Which approach should a developer take to increase the speed of completion?
Instead of using Node.js. rewrite the Lambda function using Python.
Instead of packaging the libraries in the ZIP file with the function, move them to a Lambda layer and use the layer with the function.
Allocate the maximum available CPU units to the function.
Increase the available memory to the function.
Explanations:
Changing the runtime from Node.js to Python is unlikely to have a significant impact on the performance of CPU-intensive tasks. Performance improvement would depend more on optimizing the code itself rather than switching to a different language.
Moving libraries to a Lambda layer can help with code reuse and reduce function deployment size, but it will not directly improve the CPU processing speed or efficiency of a CPU-bound task.
Allocating more CPU units is not an option in AWS Lambda. CPU resources are proportional to the memory allocation. Increasing memory also increases CPU allocation, but you cannot separately control CPU units.
Increasing the available memory for the Lambda function will automatically increase the allocated CPU power, which is the most effective way to speed up CPU-intensive processing. More memory results in more CPU power, which can reduce processing time.
I sort that the answer is:
Increase the available memory to the function.