You could create something like a custom_field table. It gets pretty messy once you start to normalize.
So you have your note table with it's common fields.
Now add:
dynamic_note_field
id label
1 publisher
2 color
3 size
dynamic_note_field_data
id dynamic_note_field_id value
1 1 Penguin
2 1 Marvel
3 2 Red
Finally, you can relate instances of your data with the fields they use through
note_dynamic_note_field_data
note_id dynamic_note_field_data_id
1 1
1 3
2 2
So now we've said: note_id 1 has two additional fields. The first one has a value "Penguin" and represents a publisher. The second one has a value of "Red" and represents a color.
So what's the point of normalizing it this far?
- You're not wasting space adding fields to every item (you relate a note with it's additional dynamic field via the m2m table).
- You're not storing redundant labels (you may continue to store redundant data however as the same publisher is likely to appear many times... this aspect is extremely subjective. If you want rich data about your publishers you typically want to take the step of turning them into their own entity rather than an ad-hoc string. Be careful when making this leap because it adds an extra level of hairiness to the db. Evaluate the use case accordingly.
The dynamic_note_field acts as your data definition. If you're interested in answering a question such as "what are the additional fields I've created" this lets you do it easily without searching all of your dynamic_note_field_data. Eventually, you might add extra info to this table such as a type
field. I like to create this separation off the bat, but that might be a violation of the YAGNI principle in your case.
Disadvantages:
It's not too bad to search for all notes that have a publisher, where that publisher is "Penguin".
What's tricky is something like "Find any note with a value of 'Penguin' in any field". You don't know up front which field's your searching. At this point you're better off with a separate index that's generated alongside your normalized db data which acts as the point of truth. Again, the nice thing about normalization is that you maintain the data in a very lossless, non-destructive state.