This won't work :) echo will run as root but the redirection is still running as the unprivileged user. Needs to be run from a privileged shell or by doing something like sudo sh -c "echo $NUM_PAGES > /proc/sys/vm/nr_hugepages"
The point gets across, though, technicality notwithstanding.
echo $NUM_PAGES | sudo tee /proc/sys/vm/nr_hugepages
I've always found it odd that there isn't a standard command to write stdin to a file that doesn't also write it to stdout. Or that tee doesn't have an option to supress writing to stdout.
> The real bottleneck is the single-threaded main loop in the postmaster.
A single-threaded event loop can do a lot of stuff. Certainly handle 4000 tasks of some sort in under 10s. Just offhand it seems like it would be eminently possible to handle incoming connections on the scale they describe in a single-threaded event loop.
Clearly the existing postgres postmaster thread is a bottleneck as it is implemented today. But I'd be interested to go deeper into what it's doing that causes it to be unable to keep up with a fairly low workload vs. what is possible to do on a single thread/core.
Can’t believe they needed this investigation to realize they need a connection pooler. It’s a fundamental component of every large-scale Postgres deployment, especially for serverless environments.
Pooling connections somewhere has been fundamental for several decades now.
Fun quick anecdote: a friend of mine worked at an EA subsidiary when Sim City (2013) was released, to great disaster as the online stuff failed under load. Got shifted over to the game a day after release to firefight their server stuff. He was responsible for the most dramatic initial improvement when he discovered the servers weren't using connection pooling, and instead were opening a new connection on almost every single query, using up all the connections on the back end DB.
EA's approach had been "you're programmers, you could build the back end", not accepting games devs accurately telling them it was a distinct skill set.
No? It sounds like they rejected the need for a connection pooler and took an alternative approach. I imagine they were aware of connection poolers and just didn't add one until they had to.
In serverless world for sure but in old-school architecture it's common to use persistent connections to a database which make connection pooler less essential. Also the last time I did check (many years ago admittedly) connection poolers didn't play well with server-size prepared statements and transactions.
Isn't this kind of the reason why teams will tend to put database proxies in front of their postgres instances, to handle massive sudden influxes of potentially short lived connections?
This sounds exactly like the problem tools like pgbouncer were designed to solve. If you're on AWS one could look at RDS Proxy.
The article is very well written but is somewhat lacking at the end.
The conclusion lists pgbouncer as one of the solutions but it does not explain it clearly.
> Many pieces of wisdom in the engineering zeitgeist are well preached but poorly understood. Postgres connection pooling falls neatly into this category. In this expedition we found one of the underlying reasons that connection pooling is so widely deployed on postgres systems running at scale. [...] an artificial constraint that has warped the shape of the developer ecosystem (RDS Proxy, pgbouncer, pgcat, etc) around it.
The artificial constraint is the single core nature of postmaster.
Other points at the end of the article that can be improved:
> we can mechnically reason about a solution.
Mechanically as in letting an AI find a solution, or as in reasoning like a mechanic, or? Furthermore:
> * Implementing jitter in our fleet of EC2 instances reduced the peak connection rate
How? Did they wait a random amount of milliseconds before sending queries to the db?
> * Eliminating bursts of parallel queries from our API servers
Also check out ProxySQL [1][2], it's an extremely powerful and battle-tested proxy. Originally it was only for MySQL/MariaDB, where it is very widely used at scale, even despite MySQL already having excellent built-in scalable threaded connection management. But ProxySQL also added Postgres support too in 2024 and that has become a major focus.
PgBouncer introduces its own problems and strictly speaking adds additional complexity to your infrastructure. It needs to be monitored and scaled separately, not to mention the different modes of session/transaction/statement connection pooling. Adding another proxy in the middle also increases latency.
Yes, currently, putting PgBouncer in the middle helps handle massive sudden influxes of potentially short lived connections. That is indeed the correct current best practice. But I hardly fault the author for wishing that postmaster could be configured to run on multiple cores so that the additional complexity of running PgBouncer for this relatively simple use-case could be eliminated.
