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From "Adrien Grand (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (LUCENE-4795) Add FacetsCollector based on SortedSetDocValues
Date Mon, 25 Feb 2013 14:20:13 GMT

    [ https://issues.apache.org/jira/browse/LUCENE-4795?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13585878#comment-13585878
] 

Adrien Grand commented on LUCENE-4795:
--------------------------------------

Not having to manage a taxonomy index is very appealing to me!

What about collecting based on segment ords and bulk translating these ords to the global
ords in setNextReader and when the collection ends? This way ordinalMap.get would be called
less often (once per value per segment instead of once per value per doc per segment) and
in a sequential way so I assume it would be faster while remaining easy to implement?
                
> 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
>         Attachments: LUCENE-4795.patch, pleaseBenchmarkMe.patch
>
>
> 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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