msokolov commented on issue #13147:
URL: https://github.com/apache/lucene/issues/13147#issuecomment-1975201779

   I ran luceneutil over wikimediumall. The index size was slightly reduced: 
   
   ```
   65200   ../indices/baseline/facets
   18923720        ../indices/baseline/index
   18988924        ../indices/baseline
   65204   ../indices/candidate/facets
   18774956        ../indices/candidate/index
   18840164        ../indices/candidate
   ```
   in a microbenchmark where I indexed random doc-only postings I saw ~28% 
index size reduction.
   
   query performance does seem to have registered some actual change: 
   
   ```
                               TaskQPS baseline      StdDevQPS 
my_modified_version      StdDev                Pct diff p-value                 
                                        [178/1805]
                       OrHighNotLow      124.19      (6.0%)      111.98      
(6.7%)   -9.8% ( -21% -    3%) 0.000         
                        LowSpanNear        1.50      (1.1%)        1.42      
(1.2%)   -4.8% (  -7% -   -2%) 0.000
                  HighTermTitleSort       86.63      (3.0%)       82.70      
(2.2%)   -4.5% (  -9% -    0%) 0.000
                MedIntervalsOrdered        3.25      (4.3%)        3.11      
(4.3%)   -4.1% ( -12% -    4%) 0.003
                         OrHighHigh       23.47      (6.7%)       22.61      
(3.3%)   -3.7% ( -12% -    6%) 0.029
                LowIntervalsOrdered        4.20      (4.1%)        4.05      
(4.1%)   -3.5% ( -11% -    4%) 0.007                                            
                                    
                        AndHighHigh       25.46      (8.5%)       24.57      
(4.9%)   -3.5% ( -15% -   10%) 0.114  
        BrowseRandomLabelTaxoFacets        2.05     (14.8%)        1.98     
(11.0%)   -3.4% ( -25% -   26%) 0.405                                           
                                     
               HighIntervalsOrdered        2.09      (5.3%)        2.02      
(5.4%)   -3.1% ( -13% -    7%) 0.063                                            
                                    
                       HighSpanNear        4.25      (1.9%)        4.13      
(2.0%)   -2.8% (  -6% -    1%) 0.000
                          OrHighMed       43.34      (3.1%)       42.18      
(2.1%)   -2.7% (  -7% -    2%) 0.001
               BrowseDateTaxoFacets        2.78      (7.6%)        2.70      
(6.6%)   -2.7% ( -15% -   12%) 0.234                                            
                                    
          BrowseDayOfYearTaxoFacets        2.81      (7.2%)        2.74      
(6.2%)   -2.5% ( -14% -   11%) 0.236                                            
                                    
                            Prefix3      126.88      (2.3%)      123.78      
(3.5%)   -2.4% (  -8% -    3%) 0.009                                            
                                    
                        MedSpanNear       11.93      (0.9%)       11.65      
(1.1%)   -2.3% (  -4% -    0%) 0.000                                            
                                    
                       OrHighNotMed      141.45      (5.1%)      138.33      
(7.0%)   -2.2% ( -13% -   10%) 0.254                                            
                                    
                         AndHighMed       36.62      (5.6%)       35.82      
(3.1%)   -2.2% ( -10% -    6%) 0.124                                            
                                    
                          MedPhrase       67.69      (2.9%)       66.22      
(2.6%)   -2.2% (  -7% -    3%) 0.013                                            
                                    
                   HighSloppyPhrase       10.38      (1.6%)       10.20      
(1.5%)   -1.8% (  -4% -    1%) 0.000                                            
                                    
                             IntNRQ        8.57     (14.4%)        8.42     
(16.1%)   -1.8% ( -28% -   33%) 0.713
                           HighTerm      271.19      (4.0%)      266.87      
