>PgDog is a sharder, connection pooler and load balancer for PostgreSQL. Written in Rust, PgDog is fast, reliable and scales databases horizontally without requiring changes to application code.

Still trying to figure out how this works technically, is the performance gain really just re-write in rust?

I hope people pronounce this as „pig-dog” and has a mascot that looks like „man-bear-pig”

Does making it "just work" here come with any caveats vs standard PG?

2M qps in production is legit. Curious how much RAM and CPU that takes on average per deployment though

we are using PG bouncer in production. Interesting, I will follow the evolution of this project

I tried out PgDog a while ago, but couldn't find a good way of handling the config except for having this users / pgdog toml file, which makes it a bit awkward to handle in kubernetes where we often do multi-tenancy in postgres - or rather having many databases on the same instance(s), and have them come and go at will.

Also had an issue with it because it cached authentication requests when doing passthrough it seems, I'd changed the roles password, but it kept using the old one, which was no bueno ;).

PgDog seems to make more sense when you really care about a few databases that need massive scale, rather than a simple proxy in front of postgres. I'll keep following the development though, it is much needed in this space, postgres can use all the investment it can get to get it past the single machine scale that it excels at currently.

It’s surprising they don’t mention advantages over other sharding systems like Citus. Maybe it’s just the fact that it’s only a proxy and not core extensions? But that could limit capabilities.

I've moved from pgbouncer to pgdog a few months ago without issue. Huge fan.

Cool work, thanks.

Wrt. the pooler, how do you compare with pgbouncer?

I'm interested because I have a postgres instance, low-traffic but still like ... tens of r(eads)ps. I was not running anything close to the machine limits but still added pgbouncer to improve performance and didn't see a noticeable difference. I was stress-testing the machine obv., I'm not talking about the 10 rps, lol.

For context, my numbers were something like 10k rps +/- 1k vanilla postgres and like 9k rps +/- 1k with pgbouncer in front of it. So ... slightly slower but big error bars so I wouldn't say for sure. I ended up not using pgbouncer as the benefit was immaterial.

Also yeah, in case you want to check it out, it's the db that backs this project: https://httpstate.com.

i am not using any tool like pgbouncer and have not run into any issues so far. Is it even required these days? Have you guys tested your setup without these connection poolers/multiplexers?

Suggestion: have more than just helm and Docker in your quickstart documentation. I'd like to try this out just to see what it can do, but not quite enough to fire up one of those systems for it.

Is there a binary I can run directly?

the reason mongo is a joy to use in scaled env is because no additional setup/software needed and all drivers natively support secondary/primary writes/reads and topological changes. so it's end to end, and adding is as a new proxy in frontend of postgres leads to all clients being incompatible or the code itself has no control anymore about when to use a secondary and what allowed stall is acceptable for a particular query. Any solutions to this by pgdog?

I'm a big PGDog fan! It really helped us scale our connection proxy needs pretty substantially and it has great features like auto mode to support Aurora failovers neatly. It's infra that just works.

I am trying to gain a basic understanding of this: Right now I have a 4TB DB on one large box. Is the idea that using a proxy tool like PGDog I could spin up 8 smaller boxes handling ~500GB each and then one medium box for the proxy?

Right now I have a project that has very heavy write traffic from multiple services and a web app that reads from this. We are starting to hit the point where no amount of indexing, query optimisation, caching or box upgrades is helping us. We are looking at maybe moving the bulk of the static data to clickhouse to reduce the DB size but I would love to hear if PgDog or other kind of sharding could be useful for this use case.

How are 3 developers going to QA this properly ?

I'm curious how this might help with our biggest downtime-causer with postgres, which is major version upgrades. Poolers do a great job for failover and load balancing, but we consistently need ~10-20 minutes of downtime once or twice a year to do upgrades. Logical replication between old->new versions could probably help, but it would still require flipping everything over to the new cluster without partial writes or anything silly. Anybody have experience with this?

  We sharded over 20 TB that we know about.

This is probably a typo, right? 20TB isn't that big. I would imagine they've sharded a lot more than that

Good stuff, although I’m not quite sure about the fast OLAP use case.

If you’re already sharding by tenant for other reasons, OK… But I see CDC to a true OLAP system as more scalable.

PostgreSQL still needs real columnar tables in the core, hopefully one day

I've loved using pgdog for the last 6 months. It's been incredibly stable. It's nifty how they've solved the LISTEN/NOTIFY on a transaction pooler problem.