I've used it at work to speed up some standard Python code (without any c-bound library usage). It sped up the code by 5 times.
I've deployed used the pypy:3.9 image on docker.
One thing I did notice is that it was significantly faster on my local machine vs when I tried to deploy it using an AWS lambda/fargate. I know this is because of virtualization/virtual-cpu, but there was not much I could do to improve it.
I agree. I only generally use Fargate with containers, and control with cron or with always on APIs, with Cloudwatch for monitoring, and it’s seems like a good midpoint between ec2 and serverless.
The APIs cost a bit more a month (they’re on quite low cpu/memory) because they’re always on, but it minimises complexity and it’s nice to know that you can move those containers over to another platform if needed.
I've tried to grapple with this notion over the last 10+ years. When I entered finance (I work for a derivatives trading desk), it was just as Facebook etc were just starting and were becoming the 'cool' places to work.
However, nowadays the banking tech sector here in the UK seems to be more mature. Our traders are quite often from a STEM background which gives them an understanding of technology, and they don't adhere to the dicky stereotype you often hear about. But most of all, I've always found my tech colleagues to be extremely smart but also down to earth, pragmatic and meritocratic which is the reason why I've ended up remaining in banking.
I've deployed used the pypy:3.9 image on docker.
One thing I did notice is that it was significantly faster on my local machine vs when I tried to deploy it using an AWS lambda/fargate. I know this is because of virtualization/virtual-cpu, but there was not much I could do to improve it.