I check-in a one-line change and CI/CD kicks off a docker build and push. A multi gigabyte binary is pushed to AWS ECR every time. I hardly notice. One benefit is the consistency. Small change. Pushed. Large change. Pushed. Dataset. Pushed. New OS, new code, new lib, new data all the same.
Maybe it doesn’t scale to frequent updates? If you are streaming me data, I can’t update my image every second..
Just reference the data as a URL to static storage, problem solved.
What binary size? What RAM consumption? With a SQLite database or something similar you'll have a small and possibly unchanged program, one or more large replaced database files, and only the unavoidable reloading of actually used index pages contributing to memory use and "warmup" after an update.
Of course if instead of relying on some efficient DBMS that manages indexing and caching you have to discard and reload a large file in its entirety after an update in order to hold all its content in memory your database is part of the problem and not part of the solution.
I find that larger database files on Cloud Run do need to be assigned a higher amount of RAM though, or they fail to start - but I'm not sure if that's a bug in how my code works. I've not yet spent much time tweaking SQLite parameters.
> I wouldn't want to be deploying a multi-gigabyte binary every time i make a change to the dataset...
To runtime startup:
> lambda warmup time
And cost:
> RAM consumption is proportional to binary size
This pattern is mostly useful for content-oriented sites with human editors. It's pretty rare for any human-edited publication to generate more than a few hundred MBs of structured data!
I forgot how to count that low
~4.1TB raid0 on a bunch of ssds xN distributed nodes we depoly, leveldb shards & client in the app.
Daily updates, blue/green needed
For small datasets (<100MB), perhaps, but I disagree with the author that this scales well. I wouldn't want to be deploying a multi-gigabyte binary every time i make a change to the dataset...