Data Science with Elasticsearch

Using Elasticsearch "significant_terms" aggregation to recommend movies by analyzing a foreground set against a background set.

Transcript

1. DATA SCIENCE WITH ELASTICSEARCH FELIPE DORNELAS
3. ABOUT ME So ware Engineer Data NERD Electronic Music enthusiast Work at ThoughtWorks
4. WHAT IS ELASTICSEARCH?
5. A real-time distributed search and analytics engine
6. Free and Open Source
7. Distributed document store Full-text search Real-time analytics
8. DISTRIBUTED DOCUMENT STORE RESTful API Automatic scale (Plug & Play) Capable of handling petabytes of data
9. FULL-TEXT SEARCH Built on Lucene Handles the human language: Synonyms, typos and misspellings Internationalization Sort results by relevance score
10. REAL-TIME ANALYTICS Lots of aggregations and metrics Gelocations Can be combined with search Real-time (no batch-processing)
11. SEARCH
12. STRUCTURED SEARCH (SQL) "Does the document match the query?" Yes or no question
13. FULL-TEXT SEARCH "How well does the document match the search"? Relevance score
14. INVERTED INDEX
15. ID Text 1 " T h e q u i c k b r o w n f o x j u m p e d o v e r t h e l a z y d o g . " 2 " Q u i c k b r o w n f o x e s l e a p o v e r l a z y d o g s i n s u m m e r . "
16. TOKENIZATION ID Tokens 1 " T h e " , " q u i c k " , " b r o w n " , " f o x " , " j u m p e d " , " o v e r " , " t h e " , " l a z y " , " d o g " 2 " Q u i c k " , " b r o w n " , " f o x e s " , " l e a p " , " o v e r " , " l a z y " , " d o g s " , " i n " , " s u m m e r "
17. NORMALIZATION
18. CAPITALIZATION " Q u i c k " → " q u i c k "
19. STEMMING " f o x e s " → " f o x "
20. REPLACING SYNONYMS " j u m p e d " ~ " l e a p " → " j u m p "
21. REMOVING COMMON WORDS " t h e "
22. Term Doc #1 Doc #2 brown dog fox in - jump lazy over quick summer -
23. SEARCH EXAMPLE " Q u i c k b r o w n f o x e s i n s u m m e r ? "
24. ELASTICSEARCH API G E T / e x a m p l e / d o c u m e n t / _ s e a r c h { " m a t c h " : { " t e x t " : " Q u i c k b r o w n f o x e s i n s u m m e r ? " } }
25. QUERY IS ALSO NORMALIZED " q u i c k " , " b r o w n " , " f o x " , " i n " , " s u m m e r "
26. MATCHING THE INVERTED INDEX Term Doc #1 Doc #2 quick brown fox in - summer -
27. SEARCH RESULTS " h i t s " : [ { " _ s c o r e " : 0 . 1 6 2 7 3 3 2 7 , " _ i d " : " 2 " , " _ s o u r c e " : { " t e x t " : " Q u i c k b r o w n f o x e s l e a p o v e r l a z y d o g s i n s u m m e r . " } } , { " _ s c o r e " : 0 . 0 1 2 7 3 3 2 7 , " _ i d " : " 1 " , " _ s o u r c e " : { " t e x t " : " T h e q u i c k b r o w n f o x j u m p e d o v e r t h e l a z y d o g . " } } ]
28. SEARCH RESULTS Document # 2 is a better match Higher relevance score than # 1 Search results are sorted by relevance
29. AGGREGATIONS
30. BUCKETS + METRICS
31. BUCKETS Collection of documents that meet a certain criteria
32. GENDER SOMEONE IDENTIFIES TO Alice ⇒ female Josh ⇒ male Karen ⇒ non-binary
33. CITIES FROM A STATE San Francisco ⇒ California Belo Horizonte ⇒ Minas Gerais
34. DAYS FROM A MONTH 2 0 1 4 - 1 0 - 2 8 ⇒ October 2 0 1 4 - 1 1 - 1 5 ⇒ November
35. METRICS Calculations on top of Buckets Ex: m i n , m a x , m e a n , s u m …
36. AGGREGATION EXAMPLE partition citzens by state then by gender then by age ranges then calculate average salary for each bucket (metric)
37. Male California age < 21 Female Non-Binary New York 21 < age < 50 age > 50 Texas ~ $ 5000/month avg salary
38. REAL-TIME ANALYTICS
39. CAR TRANSACTIONS EXAMPLE G E T / c a r s / t r a n s a c t i o n s / A V F r 1 x b V m d U Y W p F 4 6 P s 4 { " p r i c e " : 1 0 0 0 0 , " c o l o r " : " r e d " , " m a k e " : " h o n d a " , " s o l d " : " 2 0 1 4 - 1 0 - 2 8 " }
