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.

1. WRITING CUSTOM QUERIES: SCORERS'
DIVERSITY AND TRAPS
Mikhail Khludnev
Principal Engineer,
eCommerce Search Team
mkhludnev@griddynamics.com
http://goo.gl/7LJFi

2. Custom
Queries

3. Custom
Queries

4. Custom
Queries
http://nlp.stanford.edu/IR-book/

5. Custom
Queries
http://nlp.stanford.edu/IR-book/

6. Custom
Queries
Match Spotting
http://nlp.stanford.edu/IR-book/

7. Custom
Queries

8. Inverted Index

9. T[0] = "it is what it is"
T[1] = "what is it"
T[2] = "it is a banana"

10. "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"

11. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1} postings list

12. What is a Scorer?

13. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

14. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

15. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

18. while(
(doc = nextDoc())!=NO_MORE_DOCS){
println("found "+ doc +
" with score "+score());
}

20. Note: Weight is omitted for sake of compactness

22. Custom
Queries
http://nlp.stanford.edu/IR-book/

23. Doc-at-time search

24. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana

25. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana

26. "is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
"it": {0, 1, 2}

27. "is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
collect(0)
score():2
Collector

28. "is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
docID×score
0×2

29. "is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
collect(1)
score():2
Collector
0×2

30. "is": {0, 1, 2}
"what": {0, 1}
"a": {2}
"banana": {2}
Collector
0×2
1×2

31. "is": {0, 1, 2}
"a": {2}
"banana": {2}
"what": {0, 1}
collect(2)
score():3
Collector
0×2
1×2

32. "is": {0, 1, 2}
"a": {2}
"banana": {2}
"what": {0, 1}
Collector
2×3
0×2
1×2

33. Term-at-time search
see Appendix

35. doc at time term at time
complexity O(p log q + n log k) O(p + n log k)
memory q + k n

36. 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

37. q=village operations years disaster visit

38. q=+village +operations +years +disaster +visit

39. Conjunction
(+, MUST)

40. "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

41. "a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}

42. "a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}

43. "a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}

44. "a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}

45. "a": {2,3}
"banana": {2,3}
"is": {0, 1, 2, 3}
"it": {0, 1, 3}
"what": {0, 1, 3}
Collector
3 x 4

46. http://www.flickr.com/photos/fatniu/184615348/

47. Ω(n q + n log k)

48. Wrap-up
● doc-at-time vs term-at-time
● leapfrog

49. Custom
Queries
http://nlp.stanford.edu/IR-book/

50. Custom Queries
● HelloWorld
● Deeply Branched vs Flat
● Steadiness Problem
● minShouldMatch Performance Problem
● Filtering Performance Problem

51. "silver" "jeans" "dress"
silver jeans dress
Note: "foo bar" is not a phrase query, just a string

52. "silver" "jeans" "dress"
"silver jeans dress"
silver jeans dress

53. "silver" "jeans" "dress"
"silver jeans dress"
"silver jeans" "dress"
"silver" "jeans dress"
silver jeans dress

54. "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

58. 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;
}

59. Deeply Branched vs Flat

60. (+"silver jeans" +"dress")
ORmax
(+"silver jeans dress")
ORmax
(+"silver" +(
(+"jeans" +"dress")
ORmax
+"jeans dress"
)
)
ORmax
is DisjunctionMaxQuery

61. (+"silver jeans" +"dress")
ORmax
(+"silver jeans dress")
ORmax
(+"silver" +(
(+"jeans" +"dress")
ORmax
+"jeans dress"
)
)
ORmax
is DisjunctionMaxQuery

62. (+"silver jeans" +"dress")
ORmax
(+"silver jeans dress")
ORmax
(+"silver" +(
(+"jeans" +"dress")
ORmax
+"jeans dress"
)
)
ORmax
is DisjunctionMaxQuery

63. ("silver jeans" "dress")
ORmax
("silver jeans dress")
ORmax
("silver" (
("jeans" "dress")
ORmax
"jeans dress"
)
)
ORmax
is DisjunctionMaxQuery

