I’ve been doing software for 20 years in one form or another. One of the things I’ve learned is the simpler and more polished something seems to the user, it is almost always because there is a hell of a lot of complexity under the covers to make it that way.
Making something that handles the 80% is easy. Every step closer to 100% becomes non-linear in a hurry. All that polish and ease of use took months of “fit & finish” timeboxes. It took tons of input from UX and product. It involved making very hard decisions so you, the user, don’t have to.
A good example is TurboTax online. People love to hate on their business practices (for good cause) but their UX handles like 98% of your common tax scenarios in an incredibly easy to use, highly polished way. Robinhood does a pretty good job too, in my opinion—there is a lot of polish in that app that abstracts away some pretty complex stuff.
We already had something that just ran containers and that was Mesos. They had the opinion that all the stuff like service discovery could be implemented and handled by other services like Marathon. But it did not work well. Most people that was to deploy containers in an orchestrated manner want service discovery.
At least these parts are being provided by the same company (Hashicorp) so it probably won't suffer the same lack of coordination between separate projects that Mesos did.
The benefit to the kitchen sink opinionated framework that K8s does is that your deployments and descriptors are not very difficult to understand and can be shared widely. I do not think the comparison of massively sharded NoSQL to Postgres is the same because most people will not need massive sharding, but almost everyone is going to need the service discovery and other things like secrets management that K8s provides.
From the documentation:
> Nomad HCL is parsed in the command line and sent to Nomad in JSON format via the HTTP API.
So why not just JSON or even JSON at all and not MsgPack or just straight up HCL because that's over and over introduced as being machine readable and human friendly both at the same time?
JSON is fine for serialization, but I hate typing it out. There are too many quotes and commas - all keys and values have to be quoted. The last item in a list or object can't have a trailing comma, which makes re-ordering items a pain. Comments aren't supported (IMO the biggest issue).
YAML is too whitespace dependent. This is fiddly and makes copy-pasting a pain. I'm also not sure how it affects serialization, like if you want to send yaml over the network, do you also have to send all of the whitespace? That sounds like a pain. OTOH I like that quotes are usually optional and that all valid json is also valid YAML. Comments are a godsend.
HCL has the same basic structure of objects and lists, but it's much more user friendly. Keys don't need to be quoted. Commas aren't usually needed unless you compress items onto the same line. It supports functions, list/object comprehensions, interpolations, and variable references which all lead to more powerful and DRY configuration. Granted I'm not sure if these are specific to TF's HCL implementation, but I hope not.
For serialization, HCL doesn't have any advantage over JSON. Sure it's machine-readable but probably much harder to write code that works on HCL specifically than to convert to JSON and use one of the zillions of JSON libraries out there.
HCL requires a lot more code to parse and many more resources to keep in memory vs JSON. I think it completely makes sense to do it this way. K8s is the same. On the server it does everything in JSON. Your YAML gets converted prior to sending to K8s.
This means you get to build upon what you already have, instead of doing everything from scratch again because your new infrastructure-layer service needs to integrate with 5 different APIs that have slightly different structure.
But that's just basically a calculated tradeoff of Postgres (and several CP databases) trading Availability for Consistency.
If you want a transactional, consistent datastore you are gonna have to put a lock on something while writes happen. And if you want consistency it means those locks need to be on all systems in the cluster. And the entire cluster needs to hold that lock until the transaction ends. If your DB’s are 100ms apart… that is a pretty large, non negotiable overhead on all transactions.
If you toss out being fully consistent as a requirement, things get much easier in replication-land. In that case you just fucking write locally and let that change propagate out. The complexity then becomes sorting out what happens when writes to the same record on different nodes conflict… but that is a solvable problem. There will be trade offs in the solution, but it isn’t going against the laws of physics.
FWIW, it's not as bad as that sounds. There are traditional locks, and there is optimistic locking. If a there are two conflicting transactions, a traditional lock detects this before it happens (by insisting a lock is obtained before any updates are done) and if there is any chance of conflict the updates are run serially (meaning one is stopped while the other runs).
Optimistic locks let updates run with lock or blocking at all, but then at the end they check if the data they depended on (ie, data that would have been locked by the traditional mechanism) has changed. If it has they throw it all away. (Well, perhaps not quite - they may apply one of the conflicting updates to ensure forward progress is made.) The upside of this is there is if there are no conflicting updates everything runs at full speed - because there is no expensive communication about why has what lock going. The downside is a lot of work may be thrown away by what amounts to speculative execution.
Most monolithic databases use traditional locking. Two CPU's in the same data centre (or more likely on the same board) can rapidly decide who owns what lock, but cycles and I/O on a high end server are precious. Distributed ACID databases like spanner, cockroachdb and yugabytedb favour opportunistic because sending messages half way across the planet to decide who owns what lock before allowing things to proceed takes a lot of time, whereas the CPU cycles and I/O's on the low end replicated hardware are cheap.
While opportunistic locks allow an almost unlimited number of non-conflicting updates to happen concurrently, their clients still have to pay a time penalty. The decision about whether there was a conflicting update still has to be made, and it still requires packets to cross the planet, and while all this happens the client can't be sure if their data has been committed. But unlike the traditional model, they are never blocked by what any other client is doing - providing it doesn't conflict.
Even if I won $100 in the lotto today and had the money in hand, I wouldn't describe my choice which house I bought years ago as a calculated trade off between what I bought and some $10 million dollar mansion. That wasn't a feasible choice at that time. Neither was making a distributed RDBMS as an open source project decades ago, IMO.
More closely matching would be MySQL's NDB clustered storage engine, which was released in late 2004.[1] Given that Postgres and MySQL both started ab out 1996, that's quite a time after initial release.
I spent a while in the early to mid 2000's researching and implementing dual master/slave or DRBD backed MySQL HA systems as a consultant, and the options available were very limited from what I remember. There's also probably a lot better tooling these days for developers to make use of separated read/write environments, whereas is seemed fairly limited back then.
Citus is specifically designed for it.
Timescale appears to have the use case covered too.
Ultimately though, sharding is one of the easier ways to scale a database out. NoSQL just does it by eliminating joins. You can do that pretty easily with almost any solution, PG or otherwise.
It's the same reason I still like to use postgres when I can versus NoSQL until I know I need the one feature I may not be able to achieve with postgres: automatic sharding for massive global scale. The rest of the features postgres (and friends) give easily (ACID etc) are very tricky to get right in a distributed system.
It's also the same reason bash is great for tasks and small automated programs, but kind of terrible for more complex needs. The primitives are super simple, easy to grok, and crazy productive at that scale, but other languages give tools to more easily handle the complexities that start to emerge when things get more complicated.