If an SQL exception occurs in a method that performs multiple database updates, how should the exception be handled to maintain data integrity?
- Commit all changes made before the exception
- Ignore the exception and continue with the remaining updates
- Log the exception and terminate the transaction
- Rollback all changes made before the exception
When an SQL exception occurs during multiple updates, rolling back all changes made before the exception helps maintain data integrity by ensuring either all updates succeed or none do.
When designing an application that connects to a database, how should SQL exceptions be managed to provide meaningful feedback to the user?
- Display the raw SQL exception message
- Log the exception and display a user-friendly error message
- Show a generic error message to the user
- Terminate the application on exception
To provide meaningful feedback, log the SQL exception for debugging purposes and display a user-friendly error message to the user, avoiding the display of raw SQL exception messages that may expose sensitive information.
In a scenario where a transaction involves multiple SQL queries, how should exceptions be handled to ensure that either all queries succeed or none do?
- Commit each query individually
- Commit the entire transaction only if all queries succeed
- Ignore exceptions and continue with the transaction
- Rollback the entire transaction on any exception
Handling exceptions in a transaction involving multiple SQL queries requires rolling back the entire transaction on any exception to ensure data consistency鈥攅ither all queries succeed, or none do.
Which method is used to batch multiple SQL statements before sending them to the database?
- execute()
- executeBatch()
- executeQuery()
- executeUpdate()
The executeBatch() method is used to batch multiple SQL statements together before sending them to the database, which can improve performance by reducing the number of round-trips.
What is the primary purpose of using a connection pool in database interactions?
- Ensuring database security
- Managing database connections
- Optimizing SQL queries
- Reducing database load
The primary purpose of using a connection pool is to manage and reuse database connections efficiently, reducing the overhead of opening and closing connections for each database interaction.
When should you choose PreparedStatement over Statement in JDBC?
- Execute multiple queries efficiently
- Execute parameterized queries
- Improve query optimization
- Simplify complex join operations
The PreparedStatement in JDBC is used to execute parameterized queries, which helps prevent SQL injection and improves performance by reusing the compiled query plan. It is suitable when you need to execute the same SQL statement multiple times with different parameters.
What is the advantage of using CallableStatement in JDBC?
- Execute SQL queries with parameters
- Execute stored procedures
- Improve connection pooling
- Streamline complex transactions
The CallableStatement in JDBC is used to execute stored procedures, providing a way to encapsulate and execute multiple SQL statements as a single unit. This enhances code modularity, reusability, and security by allowing the use of stored procedures in the database.
How does transaction isolation level impact database performance and consistency?
- Control concurrency issues
- Ensure data consistency
- Increase transaction throughput
- Read uncommitted data
The transaction isolation level in JDBC determines the degree to which one transaction can affect another. Choosing the appropriate isolation level balances performance and consistency. Lower levels improve performance but may introduce consistency issues, while higher levels enhance consistency at the expense of performance.
How does the use of 'lazy loading' in an ORM framework like Hibernate affect database performance?
- Decreases performance by loading all data at once
- Depends on the specific use case
- Has no impact on performance
- Increases performance by loading data on demand
'Lazy loading' in Hibernate increases performance by loading data on demand, reducing the initial load time and resource consumption.
What are the implications of using optimistic vs. pessimistic locking strategies in database transactions?
- Both have similar implications
- Optimistic locking prevents blocking, may lead to conflicts
- Pessimistic locking reduces contention, but can cause blocking
- They are used interchangeably based on convenience
Optimistic locking in database transactions prevents blocking but may lead to conflicts, while pessimistic locking reduces contention but can cause blocking due to exclusive locks.