Dandandan opened a new pull request #8799:
URL: https://github.com/apache/arrow/pull/8799


   Simarly as in https://github.com/apache/arrow/pull/8795 we can optimize take 
for strings for non-null arrays as well.
   
   ```
   Benchmarking take str 512: Collecting 100 samples in estimated 5.0028 s 
(3.0M it                                                                        
        take str 512            time:   [1.6526 us 1.6593 us 1.6663 us]
                           change: [-42.763% -42.500% -42.200%] (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 take str 1024: Collecting 100 samples in estimated 5.0104 s 
(1.6M i                                                                         
       take str 1024           time:   [2.9526 us 2.9550 us 2.9576 us]
                           change: [-39.657% -39.134% -38.514%] (p = 0.00 < 
0.05)
                           Performance has improved.
   Found 13 outliers among 100 measurements (13.00%)
     13 (13.00%) high severe
   
   Benchmarking take str nulls 512: Collecting 100 samples in estimated 5.0001 
s (1                                                                            
    take str nulls 512      time:   [3.2271 us 3.2319 us 3.2371 us]
                           change: [-30.930% -30.716% -30.495%] (p = 0.00 < 
0.05)
                           Performance has improved.
   Found 4 outliers among 100 measurements (4.00%)
     4 (4.00%) high mild
   
   Benchmarking take str nulls 1024: Collecting 100 samples in estimated 5.0320 
s (                                                                             
   take str nulls 1024     time:   [7.6034 us 7.6135 us 7.6240 us]
                           change: [-22.503% -22.027% -21.564%] (p = 0.00 < 
0.05)
                           Performance has improved.
   Found 4 outliers among 100 measurements (4.00%)
     1 (1.00%) low mild
     3 (3.00%) high mild
   ```


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