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Memory adapter

FdcMemoryDataAdapter stores rows in memory and supports the normal dataset query surface, including filtering, sorting, searching, paging, total counts, aggregates, and selected-key filtering.

final adapter = FdcMemoryDataAdapter(
  rows: const [
    {
      'id': 1,
      'company': 'Cascade Coffee',
      'city': 'Portland',
      'state': 'OR',
    },
    {
      'id': 2,
      'company': 'Lakeview Market',
      'city': 'Chicago',
      'state': 'IL',
    },
  ],
);

final customers = FdcDataSet(
  fields: const [
    FdcIntegerField(name: 'id', isKey: true),
    FdcStringField(name: 'company', size: 120),
    FdcStringField(name: 'city', size: 80),
    FdcStringField(name: 'state', size: 2),
  ],
  adapter: adapter,
);

await customers.open();

Updating the source rows​

The adapter exposes a defensive copy through rows and can replace its complete source with replaceRows():

adapter.replaceRows(updatedRows);
await customers.open();

Replacing adapter rows does not implicitly reopen the dataset. Reopen when the visible dataset should reflect the new source.

Local query behavior​

Dataset filters and sorts are evaluated against the memory rows. Paging still follows the same dataset contract, which makes the memory adapter useful for testing screens before connecting a remote or SQLite backend.

Editing​

The memory adapter is writable. Dataset apply operations update its internal source rows, so it is suitable for complete local CRUD examples and automated tests.

Large local row sets​

For smaller sources, queries run inline. For sufficiently large row sets, the adapter can execute query work in a background isolate while preserving the same public API.