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https://issues.apache.org/jira/browse/SOLR-15008?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17237511#comment-17237511
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Michael Gibney commented on SOLR-15008:
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I think [this
optimization|https://github.com/apache/lucene-solr/blob/9bfaca0606968ed970d9d12d871f977e2655765b/solr/core/src/java/org/apache/solr/search/facet/FacetFieldProcessorByArrayDV.java#L94-L98]
in {{FacetFieldProcessorByArrayDV.collectDocs()}} is the reason you couldn't
do the warming with {{json.facet}} and a query that matches no docs. There's
evidently not an analogous optimization in "legacy facets", which is why that
worked (and I'd guess (\?) that this optimization won't be added to legacy
facet code anytime soon).
In any event, sounds like a good outcome, and I'm happy to have been able to
help (and no worries re: the elephant :)).
> 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, writes_commits.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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