What are the advantages and disadvantages of using row-level locking in DB2?
- Advantages: Granular control, Reduced contention
- Advantages: Improved concurrency, Reduced deadlock
- Disadvantages: Increased complexity, Higher resource consumption
- Disadvantages: Increased overhead, Potential for lock escalation
Row-level locking in DB2 provides granular control over data access, allowing transactions to lock only specific rows rather than entire tables. This approach reduces contention and improves concurrency by allowing multiple transactions to access different rows simultaneously. However, row-level locking also introduces overhead due to the need to manage individual locks for each row, and it may lead to lock escalation in situations where a transaction locks too many rows, impacting performance. Additionally, managing row-level locks adds complexity to application development and may require more system resources compared to other locking mechanisms.
What role do variables play within stored procedures in DB2?
- Controlling the execution flow
- Creating temporary tables
- Defining constraints on table columns
- Storing and manipulating data within the procedure
Variables within stored procedures in DB2 are primarily used for storing and manipulating data within the procedure, enabling dynamic processing and manipulation of data during execution.
Scenario: After completing the installation of DB2, a developer needs to configure database connections for an application. What file should they modify to accomplish this task?
- db2cli.ini
- db2connect.cfg
- db2diag.log
- db2dsdriver.cfg
The correct file to modify database connections for an application is db2dsdriver.cfg. This file contains the settings for the IBM Data Server Driver for JDBC and SQLJ. It allows developers to define data sources and connection properties for connecting to DB2 databases. db2cli.ini is used for CLI/ODBC applications, db2connect.cfg is used for remote client connectivity settings, and db2diag.log is a diagnostic log file that records messages related to DB2 errors and events, but it does not configure database connections.
What are weak entities in an ERD?
- Entities with composite attributes
- Entities with derived attributes
- Entities with strong relationships
- Entities without primary keys
Weak entities are entities that do not have a primary key attribute on their own. They rely on a relationship with another entity (owner entity) for their existence.
In DB2, what is the significance of primary keys in tables?
- They allow NULL values in the column
- They automatically create indexes
- They define the order of the rows
- They ensure uniqueness of each row
Primary keys in DB2 tables play a crucial role in ensuring the uniqueness of each row. They enforce entity integrity by ensuring that no two rows have the same values in the specified column or combination of columns. This uniqueness constraint helps maintain data accuracy and consistency.
In an ERD, what does a dotted line connecting entities signify?
- Many-to-many relationship
- Many-to-one relationship
- One-to-many relationship
- One-to-one relationship
A dotted line connecting entities in an ERD typically signifies a one-to-many relationship. This means that one instance of an entity can be associated with multiple instances of another entity, but each instance of the other entity is associated with only one instance of the first entity.
Scenario: A software development team is debating whether to denormalize their database schema to optimize performance. What factors should they consider before making this decision?
- Data integrity requirements
- Storage space availability
- Query complexity
- Development time constraints
Option 1: Data integrity requirements - Before deciding to denormalize the database schema for performance optimization, the software development team should carefully consider various factors. One crucial factor is data integrity requirements. Denormalization can lead to data redundancy and potential update anomalies, compromising data integrity. Therefore, the team must evaluate the impact of denormalization on data consistency and ensure that appropriate measures, such as establishing referential integrity constraints and enforcing data validation rules, are in place to maintain data integrity. Hence, considering data integrity requirements is essential before proceeding with denormalization for performance optimization.
When does a trigger get executed in DB2?
- Before or after a specific event like INSERT, UPDATE, or DELETE
- Only after a specific event like INSERT
- Only after an error occurs
- Only before a specific event like UPDATE
Triggers in DB2 are executed before or after a specific event like INSERT, UPDATE, or DELETE, allowing for automatic actions to be taken in response to database changes.
What role does the DB2 event monitor play in troubleshooting database issues?
- The DB2 event monitor analyzes database schemas for optimization opportunities
- The DB2 event monitor captures SQL statements executed within the database
- The DB2 event monitor logs information about database events, errors, and exceptions
- The DB2 event monitor provides real-time monitoring of database performance
The DB2 event monitor logs information about database events, errors, and exceptions, providing valuable insights for troubleshooting and performance optimization. It helps administrators identify issues and optimize database performance.
Scenario: A company's database performance is degrading due to a large volume of data. How can partitioning help improve performance in this scenario?
- Enhance security by isolating sensitive data
- Improve query performance by dividing data into smaller, manageable chunks
- Reduce disk space usage by compressing data efficiently
- Streamline backup and recovery processes by separating data into manageable units
Partitioning involves dividing large tables or indexes into smaller pieces called partitions. By doing so, queries can target specific partitions, allowing for faster query performance as only relevant data is accessed. This can significantly improve database performance in scenarios with a large volume of data.