Postgres isn't slow, your storage is
clickhouse.com
clickhouse.com
Everywhere we look we see people making the best choice for them. Big apps using Electron vs native. Ports of 250MB games like Starcraft now taking 5GB of "compatibility" libraries to run on modern PCs.
I completely agree that people need to be aware of Latency Numbers Every Programmer Should Know (https://gist.github.com/jboner/2841832), but in reality we don't all have the budget, time, or personal to do things the right way when it's the company ordering us around vs our own projects where we can do things "the right way".
Honestly, I'm glad companies are wasteful. If large companies were super conservative and agile then our economy would be in really, really bad shape. It's thanks to these inefficiencies that small startups can jump in and capitalize in ways the many bureaucracies of enterprise can't.
Then again, the dev in me would LOVE it if everyone rewrote everything in Rust or Zig and we put wasteful scripting languages to death for good.
The math has drastically changed over the last few years. For example, the on-prem database server we bought 4 years ago for $14K is now pricing out at $96K.
They can negotiate better prices based on their scale, and even on their ability to internally source key components if needed.
E.g. if AWS/Microsoft/Google/Apple buy 80% of Micron's entire output it's probably at a much better price than the rest of us competing over the other 20% are going to get.
A global supply chain storage is a global supply chain shortage.
Most famous recent example being Apple who were absorbing the rapidly increasing cost of supply until even they had to put up the prices to reflect procurement and manufacturing reality.
And this is Apple. The company that even buys its manufacturers plant equipment in return for it being dedicated to Apple.
Yes the big boys get volume discount. But that doesn't magically entitle them to last decades's pricing.
You’re trying to refute an argument I did not make.
They do not have to be cheaper than 2016. They have to be cheaper than what your business could otherwise string together paying current market prices. And they will be.
Even just going to second tier cloud providers will tend to let you rent similar capacity much cheaper.
AWS and the other big cloud providers only gets close to cost efficient when you're big enough to negotiate prices that are nowhere near their public list prices.
And by which time you are spending an ungodly amount of money per month !
Which is why second tier providers are the way to go.
You get better prices. Much clearer price models. Better customer service....
... And all without having to commit to spending more than the GDP of a small country every month on your cloud bill.
Four years ago, I needed 256gb of ram, and it was costing about $1-2k a month in the cloud, which I replaced with a server on ebay with 1,5tb of ram for about €2k- which I was pricing against equivalent to what I was renting for about $6k or so on newegg. And I think the dumbest IT procurement manager in the planet could've gotten it from Lenovo or HPE or something for $10k providing the easiest 20% savings if they move on-prem.
How much is your $96k server on AWS?
I am no fan of the idea of reinventing everything to run on cloud primitives but you can't deny reliability of online software is a lot higher now than the pre HTML5 era. Back then, having more than two nines of uptime for random backend heavy web apps was hardly common while now it's table stakes.
Some latency increase must be traded for scalability. But at least we get the required utility in return.
But without that utility, one is just hiking up the cost. Any competitor who can deliver at reduced cost holds a potent weapon that you simply cannot survive.
A company that has efficient cost structure across its operational domains holds an advantage which is near impossible to out-compete. A sense of inevitability and resilience is what reduction in cost buys you.
Cost is a weapon. One of the most fundamental weapons in almost all domains.
I don't think this is true. I am not sure about those you described, but say you have PG replicas, you can run query on replica node without hitting some network attached storage. This is obviously true for reads but not for writes.
The argument of cost is fundamental and no amount of Kafka, etc. can override it.
Considering that the topic is an article is comparing raw NVMe vs EBS performance for postgres storage and GP saying that cloud is unnecessarily inefficient, I misinterpreted your comment as implying that networked storage is always an unnecessary cost. In many cases the cost of replication and availability is lower than the cost of data loss and downtime.
Depends on your metrics. Say you're an early startup - short on man-hours and bare-metal skills, with backers who think that "cloud" sounds fast, cool, and scalable.
Or you're a VP at some big company - with C-suite mandate to deliver Cool Solutions ASAP, whatever the cost...but a byzantine mess of middle managers between you and the actual engineers. Deciding "we'll use CloudCo" can rob your steaming heap of PHB's of a lot of opportunities to make bad decisions and drag things out.
Do you want to spend thousands of dollar upfront for that DB server with sweet sweet NVME SSDs, of which your application might not even use 1% before the app get scraped?
If you can’t afford to throw down enough money on a single box to bring a business up then you’re fucked anyway.