64. B:"silver jeans dress" ORmax
T:"silver jeans dress" ORmax
S:"silver jeans dress"
B:"silver" ORmax
T:"silver" ORmax
S:"silver"
+
B:"jeans dress" ORmax
T:"jeans dress" ORmax
S:"jeans dress"
+
ORmax
ORmax
ORmax
B:"silver jeans" ORmax
T:"silver jeans" ORmax
S:"silver jeans"
+
B:"dress" ORmax
T:"dress" ORmax
S:"dress"
+
B:"jeans" ORmax
T:"jeans" ORmax
S:"jeans"
+
B:"dress" ORmax
T:"dress" ORmax
S:"dress"
+
B - BRAND
T - TYPE
S - STYLE

65. 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"

67. Steadiness problem
AFAIK 3.x only.

68. {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,..}

69. {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

71. straight jeans
silver jeans
silver jeans straight
jeans
silver
minShouldMatch=2
straight silver jeans

72. 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

73. {1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}

74. {1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}

75. {1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}

76. {1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}

77. {1, 3, 7, 10, 27,30,..}
{3, 5, 10, 27,32,..}
{ 20,27,31,..}
mm=3 { 30,37,..}

79. Filtering

82. RANDOM_ACCESS_FILTER_STRATEGY
LEAP_FROG_FILTER_FIRST_STRATEGY
LEAP_FROG_QUERY_FIRST_STRATEGY
QUERY_FIRST_FILTER_STRATEGY

83. minShouldMatch meets
Filters

84. 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

86. CONFERENCE PARTY
The Tipsy Crow: 770 5th Ave
Starts after Stump The Chump
Your conference badge gets
you in the door
TOMORROW
Breakfast starts at 7:30
Keynotes start at 8:30
CONTACT
Mikhail Khludnev
mkhludnev@griddynamics.com
http://goo.gl/7LJFi

87. Appendixes
● Term-at-time search in Lucene/Solr
● Derivation of the search complexity
● Match Spotting
● Drill Sideways Facets

88. Appendix B
Term-at-time Search
in Lucene

89. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana

90. Accumulator
... 0×1 ... 1×1 ...
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

91. Accumulator
... 0×2 ... 1×2 ... 2×1 ...
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

92. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
Accumulator
... 0×2 ... 1×2 ... 2×2 ...

93. "a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
Accumulator
... 0x2 ... 1x2 ... 2x3 ...

94. Accumulator
... 0×2 ... 1×2 ... 2×3 ...
Collector
2×3
0×2
1×2
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

95. BooleanScorer2

96. ×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

97. 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

98. org.apache.lucene.search
Collector
0×2
1×2×2 ×2
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

99. org.apache.lucene.search
Collector
0×2
1×2×1
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

100. org.apache.lucene.search
Collector
0×2
1×2×2
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

101. org.apache.lucene.search
Collector
0×2
1×2×3
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

102. org.apache.lucene.search
Collector
2×3
0×2
1×2
×3
0 1
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}

103. new BooleanScorer
new BooleanScorer2
//term-at-time
//doc-at-time
if ( collector.acceptsDocsOutOfOrder() &&
topScorer &&
required.size() == 0 &&
minNrShouldMatch == 1) {
else

104. Linked Open Hash [2K]
×1 ×1 ×5 ×2×2
0 1 2 3 4 5 6 7
×3

105. Collector
DocSetCollector TopDocsCollector
TopFieldCollector
TopScoreDocsCollector

106. 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?

107. DocList/TopDocs DocSet
Size
Ordered by
Out-of-order
collecting
k
(numHits/
rows)
N
(maxDocs)
score or
field
docID
allows* almost
could
allow
(No)

108. ?×4 6×4
9×5 2×4
2×7 7×9 1×9

109. http://www.flickr.com/photos/jbagley/4303976811/sizes/o/

110. class OutOfOrderTopScoreDocCollector
boolean acceptsDocsOutOfOrder(){ return true;
}
..
void collect(int doc) {
float score = scorer.score();
...
if (score == pqTop.score && doc > pqTop.doc) {
...
}

112. Appendix B
Derivation of the
Search Complexity

113. 1×9
7×9
2×7
2×5
9×5
6×4
...
...
≤4
...
...
k
n

114. http://en.wikipedia.org/wiki/Binary_heap

115. 6×4
log k 9×5 2×4
2×7 7×9 1×9
...
...
≤4
...
...
n

116. q
p
"a": {2}
"banana": {2}
"is": {0, 1, 2}
"it": {0, 1, 2}
"what": {0, 1}
what OR is OR a OR banana

