The article discusses a developer's experience with AWS Lambda and Aurora Serverless v1 when processing large CSV files. Initially, the setup seemed efficient, but rapid scaling of Lambda functions overwhelmed Aurora Serverless, leading to failure due to exceeding database connections. The author emphasizes the speed disparity between Lambda's scaling and Aurora's inability to respond, suggesting that cheap solutions may not always yield optimal performance. Despite the failure, the author shares this experience to guide other developers facing similar issues in their data processing pipeline.
Each new invocation of an AWS Lambda function may spin up a new environment or reuse an existing one, leading to a surge in database connections.
Aurora Serverless V1 can't scale as quickly as AWS Lambda, resulting in a 'Too many connections' error and ultimately crashing the service.
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