Hi Folks,

Apologies for a potential double posting - I sent a derivation of this in
the web interface and it was sucked into the depths of the internet, maybe
never to be seen again! I'll try to re-articulate my original post.

*Detailed Summary*
I'm working on a Google App Engine Django app with a postgres database, and
I'm looking for ways to scale update operations for model instances that
correspond to similarity values in a matrix. For example, we have two
matrices, 12K by 12K and 16K by 16K, and we want to update pairwise updates
of the values at some regular frequency (weekly or monthly). Each
similarity score is stored in its own instance of a similarity class, so
given that the input data to calculate the similarity doesn't change during
update, the operations can be considered mutually exclusive. Running on a
local machine in serial takes in the ballpark of a day. I'd like to scale
this to be faster and optimized.

*Quick Summary*
Let's discuss solutions for scaling postgres updates using Google Cloud,
where the database is for a Django site on App Engine.

*What I'm Looking At*
I'm used to scaling things with HPC, and I'm pretty excited to get a chance
to try this in cloud. I've been looking at cloud functions
<https://cloud.google.com/functions>, task queues
<https://cloud.google.com/tasks/docs/creating-queues>, and batch on GKE
<https://cloud.google.com/kubernetes-engine/docs/concepts/batch>. My
thinking is that I'd like something that we can submit a job to a queue,
and then have it process up to some maximum number of operations. I'm also
wondering if we will need to do horizontal scaling
<https://cloud.google.com/community/tutorials/horizontally-scale-mysql-database-backend-with-google-cloud-sql-and-proxysql>.
We will definitely need to think about how/if the task has permissions to
directory or indirectly make the update.

I'd ideally like a relatively simple solution, meaning that it is easy to
reproduce. Thanks muchly for your help, and apologies again for potential
double posting!

Best,

Vanessa

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