optimize window function's find_ranges_in_range

[jimexist]

Which issue does this PR close?

Optimize window function's find_ranges_in_range

Based on #564 so review that first
Closes #.

Rationale for this change

benchmark:

Benchmarking window empty over, aggregate functions: Collecting 100 samples in estimated 7.0674 s (200 iterati window empty over, aggregate functions
time: [33.115 ms 33.588 ms 34.129 ms]
change: [-17.056% -15.622% -14.056%] (p = 0.00 < 0.05)
Performance has improved.
Found 5 outliers among 100 measurements (5.00%)
4 (4.00%) high mild
1 (1.00%) high severe
Benchmarking window empty over, built-in functions: Collecting 100 samples in estimated 5.9518 s (200 iteratio window empty over, built-in functions
time: [27.812 ms 28.113 ms 28.429 ms]
change: [-23.131% -21.112% -19.100%] (p = 0.00 < 0.05)
Performance has improved.
Found 3 outliers among 100 measurements (3.00%)
3 (3.00%) high mild
Benchmarking window order by, aggregate functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 20.8s, or reduce sample count to 20.
Benchmarking window order by, aggregate functions: Collecting 100 samples in estimated 20.778 s (100 iteration window order by, aggregate functions
time: [191.15 ms 196.85 ms 202.61 ms]
change: [-13.162% -10.553% -7.7583%] (p = 0.00 < 0.05)
Performance has improved.
Benchmarking window order by, built-in functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 17.9s, or reduce sample count to 20.
Benchmarking window order by, built-in functions: Collecting 100 samples in estimated 17.919 s (100 iterations window order by, built-in functions
time: [192.66 ms 199.27 ms 206.93 ms]
change: [-8.1837% -5.1872% -1.3218%] (p = 0.00 < 0.05)
Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
1 (1.00%) high severe
Benchmarking window partition by, u64_wide, aggregate functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 27.8s, or reduce sample count to 10.
Benchmarking window partition by, u64_wide, aggregate functions: Collecting 100 samples in estimated 27.777 s window partition by, u64_wide, aggregate functions
time: [261.49 ms 265.26 ms 268.94 ms]
change: [-18.841% -17.347% -15.891%] (p = 0.00 < 0.05)
Performance has improved.
Benchmarking window partition by, u64_narrow, aggregate functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 14.1s, or reduce sample count to 30.
Benchmarking window partition by, u64_narrow, aggregate functions: Collecting 100 samples in estimated 14.061 window partition by, u64_narrow, aggregate functions
time: [133.94 ms 135.53 ms 137.16 ms]
change: [-19.379% -17.908% -16.493%] (p = 0.00 < 0.05)
Performance has improved.
Found 2 outliers among 100 measurements (2.00%)
2 (2.00%) high mild
Benchmarking window partition by, u64_wide, built-in functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 22.2s, or reduce sample count to 20.
Benchmarking window partition by, u64_wide, built-in functions: Collecting 100 samples in estimated 22.179 s ( window partition by, u64_wide, built-in functions
time: [213.65 ms 215.64 ms 217.65 ms]
change: [-20.451% -19.390% -18.336%] (p = 0.00 < 0.05)
Performance has improved.
Found 2 outliers among 100 measurements (2.00%)
1 (1.00%) low mild
1 (1.00%) high mild
Benchmarking window partition by, u64_narrow, built-in functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 11.8s, or reduce sample count to 40.
Benchmarking window partition by, u64_narrow, built-in functions: Collecting 100 samples in estimated 11.849 s window partition by, u64_narrow, built-in functions
time: [120.97 ms 122.49 ms 124.03 ms]
change: [-22.048% -20.534% -19.035%] (p = 0.00 < 0.05)
Performance has improved.
Benchmarking window partition and order by, u64_wide, aggregate functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 45.8s, or reduce sample count to 10.
Benchmarking window partition and order by, u64_wide, aggregate functions: Collecting 100 samples in estimated window partition and order by, u64_wide, aggregate functions
time: [434.30 ms 437.73 ms 441.20 ms]
change: [-6.4053% -5.1483% -3.8173%] (p = 0.00 < 0.05)
Performance has improved.
Benchmarking window partition and order by, u64_narrow, aggregate functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 64.5s, or reduce sample count to 10.
Benchmarking window partition and order by, u64_narrow, aggregate functions: Collecting 100 samples in estimat window partition and order by, u64_narrow, aggregate functions
time: [607.27 ms 610.83 ms 614.46 ms]
change: [-5.2088% -4.0570% -2.8806%] (p = 0.00 < 0.05)
Performance has improved.
Benchmarking window partition and order by, u64_wide, built-in functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 39.4s, or reduce sample count to 10.
Benchmarking window partition and order by, u64_wide, built-in functions: Collecting 100 samples in estimated window partition and order by, u64_wide, built-in functions
time: [387.44 ms 390.74 ms 394.56 ms]
change: [-0.4466% +0.8918% +2.3908%] (p = 0.21 > 0.05)
No change in performance detected.
Found 3 outliers among 100 measurements (3.00%)
2 (2.00%) high mild
1 (1.00%) high severe
Benchmarking window partition and order by, u64_narrow, built-in functions: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 38.4s, or reduce sample count to 10.
Benchmarking window partition and order by, u64_narrow, built-in functions: Collecting 100 samples in estimate window partition and order by, u64_narrow, built-in functions
time: [384.46 ms 389.51 ms 395.09 ms]
change: [+2.4390% +4.1822% +5.9735%] (p = 0.00 < 0.05)
Performance has regressed.
Found 6 outliers among 100 measurements (6.00%)
2 (2.00%) high mild
4 (4.00%) high severe

What changes are included in this PR?

Are there any user-facing changes?

[alamb]

Makes sense to me