### Executive Summary
The structure of element metadata is currently static, meaning only
predefined fields can appear in the metadata. We would like the
flexibility for end-users, at their own discretion, to define and use
additional metadata fields that make sense for their particular
use-case.
### Concepts
A key concept for dynamic metadata is _known field_. A known-field is
one of those explicitly defined on `ElementMetadata`. Each of these has
a type and can be specified when _constructing_ a new `ElementMetadata`
instance. This is in contrast to an _end-user defined_ (or _ad-hoc_)
metadata field, one not known at "compile" time and added at the
discretion of an end-user to suit the purposes of their application.
An ad-hoc field can only be added by _assignment_ on an already
constructed instance.
### End-user ad-hoc metadata field behaviors
An ad-hoc field can be added to an `ElementMetadata` instance by
assignment:
```python
>>> metadata = ElementMetadata()
>>> metadata.coefficient = 0.536
```
A field added in this way can be accessed by name:
```python
>>> metadata.coefficient
0.536
```
and that field will appear in the JSON/dict for that instance:
```python
>>> metadata = ElementMetadata()
>>> metadata.coefficient = 0.536
>>> metadata.to_dict()
{"coefficient": 0.536}
```
However, accessing a "user-defined" value that has _not_ been assigned
on that instance raises `AttributeError`:
```python
>>> metadata.coeffcient # -- misspelled "coefficient" --
AttributeError: 'ElementMetadata' object has no attribute 'coeffcient'
```
This makes "tagging" a metadata item with a value very convenient, but
entails the proviso that if an end-user wants to add a metadata field to
_some_ elements and not others (sparse population), AND they want to
access that field by name on ANY element and receive `None` where it has
not been assigned, they will need to use an expression like this:
```python
coefficient = metadata.coefficient if hasattr(metadata, "coefficient") else None
```
### Implementation Notes
- **ad-hoc metadata fields** are discarded during consolidation (for
chunking) because we don't have a consolidation strategy defined for
those. We could consider using a default consolidation strategy like
`FIRST` or possibly allow a user to register a strategy (although that
gets hairy in non-private and multiple-memory-space situations.)
- ad-hoc metadata fields **cannot start with an underscore**.
- We have no way to distinguish an ad-hoc field from any "noise" fields
that might appear in a JSON/dict loaded using `.from_dict()`, so unlike
the original (which only loaded known-fields), we'll rehydrate anything
that we find there.
- No real type-safety is possible on ad-hoc fields but the type-checker
does not complain because the type of all ad-hoc fields is `Any` (which
is the best available behavior in my view).
- We may want to consider whether end-users should be able to add ad-hoc
fields to "sub" metadata objects too, like `DataSourceMetadata` and
conceivably `CoordinatesMetadata` (although I'm not immediately seeing a
use-case for the second one).
Each partitioner has a test like `test_partition_x_with_json()`. What
these do is serialize the elements produced by the partitioner to JSON,
then read them back in from JSON and compare the before and after
elements.
Because our element equality (`Element.__eq__()`) is shallow, this
doesn't tell us a lot, but if we take it one more step, like
`List[Element] -> JSON -> List[Element] -> JSON` and then compare the
JSON, it gives us some confidence that the serialized elements can be
"re-hydrated" without losing any information.
This actually showed up a few problems, all in the
serialization/deserialization (serde) code that all elements share.
