This is as much an indictment of AWS compute as it is anything else.

Kinda comparing apples to oranges. AWS was using EBS and not local instance storage. So you’re easily looking at another order of magnitude latency when transmitting data over the network versus a local pcie bus. That’s gonna be a huge factor in what I assume is a heavy random seek load.

	▲	mjlee 3 hours ago \| parent [-]
		I wrote a longer comment already (https://news.ycombinator.com/item?id=47352526) but looking at the hot run performance and making big hand wavy guesses, the performance difference might not be as big as you'd expect.

▲

raincole 6 hours ago | parent | prev | next [-]

The article is literally saying the opposite. Quote:

> Here's the thing: if you are running Big Data workloads on your laptop every day, you probably shouldn't get the MacBook Neo.

> All that said, if you run DuckDB in the cloud and primarily use your laptop as a client, this is a great device

▲

vasco 6 hours ago | parent | prev | next [-]

But AWS beat the laptop? And there's no cost to performance analysis? Yes AWS is overpriced but how do you make that conclusion from this specific article? Because network disks were slower than SSDs? AWS also has SSD instances with local storage.

	▲	mjlee 3 hours ago \| parent [-]
		I haven't tried the newer I7i and I8g instance types (the newest instances with local storage) for myself, but AWS claims "I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances." I benchmarked I4i at ~2GB/s read, so let's say I7i gets 3GB/s. The Verge benchmarked the 256GB Neo at 1.7GB/s read, and I'd expect the 512GB SSD to be faster than that. Of course, an application specific workload will have its own characteristics, but this has to be a win for a $700 device. It's hard to find a comparable AWS instance, and any general comparison is meaningless because everybody is looking at different aspects of performance and convenience. The cheapest I* is $125/mo on-demand, $55/mo if you pay for three years up front, $30/mo if you can work with spot instances. i8g.large is 468GB NVMe, 16GB, 2 vCPUs (proper cores on graviton instances, Intel/AMD instance headline numbers include hyperthreading).

▲

api 6 hours ago | parent | prev [-]

Yeah, this is really about how ludicrously overpriced big cloud is. I’ve got a first gen M1 Max and it destroys all but the largest cloud instances (that cost its entire current market value per month!), at least in compute. It’s a laptop! A decent bare metal server in a rack will destroy any laptop.

It’s staggering. Jaw dropping. Bandwidth is even worse, like 10000X markup.

Yet cloud is how we do things. There’s a generation or maybe two now of developers who know nothing but cloud SaaS.

I watched everyone fall for it in real time.

▲

arh5451 6 hours ago | parent | next [-]

I agree and disagree, the benefit with cloud is you "don't need to manage it", it scales automatically, redundancy, and automatic backups etc. I do think you are right; in the future there will be more infrastructure as code as cost pressures become more obvious.

▲

api 6 hours ago | parent [-]

Those benefits are at least partly lies though.

The tooling — K8S with all its YAML, Terraform, Docker, cloud CLI tools, etc. — is pretty hideously ugly and complicated. I watch people struggle to beat it into shape just like they did with sysadmin automation tools like Puppet and Chef a decade or more ago. We have not removed complexity, only moved it.

The auto scaling thing is a half truth. It can do this if you deploy correctly but the zero downtime promise is only true maybe half the time. It also does this at greatly inflated cost.

Today you can scale with bare metal. Nobody except huge companies physically racks anymore. Companies like Hetzner and DataPacket have APIs to bring boxes up. There’s a delay, but you solve that by a bit of over provisioning. Very very few companies have work loads that are so bursty and irregular that they need full limitless up and down scaling. That’s one of those niche problems everyone thinks they have.

The uptime promise is false in my experience. Cloud goes down for cluster upgrades and any myriad other reasons just as often as self managed stuff. I’ve seen serious unplanned outages with cloud too. I don’t have hard numbers but I would definitely wager that if cloud is better for uptime at all it’s not enough of an improvement to justify that gigantic markup.

For what cloud charges I should, as the deploying user, receive five nines without having to think about it ever. It does not deliver that, and it makes me think about it a lot with all the complexity.

The only technical promise it makes good on, and it does do this well, is not losing data. They’ve clearly put more thought into that than any other aspect of the internal architecture. But there’s other ways to not lose data that don’t require you to pay a 10X markup on compute and a 10000X markup on transfer.

I think the real selling point of cloud is blame.

When cloud goes down, it’s not your fault. You can blame the cloud provider.

IT people like it, and it’s usually not their money anyway. Companies like it. They’re paying through the nose for the ability to tell the customer that the outage is Amazon’s fault.

Cloud took over during the ZIRP era anyway when money was infinite. If you have growth raise more. COGS doesn’t matter.

Maybe cloud is ZIRPslop.

	▲	cestith 5 hours ago \| parent [-]
		Not all IaC is Kubernetes.

▲

icedchai 5 hours ago | parent | prev | next [-]

With cloud, what you're really paying for is flexibility and scalability. You might not need either for your applications. At some startups, we needed it. We sized clusters wrong, needed to scale up in hours. This is something we wouldn't ever be able to do with our own hardware without tons of lead time.

If your application won't ever require more resources than a single server or two, then you are better off looking at other alternatives.

▲

fridder 4 hours ago | parent | prev [-]

Honestly I think the best path is hybrid with the cloud as DR and sudden load scaling.

	▲	api 2 hours ago \| parent [-]
		Metal with data streamed to cloud and cloud as hot backup is something some people already do. If the metal dies in a catastrophic way (multiple nodes at once and loss of quorum, catastrophic DC outage, etc.) you spin it up in AWS.