[staticassertion]

> It's less obvious to me how your proposal lines up with things that are less so.

Usually you have a sync wrapper around async work, maybe poll based.

As an example, I believe that Amazon's "place in cart" is completely async with queues in the background. But, of course, you may want to synchronously wait on the client side for that event to propagate around.

You get all of the benefits in your backend services - retry logic is encapsulated, failures won't cascade, scaling is trivial, etc. The client is tied to service state, but so be it.

You'll want to ensure idempotency, certainly. Actually, yeah, that belongs in the article too. Idempotent services are so much easier to reason about.

So, assuming an idempotent API, the client would "send", then poll "check", and call "send" again upon a timeout. Or, more likely, a simple backend service handles that for you, providing a sync API.

Going from Sync to Async usually means splitting up your states explicitly.

For example: Given two communication types: Sync (<->) Async (->)

We might have a sync diagram like this:

A <-> B

A calls B, and B 'calls back' into A (via a response).

The async diagram would look like one of these two diagrams:

A -> B -> A

or:

A -> B -> C

Whatever code in A happens after what the sync call would have been gets split out into its own handler.

If your system is extremely simple this may not be worth it. But you could say that about anything in the article, really.

[sbov]

> If your system is extremely simple this may not be worth it. But you could say that about anything in the article, really.

I don't know. The level of complexity this introduces seems to be way higher than anything in the original article.

E.g. for placing something in cart, its not only the next page that is reliant upon it, but anything that deals with the cart - things like checkout, removing from cart, updating quantities, etc. Adding to cart has to be mindful of queued checkout attempts. And vice versa. It sounds way messier than the comparatively isolated concepts such as CI, DI, and zero downtime deploys.

Async communication certainly seems desirable across subsystems that are only loosely connected. E.g. shopping, admin, warehouse, accounting, and reporting subsystems. But by using asynchronous comms you're actually introducing more state into your application than synchronous comms. State you should be testing - both in unit & integration tests (somewhat easy) and full end-to-end tests (much more expensive).

I'm sure Amazon has all sorts of complexities that are required at their scale. But you can heavily benefit from the techniques in the OP even if you aren't Amazon scale.

[staticassertion]

> The level of complexity this introduces seems to be way higher than anything in the original article.

I don't find it very complex at all. You send a message to a service. You want to get some state after, you query for it.

> but anything that deals with the cart - things like checkout, removing from cart, updating quantities, etc. Adding to cart has to be mindful of queued checkout attempts.

How so? Those pages just query to get the cart's state. You'd do this even in a sync system. The only difference is that on the backend this might be implemented via a poll. On subsequent pages you'd only poll the one time, since the 'add-to-cart' call was synchronous.

> But by using asynchronous comms you're actually introducing more state into your application than synchronous comms.

I don't see how. Again, with the cart example, there is always the same state - the 'cart'. You mutate the cart, and then you query for its state. If you have an expectation of its state, due to that mutation, you just poll it. You can trivially abstract that into a sync comm at your edge.

    def make_sync(mutation, query):
        mutation()
    
        while not expected_state(query()):
            # handle retry logic/ timeouts

[sbov]

Your solution seems to assume only one thing will be accessing what is being mutated at once. If another thread comes in and gets a cart (e.g. maybe the user reloads the page) and they aren't waiting on the operation to be processed anymore. If you remove it from the queue after a few seconds of failure then fine. But if the point is "self healing" it presumably hangs around for a while.

You have to deal with this to some extent in any webapp that has more than 1 OS thread or process. But if you're keeping actions around for minutes or hours instead of second you're going to have to account for a lot of weird stuff you normally wouldn't.

If you really wanted something like this, I would think you would want a concept of "stale" data and up-to-date data. If a process is OK with stale data, the service can just return whatever it sees. But if a process isn't OK with it (like, say, checkout), you probably need to wait on the queue to finish processing.

And since the front end may care about these states, you probably need to expose this concept to clients. It seems like a client should be able to know if it's serving stale data so you can warn the user.

Maybe I'm mistaken.

[Groxx]

Yeah. And this kind of system often leads to large delays without clear causes, so people re-tap many times and get into weird states.

On the extreme end of doing this well, you have stuff like Redux, which effectively does this locally plus a cache so you don't notice it. Redux has some super nice attributes, there are some definite advantages, but it is many times more complicated than a sync call.

[codebje]

> I don't know. The level of complexity this introduces seems to be way higher than anything in the original article.

HTTP isn't synchronous, we just often pretend it is. You can pretend messages are synchronous using exactly the same semantics and get exactly the same terrible failure modes when requests or responses are lost or delayed.