggressive graceful degradation and load balancer sharding kept this Go backend alive.

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#1 about 6 min

Introduction to rewarded ad tech and platform business model

How the platform connects publishers and advertisers through an offer wall system.

#2 about 2 min

Baseline traffic scale and daily backend load

Handling six billion daily API requests and maintaining high throughput operations.

#3 about 2 min

Super Bowl traffic surge challenge and autoscaling limits

Preparing for an unprecedented spike of 100,000 requests per second driven by a live television QR code.

#4 about 7 min

Event-driven Golang backend architecture and cloud deployment

Examining the microservices stack utilizing Go, AWS spot instances, and event buses.

#5 about 6 min

Load testing production traffic with Kubernetes operators

Isolating external dependencies and hammering test environments to simulate sudden load spikes.

#6 about 3 min

Mitigating DynamoDB throttling with on-demand capacity mode

Switching to flexible capacity limits to handle abrupt database traffic bursts.

#7 about 1 min

Resolving DynamoDB hot keys using in-memory caching

Using CloudWatch insights to identify overloaded partitions and implementing local application caches.

#8 about 2 min

Sharding domain traffic across multiple AWS load balancers

Routing DNS through multiple IP addresses to prevent load balancer exhaustion.

#9 about 3 min

Profiling application performance to avoid out-of-memory errors

Identifying TLS handshake bottlenecks and disabling non-essential network dependencies to prevent crashes.

#10 about 1 min

Negotiating AWS concurrent service quotas to unblock scaling

Working with cloud providers to incrementally raise infrastructure limit caps during testing.

#11 about 3 min

Implementing graceful degradation and turning off machine learning

Batching queue messages and disabling real-time recommendation models to preserve core functionality.

#12 about 3 min

Switching to on-demand instances to guarantee resource availability

Abandoning spot instances before the event to ensure stable cloud compute capacity.

#13 about 3 min

Replicating production environments and utilizing comprehensive observability tooling

Leveraging observability metrics and production-like test environments to survive massive traffic events.

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Surviving sudden scale events and malicious traffic

Justin Kitagawa · Coffee With Developers

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Managing peak traffic via scalable content delivery networks

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