Writing custom queries scorers’ diversity and traps (lucene internals)(2)
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WRITING CUSTOM QUERIES: SCORERS' DIVERSITY AND TRAPS Mikhail Khludnev Principal Engineer, eCommerce Search Team [email protected] http://goo.gl/7LJFi
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Presented by Mikhail Khludnev, Grid Dynamics Lucene has number of built-in queries, but sometimes developer needs to write own queries that might be challenging. We’ll start from the basics: learn how Lucene searches, look into few build-in queries implementations, and learn two basic approaches for query evaluation. Then I share experience which my team got when built one eCommerce Search platform, we’ll look at sample custom query or even a few ones, and talk about potential problems and caveats on that way.
Transcript of Writing custom queries scorers’ diversity and traps (lucene internals)(2)
WRITING CUSTOM QUERIES: SCORERS' DIVERSITY AND TRAPS
Mikhail KhludnevPrincipal Engineer, eCommerce Search [email protected]
http://goo.gl/7LJFi
Custom Queries
Custom Queries
Custom Queries
http://nlp.stanford.edu/IR-book/
Custom Queries
http://nlp.stanford.edu/IR-book/
Custom Queries
Match Spotting
http://nlp.stanford.edu/IR-book/
Custom Queries
Inverted Index
T[0] = "it is what it is"T[1] = "what is it"T[2] = "it is a banana"
"a": {2}"banana": {2}"is": {0, 1, 2}"it": {0, 1, 2}"what": {0, 1}
T[0] = "it is what it is"T[1] = "what is it"T[2] = "it is a banana"
"a": {2}"banana": {2}"is": {0, 1, 2}"it": {0, 1, 2}"what": {0, 1} postings list
What is a Scorer?
"a": {2}"banana": {2}"is": {0, 1, 2}"it": {0, 1, 2}"what": {0, 1}
"a": {2}"banana": {2}"is": {0, 1, 2}"it": {0, 1, 2}"what": {0, 1}
"a": {2}"banana": {2}"is": {0, 1, 2}"it": {0, 1, 2}"what": {0, 1}
while(
(doc = nextDoc())!=NO_MORE_DOCS){
println("found "+ doc +
" with score "+score());
}
Note: Weight is omitted for sake of compactness
Custom Queries
http://nlp.stanford.edu/IR-book/
Doc-at-time search
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana
"is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
"it": {0, 1, 2}
"is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}collect(0)score():2
Collector
"is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
docID×score0×2
"is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}collect(1)score():2
Collector0×2
"is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
Collector0×21×2
"is": {0, 1, 2}
"a": {2}
"banana": {2}
"what": {0, 1}collect(2)score():3
Collector0×21×2
"is": {0, 1, 2}
"a": {2}
"banana": {2}
"what": {0, 1}
Collector2×30×21×2
Term-at-time searchsee Appendix
doc at time term at time
complexity O(p log q + n log k) O(p + n log k)
memory q + k n
q=village operations years disaster visit etc map seventieth peneplains tussock sir memory character campaign author public wonder forker middy vocalize enable race object signal symptom deputy where typhous rectifiable polygamous originally look generation ultimately reasonably ratio numb apposing enroll manhood problem suddenly definitely corp event material affair diploma would dimout speech notion engine artist hotel text field hashed rottener impeding i cricket virtually valley sunday rock come observes gallnuts vibrantly prize involve
q=village operations years disaster visit
q=+village +operations +years +disaster +visit
Conjunction(+, MUST)
"a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}
what AND is AND a AND it
"a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}
"a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}
"a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}
"a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}
"a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}
Collector3 x 4
http://www.flickr.com/photos/fatniu/184615348/
Ω(n q + n log k)
Wrap-up● doc-at-time vs term-at-time
● leapfrog
Custom Queries
http://nlp.stanford.edu/IR-book/
Custom Queries
● HelloWorld
● Deeply Branched vs Flat
● Steadiness Problem
● minShouldMatch Performance Problem
● Filtering Performance Problem
"silver" "jeans" "dress"
silver jeans dress
Note: "foo bar" is not a phrase query, just a string
