Describe a scenario where caching in Entity Framework might lead to stale data issues and how to mitigate them.

  • When dealing with frequently updated data in a multi-user environment, stale data issues may arise if the cached data is not refreshed regularly. Mitigation can be achieved by implementing a cache expiration policy based on data volatility or using cache invalidation techniques such as Dependency Injection (DI) to update the cache when the underlying data changes.
  • In scenarios where concurrent transactions are updating the same data, stale data issues may occur if the cache is not properly synchronized. This can be mitigated by employing optimistic concurrency control mechanisms in Entity Framework or utilizing distributed cache locking mechanisms to ensure data consistency.
  • Stale data issues may arise when caching data retrieved using lazy loading in Entity Framework, as related entities may not be included in the cache, leading to incomplete or outdated data. To mitigate this, eager loading or explicit loading can be used to prefetch related entities and ensure data consistency.
  • When caching query results in Entity Framework, stale data issues may occur if the cache is not invalidated when underlying data changes. To address this, a cache dependency mechanism can be implemented to automatically refresh the cache when relevant data is modified.
Option 4: When caching query results in Entity Framework, stale data issues may occur if the cache is not invalidated when underlying data changes. To address this, a cache dependency mechanism can be implemented to automatically refresh the cache when relevant data is modified. This ensures that the cached data remains up-to-date and consistent with the underlying database, mitigating the risk of stale data issues.
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