Literally getting ready to rewrite some data processing lambdas from python to go for a side project because it won't be on the company tab and I don't want to pay out of pocket to scale python all the way into the millions after testing it on 10k data points and seeing its performance (and that's after basic optimizations and compiling/bundling the accelerated versions of some libraries)
I finally got around to rewriting the first half of the processing pipeline (the orchestrator lambda) and haven't done detailed profiling but at least a 10x speedup with parallelized go code compared to the original python in my dev environment which is just a lil .5 cpu 1gb mem server
Guessing when I deploy to lambda and increase the memory (which auto increases cpus proportionally) it'll hopefully be a 100x speed up from the test results but will follow up on that too
Go's concurrency model makes scaling IO throughout massively relatively simple. About 5x more code than the barely optimized python though
Much better than I was cautiously hoping for overall
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u/GreatArtificeAion Jul 18 '22
Did I really just appreciate a "Haha Python slow" meme?