117. doc at time term at time
complexity O(p + n log k)
memory

118. 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

119. doc at time term at time
complexity O(p log q + n log k) O(p + n log k)
memory

120. Custom
Queries
Match Spotting
http://nlp.stanford.edu/IR-book/
Appendix C

121. 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"

122. 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

123. 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"

124. 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"

125. 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"

126. 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"

127. 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"

128. BRAND:"silver jeans" TYPE:"dress" (4)
TYPE:"dress" STYLE:"silver","jeans" (3)
TYPE:"jeans dress" STYLE:"silver"
TYPE:"jeans dress" STYLE:"silver"

129. BRAND:"silver jeans" TYPE:"dress" (4)
TYPE:"dress" STYLE:"silver","jeans" (3)
TYPE:"jeans dress" STYLE:"silver" (2)

130. BRAND:"silver jeans" TYPE:"dress" (4)
TYPE:"dress" STYLE:"silver","jeans" (3)
TYPE:"jeans dress" STYLE:"silver" (2)
silver jeans dress

131. BRAND:"silver jeans" TYPE:"dress" (4)
TYPE:"dress" STYLE:"silver","jeans" (3)
TYPE:"jeans dress" STYLE:"silver" (2)
silver jeans dress

132. Appendix D
Drill Sideways Facets

139. +CATEGORY: Denim
+FIT: Straight
+WASH: Dark&B

140. +CATEGORY: Denim
+FIT: Straight
+WASH: Dark&B
+CATEGORY: Denim
+WASH: Dark&B

141. +CATEGORY: Denim
+FIT: Straight
+WASH: Dark&B
+CATEGORY: Denim
+WASH: Dark&B
+CATEGORY: Denim
+FIT: Straight

142. +CATEGORY: Denim
FIT: Straight
WASH: Dark&Black
...
/minShouldMatch=Ndrilldowns
-1

143. +CAT: Denim
FIT: Straight
WASH: Dark

144. +CAT: Denim
FIT: Straight
WASH: Dark
totalHits
3
near miss
2
near miss
2

145. +CAT: Denim
FIT: Straight
WASH: Dark
totalHits
3
near miss
2
near miss
2

146. +CAT: Denim
FIT: Straight
WASH: Dark
totalHits
3
near miss
2
near miss
2

147. Doc at time
base query is highly selective

148. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...

149. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...

150. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...

151. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...
TopDocsCollector

152. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...
TopDocsCollector

153. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...
TopDocsCollector

154. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...
TopDocsCollector

155. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...
TopDocsCollector

156. +CAT:D..{1, 7, 9, 15 }
FIT:S.. {2, 7, 8, 9, 10,12}
WASH:D..{2, 7, 11,13,15}
...
TopDocsCollector

157. Term at time
drilldown queries are highly selective

158. +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 15108 9
...

159. +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
miss
Wash
8 9
...
hits
1
miss
Wash
hits
2
miss
no
hits
1
miss
Wash

160. +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
Fit
Cat
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
miss
Wash
Cat
8 9
...
hits
3
miss
hits
2
miss
Wash

161. hits
1
miss
Wash
Cat
hits
1
miss
Fit
Cat
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
miss
Wash
Cat
8 9
...
hits
3
miss
no
hits
2
miss
Wash

162. hits
2
miss
Fit
1 2 7 11 12 13 15108 9
...
hits
3
miss
no
hits
2
miss
Wash
TopDocsCollector

163. TopDocsCollector
hits
2
miss
Fit
1 2 7 11 12 13 15108 9
...
hits
3
miss
no
hits
2
miss
Wash

164. TopDocsCollector
hits
2
miss
Fit
1 2 7 11 12 13 15108 9
...
hits
3
miss
no
hits
2
miss
Wash

165. Overflow

166. UML
http://www.flickr.com/photos/kristykay/2922670979/lightbox/

167. Custom Queries
..hm what for ?

168. qf=STYLE TYPE
denim dress

169. qf=STYLE TYPE
denim dress
DisjunctionMaxQuery((
(STYLE:denim OR TYPE:denim) |
(STYLE:dress OR TYPE:dress)
))

170. qf=STYLE TYPE
denim dress
( DisjunctionMaxQuery((
STYLE:denim | TYPE:denim ))
)OR( DisjunctionMaxQuery((
STYLE:dress | TYPE::dress ))
)