Re: [hibernate-dev] Coordinates storage in Lucene index for spatial functionality

On a side note, what do you need System.exit(0); for ? You should close the SessionFactory.

Cheers, Sanne On 15 May 2012 14:04, Nicolas Helleringer <nicolas.helleringer(a)gmail.com&gt; wrote: > I did the seed on the random generator. > > Here are some results: > > Degrees 2K calls > Mean time with Grid : 4.769457488717949 ms. Average number of docs fetched > : 2524.982564102564 > Mean time with Grid + Distance filter : 6.501712946153845 ms. Average number > of docs fetched : 426.1876923076923 > Mean time with DoubleRange : 14.336663392307692 ms. Average number of docs > fetched : 543.6035897435897 > Mean time with DoubleRange + Distance filter : 19.7123163574359 ms. Average > number of docs fetched : 426.1876923076923 > > Radians 2K calls > Mean time with Grid : 4.430686068205128 ms. Average number of docs fetched > : 2524.982564102564 > Mean time with Grid + Distance filter : 6.717519717948718 ms. Average number > of docs fetched : 426.1876923076923 > Mean time with DoubleRange : 14.35186034 ms. Average number of docs fetched > : 543.6035897435897 > Mean time with DoubleRange + Distance filter : 20.073972284102563 ms. > Average number of docs fetched : 426.1876923076923 > > Radians 50k calls > Mean time with Grid : 4.440979528643216 ms. Average number of docs fetched > : 2459.169386934673 > Mean time with Grid + Distance filter : 6.722681398331658 ms. Average number > of docs fetched : 416.2335879396985 > Mean time with DoubleRange : 14.532376860201005 ms. Average number of docs > fetched : 530.2923618090452 > Mean time with DoubleRange + Distance filter : 20.21980649284422 ms. Average > number of docs fetched : 416.2335879396985 > > On the random part you can see by looking at the average umber of docs on > the 2k calls that the seed did its works, the requests are the same. > > As you can see there is not such a difference between 2k and 50k calls runs. > > What I have investigated too is the overhead of the distance filter over the > double range approach. I do fear that the wrapping > of the lat,long range query in a QueryWrapperFilter is costly but i cannnot > prove it, yet. > > Back to the main question : does radian storage gives better performance ? I > cannot say with my test env. It seems pretty close to me. > Maybe if someone manages to launch the bench on a different environnement. > > Niko > > PS : both branches are up to date in my github > : https://github.com/nicolashelleringer/hibernate-search/tree/HSEARCH-923 & https://github.com/nicolashelleringer/hibernate-search/tree/HSEARCH-923-R... > > 2012/5/14 Nicolas Helleringer <nicolas.helleringer(a)gmail.com&gt; >>> >>> maybe even simpler set a constant as the seed of your random >>> generator: should provide a reproducible sequence of values. >> >> /facepalm >> I should have guess that :s >> >> Niko >> >>> >>> >> >>> >> On 11 May 2012 08:40, Nicolas Helleringer >>> >> <nicolas.helleringer(a)gmail.com&gt; >>> >> wrote: >>> >> > There, back and again ... >>> >> > >>> >> > After fixing a bug in grid search here are some updated results on >>> >> > 2k >>> >> > calls >>> >> > >>> >> > Degrees : >>> >> > Mean time with Grid : 4.4897266425641025 ms. Average number of docs >>> >> > fetched >>> >> > : 2506.96 >>> >> > Mean time with Grid + Distance filter : 6.4930799487179485 ms. >>> >> > Average >>> >> > number of docs fetched : 425.33435897435896 >>> >> > Mean time with DoubleRange : 14.430638703076923 ms. Average number >>> >> > of >>> >> > docs >>> >> > fetched : 542.0410256410256 >>> >> > Mean time with DoubleRange + Distance filter : 20.483300545128206 >>> >> > ms. >>> >> > Average number of docs fetched : 425.33435897435896 >>> >> > >>> >> > Radians : >>> >> > Mean time with Grid : 5.650845744102564 ms. Average