Mastering Spring & MSA Transactions – Part 8: Beyond Propagation: Mastering Transaction Isolation for Safer Concurrency

In the previous article, we explored the various propagation modes (REQUIRES_NEW, NOT_SUPPORTED, etc.), showing how different pieces of your application can start new transactions or share existing ones. However, even if you’ve got propagation nailed down, that alone won’t guarantee your data remains consistent when multiple users or services hit the database at the same time.

Enter transaction isolation. It tackles a different angle of concurrency:

• How does your system deal with partially updated data?
• Can one transaction read data that’s not yet committed by another?
• What if a row changes mid-transaction, or new rows appear after you’ve already scanned a table?

Answering these questions requires understanding the isolation levels that a database supports, and how to configure them in Spring. Let’s dive in.

1) Quick Refresher on Concurrency Anomalies

1. Dirty Reads
   • Transaction A sees uncommitted changes made by Transaction B. If B rolls back later, A has effectively read “ghost data.”
2. Non-Repeatable Reads
   • A transaction re-reads the same row but gets a different value the second time (because another transaction modified it in between).
3. Phantom Reads
   • A transaction repeats a range query (e.g., SELECT * FROM accounts WHERE balance > 100), and new rows that match the criteria appear or vanish in the interim.

Different isolation levels exist to mitigate these anomalies in varying degrees.

2) The Four Core Isolation Levels

2.1) READ_UNCOMMITTED

• Allows dirty reads. Potentially the highest concurrency but the riskiest.
• Almost never recommended in real production usage, because reading uncommitted data can lead to big consistency issues.

2.2) READ_COMMITTED (Common Default)

• Prevents dirty reads but still vulnerable to non-repeatable and phantom reads.
• It’s the default for many databases (Oracle, SQL Server, PostgreSQL). Often sufficient for typical CRUD operations.

2.3) REPEATABLE_READ

• Prevents non-repeatable reads. Some engines like MySQL InnoDB also address phantom reads here (though that’s vendor-specific).
• If you must ensure that once you read a row, it doesn’t magically change mid-transaction, consider REPEATABLE_READ.

2.4) SERIALIZABLE

• Most strict. Practically simulates transactions running one by one in sequence.
• Eliminates dirty, non-repeatable, and phantom reads, but can severely impact performance with locking or forced rollbacks.

3) Real Examples: When to Use Which?

1. Most Ordinary Logic: READ_COMMITTED
   • Default in many DBs. Balances concurrency and data safety.
   • E.g., standard web CRUD operations, order processing, user profile updates—cases where occasionally seeing fresh data is acceptable.
2. Inventory or Balance: REPEATABLE_READ
   • If you fetch an item’s stock or account balance multiple times in the same transaction, you don’t want the value to spontaneously change.
   • MySQL’s InnoDB can also block phantom reads under REPEATABLE_READ.
3. Extreme Safety: SERIALIZABLE
   • For high-stakes finance or data that must be 100% consistent.
   • If concurrency is intense, you might face deadlocks or heavy rollback overhead. Typically used in small, critical sections only.
4. Rarely: READ_UNCOMMITTED
   • Might be considered for read-only “reporting” if ephemeral inaccuracies are acceptable. But it’s generally too dangerous for normal usage.

4) Configuring Isolation in Spring

In Spring, you specify isolation via the @Transactional annotation:

@Transactional(isolation = Isolation.REPEATABLE_READ)
public void updateBalance(int accountId, BigDecimal amount) {
    // ensures non-repeatable reads are prevented
}

• If you don’t set isolation, Isolation.DEFAULT applies, meaning you defer to the database’s default.
• As with propagation, you can configure isolation on individual methods or at the class level (and method-level overrides the class-level).

5) Performance & Locking Trade-Offs

1. Higher Isolation = More Locking
   • REPEATABLE_READ or SERIALIZABLE can trigger longer lock retention or heavier versioning (MVCC overhead).
2. Deadlock Potential
   • Strict isolation fosters concurrency conflicts, meaning your DB might forcibly roll back one transaction if they collide.
3. Vendor Differences
   • MySQL’s REPEATABLE_READ can block phantom reads, but Postgres’s approach to the same level might differ slightly.
4. Pilot in a Real-Load Environment
   • Don’t set SERIALIZABLE system-wide without load testing. Partial usage in just the critical code path is more common.

6) Putting It All Together After Propagation

In the previous article, we learned how calls can start new transactions, join existing ones, or skip transactions altogether—Propagation. But concurrency goes beyond how many transactions are open. We also need to define what data changes each transaction sees while it’s active.

• Propagation solves “Should we create or reuse a transaction?”
• Isolation solves “What level of data consistency do we enforce inside that transaction?”

By combining them wisely, you keep your operations from stepping on each other’s toes while still performing smoothly under load.

Final Advice

• Stick with READ_COMMITTED for most standard logic.
• Use REPEATABLE_READ when you truly require no mid-transaction changes to re-read data.
• SERIALIZABLE is for extreme correctness; try it on small, critical areas only, not the entire app.
• Test concurrency under realistic conditions to ensure your chosen level doesn’t cripple performance or cause excessive locks.

Done right, Transaction Isolation is your shield against concurrency anomalies. Combined with the right Propagation choices, it helps your application handle complicated multi-user operations reliably—and that’s what safe data handling is all about.