"silver" "jeans" "dress""silver jeans dress"
silver jeans dress
"silver" "jeans" "dress""silver jeans dress""silver jeans" "dress""silver" "jeans dress"
silver jeans dress
"silver" "jeans" "dress""silver jeans dress""silver jeans" "dress""silver" "jeans dress"
"silver" "dress""silver jeans" "jeans""silver jeans""jeans" "dress"
silver jeans dress
Note: "foo bar" is not a phrase query, just a string
boolean verifyMatch(){ int sumLength=0; for(Scorer child:getChildren()){ if(child.docID()==docID()){ TermQuery tq=child.weight.query; sumLength += tq.term.text.length; } } return sumLength>=expectedLength;}
Deeply Branched vs Flat
(+"silver jeans" +"dress")ORmax
(+"silver jeans dress")ORmax
(+"silver" +((+"jeans" +"dress")
ORmax +"jeans dress"
) )
ORmax is DisjunctionMaxQuery
(+"silver jeans" +"dress")ORmax
(+"silver jeans dress")ORmax
(+"silver" +((+"jeans" +"dress")
ORmax +"jeans dress"
) )
ORmax is DisjunctionMaxQuery
(+"silver jeans" +"dress")ORmax
(+"silver jeans dress")ORmax
(+"silver" +((+"jeans" +"dress")
ORmax +"jeans dress"
) )
ORmax is DisjunctionMaxQuery
("silver jeans" "dress")ORmax
("silver jeans dress")ORmax
("silver" (("jeans" "dress")
ORmax "jeans dress"
) )
ORmax is DisjunctionMaxQuery
B:"silver jeans dress" ORmaxT:"silver jeans dress" ORmaxS:"silver jeans dress"
B:"silver" ORmaxT:"silver" ORmaxS:"silver"
+B:"jeans dress" ORmaxT:"jeans dress" ORmaxS:"jeans dress"
+
ORmax
ORmax
ORmax
B:"silver jeans" ORmaxT:"silver jeans" ORmaxS:"silver jeans"
+B:"dress" ORmaxT:"dress" ORmaxS:"dress"
+
B:"jeans" ORmaxT:"jeans" ORmaxS:"jeans"
+B:"dress" ORmaxT:"dress" ORmaxS:"dress"
+
B - BRANDT - TYPES - STYLE
B:"silver" T:"silver" S:"silver"
B:"jeans" T:"jeans" S:"jeans"
B:"dress" T:"dress" S:"dress"
B:"silver jeans" T:"silver jeans" S:"silver jeans"
B:"silver jeans dress" T:"silver jeans dress"
S:"silver jeans dress"
B:"jeans dress" T:"jeans dress" S:"jeans dress"
Steadiness problemAFAIK 3.x only.
{1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{2,3, 27,31,..}
{..., 30,37,..}
3
3 20
3 30 30
{..., 30, 31,32,..}{..., 20, 27,32,..}
{1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{2,3, 27,31,..}
{..., 30,37,..}
5
7 20
27 30 30
{..., 30, 31,32,..}{..., 20, 27,32,..}
3docID=
3.x
straight jeans
silver jeans
silver jeans straight
jeans
silver
minShouldMatch=2
straight silver jeans
int nextDoc() {while(true) {
while (subScorers[0].docID() == doc) { if (subScorers[0].nextDoc() != NO_DOCS) { heapAdjust(0); } else { .... } } ... if (nrMatchers >= minimumNrMatchers) { break; }
}return doc;
}
org.apache.lucene.search.DisjunctionSumScorer
{1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}
{1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}
{1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}
{1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}
{1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}
Filtering
RANDOM_ACCESS_FILTER_STRATEGY
LEAP_FROG_FILTER_FIRST_STRATEGY
LEAP_FROG_QUERY_FIRST_STRATEGY
QUERY_FIRST_FILTER_STRATEGY
minShouldMatch meets Filters
http://localhost:8983/solr/collection1/select
?q={!cache=false}village AND village operations years disaster visit etc
map seventieth peneplains tussock sir memory character campaign author
public wonder forker middy vocalize enable race object signal symptom
deputy where typhous rectifiable polygamous originally look generation
ultimately reasonably ratio numb apposing enroll manhood problem
suddenly definitely corp event material affair diploma would dimout speech
notion engine artist hotel text field hashed rottener impeding i cricket
virtually valley sunday rock come observes gallnuts vibrantly prize involve
explanation module&
qf=text_all&
defType=edismax&
mm=32&
fq= id:yes_49912894 id:nurse_30134968
CONFERENCE PARTYThe Tipsy Crow: 770 5th AveStarts after Stump The ChumpYour conference badge getsyou in the door
TOMORROW Breakfast starts at 7:30Keynotes start at 8:30
CONTACTMikhail [email protected]
http://goo.gl/7LJFi
Appendixes● Term-at-time search in Lucene/Solr● Derivation of the search complexity● Match Spotting● Drill Sideways Facets
Appendix B
Term-at-time Search in Lucene
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana
Accumulator... 0×1 ... 1×1 ...