number of docs >>> >> > fetched >>> >> > : 5074.830769230769 >>> >> > Mean time with Grid + Distance filter : 8.627138825128204 ms. >>> >> > Average >>> >> > number >>> >> > of docs fetched : 426.7902564102564 >>> >> > Mean time with DoubleRange : 15.337755502564102 ms. Average number >>> >> > of >>> >> > docs >>> >> > fetched : 1087.705641025641 >>> >> > Mean time with DoubleRange + Distance filter : 20.82852138769231 ms. >>> >> > Average >>> >> > number of docs fetched : 426.7902564102564 >>> >> > >>> >> > Next thing I do not explain yet is the distance filter overhead >>> >> > mismatch >>> >> > : >>> >> > It is less on grid search with more docs to test than on >>> >> > DoubleRange. >>> >> > >>> >> > Niko >>> >> > >>> >> > >>> >> > 2012/5/7 Nicolas Helleringer <nicolas.helleringer(a)gmail.com&gt; >>> >> >> >>> >> >> Here are some results : >>> >> >> >>> >> >> Mean time with Grid : 4.9297471630769225 ms. Average number of docs >>> >> >> fetched : 2416.373846153846 >>> >> >> Mean time with Grid + Distance filter : 6.48634534 ms. Average >>> >> >> number >>> >> >> of >>> >> >> docs fetched : 425.84 >>> >> >> Mean time with DoubleRange : 15.39593650051282 ms. Average number >>> >> >> of >>> >> >> docs >>> >> >> fetched : 542.72 >>> >> >> Mean time with DoubleRange + Distance filter : 21.158394677435897 >>> >> >> ms. >>> >> >> Average number of docs fetched : 425.8779487179487 >>> >> >> >>> >> >> Sounds weird that with distance filter the two results are note the >>> >> >> same. >>> >> >> I shall investigate that. >>> >> >> >>> >> >> Niko >>> >> >> >>> >> >> 2012/5/7 Emmanuel Bernard <emmanuel(a)hibernate.org&gt; >>> >> >>> >>> >> >>> Do you know the average amount of POI that were filtered in memory >>> >> >>> but >>> >> >>> the DistanceFilter during these runs? >>> >> >>> >>> >> >>> Emmanuel >>> >> >>> >>> >> >>> On 7 mai 2012, at 10:31, Nicolas Helleringer wrote: >>> >> >>> >>> >> >>> Hi all, >>> >> >>> >>> >> >>> I have done a radian patch/branch and some benchmarks on geonames >>> >> >>> french >>> >> >>> database. >>> >> >>> >>> >> >>> Benchs are on 2k calls each run. >>> >> >>> >>> >> >>> Radians: >>> >> >>> run 1 >>> >> >>> Mean time with Grid : 4.808043092820513 ms >>> >> >>> Mean time with Grid + Distance filter : 6.571108878461538 ms >>> >> >>> Mean time with DoubleRange : 14.62661525128205 ms >>> >> >>> Mean time with DoubleRange + Distance filter : 20.143597923076925 >>> >> >>> ms >>> >> >>> >>> >> >>> run 2 >>> >> >>> Mean time with Grid : 5.290368523076923 ms >>> >> >>> Mean time with Grid + Distance filter : 6.706567517435897 ms >>> >> >>> Mean time with DoubleRange : 14.878960702564102 ms >>> >> >>> Mean time with DoubleRange + Distance filter : 20.75806591948718 >>> >> >>> ms >>> >> >>> >>> >> >>> Degrees: >>> >> >>> run 1 >>> >> >>> Mean time with Grid : 5.101956610769231 ms >>> >> >>> Mean time with Grid + Distance filter : 6.548685109230769 ms >>> >> >>> Mean time with DoubleRange : 14.767478146153845 ms >>> >> >>> Mean time with DoubleRange + Distance filter : 20.668063972820512 >>> >> >>> ms >>> >> >>> >>> >> >>> run 2 >>> >> >>> Mean time with Grid : 4.683360031282051 ms >>> >> >>> Mean time with Grid + Distance filter : 6.7065247435897435 ms >>> >> >>> Mean time with DoubleRange : 14.617140157948716 ms >>> >> >>> Mean time with DoubleRange + Distance filter : 20.074868595897435 >>> >> >>> ms >>> >> >>> >>> >> >>> The radian branch is here for review >>> >> >>> >>> >> >>> >>> >> >>> : https://github.com/nicolashelleringer/hibernate-search/tree/HSEARCH-923-R... >>> >> >>> >>> >> >>> While moving from degrees to radians I have seen that DSL has >>> >> >>> still >>> >> >>> some >>> >> >>> work to do. >>> >> >>> I shall focus on that now. >>> >> >>> >>> >> >>> Niko >>> >> >>> >>> >> >>> 2012/5/3 Sanne Grinovero <sanne(a)hibernate.org&gt; >>> >> >>>> >>> >> >>>> >>> >> >>>> On May 3, 2012 10:10 AM, "Emmanuel Bernard" >>> >> >>>> <emmanuel(a)hibernate.org&gt; >>> >> >>>> wrote: >>> >> >>>> > >>> >> >>>> > How comes the DistanceFilter has to compute the distance for >>> >> >>>> > the >>> >> >>>> > whole >>> >> >>>> > corpus? >>> >> >>>> >>> >> >>>> You're right in that's not always the case, but it's possible. If >>> >> >>>> there >>> >> >>>> are more filters enabled and they are executed first, our filter >>> >> >>>> will >>> >> >>>> need >>> >> >>>> to do the math only on the matched documents by the previous >>> >> >>>> filters, >>> >> >>>> but if >>> >> >>>> there are no other constraints or filters our DistanceFilter >>> >> >>>> might >>> >> >>>> need to >>> >> >>>> process all documents in all segments. This happens also when a >>> >> >>>> limit >>> >> >>>> is >>> >> >>>> enabled on the collector - although limited to the current index >>> >> >>>> segment - >>> >> >>>> when the filter needs to be cached as it needs to evaluate each >>> >> >>>> document in >>> >> >>>> the segment. >>> >> >>>> >>> >> >>>> In our case this DistanceFilter is only applied after RangeQuery >>> >> >>>> was >>> >> >>>> applied on both longitude and latitude, so I'm not sure if this >>> >> >>>> is a >>> >> >>>> big >>> >> >>>> problem; personally I was just wondering but I'd be fine in >>> >> >>>> keeping >>> >> >>>> this as >>> >> >>>> a possible future improvement - but if we go for a separate >>> >> >>>> issue, >>> >> >>>> let's >>> >> >>>> keep in mind that that the index format would not be backwards >>> >> >>>> compatible. >>> >> >>>> >>> >> >>>> >>> >> >>>> >>> >> >>>> > By the way the actual storage (say via Hibernate ORM, or >>> >> >>>> > Infinispan) >>> >> >>>> > does not need to store in radian, so we don't need to do a >>> >> >>>> > conversion when >>> >> >>>> > reading an entity. >>> >> >>>> >>> >> >>>> Right, another reason to index only in whatever format makes >>> >> >>>> querying >>> >> >>>> more efficient. >>> >> >>>> >>> >> >>>> -- Sanne >>> >> >>>> >>> >> >>>> >>> >> >>>> > >>> >> >>>> > On 3 mai 2012, at 10:45, Sanne Grinovero wrote: >>> >> >>>> > >>> >> >>>> > > The reason for my comment is that the code is doing a >>> >> >>>> > > conversion >>> >> >>>> > > to >>> >> >>>> > > radians in the DistanceFilter, which needs to be extremely >>> >> >>>> > > efficient >>> >> >>>> > > as it's not only applied on the resultset but potentially on >>> >> >>>> > > the >>> >> >>>> > > whole >>> >> >>>> > > corpus of all Documents in the index. >>> >> >>>> > > So even if it's true that conversion would be needed on the >>> >> >>>> > > final >>> >> >>>> > > results, we always expect people to retrieve only a limited >>> >> >>>> > > amount >>> >> >>>> > > of >>> >> >>>> > > entities (like with pagination), while the index might need >>> >> >>>> > > to >>> >> >>>> > > perform >>> >> >>>> > > this computation millions of times per query. >>> >> >>>> > > >>> >> >>>> > > If I look at the complexity of Point.getDistanceTo(double, >>> >> >>>> > > double), >>> >> >>>> > > I >>> >> >>>> > > get a feeling that that method will hardly provide speedy >>> >> >>>> > > queries >>> >> >>>> > > because of the complex computations in it - this is just >>> >> >>>> > > speculation >>> >> >>>> > > at this point of course, to be sure we'd need to compare them >>> >> >>>> > > with a >>> >> >>>> > > large enough dataset, but it seems quite obvious that storing >>> >> >>>> > > normalized radians should be more efficient as it would avoid >>> >> >>>> > > a >>> >> >>>> > > good >>> >> >>>> > > deal of math to be executed on each Document in the index. >>> >> >>>> > > >>> >> >>>> > > Also if we assume people might want to use radians in their >>> >> >>>> > > user >>> >> >>>> > > data >>> >> >>>> > > (I know some who definitely