(5.1%)   -1.6% ( -10% -    7%) 0.271
                    MedSloppyPhrase        8.12      (1.9%)        8.00      
(2.5%)   -1.6% (  -5% -    2%) 0.028
                         HighPhrase       39.43      (3.8%)       38.94      
(3.1%)   -1.2% (  -7% -    5%) 0.257
                            MedTerm      235.50      (3.4%)      232.58      
(4.7%)   -1.2% (  -9% -    7%) 0.339
                          LowPhrase       46.81      (2.8%)       46.27      
(2.3%)   -1.2% (  -6% -    4%) 0.157
                      OrHighNotHigh      147.42      (4.7%)      145.78      
(6.2%)   -1.1% ( -11% -   10%) 0.525
                         TermDTSort       88.33      (2.8%)       87.38      
(1.8%)   -1.1% (  -5% -    3%) 0.151
              HighTermDayOfYearSort      152.37      (2.1%)      150.79      
(1.8%)   -1.0% (  -4% -    2%) 0.093
                            LowTerm      254.01      (1.9%)      251.72      
(2.6%)   -0.9% (  -5% -    3%) 0.207
                    LowSloppyPhrase       24.52      (0.9%)       24.32      
(1.4%)   -0.8% (  -3% -    1%) 0.029
                      OrNotHighHigh      199.37      (3.8%)      197.74      
(4.9%)   -0.8% (  -9% -    8%) 0.557
                  HighTermMonthSort     1581.75      (2.6%)     1569.14      
(2.1%)   -0.8% (  -5% -    4%) 0.292
                       OrNotHighMed      134.43      (2.7%)      133.51      
(3.3%)   -0.7% (  -6% -    5%) 0.471
                          OrHighLow      279.41      (2.1%)      277.84      
(2.2%)   -0.6% (  -4% -    3%) 0.412
                             Fuzzy1       64.73      (1.5%)       64.48      
(0.7%)   -0.4% (  -2% -    1%) 0.302
             OrHighMedDayTaxoFacets        3.84      (6.3%)        3.83      
(5.4%)   -0.4% ( -11% -   12%) 0.845
            AndHighMedDayTaxoFacets       31.84      (1.2%)       31.74      
(1.5%)   -0.3% (  -2% -    2%) 0.444
                             Fuzzy2       36.90      (1.3%)       36.80      
(0.8%)   -0.3% (  -2% -    1%) 0.383
        BrowseRandomLabelSSDVFacets        1.57      (5.5%)        1.57      
(3.8%)   -0.2% (  -9% -    9%) 0.906
                           PKLookup      140.43      (1.7%)      140.30      
(2.1%)   -0.1% (  -3% -    3%) 0.876
                         AndHighLow      279.44      (2.2%)      279.34      
(2.3%)   -0.0% (  -4% -    4%) 0.958
                       OrNotHighLow      345.34      (1.7%)      345.21      
(1.9%)   -0.0% (  -3% -    3%) 0.948
                            Respell       33.36      (1.5%)       33.38      
(1.3%)    0.1% (  -2% -    2%) 0.881
               MedTermDayTaxoFacets       10.12      (2.4%)       10.13      
(2.4%)    0.1% (  -4% -    4%) 0.912
          BrowseDayOfYearSSDVFacets        2.32      (5.4%)        2.33      
(3.3%)    0.1% (  -8% -    9%) 0.953
               HighTermTitleBDVSort        4.74      (3.3%)        4.74      
(4.0%)    0.1% (  -6% -    7%) 0.902                                            
                                    
                           Wildcard      136.61      (2.5%)      136.82      
(2.2%)    0.2% (  -4% -    4%) 0.831  
               BrowseDateSSDVFacets        0.68     (13.1%)        0.68     
(13.0%)    0.4% ( -22% -   30%) 0.928                                           
                                     
              BrowseMonthTaxoFacets        2.84      (3.8%)        2.87      
(1.3%)    1.1% (  -3% -    6%) 0.207                                            
                                    
              BrowseMonthSSDVFacets        2.38      (5.1%)        2.41      
(4.0%)    1.3% (  -7% -   11%) 0.362
           AndHighHighDayTaxoFacets        3.23      (3.6%)        3.34      
(2.9%)    3.5% (  -2% -   10%) 0.001
   ```
   
   so this looks positive. I can try tuning the decision parameter controlling 
which encoding to use to see what impact that may have. I guess what I wonder 
is whether the added complexity is worth chasing this, but I'm pretty 
encouraged that the overhead of the conditionals isn't overwhelming the 
"within-block skipping" this affords.


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