infraXstructure: Adam Sznajder, Optymalizacja kosztów w Amazon Web Services - od właściwego nastawienia do oszczędności.

1. A D A M S Z N A J D E R
AW S C O S T
O P T I M I S AT I O N
F R O M R I G H T M I N D S E T TO
R E A L S AV I N G S
F O L L O W M E : @ A S Z N A J D E R

2. C L O U D S O F T WA R E
E N G I N E E R
S I L I C O N VA L L E Y
S TA R T U P
B I G M A C H I N E D ATA
A N A LY S I S
H U G E AW S
B I L L S
F O L L O W M E : @ A S Z N A J D E R

3. WA R S AW T E A M
C O S T S - D R I V E N
D E V E L O P M E N T
F O L L O W M E : @ A S Z N A J D E R

4. C L O U D S U P P O R T S
I N N O VAT I O N
F O L L O W M E : @ A S Z N A J D E R

5. B U R N I N G
L O T S O F $ $ $
V E RY L O W
AWA R E N E S S
M I S S I N G T E C H N I C A L
K N O W L E D G E
L O W
P R I O R I T Y
F O L L O W M E : @ A S Z N A J D E R

6. F O L L O W M E : @ A S Z N A J D E R
R I G H T
M I N D S E T
I S C R I T I C A L

7. F O L L O W M E : @ A S Z N A J D E R
R E S P O N S I B I L I T Y
D I F F I C U LT
D E C I S I O N S
G E T T I N G
S H * T D O N E

8. F O L L O W M E : @ A S Z N A J D E R
K N O W Y O U R B I L L S
T R A C K
C O S T
D ATA - D R I V E N
A P P R O A C H

9. F O L L O W M E : @ A S Z N A J D E R
A U TO M AT E
P E O P L E A R E
L A Z Y
A U TO M AT I C C O S T
G U A R D S

10. F O L L O W M E : @ A S Z N A J D E R
H O W TO C U T
C O S T S I N P R A C T I C E

11. PAY U P F R O N T
C O S T R E D U C T I O N
S AV E 3 1 - 7 6 %
L O W H A N G I N G
F R U I T
F O L L O W M E : @ A S Z N A J D E R

12. S 3
3 T Y P E S
S M A R T S TO R A G E
I N F R E Q U E N T
A C C E S S
F O L L O W M E : @ A S Z N A J D E R

13. AW S TO O L B O X
U S E R I G H T
TO O L
D B S , C A C H E S . . .
F O L L O W M E : @ A S Z N A J D E R

14. D Y N A M O D B
N O S Q L
K E Y- VA L U E
S TO R E
O N D E M A N D
C A PA C I T Y
F O L L O W M E : @ A S Z N A J D E R

15. $ 2 3 K L E S S E A C H M O N T H !

16. N O D E 1
N O D E 2
N O D E 3
Hash Space N1-N2
Hash Space N3-N1 Hash Space N2-N3
C O N S I S T E N T H A S H I N G
N O D E 2
N O D E 3
CustomerId
Timestamp
Count

17. N O D E 1
N O D E 2
N O D E 3
Hash Space N1-N2
Hash Space N3-N1 Hash Space N2-N3
K E Y I S H A S H E D
N O D E 2
N O D E 3
CustomerId
Timestamp
Count
hashValue
h(key)

18. N O D E 1
N O D E 2
N O D E 3
Hash Space N1-N2
Hash Space N3-N1 Hash Space N2-N3
F I N D I N G PA R T I T I O N
N O D E 2
N O D E 3
CustomerId
Timestamp
Count
hashValue
h(key)
N1 < <= N2

19. N O D E 1
N O D E 2
N O D E 3
Hash Space N1-N2
Hash Space N3-N1 Hash Space N2-N3
1 0 0 W C U ! = 1 0 0 W C U
N O D E 2
N O D E 3
Partition WCU = Bought WCU / Partitions Number

20. N O D E 1
N O D E 2
N O D E 3
Hash Space N1-N2
Hash Space N3-N1 Hash Space N2-N3
1 0 0 W C U ! = 1 0 0 W C U
N O D E 2
N O D E 3
Partition WCU = Bought WCU / Partitions Number
33.3 WCU = 100 WCU / 3

21. N O D E 1
N O D E 2
N O D E 3
Hash Space N1-N2
Hash Space N3-N1 Hash Space N2-N3
H O T PA R T I T I O N
N O D E 2
N O D E 3
CustomerId
Timestamp
Count
hashValue
h(key)
N1 < <= N2
100 Writes / sec
Only 33.3 WCU!