I know whole businesses that make a lot of money and never needed more than that box. (And a hot spare)
However, these days, I notice my DBA skills atrophying because LLMs have encoded most of the answers and diagnostics I need in their weights. Coupled with something like Nix, spinning up new servers including backups and everything, it's easier easier than ever to run your own production database instance.
It would really suck to learn that your LLM-generated disaster recovery plan isn't viable when attempting to use it to recover from an actual disaster.
This isn't a whole lot different than what you should be doing to ensure systems not managed by LLMs are designed and operating properly, of course, which I guess is sort of my point.
Hiding behind the LLM, or even a cloud service like RDS makes you think you can get out of a corner, but you are nowhere near that. Practice, drills, experience are the only solution here. The illusion of that is dangerous.
I spent a couple of days recently digging one of our associated businesses out of the shit because they killed a postgres on RDS instance (AWS bug!) and couldn't google or LLM their way out of it. It hit the trade press and did a lot of damage to them.
If you're not going to do the work, don't take on customers.
Of course you need understanding for when shit hits the fan, but that applies regardless of whether it's a human or an LLM writing the code.
LLMs remove personal exploration from tasks and that's where you really get to know things.
When you optimise for fast and not correct, things are fucked.
I think you overestimate the level of DBA skills smaller startups have in house.
If you have found a way for a company to hold off on taking on customers until they can afford a proper DBA, I'd love to hear more about it. But it sounds backwards to me. In my experience, it typically goes the other way around: only after taking on enough customers to be decently profitable can a company afford a real DBA.
Until then, the choice used to be between managed database services or striking luck with a developer that had some database interest. These days, it's between managed database services, striking luck, or giving the developers access to an LLM. The LLM doesn't replace DBA skills that used to be there. There weren't any DBA skills there to begin with.
The day it broke, it's a PITA.
You need that RAID card which is not available anywhere around you.
You need to visit your Data Center, but you suddenly have issues with security and you need to arrange all that stuff.
You need to buy DDR stick but you need to wait a month for delivery.
The guy who knows how to insert a stick took a vacation week ago, and everyone else is too afraid to touch big iron box.
With cloud, you either don't notice that, because they have scale and they have spare parts and they have personnel 24/7. Or worst case you order identical server and deploy your app, hopefully in a few hours restoring from backup.
And for some people it might never broke. But it's all the uncertainty and fear that leads people to cloud solutions. You don't plan for average outcome, you plan for worst outcome.
And you're not immune from those issues in AWS. What happens when you run out of EBS IOPS or have to change instance type and it won't boot? What happens when your instance fails? What happens if you hit a quota limit in the middle of a failover?
AWS support is fucking awful unless you pay $5k a month or 10% of your total spend (whatever is greater) and then it's only mediocre.
If you're small enough to not be able to justify spares, then rent servers instead. It's still cheaper than the big cloud providers.
> You need to visit your Data Center, but you suddenly have issues with security and you need to arrange all that stuff.
You rarely do. Anywhere beyond the cheapest third-rate colo provider provides "remote hands" services.
Even when managing several racks worth of hardware, I'd go into the colo facilities a couple of times a year at most.
> and they have spare parts and they have personnel 24/7
Even tiny colo-providers still have personnel 24/7, and you can buy fractional devops services for small retainers from any number of providers (like me).
I can't comment on total costs / costs over time though.
I remember we ran a pretty large UK website on 5 x Pentium II boxes, using a Perl stack which is not exactly memory efficient. Later we ran a chat server for hundreds of thousands of school kids on a single server which was (if I remember this right) a Pentium III with 128 MB (yes, megabytes) of memory and a spinning hard disk.
However, the main cost would be colocation - it's risen quite a bit.
I wouldn't use Ryzen on a production server myself.
I doubt there's much of a meaningful difference other than a jumper for the brand. Retail pricing and availability might differ.
Where the money gets wasted, is doing a different server per project, putting everything in different resource groups, and then overprovisioning those servers many times over. So much wasted capacity because of simple project structure preferences.
And then there is also the fact some features are locked behind more expensive models, which is also rarely cost efficient.
All I am saying is, if you simply create 1 database server on the cloud, with a database per app, and different schema's, isolated users, ... it can stay super cheap. But good look getting through an enterprise cybersecurity audit or ISO27000+SOC2 certification.
It's absolutely fine. Everyone has just gone stupid recently.