40. BEST SELLING CAR COLOR G E T / c a r s / t r a n s a c t i o n s / _ s e a r c h ? s e a r c h _ t y p e = c o u n t { " a g g s " : { " c o l o r s " : { " t e r m s " : { " f i e l d s " : " c o l o r " } } } }
41. BEST SELLING CAR COLOR { " c o l o r s " : { " b u c k e t s " : [ { " k e y " : " r e d " , " d o c _ c o u n t " : 1 6 } , { " k e y " : " b l u e " , " d o c _ c o u n t " : 8 } , { " k e y " : " g r e e n " , " d o c _ c o u n t " : 8 } ] } }
42. AVERAGE CAR COLOR PRICE G E T / c a r s / t r a n s a c t i o n s / _ s e a r c h ? s e a r c h _ t y p e = c o u n t { " a g g s " : { " c o l o r s " : { " t e r m s " : { " f i e l d " : " c o l o r " } , " a g g s " : { " a v g _ p r i c e " : { " a v g " : { " f i e l d " : " p r i c e " } } } } } }
43. AVERAGE CAR COLOR PRICE { " c o l o r s " : { " b u c k e t s " : [ { " k e y " : " r e d " , " d o c _ c o u n t " : 1 6 , " a v g _ p r i c e " : { " v a l u e " : 3 2 5 0 0 . 0 } } , { " k e y " : " b l u e " , " d o c _ c o u n t " : 8 , " a v g _ p r i c e " : { " v a l u e " : 2 0 0 0 0 . 0 } } , { " k e y " : " g r e e n " , " d o c _ c o u n t " : 8 , " a v g _ p r i c e " : { " v a l u e " : 2 1 0 0 0 . 0 } } ] } }
44. CAR SALES REVENUE HISTOGRAM G E T / c a r s / t r a n s a c t i o n s / _ s e a r c h ? s e a r c h _ t y p e = c o u n t { " a g g s " : { " p r i c e " : { " h i s t o g r a m " : { " f i e l d " : " p r i c e " , " i n t e r v a l " : 2 0 0 0 0 } , " a g g s " : { " r e v e n u e " : { " s u m " : { " f i e l d " : " p r i c e " } } } } } }
45. CAR SALES REVENUE HISTOGRAM { " p r i c e " : { " b u c k e t s " : [ { " k e y " : 0 , " d o c _ c o u n t " : 1 2 , " r e v e n u e " : { " v a l u e " : 1 4 8 0 0 0 . 0 } } , { " k e y " : 2 0 0 0 0 , " d o c _ c o u n t " : 1 6 , " r e v e n u e " : { " v a l u e " : 3 8 0 0 0 0 . 0 } } , { " k e y " : 4 0 0 0 0 , " d o c _ c o u n t " : 0 , " r e v e n u e " : { " v a l u e " : 0 . 0 } } , { " k e y " : 6 0 0 0 0 , " d o c _ c o u n t " : 0 , " r e v e n u e " : { " v a l u e " : 0 . 0 } } , { " k e y " : 8 0 0 0 0 , " d o c _ c o u n t " : 4 , " r e v e n u e " : { " v a l u e " : 3 2 0 0 0 0 . 0 } } ] } }
46. CAR SALES REVENUE HISTOGRAM
47. TIME-SERIES DATA Any data with a timestamp Ex: server logs, sales history, stock prices
48. HOW MANY CARS SOLD PER MONTH? G E T / c a r s / t r a n s a c t i o n s / _ s e a r c h ? s e a r c h _ t y p e = c o u n t { " a g g s " : { " s a l e s " : { " d a t e _ h i s t o g r a m " : { " f i e l d " : " s o l d " , " i n t e r v a l " : " m o n t h " , " f o r m a t " : " y y y y - M M - d d " } } } }
49. HOW MANY CARS SOLD PER MONTH? { " s a l e s " : { " b u c k e t s " : [ { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 1 - 0 1 " , " d o c _ c o u n t " : 4 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 2 - 0 1 " , " d o c _ c o u n t " : 4 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 3 - 0 1 " , " d o c _ c o u n t " : 0 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 4 - 0 1 " , " d o c _ c o u n t " : 0 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 5 - 0 1 " , " d o c _ c o u n t " : 4 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 6 - 0 1 " , " d o c _ c o u n t " : 0 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 7 - 0 1 " , " d o c _ c o u n t " : 4 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 8 - 0 1 " , " d o c _ c o u n t " : 4 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 0 9 - 0 1 " , " d o c _ c o u n t " : 0 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 1 0 - 0 1 " , " d o c _ c o u n t " : 4 } , { " k e y _ a s _ s t r i n g " : " 2 0 1 4 - 1 1 - 0 1 " , " d o c _ c o u n t " : 8 } ] } }
