The second half of this statement negates the first :-)
You have confused pessimistic concurrency with optimistic concurrency. An optimistic system detects collision at commit time and the actual order of operations in your transaction is irrelevant. You will never experience a deadlock, only aborts/retries.
In a pessimistic concurrency scenario, you take explicit locks as you access resources. Those locks block access by other transactions. This creates the dining philosophers problem and the risk of deadlocks. When locking resources, you must access resources in a consistent order to prevent deadlocks.
Please avoid database locks and let smart databases do what they are supposed to!
Also, I found this and similar on SO: https://stackoverflow.com/questions/17431338/optimistic-lock...
So if this technique actually requires me to write my database access code at this level, it means that using something like an ORM is nearly impossible in a way that makes an ORM useful.
If you put Postgres in repeatable read or serializable[1] mode, it manages optimistic concurrency for you. Collisions will produce an error in the second transaction attempting to commit. The only "trick" is that your client code should be prepared to retry transactions that have concurrency failures, and this is very easily abstracted by a library. Your SQL or ORM code does not change (I use Hibernate, fwiw).
Some good reading: https://www.postgresql.org/docs/current/static/transaction-i...
[1] Repeatable Read won't detect collisions caused by phantom rows, so pick carefully. Most CRUD apps don't care about phantom rows.
The solution comes in two parts:
1) Ensure your call to the card processor is idempotent and retry it. For example, grep Stripe's documentation for the "Idempotency-Key" header. Every processor should have a similar mechanism. Conveniently, if your database transaction unit-of-work includes the remote call, it will retry automatically in an optimistic scenario.
2) Get a webhook call from the credit card processor. Even though you are retrying your transaction, you can't guarantee that your server didn't crash without a successful commit. This potentially leaves a charged card without recording the purchase; the webhook will sync it up afterwards.
Distributed systems are hard if you think past the happy path. I wish every REST API would have an equivalent of Idempotency-Key.
But this brings me back to my original point: having a library that blindly re-tries POST requests that result database transactions on every conflict is not going to work. Until every external API, from printers to credit card processors, has an Idempotency Key equivalent you just can't do that generically, and you will have to do that with a custom bit of code for every situation.
Isn't it easier to just acquire an advisory lock for the critical bit and take your own database's concurrency contention out of the equation?
A lock is seductive because in the happy path it looks like you have a distributed transaction. In practice making reliable calls across distributed systems involves more complexity. The hard part is figuring out what to do when your remote call times out and you aren't sure if it succeeded; once you resolve that, you can usually work with any concurrency scenario.
If your database doesn't fully support ACID transactions, yes, get a better DB. And no you don't need to use named locks all the time. As an example, I have a codebase where I use the standard transactions semantics everywhere using the Django ORM, except one very specific and critical bit of code where I want to be dead certain that a thing can't happen twice in a row. So basically out of, say, 100 or so places where I commit a transaction, only one actually uses named locks, but where it does, it solves the problem in a very elegant way.
So I agree with you that you shouldn't litter your code with these. But you absolutely can use them to either (a) guard against complex and critical parts of your code or (b) use these to quickly augment a huge existing codebase that keeps running into deadlocks or worse yet lock timeouts.