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
Accumulator... 0×2 ... 1×2 ... 2×1 ...
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
Accumulator... 0×2 ... 1×2 ... 2×2 ...
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
Accumulator... 0x2 ... 1x2 ... 2x3 ...
Accumulator... 0×2 ... 1×2 ... 2×3 ...
Collector2×30×21×2
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
BooleanScorer2
×1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
Hashtable[2]
org.apache.lucene.search.BooleanScorer
×1 0 1
chunk
x2
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
org.apache.lucene.search.BooleanScorer
x2 0 1
chunk
org.apache.lucene.search
Collector0×21×2×2 ×2
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
org.apache.lucene.search
Collector0×21×2×1
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
org.apache.lucene.search
Collector0×21×2×2
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
org.apache.lucene.search
Collector0×21×2×3
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
org.apache.lucene.search
Collector2×30×21×2
×3
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
new BooleanScorer
new BooleanScorer2
//term-at-time
//doc-at-time
if ( collector.acceptsDocsOutOfOrder() && topScorer &&
required.size() == 0 && minNrShouldMatch == 1) {
else
Linked Open Hash [2K]
×1 ×1 ×5 ×2 ×2
0 1 2 3 4 5 6 7
×3
Collector
DocSetCollector TopDocsCollector
TopFieldCollector
TopScoreDocsCollector
long [952045] = { 0, 0, 0, 0, 2050, 0, 0, 8, 0, 0, 0,... }
int [2079] = {4, 12, 45, 67, 103, 673, 5890, 34103,...}
int [100] = {8947, 7498,1, 230, 2356, 9812, 167,....}
DocSet or DocList?
DocList/TopDocs DocSet
Size
Ordered by
Out-of-order collecting
k(numHits/
rows)
N(maxDocs)
score or field
docID
allows* almost could allow (No)
?×4 6×4
9×5 2×4
2×7 7×9 1×9
http://www.flickr.com/photos/jbagley/4303976811/sizes/o/
class OutOfOrderTopScoreDocCollector
boolean acceptsDocsOutOfOrder(){ return true; } .. void collect(int doc) { float score = scorer.score(); ... if (score == pqTop.score && doc > pqTop.doc) { ...}
Appendix B
Derivation of the Search Complexity
1×97×92×72×59×56×4
...
...≤4......
k
n
http://en.wikipedia.org/wiki/Binary_heap
6×4
log k 9×5 2×4
2×7 7×9 1×9
...
...≤4......