I wish more applications would adopt the "H" option that Jenkins uses in it's cron notation - essentially it is a randomiser, based on some sort of deterministic hashing function. So you say you want this job to run hourly and it will always run at the same minute past the hour, but you don't know (or care) what that minute that is. Designed to prevent the thundering herd problem with scheduled work.
Good reminder to always remember Chesterton's Fence. The post indicates that the bottleneck occurs when "many thousands" of EC2 instances are connecting simultaneously. In order for this to happen, presumably someone had to turn `max_connections` way up on their database server to make this to work at all. Seems like the issue could have been avoided at that point with a bit more understanding about why the default is an order of magnitude or more lower than whatever they tuned it to.
Why do you need a connection to a database during the meeting? Doesn't it make more sense to record the meeting data to some local state first, and then serialize it to database at the end of the meeting or when a database connection is available? Or better yet, have a lightweight API service that can be scaled horizontally that is responsible for talking to the database and maintains its own pool of connections.
They probably don't even need a database anyway for data that is likely write once, read many. You could store the JSON of the meeting in S3. It's not like people are going back in time and updating meeting records. It's more like a log file and logging systems and data structures should be enough here. You can then take that data and ingest it into a database later, or some kind of search system, vector database etc.
Database connections are designed this way on purpose, it's why connection pools exist. This design is suboptimal.
It took me a long time to realize this but yes asking people to just open and write to files (or S3) is in fact asking a lot.
What you describe makes sense, of course, but few can build it without it being drastically worse than abusing a database like postgres. It's a sad state of affairs.
Note that they were running Postgres on a 32 CPU box with 256GB of ram.
I'm actually surprised that it handled that many connections. The data implies that they have 4000 new connections/sec...but is it 4000 connections handled/sec?
32 vCPU, meaning an undetermined slice of a CPU that varies depending on what else is running on the box (and the provider has an incentive to run as many VMs on the box as possible).
It’s likely an actual CPU would’ve handled this load just fine.
I'm not working at this company but I found that these types of problems can often be simplified in the architecture.
> Most meetings start on the hour, some on the half, but most on the full. It sounds obvious to say it aloud, but the implication of this has rippled through our entire media processing infrastructure.
When you can control when it happens, you can often jitter things. For instance the naive approach of rate limiting users down to quantized times (eg: the minute, the hour, etc.) leads to every client coming back at the same time. The solution there is to apply a stable jitter so different clients get different resets.
That pattern does not go all that well with meetings as they need to happen when they happen, which is going to be mostly the hour and 30 minutes etc. However often the lead up time to those meetings is quite long, so you can do the work needed that should happen on the hour, quite a bit ahead of time and then apply the changes in one large batch on the minute.
You have similar problems quite often with things like weekly update emails. At scale it can take you a lot of time to prepare all the updates, often more than 12 hours. But you don't want the mails to come in at different times of the day so you really need to get the reports prepared and then send them out when ready.
My first thought was "why even use big databases, you have perfect workload to shard it between a bunch of instances and as a bonus any downtime would only affect smaller part of customers"
This is not a big database usecase. It just needs one to not do silly things like opening a new database session for every query when it's well documented that this is expensive.
Great investigation. Slight aside, but I found the few typos that I noticed to make me feel better about continuing to read -- as a sign that the post wasn't AI-generated.
Cool debugging, but…
1) if you have very spiky loads (on the hour) and you can distribute them a little it’s obvious that this is will be good thing.
2) they had the answer all along in their telemetry
Sometimes wisdom beats effort
Sharding is often not easy. Depending on the application, it may add significant complexity to the application. For example, what do you do if you have data related to multiple customers? How do you handle customers of significantly different sizes?
And that is assuming you have a solution for things like balancing, and routing to the correct shard.
The article addresses this, sort of. I don't understand how you can run multiple postmasters.
