Couldn't be a better why us :)
Instacart doesn't need "100,000s of grocery delivery orders per minute".
There must be some 0s added for the sake of the story.
Could just be looking at the "orders" endpoint in their app which might also include incremental updates as shoppers get items from the store. It's a fairly ambiguous statement
It might make 100k row level changes per minute, but that’s a different metric.
https://www.sec.gov/Archives/edgar/data/1579091/000157909126...
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.
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-...
For both MySQL and PostgreSQL you will need to use some kind of logical upgrades if you want no downtime.
At this point i wonder if i'll ever see that.
Multi-master is hard. The main issue is what to do with commit/replication lag. It's far "easier" if support for eventual consistency is ok with your use case. In some cases it's not. Also, the problems related to read-only lag can happen on multi-master instances. If somebody does a giant long running query on one of the masters, the target instance needs to hold the data state for the query, even if the underlying DB is getting updates. It also needs to still keep up with other masters. This means the whole cluster can slow down if the multi-master replication is synchronous. Depending on a variety of factors, that can chew up disk space, memory, etc.
There are ways of dealing with these issues (and others), but it comes with tradeoffs with performance, etc.
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.
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.
We sharded over 20 TB that we know about.
1. Control plane to manage multi-node deployments; "works out of the box" experience to make PgDog easy to deploy and use
2. QoS (quality of service): automatically block bad queries from taking down the database
Last but not least, you get SLA-backed support from us (up to P0).
New features are broken down into two categories:
1. Sharding / running Postgres at scale: always open source.
2. Infra management / making it easy to run PgDog at scale: enterprise.
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. Neki as you mentioned 2. PgDog 3. Multigres, headed by original creator of Vitesse
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?
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.
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.
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?
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.
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;
This kind of tool will help in this case?
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.
its probably the easiest database to run at scale. run & forget. you just have to do a little more work on the data modeling part before you write your application i.e consider your query patterns.
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...
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.
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 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.
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.
As long as they don't get undercut by the equivalent of AWS https://aws.amazon.com/rds/proxy/ which is a managed pgbouncer.
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.
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'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.
Load balancer with health checks and failover, works out of the box. :) Battle-tested at this point too, so could be worth a look.
So much so, we re-wrote the DynamoSDK to squeeze out more optimizations so we could be the same cost even though we were a layer in front of dynamo. We used key encoding and other various technique as well as managed capacity (on demand vs reserved) to transparently optimize workloads for price. In our experience we saw dramatic gains vs just vanilla SDK usage.
If you're curious, here was the marketing website, but we're now part of Databricks: https://stately.cloud/
Take for example AuroraDB: the sheer engineering it took to make SQL do scalable OLTP at all tells you how much that flexibility actually costs to keep.
> PgDog does not detect primary failure and will not call pg_promote(). It is expected that the databases are managed externally by another tool, like Patroni or AWS RDS, which handle replica promotion.
look, you guys can use a database tool from a guy who thinks he supports replication when the LLM he used to do everything correctly admitted that it doesn't support replication. i won't.
https://github.com/patroni/patroni