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https://issues.apache.org/jira/browse/SOLR-15008?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17236213#comment-17236213
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Michael Gibney commented on SOLR-15008:
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Thanks for the extra information; I think I'm no longer confused about the
profiling indicating {{OrdinalMap}} as the culprit! The key is that with a 60s
autoSoftCommit interval, and 8(shards)x3(replicas/shard)=24 cores/replicas,
assuming random drift among the actual commit times on each replica, one of the
24 shards will have a "cold" searcher an average every 2.5 seconds. A
distributed request then (by default) randomly picks 8 replicas from among
those 24, but a subsequent request (by default) picks a _different_ random 8
replicas. If even _one_ of those replicas has a cold searcher, the entire
top-level request would have to wait for the {{OrdinalMap}} on that one replica.
There are a couple of approaches that could evaluate/address this hypothesis:
# configure stable replica routing using the {{replica.base}} property of the
[shards.preference
parameter|https://lucene.apache.org/solr/guide/8_7/distributed-requests.html#shards-preference-parameter]
(note: I'd strongly recommend this anyway for most use cases, but only for
v8.5.2+, because of SOLR-14471). This would not solve the problem, but would
decrease the likelihood of picking a "cold" replica.
# if this hypothesis is correct, then {{distrib=false}} queries against a
specific core/replica should exhibit behavior tied more clearly to the 60s
autoSoftCommit interval
# ultimately the fix would be to add a nominal facet on the relevant field(s)
to one of your configured static warming queries, for the sole purpose of
building the {{OrdinalMap}}.
I'm curious to know if this helps. I still think "facet on actual values" could
help this particular use case (low-cardinality domain, high-cardinality field)
-- but I'd expect the underlying issue ({{OrdinalMap}} building) to equally
affect the high-cardinality domain use case, so if both use cases are equally
helped by configuring a warming query, that may ultimately be the way to go.
(If warming queries do indeed help here, the only argument I can see for
pursuing facet-by-value would be if you expect to facet on a field
_exclusively_ for low-cardinality domains, _and_ the field is sufficiently
high-cardinality that either CPU of building {{OrdinalMap}} in a warming query,
or memory of keeping it hanging around on the heap, is deemed prohibitively
expensive).
> 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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