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From Steven Bower <sbo...@alcyon.net>
Subject Re: Performance question on Spatial Search
Date Tue, 30 Jul 2013 18:00:26 GMT
Until I get the data refed I there was another field (a date field) that
was there and not when the geo field was/was not... i tried that field:*
and query times come down to 2.5s .. also just removing that filter brings
the query down to 30ms.. so I'm very hopeful that with just a boolean i'll
be down in that sub 100ms range..

steve


On Tue, Jul 30, 2013 at 12:02 PM, Steven Bower <sbower@alcyon.net> wrote:

> Will give the boolean thing a shot... makes sense...
>
>
> On Tue, Jul 30, 2013 at 11:53 AM, Smiley, David W. <dsmiley@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" <sbower@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) <
>> >DSMILEY@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 &lt;
>> >>
>> >> > erickerickson@
>> >>
>> >> > &gt;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 &lt;
>> >>
>> >> > smb-apache@
>> >>
>> >> > &gt;
>> >> >> 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.
>> >>
>>
>>
>

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