Ghislain Fourny. Big Data 2. Lessons learnt from the past
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1 Ghislain Fourny Big Data 2. Lessons learnt from the past
2 Mr. Databases: Edgar Codd Wikipedia 2
3 Data Independence (Edgar Codd) Logical data model Lorem Ipsum Dolor sit amet Physical storage Consectetur Adipiscing Elit. In Imperdiet Ipsum ante 3
4 Data Independence (Edgar Codd) Logical data model Physical storage 4
5 Data Independence (Edgar Codd) Logical data model Physical storage 5
6 Data Shapes Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam vel erat nec dui aliquet vulputate sed quis nulla. Donec eget ultricies magna, eu dignissim elit. Nullam sed urna nec nisl rhoncus ullamcorper placerat et enim. Integer varius ornare libero quis consequat. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean eu efficitur orci. Aenean ac posuere tellus. Ut id commodo turpis. Praesent nec libero metus. Praesent at turpis placerat, congue ipsum eget, scelerisque justo. Ut volutpat, massa ac lacinia cursus, nisl dui volutpat arcu, quis interdum sapien turpis in tellus. Suspendisse potenti. Vestibulum pharetra justo massa, ac venenatis mi condimentum nec. Proin viverra tortor non orci suscipit rutrum. Phasellus sit amet euismod diam. Nullam convallis nunc sit amet diam suscipit dapibus. Integer porta hendrerit nunc. Quisque pharetra congue porta. Suspendisse vestibulum sed mi in euismod. Etiam a purus suscipit, accumsan nibh vel, posuere ipsum. Nulla nec tempor nibh, id venenatis lectus. Duis lobortis id urna eget tincidunt. 6
7 Data Shapes Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam vel erat nec dui aliquet vulputate sed quis nulla. Donec eget ultricies magna, eu dignissim elit. Nullam sed urna nec nisl rhoncus ullamcorper placerat et enim. Integer varius ornare libero quis consequat. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean eu efficitur orci. Aenean ac posuere tellus. Ut id commodo turpis. Praesent nec libero metus. Praesent at turpis placerat, congue ipsum eget, scelerisque justo. Ut volutpat, massa ac lacinia cursus, nisl dui volutpat arcu, quis interdum sapien turpis in tellus. Suspendisse potenti. Vestibulum pharetra justo massa, ac venenatis mi condimentum nec. Proin viverra tortor non orci suscipit rutrum. Phasellus sit amet euismod diam. Nullam convallis nunc sit amet diam suscipit dapibus. Integer porta hendrerit nunc. Quisque pharetra congue porta. Suspendisse vestibulum sed mi in euismod. Etiam a purus suscipit, accumsan nibh vel, posuere ipsum. Nulla nec tempor nibh, id venenatis lectus. Duis lobortis id urna eget tincidunt. 7
8 Take-away Concepts 8
9 Take-away Concepts Table 9
10 Take-away Concepts Table Collection 10
11 Take-away Concepts Attribute 11
12 Take-away Concepts Attribute Column Field Property 12
13 Take-away Concepts Primary Key 13
14 Take-away Concepts Primary Key Row ID Name 14
15 Take-away Concepts Row 15
16 Take-away Concepts Row Business Object Item Entity Document Record 16
17 Relational Algebra 17
18 Function f B A or f A B A B 18
19 Partial function f A B A B 19
20 Relation R A B or R P(A B) A B 20
21 Relation as a list 21
22 Relation as a list, n-way 22
23 Relations seen from set theory Given a family of sets A. /0.01 A relation is a subset of their Cartesian product 1 R 2 A 3 = A / A 5 37/ A 1 23
24 Relation as a list, n-way 24
25 Relational table A B C 25
26 Relational Algebra 26
27 Summary of relational queries Union Intersection Substraction Set queries Selection Projection Filter queries Relation renaming Attribute renaming Renaming queries Cartesian product Natural join Theta join Binary queries 27
28 Selection 28
29 Selection queries: duplicates R A B C string integer boolean foo 1 true bar 2 false foo 3 false foobar 4 true S = σ :05 (R) S A B C string integer boolean foo 1 true bar 2 false 29
