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- Hacker News
- My recommendation to everyone out there willing to put in the effort of writing an article; don't put a naff low effort AI generated image at the top of it (or anywhere in it..). It turns me off before I've even read the first word.by issung
- I went through this process when I was designing the sync server for Digital Carrot.
In the end, I decided to just go with the simplest solution possible. In my case it's just a barebones Go gRPC service that uses an in memory channel to send notifications between connected clients.
The reality is that this simple Go server will scale up to about 1000 simultaneously connected customers on about 2gb of RAM. I don't expect to have more than that many paying customers, and if I do I can always just throw a bigger VM at the problem.
Engineers love to over complicate things in the name of infinite scalability, when in reality you can save a lot of time and effort by just understanding the scope of the actual problem you're trying to solve. Fingers crossed that this will become an issue for me some day, but until then most of us just don't need to worry about it!
by newswangerd - 1000 connections is a fairly low number by modern standards, c10k “challenges” are like twenty years old by now.
The real questions are:
- how many messages per second are you processing on that 2gb machine (and using how many cpus)?
- does your message processing involve transaction handling, including saving data ti disk durably?
No offense but it really seems you’re comparing apples and oranges, with your use case being much much simpler than the one described.
by znpy - I had a similar setup, scaled pretty well with some GOGC tuning. I had a small, simple "router" using channels https://github.com/urjitbhatia/gopipe and except for the per connection 16ish kb network overhead per socket, you can get away with a lot of performance with a small hand rolled service.by anachronox
- 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.
by elendilm - Yeah, with that machine spec, I think if anything, the post goes to show that LISTEN/NOTIFY does actually _not_ scale well. A CDC-based solution would give you a multiple of these numbers on much smaller (and cheaper) hardware.
- 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.by Latty
- Could you not send a message that pointed to the state that changed in a non-row-specific way?by pgwhalen
- As an implementer of a scalable notification system I'd be sure to cap the notification size.
- Keeps messages O(1) so I can focus on scaling in the amount of notifications
- Tells whoever runs into this that,
- I didn't planned for arbitrarily large messages as they *might* otherwise grind performance to a halt. - They *might* be misusing my notification systemby dietr1ch - 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.
by dietr1ch - If you mean the optimizations coming in Postgres 19, the original post addresses this:
> As an aside, there’s been some online discussion of a Postgres patch (https://github.com/postgres/postgres/commit/282b1cde9dedf456...) related to this issue. This patch (to be released in Postgres 19) does not remove the global lock or fix the bottleneck we observed. Instead, it optimizes the narrower case where there are many notification channels and each listener is waiting only on a specific channel.
by KraftyOne - I feel like the article is leaving the most important part out ; how to allocate that "offset" (sequence number) which allows consumers to keep tabs on how far they have read and query if there are new messages in the fallback. There are a few schemes, some more creative than others, but it is not a trivial problem to solve without complexity or possibility of lock contention (or races with consumers if you do it the wrong way). Most people I guess have writers acquire a lock, for instance on a single row in a state table, for allocating the next sequence number for an event topic.
What is the best way? Perhaps a tool that reads CDC and writes allocated event sequence numbers to another table would work ok, or would that have long latency?
And that CDC processor should then do the NOTIFY too.
Also on the consumer side in some usecases batching can drastically improve throughput. You don't even use LISTEN/NOTIFY then. Just run the consumer in a loop and each iteration process all new unprocessed messages, and store your last sequence number processed between iterations.
by dagss - Related, presumably:
Postgres LISTEN/NOTIFY does not scale - https://news.ycombinator.com/item?id=44490510 - July 2025 (321 comments)
by dang - 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
by sandeepkd - Agreedby elendilm
- Just sharing a data point and experience.
We had a lot of success with LISTEN/NOTIFY when we paired it with a Rust graphql subscription broker. 10s of thousands of subscriptions, but only 3 or 4 LISTEN connections (one for each host). All changes would be pushed out to all hosts, who would each manage the actual user subscriptions and choose what to actually publish.
This worked super well. In general, moving from hundreds of Ruby or Node hosts to just a few Rust hosts just allows so many simplifications and things that "don't scale" to actually work quite well.
by phamilton - I‘m interested, which graphql library for Rust can you recommend?by hoodaly
- 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
by nzoschke - 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.
by vhiremath4 - Pretty sure if you didn't run the DB on networked disks you would have been fine.by winrid
- I feel this is a management problem, and I disagree with the takeaway about adding a piece to the infra stack. "My devs don't want to work on part of our stack" is not a valid reason to add a new network node imo. Just make them work on it.by eudamoniac
- It is so rare that a new moving part beats other factors. But the one thing I will say is that there's tremendous advantages to of all things, your queuing system being independent of other architectural pieces : it's the one component built to natively store and forward, so if you can keep its lifecycle separate then you can harness that to decouple other systems from each other during updates, troubleshooting, patch windows etc.by zmmmmm
- Not saying this in a mean way:
It seems to me this can be boiled down to "using things without understanding how they work doesn't scale". Yes, vanilla listen/notify doesn't scale. But the OP actually figured out how to make it scale. So your engineers don't have to.
As the community builds, over time, distributed systems, it also understands which brick can do what, and it turns out that starting with less bricks and adding some when you actually need them makes for healthier systems.
by hmaxdml - "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.
by jerf - In this case it does by hitting hardware maximum. It scales until the max it is possible, due the bottleneck being in the hardware, not in the database or i/o. Check the article again:
> At maximum throughput, Postgres CPU is fully utilized, showing the database is actually saturated instead of bottlenecked on contention.
by nudpiedo - I think if you expect to be under 60K/s and suddenly find yourself at 20K/s heading for 200K/s-- you have a better problem than if you built for 1M/s and actual load is 20K/s. The unexpected success of the former will pay for a lot of band-aids and scaling, while you're pretty stuck with the cost structure and upfront spent capital in the latter.
IMO one should design for actual anticipated scale with moderate margin, only exceeding this when it's relatively "free" to do so. (If you can buy bigger hardware for a few K, or if solutions are equivalent other than scalability, pick the bigger solution).
by mlyle - > 5 orders of magnitude too small for another.
Nitpick on an otherwise good post, but I don’t think there are very many 6billion RPS systems out there, and those that do exist are almost certainly using bespoke, purpose-built tools
by zbentley