[jitl]

Postgres has really not solved problems that come with being a networked server and will collapse under concurrent connections far before you start to feel it with SQLite. 5000 concurrent connections will already start to deadlock your Postgres server; each new connection in Postgres is a new Postgres process and the state for the connection needs to be written to various internal tracking tables. It has a huge amount of overhead; connection pooling in PG is required and often the total system has a rather low fixed limit compared to idk, writing 200 lines of python code or whatever and getting orders of magnitude more connections out of a single machine.

[anarazel]

A connection definitely has overhead in PG, but "5000 concurrent connections will already start to deadlock your Postgres server" is bogus. People completely routinely run with more connections.

Check the throughput graphs from this blog post from 2020 (for improvements I made to connection scalability):

https://techcommunity.microsoft.com/blog/adforpostgresql/imp...

That's for read-mostly work. If you do write very intensely, you're going to see more contention earlier. But that's way way worse with sqlite, due to its single writer model.

EDIT: Corrected year.

[jitl]

Yeah, I think I'm conflating our fear of >5000 connections for our Postgres workload (read-write that is quite write heavy) with our SQLite workload, which is 99.9% read.

The way our SQLite workload works is that we have a pool of hundreds of read connections per DB file, and a single writer thread per DB file that keeps the DB up to date via CDC from Postgres; basically using SQLite as a secondary index "scale out" over data primarily written to Postgres. Because we're piping Postgres replication slot -> SQLite, we don't suffer any writer concurrency and throughput is fine to keep up with the change rate so far. Our biggest bottleneck is reading the replication slot on the Postgres side into Kafka with Debezium.