In this lesson, we’ll dive deep into Transaction Isolation Levels. You’re already familiar with Isolation as a concept, now it’s time to understand how it works in detail and why it’s crucial for maintaining data consistency in concurrent environments.
Some Processes Require More Isolation Than Others
Ever cooked spaghetti bolognese? It’s a simple dish, pasta, minced meat, onions, tomatoes, but it requires a specific order of steps to turn out right. Here’s how you do it:
- Heat oil in a pan, sauté chopped onions until soft
- Add minced meat, cook until browned
- Season with salt and pepper
- Add finely chopped tomatoes, cover, and simmer
- Meanwhile, boil water in a separate pot and cook the pasta
What do we observe? There are three distinct, isolated processes happening:
- Cooking onions and meat
- Simmering meat with tomatoes
- Boiling pasta separately
Each step depends on the previous one. If you throw everything into a single pot and boil it together, you won’t get spaghetti bolognese, you’ll get an unappetizing mushy mess. It’s still food, but not something you’d want to eat.
Transactions work the same way. If you don’t isolate them properly, data gets mixed up, leading to inconsistencies. Just like cooking, isolation ensures a clean and predictable outcome. Without proper control, systems risk data corruption, inconsistent reports, or worse - failed transactions that never should have been executed.
What Happens Without Proper Isolation?
Transaction Isolation Levels define how much one transaction can interfere with another. Without isolation, you might encounter the following issues:
1. Dirty Read
A dirty read happens when a transaction reads uncommitted changes from another transaction. Example:
- T1 updates a user’s name from “Alex” to “Kevin” but hasn’t committed yet
- T2 reads the updated value “Kevin”
- Suddenly, T1 is rolled back, and “Kevin” never officially existed
T2 just acted on phantom data - something that was never truly saved. If T2 depends on this incorrect value for further processing, it could lead to cascading failures.
2. Non-Repeatable Read
A non-repeatable read occurs when the same query gives different results within a single transaction:
- T1 reads a user’s name: “George”
- T2 updates “George” to “Hank” and commits
- T1 reads the name again and now sees “Hank”
T1 sees inconsistent data within its own execution. This could break business rules that assume data remains unchanged throughout the transaction’s lifecycle.
3. Phantom Read
A phantom read happens when identical queries return different numbers of rows:
- T1 selects all users named “Laurene”, there are 10
- T2 inserts two new “Laurene” users and commits
- T1 runs the same query again but now gets 12 rows
This can lead to incorrect assumptions, logical errors, and flawed calculations.
The Remedy: Transaction Isolation Levels
These issues aren’t always problems - some systems tolerate them. But when accuracy is critical, we use Transaction Isolation Levels to control them. The SQL standard defines four levels:
1. Read Uncommitted (Not Available in PostgreSQL)
- Allows all anomalies: Dirty reads, non-repeatable reads, and phantom reads
- Barely any isolation, transactions see each other’s uncommitted changes
- PostgreSQL doesn’t even support it because it’s too risky
- Common in some databases for read-heavy workloads where absolute accuracy isn’t a priority
2. Read Committed (Default in PostgreSQL)
- Prevents dirty reads - you only see committed data
- But you can still experience non-repeatable reads and phantom reads
- PostgreSQL uses MVCC (Multi-Version Concurrency Control) to prevent locking rows, improving performance
- Works well for applications that prioritize performance over strict consistency
3. Repeatable Read
- Prevents dirty and non-repeatable reads - you always see a consistent snapshot
- Phantom reads can still occur
- Ideal for banking transactions where data must remain unchanged throughout
- PostgreSQL handles this using Serializable Snapshot Isolation (SSI), reducing contention while maintaining accuracy
4. Serializable (Strictest Isolation)
- Eliminates all anomalies, including phantom reads
- Transactions behave as if they run one after another
- PostgreSQL does not force sequential execution - it uses predicate locking to detect and reject conflicting transactions
- Downside: High overhead, potential for increased transaction rollbacks
- Used in scenarios where data integrity is critical, like financial transactions and audits
Choosing the Right Isolation Level
Each isolation level offers a trade-off between consistency and performance.
- Low isolation (Read Committed) allows faster execution but increases the risk of anomalies.
- High isolation (Serializable) ensures correctness but may slow the system due to strict locking mechanisms.
Summary Table
| Isolation Level | Dirty Reads | Non-Repeatable Reads | Phantom Reads |
|---|---|---|---|
| Read Uncommitted | Possible | Possible | Possible |
| Read Committed | No | Possible | Possible |
| Repeatable Read | No | No | Possible |
| Serializable | No | No | No |
Final Thoughts
Transaction isolation is about balancing data consistency and performance. Higher isolation levels mean fewer anomalies, but they also increase resource consumption and potential slowdowns.
When designing your system, consider:
- Business requirements: Does strict consistency matter more than speed?
- Concurrency levels: Will multiple users access the same data at once?
- Database capabilities: Does your database support efficient locking mechanisms like MVCC?
Experiment with different isolation levels using the Lab module and observe their impact firsthand. Understanding transaction isolation is a critical skill for any database professional, mastering it ensures your applications handle concurrency safely and efficiently.