Wikibase/Indexing/Benchmarks

Titan benchmarks
Made on einsteinium with external cassandra cluster.

Shorter lookups
These are short lookups that must be fast.

Checking random element without fetching property
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
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
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
This mostly measured cache performance, but for supernode that has tons of incoming edges. w.benchmark { 10000.times { def a = g.V('wikibaseId', 'Q5').labelEn.next; } } [9611, 8339, 8174, 8360, 8815]

Average: 8659.8

Time: 0.8ms

Checking supernode out - first human
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
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
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
def a = g.listOf('Q5')[0].next

Check if random entry is a human - non-cached
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
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
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
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
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
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?
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
These may generate long lists and are expected to be slower.

List of countries by population
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
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
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
This one has 36K+ entries, takes a lot of time. Maybe there's more optimal way to write the same query. w.benchmark { g.wd('Q36180').in('P106').has('P31link', CONTAINS, 'Q5').filter{!it.out('P106').has('wikibaseId', 'Q482980').hasNext}.dump("wauthors", "wikibaseId", "labelEn") } 127.818s

List of humans with no date of death
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.