And if for some reason you refuse to, then this "barrier" or "hooks" approach to testing will in practice not help. It requires you to already know the potential race conditions, but if you are already aware of them then you will already write your code to avoid them. It is the non-obvious race conditions that should scare you.

To find these, you should use randomized testing that runs many iterations of different interleavings of transaction steps. You can build such a framework that will hook directly into your individual DB query calls. Then you don't have to add any "hooks" at all.

But even that won't find all race condition bugs, because it is possible to have race conditions surface even within a single database query.

You really should just use SERIALIZABLE and save yourself all the hassle and effort and spending hours writing all these tests.

Use transactions table (just a name, like orders)

On it have an Insert trigger.

It should make a single update with simple “update … set balance += amount where accoundId = id”. This will be atomic thanks to db engine itself.

Also add check constraint >= 0 for balance so it would never become negative even if you have thousands of simultaneous payments. If it becomes negative, it will throw, insert trigger will rethrow, no insert will happen, your backend code will catch it.

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That’s it: insert-trigger and check constraint.

No need for explicit locking, no stored procedures, no locks in you backend also, nada. Just a simple insert row. No matter the load and concurrent users it will work like magic. Blazingly fast too.

That’s why there is ACID in DBs.

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Shameless plug: learn your tool. Don’t approach Postgresql/Mssql/whathaveyousql like you’re a backend engineer. DB is not a txt file.

I think you can do better than explicit barrier() calls. My hunch is the test middleware layer can intercept calls and impose a deterministic ordering.

(There are a few papers around looking into more complex OS level frameworks to systematically search for concurrency bugs, but these would be tough to drop into the average web app.)

Or to put another way; as others have observed, this could be solved with atomic updates and in some case SERIALIZABLE. These are right tools for balance operations - and if they’re used I’m not sure they need testing in this manner?

> The simplest case: no transaction, just a SELECT and an UPDATE with a barrier between them:

There is no context where you do not have a transaction. Postgres requires them.

It's likely that the library the author is using is doing automatic implicit transactions, but it seems like the author needs to understand their tools a bit better.

IMHO you should never write code like that, you can either do UPDATE employees SET salary = salary + 500 WHERE employee_id = 101;

Or if its more complex just use STORED PROCEDURE, there is no point of using database if you gonna do all transactional things in js

Ended up using SELECT FOR UPDATE on the waitlist row before the count check. Simple but effective. The barrier testing approach described here would have caught this much earlier in development instead of discovering it under load.

One thing I'd add: in Go, it's tempting to handle this at the application level with mutexes, but that breaks the moment you have multiple instances. Pushing the serialization down to Postgres is almost always the right call for correctness.

There's essentially no difference between putting the logic in the app vs a stored procedure (other than round trip time)

await db().transaction(async (tx) => { await hooks?.onTxBegin?.();

  const [order] = await tx.select().from(orders)
    .where(eq(orders.id, input.id))
    .for("update");

  const [status] = await tx.select().from(orderStatuses)
    .where(eq(orderStatuses.orderId, input.id))
    .orderBy(desc(orderStatuses.createdAt))
    .limit(1);

  if (input.status === status.code)
    throw new Error("Status already set");

  await tx.insert(orderStatuses).values({ ... });

You need the transaction + SELECT FOR UPDATE because the validation depends on current state, and two concurrent requests could both pass the duplicate check. The hooks parameter is the barrier injection point from the article - that's how you test that the lock actually prevents the race.