30 Projection 30
31 Projection R A B C string integer boolean foo 1 true bar 2 false foo 3 false foobar 4 true S = π =,? (R) A string foo bar foo foobar S C boolean true false false true 31
32 Grouping A B C D 32
33 Grouping G R A G R A G R A G R A string integer string integer string integer string integer foo 19 foo 19 foo 19 foo 69 bar 28 foo 4 4 bar 677 bar 265 foo foobar 3510 foo 4 bar 28 bar 28 foobar 54 bar foo 46 bar bar 245 bar foobar 3456 foobar 54 foobar 54 bar 139 foobar
34 Sorting
35 Cartesian Product 35
36 Cartesian product R A B C string integer boolean foo 1 true bar 2 false S D E string integer foo 1 bar 2 foo 3 T = R S T A B C D E string integer boolean string integer foo 1 true foo 1 foo 1 true bar 2 foo 1 true foo 3 bar 2 false foo 1 bar 2 false bar 2 bar 2 false foo 3 36
37 Joining A B A B A A A B 37
38 Join R A B C string integer boolean foo 1 true bar 2 false S D B string integer bar 1 bar 2 foo 3 T = R S T A B C D string integer boolean string foo 1 true bar bar 2 false bar 38
39 Normal Forms 39
40 From your Bachelor: Normal Forms Update anomaly Consistency Delete anomaly Insert anomaly 40
41 1st Normal Form (tabular) The Key 41
42 1st Normal Form: counter-example Legi Name Lecture City State PLZ Alan Turing Bletchley Park UK MK3 6EB Lecture ID Lecture Name xxx-xxxx-xxx Cryptography L Big Data Georg Cantor Pfäffikon SZ 8808 Lecture ID L Lecture Name Big Data L Set theory Felix Bloch Pfäffikon ZH 8330 Lecture ID Lecture Name L Set theory 42
43 1st Normal Form Legi Name Lecture ID Lecture Name City State PLZ Alan Turing xxx-xxxx-xxx Cryptography Bletchley Park UK MK3 6EB Alan Turing L Big Data Bletchley Park UK MK3 6EB Georg Cantor L Big Data Pfäffikon SZ Georg Cantor L Set theory Pfäffikon SZ Felix Bloch L Set theory Pfäffikon ZH
44 2nd Normal Form (not joined) The Whole Key 44
45 2nd Normal Form: Counter-example Legi Name Lecture ID Lecture Name City State PLZ Alan Turing xxx-xxxx-xxx Cryptography Bletchley Park UK MK3 6EB Alan Turing L Big Data Bletchley Park UK MK3 6EB Georg Cantor L Big Data Pfäffikon SZ Georg Cantor L Set theory Pfäffikon SZ Felix Bloch L Set theory Pfäffikon ZH
46 2nd Normal Form: Example Legi Name City State PLZ Alan Turing Bletchley Park UK MK3 6EB Alan Turing Bletchley Park UK MK3 6EB Georg Cantor Pfäffikon SZ 8808 Legi Lecture ID xxx-xxxx-xxx L L L L Lecture ID Lecture Name Georg Cantor Pfäffikon SZ Felix Bloch Pfäffikon ZH 8330 xxx-xxxx-xxx L L Cryptography Big Data Set theory 46
47 3rd Normal Form Nothing But The Key 47
48 3rd Normal Form: Counter-Example Legi Name City State PLZ Alan Turing Bletchley Park UK MK3 6EB Alan Turing Bletchley Park UK MK3 6EB Georg Cantor Pfäffikon SZ Georg Cantor Pfäffikon SZ Felix Bloch Pfäffikon ZH
49 3rd Normal Form: Example Legi Name City State Alan Turing Bletchley Park UK City State PLZ Bletchley Park UK MK3 6EB Alan Turing Bletchley Park UK Bletchley Park UK MK3 6EB Georg Cantor Pfäffikon SZ Pfäffikon SZ Georg Cantor Pfäffikon SZ Pfäffikon SZ Felix Bloch Pfäffikon ZH Pfäffikon ZH
50 Data Denormalization 3NF 2NF 50
51 Data Denormalization 3NF 1NF 51
52 Data Denormalization 3NF 0NF 52
53 SQL Brush-Up 53
54 SQL History Don Chamberlin Raymond Boyce 54
55 The early days (early 1970s) First commercial relational database System R + SEQUEL First commercial relational query language Almaden (San Jose) First customer (1977) 55
56 SEQUEL Structured English QUEry Language Declarative language Set-based (Manipulates entire relations with a single command) 56
57 Renaming SEQUEL (Trademark issue) SQL ESS-kew-EL or SEE-kwəl 57
58 SQL is a declarative language Logical model "What, not how" Physical execution 58
59 SQL is a declarative language Logical model "What, not how" Query Plan 59
