Will give the boolean thing a shot... makes sense...
On Tue, Jul 30, 2013 at 11:53 AM, Smiley, David W. <dsmi...@mitre.org>wrote:
> I see the problem ‹ it's +pp:*. It may look innocent but it's a
> performance killer. What your telling Lucene to do is iterate over
> *every* term in this index to find all documents that have this data.
> Most fields are pretty slow to do that. Lucene/Solr does not have some
> kind of cache for this. Instead, you should index a new boolean field
> indicating wether or not 'pp' is populated and then do a simple true check
> against that field. Another approach you could do right now without
> reindexing is to simplify the last 2 clauses of your 3-clause boolean
> query by using the "IsDisjointTo" predicate. But unfortunately Lucene
> doesn't have a generic filter cache capability and so this predicate has
> no place to cache the whole-world query it does internally (each and every
> time it's used), so it will be slower than the boolean field I suggested
> you add.
>
>
> Nevermind on LatLonType; it doesn't support JTS/Polygons. There is
> something close called SpatialPointVectorFieldType that could be modified
> trivially but it doesn't support it now.
>
> ~ David
>
> On 7/30/13 11:32 AM, "Steven Bower" <sbo...@alcyon.net> wrote:
>
> >#1 Here is my query:
> >
> >sort=vid asc
> >start=0
> >rows=1000
> >defType=edismax
> >q=*:*
> >fq=recordType:"xxx"
> >fq=vt:"X12B" AND
> >fq=(cls:"3" OR cls:"8")
> >fq=dt:[2013-05-08T00:00:00.00Z TO 2013-07-08T00:00:00.00Z]
> >fq=(vid:86XXX73 OR vid:86XXX20 OR vid:89XXX60 OR vid:89XXX72 OR
> >vid:89XXX48
> >OR vid:89XXX31 OR vid:89XXX28 OR vid:89XXX67 OR vid:90XXX76 OR vid:90XXX33
> >OR vid:90XXX47 OR vid:90XXX97 OR vid:90XXX69 OR vid:90XXX31 OR vid:90XXX44
> >OR vid:91XXX82 OR vid:91XXX08 OR vid:91XXX32 OR vid:91XXX13 OR vid:91XXX87
> >OR vid:91XXX82 OR vid:91XXX48 OR vid:91XXX34 OR vid:91XXX31 OR vid:91XXX94
> >OR vid:91XXX29 OR vid:91XXX31 OR vid:91XXX43 OR vid:91XXX55 OR vid:91XXX67
> >OR vid:91XXX15 OR vid:91XXX59 OR vid:92XXX95 OR vid:92XXX24 OR vid:92XXX13
> >OR vid:92XXX07 OR vid:92XXX92 OR vid:92XXX22 OR vid:92XXX25 OR vid:92XXX99
> >OR vid:92XXX53 OR vid:92XXX55 OR vid:92XXX27 OR vid:92XXX65 OR vid:92XXX41
> >OR vid:92XXX89 OR vid:92XXX11 OR vid:93XXX45 OR vid:93XXX05 OR vid:93XXX98
> >OR vid:93XXX70 OR vid:93XXX24 OR vid:93XXX39 OR vid:93XXX69 OR vid:93XXX28
> >OR vid:93XXX79 OR vid:93XXX66 OR vid:94XXX13 OR vid:94XXX16 OR vid:94XXX10
> >OR vid:94XXX37 OR vid:94XXX69 OR vid:94XXX29 OR vid:94XXX70 OR vid:94XXX58
> >OR vid:94XXX08 OR vid:94XXX64 OR vid:94XXX32 OR vid:94XXX44 OR vid:94XXX56
> >OR vid:95XXX59 OR vid:95XXX72 OR vid:95XXX14 OR vid:95XXX08 OR vid:96XXX10
> >OR vid:96XXX54 )
> >fq=gp:"Intersects(POLYGON((47.0 30.0, 47.0 27.0, 52.0 27.0, 52.0 30.0,
> >47.0
> >30.0)))" AND NOT pp:"Intersects(POLYGON((47.0 30.0, 47.0 27.0, 52.0 27.0,
> >52.0 30.0, 47.0 30.0)))" AND +pp:*
> >
> >Basically looking for a set of records by "vid" then if its gp is in one
> >polygon and is pp is not in another (and it has a pp)... essentially
> >looking to see if a record moved between two polygons (gp=current,
> >pp=prev)
> >during a time period.
