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https://issues.apache.org/jira/browse/LUCENE-4795?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13600355#comment-13600355
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Michael McCandless commented on LUCENE-4795:
--------------------------------------------
{quote}
So rather perhaps we should:
* Add a FacetField (extends SSDVF) which takes a CP (potentially
FacetIndexingParams as well).
* It will call super(CLP.DEFAULT_FIELD, new BytesRef(cp.toString())) (we can
optimize that later, e.g. have CP expose a BytesRef API too if we want).
* Potentially, allow (or not) to define the field type.
{quote}
I agree it's awkward now.
But ... FacetField makes me nervous, just because it's too close to
FacetFields and users may think they can mix & match the two
approaches. It's trappy ... maybe SortedSetDocValuesFacetField
instead?
But you'd need to provide it with this separator... hmm, or maybe we
can use the same sep as FIP.
Separately, I wonder whether facet module should escape the delimiter
when it appears in a cat path label, in general (and, here)? This way
the app does not have to ensure it never appears in any label (which I
think is tricky for some apps to do, eg a search server like
ElasticSearch/Solr can't do this).
bq. Any reason why you don't get a hold of the returned FRN?
I wanted to keep it simple for starters ... but I'll fix to reuse the
rejected entry.
> 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, 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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