Advisory locks let you serialize on an arbitrary key (like a hash of the entity type + parent ID) without needing a dummy row or separate lock table. They auto-release on transaction end, so no cleanup.
The barrier testing approach from the article would work nicely here too - inject the barrier between acquiring the advisory lock and the subsequent insert, then verify the second transaction blocks until the first commits.
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.
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
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.
Add a numeric version column to the table being updated, read and increment it in the application layer and use the value you saw as part of the where clause in the update statement. If you see ‘0 rows updated’ it means you were beaten in a race and should replay the operation.
Optimistic updates looks great when there is no contention, and they will beat locking in a toy benchmark, but if you're not very careful they can cause insane amplification under load.
It's a similar trap as spinlocks. People keep re-discovering this great performance hack that avoids the slow locks in the standard. And some day the system has a spike that creates contention, and now you have 25 instances with 24 of them spinning like crazy, slowing to a crawl the only one that could be making progress.
It's possible to implement this pattern correctly, and it can be better in some specific situations. But a standard FOR UPDATE lock will beat the average badly implemented retry loop nine times out of ten.
WITH
o AS (
SELECT FROM orders
WHERE orders.id = $1
),
os AS (
SELECT FROM orderStatuses
WHERE orderStatuses.orderId = $1
ORDER BY DESC orderStatuses.createdAt
LIMIT 1
)
INSERT INTO orderStatuses ...
WHERE EXISTS (SELECT 1 FROM os WHERE os.code != $2)
RETURNING ...something including the status differ check...