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https://issues.apache.org/jira/browse/LUCENE-4702?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17007358#comment-17007358
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Adrien Grand commented on LUCENE-4702:
--------------------------------------
Indeed I had disabled PKLookup for another benchmark and forgot to enable it
again. Let me run the benchmark now. The slowdown is going to depend on the
format of ids... For random binary ids like uuids, I expect suffixes won't be
compressed, so the lookup rate should remain almost the same. For compressible
ids like Flake that likely get compressed with LZ4 there will be a noticeable
slowdown. Ids of the nightly benchmarks are base36 integers, so they should
compress with the new "lowercase ascii" compression that was introduced in this
change, which should give a slowdown, but not as much as with LZ4.
Here are the stats:
{noformat}
index FST:
72 bytes
terms:
6647577 terms
39885462 bytes (6.0 bytes/term)
blocks:
189932 blocks
184655 terms-only blocks
5277 sub-block-only blocks
0 mixed blocks
0 floor blocks
189932 non-floor blocks
0 floor sub-blocks
14059998 term suffix bytes before compression (69.0 suffix-bytes/block)
13110492 compressed term suffix bytes (0.93 compression ratio - compression
count by algorithm: LOWERCASE_ASCII: 189932)
6647577 term stats bytes (35.0 stats-bytes/block)
32616851 other bytes (171.7 other-bytes/block)
by prefix length:
0: 1
2: 4
3: 143
4: 5129
5: 184655
{noformat}
The compression ratio is small because we spend as many bytes on storing suffix
lengths (though most of them are the same! we should fix it) and actual
suffixes. Here are the benchmark results:
{noformat}
TaskQPS baseline StdDev QPS patch StdDev
Pct diff
Respell 188.47 (6.5%) 154.44 (4.8%)
-18.1% ( -27% - -7%)
Wildcard 145.26 (4.7%) 132.99 (3.4%)
-8.4% ( -15% - 0%)
PKLookup 201.32 (3.2%) 187.85 (2.8%)
-6.7% ( -12% - 0%)
Fuzzy2 48.07 (3.6%) 46.37 (2.9%)
-3.5% ( -9% - 3%)
Term 1347.18 (4.5%) 1327.14 (4.3%)
-1.5% ( -9% - 7%)
Prefix3 58.29 (6.3%) 57.53 (6.4%)
-1.3% ( -13% - 12%)
TermBGroup1M 45.57 (6.0%) 45.08 (6.6%)
-1.1% ( -12% - 12%)
TermGroup1M 28.98 (6.6%) 28.79 (7.0%)
-0.7% ( -13% - 13%)
AndMedOrHighHigh 34.19 (3.4%) 33.98 (3.3%)
-0.6% ( -7% - 6%)
OrHighMed 43.14 (3.1%) 42.96 (3.1%)
-0.4% ( -6% - 6%)
Phrase 24.08 (4.3%) 23.99 (4.3%)
-0.4% ( -8% - 8%)
Fuzzy1 44.45 (2.7%) 44.32 (2.4%)
-0.3% ( -5% - 4%)
TermDayOfYearSort 67.17 (6.3%) 66.99 (6.3%)
-0.3% ( -12% - 13%)
AndHighMed 39.44 (4.1%) 39.33 (4.1%)
-0.3% ( -8% - 8%)
AndHighHigh 35.90 (4.3%) 35.82 (4.1%)
-0.2% ( -8% - 8%)
TermGroup10K 45.35 (6.2%) 45.30 (6.5%)
-0.1% ( -12% - 13%)
IntervalsOrdered 0.98 (8.1%) 0.98 (8.0%)
-0.1% ( -14% - 17%)
TermBGroup1M1P 31.02 (3.4%) 31.02 (3.4%)
-0.0% ( -6% - 7%)
IntNRQ 125.94 (7.6%) 125.93 (6.1%)
-0.0% ( -12% - 14%)
AndHighOrMedMed 37.36 (2.1%) 37.37 (1.8%)
0.0% ( -3% - 4%)
TermGroup100 25.90 (2.6%) 25.92 (2.8%)
0.1% ( -5% - 5%)
OrHighHigh 12.01 (2.8%) 12.02 (2.8%)
0.1% ( -5% - 5%)
SloppyPhrase 2.77 (1.3%) 2.78 (1.4%)
0.2% ( -2% - 2%)
TermDTSort 68.26 (4.2%) 68.39 (3.5%)
0.2% ( -7% - 8%)
SpanNear 10.99 (1.3%) 11.02 (1.5%)
0.2% ( -2% - 3%)
TermMonthSort 40.55 (2.4%) 40.69 (1.4%)
0.3% ( -3% - 4%)
TermTitleSort 39.74 (2.1%) 39.92 (1.2%)
0.4% ( -2% - 3%)
{noformat}
> Terms dictionary compression
> ----------------------------
>
> Key: LUCENE-4702
> URL: https://issues.apache.org/jira/browse/LUCENE-4702
> Project: Lucene - Core
> Issue Type: Wish
> Reporter: Adrien Grand
> Assignee: Adrien Grand
> Priority: Trivial
> Attachments: LUCENE-4702.patch, LUCENE-4702.patch
>
> Time Spent: 3h 10m
> Remaining Estimate: 0h
>
> I've done a quick test with the block tree terms dictionary by replacing a
> call to IndexOutput.writeBytes to write suffix bytes with a call to
> LZ4.compressHC to test the peformance hit. Interestingly, search performance
> was very good (see comparison table below) and the tim files were 14% smaller
> (from 150432 bytes overall to 129516).