n
q
p
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana
doc at time term at time
complexity O(p + n log k)
memory
q
p
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana
1
1 2
2
doc at time term at time
complexity O(p log q + n log k) O(p + n log k)
memory
Custom Queries
Match Spotting
http://nlp.stanford.edu/IR-book/
Appendix C
BRAND:"silver jeans" TYPE:"dress" STYLE:"white"
BRAND:"alfani" TYPE:"dress" STYLE:"silver","jeans"
BRAND:"chaloree" TYPE:"dress" STYLE:"silver"
BRAND:"style&co" TYPE:"jeans dress" STYLE:"silver"
BRAND:"silver jeans" TYPE:"dress" STYLE:"black"
BRAND:"silver jeans" TYPE:"dress" STYLE:"white"
BRAND:"silver jeans" TYPE:"jacket" STYLE: "black"
BRAND:"angie" TYPE:"dress" STYLE:"silver","jeans"
BRAND:"chaloree" TYPE:"jeans dress" STYLE:"silver"
BRAND:"silver jeans" TYPE:"dress" STYLE:"blue"
BRAND:"dotty" TYPE:"dress" STYLE:"silver","jeans"
BRAND:"chaloree" STYLE:"jeans" "dress"
BRAND:"silver jeans" TYPE:"dress" STYLE:"white"
BRAND:"alfani" TYPE:"dress" STYLE:"silver","jeans"
BRAND:"chaloree" TYPE:"dress" STYLE:"silver"
BRAND:"style&co" TYPE:"jeans dress" STYLE:"silver"
BRAND:"silver jeans" TYPE:"dress" STYLE:"black"
BRAND:"silver jeans" TYPE:"dress" STYLE:"white"
BRAND:"silver jeans" TYPE:"jacket" STYLE: "black"
BRAND:"angie" TYPE:"dress" STYLE:"silver","jeans"
BRAND:"chaloree" TYPE:"jeans dress" STYLE:"silver"
BRAND:"silver jeans" TYPE:"dress" STYLE:"blue"
BRAND:"dotty" TYPE:"dress" STYLE:"silver","jeans"
BRAND:"chaloree" STYLE:"jeans" "dress"
silver jeans dress
BRAND:"silver jeans" TYPE:"dress" STYLE:"white"
BRAND:"alfani" TYPE:"dress" STYLE:"silver","jeans"BRAND:"chaloree" TYPE:"dress" STYLE:"silver"
BRAND:"style&co" TYPE:"jeans dress" STYLE:"silver"BRAND:"silver jeans" TYPE:"dress" STYLE:"black"
BRAND:"silver jeans" TYPE:"dress" STYLE:"white"
BRAND:"silver jeans" TYPE:"jacket" STYLE: "black"
BRAND:"angie" TYPE:"dress" STYLE:"silver","jeans"BRAND:"chaloree" TYPE:"jeans dress" STYLE:"silver"BRAND:"silver jeans" TYPE:"dress" STYLE:"blue"
BRAND:"dotty" TYPE:"dress" STYLE:"silver","jeans"BRAND:"chaloree" STYLE:"jeans" "dress"
BRAND:"silver jeans" TYPE:"dress" TYPE:"dress" STYLE:"silver","jeans"
TYPE:"jeans dress" STYLE:"silver"BRAND:"silver jeans" TYPE:"dress" BRAND:"silver jeans" TYPE:"dress"
TYPE:"dress" STYLE:"silver","jeans"TYPE:"jeans dress" STYLE:"silver"
BRAND:"silver jeans" TYPE:"dress" TYPE:"dress" STYLE:"silver","jeans"
BRAND:"silver jeans" TYPE:"dress" TYPE:"dress" STYLE:"silver","jeans"
TYPE:"jeans dress" STYLE:"silver"BRAND:"silver jeans" TYPE:"dress"
BRAND:"silver jeans" TYPE:"dress"
TYPE:"dress" STYLE:"silver","jeans"TYPE:"jeans dress" STYLE:"silver"
BRAND:"silver jeans" TYPE:"dress" TYPE:"dress" STYLE:"silver","jeans"
BRAND:"silver jeans" TYPE:"dress" (4)TYPE:"dress" STYLE:"silver","jeans"
TYPE:"jeans dress" STYLE:"silver"
TYPE:"dress" STYLE:"silver","jeans"TYPE:"jeans dress" STYLE:"silver"
TYPE:"dress" STYLE:"silver","jeans"
BRAND:"silver jeans" TYPE:"dress" (4)TYPE:"dress" STYLE:"silver","jeans"
TYPE:"jeans dress" STYLE:"silver"
TYPE:"dress" STYLE:"silver","jeans"
TYPE:"jeans dress" STYLE:"silver"
TYPE:"dress" STYLE:"silver","jeans"
