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From "Michael McCandless (JIRA)" <j...@apache.org>
Subject [jira] [Created] (LUCENE-4795) Add FacetsCollector based on SortedSetDocValues
Date Mon, 25 Feb 2013 13:58:46 GMT
Michael McCandless created LUCENE-4795:
------------------------------------------

             Summary: Add FacetsCollector based on SortedSetDocValues
                 Key: LUCENE-4795
                 URL: https://issues.apache.org/jira/browse/LUCENE-4795
             Project: Lucene - Core
          Issue Type: Improvement
          Components: modules/facet
            Reporter: Michael McCandless


Recently (LUCENE-4765) we added multi-valued DocValues field
(SortedSetDocValuesField), and this can be used for faceting in Solr
(SOLR-4490).  I think we should also add support in the facet module?

It'd be an option with different tradeoffs.  Eg, it wouldn't require
the taxonomy index, since the main index handles label/ord resolving.

There are at least two possible approaches:

  * On every reopen, build the seg -> global ord map, and then on
    every collect, get the seg ord, map it to the global ord space,
    and increment counts.  This adds cost during reopen in proportion
    to number of unique terms ...

  * On every collect, increment counts based on the seg ords, and then
    do a "merge" in the end just like distributed faceting does.

The first approach is much easier so I built a quick prototype using
that.  The prototype does the counting, but it does NOT do the top K
facets gathering in the end, and it doesn't "know" parent/child ord
relationships, so there's tons more to do before this is real.  I also
was unsure how to properly integrate it since the existing classes
seem to expect that you use a taxonomy index to resolve ords.

I ran a quick performance test.  base = trunk except I disabled the
"compute top-K" in FacetsAccumulator to make the comparison fair; comp
= using the prototype collector in the patch:

{noformat}
                    Task    QPS base      StdDev    QPS comp      StdDev                Pct
diff
               OrHighLow       18.79      (2.5%)       14.36      (3.3%)  -23.6% ( -28% -
 -18%)
                HighTerm       21.58      (2.4%)       16.53      (3.7%)  -23.4% ( -28% -
 -17%)
               OrHighMed       18.20      (2.5%)       13.99      (3.3%)  -23.2% ( -28% -
 -17%)
                 Prefix3       14.37      (1.5%)       11.62      (3.5%)  -19.1% ( -23% -
 -14%)
                 LowTerm      130.80      (1.6%)      106.95      (2.4%)  -18.2% ( -21% -
 -14%)
              OrHighHigh        9.60      (2.6%)        7.88      (3.5%)  -17.9% ( -23% -
 -12%)
             AndHighHigh       24.61      (0.7%)       20.74      (1.9%)  -15.7% ( -18% -
 -13%)
                  Fuzzy1       49.40      (2.5%)       43.48      (1.9%)  -12.0% ( -15% -
  -7%)
         MedSloppyPhrase       27.06      (1.6%)       23.95      (2.3%)  -11.5% ( -15% -
  -7%)
                 MedTerm       51.43      (2.0%)       46.21      (2.7%)  -10.2% ( -14% -
  -5%)
                  IntNRQ        4.02      (1.6%)        3.63      (4.0%)   -9.7% ( -15% -
  -4%)
                Wildcard       29.14      (1.5%)       26.46      (2.5%)   -9.2% ( -13% -
  -5%)
        HighSloppyPhrase        0.92      (4.5%)        0.87      (5.8%)   -5.4% ( -15% -
   5%)
             MedSpanNear       29.51      (2.5%)       27.94      (2.2%)   -5.3% (  -9% -
   0%)
            HighSpanNear        3.55      (2.4%)        3.38      (2.0%)   -4.9% (  -9% -
   0%)
              AndHighMed      108.34      (0.9%)      104.55      (1.1%)   -3.5% (  -5% -
  -1%)
         LowSloppyPhrase       20.50      (2.0%)       20.09      (4.2%)   -2.0% (  -8% -
   4%)
               LowPhrase       21.60      (6.0%)       21.26      (5.1%)   -1.6% ( -11% -
  10%)
                  Fuzzy2       53.16      (3.9%)       52.40      (2.7%)   -1.4% (  -7% -
   5%)
             LowSpanNear        8.42      (3.2%)        8.45      (3.0%)    0.3% (  -5% -
   6%)
                 Respell       45.17      (4.3%)       45.38      (4.4%)    0.5% (  -7% -
   9%)
               MedPhrase      113.93      (5.8%)      115.02      (4.9%)    1.0% (  -9% -
  12%)
              AndHighLow      596.42      (2.5%)      617.12      (2.8%)    3.5% (  -1% -
   8%)
              HighPhrase       17.30     (10.5%)       18.36      (9.1%)    6.2% ( -12% -
  28%)
{noformat}

I'm impressed that this approach is only ~24% slower in the worst
case!  I think this means it's a good option to make available?  Yes
it has downsides (NRT reopen more costly, small added RAM usage,
slightly slower faceting), but it's also simpler (no taxo index to
manage).


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