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Titan benchmarks edit
Made on einsteinium with external cassandra cluster.
Shorter lookups edit
These are short lookups that must be fast.
Checking random element without fetching property edit
w.measure(10000) { def a = g.V('wikibaseId','Q'+(random.nextInt(10000000) as String)).hasNext(); }
[18816, 13342, 15188, 12626, 12289]
Average: 14452.2
Time: 1.44522 ms
Checking random element edit
w.benchmark { 10000.times { def a = g.V('wikibaseId','Q'+(random.nextInt(10000000) as String)).labelEn.hasNext(); } }
[39330, 28555, 30037, 27755, 35049]
Average: 32145.2
Time: 3.21452 ms
Checking fixed node edit
This mostly measured cache performance.
w.measure(10000) { a = g.V('wikibaseId', 'Q30').labelEn.hasNext() }
[10889, 9779, 8969, 8930, 9467]
Average: 9606.8
Time: 0.9ms
Checking supernode edit
This mostly measured cache performance, but for supernode that has tons of incoming edges.
w.measure(10000) { def a = g.V('wikibaseId', 'Q5').labelEn.next(); }
[9611, 8339, 8174, 8360, 8815]
Average: 8659.8
Time: 0.8ms
Checking supernode out - first human edit
Navigating "wide" link out of supernode.
w.measure(100) { def a = g.V('wikibaseId', 'Q5').in("P31")[0].next(); }
[8689, 7015, 7194, 8082, 8515]
Average: 7899
Time: 0.7899 ms
Random human edit
This may stretch the cache a little more, but still be cacheable.
w.measure(10000) { def a = g.V('wikibaseId', 'Q5').in("P31")[random.nextInt(10000)].next(); }
[21395, 21192, 21288, 20017, 21699]
Average: 21118.2
Time: 2.11182 ms
Random human with name, bigger spread edit
This is probably outside of current cache size. Also, [] probably does linear scan, so it behaves worse quadratically, as expected.
w.measure(100) { def a = g.V('wikibaseId', 'Q5').in("P31")[random.nextInt(100000)].labelEn.next(); }
[27543, 24389, 24191, 23185, 26852]
Average: 25232
Time: 252.32 ms
Random human with name - cached edit
def a = g.listOf('Q5')[0].next()
Check if random entry is a human - non-cached edit
This is using "out" link to Q5.
w.measure(1000) { def a = g.V('wikibaseId', 'Q'+(random.nextInt(10000000) as String)).out("P31").has('wikibaseId', 'Q5').hasNext(); }
[6509, 3882, 4626, 4165, 3371]
Average: 4510.6
Time: 4.5106 ms
Check if random entry is a human - cached edit
This uses "link" property on the vertex itself. Surprisingly, not much difference!
w.measure(10000) { def a = g.V('wikibaseId', 'Q'+(random.nextInt(10000000) as String)).has('P31link', CONTAINS, 'Q5').hasNext(); }
[54131, 52634, 43485, 41180, 44011]
Average: 47088.2
Time: 4.70882 ms
Check if random entry is human and not disambiguation edit
Simplistic approach - just go by out links w.measure(1000) { def a = g.V('wikibaseId', 'Q'+(random.nextInt(10000000) as String)).as('x').out("P31").has('wikibaseId', 'Q5').back('x').filter{!it.out('P31').has('wikibaseId', 'Q4167410').hasNext()}.hasNext(); } [9069, 7610, 5076, 4825, 6499]
Average: 6615.8
Time: 6.6158 ms
More sophisticated condition handling using link property: w.measure(1000) { def a = g.V('wikibaseId', 'Q'+(random.nextInt(10000000) as String)).filter{'Q5' in it.P31link && !('Q4167410' in it.P31link);}.hasNext(); } [4489, 3696, 3677, 3597, 3480]
Average: 3787.8
Time: 3.7878 ms
Collect 1000 non-empty names edit
Using link property:
w.measure(1000) {t = []; g.V('P31link', 'Q5').labelEn.filter{it != null}[0..1000].aggregate(t).iterate(); assert t.size() == 1001;}
[29682, 29685, 31022, 30879, 28966]
Average: 30046.8
Time: 30.0468 ms
Using "in" edge. Now there's a big difference:
w.measure(100) {t = []; g.V('wikibaseId', 'Q5').in('P31').labelEn.filter{it != null}[0..1000].aggregate(t).iterate(); assert t.size() == 1001;}
[13203, 11387, 11429, 11385, 11359]
Average: 11752.6
Time: 117.526 ms
Find country edit
This would be heavily cached.
w.measure(1000) { def a = g.V('wikibaseId', 'Q1013639').toCountry().labelEn.next(); }
[2905, 2625, 2504, 2358, 2436]
Average: 2565.6
Time: 2.5656 ms
Find country of random neighborhood edit
This one may have less luck with caching.
w.measure(100) { def a = g.listOf('Q123705').shuffle()[0].toCountry().labelEn.hasNext(); }
[17432, 17212, 16752, 16681, 16310]
Average: 16877.4
Time: 168.774 ms
Check if random neighborhood is in Finland? edit
w.measure(100) { g.listOf('Q123705').shuffle()[0].toCountry().has('wikibaseId', 'Q33').hasNext(); }
[17707, 17807, 17310, 17461, 18288]
Average: 17714.6
Time: 177.146 ms
Longer list queries edit
These may generate long lists and are expected to be slower.
List of countries by population edit
The list is small, so most probably it's cacheable.
w.measure(100) { t= []; g.listOf('Q6256').as('c').groupBy{it}{it.claimValues('P1082').preferred().latest()}.cap.scatter.filter{it.value.size()>0}.transform{it.value = it.value.P1082value.collect{it?it as int:0}.max(); it}.order{it.b.value <=> it.a.value}.transform{[it.key.wikibaseId, it.key.labelEn, it.value]}.aggregate(t).iterate(); }
[2885, 2838, 2811, 2803, 2776]
Average: 2822.6
Time: 28.226 ms
List of all occupations edit
Probably caches too.
w.measure(100) { t = []; g.wd('Q28640').treeIn('P279').instances().dedup().aggregate(t).iterate(); assert t.size() == 2777}
[4647, 4530, 4593, 4549, 4479]
Average: 4559.6
Time: 45.596 ms
List of potential nationalities edit
WDQ produces 571815 results.
g.listOf('Q5').as('humans').claimValues('P569').filter{it.P569value != 'somevalue' && it.P569value > Date.parse('yyyy', '1750')} .back('humans').claimVertices('P19').toCountry().as('countries').select(['humans', 'countries']){it.labelEn}{it.labelEn}
List of humans having occupation writer but not author edit
This one has 36K+ entries, takes a lot of time. Maybe there's more optimal way to write the same query.
w.benchmark { g.V.has('P106link', 'Q36180').filter{'Q5' in it.P31link && !('Q482980' in it.P106link)}.dump("authors", "wikibaseId", "labelEn") } w.benchmark { t = []; g.V.has('P106link', 'Q36180').as('w').has('P106link', 'Q482980').aggregate(t).optional('w').except(t).dump("authors", "wikibaseId", "labelEn") }
86.017s
List of humans with no date of death edit
WDQ produces 14431 results.
w.benchmark { g.listOf('Q5').as('humans').claimValues('P569').filter{it.P569value && it.P569value < Date.parse('yyyy', '1880')}.back('humans').filter{!it.out('P570').hasNext()}.dump("undead", "wikibaseId", "labelEn"); }
4763.817 s
too slow, probably needs value index.