In a Blazor Server app using EF Core with REST endpoints, duplicate tracking errors occur when saving. For example:
async Task SubmitEntry() {
if (entry != null)
entry.Key = await Repo.InsertAsync(entry);
}
How can this be resolved?
In a Blazor Server app using EF Core with REST endpoints, duplicate tracking errors occur when saving. For example:
async Task SubmitEntry() {
if (entry != null)
entry.Key = await Repo.InsertAsync(entry);
}
How can this be resolved?
Based on my experience managing similar issues in a Blazor Server application, the problem can be mitigated by decoupling the lifecycle of the DbContext used for data operations. Instead of working with a long-lived context across multiple operations, creating a fresh instance for each transaction helps prevent unintended duplicate tracking. It is also beneficial to detach any entities post-insert to ensure that subsequent operations obtain a clean slate. This practice avoids lingering tracked state which can cause issues during subsequent save operations, especially in concurrent REST API scenarios.
i ran into this too. i fixed it by detaching the entity right after the insert to reset tracking. using a new context for each operashun also solved it. hope this helps!
hey, has anyone tried reinitating the dbcontext completely and clearing local tracking for each call? i encountered similar issues and found that resetting context state sometimes helps. what tweaks have you guys tried for those duplicate errors? keen to hear your experinces.
In my experience, addressing duplicate tracking issues in EF Core within a Blazor Server environment involves ensuring that each transactional operation is isolated. I have found that rather than reusing a single context across REST calls, encapsulating each operation in its own context instance avoids inadvertent state sharing. Additionally, using methods such as explicit state management or AsNoTracking queries for read operations can further mitigate conflicts. This approach allows the system to remain robust under high concurrency, resulting in more predictable and stable behavior during data manipulations.