Stateless Row Conversion

[tustvold]

Is your feature request related to a problem or challenge? Please describe what you are trying to do.

Currently row conversion is stateful, relying on a separate RowConverter to maintain this global state.

This has a number of drawbacks:

• Prevents parallelizing row conversion across multiple threads
• Complicates APIs which have to manage mutable access to a RowConverter - https://github.com/apache/arrow-datafusion/blob/cf229b82cb53f1d9f430a2fa4ff5191ba52e5cef/datafusion/core/src/physical_plan/sorts/stream.rs#L38
• State accumulation can be problematic, necessitating workarounds like Implement CardinalityAwareRowConverter while doing streaming merge datafusion#7401
• The dictionary interning logic is very expensive - Make dictionary preservation optional in row encoding #3831

Describe the solution you'd like

I would like to propose removing the dictionary preservation logic, instead always hydrating the dictionaries values when encoding. This in turn would allow simplifying the API to no longer have a notion of a stateful RowConverter.

This may represent a performance regression for dictionaries with small number of values. We should definitely quantify this, but it is my expectation that this will only occur for dictionaries with a very low number of values. It is currently the case that even arrays with low numbers of distinct values may contain non-trivial number of values as a result of the way dictionaries are handled by the various kernels and readers, and so I'm inclined to not weigh this very highly.

Describe alternatives you've considered

Additional context

[alamb]

I personally think this proposal is very compelling because using high cardinality dictionaries has caused us significant pain in IOx due to the memory accumulated in the row converters. We have several non trivial PRs to properly account for and try to avoid using dictionary encoding for these cases -- see apache/datafusion#7130 and apache/datafusion#7401 for example

Also, a stateful row converter has caused other operations like apache/datafusion#7379 from @wiedld become significantly more complicatated as it can't be shared between threads

Thus, in my mind the ideal outcome would be that we can remove the stateful row conversion and minimize the performance penalty for relatively low cardinality dictionaries. I am hopeful that @tustvold 's ideas to make string encoding more efficient (e.g. #4812) could be part of this answer

[alamb]

The one case I can think of where a stafeful row converter is likely to have massive benefit is with low cardinality dictionaries that have very large individual values (e.g. 2MB strings for each entry). I have no idea how common that type of data is in practice.

[tustvold]

label_issue.py automatically added labels {'arrow'} from #4819

[tustvold]

label_issue.py automatically added labels {'arrow-flight'} from #4819