22. N O D E 1
N O D E 2
N O D E 3
Hash Space N1-N2
Hash Space N3-N1 Hash Space N2-N3
H O T PA R T I T I O N
N O D E 2
N O D E 3
CustomerId
Timestamp
Count
hashValue
h(key)
N1 < <= N2
100 Writes / sec
Increase to 100 WCU

23. N O D E 1
N O D E 2
N O D E 3
Hash Space N1-N2
Hash Space N3-N1 Hash Space N2-N3
H O T PA R T I T I O N
N O D E 2
N O D E 3
CustomerId
Timestamp
Count
hashValue
h(key)
N1 < <= N2
100 Writes / sec
Increase to 100 WCU
TOTAL COST = Hot Partition WCU * Number of partitions

24. M I S TA K E S C O S T A L O T !

25. M I S TA K E S C O S T A L O T !
Paid

26. M I S TA K E S C O S T A L O T !
Paid
Used
Paid
Used
= ~100

27. A U TO S C A L I N G
J U S T E N O U G H
R E S O U R C E S
F O L L O W M E : @ A S Z N A J D E R

28. N O D E 2
N O D E 3
R E A L E X A M P L E
Cluster CPU

29. N O D E 2
N O D E 3
TA K I N G A D VA N TA G E
SAVED $$$
Old Cluster CPU
New Cluster CPU

30. P R O B L E M S TO S O LV E
S TAT E L E S S
S E RV I C E
S C A L I N G
S T R AT E G Y
C L U S T E R
M A N A G E M E N T
F O L L O W M E : @ A S Z N A J D E R

31. S P O T I N S TA N C E S
E C 2
M A R K E T
G E N E R A L LY
C H E A P E R
S P I K E S H U R T
F O L L O W M E : @ A S Z N A J D E R

32. B AT C H P R O C E S S I N G
C H E A P
E C 2 L O S S
S AV E PA R T I A L
R E S U LT S
M A C H I N E
L E A R N I N G
F O L L O W M E : @ A S Z N A J D E R

33. P R E D I C T S P I K E S
D I F F I C U LT
B U T P O S S I B L E
U S E D W I S E LY
V E RY P O W E R F U L
F O L L O W M E : @ A S Z N A J D E R

34. A U TO S C A L E
U S I N G S P O T S
U S E R I
A S A B A S E
F O L L O W M E : @ A S Z N A J D E R
S P O T S + A U TO S C A L I N G

35. N O D E 2
N O D E 3
S P O T S + A U TO S C A L I N G
Reserved Instances
Spots

36. L E A R N H O W
D O E S Y O U R
S Y S T E M W O R K
F O L L O W M E : @ A S Z N A J D E R

37. F I N D C O N S T R A I N T S
C P U
M E M O RY
N E T W O R K
F O L L O W M E : @ A S Z N A J D E R

38. F I N D T H E R O O T S O F Y O U R
P R O B L E M S
F O L L O W M E : @ A S Z N A J D E R

39. R E A L E X A M P L E
M AT C H I N G P R O C E S S I N G
Messages
Search Expressions
Matching Messages

40. T H I S R E A L LY W O R K S !

41. T H A N K YO U
C A P TA I N
O B V I O U S
F O L L O W M E : @ A S Z N A J D E R

42. Things rarely get stuck because of lack of
time. They get stuck because the doing of
them has not been defined.
David Allen, Getting Things Done
TA K E H O M E
F O L L O W M E : @ A S Z N A J D E R

43. Q U E S T I O N S ?
L E T ' S M E E T !
F O L L O W M E : @ A S Z N A J D E R

44. ADAM SZNAJDER
adam.sznajder@gmail.com
@asznajder
asznajder.github.io

45. H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / PA S Z C Z A K 0 0 0 / 1 3 4 4 6 6 0 2 8 4 4 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / E S C H I P U L / 8 3 5 8 0 8 6 4 9 3 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / A I G L E _ D O R E / 2 4 0 6 5 8 3 9 2 9 9 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / S O L D I E R S M E D I A C E N T E R / 11 9 8 6 9 8 9 5 2 3
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / 3 1 0 2 9 8 6 5 @ N 0 6 / 1 4 8 1 0 9 0 3 2 8 0 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / R E D N U H T / 4 7 9 3 7 0 0 8 8 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / W O O D H E A D / 2 1 2 0 3 3 6 7 0 2 4
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / V I N O T H C H A N D A R / 5 7 9 3 0 5 9 5 8 0 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / A R T- S A R A H / 5 1 8 1 9 0 4 6 1 5 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / 1 3 2 0 8 4 5 2 2 @ N 0 5 / 1 7 0 8 6 5 7 0 2 1 8 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / C E N T R A L A S I A N / 3 9 8 8 7 5 8 4 4 2 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / O L A F _ S C H R E I B E R / 8 0 3 2 2 6 5 8 2 5 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / Z A P T H E D I N G B AT / 3 6 3 4 8 8 4 1 5 5
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / M A N O F TA S T E - D E / 9 4 8 3 6 0 2 8 1 7 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / F L O R I A N R I C / 7 2 6 3 3 8 2 5 5 0 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / B R U C E _ A L D R I D G E / 8 3 7 8 9 2 2 3 9 3 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / K 1 L LY R I D 0 L S / 4 5 4 7 8 4 3 6 4 4 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / F I S C H L E R / 6 1 8 1 2 9 5 8 3 8 /
H T T P S : / / W W W. F L I C K R . C O M / P H O TO S / V I L L A G E 9 9 9 1 / 7 5 1 9 4 9 6 6 4 6 /
I M A G E S S O U R C E S