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brandonbullen avatar brandonbullen commented on June 2, 2024 1

Thanks for the suggestions, I completely looked over this as an option to solve the problem! I'll give it a shot and reach out if I encounter issues here.

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alexcasalboni avatar alexcasalboni commented on June 2, 2024

Hi @brandonbullen 👋 thanks for reaching out!

I think the best solution for this specific use case would be using preProcessorARN and postProcessorARN (doc here) to create the exact input/output condition for each function invocation, while using parallelInvocation=false to avoid conflicts between different power values (which happen in parallel anyway).

This way, you can create/remove the messages from your queue(s) without interfering with the invocation time (or the code) or the function you're power-tuning.

I understand this requires you to write additional code, but it's the most flexible way to address all possible use cases for a tool such as Lambda Power Tuning :)

Let me know if you have any doubts or encounter issues.

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alexcasalboni avatar alexcasalboni commented on June 2, 2024

Great!

Let me know :)

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