People have zero concept of how incredibly absurdly fast even a slow computer is. Storage is literally 1000x faster than it was 15 years ago. My first job was firmware for enterprise storage arrays and IIRC we were aiming for 3M IOPS. That's not much more than what kids can put in their PS5 now, and these were arrays running companies like Walmart and JPMC.
Which is to say, it's good advice.
It lets you load your dedicated servers far higher than what you can safely do if you don't have the option of using a cloud provider for burst traffic, but in practice most people will very rarely need it.
In 30 years of hosting on-prem or in colo facilities, I've never once bought hardware up front for corporate accounts.
With the multi million dollars A MONTH purchase commitment contracts I've seen in my previous employers, you could use a single month budget to buy hardware that could serve our applications for years.
totally different words
sell a lie, blame the victim?
No thanks.
Kestrel (AspNetCore) + SQLite + NVMe all on the same machine is where things start to get really spicy in my experience. Imagine handling an entire form submission that mutates database state in tens to low-hundreds of microseconds. Total wall clock time required to service any HTTP request can be well under a single millisecond. If your average cpu time per request is 30uS, you could approach half a million per second on a 16-32 core machine.
optane
Edit: optane also shows what the SSD/nvme sequential-throughput marketing wars have cost us. Latency and random 4k iops always should have been the focus (in research, development, reviews, and benchmarks), not sequential throughput.
If the pricing is competitive it will easily be replacing DRAM for many lower-level uses, thanks to its support for better PCIe standards (5.0 at least, while aftermarket Optane is stuck on 4.0).
I love toying with optane stuff but yeah it's starting to show it's age and the gen4 gear is both old-ish and still expensive.
So a new toy would be cool
In a load test with 512 players all active on one world, command acks were 4-6 ms at the median and under 50 ms at p95, on a single Ryzen 9 3900X box. And most of that time is game logic and fanning out updates over websockets, not the database.
The part people worry about is durability, because SSDs have limited write cycles. So I took care to have backups of the state, recovery in short time etc.
I stream the WAL to object storage with Litestream, with one gotcha: I had to turn off SQLite’s own auto-checkpoint and let only Litestream checkpoint, or the two step on each other.
SQLite does a great job to support single writer and multiple readers. That's what we use for aggregation right now. DuckDB is purely post analytics.
Persistent volume management, including:
Support for Local Persistent Volumes with PVC templates.
Reuse of Persistent Volumes storage in Pods.
Separate volumes for WAL files and tablespaces.
> While the standard approach ensures service reliability and leverages Kubernetes' self-healing capabilities, there are scenarios where operating with a temporarily degraded cluster may be acceptable. This is particularly relevant for PostgreSQL clusters relying on node-local storage, where the storage is local to the Kubernetes worker node running the PostgreSQL database. Node-local storage, or simply local storage, is employed to enhance performance.
[0] https://cloudnative-pg.io/docs/devel/#main-features [1] https://cloudnative-pg.io/docs/devel/kubernetes_upgrade/#tem...
I think there's a generation of devs that just takes big memory pools and SSDs for granted; there's a mental disconnect to the fact databases map the underlying storage.
It becomes obvious once I bring it up with them in discussion and I can almost see the bulb go off (happened while discussing partitioning and sharding), but they never make the connection that a query is mapping to some artifacts sitting on storage.
In our case the primary index on a table was a something like a sequential ID but the table stored data from multiple tenants so each page had data from multiple tenants
Took a while to convince people that the DB was always going to read 16KB chunks so we could reduce IO by changing the primary index so it was based around an ID that was unique to the tenant
Yikes. I'm mostly frontend and even to me this seems pretty clear.
> This pattern is not only theoretical, the talk cites public examples from Instacart, which has described PostgreSQL on NVMe for a latency-sensitive search workload
article basically teaches that computers with local storage has better latency. Wisdom of back to basics.
Not that it teaches a bad thing or in a bad way, just me expected from Clickhouse guys less obvious things
They also told me that they've started as a h/w shop and that they have competitors who are building ASICs rather than programming FPGAs to do the same (with, potentially, even more performance gains).
This completely changed the perspective for me. Before, I had this idea that if I need to optimize database performance, I'd first look at indices. Then, at how much memory is available to the database for various tasks. Only after that, I'd look at I/O performance, but, mostly, because of fsync configuration.
It wouldn't even appear to me as a possibility that the compute can become the culprit of optimization. But, apparently, it's a new hot thing.
