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https://issues.apache.org/jira/browse/SOLR-9142?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15454221#comment-15454221
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David Smiley commented on SOLR-9142:
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bq. Perhaps we could use the "Bits" interface instead when we want/require a
fast random access set.
Do you mean this?: Code that needs a fast set would be changed to work on a
Bits interface, and we'd change HashDocSet to be a hypothetical HashBits and
implement Bits. Meanwhile any DocSet that is already fast for random sets
could be enhanced to either implement Bits or expose the Bits? +1 to that.
bq. I was surprised this adds a method (dvhash).
Even if we had a heuristic to auto-pick this, nonetheless sometimes the user
knows best. Ok; I could imagine the herustic number itself might be tunable to
get the intended effect. So if we can add auto-tuning in before v6.3 then we
don't need a method=dvhash.
bq. Although perhaps convenient for testing things out, it would be tedious in
production since the best method will depend on the domain size, which will
often not be known ahead of time by the user. For the normal "dv" method, we
should definitely make it pick hashing when the domain is much smaller than the
number of unique terms in the field. We already do stuff like this in the DV
faceting to pick whether we accumulate global ords, or accumulate local
(per-seg) ords first and then do a mapping at the end to global ords.
Certainly that would be nice... it's a TODO in the method selection code to
auto-pick dvhash. If it was trivial I would have added it... but the
method-selection code doesn't conveniently have access to the Terms/DocValues
to know the stats, furthermore we might want to try to get stats from Terms
and/or DocValues depending on which is available. There are other TODOs as well
like supporting multi-valued and prefix.
> JSON Facet, add hash table method for terms
> -------------------------------------------
>
> Key: SOLR-9142
> URL: https://issues.apache.org/jira/browse/SOLR-9142
> Project: Solr
> Issue Type: Improvement
> Components: Facet Module
> Reporter: Varun Thacker
> Assignee: David Smiley
> Fix For: 6.3
>
> Attachments: SOLR_9412_FacetFieldProcessorByHashDV.patch,
> SOLR_9412_FacetFieldProcessorByHashDV.patch,
> SOLR_9412_FacetFieldProcessorByHashDV.patch,
> SOLR_9412_FacetFieldProcessorByHashDV.patch,
> SOLR_9412_FacetFieldProcessorByHashDV.patch
>
>
> I indexed a dataset of 2M docs
> {{top_facet_s}} has a cardinality of 1000 which is the top level facet.
> For nested facets it has two fields {{sub_facet_unique_s}} and
> {{sub_facet_unique_td}} which are string and double and have cardinality 2M
> The nested query for the double field returns in the 1s mark always. The
> nested query for the string field takes roughly 10s to execute.
> {code:title=nested string facet|borderStyle=solid}
> q=*:*&rows=0&json.facet=
> {
> "top_facet_s": {
> "type": "terms",
> "limit": -1,
> "field": "top_facet_s",
> "mincount": 1,
> "excludeTags": "ANY",
> "facet": {
> "sub_facet_unique_s": {
> "type": "terms",
> "limit": 1,
> "field": "sub_facet_unique_s",
> "mincount": 1
> }
> }
> }
> }
> {code}
> {code:title=nested double facet|borderStyle=solid}
> q=*:*&rows=0&json.facet=
> {
> "top_facet_s": {
> "type": "terms",
> "limit": -1,
> "field": "top_facet_s",
> "mincount": 1,
> "excludeTags": "ANY",
> "facet": {
> "sub_facet_unique_s": {
> "type": "terms",
> "limit": 1,
> "field": "sub_facet_unique_td",
> "mincount": 1
> }
> }
> }
> }
> {code}
> I tried to dig deeper to understand why are string nested faceting that slow
> compared to numeric field
> Since the top facet has a cardinality of 1000 we have to calculate sub facets
> on each of them. Now the key difference was in the implementation of the two .
> For the string field, In {{FacetField#getFieldCacheCounts}} we call
> {{createCollectAcc}} with nDocs=0 and numSlots=2M . This then initializes an
> array of 2M. So we create a 2M array 1000 times for this one query which from
> what I understand makes this query slow.
> For numeric fields {{FacetFieldProcessorNumeric#calcFacets}} uses a
> CountSlotAcc which doesn't assign a huge array. In this query it calls
> {{createCollectAcc}} with numDocs=2k and numSlots=1024 .
> In string faceting, we create the 2M array because the cardinality is 2M and
> we use the array position as the ordinal and value as the count. If we could
> improve on this it would speed things up significantly? For sub-facets we
> know the maximum cardinality can be at max the top level bucket count.
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