> Most online resources chalk this up to connection churn, citing fork rates and the pid-per-backend yada, yada. This is all true but in my opinion misses the forest from the trees. The real bottleneck is the single-threaded main loop in the postmaster. Every operation requiring postmaster involvement is pulling from a fixed pool, the size of a single CPU core. A rudimentary experiment shows that we can linearly increase connection throughput by adding additional postmasters on the same host.
But, I'm hopeful that in 2-3 years from now, we'll see this bear fruition. The recent asynchronous read I/O improvements in Postgres 18 show that Postgres can evolve, one just needs to be patient, potentially help contribute, and find workarounds (connection pooling, in this case).
maybe this is silly but
these days cloud resources are so cheap. just loading up instances and putting this stuff into memory and processing it is so fast and scalable. even if you have billions of things to process daily you can just split if needed.
you can keep things synced across databases easily and keep it super duper simple.
Yeah you can get an AMD 9454P with 1TB of memory and 20TB of redundant NVME storage for like 1000$ a month, its crazy how cheap compute and storage is these days.
If people are building things which actually require massive amounts of data stored in databases they should be able to charge accordingly.
I think this is the kind of investigation that AI can really accelerate. I imagine it did. I would love to see someone walk through a challenging investigation assisted by AI.
kayson|26 days ago
This won't work :) echo will run as root but the redirection is still running as the unprivileged user. Needs to be run from a privileged shell or by doing something like sudo sh -c "echo $NUM_PAGES > /proc/sys/vm/nr_hugepages"
The point gets across, though, technicality notwithstanding.
thayne|26 days ago
timetoogo|26 days ago
jstrong|26 days ago
> The real bottleneck is the single-threaded main loop in the postmaster.
A single-threaded event loop can do a lot of stuff. Certainly handle 4000 tasks of some sort in under 10s. Just offhand it seems like it would be eminently possible to handle incoming connections on the scale they describe in a single-threaded event loop.
Clearly the existing postgres postmaster thread is a bottleneck as it is implemented today. But I'd be interested to go deeper into what it's doing that causes it to be unable to keep up with a fairly low workload vs. what is possible to do on a single thread/core.
cylemons|25 days ago
paulkre|26 days ago
Twirrim|25 days ago
Fun quick anecdote: a friend of mine worked at an EA subsidiary when Sim City (2013) was released, to great disaster as the online stuff failed under load. Got shifted over to the game a day after release to firefight their server stuff. He was responsible for the most dramatic initial improvement when he discovered the servers weren't using connection pooling, and instead were opening a new connection on almost every single query, using up all the connections on the back end DB. EA's approach had been "you're programmers, you could build the back end", not accepting games devs accurately telling them it was a distinct skill set.
foota|26 days ago
jstrong|26 days ago
modin|26 days ago
citrin_ru|26 days ago
vel0city|26 days ago
This sounds exactly like the problem tools like pgbouncer were designed to solve. If you're on AWS one could look at RDS Proxy.
pmontra|26 days ago
The conclusion lists pgbouncer as one of the solutions but it does not explain it clearly.
> Many pieces of wisdom in the engineering zeitgeist are well preached but poorly understood. Postgres connection pooling falls neatly into this category. In this expedition we found one of the underlying reasons that connection pooling is so widely deployed on postgres systems running at scale. [...] an artificial constraint that has warped the shape of the developer ecosystem (RDS Proxy, pgbouncer, pgcat, etc) around it.
The artificial constraint is the single core nature of postmaster.
Other points at the end of the article that can be improved:
> we can mechnically reason about a solution.
Mechanically as in letting an AI find a solution, or as in reasoning like a mechanic, or? Furthermore:
> * Implementing jitter in our fleet of EC2 instances reduced the peak connection rate
How? Did they wait a random amount of milliseconds before sending queries to the db?
