How to Build 1 Million Requests per Minute with Golang

Handling High-Volume POST Requests with Go and Elastic Beanstalk

Facing the challenge of processing a massive influx of POST requests – millions per minute – from numerous endpoints is a common concern for scalable web applications. Our team encountered this problem while developing an anonymous telemetry and analytics system. The core requirement was to efficiently handle JSON documents containing multiple payloads, destined for storage in Amazon S3 for subsequent map-reduce processing.

Traditional solutions often involve complex architectures like:

• Worker-tier systems: Utilizing tools such as Sidekiq, Resque, DelayedJob, Elastic Beanstalk Worker Tier, and RabbitMQ.
• Separate clusters: Deploying distinct front-end and worker clusters to scale background processing capabilities.

However, recognizing the potential for extreme traffic, we strategically chose Go as the development language. With approximately two years of Go experience, our team believed its concurrency features and performance would be ideal.

The solution we implemented prioritized simplicity and efficiency. Instead of a complex queue-based system, we leveraged the power of Elastic Beanstalk auto-scaling and Go's built-in concurrency. This approach allowed a small cluster – just four machines, potentially less powerful than a modern MacBook Pro – to handle 1 million POST requests per minute writing to an Amazon S3 bucket.

This demonstrates that sometimes, the most effective solution is the simplest. Go's concurrency model, combined with Elastic Beanstalk's auto-scaling, provides a powerful and straightforward way to build highly scalable applications capable of handling demanding workloads. This approach significantly reduces deployment complexity and operational overhead.