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https://issues.apache.org/jira/browse/LUCENE-4123?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13291884#comment-13291884
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Michael McCandless commented on LUCENE-4123:
--------------------------------------------
Results for 5M doc index:
{noformat}
Task QPS base StdDev base QPS cachedStdDev cached Pct
diff
Respell 104.06 7.63 108.59 7.55 -9% -
20%
TermGroup1M 57.94 1.59 60.70 0.30 1% -
8%
TermBGroup1M 103.28 2.54 108.51 2.54 0% -
10%
Fuzzy2 43.07 2.96 45.32 3.06 -8% -
20%
Fuzzy1 72.64 4.73 76.92 4.38 -6% -
19%
TermBGroup1M1P 90.14 3.03 95.95 3.81 -1% -
14%
IntNRQ 16.01 0.95 17.17 0.33 0% -
16%
PKLookup 86.21 2.51 92.55 2.59 1% -
13%
Wildcard 65.51 3.13 71.00 1.45 1% -
16%
OrHighMed 21.64 1.83 23.56 1.24 -4% -
25%
Prefix3 105.33 4.94 114.75 2.46 1% -
16%
OrHighHigh 17.39 1.45 18.97 0.95 -4% -
24%
AndHighHigh 30.05 1.14 33.42 0.88 4% -
18%
Term 243.13 9.03 273.92 8.26 5% -
20%
SloppyPhrase 15.80 0.28 17.84 0.78 6% -
19%
SpanNear 10.52 0.14 11.97 0.25 9% -
17%
AndHighMed 117.60 3.54 135.91 2.49 10% -
21%
Phrase 20.15 0.78 24.22 0.26 14% -
26%
{noformat}
> 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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