60 SQL is a declarative language Logical model "What, not how" Parallelism 60
61 SQL is a functional language "Expressions that nest" 61
62 The simplest query (almost idempotent) persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT * FROM persons The output relation has no name name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 62
63 A selecting query: WHERE persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT * FROM persons WHERE last_name = 'Crusher' persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea Beverly C Crusher FALSE AD234F7 63
64 A projecting query persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT name, birth_date FROM persons name birth_date varchar(30) date James Beverly Spock
65 A projecting query persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT DISTINCT gender, birth_date FROM persons gender birth_date boolean date TRUE FALSE
66 A renaming query persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT name AS who, birth_date AS when FROM persons who when varchar(30) date James Beverly Spock
67 A sorting query: ORDER BY persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT * FROM persons ORDER BY birth_date persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea Spock NULL NULL TRUE AD234F7 James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 67
68 Sorting options: ascending (default) persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT * FROM persons ORDER BY birth_date ASC persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea Spock NULL NULL TRUE AD234F7 James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 68
69 Sorting options: descending persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT * FROM persons ORDER BY birth_date DESC persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea Beverly C Crusher FALSE AD234F7 James T Kirk TRUE AD10E7 Spock NULL NULL TRUE AD234F7 69
70 Sorting options: NULLs first persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea James T Kirk TRUE AD10E7 Beverly C Crusher FALSE AD234F7 Spock NULL NULL TRUE AD234F7 SELECT * FROM persons ORDER BY last_name NULLS FIRST persons name middle_initial last_name birth_date gender passport_scan varchar(30) char(1) text date boolean bytea Spock NULL NULL TRUE AD234F7 Beverly C Crusher FALSE AD234F7 James T Kirk TRUE AD10E7 70
71 A grouping query: GROUP BY persons Grouping column name middle_initial last_name century gender varchar(30) char(1) text integer boolean James T Kirk 23 TRUE Beverly C Crusher 24 FALSE Spock NULL NULL 23 TRUE Kathryn NULL Janeway 24 FALSE SELECT century, COUNT(*) AS count FROM persons GROUP BY century century count integer bigint
72 Aggregation operators MAX MIN SUM AVG COUNT BOOL_AND/EVERY BOOL_OR STRING_AGG The most common Advanced aggregations 72
73 Post-aggregation selection: HAVING persons name middle_initial last_name century captain varchar(30) char(1) text integer boolean James T Kirk 23 TRUE Beverly C Crusher 24 FALSE Jean-Luc NULL Picard 24 TRUE Kathryn NULL Janeway 24 TRUE SELECT century AS c FROM persons GROUP BY century HAVING COUNT(*) > 2 century integer 24 73
74 Semi-outer joins: left persons name middle_initial last_name century captain varchar(30) char(1) text integer boolean James T Kirk 23 TRUE Beverly C Crusher 24 FALSE Jean-Luc NULL Picard 24 TRUE Kathryn NULL Janeway 24 TRUE spaceships warp spaceship_name captain_name code numeric varchar(30) text integer 5 USS Enterprise A Kirk NCC-1701-A 4 USS Enterprise Kirk NCC USS Enterprise D Picard NCC-1701-D USS Voyager Janeway NCC SELECT * FROM persons LEFT OUTER JOIN spaceships ON persons.last_name = spaceships.captain_name name middle_initial last_name captain century warp spaceship_name captain_name code varchar(30) char(1) text boolean integer numeric varchar(30) text integer James T Kirk TRUE 23 5 USS Enterprise A Kirk NCC-1701-A James T Kirk FALSE 24 4 USS Enterprise Kirk NCC-1701 Beverly C Crusher 24 FALSE NULL NULL NULL NULL Jean-Luc NULL Picard TRUE USS Enterprise D Picard NCC-1701-D Kathryn NULL Janeway TRUE USS Voyager Janeway NCC
