Is writing multiple INSERTS versus UPDATE faster for temporary POSTGRES databases?

Question

I am re-designing a project I built a year ago when I was just starting to learn how to code. I used MEAN stack, back then and want to convert it to a PERN stack now. My AWS knowledge has also grown a bit and I'd like to expand on these new skills.

The application receives real-time data from an api which I clean up to write to a database as well as broadcast that data to connected clients.

To better conceptualize this question I will refer to the following items:

api-m1 : this receives the incoming data and passes it to my schema I then send it to my socket-server.

socket-server: handles the WSS connection to the application's front-end clients. It also will write this data to a postgres database which it gets from Scraper and api-m1. I would like to turn this into clusters eventually as I am using nodejs and will incorporate Redis. Then I will run it behind an ALB using sticky-sessions etc.. for multiple EC2 instances.

RDS: postgres table which socket-server writes incoming scraper and api-m1 data to. RDS is used to fetch the most recent data stored along with user profile config data. NOTE: RDS main data table will have max 120-150 UID records with 6-7 columns

To help better visualize this see img below.

From a database perspective, what would be the quickest way to write my data to RDS. Assuming we have during peak times 20-40 records/s from the api-m1 + another 20-40 records/s from the scraper? After each day I tear down the database using a lambda function and start again (as the data is only temporary and does not need to be saved for any prolonged period of time).

1.Should I INSERT each record using a SERIAL id, then from the frontend fetch the most recent rows based off of the uid?

2.a Should I UPDATE each UID so i'd have a fixed N rows of data which I just search and update? (I can see this bottlenecking with my Postgres client.

2.b Still use UPDATE but do BATCHED updates (what issues will I run into if I make multiple clusters i.e will I run into concurrency problems where table record XYZ will have an older value overwrite a more recent value because i'm using BATCH UPDATE with Node Clusters?

My concern is UPDATES are slower than INSERTS and I don't want to make it as fast as possible. This section of the application isn't CPU heavy, and the rt-data isn't that intensive.

Answer 1

To make my comments an answer:

You don't seem to need SQL semantics for anything here, so I'd just toss RDS and use e.g. Redis (or DynamoDB, I guess) for that data store.