To optimize query performance, Entity Framework can pre-generate views using ________.
- Code-First Migrations
- Database Views
- Model-First Approach
- Stored Procedures
Entity Framework can pre-generate views using database views. This technique helps in improving query performance by reducing the overhead of dynamically generating SQL queries during runtime.
For complex queries, using ________ over LINQ can sometimes result in better performance in Entity Framework.
- Entity SQL
- Object-SQL Mapping
- Raw SQL Queries
- Stored Procedures
In Entity Framework, using raw SQL queries directly instead of LINQ can sometimes lead to better performance, especially for complex queries, as it allows developers to optimize the query execution plan.
In a scenario with high transaction rates, what Entity Framework strategies can be employed to optimize performance?
- Optimistic concurrency control
- Batch processing
- Database sharding
- Connection resiliency
Option 4, Connection resiliency, involves techniques such as connection pooling and retry logic to handle transient failures, ensuring that the application remains responsive even under high transaction rates. These strategies help optimize performance by efficiently managing connections and ensuring the application's resilience to temporary network issues.
What happens in Entity Framework when you query an entity that has already been retrieved in the current context?
- It fetches the entity from the database again, ignoring any previously retrieved instances.
- It merges the changes from the database into the tracked entity, ensuring data consistency.
- It returns the entity from the cache, avoiding a database round trip.
- It throws an exception indicating that the entity is already being tracked by the context.
Entity Framework utilizes its change tracking mechanism to detect changes made to entities within the context. When an entity is queried again, Entity Framework returns the already retrieved instance from the cache, ensuring data consistency and avoiding unnecessary database queries.
How does Entity Framework handle second-level caching?
- By default, Entity Framework does not support second-level caching
- Entity Framework generates cache files on the disk
- Entity Framework provides built-in support for second-level caching
- Entity Framework relies on third-party libraries for caching
Entity Framework provides built-in support for second-level caching. It allows caching query results in memory or using external caching providers like Redis. This improves performance by reducing database round trips and query execution time.
What is the impact of disabling tracking on caching in Entity Framework?
- Disabling tracking disables caching
- Disabling tracking has no impact on caching
- Disabling tracking improves caching performance
- Disabling tracking increases memory consumption
Disabling tracking improves caching performance in Entity Framework. When tracking is disabled, Entity Framework does not keep track of changes to entities, resulting in reduced overhead and improved performance when caching query results.
________ in Entity Framework can be used to track changes in entities more efficiently for performance optimization.
- Change Tracking
- Eager Loading
- Explicit Loading
- Lazy Loading
Entity Framework's change tracking mechanism efficiently tracks changes in entities, allowing it to optimize performance by minimizing unnecessary database operations during save operations.
Consider a situation where an Entity Framework application experiences slow response times due to large object graphs. How would you address this for performance improvement?
- Lazy loading
- Eager loading
- Proxy generation
- Entity splitting
Option 2, Eager loading, retrieves related entities along with the main entity in a single query, reducing the number of database round trips and mitigating the impact of large object graphs on performance. By eagerly loading related data, the application can fetch all necessary information upfront, improving response times and overall performance, especially in scenarios with large object graphs.
How would you optimize an Entity Framework application that requires frequent data writes and reads from a high-latency database?
- Asynchronous programming
- Database indexing
- Connection pooling
- Query caching
Option 1, Asynchronous programming, allows the application to perform multiple operations concurrently, reducing the impact of latency on performance. By utilizing asynchronous programming techniques such as async/await, the application can continue executing other tasks while waiting for database operations to complete, thereby improving overall responsiveness and throughput, especially in scenarios involving frequent data reads and writes to a high-latency database.
What type of caching is natively supported by Entity Framework?
- Eager loading
- Explicit loading
- Lazy loading
- No caching
Entity Framework natively supports eager loading, which is the process of loading related entities along with the main entity being queried. This helps reduce the number of database queries by fetching all required data in a single query, enhancing performance.