vegarsti commented on PR #18500:
URL: https://github.com/apache/datafusion/pull/18500#issuecomment-3506126893

   Updated benchmark results after reworking those cc @Jefffrey Baseline is 
main implementation of reverse but new benchmark. I didn't end up using rng but 
just setting variable array sizes and nullability. I reckon that's fine! 
Reversing the indexes directly for FixedSizeList was ~10% better (from 41e1044) 
than the previous thing I had, so thanks @Jefffrey!
   
   ```
   08:49 ~/dev/datafusion improve-perf-list-reverse # cargo bench --bench 
array_reverse
      Compiling datafusion-functions-nested v50.3.0 
(/Users/vegard/dev/datafusion/datafusion/functions-nested)
       Finished `bench` profile [optimized] target(s) in 42.08s
        Running benches/array_reverse.rs 
(target/release/deps/array_reverse-2c473eed34a53d0a)
   Gnuplot not found, using plotters backend
   Benchmarking array_reverse_list: Warming up for 3.0000 s
   Warning: Unable to complete 100 samples in 5.0s. You may wish to increase 
target time to 6.3s, or reduce sample count to 70.
   array_reverse_list      time:   [62.201 ms 62.551 ms 62.946 ms]
                           change: [−70.137% −69.965% −69.785%] (p = 0.00 < 
0.05)
                           Performance has improved.
   Found 8 outliers among 100 measurements (8.00%)
     5 (5.00%) high mild
     3 (3.00%) high severe
   
   Benchmarking array_reverse_list_view: Warming up for 3.0000 s
   Warning: Unable to complete 100 samples in 5.0s. You may wish to increase 
target time to 6.3s, or reduce sample count to 70.
   array_reverse_list_view time:   [61.649 ms 61.905 ms 62.185 ms]
                           change: [−16.122% −15.623% −15.087%] (p = 0.00 < 
0.05)
                           Performance has improved.
   Found 6 outliers among 100 measurements (6.00%)
     5 (5.00%) high mild
     1 (1.00%) high severe
   
   array_reverse_fixed_size_list
                           time:   [4.7936 ms 4.8292 ms 4.8741 ms]
                           change: [−76.435% −76.196% −75.951%] (p = 0.00 < 
0.05)
                           Performance has improved.
   Found 20 outliers among 100 measurements (20.00%)
     8 (8.00%) low mild
     5 (5.00%) high mild
     7 (7.00%) high severe
   ```


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