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https://issues.apache.org/jira/browse/LUCENE-2075?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12780977#action_12780977
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Yonik Seeley commented on LUCENE-2075:
--------------------------------------

bq. I agree the test is synthetic, so the blowup we're seeing is a worse case 
sitatuion, but are you really sure this can never be hit in practice?

I'm personally comfortable that Solr isn't going to hit this for it's uses of 
the cache... it's simply the relative cost of generating a cache entry vs doing 
some cleaning.

bq. But still I'm more comfortable w/ the simplicity of the double-barrel 
approach. In my tests its performance is in the same ballpark as 
ConcurrentLRUCache;

But it wouldn't be the same performance in Lucene - a cache like LinkedHashMap 
would achieve a higher hit rate in real world scenarios.


> Share the Term -> TermInfo cache across threads
> -----------------------------------------------
>
>                 Key: LUCENE-2075
>                 URL: https://issues.apache.org/jira/browse/LUCENE-2075
>             Project: Lucene - Java
>          Issue Type: Improvement
>          Components: Index
>            Reporter: Michael McCandless
>            Priority: Minor
>             Fix For: 3.1
>
>         Attachments: ConcurrentLRUCache.java, LUCENE-2075.patch, 
> LUCENE-2075.patch, LUCENE-2075.patch, LUCENE-2075.patch, LUCENE-2075.patch, 
> LUCENE-2075.patch
>
>
> Right now each thread creates its own (thread private) SimpleLRUCache,
> holding up to 1024 terms.
> This is rather wasteful, since if there are a high number of threads
> that come through Lucene, you're multiplying the RAM usage.  You're
> also cutting way back on likelihood of a cache hit (except the known
> multiple times we lookup a term within-query, which uses one thread).
> In NRT search we open new SegmentReaders (on tiny segments) often
> which each thread must then spend CPU/RAM creating & populating.
> Now that we are on 1.5 we can use java.util.concurrent.*, eg
> ConcurrentHashMap.  One simple approach could be a double-barrel LRU
> cache, using 2 maps (primary, secondary).  You check the cache by
> first checking primary; if that's a miss, you check secondary and if
> you get a hit you promote it to primary.  Once primary is full you
> clear secondary and swap them.
> Or... any other suggested approach?

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