That's interesting. After re-reading your earlier email, I think that I misunderstood what you were saying.
Since this is a mod_perl listserv, I imagine that the advice will always be to use mod_perl rather than starman? Personally, I'd say either option would be fine. In my experience, the key advantage of mod_perl or starman (say over CGI) is that you can pre-load libraries into memory at web server startup time, and that processes are persistent (although they do have limited lifetimes of course). You could use a framework like Catalyst or Mojolicious (note Dancer is another framework, but I haven't worked with it) which can support different web servers, and then try the different options to see what suits you best. One thing to note would be that usually people put a reverse proxy in front of starman like Apache or Nginx (partially for serving static assets but other reasons as well). Your stack could be less complicated if you just went the mod_perl/Apache route. That said, what OS are you planning to use? It's worth checking if mod_perl is easily available in your target OS's package repositories. I think Red Hat dropped mod_perl starting with RHEL 8, although EPEL 8 now has mod_perl in it. Something to think about. David Cook -----Original Message----- From: Wesley Peng <m...@yonghua.org> Sent: Wednesday, 5 August 2020 1:00 PM To: dc...@prosentient.com.au; modperl@perl.apache.org Subject: Re: Question about deployment of math computing Hi dc...@prosentient.com.au wrote: > If your app isn't human-facing, then I don't see why a little delay would be > a problem? Our app is not human facing. The application by other department will request the result from our app via HTTP. The company has huge big-data stack deployed, such as Hadoop/Flink/Storm/Spark etc, all these solutions have been existing there. The data traffic each day is as huge as xx PB. But, those stacks have complicated privileges control layer, they are most time running as backend service, for example, offline analysis, feature engineering, and some real time streaming. We train the modes in backend, and use the stacks mentioned above. But, once the mode finished training, they will be pushed to online as prediction service and serve as HTTP API, b/c third party apps will only like to request the interface via HTTP way. Thanks.
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