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https://issues.apache.org/jira/browse/LUCENE-4123?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13404747#comment-13404747
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Uwe Schindler commented on LUCENE-4123:
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bq. Are we sure the catch + rethrow adds no cost?
Yes! It is definitely also less work for hotspot than asserts :-)
In general, throwing exceptions instead of if statements is used because of
this. The exception matrix is in the metadata of a method and just defines the
goto statements in the exceptional case. If you dont catch exception, this
matrix only contains the bubble-up entry, otherwise the jvm goes to our
bytecode that simply rethrows. this rethrowing is seldom, so overhead
bywrapping the inner exception is neglectible.
Exceptions for array indexes are implemented by traps on most processors, so in
the exceptional case (AIOOBE) is not happening it does not exist.
bq. I won't have time for this any time soon so if you want to work on it Uwe
feel free!
I have some time. The RAMDir issue is also open (it should also use index
exceptions instead of if statements), so can look into it next week.
> Add CachingRAMDirectory
> -----------------------
>
> Key: LUCENE-4123
> URL: https://issues.apache.org/jira/browse/LUCENE-4123
> Project: Lucene - Java
> Issue Type: Bug
> Components: core/store
> Reporter: Michael McCandless
> Assignee: Michael McCandless
> Attachments: LUCENE-4123.patch
>
>
> The directory is very simple and useful if you have an index that you
> know fully fits into available RAM. You could also use FileSwitchDir if
> you want to leave some files (eg stored fields or term vectors) on disk.
> It wraps any other Directory and delegates all writing (IndexOutput) to
> it, but for reading (IndexInput), it allocates a single byte[] and fully
> reads the file in and then serves requests off that single byte[]. It's
> more GC friendly than RAMDir since it only allocates a single array per
> file.
> It has a few nocommits still, but all tests pass if I wrap the delegate
> inside MockDirectoryWrapper using this.
> I tested with 1M Wikipedia english index (would like to test w/ 10M docs
> but I don't have enough RAM...); it seems to give a nice speedup:
> {noformat}
> Task QPS base StdDev base QPS cachedStdDev cached
> Pct diff
> Respell 197.00 7.27 203.19 8.17 -4% -
> 11%
> PKLookup 121.12 2.80 125.46 3.20 -1% -
> 8%
> Fuzzy2 66.62 2.62 69.91 2.85 -3% -
> 13%
> Fuzzy1 206.20 6.47 222.21 6.52 1% -
> 14%
> TermGroup100K 160.14 6.62 175.71 3.79 3% -
> 16%
> Phrase 34.85 0.40 38.75 0.61 8% -
> 14%
> TermBGroup100K 363.75 15.74 406.98 13.23 3% -
> 20%
> SpanNear 53.08 1.11 59.53 2.94 4% -
> 20%
> TermBGroup100K1P 222.53 9.78 252.86 5.96 6% -
> 21%
> SloppyPhrase 70.36 2.05 79.95 4.48 4% -
> 23%
> Wildcard 238.10 4.29 272.78 4.97 10% -
> 18%
> OrHighMed 123.49 4.85 149.32 4.66 12% -
> 29%
> Prefix3 288.46 8.10 350.40 5.38 16% -
> 26%
> OrHighHigh 76.46 3.27 93.13 2.96 13% -
> 31%
> IntNRQ 92.25 2.12 113.47 5.74 14% -
> 32%
> Term 757.12 39.03 958.62 22.68 17% -
> 36%
> AndHighHigh 103.03 4.48 133.89 3.76 21% -
> 39%
> AndHighMed 376.36 16.58 493.99 10.00 23% -
> 40%
> {noformat}
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