* * *
From my experience with storage testing, which also included quite a bit of pg-bench exposure... My impression was this: the sort of query that is used for performance benchmark matters the most. It will define the optimization direction. Blindly trying to add or speed-up one resource or another is not going to be an efficient strategy. In the database world, there's this idea of an "ideal" benchmark. Without too much detail, it imagines an "ideal" business accounting back-end, the sort of queries it would have to execute. It defines an "ideal" unit that roughly encompasses one "sale", and then it measures how many such "sales" a business can perform per unit of time. This will translate into some ratio of selects / inserts / deletes / updates with some index hits and some linear searches etc. But it's all extremely vague and it's very hard to adapt this benchmark to the actual business needs of the database being tested.
There's also a question what to optimize for: the resource usage? the response speed? the concurrency? In some scenarios, I can see how storage would factor in, but there are also certainly scenarios where using a faster, in some sense, storage will not result in any meaningful gains.
Trying to grow a business that relies on bespoke hardware in the age of cloud monopoly is an uphill battle.
Though it looks like they didn't follow up on these and can't find them for sale
The company I talked to explained the use of FPGA by saying that FPGAs are offered by major cloud vendor, and so are a commercially viable direction. While making their own dedicated chips is not only a lot more expensive, but also doesn't have a lot of customers (people don't want to maintain their own h/w, especially of a bespoke variety). The bespoke part might be why Samsung didn't pursue the idea. Essentially, if eg. AWS doesn't buy this from you and makes it an offering that customers may reasonably rely on, it's too hard to sell.
We’ve spent a fair bit of time on this at ClickHouse through our operational expertise huilding Postgres managed services multiple times. We’re fortunate to have deep internal and partner Postgres expertise, folks who originally built WAL-G back in the Citus Data days and are active contributors now and solid partnership with Ubicloud.
It’s definitely more engineering work, but the performance/scalability gains make it worth it. Relaying this based on 100s of customers who migrated to ClickHouse Managed Postgres - new, powerful world surpassing previous expectations.
With application-level replication, you can amortize it to a few steps (e.g. on commit, do 2PC).
Obviously there's nuance, esp if you have to read from another node, but it's why moving replication as far up the stack as possible generally performs better with DBs -- the DB knows when to touch the network better than the disk.
Including relevant columns in the index can massively improve the performance, because postgres does not need to read the randomly sorted tuple in the original table.
The indices will bloat in size (postgres does not vacuum them in the way a table is vacuumed), and begin to bottleneck primary writer latency.
You should repack the indices every once in a while (you can set up monitors on index bloat) with something like pg_repack, and make sure that your indices are minimally-sized (i.e. omit rows that are never scanned, use pg_stat to verify which indices are actually used by the primary or read-replicas).
Since I’m a freelance programmer, I mainly work on small-to-medium-sized projects, so I don’t know much about this kind of infrastructure programming.
When I think of "SAN" I think of Fibre Channel, which can be quite fast. EBS sounds more like iSCSI.
If that's the problem, we should say "Postgres isn't slow, you're just on ACID".
fsync itself doesn't guarantee your data will survive, so it is not essential.
Other ideas to try: you have N replicas, and you ask them to do in-memory commit. M server simultanious death is probably more unlikely compared to single server storage system death.
Yes, there are various levers you can pull and it's rare to find a solution that will be optimal for every use case. But, in general, sticking with the most mainstream solution is usually better than going off-piste unless you are very certain that it doesn't cover your niche.
I think I'm a bit of an outlier here on that k never really understood why durability requires fsync. It doesn't help you if the storage dies. If durable storage is important to you, then isn't the only solution to synchronously replicate to a quorum of machines at which point an fsync isn't that relevant? What am I missing?
"Storage dies" is a broad set of causes. If there is power loss or a server crash due to a multitude of reasons, you want committed transactions to actually be committed.
Having replicas doesn't change the problem. If the orchestrator requires at least N replicas to be done committing the data, you'll have to wait for fsync in N of them.
Without fsync, the data anywhere could be sitting on a write back cache in memory instead of being flushed to the disk.
Well written databases are frequently I/O bottlenecked and getting a good storage/network setup can thus grant massive performance gains. But this is hard, operationally. The difficulty of developing the right opinions about hardware design, acquiring it, properly wiring it up, tuning it and ensuring really excellent performance across the stack was recognized by Oracle decades ago, which led to the creation of ExaData.
ExaData is a bit like Oxide, if you've encountered them. It's basically a cloud database in a pre-manufactured rack. You buy an ExaData rack and it turns up at your on-prem colo facility, you plug it in, and now you have a fast database. Internally it has a dedicated high speed InfiniBand or RoCE RDMA capable backplane network and handles replicated between storage devices itself. There's an alerting system and repl-like tool that guides you through swapping out failed storage devices, you can expand it by adding more racks, and there are various cloud hosted versions of all of this if your budget doesn't stretch to your own full system.
