Hey, David, I´ve been reading the thread and I think that is one of the most educative mail-threads I´ve read in Solr mailing list. Just for curiosity: internally for Solr, is it the same a query like "field:*" and "field:[* TO *]"? I think that it´s expected to receive the same number of numFound documents, but I would like to know the internal behavior of Solr.
Best regards, - Luis Cappa 2013/7/30 Smiley, David W. <dsmi...@mitre.org> > Steve, > The FieldCache and DocValues are irrelevant to this problem. Solr's > FilterCache is, and Lucene has no counterpart. Perhaps it would be cool > if Solr could look for expensive field:* usages when parsing its queries > and re-write them to use the FilterCache. That's quite doable, I think. > I just created an issue for it: > https://issues.apache.org/jira/browse/SOLR-5093 but don't expect me to > work on it anytime soon ;-) > > > ~ David > > On 7/30/13 2:02 PM, "Steven Bower" <sbo...@alcyon.net> wrote: > > >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.JtsSpatialConte > >>>>>>xtFa > >>>> >>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-Sear > >>>>ch > >>>> >>-tp4081150p4081309.html > >>>> >> Sent from the Solr - User mailing list archive at Nabble.com. > >>>> >> > >>>> > >>>> > >>> > >> > > -- - Luis Cappa