75 Semi-outer joins: right persons name middle_initial last_name century captain varchar(30) char(1) text integer boolean James T Kirk 23 TRUE Beverly C Crusher 24 FALSE Kathryn NULL Janeway 24 TRUE spaceships warp spaceship_name captain_name code numeric varchar(30) text integer 5 USS Enterprise A Kirk NCC-1701-A 4 USS Enterprise Kirk NCC USS Enterprise D Picard NCC-1701-D USS Voyager Janeway NCC SELECT * FROM persons RIGHT OUTER JOIN spaceships ON persons.last_name = spaceships.captain_name name middle_initial last_name captain century warp spaceship_name captain_name code varchar(30) char(1) text boolean integer numeric varchar(30) text integer James T Kirk TRUE 23 5 USS Enterprise A Kirk NCC-1701-A James T Kirk FALSE 24 4 USS Enterprise Kirk NCC-1701 NULL NULL NULL NULL NULL 9.2 USS Enterprise D Picard NCC-1701-D Jean-Luc NULL Picard TRUE USS Enterprise D Picard NCC-1701-D Kathryn NULL Janeway TRUE USS Voyager Janeway NCC
76 Full outer joins persons name middle_initial last_name century captain varchar(30) char(1) text integer boolean James T Kirk 23 TRUE Beverly C Crusher 24 FALSE Kathryn NULL Janeway 24 TRUE spaceships warp spaceship_name captain_name code numeric varchar(30) text integer 5 USS Enterprise A Kirk NCC-1701-A 4 USS Enterprise Kirk NCC USS Enterprise D Picard NCC-1701-D USS Voyager Janeway NCC SELECT * FROM persons FULL OUTER JOIN spaceships ON persons.last_name = spaceships.captain_name name middle_initial last_name captain century warp spaceship_name captain_name code varchar(30) char(1) text boolean integer numeric varchar(30) text integer James T Kirk TRUE 23 5 USS Enterprise A Kirk NCC-1701-A James T Kirk FALSE 24 4 USS Enterprise Kirk NCC-1701 NULL NULL NULL NULL NULL 9.2 USS Enterprise D Picard NCC-1701-D Beverly C Crusher 24 FALSE NULL NULL NULL NULL Kathryn NULL Janeway TRUE USS Voyager Janeway NCC
77 Natural join persons name middle_initial last_name century captain varchar(30) char(1) text integer boolean James T Kirk 23 TRUE Beverly C Crusher 24 FALSE Kathryn NULL Janeway 24 TRUE spaceships warp spaceship_name last_name code numeric varchar(30) text integer 5 USS Enterprise A Kirk NCC-1701-A 4 USS Enterprise Kirk NCC USS Enterprise D Picard NCC-1701-D USS Voyager Janeway NCC SELECT * FROM persons NATURAL FULL OUTER JOIN spaceships name middle_initial last_name captain century warp spaceship_name code varchar(30) char(1) text boolean integer numeric varchar(30) integer James T Kirk TRUE 23 5 USS Enterprise A NCC-1701-A James T Kirk FALSE 24 4 USS Enterprise NCC-1701 NULL NULL NULL NULL NULL 9.2 USS Enterprise D NCC-1701-D Beverly C Crusher 24 FALSE NULL NULL NULL Kathryn NULL Janeway TRUE USS Voyager NCC
78 WHERE vs. HAVING: query plan FROM WHERE GROUP HAVING SELECT σ γ σ π Pre-grouping Post-grouping 78
79 A union query: UNION spaceships1 warp name last_name code successor numeric varchar(30) text varchar varchar 5 USS Enterprise A Kirk NCC-1701-A NCC-1701-B 6 USS Enterprise B Kirk NCC-1701-B NCC-1701-C 7 USS Enterprise C Kirk NCC-1701-C NCC-1701-D spaceships2 warp name last_name code successor numeric varchar(30) text varchar varchar 7 USS Enterprise C Kirk NCC-1701-C NCC-1701-D 4 USS Enterprise Kirk NCC-1701 NCC-1701-A duplicate elimination 9.2 USS Enterprise D Picard NCC-1701-D NCC-1701-E SELECT * FROM spaceships1 UNION spaceships2 warp name last_name code successor numeric varchar(30) text varchar varchar 5 USS Enterprise A Kirk NCC-1701-A NCC-1701-B 6 USS Enterprise B Kirk NCC-1701-B NCC-1701-C 7 USS Enterprise C Kirk NCC-1701-C NCC-1701-D 4 USS Enterprise Kirk NCC-1701 NCC-1701-A 9.2 USS Enterprise D Picard NCC-1701-D NCC-1701-E 79