> {noformat}
> TaskQPS baseline StdDevQPS compressed StdDev
> Pct diff
> Fuzzy1 111.50 (2.0%) 78.78 (1.5%)
> -29.4% ( -32% - -26%)
> Fuzzy2 36.99 (2.7%) 28.59 (1.5%)
> -22.7% ( -26% - -18%)
> Respell 122.86 (2.1%) 103.89 (1.7%)
> -15.4% ( -18% - -11%)
> Wildcard 100.58 (4.3%) 94.42 (3.2%)
> -6.1% ( -13% - 1%)
> Prefix3 124.90 (5.7%) 122.67 (4.7%)
> -1.8% ( -11% - 9%)
> OrHighLow 169.87 (6.8%) 167.77 (8.0%)
> -1.2% ( -15% - 14%)
> LowTerm 1949.85 (4.5%) 1929.02 (3.4%)
> -1.1% ( -8% - 7%)
> AndHighLow 2011.95 (3.5%) 1991.85 (3.3%)
> -1.0% ( -7% - 5%)
> OrHighHigh 155.63 (6.7%) 154.12 (7.9%)
> -1.0% ( -14% - 14%)
> AndHighHigh 341.82 (1.2%) 339.49 (1.7%)
> -0.7% ( -3% - 2%)
> OrHighMed 217.55 (6.3%) 216.16 (7.1%)
> -0.6% ( -13% - 13%)
> IntNRQ 53.10 (10.9%) 52.90 (8.6%)
> -0.4% ( -17% - 21%)
> MedTerm 998.11 (3.8%) 994.82 (5.6%)
> -0.3% ( -9% - 9%)
> MedSpanNear 60.50 (3.7%) 60.36 (4.8%)
> -0.2% ( -8% - 8%)
> HighSpanNear 19.74 (4.5%) 19.72 (5.1%)
> -0.1% ( -9% - 9%)
> LowSpanNear 101.93 (3.2%) 101.82 (4.4%)
> -0.1% ( -7% - 7%)
> AndHighMed 366.18 (1.7%) 366.93 (1.7%)
> 0.2% ( -3% - 3%)
> PKLookup 237.28 (4.0%) 237.96 (4.2%)
> 0.3% ( -7% - 8%)
> MedPhrase 173.17 (4.7%) 174.69 (4.7%)
> 0.9% ( -8% - 10%)
> LowSloppyPhrase 180.91 (2.6%) 182.79 (2.7%)
> 1.0% ( -4% - 6%)
> LowPhrase 374.64 (5.5%) 379.11 (5.8%)
> 1.2% ( -9% - 13%)
> HighTerm 253.14 (7.9%) 256.97 (11.4%)
> 1.5% ( -16% - 22%)
> HighPhrase 19.52 (10.6%) 19.83 (11.0%)
> 1.6% ( -18% - 25%)
> MedSloppyPhrase 141.90 (2.6%) 144.11 (2.5%)
> 1.6% ( -3% - 6%)
> HighSloppyPhrase 25.26 (4.8%) 25.97 (5.0%)
> 2.8% ( -6% - 13%)
> {noformat}
> Only queries which are very terms-dictionary-intensive got a performance hit
> (Fuzzy, Fuzzy2, Respell, Wildcard), other queries including Prefix3 behaved
> (surprisingly) well.
> Do you think of it as something worth exploring?
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