How do I use background contexts and merge policies?

Using background contexts and merge policies in Core Data can greatly enhance the performance of your app when dealing with large datasets or long-running tasks. Background contexts allow you to perform data operations in a separate thread, while merge policies help you manage conflicts that arise when multiple contexts are accessing the same data.

Background Contexts

To create a background context, you can use the following approach:

let backgroundContext = persistentContainer.newBackgroundContext() backgroundContext.perform { // Perform your data operations here }

Merge Policies

Core Data provides several merge policies to resolve conflicts:

  • NSErrorMergePolicy: Default behavior that raises an error on conflict.
  • MergeByPropertyObjectTrumpMergePolicy: The changes from the object in the context that is being saved to the managed object context will overwrite conflicting values.
  • MergeByPropertyStoreTrumpMergePolicy: The changes from the object in the persistent store will overwrite conflicting values.

Set a merge policy on a managed object context like this:

let mainContext = persistentContainer.viewContext mainContext.mergePolicy = NSMergeByPropertyObjectTrumpMergePolicy

Background Contexts Merge Policies Core Data Performance Data Management

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?