Plus, by strategically benefiting from SQLite’s support for UNIQUE ON CONFLICT REPLACE clauses with certain posts during table creation, upgrading records atomically throughout the sync process needed without any work whatsoever.
Transactions with Core Data (an in-memory object graph) aren’t fully supported within the strictest sense. By utilizing separate child ManagedObjectContexts for background thread processing (thread confinement) together with proper positioning of synchronized blocks I could implement proper handling of information updates and syncing while staying away from incorrect overwriting. iphone developer suggestion for efficiently upgrading or creating objects was just marginally useful, and overall I discovered using Core Data cumbersome despite its purported advantages. Furthermore, whereas SQLite in Android enabled fast loading Wise Queues, within the iOS application all filtering needed to be carried out in code, that is reduced even by using extensive caching.
Adding fairly robust full text search abilities towards the Android application was simple. I modeled my implementation after search within the Google I/O application, while using FTS3 feature of SQLite to produce a virtual table populated by a number of triggers set up for grabs that stored a user’s tasks. After that it had been only a matter of creating looking interface and adding storage for search history.
Core Data in iOS doesn’t support full text search natively, and so i made a decision to implement fundamental search functionality by using LIKE clauses inside a predicate for task descriptions and notes. That is certainly less effective as full text search, however i reasoned still iphone developer covered a lot of real existence use cases.
I’ll only mention a couple of APIs which i utilized in building various options that come with the GQueues apps to compare reasons.
Regular Expressions were crucial for applying the fast Add parsing in GQueues and fortunately both Android and iOS provided RegEx support natively.