7. Branch mispredictions

Time to go back to the past. In Section 2, I told you that the implementation we had of abs_i64 wasn't the most efficient one you could possibly write. Time to see how to improve it!

Which algorithm do you think would win?

1. for each row:
   • check if it's null or not
   • if it's not null, calculate its absolute value
2. for each row:
   • calculate its absolute value, even if we don't need it because it's a null row

If you've not come across the concept of branch mispredictions before, then the answer may surprise you, because the second one is faster here. This is because .abs is a very fast operation, and wasting time checking whether each element is null or not actually slows us down!

Here's how you can make abs_i64 faster:

#[polars_expr(output_type=Int64)]
fn abs_i64(inputs: &[Series]) -> PolarsResult<Series> {
    let s = &inputs[0];
    let ca = s.i64()?;
    let out = ca.apply_values(|x| x.abs());
    Ok(out.into_series())
}

For operations more complex than .abs, it may be that computing the operation for only the non-null values is cheaper. In general, you should measure, not guess. If you're just starting out with plugins and only need to beat .map_elements, then either of these solutions will blow it out of the water.

Practice!

Can you go back and make a faster version of sum_i64?