https://github.com/sandysanthosh/transaction-sb
https://github.com/sandysanthosh/transaction-sb
Last synced: 6 months ago
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- Host: GitHub
- URL: https://github.com/sandysanthosh/transaction-sb
- Owner: sandysanthosh
- License: apache-2.0
- Created: 2024-11-06T14:06:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-06T14:07:20.000Z (about 1 year ago)
- Last Synced: 2025-02-28T20:56:18.540Z (11 months ago)
- Size: 7.81 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
When a transaction fails in Spring Boot, you need to ensure that the system gracefully handles the failure while maintaining data integrity. Here are several strategies to handle transaction failures in Spring Boot effectively:
1. Using @Transactional Annotation
Spring Boot provides the @Transactional annotation to manage transactions.
If any exception occurs within a method annotated with @Transactional, the transaction is rolled back by default, preserving data integrity.
Example:
@Transactional
public void performTransaction() {
// Database operations
// If any exception occurs, this transaction will automatically roll back
}
2. Configuring Rollback Rules
By default, Spring rolls back transactions on runtime exceptions (unchecked exceptions).
You can specify rollback behavior for specific exceptions by configuring the @Transactional annotation.
@Transactional(rollbackFor = { CustomException.class, SQLException.class })
public void performTransaction() {
// Custom logic
}
3. Exception Handling and Custom Rollbacks
Use try-catch blocks within the @Transactional method to catch exceptions and perform custom rollback actions or compensate for failed transactions.
For non-transactional methods, you can manually manage transactions by injecting PlatformTransactionManager or using TransactionTemplate.
@Autowired
private TransactionTemplate transactionTemplate;
public void performTransaction() {
transactionTemplate.execute(status -> {
try {
// Perform operations
} catch (Exception e) {
status.setRollbackOnly(); // Manually roll back if needed
throw e;
}
return null;
});
}
4. Handling Distributed Transactions (Saga or 2PC)
In a microservices architecture, use Saga patterns or a distributed transaction coordinator like JTA for complex, distributed transactions.
Saga Pattern: Compensating actions are triggered when part of a transaction fails.
Two-Phase Commit (2PC): If consistency is critical, 2PC ensures a transaction is either fully completed across services or rolled back.
5. Using Retry Mechanism
Sometimes, transaction failures may be transient (e.g., network issues or lock contentions). You can implement retries using @Retryable with Spring Retry.
@Retryable(value = { SQLException.class }, maxAttempts = 3)
@Transactional
public void performTransactionWithRetry() {
// Operation that might fail transiently
}
6. Monitoring and Logging
Logging transaction failures can help in diagnosing issues. Use @AfterThrowing advice with Spring AOP to log any transaction failures for better visibility.
Also, consider using Spring Boot Actuator to monitor transactions and track failure rates.
7. Handling Deadlocks and Optimistic Locking
For concurrent transactions, consider using optimistic or pessimistic locking mechanisms to avoid deadlocks and ensure transaction success.
With optimistic locking (using versioning), a transaction fails if there’s a version conflict, and you can retry or handle the exception as needed.
8. Application-Level Error Handling
Use a global exception handler (e.g., @ControllerAdvice) to manage transaction errors in a centralized manner.
This allows you to return appropriate HTTP responses or redirect users upon failure.
Example Code Snippet for Custom Rollback:
@Transactional
public void updateData() {
try {
// Operation 1
// Operation 2 (may fail)
} catch (DataAccessException e) {
// Handle exception or rollback
throw new CustomException("Transaction failed", e);
}
}
By using these strategies, you can ensure that your Spring Boot application handles transaction failures gracefully, with minimal impact on data integrity. Let me know if you’d like further examples for any specific approach!