🔥 The DB Grill 🔥

Alright, settle down, kids. Let me put down my coffee—the kind that's been stewing since the system IPL'd this morning—and take a look at this... this blog post.

It's just delightful to see the younger generation discovering the foundational principles of data integrity. Truly, a stunning demonstration of a race condition. You needed multiple threads and a time.sleep() to prove that doing a write and then a separate read might give you an inconsistent result? Bless your hearts. Back in my day, we called that "Tuesday." We didn't need a "simulation" with a fancy Python script; we had three hundred CICS terminals hitting the same VSAM KSDS file for airline reservations, and if you didn't get your locking right, you'd have a planeload of people all booked for seat 14B. You learned about atomicity right after you learned which end of the punch card went into the reader.

And the solution! My goodness, the sheer ingenuity. An atomic read-write operation in a single call! You call it findOneAndUpdate(). We... well, we just called it a transaction. I seem to recall some preliminary work on this concept in DB2, oh, around 1985. It’s a real marvel of modern MongoDB that you can now perform this failure-resilient and safely retryable operation. We had to settle for a crusty old thing called a "transaction log" and a primitive ritual known as "two-phase commit." It was terribly dull, I assure you. No lightweight document-level locks for us, just boring old row-level and page-level locks that, you know, actually worked across the entire dataset.

I'm particularly impressed by this whole business of making the operation idempotent by storing a copy of the document.

To support this, MongoDB stores a document image... in an internal system collection (config.image_collection) that is replicated independently of the oplog, as part of the same transaction...

Fascinating. So, to avoid a transaction, you've implemented... a more complicated, hidden transaction that writes to two different places? Brilliant. We used to do something similar. It was called "hauling tape reels to the off-site vault in a station wagon." Seemed a bit less convoluted, but what do I know? I'm just a relic who still thinks in EBCDIC.

And this comparison to PostgreSQL is just the chef's kiss. It seems that with a proper database, you have to understand things like transaction isolation levels. You might even get a "serialization error" and have to—gasp!—retry the transaction. The horror. It's almost as if the database is designed to guarantee consistency across the entire system, rather than just hoping for the best within a single, glorified JSON blob. These precocious PostgreSQL programmers and their pesky, predictable ACID properties.

But the real pearl of wisdom is saved for the end. This is the part I'm going to have printed on a coffee mug.

If you design your schema so that business logic fits in a single document, findOneAndUpdate() can perform conditional checks, apply updates, and return the updated document atomically...

Let me translate that for the folks in the back who still write COBOL. "If you abandon decades of normalization theory and stuff your entire universe into one massive, unmanageable record because your database can't handle a simple join, then you can perform a basic update without tripping over your own feet."

It's a bold strategy.

You haven't discovered a revolutionary feature. You've just found the one weird trick to make a document store behave like a real database, but only for one row at a time.

Call me when you invent a foreign key.