The software stack on top of that provides fast multi-master synchronous writes by using RAM as a shared disk cache. If a node needs to read a block off disk it will prefer to read it directly out of the RAM of another node using RDMA across the dedicated backplane than go to storage, so if you buy the high capacity variant then you have a three tier storage system: HDDs for cold data, flash as a cache and DRAM as another cache on top of that, all kept in sync using cache coherency protocols.
There are lots of optimizations on top of that which a Postgres-on-EBS architecture can't match, for example, the storage nodes that handle the disks and serve blocks can have some of the query computations pushed down to them to reduce IO across the backplane and accelerate query execution. A final 'optimization' is that Oracle uses a different MVCC algorithm that doesn't require vacuuming and can be more IO efficient in general (doesn't copy whole rows); there are patch sets that switch Postgres to an Oracle-style MVCC engine but none of them were ever merged.
The whole thing acts as a single coherent database, so e.g. rolling upgrades across the rack's cluster don't cause downtime because transactions can be handed off across nodes. Clients can discover the IP addresses of the individual servers automatically and shift their traffic across nodes in response to load balancing requests from the system, etc.
I'm not sure it's a good idea to run a Postgres cluster like the one described in the article unless you have a lot of appetite for manual operations. If you can tolerate downtime or if you're renting bare metal where you can configure a proper RAID setup, then OK, but if you care a lot about query performance to begin with then a commercial database will win in a lot of ways (doesn't have to be Oracle, commercial RDBMS in general is a much more competitive space than open source).
Obligatory disclaimer: opinions above are not corporate approved.
There. Fixed it for you.
Too many times I see people making the same avoidable mistakes time and time again.
They get stuck in the dream of "portable" schemas and don't use their brain to design a database-specific schema.
They have no clue how to write SQL queries (or again, have stupid dreams of "portable" queries). As a result performance suffers.
Then they come to your desk complaining the databse is slow, and insisting that they absolutely MUST have a Redis caching layer.
I'm sick and tired of people using databases as blackbox dumping grounds. If slow storage was always the problem, my life would be far less stressful. Since the prescription would be easier for the doctor to write. ;)
And then you push it to production and get completely different results
Statistics change everything. Queries which produced beautiful plans in local and staging environments breakdown when given production statistics, some users get the same plans you did, others get nasty nested loops, indexes don't get used, and debugging and fixing it requires poking around in prod statistics which is never a good place to be. Don't even get me started on how RLS impacts query optimisation
Postgres is great but the authors refusal to add plan hints is a decision that comes to bite me at least a few times a year
At least run some plans on the real database with real sized data and real statistics.
Similar to how mocks/stubs are great until they aren't.
That won't magic away statistics problems, but it's still good practice to acknowledge the lower app environments tend to not reflect performance of the production one.
Sure. But to be clear: I am not denying storage is a genuine problem for some people.
I am just saying in the majority of cases it's probably not "the storage". For most people "the storage" will be quite far down the list of likely causes of a "slow" database.
Throw all the Enterprise SSDs or other storage solutions you like at a shitty schema and it will still struggle under load (or sometimes just struggle full stop).
Optimizing column layout has been enough for me to leave OLAP workloads in Postgres a few times when I otherwise would've had to spin up something dedicated for them.
Lots of things in PGSQL benefit from thinking about how your use case is manipulating the underlying B-tree, if you work in performance sensitive stuff the devil is really in the details. And having to rebuild indexes that have decayed over time is painful.
Erm. Isn't this kind of a schema specific question ?
I mean, sometimes I see schemas that are so goddam awful that the answer to your question would be "anything, literally anything".
Other times its the old story of failing to use or misconfiguring indexes.
And then, yes you end up in the realms of Postgres specific stuff. What you do there clearly depends on what you're looking at.
But the point is, I could probably count on one hand the number of times the problem has been "the storage" or "the database". Most of the time its "the developer". :D
On the other side if dev(ops) does not know that local storage is faster than network/cluster filesystems…
Happy to still be here among my fellow humans. Hackernews the last bastion it feels like.
Can we somehow filter out slop from the front page? That would be great :).
But using detectors to label so people can choose seems entirely reasonable to me even with a risk of false positives, and I think a lot of people who are generally pro-AI would feel that's fine.