I've used Postgres at a few places and the #1 problem was always high availability, not scaling. One Postgres cluster could easily handle 100000 transactions per minute, but when a primary node went down it was a page and manually failing over to the spare then manually replacing the spare. The manual tooling was very finicky but at least it worked, no automated solution came even close. Lack of a good HA story is why I avoid self-managed Postgres as much as possible.

I'm really happy that there's more options for Postgres sharding and I applaud Pgdog and the team's efforts and energy.

Having said that, this makes it a no-go for me:

> shard_number = hash(data) % num_shards

https://docs.pgdog.dev/features/sharding/basics/#terminology

Most sharding solutions distribute the hash value over linear ranges, that then split across "virtual shards", that are then placed on the physical shards or worker. This allows for shard replacement when needed. For example, Citus works this way, and even adds convenience functions for shard migration (using logical migration) in an automated way. That's all I'd need.

Operationally, it's worlds apart. With modulo distribution the only way to replace data is to reshard everything --something you don't want to do however fast the operation may be.

Couldn't be a better why us :)

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.

  We sharded over 20 TB that we know about.

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.

https://open.spotify.com/episode/6qgpfiW68KcvRASs6649Fb

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.

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

Just to say we're happy pgdog users here! One feature we quite like (of the proxy) is the handling of different connection settings per connection (i.e. statement_timeout). When we investigated RDS proxy (ages ago) it wasn't supported, I think the same was true for pgbouncer so it required a bunch of application changes. With pgdog, it just works transparently.

Edit: It also might be interesting to point out how your solution differs from what the folks at Planetscale are building https://planetscale.com/neki

1. pool exhaustion from idle connections inside open long-running transactions

2. SQLAlchemy's client-side pool using dead connections that PgBouncer had already killed, causing periodic request errors

3. Some tasks have to bypass PgBouncer when they use SET or prepared statements

I've already sharded large datasets at the application layer, but looks like PgDog solves the above problems for any future work?

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

This is years of product development with a three person team. If Enterprise sales and support are a big part of your business plan it will suck up a lot more than that.

Is there a binary I can run directly?

My question is, has any of them been talked about being upstreamed to postgres itself? Or, adding a custom built in feature to postgres itself?

Don't pay a startup for your DB proxy, you should own that layer yourself inside of your infrastructure.

This kind of tool will help in this case?

i'm sure you'll get 100x comments about "why not just have one fast SSD? it can do 2000 trillion writes/s"

Would love to hear the advantages of moving to PgDog.

https://github.com/pgdogdev/pgdog/commit/36434f93f03dec1d7d4...

I want to have as much fun as the next developer, but that makes me worry, what with supply chain attacks in the news and all.

I don't know how the pg scaling story gets fixed unless certain things are rewritten. that's my fear of going all in pg.

mysql has vitess etc & even upgrades are easier. though pg is more extensible.

Surfacing where and how PG is better than Dynamo or any other database is probably a good starting point instead of calling out PG a silver bullet for everything. At the end of the day its all a trade-off.

This just seems like fanboyism to me. At the very least, you need to qualify what scenarios you think it's useful for.

I don't doubt that Postgres is good for all the projects you've ever worked on. Generalizing from that, though, is hubristic.

Not quite. The reason "DBs" like those exist is purely due to fashion. Lets not kid ourselves into thinking they do anything better, save the exception of making data hard to access, which might be a project goal in some cases.