would never touch decimals for >>> >> >>>> > > such a >>> >> >>>> > > use >>> >> >>>> > > case), there would be no need at all to convert the end >>> >> >>>> > > result. >>> >> >>>> > > >>> >> >>>> > > Some more thoughts inline: >>> >> >>>> > > >>> >> >>>> > > On 3 May 2012 09:12, Nicolas Helleringer >>> >> >>>> > > <nicolas.helleringer(a)gmail.com&gt; wrote: >>> >> >>>> > >> Hi all, >>> >> >>>> > >> >>> >> >>>> > >> Sanne and I have been wondering about the way the spatial >>> >> >>>> > >> branch/module/functionality for Hibernate Search shall store >>> >> >>>> > >> its >>> >> >>>> > >> coordinates in the Lucene index. >>> >> >>>> > >> >>> >> >>>> > >> Today it is implemented with decimal degree for : >>> >> >>>> > >> - easy debugging/readability >>> >> >>>> > >> - ease of conversion on storage as we want to accept mainly >>> >> >>>> > >> decimal >>> >> >>>> > >> degree >>> >> >>>> > >> from users data >>> >> >>>> > > >>> >> >>>> > > Valid points, but consider that "storage" is going to be way >>> >> >>>> > > slower >>> >> >>>> > > anyway, and typically you'll process a Document to evaluate >>> >> >>>> > > it >>> >> >>>> > > for a >>> >> >>>> > > hit many many orders of magnitude more frequently than the >>> >> >>>> > > times >>> >> >>>> > > you >>> >> >>>> > > store it. >>> >> >>>> > > >>> >> >>>> > >> >>> >> >>>> > >> Sanne pointed out that when the search is done there is >>> >> >>>> > >> quite a >>> >> >>>> > >> few >>> >> >>>> > >> conversion to radians for distance calculation and suggested >>> >> >>>> > >> that >>> >> >>>> > >> we may >>> >> >>>> > >> store directly coordinates under their radians form. >>> >> >>>> > >> >>> >> >>>> > >> I have tried a patch to implement this and as I was coding >>> >> >>>> > >> it I >>> >> >>>> > >> feel that >>> >> >>>> > >> the code was less readable, in the coordinates normalisation >>> >> >>>> > >> mainly >>> >> >>>> > >> and >>> >> >>>> > >> that there was as many conversion as before. >>> >> >>>> > >> Conversions had moved from search to import / export of >>> >> >>>> > >> coordinates >>> >> >>>> > >> in and >>> >> >>>> > >> out the spatial module scope to user scope. >>> >> >>>> > > >>> >> >>>> > > I'm sure the amount of points in the code in which they are >>> >> >>>> > > converted >>> >> >>>> > > won't change. I'm concerned about the cardinality of the >>> >> >>>> > > collections >>> >> >>>> > > on which it's applied ;) >>> >> >>>> > > "Less readable" isn't nice, but we can work on that I guess? >>> >> >>>> > > >>> >> >>>> > >> >>> >> >>>> > >> What the docs does not tell (yet), is that we are waiting >>> >> >>>> > >> for >>> >> >>>> > >> WGS >>> >> >>>> > >> 84 (this >>> >> >>>> > >> is a coordinate system) decimal degree coordinates input, as >>> >> >>>> > >> these >>> >> >>>> > >> are >>> >> >>>> > >> quite a de facto standard (GPS output this way). >>> >> >>>> > > >>> >> >>>> > > How does it affect this? >>> >> >>>> > > >>> >> >>>> > >> >>> >> >>>> > >> Today this is not the purpose of Hibernate Search spatial >>> >> >>>> > >> initiative to >>> >> >>>> > >> handle projections. There are opensource libs to handle that >>> >> >>>> > >> on >>> >> >>>> > >> user side >>> >> >>>> > >> very well (Proj4j) >>> >> >>>> > >> >>> >> >>>> > >> So. The question is : shall we store as radians or decimal >>> >> >>>> > >> degree ? >>> >> >>>> > >> >>> >> >>>> > >> Niko >>> >> >>>> > >> >>> >> >>>> > >> P.S : Hope it is clear. If not ask for more. >>> >> >>>> > > >>> >> >>>> > > Thanks! >>> >> >>>> > > Sanne >>> >> >>>> > > _______________________________________________ >>> >> >>>> > > hibernate-dev mailing list >>> >> >>>> > > hibernate-dev(a)lists.jboss.org >>> >> >>>> > > https://lists.jboss.org/mailman/listinfo/hibernate-dev >>> >> >>>> > >>> >> >>> >>> >> >>> >>> >> >>> >>> >> >> >>> >> > >>> > >>> > >> >> >