Performance issues querying wide rows

Querying a small subset of rows from a wide table (e.g., a table with 6000 columns) can be quite slow in the following case:

• the table has many rows (most of which will be filtered out)
• the projection includes every column of a wide table (i.e., select *)
• predicate push down is not helping: either matching rows are sprinkled fairly evenly throughout the table, or predicate push down is switched off

Even if the filter involves only a single column and the returned result includes just a few rows, the query can run much longer compared to an equivalent query against a similar table with fewer columns.

According to initial profiling, it appears that most time is spent realizing the entire row in the scan, just so the filter can look at a tiny subset of columns and almost certainly throw the row away. The profiling shows 74% of time is spent in FileSourceScanExec, and that time is spent across numerous writeFields_0_xxx method calls.

If Spark must realize the entire row just to check a tiny subset of columns, this all sounds reasonable. However, I wonder if there is an optimization here where we can avoid realizing the entire row until after the filter has selected the row.