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https://issues.apache.org/jira/browse/LUCENE-4795?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13602063#comment-13602063
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Shai Erera commented on LUCENE-4795:
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bq. At search time they make two FacetCollectors
They should be making two FacetAccumulators and a delegating one. It's more
efficient than having two collectors which track matching documents per-segment.
bq. The new method uses much less RAM for flat fields
I scanned the issue but did not find the right numbers. Do you have them? How
much more RAM does the taxo consume (with its 3 int[]) on the non-hierarchical
dimensions only, vs the sorted set approach?
{quote}
Also, I don't like the risk that the app will try to add both "styles" of
facet field to a Document and expect it to work ... they will get a
cryptic exception about DocValues type changing for a single field.
{quote}
I think that's fine? It's an exception that the app will hit quite fast
(hopefully during testing). And if you put the sorted set stuff in its own
package, then we should document that this does not work with FacetFields,
unless the categories are put in different CLPs (using PerDimIndexingParams).
IMO, that that you add "sdv" to sorted set field is premature optimization. No
one has used the new method yet, let alone tried to use both of them. Why not
do it when it will really hit someone?
> 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
> Assignee: Michael McCandless
> Attachments: LUCENE-4795.patch, LUCENE-4795.patch, LUCENE-4795.patch,
> 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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