BRAND:"silver jeans" TYPE:"dress" (4)TYPE:"dress" STYLE:"silver","jeans" (3)
TYPE:"jeans dress" STYLE:"silver"
TYPE:"jeans dress" STYLE:"silver"
BRAND:"silver jeans" TYPE:"dress" (4)TYPE:"dress" STYLE:"silver","jeans" (3)
TYPE:"jeans dress" STYLE:"silver" (2)
BRAND:"silver jeans" TYPE:"dress" (4)TYPE:"dress" STYLE:"silver","jeans" (3)TYPE:"jeans dress" STYLE:"silver" (2)
silver jeans dress
BRAND:"silver jeans" TYPE:"dress" (4)TYPE:"dress" STYLE:"silver","jeans" (3)TYPE:"jeans dress" STYLE:"silver" (2)
silver jeans dress
Appendix D
Drill Sideways Facets
+CATEGORY: Denim +FIT: Straight +WASH: Dark&B
+CATEGORY: Denim +FIT: Straight +WASH: Dark&B
+CATEGORY: Denim +WASH: Dark&B
+CATEGORY: Denim +FIT: Straight +WASH: Dark&B
+CATEGORY: Denim +WASH: Dark&B
+CATEGORY: Denim +FIT: Straight
+CATEGORY: Denim FIT: Straight WASH: Dark&Black ... /minShouldMatch=Ndrilldowns-1
+CAT: Denim
FIT: Straight
WASH: Dark
+CAT: Denim
FIT: Straight
WASH: Dark
totalHits3
near miss2
near miss2
+CAT: Denim
FIT: Straight
WASH: Dark
totalHits3
near miss2
near miss2
+CAT: Denim
FIT: Straight
WASH: Dark
totalHits3
near miss2
near miss2
Doc at timebase query is highly selective
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
TopDocsCollector
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
TopDocsCollector
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
TopDocsCollector
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
TopDocsCollector
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
TopDocsCollector
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
TopDocsCollector
Term at timedrilldown queries are highly selective
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
hits 1
miss Fit
hits 1
miss Fit
hits 1
miss Fit
hits 1
miss Fit
hits 1
miss Fit
1 2 7 11 12 13 1510
8 9...
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
hits 1
miss Fit
hits 1
miss Fit
hits 1
miss Fit
hits 2
miss no
1 2 7 11 12 13 1510
hits 1
miss Wash
hits 1
missWash
8 9...
hits 1
miss Wash
hits 2
miss no
hits 1
miss Wash
+CAT:D..{1, 7, 9, 15 }FIT:S.. {2, 7, 8, 9, 10,12}WASH:D..{2, 7, 11,13,15}...
hits 1
miss Wash Cat
hits 1
miss FitCat
hits 1
miss Wash Cat
hits 1
miss Fit Cat
hits 2
miss Fit
hits 2
miss Cat
1 2 7 11 12 13 1510
hits 1
missWash Cat
8 9...
hits 3
miss
hits 2
miss Wash
hits 1
miss Wash Cat
hits 1
miss FitCat
hits 1
miss Wash Cat
hits 1
miss Fit Cat
hits 2
miss Fit
hits 2
miss Cat
1 2 7 11 12 13 1510
hits 1
missWash Cat
8 9...
hits 3
miss no
hits 2
miss Wash
hits 2
miss Fit
1 2 7 11 12 13 15108 9...
hits 3
miss no
hits 2
miss Wash
TopDocsCollector
TopDocsCollector
hits 2
miss Fit
1 2 7 11 12 13 15108 9...
hits 3
miss no
hits 2
miss Wash
TopDocsCollector
hits 2
miss Fit
1 2 7 11 12 13 15108 9...
hits 3
miss no
hits 2
miss Wash
Overflow
UML
http://www.flickr.com/photos/kristykay/2922670979/lightbox/
Custom Queries ..hm what for ?
qf=STYLE TYPE
denim dress
qf=STYLE TYPE
denim dress
DisjunctionMaxQuery((
(STYLE:denim OR TYPE:denim) |
(STYLE:dress OR TYPE:dress)
))
qf=STYLE TYPEdenim dress
( DisjunctionMaxQuery((
STYLE:denim | TYPE:denim ))
)OR( DisjunctionMaxQuery((
STYLE:dress | TYPE::dress ))
)
