This solves the thousands of clients case for read in a way that is transparent to the clients.
Yes it's required at large scale, especially if you want to distribute reads or shard to a particular geographical area.
As long as they don't get undercut by the equivalent of AWS https://aws.amazon.com/rds/proxy/ which is a managed pgbouncer.
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.
You’d need a ton of faith in these 3 people.
Feels more like it would work better inside of a bigger organization.
The QA tester in me is kinda risk adverse.
Couldn't be a better why us :)
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.
This is for DBs that are ~1-1.5TB but doesnt have a huge amount of churn/qps
Effectively what is described here https://www.pgedge.com/blog/always-online-or-bust-zero-downt...
If you use something like CloudNativePG they automate parts of the process with cli tools and declarative syntax. Otherwise you take the time to figure it out by hand. It might sound complicated, but just practice on your staging DB, and if all goes well you do the same procedure in prod.
Edit: Apparently Postgres 19 has a patch for one-shot logical replication of sequences! https://www.depesz.com/2025/11/11/waiting-for-postgresql-19-...
At this point i wonder if i'll ever see that.
Multi master, from even a conceptual perspective, is incredibly complicated. Databases, transactions, consistency, parallelism are all very complicated.
It’s something that always seems promising at the start but as soon as maintenance and long term improvements enter the picture(ie integrating new Postgres versions), the complexity becomes too much.
For both MySQL and PostgreSQL you will need to use some kind of logical upgrades if you want no downtime.
This kind of tool will help in this case?
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.
That's exactly right. Get in touch (lev@pgdog.dev), happy to help or at the very least tell you what current works (or doesn't) so you know what your options are.
We sharded over 20 TB that we know about.
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?
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.
In all seriousness, we audit every single line of code that goes in and only people who work for PgDog Inc are allowed to merge code.
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.
We'll get there.
You could also build a watcher side car that watches for changes of the pgdog_users.toml and have pgdog refresh itself then too with this combination. We thought about that but prefer to control the reloads for our needs.
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?
I had to disable application pooling as it was causing read only transactions I could couldnt pin down the cause.
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
SELECT tenant_id, COUNT(clicks)
FROM users
GROUP BY tenant_id
ORDER BY 2 DESC
LIMIT 25;
Still trying to figure out how this works technically, is the performance gain really just re-write in rust?
Edit:
Performance gains are from having the ability to load balance reads (horizontal scaling for read queries) and scale out writes (with sharding). Once instance bottleneck in Postgres has many faces:
1. Behind schedule vacuums because of too many dead tuples (too many writes)
2. The WALWriter is single-threaded and IO-bound - Postgres can only do about 200-300MB/sec in writes per instance (real prod numbers on EC2 with NVMes and ZFS, basically best case scenario).
3. Bulkheading: single primary is a single point of failure. With 12 primaries, if one fails, 91% of your customers don't notice.
The list goes on. Rust is just a side effect. We love it because it's fast and correct - the perfect match for a database product.
The same old processes vs. threads debate, plus having the ability to scale the coordinator past a single machine. So, if you're OLTP, definitely consider PgDog. OLAP - Citus still wins because of its advanced query engine. We'll get there.
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.
Is there a binary I can run directly?
Then again, sharding on a single host probably isn't very useful anyway - but it might work with docker in swarm mode?
You _could_ make that ACID, but it's not going to be faster than a single machine.
Expanding on that a bit, mongo drivers even have a shared specification of the state machine for monitoring topology changes[1] and algorithm for selecting the server to send an operation to[2] (along with various declarative test cases that the drivers use to validate them alongside the specs in the repo). I think people sometimes underestimate how important the client-side work is to this sort of experience; for all of the faults mongo has had over the years, the amount of investment that they put into the client libraries is something I've never seen anywhere else (although having spent several years working on some of these libraries, my take is likely very biased).
[1]: https://github.com/mongodb/specifications/blob/master/source... [2]: https://github.com/mongodb/specifications/blob/master/source...
1. Let it crash. Increase the RAM, try again.
2. Page to disk (swap), make it slow but ultimately work.
Both have their trade-offs. There is no free lunch here.