I'm using faust to process some data, the example model looks like this: class MyModel(faust.Record): entry_id: uuid.uuid4 metadata: json.loads timestamp: lambda x: pd.Timestamp(x, tz=None) _offset: int # ... some other int, str, bool fields Note I'm using functions as field descriptors to process incoming fields. Now, I need to pre-fetch some rows first from database: # cols is a list of declared 'fields' for a type, cls = MyModel cols = get_class_variables(cls) # this returns a DataFrame with given columns df = client.select( f'SELECT {", ".join(cols)} FROM ... WHERE ...', columns=cols) # here I want values to be list[MyModel] values = [??? for row in df.to_dict(orient='records')] I tried the following for ??? cls(**row) cls.loads(row) cls.from_data(row) none of them seems to work, it just "casts" the dictionary into the type (i.e. metadata is still string, not parsed json). Any idea what function should I use for the dictionary fields to be processed according to the model? I want it to be as generic as possible since I'm using the same code for many models. Continue reading...
