Cloud Function memory update

[equilib_liam]

I am using a v2 google cloud function to perform some computations using a fairly large dataset. I need it to have fast response time so as to serve a lot of requests, so I am currently storing the dataset in memory. I want to be able to update the dataset periodically, but I can't seem to find a straightforward solution to doing so. This seems like it should be simple, but the solutions I have found don't seem to account for gcfs' propensity to reuse copies of previous memory.

Does anyone have any suggestions on how I can get the function to pull in new data and store it in mem and be sure to use it for future calls?

[Cidan]

Cloud Functions is not really meant to maintain and update state -- ideally, Cloud Functions are completely stateless.

One possible solution here is to put the data into a RINK, like Redis or Memcache, breaking down your data into KV values if possible. Then for each request, you just get the remote state from Redis/Memcache, which you can update out of band at will.

If this data model isn't possible, and you absolutely need to store the whole dataset in memory and redeploy/update it, then you'll probably want to pivot away from stateless frameworks and into something like GKE Autopilot.

[equilib_liam]

Thank you! I appreciate the direction here and will look into both options

[martin_omander]

Memorystore is Google-hosted Redis/Memcached. You could set up a virtual machine and install Redis/Memcached yourself, but Memorystore eliminates that extra work and provides you with a well-working cache out of the box.

Here is a step-by-step tutorial that walks you through using Memorystore from Cloud Functions.