LARGE questions

Best practices for structuring a large React application include organizing components into feature-based directories, using hooks and context for state management, adopting a modular approach with code splitting, and maintaining a consistent naming convention and folder structure.

Performance optimization in React applications with large-scale data can be achieved using techniques such as virtualization with libraries like react-window, memoization with useMemo and useCallback, and efficient state management to prevent unnecessary re-renders.

Common patterns for state management in large React applications include using context for global state, adopting state management libraries like Redux or Zustand, implementing state normalization, and employing custom hooks to encapsulate state logic and improve modularity.

`ScrollView` renders all of its children at once, making it suitable for a small number of items or when the content is not dynamically changing. On the other hand, `FlatList` is optimized for rendering large lists of data by recycling items that are off-screen, which helps with performance and memory usage.

In PostgreSQL, large objects (LOBs) are handled using the `pg_largeobject` system catalog and associated functions. You can store large objects like files or images using `lo_create()`, `lo_write()`, and `lo_read()` functions. For example, to store a file: `SELECT lo_create(0);` to create a new large object, and then use `lo_write()` to write data. You can retrieve it with `lo_read()` and manage large objects using the `pg_largeobject` catalog.

Use a min-heap of size K to keep track of the K largest elements. For each element, if it is larger than the smallest element in the heap, replace the smallest. For example, in [3, 2, 1, 5, 6, 4], the 2nd largest element is 5.