?? The Hidden Dangers of Weak Isolation Levels – And Why You Should Care!

Ever had that feeling where you double-check your bank balance, and it's lower than expected? Or maybe you clicked “Buy Now” on a limited-stock product, and somehow both you and another buyer ended up "owning" the same item? ???? Welcome to the world of weak isolation levels in databases—where concurrency bugs can lead to real-world chaos!

But don’t worry—I got you! In this post, we'll break down why weak isolation matters, how it affects your data, and what “Read Committed” actually does. And the best part? We’re not just talking—we’re implementing it hands-on! ??. Let’s make this fun, engaging, and super simple! ??

???♂? The Sneaky Nature of Concurrency Bugs

Concurrency bugs are like ghosts in your code. ?? They don’t show up during testing but haunt your system in production at the worst possible time. These bugs happen when multiple transactions access and modify the same data at the same time without proper isolation.

?? Example: Two users, Alice and Bob, both try to buy the last available car in an online marketplace.

• The system updates the listing to reflect the new owner.
• It generates an invoice and sends it to the buyer.
• Oops! Alice gets the invoice, but Bob owns the car. ??

Why? Because the system didn’t properly handle concurrent transactions! This is exactly why we need transaction isolation levels in databases.

??? Isolation Levels: Keeping Transactions in Check

Isolation ensures that transactions don't step on each other's toes while running. The strongest isolation level, Serializable, makes transactions run as if they were executed one after another—but at a huge performance cost! ??

Since not all systems need that level of strictness, many databases use weaker isolation levels, which are faster but introduce risks. The most common one?

Read Committed: The “No Dirty Reads” Rule

This is the most basic isolation level and is the default in many databases like PostgreSQL, Oracle, and SQL Server. It makes two key promises:

? No Dirty Reads – You will only read committed data. You’ll never see uncommitted changes from another transaction. ? No Dirty Writes – You won’t overwrite another transaction’s uncommitted data.

Sounds reasonable, right? Let’s break it down further.

?? No Dirty Reads: Why It Matters

Imagine a transaction updates a database but hasn’t committed yet. If another transaction reads this uncommitted data, it’s called a dirty read—and it's dangerous. ??

?? Example: You check your salary account, and it shows $10,000.

• But wait! Your boss just initiated a salary deduction (because, let’s say, you “accidentally” took too many coffee breaks ???).
• If your banking app allows dirty reads, you might see the original amount before the deduction finalizes.
• You happily withdraw all $10,000, but—oops! The deduction goes through later, and now your balance is negative. ??

Read Committed prevents this by ensuring you never see uncommitted data.

?? No Dirty Writes: Preventing Data Corruption

If two transactions try to update the same row at the same time, who wins? Without proper control, the last write wins, potentially overwriting uncommitted changes.

?? Example: The Race for the Last Car

• Alice and Bob both try to buy the same car at the exact same time.
• Alice updates the listing table first but hasn't committed yet.
• Bob also updates the listing and commits successfully.
• If Alice’s update then commits without checking Bob’s change, we have data corruption!

To prevent this, Read Committed ensures no transaction overwrites another's uncommitted data. If Alice starts writing first, Bob must wait for Alice’s transaction to finish before making changes.

?? How Databases Implement Read Committed

Most databases automatically handle Read Committed isolation using row-level locking.

? When a transaction writes to a row, it locks it.

? Other transactions trying to write must wait until the first one finishes.

? Reads don’t require locks—they always see the last committed value. The database keeps both the old committed value and the new uncommitted value set by an active transaction. Until the transaction commits, other transactions continue reading the old value.

This approach prevents dirty reads while keeping things efficient. However, it does NOT prevent all concurrency issues (like lost updates or phantom reads), which we’ll cover in the next post! ??

??? We’re Not Just Talking—We’re Building It!

?? We’ll simulate database transactions, lock handling, and race conditions so you get a practical, hands-on understanding of how it works under the hood.

?? Want to see the code? Check it out here (give a ??): ?? Read Committed C++ Implementation