Quick Answer: Database transaction isolation levels control the trade-off between concurrency (speed) and data consistency (safety) when multiple queries run simultaneously. The four standard levels are Read Uncommitted, Read Committed (Postgres default), Repeatable Read, and Serializable. Higher isolation prevents data anomalies like dirty reads and phantom reads but reduces performance.
Most engineers interact with a database every single day, yet a surprisingly low number know what transaction isolation levels actually do. Even fewer realize that Postgres isn't using the safest one out of the box. I look at transaction isolation as a strict trade-off between concurrency and isolation. It is a balancing act between speed and danger.
What is the difference between concurrency and isolation in databases?
Concurrency dictates how many transactions your database can process at the exact same time, which gives you speed. Isolation ensures those simultaneous transactions don't interfere with each other, which gives you data safety. You cannot maximize both; increasing isolation forces transactions to wait, which inherently reduces concurrency.
Think of it like designing a highway. If you remove all speed limits and lanes (maximum concurrency), cars get to their destination incredibly fast, but the risk of a multi-car pileup (data corruption) goes through the roof. If you force every single car to drive in one single-file lane at 10 miles per hour (maximum isolation), nobody crashes, but traffic backs up for miles. Database engines let you choose the speed limit that fits your risk tolerance.
What are the four database transaction isolation levels?
The SQL standard defines four progressive isolation levels: Read Uncommitted, Read Committed, Repeatable Read, and Serializable. Each step up the ladder fixes a specific data anomaly caused by the previous level by locking down data more aggressively.
| Isolation Level |
Prevents Dirty Reads |
Prevents Non-Repeatable Reads |
Prevents Phantom Reads |
Performance Impact |
| Read Uncommitted |
No |
No |
No |
Lowest overhead (Fastest) |
| Read Committed |
Yes |
No |
No |
Low overhead |
| Repeatable Read |
Yes |
Yes |
No |
Medium overhead |
| Serializable |
Yes |
Yes |
Yes |
Highest overhead (Slowest) |
How does Read Uncommitted cause dirty reads?
Read Uncommitted allows an active transaction to read data that another transaction has written but hasn't yet saved (committed). If that other transaction encounters an error and rolls back, your query just read data that technically never existed.
Imagine your team is building a fintech app. Transaction A deducts $100 from an account, but hasn't committed yet. Transaction B (checking the balance) reads this uncommitted state and shows the user a lower balance. If Transaction A fails and rolls back, Transaction B has just served the user a "dirty read" of phantom money.
Why does Postgres default to Read Committed?
Read Committed restricts your queries so they can only read data that has been successfully saved to the database, entirely preventing dirty reads. Postgres and most major database systems use this as their default because it offers a highly practical baseline for standard web applications.
However, Read Committed introduces a new problem. If your transaction reads a specific row, and a concurrent transaction updates that row before your transaction finishes, a second read of that same row within your transaction will return completely different data. Your data shifts right under your feet.
How does Repeatable Read fix shifting data?
Repeatable Read solves shifting data by taking a stable snapshot of the database at the start of your transaction. Even if other transactions modify and commit changes to those rows in the background, any subsequent reads in your transaction will return the exact same snapshot data.
This sounds bulletproof, but it leaves you exposed to "phantom reads." If you are running a broad query—like selecting all users from a specific city—and you run it multiple times, another transaction might insert brand-new rows that match your filter. Those new rows will suddenly appear in your subsequent reads like phantoms.
When should you use Serializable isolation?
You should use Serializable isolation only when absolute data accuracy is mandatory and you cannot risk any concurrent data modification whatsoever. It essentially asks all of your transactions to wait in a single-file line, executing one after the other.
This gives you the absolute most isolation, but the least performance. There really isn't a universally "best" isolation level. It's always a conversation about trade-offs and deciding which level of speed versus danger is most appropriate for your specific use case.
Frequently Asked Questions
How do I check the current transaction isolation level in Postgres?
You can check the default isolation level for your entire Postgres instance by running SHOW transaction_isolation;. This will typically return read committed unless your database administrator has explicitly modified the configuration file.
Can you change the isolation level per transaction?
Yes, you can specify the isolation level at the start of a transaction. By using the BEGIN ISOLATION LEVEL command followed by the level you want (like SERIALIZABLE), you can enforce stricter rules for a specific critical operation without slowing down the rest of the database.
Which isolation level prevents phantom reads?
Serializable is the only standard isolation level that completely guarantees the prevention of phantom reads. It achieves this by locking the exact range of rows your query evaluates, preventing any concurrent transactions from inserting new data into that range until you commit.