50. HOW MANY CARS SOLD PER MONTH?
51. COMMON SETUP - ELK Logstash ⇒ Elasticsearch ⇒ Kibana
52. LOGSTASH Collect and stream logs into Elasticsearch
53. KIBANA An analytics dashboard for Elasticsearch
55. MOVIE RECOMMENDATIONS
56. THE MOVIELENS DATA SETS Movies Catalog User Movie Recommendations (0 to 5) User Movie Tags
57. THE MOVIELENS 10M DATASET 10 million ratings 10,000 movies 72,000 users Released on 2009
58. M O V I E S . D A T M o v i e I D : : T i t l e : : G e n r e s
59. M O V I E S . D A T 1 : : T o y S t o r y ( 1 9 9 5 ) : : A d v e n t u r e | A n i m a t i o n | C h i l d r e n | C o m e d y | F a n t a s y 2 : : J u m a n j i ( 1 9 9 5 ) : : A d v e n t u r e | C h i l d r e n | F a n t a s y 3 : : G r u m p i e r O l d M e n ( 1 9 9 5 ) : : C o m e d y | R o m a n c e 4 : : W a i t i n g t o E x h a l e ( 1 9 9 5 ) : : C o m e d y | D r a m a | R o m a n c e 5 : : F a t h e r o f t h e B r i d e P a r t I I ( 1 9 9 5 ) : : C o m e d y 6 : : H e a t ( 1 9 9 5 ) : : A c t i o n | C r i m e | T h r i l l e r 7 : : S a b r i n a ( 1 9 9 5 ) : : C o m e d y | R o m a n c e 8 : : T o m a n d H u c k ( 1 9 9 5 ) : : A d v e n t u r e | C h i l d r e n 9 : : S u d d e n D e a t h ( 1 9 9 5 ) : : A c t i o n 1 0 : : G o l d e n E y e ( 1 9 9 5 ) : : A c t i o n | A d v e n t u r e | T h r i l l e r ( . . . )
60. R A T I N G S . D A T U s e r I D : : M o v i e I D : : R a t i n g : : T i m e s t a m p
61. R A T I N G S . D A T 2 : : 1 1 0 : : 5 : : 8 6 8 2 4 5 7 7 7 2 : : 1 5 1 : : 3 : : 8 6 8 2 4 6 4 5 0 2 : : 2 6 0 : : 5 : : 8 6 8 2 4 4 5 6 2 2 : : 3 7 6 : : 3 : : 8 6 8 2 4 5 9 2 0 2 : : 5 3 9 : : 3 : : 8 6 8 2 4 6 2 6 2 2 : : 5 9 0 : : 5 : : 8 6 8 2 4 5 6 0 8 2 : : 6 4 8 : : 2 : : 8 6 8 2 4 4 6 9 9 2 : : 7 1 9 : : 3 : : 8 6 8 2 4 6 1 9 1 2 : : 7 3 3 : : 3 : : 8 6 8 2 4 4 5 6 2 2 : : 7 3 6 : : 3 : : 8 6 8 2 4 4 6 9 8 ( . . . )
62. LOADING DATA SETS Python script to load the data sets into Elasticsearch Using the new e l a s t i c s e a r c h - d s l library One line at a time...
