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https://issues.apache.org/jira/browse/LUCENE-4795?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13585903#comment-13585903
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Michael McCandless commented on LUCENE-4795:
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bq. If you want to integrate that with the current classes, all you need to do
is to implement a partial TaxonomyReader, which resolves ordinals to CPs using
the global ord map? Or actually make that TR the entity that's responsible to
manage to global ordinal map, so that TR.doOpenIfChanged opens the new segments
and updates the global map?
That sounds great!
bq. Since this taxonomy, at least currently, doesn't support hierarchical
facets, you'll need to hack something as a ParallelTaxoArray, but that should
be easy .. I think.
OK.
I think it could be hierarchical w/o so much work, ie on reopen as it
walks the terms it should be able to easily build up the parent/child
arrays since the terms are in sorted order. Hmm, except, with SSDV
you cannot have a term/ord that had no docs indexed. So the
"ancestor" ords would not exist... hmm. Better start
non-hierarchical.
I guess if we are non-hierarchical then we don't really need to
integrate at indexing time? Ie, app can just add the facet values
using SortedSetDVF.
bq. Is the only benefit in this approach that you don't need to manage a
sidecar taxonomy index?
I think so?
> 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, 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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