> >
> >#2 Yes on JTS (unless from my query above I don't) however this is only an
> >initial use case and I suspect we'll need more complex stuff in the future
> >
> >#3 The data is distributed globally but along generally fixed paths and
> >then clustering around certain areas... for example the polygon above has
> >about 11k points (with no date filtering). So basically some areas will be
> >very dense and most areas not, the majority of searches will be around the
> >dense areas
> >
> >#4 Its very likely to be less than 1M results (with filters) .. is there
> >any functinoality loss with LatLonType fields?
> >
> >Thanks,
> >
> >steve
> >
> >
> >On Tue, Jul 30, 2013 at 10:49 AM, David Smiley (@MITRE.org) <
> >dsmi...@mitre.org> wrote:
> >
> >> Steve,
> >> (1) Can you give a specific example of how your are specifying the
> >>spatial
> >> query? I'm looking to ensure you are not using "IsWithin", which is not
> >> meant for point data. If your query shape is a circle or the bounding
> >>box
> >> of a circle, you should use the geofilt query parser, otherwise use the
> >> quirky syntax that allows you to specify the spatial predicate with
> >> "Intersects".
> >> (2) Do you actually need JTS? i.e. are you using Polygons, etc.
> >> (3) How "dense" would you estimate the data is at the 50m resolution
> >>you've
> >> configured the data? If It's very dense then I'll tell you how to raise
> >> the
> >> "prefix grid scan level" to a # closer to max-levels.
> >> (4) Do all of your searches find less than a million points, considering
> >> all
> >> filters? If so then it's worth comparing the results with LatLonType.
> >>
> >> ~ David Smiley
> >>
> >>
> >> Steven Bower wrote
> >> > @Erick it is alot of hw, but basically trying to create a "best case
> >> > scenario" to take HW out of the question. Will try increasing heap
> >>size
> >> > tomorrow.. I haven't seen it get close to the max heap size yet.. but
> >> it's
> >> > worth trying...
> >> >
> >> > Note that these queries look something like:
> >> >
> >> > q=*:*
> >> > fq=[date range]
> >> > fq=geo query
> >> >
> >> > on the fq for the geo query i've added {!cache=false} to prevent it
> >>from
> >> > ending up in the filter cache.. once it's in filter cache queries come
> >> > back
> >> > in 10-20ms. For my use case i need the first unique geo search query
> >>to
> >> > come back in a more reasonable time so I am currently ignoring the
> >>cache.
> >> >
> >> > @Bill will look into that, I'm not certain it will support the
> >>particular
> >> > queries that are being executed but I'll investigate..
> >> >
> >> > steve
> >> >
> >> >
> >> > On Mon, Jul 29, 2013 at 6:25 PM, Erick Erickson <
> >>
> >> > erickerickson@
> >>
> >> > >wrote:
> >> >
> >> >> This is very strange. I'd expect slow queries on
> >> >> the first few queries while these caches were
> >> >> warmed, but after that I'd expect things to
> >> >> be quite fast.
> >> >>
> >> >> For a 12G index and 256G RAM, you have on the
> >> >> surface a LOT of hardware to throw at this problem.
> >> >> You can _try_ giving the JVM, say, 18G but that
> >> >> really shouldn't be a big issue, your index files
> >> >> should be MMaped.
> >> >>
> >> >> Let's try the crude thing first and give the JVM
> >> >> more memory.
