I am curious why the field:* walks the entire terms list.. could this be
discovered from a field cache / docvalues?
steve
On Tue, Jul 30, 2013 at 2:00 PM, Steven Bower <sbo...@alcyon.net> wrote:
> 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 <sbo...@alcyon.net> wrote:
>
>> 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.
>>> >>
>>>
>>>
>>
>
