I suspect the intention is something more like IPFS (https://ipfs.io), built on some distributed data structure like a DHT. With that in mind:

- "Infinitely scalable" in the sense of the internet, I suppose? If we had a way of paying for storage and indexing in a decentralized way, then you could switch from one 'provider' to another, or host your own data. Data would not be siloed in the way it is by AWS.

- I assume "serverless" means you don't have some specific upstream server you must hit with requests. You could submit queries to a local job, which could pass them to any node in the network.

- "Fundamentally reliable" because data is replicated, and jobs can be executed by any member of the network.

- "Distributed globally" because you could store data from any internet-connected device, "available locally" because, again, you don't have some single-point-of-failure server you need to connect to, or VPN you need to join, or whatever: your computer would be a node on the network, as capable of accessing data and running jobs as any other node.

And "intelligence" and "no performance cost" are just aspirations.

> I suspect the intention is something more like IPFS (https://ipfs.io), built on some distributed data structure like a DHT.

the company's products are all built on top of sharded mysql .. unless they plan to throw away everything they have done so far, i do not think these assumptions are correct!

Ah, fair enough. I didn't look into the company, I've just come across suggestions along this line.

What does 'serverless' mean exactly? 1) i think server-less, means ops-less, no manager of server even at scale,maybe you need optimization query but not to deploy databases, migration and that. 3)Scale should not come at the cost of performance is easier said than done, is the same idea automatic cache an thinks like this 2) i think the same i will still use monolith like PostgreSQL use a doubt have more intensive workflows than medium.

> maybe you need optimization query but not to deploy databases, migration and that

Well, how do you indicate to it that you want to create a database? Does the database read your mind too? How do you indicate that you want to provision some more because there's a big event coming up?

> Scale should not come at the cost of performance is easier said than done

That's essentially my exact point, yeah. It's not a novel aspiration, and they don't contribute any kind of solution. It's like saying "computers should cost less and also be gooder".

> i think the same i will still use monolith like PostgreSQL

Yeah, I think that's probably sensible for many people, though I'd also welcome more innovation. Kleppmann's idea of 'unbundling the database' – i.e. the modern database becoming fragmented into several distinct components – is I think very promising and very probable. (Of course, your business and your production environment may not be somewhere you wish to be a hotbed of experimentation.)

It is possible to design scale-out database engines with very fast and elastic load following though it isn't common. In these kinds of systems, you don't provision for load, the system automatically adjusts to the load as it happens. In good designs you can often shed load in milliseconds once the additional server capacity is online, so the latency is often a function of how quickly you can bootstrap more server images.

These kinds of fast-twitch load shedding mechanics were not designed for elasticity -- it would not be worth the engineering investment in most cases. They were typically developed to support scale-out of data models for which uniform sharding is intrinsically impossible, requiring real-time adaptive resharding instead. If you have super-fast load shedding for extremely and unpredictably biased data distributions, you are 90% of the way to a really nice implementation of elastic capacity, just add hardware provisioning.

These setups are nice as a user, because the sharded nature of a table is (necessarily) completely transparent. You can create an empty new table and insert trillions of records without every having to manage sharding or cluster capacity as the table grows. In this sense, the fact that it is running on a cluster of discrete servers does not leak through the database abstraction presented to the user.

> How do you indicate that you want to provision some more because there's a big event coming up?

You literally do not. As load starts to increase, you scale up automatically. The word elastic has been used to represent this pattern in the last few generations of cloud and/or computing infra.

What's the alternative? Manually ssh into some box and crank up mysql instance by hand? like its 1996?

Yeah, I appreciate what 'auto' and 'scaling' signifies. I've implemented an autoscaler on a huge Kubernetes cluster in the past. That's precisely where my doubt comes from.

I was about to write out a huge example, but I figure I'll just express the core logic simply. First, it takes a chunk of time to determine that increased traffic is not just random variance. Then it takes time to allocate and provision machines. And often the traffic spikes for you and for your co-tenants are not statistically independent, so the provider struggles to allocate machines in time when it most matters.

And how much do you scale up? 10x right away? Can't do that: vastly expensive, and anyway it could be a retry storm exacerbating things. 2x and then go from there? Well, if your business is serving ads during the Superbowl break, that's not gonna work. Etc. This all starts to look increasingly absurd against the backdrop of being able to just push a button and do it yourself.

I'm not suggesting never doing autoscaling. I'm just saying that wise men don't speak in absolutes, or make architectural decisions based on toy examples. Nor am I particularly bothered about whether I'm doing things "like it's 1996" - I'm not in the fashion business, so I'm purely interested in finding the optimal solution to my problem, and I couldn't care less whether it's flaming hot or whether it's from the Iron Age.

I think that only applies if the incoming load is gradual. Any sane elastic configuration has some timeouts and a measurement period meant to prevent unwanted scale-up just because of transient load, and during that time you can get hit hard enough to degrade/take down your service before your additional capacity has come online.

It makes sense to get out in front of known massive load events before they hit your service. If I'm launching a new service that I expect to hit the front page of HN, I'm spinning up capacity first and asking questions later. A couple hours of running large instances or extra containers costs much less than potential lost sales from users getting timeouts.

> Well, how do you indicate to it that you want to create a database? Does the database read your mind too?

I'm not sure what you mean. You still instantiate resources with serverless products. With an AWS Lambda function, you go to the Lambda web console, click "Create a function," and type in the code for that function (of course this can also done via the AWS API). There's no mind-reading going on. For AWS Aurora, you still go to the RDS web console, click "Create a database," choose Aurora as the database engine, etc.

I was joking, with that particular sentence. My point was that there is someone managing the server irrespective, and that there's not a particularly clear metaphysical distinction between 'create my database in this way' and 'instruct someone else to create my database in this way'. Not in the computing world, where everything's already under 17 layers of abstraction.

Maybe the the article would be better titled "Aspirations for a Future Database".

Yeah, I'd agree with that. But even then, it would be more valuable if it committed to at least some meaningful, non-platitudinous positions. Something with which at least one person on the planet might disagree. Not "it would be nice if databases were fast, scalable, reliable, personally tailored to everyone on the planet, ...".

For example, my predictions for future databases would be:

- They will take over more functions of the average backend codebase: instead of database users, they will have a concept of application users, along with their privileges, and simple CRUD logic will be executed by the database.

- Horizontal scaling will be less important than we currently think. Consensus will be handled at a lower level, by networked filesystems or storage engines. (Zookeeper, in the Java world, is a proto-example of what I mean.) This is one instance of the trend that...

- Databases will be 'unbundled' (Kleppmann's term). Many of the dull uniform bits will be shared rather than reimplemented. This will happen either through libraries or - more likely and preferably - through separate pieces of software, implementing an interface, which the user will compose. (Rebundling will occur for users who just want a click-and-tick experience.)

- Self-optimising will - I agree with the article here - widen in scope. Users won't have to perform housekeeping tasks like creating indices on commonly-queried fields.

- Databases won't target a filesystem but block storage. This will accompany a convergence of disk (NVMe) and RAM (NVRAM) towards persistent random-access storage of state. Databases, along with applications, won't think in terms of a rigid distinction between "what's in my process's memory" and "what do I have to expressly commit to the disk with a syscall". Tuple spaces are a precursor.

Agreed. And I like your list a lot better, thanks!