Postgres LISTEN/NOTIFY actually scales

"Scale" isn't a binary, it's a continuum. "Scales to 60K/s" can be 5 orders of magnitude more than one system needs and 5 orders of magnitude too small for another. Personally I'd knock the general "premature optimization" off the list of "most common developer errors" and put in its place "using techs with the wrong scaling factors". If you use something too small and you exceed its needs, the failure is obvious, but the other way around is a problem too. Bringing in the overhead and management issues of the super scalable techs, as well as their limitations they impose so that they can scale, to a system that would actually be better off with a richer model whose richness prevents it from scaling but would save a lot of effort is also a bad choice.

The ceiling of LISTEN/NOTIFY is small enough that you need to pay attention, and I personally like to have at least an order of magnitude of slack left over even after my most pessimistic load numbers are accounted for, but it's still plenty for a lot of projects, and the integration with the rest of the DB, its availability, its not being another service you have to devops, it's definitely not something that should be simply dismissed out of hand as an option. Even the original 2K/s number they cite is a lot of messages for some systems that are more properly measured in seconds per message.

The tested machine appears big and with 96 vcpu + 384 gb yielding 20k writes sounds too low. Jumping to 60k is very good. But still too small a throughput for what the machine is capable of.

Batching ensures you run at cpu & memory speeds and only pay significant latency for the flush - which usually linux kernel coalesces well if concurrent.

I continue to love DBOS for how it just leverages Postgres (and now SQLite) properly. It's effortless to drop into an existing CRUD stack.

Once you start down the "durable workflows" path, you start seeing them everywhere.

My latest experiments are treating individual emails as durable workflows, where you, the people you're communicating with, agents and tools like GitHub or Attio all take turns in the flow.

https://housecat.com/blog/gmail-durable-workflows-sandbox-vm

Related, presumably:

Postgres LISTEN/NOTIFY does not scale - https://news.ycombinator.com/item?id=44490510 - July 2025 (321 comments)

I think a lot of these kind of posts are standalone assessment of your problems, understanding and solutions. Its debatable to term something as lack of expertise if one is trying to work with default settings of a tool and expecting a certain performance. Everyone is doing a continuous learning with the failures.

1. What I find interesting is that the experiment seems to be using a DB server with 96 cores, 384 GB RAM (https://github.com/dbos-inc/dbos-postgres-benchmark/blob/mai...). This is very critical part of any such experiment, it should have been called out. The database is vertically scalable and that too has its limits

2. Who is making connection, and from where has its own impact on performance and overall latency

3. 60k may seem big number, however in real world the things which bring the systems down are the bursts of traffic, not the regular traffic.

Personally I would never start with such a big server unless I am a big business. Its > 100K cost for one production DB cluster if I include read replicas and cross region redundancy

I recall that in the first release that supported LISTEN/NOTIFY there was a performance issue around it (poor locking IIRC), which today the "bad post" mentioned in here corrects in a errata just after their first paragraph.

Since the correction apparently dates from May 8th, I think that a post from July 24th might want to acknowledge that the popular post asserting this feature doesn't (didn't?) scale was not made in bad faith or was even wrong about their claims at the time.

I once was the CTO of a company that serviced about 100k requests per day across all our services. We grew to millions and eventually 10's of millions, but, somewhere along the way, an engineer on our team decided he wanted to build a queue off LISTEN/NOTIFY semantics in order to take advantage of strong consistency with the rest of our data model, which seemed reasonable given LISTEN/NOTIFY is not that hard to understand and we did need consistency guarantees for this workflow and this would remove the need for yet another place data got stored and transfered.

In practice, this eventually ended up being very awkward because extending the functionality (since we had "built" it) and had to work around internal pg semantics (we should have just moved off much sooner). It also did not scale well. We ended up getting a ton of disk contention on our RDS instance in non-obvious ways, and the vacuum runs on that table was a nightmare. Additionally, it was hard to get other engineers to really debug and take ownership of the system because they automatically viewed a queue (very easy to understand) implemented in a foreign way (off pg internals) as something "scary". It was emotional, not rational, but we are emotional beings, and I do not blame them. These were good engineers with a lot of other things on their plates.

Obviously, this is all hand-wavy without discussing the internal schema, indeces, etc. that we had set up, but my main takeaway with core technology from this experience was to always reach for the dumb, expected, simple thing. Even if it adds another moving piece in the infra stack. Unless I need very strong data consistency guarantees, it's always better to use something like SQS, Redis queues, etc. where the understanding is that it is just a queue (or at least the API contract suggests simplicity), and then everything needs to work around it.

The fewer mechanistic responsibilities per core data store, the better in my experience.

One way it explicitly doesn't scale (unless something has changed since I last checked and my quick search failed me) is the hard limit on 8000 bytes of data in a notification. If your notification doesn't make sense to exist as a row (where you can just give an ID), then that makes it hard to use. I had a web game and the events were transient descriptions of changes in state that didn't make sense to store in the database, and could be bigger than that, so it didn't work for that use case.