Database Transaction Isolation Levels: Dirty, Non-repeatable & Phantom Reads

Database transaction isolation levels are a critical aspect of building reliable backend systems. When multiple transactions execute simultaneously, several anomalies can emerge that threaten data integrity. These anomalies—dirty reads, non-repeatable reads, and phantom reads—occur when one transaction interferes with another’s view of the database. Understanding these phenomena is essential for implementing appropriate isolation levels and ensuring system reliability. This article examines each anomaly in detail, explains their implications for data consistency, and demonstrates how transaction isolation levels mitigate these issues in production environments.

Dirty Reads: The Perils of Uncommitted Data:

Dirty reads occur when a transaction reads data written by a concurrent uncommitted transaction. This anomaly exposes your application to potentially invalid data that may later be rolled back.

Consider this scenario:

1. Transaction A updates a customer’s account balance from $1000 to $800
2. Transaction B reads the updated balance ($800) before Transaction A commits
3. Transaction A encounters an error and rolls back, restoring the balance to $1000
4. Transaction B continues processing with invalid data ($800)

The consequences of dirty reads include:

• Business logic operating on phantom data
• Cascading errors through dependent systems
• Incorrect reporting or analytics
• Potential financial or data integrity issues

In code, this looks like:

-- Transaction A
BEGIN;
UPDATE accounts SET balance = 800 WHERE account_id = 123; -- Original balance: 1000
-- (Transaction hasn't committed yet)

-- Transaction B (concurrent)
BEGIN;
SELECT balance FROM accounts WHERE account_id = 123; -- Returns 800
-- Application logic uses invalid balance of 800

-- Transaction A rolls back
ROLLBACK;

-- Transaction B continues with invalid data

Production systems prevent dirty reads by implementing READ COMMITTED isolation level or higher, ensuring transactions only see committed data.

Non-repeatable Reads: The Challenge of Data Stability

Non-repeatable reads occur when a transaction reads the same row twice and gets different results because another transaction modified the data between reads.

Key characteristics:

• Data changes during the course of a single transaction
• Multiple reads of the same record yield different values
• Particularly problematic for multi-step processing and reporting

Consider this scenario:

1. Transaction A reads a product price ($50)
2. Transaction B updates and commits the price to $60
3. Transaction A reads the same product price again, now getting $60
4. Transaction A’s business logic fails due to inconsistent data

This common scenario demonstrates why non-repeatable reads disrupt:

-- Transaction A
BEGIN;
SELECT price FROM products WHERE product_id = 456; -- Returns $50
-- Some processing occurs

-- Transaction B (concurrent)
BEGIN;
UPDATE products SET price = 60 WHERE product_id = 456;
COMMIT;

-- Transaction A continues
SELECT price FROM products WHERE product_id = 456; -- Now returns $60!
-- Business logic fails because price changed unexpectedly

Production systems prevent non-repeatable reads by implementing REPEATABLE READ isolation level or higher, ensuring data stability throughout a transaction’s execution.

Phantom Reads: The Problem with Range Queries

Phantom reads occur when a transaction executes the same query twice but gets different sets of rows. This happens when another transaction inserts or deletes rows that match the query’s conditions.

Unlike non-repeatable reads (which involve changes to existing rows), phantom reads involve the appearance or disappearance of rows that match query criteria.

Consider this scenario:

1. Transaction A queries orders placed on a specific date, finding 10 records
2. Transaction B inserts a new order for the same date and commits
3. Transaction A queries again and now finds 11 records
4. Transaction A’s aggregation logic becomes invalid

This anomaly manifests in code as:

-- Transaction A
BEGIN;
SELECT COUNT(*) FROM orders WHERE order_date = '2025-05-18'; -- Returns 10
-- Calculate averages or other aggregates

-- Transaction B (concurrent)
BEGIN;
INSERT INTO orders (order_id, order_date, amount) VALUES (789, '2025-05-18', 250.00);
COMMIT;

-- Transaction A continues
SELECT COUNT(*) FROM orders WHERE order_date = '2025-05-18'; -- Now returns 11!
-- Previously calculated aggregates are now incorrect

Production systems prevent phantom reads by implementing SERIALIZABLE isolation level, which guarantees transaction execution as if they were sequential, regardless of query type.

Choosing The Right Database Transaction Isolation Levels:

Database systems implement isolation levels to control these anomalies:

• READ UNCOMMITTED: Permits all three anomalies (rarely used in production)
• READ COMMITTED: Prevents dirty reads but allows non-repeatable and phantom reads
• REPEATABLE READ: Prevents dirty and non-repeatable reads but allows phantom reads
• SERIALIZABLE: Prevents all anomalies but reduces concurrency

Selecting the appropriate isolation level requires balancing consistency requirements against performance considerations:

Conclusion

Database transaction isolation levels directly address fundamental challenges in concurrent systems. Dirty reads expose applications to uncommitted and potentially invalid data. Non-repeatable reads create inconsistency within a single transaction’s view of data. Phantom reads undermine the stability of result sets for range queries. Understanding these anomalies enables engineers to select appropriate transaction isolation levels and design robust data access patterns.

When implementing transaction management in production systems:

• Use the highest practical isolation level based on consistency requirements
• Consider the performance implications of stricter isolation
• Implement retry mechanisms for serialization failures
• Design transactions to be short-lived and focused
• Document isolation level requirements in data access code

By addressing these anomalies through proper database transaction isolation levels, backend engineers can build reliable, consistent systems that maintain data integrity despite concurrent operations. The tradeoffs between isolation and performance remain a critical architectural decision point in any database-driven application. For more details on transaction level PostgreSQL Documentation on Transaction Isolation