Seeding a Core Data Store With Remote JSON Data

Importing large JSON datasets into a Core Data store can be challenging due to the potential size and dynamics of the data. Stream recently enhanced its SDK's data-importing strategy to efficiently handle vast datasets without performance degradation. Key issues include the expensive nature of database reads and writes, which can be mitigated by caching fetched data to reduce redundant fetches, and optimizing the number of database interactions by consolidating updates into single transactions. The use of a caching strategy, which involves storing unique items in a dictionary to prevent repeated database queries, significantly reduces fetch requests and improves import speed. Stream's approach also involves pre-processing JSON data to minimize repeated data processing, further enhancing efficiency. While the optimal import strategy can vary depending on specific use-cases and data characteristics, the outlined methods provide a framework for improving data import performance by reducing database load and optimizing data handling.