The timelines matter as well: They were working on EUV at Zeiss (who make the lensing/mirroring systems) already in 2005. That's about 20 years of development.
Slight problem with that if you would like to live in a functioning, thriving democracy: democracy in the sense of "one person, one vote" requires or at least greatly benefits from a broadly educated population. It's not sufficient, but very likely necessary.
You're right -- the theoretical particle physicists at my faculty were using Mathematica very heavily when I was still in academia and maintained a dedicated compute cluster for it.
They really did not appreciate the debugging experience, but maybe that's improved in 15 years. :)
I realize you're making a general point about space/IO ratios and the below is orthogonal, no contradiction.
It's actually a lot less user-facing per disk IO capacity that you will be able to "sell" in a large distributed storage system. There's constant maintenance churn to keep data available:
- local hardware failure
- planned larger scale maintenance
- transient, unplanned larger scale failures
(etc)
In general, you can fall back to using reconstruction from the erasure codes for serving during degradation. But that's a) enormously expensive in IO and CPU and b) you carry higher availability and/or durability risk because you lost redundancy.
Additionally, it may make sense to rebalance where data lives for optimal read throughput (and other performance reasons).
So in practice, there's constant rebalancing going on in a sophisticated distributed storage system that takes a good chunk of your HDD IOPS.
This + garbage collection also makes tape really unattractive for all but very static archives.
I think Chips and Cheese is more like a fine replacement for realworldtech.com sans the toxic and highly educational and entertaining forums. Anandtech was much more accessible to the general tech public, but also more commercial and thus hit and miss on the content (no judgement intended, gotta eat).
Google's internal systems have been written against the Colossus semantics for many, many years and thus benefit from it's upsides (performance, cost efficiency, reliability, strong isolation for a multi tenant system, ability to scale byte and IO usage fairly independently, tremendously good abstraction against and automation of underlying physical maintenance, etc) while not really having too much of an issue with any of the conscious trade-offs (like no random writes).
On the other hand, if you've been building your applications against expectations of different semantics (like POSIX), retrofitting this into your existing application is really hard, and potentially awkward. This is (IMO) why there hasn't been an overtly Colossus based Google Cloud offering previously. (Though it's well publicized that both Persistent Disk and GCS use Colossus in their implementation.)
One of the reasons why it would be extremely hard to just set up or build CFS elsewhere or on a different abstraction level is that while it may look quite achievable to implement the high level architecture, there is vast complexity in the practical implementation side. The tremendous user isolation it affords for an MT system, the resilience it has against various types of failures and high throughput planned maintenance, the specialization it and its dependencies have to use specific hardware optimally.
(I work on Google storage part time, I am not a Colossus developer.)
Concur, Colossus is one of the examples where Google built what almost feels like magic technology. I work on Google Storage (among other things), and I've wished for a Cloud offering that exposes Colossus for years.
I don't know that it took "AI branding" to convince anybody. I think these workloads potentially enabled additional demand/market for such a product that may not have been there before.
One of the challenges with exposing native Colossus was always that it's just different enough from how people elsewhere are used to use Storage that there was a lot of uncertainty about the addressable market of a "native" Colossus offering. It's not a POSIX file system. Some of the specific differences (eg. no random writes) are part of what makes Colossus powerful and performant on HDDs, but it means you have to write your application to work well within its constraints. Google has been doing that for a long time. If you haven't, even if it's an amazing product, is it worth rewriting your applications or middleware?
Rapid Storage basically addresses this by adding the object store API on top if it (TIL from this thread that there's a lower abstraction client in the works as well).
Anyway, the team behind this is awesome. Awesome tech, awesome people. Seeing this launched at Next and seeing some appreciation on HN makes me very grateful.
Any remaining reasonable use cases would be sufficiently corner-casey that that the first order approximation is "if you want it to behave like a database, no, you do not want MyISAM".
This being said, at least some years ago, a use case I saw that held SOME water then was generating MyISAM tables offline, importing them as-is into a running MySQL (or taking an instance offline and bringing it back up) and then serving from it read-only. At least at the time, this provided better RO performance than InnoDB. I wouldn't be surprised if that was still true. Please don't do that at home!
Also, I think until the previous-to-most-recent release, some internal tables were still MyISAM, causing MySQL overall to have some very rare cases of not being crash safe. Again, I think that's since been resolved in 5.8(?).