63. LOADING DATA SETS ...it took too long to load the 10M data set Improved time by using the bulk API
64. THE MOVIE TYPE G E T / m o v i e l e n s / m o v i e / 2 { " n a m e " : " J u m a n j i ( 1 9 9 5 ) " }
65. THE USER RATING TYPE G E T / m o v i e l e n s / r a t i n g s / A V F r 1 x b V m d U Y W p F 4 6 P s 4 { " u s e r " : 1 5 , " r e c o m m e n d e d _ m o v i e s " : [ 1 2 2 , 1 8 5 , 2 3 1 , 2 9 2 , 3 1 6 , 3 2 9 ] } Recommended movies have s c o r e > = 4
66. MOVIE RECOMMENDATIONS Given Talladega Nights, starring Will Ferrel We want to find comedies in similar style
67. RECOMMENDING BASED ON POPULARITY Find all users who recommended a movie Agregate their recommendations Take the top five most-popular
68. Find the Talladega Nights ID: G E T / m o v i e l e n s / m o v i e / _ s e a r c h { " q u e r y " : { " m a t c h " : { " t i t l e " : " T a l l a d e g a N i g h t s " } } }
69. Find the Talladega Nights ID: { " h i t s " : [ { " _ i d " : " 4 6 9 7 0 " , " _ s o u r c e " : { " t i t l e " : " T a l l a d e g a N i g h t s : T h e B a l l a d o f R i c k y B o b b y ( 2 0 0 6 ) " } } ] }
70. Find the most popular movies from people who also like Talladega Nights: G E T / m o v i e l e n s / r a t i n g s / _ s e a r c h ? s e a r c h _ t y p e = c o u n t { " q u e r y " : { " f i l t e r e d " : { " f i l t e r " : { " t e r m " : { " m o v i e " : 4 6 9 7 0 } } } } , " a g g s " : { " m o s t _ p o p u l a r " : { " t e r m s " : { " f i e l d " : " m o v i e " , " s i z e " : 6 } } } }
71. A er correlating the ids to the titles, we got: 1. Matrix, The 2. Shawshank Redemption 3. Pulp Fiction 4. Fight Club 5. Star Wars Episode IV: A New Hope
72. Very good list! But almost everyone likes them! These are universally well-liked movies.
73. Findind the most popular movies from all the time: G E T / m o v i e l e n s / r a t i n g s / _ s e a r c h ? s e a r c h _ t y p e = c o u n t { " a g g s " : { " m o s t _ p o p u l a r " : { " t e r m s " : { " f i e l d " : " m o v i e " , " s i z e " : 5 } } } }
74. A er correlating the ids to the titles, we got: 1. Shawshank Redemption 2. Silence of the Lambs, The 3. Pulp Fiction 4. Forrest Gump 5. Star Wars Episode IV: A New Hope
75. S I G N I F I C A N T _ T E R M S Aggregation based on statistics Finds uncommonly common terms in a data set i.e., statistic annomalies
76. FOREGROUND GROUP Popular movies among people who enjoy Taladega Nights
77. BACKGROUND GROUP Most popular movies among the entire user base
78. Using the s i g n i f i c a n t _ t e r m s aggregation: G E T / m o v i e l e n s / r a t i n g s / _ s e a r c h ? s e a r c h _ t y p e = c o u n t { " q u e r y " : { " f i l t e r e d " : { " f i l t e r " : { " t e r m " : { " m o v i e " : 4 6 9 7 0 } } } } , " a g g s " : { " m o s t _ s i g n i f i c a n t " : { " s i g n i f i c a n t _ t e r m s " : { " f i e l d " : " m o v i e " , " s i z e " : 6 } } } }
79. Returned movies: { " a g g r e g a t i o n s " : { " m o s t _ s i g n i f i c a n t " : { " d o c _ c o u n t " : 2 7 1 , " b u c k e t s " : [ { " k e y " : 4 6 9 7 0 , " d o c _ c o u n t " : 2 7 1 , " b g _ c o u n t " : 2 7 1 } , { " k e y " : 5 5 2 4 5 , " d o c _ c o u n t " : 5 9 , " b g _ c o u n t " : 1 8 5 } , { " k e y " : 8 6 4 1 , " d o c _ c o u n t " : 1 0 7 , " b g _ c o u n t " : 7 6 2 } , { " k e y " : 5 8 1 5 6 , " d o c _ c o u n t " : 1 7 , " b g _ c o u n t " : 2 8 } , { " k e y " : 5 2 9 7 3 , " d o c _ c o u n t " : 9 5 , " b g _ c o u n t " : 8 5 7 } , { " k e y " : 3 5 8 3 6 , " d o c _ c o u n t " : 1 2 8 , " b g _ c o u n t " : 1 6 1 0 } ] } } }
80. A er correlating the ids to the titles, we got: 1. Blades of Glory 2. Anchorman: The Legend of Ron Burgundy 3. Semi-Pro 4. Knocked Up 5. 40-Year-Old Virgin, The
81. THANK YOU! @felipead → , , GitHub Twitter MixCloud