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https://issues.apache.org/jira/browse/SOLR-15008?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17235549#comment-17235549
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Michael Gibney commented on SOLR-15008:
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Interesting; I'm surprised that profiling indicated {{OrdinalMap}} building,
since I'm pretty sure the {{OrdinalMap}} instances (as accessed via
{{FacetFieldProcessorByArrayDV}} are already cached in the way you're
suggesting:
# in
[FacetFieldProcessorByArrayDV.findStartAndEndOrds(...)|https://github.com/apache/lucene-solr/blob/40e2122b5a5b89f446e51692ef0d72e48c7b71e5/solr/core/src/java/org/apache/solr/search/facet/FacetFieldProcessorByArrayDV.java#L60]
# in
[FieldUtil.getSortedSetDocValues(...)|https://github.com/apache/lucene-solr/blob/1d85cd783863f75cea133fb9c452302214165a4d/solr/core/src/java/org/apache/solr/search/facet/FieldUtil.java#L55]
# in
[SlowCompositeReaderWrapper.getSortedSetDocValues(...)|https://github.com/apache/lucene-solr/blob/c02f07f2d5db5c983c2eedf71febf9516189595d/solr/core/src/java/org/apache/solr/index/SlowCompositeReaderWrapper.java#L197-L211]
Do you have more information about the total numbers involved (high-cardinality
field -- specifically how high per core? how many documents overall per core?
how many cores? does the latency manifest even across a single indexSearcher --
i.e., no intervening updates?). A couple of things that might be worth doing in
the meantime, just as a sanity check:
# disable refinement for the facet field ({{"refinement":"none"}}) -- among
other things, this would take the {{filterCache}} out of the equation
# if possible, try optimizing each replica to a single segment, which should
take {{OrdinalMap}} out of the equation (this of course strictly diagnostic,
not a "workaround" suggestion).
{quote}Allow faceting on actual values (a Map) rather than ordinals
{quote}
Interesting -- even if {{OrdinalMap}} is already getting cached (as I think it
is?), this would be one way to avoid the overhead of allocating a
{{CountSlotArrAcc}} backed by an int array of a size matching the field
cardinality (this is why I asked more specifically about the cardinality of the
field involved). I'm not sure how big a problem this is in practice, but I
imagine a value-Map-based faceting implementation would probably perform better
for this type of use case ... not 100% sure though, and not sure how _much_
better ... (I think {{FacetFieldProcessorByHashDV}} was designed to meet this a
similar use case, but it only works for single-valued fields).
> Avoid building OrdinalMap for each facet
> ----------------------------------------
>
> Key: SOLR-15008
> URL: https://issues.apache.org/jira/browse/SOLR-15008
> Project: Solr
> Issue Type: Improvement
> Security Level: Public(Default Security Level. Issues are Public)
> Components: Facet Module
> Affects Versions: 8.7
> Reporter: Radu Gheorghe
> Priority: Major
> Labels: performance
> Attachments: Screenshot 2020-11-19 at 12.01.55.png
>
>
> I'm running against the following scenario:
> * [JSON] faceting on a high cardinality field
> * few matching documents => few unique values
> Yet the query almost always takes a long time. Here's an example taking
> almost 4s for ~300 documents and unique values (edited a bit):
>
> {code:java}
> "QTime":3869,
> "params":{
> "json":"{\"query\": \"*:*\",
> \"filter\": [\"type:test_type\", \"date:[1603670360 TO 1604361599]\",
> \"unique_id:49866\"]
> \"facet\":
> {\"keywords\":{\"type\":\"terms\",\"field\":\"keywords\",\"limit\":20,\"mincount\":20}}}",
> "rows":"0"}},
>
> "response":{"numFound":333,"start":0,"maxScore":1.0,"numFoundExact":true,"docs":[]
> },
> "facets":{
> "count":333,
> "keywords":{
> "buckets":[{
> "val":"value1",
> "count":124},
> ...
> {code}
> I did some [profiling with our Sematext
> Monitoring|https://sematext.com/docs/monitoring/on-demand-profiling/] and it
> points me to OrdinalMap building (see attached screenshot). If I read the
> code right, an OrdinalMap is built with every facet. And it's expensive since
> there are many unique values in the shard (previously, there we more smaller
> shards, making latency better, but this approach doesn't scale for this
> particular use-case).
> If I'm right up to this point, I see a couple of potential improvements,
> [inspired from
> Elasticsearch|#search-aggregations-bucket-terms-aggregation-execution-hint]:
> # *Keep the OrdinalMap cached until the next softCommit*, so that only the
> first query takes the penalty
> # *Allow faceting on actual values (a Map) rather than ordinals*, for
> situations like the one above where we have few matching documents. We could
> potentially auto-detect this scenario (e.g. by configuring a threshold) and
> use a Map when there are few documents
> I'm curious about what you're thinking:
> * would a PR/patch be welcome for any of the two ideas above?
> * do you see better options? am I missing something?
>
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