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# From 0 to 1.000.000: How to build a serverless raffle service for hyperscale

Synchronous serverless APIs fail during extreme traffic spikes. Learn how a store-first architecture decoupling API Gateway and SQS effortlessly scales past 100,000 requests per second without upfront provisioning.

- **Speakers:** Marco Plaul, [Martin Sakowski](https://www.wearedevelopers.com/@martin-sakowski)
- **Event:** World Congress 2023
- **Published:** October 23, 2023
- **Duration:** 30:03
- **URL:** https://www.wearedevelopers.com/videos/793-from-0-to-1-000-000-how-to-build-a-serverless-raffle-service-for-hyperscale

## Summary

Building serverless web applications to handle hyperscale traffic—such as retail raffles, live TV voting, and sneaker drops—requires designing for abrupt spikes that can exceed 100,000 requests per second. A traditional synchronous architecture connecting an API Gateway directly to AWS Lambda often falls short during sudden traffic bursts, as account concurrency constraints can take upward of 15 minutes to fully autoscale. While workarounds like provisioned concurrency are available, they require upfront planning, add unnecessary costs, and compromise the inherently scalable nature of a truly serverless application.

To overcome these hyperscale bottlenecks, architects must adopt a "store first, process later" paradigm that intelligently decouples data ingestion from backend computation. By configuring a direct integration between Amazon API Gateway and Amazon SQS, massive influxes of incoming requests are seamlessly buffered without immediately triggering Lambda scaling functions. The architecture then utilizes Lambda event source mapping to consume and micro-batch up to 100 messages at a time, drastically reducing HTTP overhead and enabling highly efficient batch writes to an on-demand DynamoDB table.

Sustaining such immense load ultimately requires proactive capacity planning and a deep understanding of component-level service quotas. While an SQS queue effectively functions as an infinite buffer, developers must still initiate load tests to unlock and elevate legacy thresholds—such as DynamoDB's initial write bottlenecks, which double only under consecutive sustained load. Writing clean, highly optimized Lambda code is just as vital, as faster execution reduces billable compute time and maximizes environment reuse. Furthermore, comprehensive client-side resilience is critical; by minimizing the number of API calls required to register a user and implementing rigorous exponential backoff routines, engineering teams can successfully process chaotic hyperscale events without accidentally DDoSing their own infrastructure.

**Keywords:** hyperscale serverless architecture, aws lambda burst concurrency, api gateway throughput limits, amazon sqs integration, dynamodb on-demand scaling, asynchronous serverless processing, lambda event source mapping, batch writing to dynamodb, handling sudden traffic bursts, serverless load testing strategies, exponential backoff implementation, decoupling serverless microservices, serverless component quotas, artillery load testing setup, retail raffle web applications

## Chapters

1. **Overcoming traffic burst challenges in hyperscale web applications** (00:03) — Unexpected traffic spikes require robust architectures capable of processing millions of concurrent user registrations seamlessly.
1. **Analyzing concurrency bottlenecks in standard serverless architectures** (03:35) — Synchronous architecture patterns struggle to scale instantly due to default account quotas and burst concurrency limitations.
1. **Addressing synchronous invocation flaws between API Gateway and Lambda** (09:34) — One-to-one synchronous lambda invocations for initial requests rapidly exhaust account concurrency and reduce database write efficiency.
1. **Decoupling frontend requests using Amazon SQS integrations** (10:40) — Direct service integrations capture high-volume API requests into a buffer queue to prevent immediate backend overload.
1. **Processing queue messages efficiently with event source mapping** (12:53) — Batching queue messages into single lambda invocations drastically minimizes compute concurrency and optimizes database write operations.
1. **Configuring distributed load testing for serverless applications** (14:21) — Leveraging distributed functions to simulate extreme traffic scenarios helps validate upstream infrastructure configurations prior to launch.
1. **Monitoring backend ingestion during a simulated traffic burst** (19:04) — Live telemetry dashboards reveal how queueing mechanisms effectively absorb millions of incoming requests without dropping data.
1. **Best practices for implementing durable hyperscale serverless systems** (23:57) — Building asynchronous workflows and proactively managing resource quotas ensures reliable application stability during critical high-traffic business events.
1. **Navigating caching and downstream throttling in event-driven setups** (25:52) — Accommodating immediate client feedback requirements necessitates fast caching layers while exponential backoff configurations safeguard vulnerable legacy downstream components.

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