> >> >>
> >> >> FWIW
> >> >> Erick
> >> >>
> >> >> On Mon, Jul 29, 2013 at 4:45 PM, Steven Bower <
> >>
> >> > smb-apache@
> >>
> >> > >
> >> >> wrote:
> >> >> > I've been doing some performance analysis of a spacial search use
> >>case
> >> >> I'm
> >> >> > implementing in Solr 4.3.0. Basically I'm seeing search times alot
> >> >> higher
> >> >> > than I'd like them to be and I'm hoping people may have some
> >> >> suggestions
> >> >> > for how to optimize further.
> >> >> >
> >> >> > Here are the specs of what I'm doing now:
> >> >> >
> >> >> > Machine:
> >> >> > - 16 cores @ 2.8ghz
> >> >> > - 256gb RAM
> >> >> > - 1TB (RAID 1+0 on 10 SSD)
> >> >> >
> >> >> > Content:
> >> >> > - 45M docs (not very big only a few fields with no large textual
> >> >> content)
> >> >> > - 1 geo field (using config below)
> >> >> > - index is 12gb
> >> >> > - 1 shard
> >> >> > - Using MMapDirectory
> >> >> >
> >> >> > Field config:
> >> >> >
> >> >> >
> >> > <fieldType name="geo" class="solr.SpatialRecursivePrefixTreeFieldType"
> >> >>
> >> > > distErrPct="0.025" maxDistErr="0.00045"
> >> >> >
> >> >>
> >>
> >>spatialContextFactory="com.spatial4j.core.context.jts.JtsSpatialContextFa
> >>ctory"
> >> >> > units="degrees"/>
> >> >> >
> >> >> >
> >> > <field name="geopoint" indexed="true" multiValued="false"
> >> >>
> >> > > required="false" stored="true" type="geo"/>
> >> >> >
> >> >> >
> >> >> > What I've figured out so far:
> >> >> >
> >> >> > - Most of my time (98%) is being spent in
> >> >> > java.nio.Bits.copyToByteArray(long,Object,long,long) which is being
> >> >> > driven by
> >> >> BlockTreeTermsReader$FieldReader$SegmentTermsEnum$Frame.loadBlock()
> >> >> > which from what I gather is basically reading terms from the .tim
> >>file
> >> >> > in blocks
> >> >> >
> >> >> > - I moved from Java 1.6 to 1.7 based upon what I read here:
> >> >> >
> >> >>
> >>
> http://blog.vlad1.com/2011/10/05/looking-at-java-nio-buffer-performance/
> >> >> > and it definitely had some positive impact (i haven't been able to
> >> >> > measure this independantly yet)
> >> >> >
> >> >> > - I changed maxDistErr from 0.000009 (which is 1m precision per
> >>docs)
> >> >> > to 0.00045 (50m precision) ..
> >> >> >
> >> >> > - It looks to me that the .tim file are being memory mapped fully
> >>(ie
> >> >> > they show up in pmap output) the virtual size of the jvm is ~18gb
> >> >> > (heap is 6gb)
> >> >> >
> >> >> > - I've optimized the index but this doesn't have a dramatic impact
> >>on
> >> >> > performance
> >> >> >
> >> >> > Changing the precision and the JVM upgrade yielded a drop from ~18s
> >> >> > avg query time to ~9s avg query time.. This is fantastic but I
> >>want to
> >> >> > get this down into the 1-2 second range.
> >> >> >
> >> >> > At this point it seems that basically i am bottle-necked on
> >>basically
> >> >> > copying memory out of the mapped .tim file which leads me to think
> >> >> > that the only solution to my problem would be to read less data or
> >> >> > somehow read it more efficiently..
> >> >> >
> >> >> > If anyone has any suggestions of where to go with this I'd love to
> >> know
> >> >> >
> >> >> >
> >> >> > thanks,
> >> >> >
> >> >> > steve
> >> >>
> >>
> >>
> >>
> >>
> >>
> >> -----
> >> Author:
> >> http://www.packtpub.com/apache-solr-3-enterprise-search-server/book
> >> --
> >> View this message in context:
> >>
> >>
> http://lucene.472066.n3.nabble.com/Performance-question-on-Spatial-Search
> >>-tp4081150p4081309.html
> >> Sent from the Solr - User mailing list archive at Nabble.com.
> >>
>
>
