Comment by 0xbadcafebee
2 days ago
RBDMS are not designed for write-heavy applications, they are designed for read-heavy analysis. Also, an RDBMS is not a message queue or an RPC transport.
I feel like somebody needs to write a book on system architecture for Gen Z that's just filled with memes. A funny cat pic telling people not to use the wrong tool will probably make more of an impact than an old fogey in a comment section wagging his finger.
People have been using RDBMS' for write-heavy workflows for forever. Some people even use stored procs or triggers for getting complicated write operations to work properly.
Databases can do a lot of stuff, and if you're not hurting for DB performance it can be a good idea to just... do it in the database. The advantage is that, if the DB does it, you're much less likely to break things. Putting data constraints in application code can be done, but then you're just waiting for the day those constraints are broken.
That is the same logic that led every failed design I've seen in my career to take months (if not years) and tons of money to fix. "YOLO engineering" is simple at first and a huge pain in the ass later. Whereas actually correct engineering is slightly painful at first and saves your ass later.
The people who design it walk away after a few years, so they don't give a crap what happens. The rest of us have to struggle to support or try to replace whatever the lumbering monstrosity is.
But those rules of thumb aren't true. People use Postgres for job queues and write-heavy applications.
You'd have to at least accompany your memes with empirics. What is write-heavy? A number you might hit if your startup succeeds with thousands of concurrent users on your v1 naive implementation?
Else you just get another repeat of everyone cargo-culting Mongo because they heard that Postgres wasn't web scale for their app with 0 users.
There are lots of ways to empirically tell what solutions are right for what applications. The simplest is using basic computer science like applying big-O notation, or using something designed as a message queue to do message queueing, etc. Slightly more complicated are simple benchmarks with immutable infrastructure.
There are OLTP and OLAP RDBMSes. Only OLAP ones are designed for read-heavy analyses.