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Posted to dev@lucene.apache.org by "Robert Muir (JIRA)" <ji...@apache.org> on 2015/05/27 14:50:18 UTC

[jira] [Updated] (LUCENE-6504) implement norms with random access API

     [ https://issues.apache.org/jira/browse/LUCENE-6504?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Robert Muir updated LUCENE-6504:
--------------------------------
    Attachment: LUCENE-6504.patch

Here is a patch. We just use these methods to get signed values as a very simplistic compression. 

Generally speaking the performance seems ok, I think its the right tradeoff.

{noformat}
Report after iter 19:
Chart saved to out.png... (wd: /home/rmuir/workspace/util/src/python)
                    Task   QPS trunk      StdDev   QPS patch      StdDev                Pct diff
                 LowTerm      983.09      (3.8%)      903.02      (4.6%)   -8.1% ( -15% -    0%)
            HighSpanNear      158.40      (2.7%)      148.01      (2.0%)   -6.6% ( -11% -   -1%)
            OrNotHighMed      213.20      (3.9%)      199.50      (2.8%)   -6.4% ( -12% -    0%)
            OrNotHighLow     1170.07      (2.7%)     1134.55      (2.4%)   -3.0% (  -7% -    2%)
             AndHighHigh       87.91      (1.9%)       86.21      (1.8%)   -1.9% (  -5% -    1%)
                  IntNRQ        8.64      (5.6%)        8.48      (8.0%)   -1.8% ( -14% -   12%)
              AndHighMed      123.04      (1.8%)      120.85      (1.7%)   -1.8% (  -5% -    1%)
                  Fuzzy2       60.37      (1.3%)       59.35      (1.8%)   -1.7% (  -4% -    1%)
                Wildcard       44.77      (3.2%)       44.06      (4.6%)   -1.6% (  -9% -    6%)
             MedSpanNear      150.07      (3.1%)      148.15      (2.6%)   -1.3% (  -6% -    4%)
             LowSpanNear       30.53      (1.2%)       30.15      (1.5%)   -1.2% (  -3% -    1%)
               LowPhrase       33.89      (2.1%)       33.49      (3.7%)   -1.2% (  -6% -    4%)
                 Prefix3      210.10      (3.8%)      207.61      (5.3%)   -1.2% (  -9% -    8%)
              AndHighLow     1180.40      (2.1%)     1166.82      (2.5%)   -1.2% (  -5% -    3%)
                 Respell       81.67      (1.5%)       81.41      (2.4%)   -0.3% (  -4% -    3%)
                  Fuzzy1       97.84      (1.3%)       97.64      (1.7%)   -0.2% (  -3% -    2%)
         LowSloppyPhrase      120.00      (3.0%)      120.42      (3.0%)    0.4% (  -5% -    6%)
               MedPhrase      263.96      (4.8%)      265.05      (7.1%)    0.4% ( -10% -   12%)
         MedSloppyPhrase       15.97      (4.5%)       16.08      (4.6%)    0.7% (  -8% -   10%)
                 MedTerm      175.47      (4.4%)      177.64      (5.7%)    1.2% (  -8% -   11%)
              HighPhrase       17.80      (6.3%)       18.20      (9.3%)    2.2% ( -12% -   19%)
               OrHighMed       54.02      (6.8%)       56.18      (7.7%)    4.0% (  -9% -   19%)
               OrHighLow       52.35      (6.9%)       54.61      (7.7%)    4.3% (  -9% -   20%)
        HighSloppyPhrase       12.85     (10.3%)       13.41     (11.9%)    4.4% ( -16% -   29%)
              OrHighHigh       25.34      (7.2%)       26.77      (8.3%)    5.6% (  -9% -   22%)
                HighTerm      119.17      (4.7%)      128.45      (7.1%)    7.8% (  -3% -   20%)
            OrHighNotLow      110.06      (6.4%)      119.06      (7.0%)    8.2% (  -4% -   23%)
            OrHighNotMed       91.03      (6.1%)       98.91      (6.4%)    8.7% (  -3% -   22%)
           OrNotHighHigh       51.53      (5.6%)       56.04      (6.4%)    8.8% (  -3% -   21%)
           OrHighNotHigh       30.45      (5.7%)       33.27      (6.2%)    9.2% (  -2% -   22%)
{noformat}

> implement norms with random access API
> --------------------------------------
>
>                 Key: LUCENE-6504
>                 URL: https://issues.apache.org/jira/browse/LUCENE-6504
>             Project: Lucene - Core
>          Issue Type: Improvement
>            Reporter: Robert Muir
>         Attachments: LUCENE-6504.patch
>
>
> We added this api in LUCENE-5729 but we never explored implementing norms with it. These are generally the largest consumer of heap memory and often a real hassle for users.



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