> * Eliminating bursts of parallel queries from our API servers
How?
evanelias|26 days ago
[1] https://proxysql.com/
[2] https://github.com/sysown/proxysql
solatic|26 days ago
Yes, currently, putting PgBouncer in the middle helps handle massive sudden influxes of potentially short lived connections. That is indeed the correct current best practice. But I hardly fault the author for wishing that postmaster could be configured to run on multiple cores so that the additional complexity of running PgBouncer for this relatively simple use-case could be eliminated.
haki|26 days ago
We have a habbit of never scheduling long running processes at round hours. Usually because they tend to be busier.
https://hakibenita.com/sql-tricks-application-dba#dont-sched...
abrookewood|26 days ago
ahoka|26 days ago
_bohm|25 days ago
iamleppert|25 days ago
They probably don't even need a database anyway for data that is likely write once, read many. You could store the JSON of the meeting in S3. It's not like people are going back in time and updating meeting records. It's more like a log file and logging systems and data structures should be enough here. You can then take that data and ingest it into a database later, or some kind of search system, vector database etc.
Database connections are designed this way on purpose, it's why connection pools exist. This design is suboptimal.
xyzzy_plugh|25 days ago
What you describe makes sense, of course, but few can build it without it being drastically worse than abusing a database like postgres. It's a sad state of affairs.
mannyv|26 days ago
I'm actually surprised that it handled that many connections. The data implies that they have 4000 new connections/sec...but is it 4000 connections handled/sec?
Nextgrid|26 days ago
It’s likely an actual CPU would’ve handled this load just fine.
the_mitsuhiko|26 days ago
> Most meetings start on the hour, some on the half, but most on the full. It sounds obvious to say it aloud, but the implication of this has rippled through our entire media processing infrastructure.
When you can control when it happens, you can often jitter things. For instance the naive approach of rate limiting users down to quantized times (eg: the minute, the hour, etc.) leads to every client coming back at the same time. The solution there is to apply a stable jitter so different clients get different resets.
That pattern does not go all that well with meetings as they need to happen when they happen, which is going to be mostly the hour and 30 minutes etc. However often the lead up time to those meetings is quite long, so you can do the work needed that should happen on the hour, quite a bit ahead of time and then apply the changes in one large batch on the minute.
You have similar problems quite often with things like weekly update emails. At scale it can take you a lot of time to prepare all the updates, often more than 12 hours. But you don't want the mails to come in at different times of the day so you really need to get the reports prepared and then send them out when ready.
rsanek|26 days ago
PunchyHamster|26 days ago
My first thought was "why even use big databases, you have perfect workload to shard it between a bunch of instances and as a bonus any downtime would only affect smaller part of customers"
ants_a|25 days ago
rsanek|26 days ago
truekonrads|25 days ago
j16sdiz|25 days ago
ya, right. just make up some reason not following the best practices
atherton94027|26 days ago
hinkley|26 days ago
atsjie|26 days ago
thayne|26 days ago
And that is assuming you have a solution for things like balancing, and routing to the correct shard.
levkk|26 days ago
eatonphil|26 days ago
> Most online resources chalk this up to connection churn, citing fork rates and the pid-per-backend yada, yada. This is all true but in my opinion misses the forest from the trees. The real bottleneck is the single-threaded main loop in the postmaster. Every operation requiring postmaster involvement is pulling from a fixed pool, the size of a single CPU core. A rudimentary experiment shows that we can linearly increase connection throughput by adding additional postmasters on the same host.
vivzkestrel|26 days ago
lfittl|26 days ago
Since Postgres is a mature project, this is a non-trivial effort. See the Postgres wiki for some context: https://wiki.postgresql.org/wiki/Multithreading
But, I'm hopeful that in 2-3 years from now, we'll see this bear fruition. The recent asynchronous read I/O improvements in Postgres 18 show that Postgres can evolve, one just needs to be patient, potentially help contribute, and find workarounds (connection pooling, in this case).
moomoo11|26 days ago
you can keep things synced across databases easily and keep it super duper simple.
carshodev|26 days ago
If people are building things which actually require massive amounts of data stored in databases they should be able to charge accordingly.
sgt|26 days ago
clarity_hacker|26 days ago
[deleted]
ltxdsf|26 days ago
[deleted]
parentheses|26 days ago
rcakebread|21 days ago