80 An intersection query: INTERSECT spaceships1 warp name last_name code successor numeric varchar(30) text varchar varchar 5 USS Enterprise A Kirk NCC-1701-A NCC-1701-B 6 USS Enterprise B Kirk NCC-1701-B NCC-1701-C 7 USS Enterprise C Kirk NCC-1701-C NCC-1701-D spaceships2 warp name last_name code successor numeric varchar(30) text varchar varchar 7 USS Enterprise C Kirk NCC-1701-C NCC-1701-D 4 USS Enterprise Kirk NCC-1701 NCC-1701-A duplicate elimination SELECT * FROM spaceships1 INTERSECT spaceships2 warp name last_name code successor numeric varchar(30) text varchar varchar 7 USS Enterprise C Kirk NCC-1701-C NCC-1701-D 80
81 An set substraction query: EXCEPT spaceships1 warp name last_name code successor numeric varchar(30) text varchar varchar 5 USS Enterprise A Kirk NCC-1701-A NCC-1701-B 6 USS Enterprise B Kirk NCC-1701-B NCC-1701-C 7 USS Enterprise C Kirk NCC-1701-C NCC-1701-D spaceships2 warp name last_name code successor numeric varchar(30) text varchar varchar 7 USS Enterprise C Kirk NCC-1701-C NCC-1701-D 4 USS Enterprise Kirk NCC-1701 NCC-1701-A duplicate elimination SELECT * FROM spaceships1 EXCEPT spaceships2 warp name last_name code successor numeric varchar(30) text varchar varchar 5 USS Enterprise A Kirk NCC-1701-A NCC-1701-B 6 USS Enterprise B Kirk NCC-1701-B NCC-1701-C 81
82 Nesting Table expression SELECT pname, warp FROM ( Table expression SELECT persons.name AS pname, last_name, century, spaceships.name AS sname, warp FROM persons FULL OUTER JOIN spaceships USING last_name ) 82
83 Three-valued logics: OR OR TRUE FALSE UNKNOWN TRUE TRUE TRUE TRUE FALSE TRUE FALSE UNKNOWN UNKNOWN TRUE UNKNOWN UNKNOWN This is common sense. e.g., <anything> OR TRUE = TRUE thus UNKNOWN OR TRUE = TRUE 83
84 Some terminology Data Schema DML: Data Manipulation Language (Query, insert, remove rows) DDL: Data Definition Language (Create or table/schema, drop it) 84
85 Some terminology Create Read Update Delete 85
86 Transactions 86
87 The good old times of databases: ACID Atomicity Consistency Isolation Durability 87
88 Atomicity Either the entire transaction is applied, or none of it (rollback). 88
89 Consistency After a transaction, the database is in a consistent state again. 89
90 Isolation A transaction feels like nobody else is writing to the database. 90
91 Durability Updates made do not disappear again. 91
92 The new era: the CAP theorem Yet another impossibility triangle 92
93 The new era: the CAP theorem Consistency Availability Partition tolerance 93
94 (Atomic) Consistency All nodes see the same data. 94
95 Availability It is possible to query the database at all times. 95
96 Partition tolerance The database continues to function even if the network gets partitioned. 96
97 Performance 97
98 No such thing as "one size fits all" Data shapes matter 98
99 Optimize for read vs. write intensive OnLine Transaction Processing OnLine Analytical Processing OLTP OLAP Write-intensive Read-intensive 99
100 Indices 100
101 Data Scale-Up 101
102 Data can have... Lots of rows 102
103 Data can have... Lots of columns 103
104 Data can have... Lots of nesting 104
105 The rest of the lecture: Scaling up Data in the Large Lots of rows Object Storage Lots of rows Distributed File Systems Lots of rows/columns Column storage Lots of rows Massive Parallel Processing Data in the Small Lots of nesting Syntax Lots of nesting Data Models Lots of nesting Querying Lots of nesting Document Stores Data in the Very Small Lots of rows Graph Databases Lots of columns Data Warehousing Lots of columns Business reporting 105
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