The advice to "Allow table dependency" through normalization is counterproductive to achieving parallel data fetching in mobile local storage. Normalization creates foreign key relationships, which impose a sequential order on data synchronization. The process must sync the primary table's data first before it can sync the dependent table's data to resolve the relationships. This serialization directly prevents the parallel, independent fetching of data, which is a key strategy for optimizing mobile app performance and data sync speed. The goal for parallel sync is to have independent, denormalized local entities where possible.

B. Use Fetch Data : Avoid cascading aggregates in Onlnitialize and OnReady

This is suitable advice. Using separate, independent Fetch Data from Local Storage actions allows the platform to run them in parallel, improving screen load times.

C. Avoid generic tables : Contains too much data, not all are relevant

This is suitable advice. Generic tables force the synchronization of large, often irrelevant, datasets, which increases sync time and consumes more resources, hindering overall performance.

1. OutSystems - Official Documentation

"Offline Data Synchronization Patterns": In the section discussing the Parallel Sync pattern

the documentation emphasizes the need for data independence. It states

"To fetch data in parallel

the Aggregates must be independent." This principle applies directly to the design of local entities; dependencies between them would prevent independent

and therefore parallel

synchronization.

2. OutSystems - Official Documentation

"Mobile and Reactive Web Apps Performance Best Practices": The section on "Fetching data" advises against creating data-fetching chains (cascading aggregates). It recommends using multiple independent Aggregates on a screen so they can be executed in parallel by the platform

which supports the validity of option B as good advice.

3. OutSystems - Training Material

"Architecting Sustainable Applications": The module on "Mobile Architecture" discusses designing a resilient and performant local storage. It advocates for creating a local data model that is optimized for the specific use cases of the mobile app

which often involves denormalization to avoid complex joins and dependencies

thereby improving data access and synchronization performance. This supports the reasoning against option A and for option C.