The parquet reader currently supports project pushdown, for limiting the number of columns read, however it does not use filter pushdown to read a subset of the requested columns. This is particularly useful with parquet files that contain statistics, most importantly min and max values on pages. Evaluating predicates against these values could save some major reading and decoding time.
The largest barrier to implementing this is the current design of the reader. Firstly, we currently have two separate parquet readers, one for reading flat files very quickly and another or reading complex data. There are enhancements we can make the the flat reader, to make it support nested data in a much more efficient manner. However the speed of the flat file reader currently comes from being able to make vectorized copies out the the parquet file. This design is somewhat at odds with filter pushdown, as we will only can make useful vectorized copies if the filter matches a large run of values within the file. This might not be too rare a case, assuming files are often somewhat sorted on a primary field like date or a numeric key, and these are often fields used to limit the query to a subset of the data. However for cases where we are filter out a few records here and there, we should just make individual copies.
We need to do more design work on the best way to balance performance with these use cases in mind.