Comment by gkbrk
2 days ago
If it's designed well, your application also opens a pool of connections and re-uses them each time the code needs a DB connection.
2 days ago
If it's designed well, your application also opens a pool of connections and re-uses them each time the code needs a DB connection.
The issue is language ecosystems that don't use client side connection pooling because they're single threaded (node, Python). So scaling up the number of web server threads means scaling the number of Postgres processes, which are expensive.
Python has threads and connection pools work fine on async workers as well.
Yes but they can’t be shared. If you size your pool to hold 10 connections (because asyncio can handle that and more), then deploy with uvicorn —-workers 4 (which should match the number of cores on your app server), and then deploy to 3 app servers (for redundancy) then you’ve now got 120 open connections to Postgres. Run anything more than a trivial query and you’re easily at gigabytes of ram.
2 replies →
This solves the latency but not the overhead--you'll end up with a full pool of connections for each running instance of your application.
How many running instances do you need though? e.g. Scala web frameworks should be able to do thousands of RPS on a single core without the application developer really trying to optimize anything, and I always hear that even Ruby, Python, etc. are also fast enough to be IO bound so you should just need 2 copies for redundancy, right? Then give each like 8-16 connections.
If I'm implementing my own connection pool, I'll certainly make the number of connections configurable.
That's fine but doesn't address the issue that PgBouncer does. Your application connection pool can multiplex all the connections needed in one application. PgBouncer can multiplex the connections across all applications (whether different apps or many instances of the same app).