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Konstantin Osipov,
kostja@tarantool.org
October 28th, 2013
Случай в квадрате 36-80
•

•

•

B-tree – most
popular disk-based
data structure
B-tree balances
INSERT, UPDATE
and SELECT speed
DELETEs can be
slow
СУБД быстрая, настраивать
надо уметь
B-tree: внутреннее устройство
Что означает сache-oblivious?
Что означает сache-oblivious? (2)

BLOCK­MULT(A,B,C,n):
1 for i = 1 to n/s do:
2    for j = 1 to n/s do:
3         for k = 1 to n/s do:
4             ORD­MULT(Aik, Bkj, Cij, s)
LSM-tree: архитектура
LSM-tree: архитектура (2)
LevelDB: архитектура
LevelDB: insert RPS
LSM-tree: применение
●

Данные с разной степенью актуальности
–
–

Стена в соцсети

–

Чаты

–
●

Ленты сообщений

События

Сегрегация данных
–

Данные в LSM, индекс в памяти
COLA: архитектура
O(logB(N)) vs. O(logB(N)/B)
PUT(37), PUT(16)

Self-Balancing Tree

Memory
Disk

WAL:
16 37 Self-Balancing Tree Memory

Disk

WAL: 37, 16
7 41 Self-Balancing Tree Memory
16 37 Sorted String Table

WAL: 41, 7, 37, 16

Disk
Memory
7 37
7 16 37 41

WAL: 41, 7, 28, 16

Disk
10 28

Memory

7 37

Disk

7 16 37 41

WAL: 10, 28, 41, 7, 37, 16
Memory
10 28
7 16 37 41

WAL: 10, 28, 41, 7, 37, 16

Disk
2 47

Memory

10 28

Disk

7 16 37 41

WAL: 47, 2, 10, 28, 41, 7, 37, 16
2 28
2 10 28 41
2

7 10 16 28 37 41 47

WAL: 47, 2, 10, 28, 41, 7, 37, 16

Memory
Disk
6 49
2 28
2 10 28 41
2

7 10 16 28 37 41 47

WAL: 49, 6, 47, 2, 10, 28, 41, 7, 37, 16

Memory
Disk
6 49
2 10 28 41
2

7 10 16 28 37 41 47

WAL: 49, 6, 47, 2, 10, 28, 41, 7, 37, 16

Memory
Disk
23 32
6 49
2 10 28 41
2

7 10 16 28 37 41 47

WAL: 32, 23, 49, 6, 47, 2, 10, 28, 41, 7, 37, 16

Memory
Disk
6 32
6 23 32 49
2

7 10 16 28 37 41 47

WAL: 32, 23, 49, 6, 47, 2, 10, 28, 41, 7, 37, 16

Memory
Disk
30 45

Memory

6 32

Disk

6 23 32 49
2

7 10 16 28 37 41 47

WAL: 30, 45, 32, 23, 49, 6, 47, 2, 10, 28, 41, 7, 37, 16
14 38

Memory

30 45

Disk

6 23 32 49
2

7 10 16 28 37 41 47

WAL: 38, 14, 30, 45, 32, 23, 49, 6, 47, 2, 10, 28, 41, 7, 37, 16
6 10

Memory

2 30

Disk

2 14 30 41
2
2

6

7 14 23 30 37 41 47

7 10 14 16 23 28 30 32 37 38 41 45 47 49

WAL: 10, 6, 38, 14, 45, 30, 45, 32, 23, 49, 6, 47, 2, 10, 28, 41, 7, 37, 16
Memory
22 37

Disk

10 25 36 42
3
2

6

8 15 26 35 40 45 48

7 10 14 16 23 28 30 32 37 38 41 45 47 49

WAL: 37, 22, 36, 10, 25, 42, 10, 6, 38, 14, 45, 30, 45, 32, 23, 49, 6, 47, 2,
10, 28, 41, 7, 37, 16
GET(16)

Memory
22 37

Disk

10 25 36 42
3
2

6

8 15 26 35 40 45 48

7 10 14 16 23 28 30 32 37 38 41 45 47 49

WAL: 37, 22, 36, 10, 25, 42, 10, 6, 38, 14, 45, 30, 45, 32, 23, 49, 6, 47, 2,
10, 28, 41, 7, 37, 16
GET(16)

Memory
22 37

Disk

10 25 36 42
3
2

6

8 15 26 35 40 45 48

7 10 14 16 23 28 30 32 37 38 41 45 47 49

WAL: 37, 22, 36, 10, 25, 42, 10, 6, 38, 14, 45, 30, 45, 32, 23, 49, 6, 47, 2,
10, 28, 41, 7, 37, 16
BitCask: архитектора AOF
BitCask: архитектура keydir
Sophia: архитектура
Memory

15

26

40

84

Key-value index

10, 25

26, 31

39, 85

86, 96

Page index

split

Disk

10, 15, 16, 25

26, 31

39, 40, 84, 85

86, 96
39, 16, 85, 96

Insert

Memory
Key-value index

Disk

WAL

Page index
Insert

Memory

16

39

85

96

Key-value index

Disk

Page index
31, 25, 10, 86

Insert
16, 96

Memory
Key-value index

Disk

Page index

WAL
16, 39, 85, 96
Memory

10

25

31

86

Key-value index

16, 96

Page index

merge

Disk

16, 39, 85, 96
Memory

10

25

31

86

Key-value index

10, 31

39, 96

Page index

split

Disk

10, 16, 25, 31
39, 85, 86, 96
15, 26, 40, 84

Insert
10, 31

Memory
Key-value index

Disk

WAL

39, 96

Page index

10, 16, 25, 31
39, 85, 86, 96
Memory

15

26

40

84

Key-value index

10, 31

39, 96

Page index

merge

Disk

10, 16, 25, 31
39, 85, 86, 96
Memory

15

26

40

84

Key-value index

10, 25

26, 31

39, 85

86, 96

Page index

split

Disk

10, 15, 16, 25

26, 31

39, 40, 84, 85

86, 96
Эпилог: choose your db wisely
?
Links
●

●

●
●
●

●
●
●
●
●

Bitcask A Log-Structured Hash Table for Fast Key/Value Data, Justin Sheehy David Smith with
inspiration from Eric Brewer
The Log-Structured Merge-Tree (LSM-Tree) Patrick O'Neil , Edward Cheng, Dieter Gawlick,
Elizabeth O'Neil
Cache-Oblivious Algorithms by Harald Prokop (Master theses)
Space/time trade-offs in hash coding with allowable errors, Burton H. Bloom
Data Structures and Algorithms for Big Databases, Michael A. Bender Stony Brook & Tokutek
Bradley C. Kuszmaul (XLDB tutorial)
http://github.com/pmwkaa/sophia, http://sphia.org
http://codecapsule.com/2012/12/30/implementing-a-key-value-store-part-3-comparative-analysis-of-the-ar
http://stackoverflow.com/questions/6079890/cache-oblivious-lookahead-array
http://www.youtube.com/watch?v=88NaRUdoWZM(Tim Callaghan: Fractal Tree indexes)
http://code.google.com/p/leveldb/downloads/list

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Highload2o013 osipv