I hope this won’t be counted as some form of self-promotion, even though I am sharing a post from my own blog.
As a tech worker who works in a Cloud shop, I wanted to elaborate the many reasons why I find working with Clouds terrible, from multiple points of view.
I tried to organize my thoughts in a (relatively long) post, in which both technical aspects and political aspects (which are very related) are covered.
I am sure many people will have different perspectives, and this could be potentially also a nice prompt for a discussion.
there are too many points of failure for me to ever be comfortable using the cloud as a primary storage option.
i’ve always maintained this opinion when “the cloud” started being touted as being the future. and yet more corporations (including mine) are reliant on it. i mean sure, i can log in on my home computer and have some access to stuff as though i were physically at the office but that convenience ain’t worth the headache if the main storage site crashes.
If everything that you run is local as in the same physical location and there is no requirement for external or internet access then sure. Not everyone has that luxury. Otherwise, There are the same number of points of failure in a non-cloud configuration. You just feel more comfortable with those because you have direct hands on control.
You write “actually following best practice instead of faking it and lying” funny.
Are you implying that the various cloud vendors lie about the way they configure their environments or admins don’t have emotional biases or something else entirely?
There are places that actually do that?
Can you provide a list, because I’d like to work there.
(I do not have 25 years of sysadmin angst over nobody ever doing shit right until after it’s on fire.)
Proton runs fully on their own hardware, they have some positions open!
Having done everything from building my own servers 30 years ago to managing hundreds of servers in data centers to now managing hundreds of instances and other services in AWS, I’ll gladly stick with AWS. The hardware management alone makes it well worth the overhead.
25 or so years ago I had to troubleshoot a hardware issue in a SCSI-based server with 6 hard drives in it. A drive appeared to be failing so I replaced it and immediately another drive failed, then another, and so on. After almost a full day of troubleshooting later and we realized the power supply was actually the culprit and could no longer provide sufficient power to the full set of hard drives.
20 years ago while managing 700+ servers in a datacenter we had to manage a recall of about 400 of them thanks to the Capacitor plague that caused a handful of our servers to literally burst into flames.
Hardware failures like the above and dozens of others were mitigated in most cases thanks to redundancies in the software we wrote. But dealing with hardware failures and the resulting software recovery was a real PITA.
With AWS I may occasionally have a Linux instance lock up due to a hardware failure but it’s usually fairly easy to reboot the instance and have it migrate to new hardware. It’s also trivial to migrate a server to run on more (or less) number of CPU’s, RAM, etc. with only a couple of minutes of downtime.
The more advanced services AWS offers like object storage, queues, databases, etc. are even more resilient. We occasionally get notified that a replica for one of these services had failed or was determined to be on hardware that was failing, and it was automatically replaced with a new replica.
I’d much rather work this way than the way I did 20+ years ago.
Why not outsourcing just the hardware then? Dedicated servers and Kubernetes slapped on them. Hardware failure mitigated for the most part, and the full effort goes into making the cluster as resilient as possible, for 1/5 of the cost of AWS. If machines burn, it’s not your problem (you can have them spread over multiple sites, DCs, rooms, racks) anymore.
We did that (with Rackspace) for years before migrating to AWS. AWS is still far better from a service & flexibility perspective.
My employers website has certain times of the year where we see a huge increase in web traffic. When we had a hosted solution it took weeks of preparation to provision additional web servers to handle that load. We had to submit formal requests for additional servers, document how to wire them into our network & required firewall rules, etc. Then we had to wait an arbitrary number of days for them to do the work. And then we had to repeat that whole process when we no longer needed the additional capacity.
With AWS we just define an auto scaling group and additional web servers are spun up automatically when demand is high, and frees them up again when no longer needed. Even if we didn’t use auto scaling we could easily automate this sort of thing via terraform or other tools and spin up additional instances in minutes instead of days.
If the storage “crashes” it doesn’t matter if it’s in the cloud or on-prem.
With the cloud you get two substantial advantages:
Of course all this costs big bucks, but technically it’s superior, easier and less risky.
AWS engineers’ first responsibility is to shareholders
Mike’s responsibility is to your same boss.
They are not the same.
Bonus: you can see Mike’s certs are real.
It’s not about responsibility (and only the c suite reports to the shareholders, not Mike), it’s about capability, visibility, tooling and availability.
That’s easily mitigated just following established standards. Redundancy is cheaper than anything else in the aftermath and documentation can be done easy with automation.
You don’t, you rent rack space in a location far enough away but close enough to get the data in a few hours.
It’s neither superior, easier or less risky, it’s just a shift in responsibility. And in most cases, it’s so expensive that a second or third on site engineer is payed for.
And what is simpler and faster, renting rack space in another continent (and buying, shipping, racking and initializing) or editing your terraform file?
Why on another continent? Except maybe VDI, some direct calls to some LLM or some insane scales, there’s nothing really that needs those round trip times.
Also data rules / data privacy. Some things need to have the original in Europe; China & Russia also need their data separated from others.
Because the customer demands it.
Not OP, but they are comparable efforts, especially since it’s a relatively infrequent activity. You can rent dedicated boxes with off-the-sheld hardware almost instantly, if you don’t want to deal with the hardware procurement, and often you can do that via APIs as well. And of course both options are much, much, much cheaper than the Cloud solution.
For sure speed in general is something Cloud provide. I would say it’s a very bad metric though in this context.
Full-ACK.
My last customer (global insurance company) provisions several systems a day. Now moving to hundreds via Jenkins. Frequency is environment dependent.
If your compute needs expand that much everyday, and possibly shrink in others, than your use-case is one that can benefit from Cloud (I covered this in the post).
That said, if provisioning means recycle, then it’s obviously not a problem.
This is a very rare requirement. Most companies’ load is fairly stable and relatively predictable, which means that with a proper capacity planning, increasing compute resources is something that happens rarely too. So rarely that even a lead time for hardware is acceptable.
So if I may ask (and you can tell), what is the purpose of provisioning that many systems each day? Are they continuously expanding?
Agree to disagree. Banking, telecommunications, insurance, automotive, retail are all industries where I have seen wild load fluctuations. The only applications where I have seen constant load are simulations: weather, oil&gas, scientific. That’s where it makes sense to deploy your own hardware. For all else, server less or elastic provisioning makes economic sense.
Edit to answer the last question: to test variable loads, in the last one. Imagine a hurricane comes around and they have to recalculate a bunch of risk components. But can be as simple as running CI/CD tests.
Systems are always overspecced, obviously. Many companies in those industries are dynosaurs which run on very outdated systems (like banks) after all, and they all existed before Cloud was a thing.
I also can’t talk for other industries, but I work in fintech and banks have a very predictable load, to the point that their numbers are almost fixed (and I am talking about UK big banks, not small ones).
I imagine retail and automotive are similar, they have so much data that their average load is almost 100% precise, which allows for good capacity planning, and their audience is so wide that it’s very unlikely to have global spikes.
Industries that have variable load are those who do CPU intensive (or memory) tasks and have very variable customers: media (streaming), AI (training), etc.
I also worked in the gaming industry, and while there are huge peaks, the jobs are not so resource intensive to need anything else than a good capacity planning.
I assume however everybody has their own experiences, so I am not aiming to convince you or anything.