| Hi there, Hey there, I lead Developer Advocacy at AWS for Serverless (https://twitter.com/chrismunns). I'll give you that this 80% number seems pretty out there. I don't know how that is measured or what it would be referencing. If you step back and remove all the commercial software from the argument (something like 50%+ of enterprise workloads, the kind of things you buy from a 3rd party and just run it, like Sharepoint, SAP, or similar) and then look at how many business applications take on a trivial amount of load over time, then the author's post becomes more of an outlier. Few folks have apps that do 100rps realistically. And so for data processing/streaming/batch or web/api workloads serverless actually does work out pretty well. Is this 80%, I am not sure. There is 100% an inflection point where if your operator cost is low enough(human work+3p tools+process+care and feeding) then the "metal to metal" costs can be comparable. Even the author admits that's leaving something on the floor and so it really comes down to what your organization values most. I would love for most of our serverless app workloads to be top-down organizationally driven but the reality of it is that it comes often from developers themselves and/or line of business organizations with skin in the game of seeing things move faster in most organizations. This will then typically require buy in from security and ops groups. If these folks you know have the trick to driving top down incompetent strategic management towards serverless I'd buy in on that newsletter. In terms of HN sentiment and in being a member of this community for almost a decade, I don't know if I'd say it widely represents most of the dev world as it tends to lean way more open-source and less enterprisey. I think there's also a larger number of people that represent IT vendors that would love to see AWS fail here :) Thanks, - Chris Munns - AWS - Serverless - https://twitter.com/chrismunns |
This is my field of expertise. Serverless in the sense of lambda/functions is not usable for serious analytics pipelines due to the max allowed image size being smaller than the smallest NLP models or even lightweight analytics python distributions. You can't use lambda on the ETL side and you can't use lambda on the query side unless your queries are trivial enough to be piped straight through to the underlying store. And if your workload is trivial, you should just use clickhouse or straight up postgres because it vastly outperforms serverless stacks in cost and performance[1]
For non-trivial pipelines, tools like spark and dask dominate. And it just so happens that both have plugins to provision their own resources through kubernetes instead of messing around with serverless/paas noise.
And PasS products, well.
https://weekly-geekly.github.io/articles/433346/index.html
>One table instead of 90
>Service requests are executed in milliseconds
>The cost has decreased by half
>Easy removal of duplicate events
Please explain.
[1] https://blog.cloudflare.com/http-analytics-for-6m-requests-p...
IaaS is the peak value proposition of cloud vendors. Serverless/PaaS are grossly overpriced products aimed at non-technical audiences and are mostly snake oil. Change my mind.