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https://issues.apache.org/jira/browse/LUCENE-7905?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16085139#comment-16085139
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Robert Muir commented on LUCENE-7905:
-------------------------------------

I think its good to pull out OrdinalMap into its own class from MultiDocValues, 
but i think its a little trappy that it then has no warnings on it and just 
some cool sounding javadocs.

MultiDocValues still warns you twice: 
https://github.com/apache/lucene-solr/blob/master/lucene/core/src/java/org/apache/lucene/index/MultiDocValues.java#L40-L46

But I think we should have some kind of doc about the cost of this thing in 
OrdinalMap now that its separated? The "tax" of multiple segments is real here, 
makes it a hotspot just like it is for blocktree term dictionaries at merge.

> Optimizations for OrdinalMap
> ----------------------------
>
>                 Key: LUCENE-7905
>                 URL: https://issues.apache.org/jira/browse/LUCENE-7905
>             Project: Lucene - Core
>          Issue Type: Improvement
>            Reporter: Michael McCandless
>            Assignee: Michael McCandless
>             Fix For: 7.1
>
>         Attachments: LUCENE-7905.patch, LUCENE-7905.patch, 
> LUCENE-7905-specialized.patch
>
>
> {{OrdinalMap}} is a useful class to quickly map per-segment ordinals to 
> global space, but it's fairly costly to build, which must typically be done 
> on every NRT refresh.
> I'm using it quite heavily in two different places, one for 
> {{SortedSetDocValuesFacetCounts}}, and another custom usage, and I found some 
> small optimizations to improve its construction time.
> I switched it to use a simple priority queue to merge the terms instead of 
> the more general {{MultiTermsEnum}}, which does extra work since it must also 
> provide postings, implement seekExact, etc.
> I also pulled {{OrdinalMap}} out into its own oal.index class.
> When testing construction time for my case the patch is ~16% faster (159.9s 
> -> 134.2s) in one case with 91.4 M terms and ~9% faster (115.6s -> 105.7s) in 
> another case with 26.6 M terms.



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