I came across this paper making a case for indices that use machine learning to optimise search.
The gist seems to be to use a linear regression model or feed a tensor flow model when a more complicated distribution is needed for the data and allow SIMD instructions working on top of GPUs / TPUs to speed up lookups. The speedup observed is anywhere from 40-60%.
That result looks impressive but I don't have enough context on say rebuilding a neural net on every DML operation. The equivalent operation that I can relate to on PG would be to rebalance the B-tree for DML operations.
In your opinion, would a ML model work for a table whose operations are both write and read heavy? I'd love to hear your thoughts on the paper.
Thanks for reading
- Deepak
