database performance analysis with time series

ANALYZING ORACLE PERFORMANCE USING TIME SERIES MODELS

Chen (Gwen) Shapirahttp://prodlife.wordpress.com

Why?

• Abnormal Data• Changes• Trends• SLAs

See

• Techniques• Use Cases• Real Data

Techniques

Preparing Data

Time

x

0 20 40 60 80 100

050

100

150

Missing data

Discontinuity

Outlier

Trend

Trend

Moving Average Trend

Different Windows

Time

p

0 10 20 30 40 50

15

00

16

00

17

00

18

00

19

00

20

00

21

00

Remove Trend

Seasonality

-2 -1 0 1 2

15

00

16

00

17

00

18

00

19

00

20

00

21

00

Correlation

Theoretical Quantiles

Sa

mp

le Q

ua

ntil

es

0 5 10 15 20 25

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Lag

AC

F

Correlogram

Seasonal Effect

5 10 15 20

01

23

45

Hourly CPU Utilization Average

Hour

Ave

rag

e U

tiliza

tio

n

Components

More AutoCorrelation

0 5 10 15 20 25

0.0

0.2

0.4

0.6

0.8

1.0

Lag

AC

F

Autocorrelation without Seasonality

Xt = 0.33Xt-1 + 0.07Xt-2 – 0.09Xt-3 + e

Test Model

Use Cases

Fake Incident

Detect By

• Remove trend• Remove Seasonality• Mark “normal data”• What’s left?

Spot the Incident

Time

fc_

nt_

ns

0 100 200 300 400 500 600 700

-20

24

“I have seen the future and it is very much like the present, only longer”

Kehlog Albran

Exponential Smoothing

• Calculate moving average of future• Add seasonality

Holt-Winters filtering

Time

Ob

se

rve

d / F

itte

d

2 4 6 8 10 12 14

55

60

65

70

75

80

AutoCorrelation

Use the model:Xt = aXt-1…To calculate Xt+1,Xt+2…

AR Model Prediction

Time

fc_

nt_

ns

550 600 650 700

-20

24

Real Data 1:Redo Blocks per Hour

Holiday

0 5 10 15 20 25

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Lag

AC

F

Auto-Correlation of Redo Data

Seasonality

Abnormal DataRedo Generation without Seasonality

Time

x_

ns

0 100 200 300 400

-4e

+0

6-2

e+

06

0e

+0

02

e+

06

4e

+0

66

e+

06

8e

+0

6

Real Data 2:CPU on DB Server

Seasonality?

Partial AutoCorrelation

0 5 10 15 20 25 30

0.0

0.2

0.4

0.6

Lag

Pa

rtia

l A

CF

Partial Correlogram

Check Fit of Model

PredictionAR Model Prediction

Time

cp

u2

600 650 700 750 800 850 900

-10

01

02

03

04

05

0

Conclusions

• Use moving average to describe trend• Look for seasonality• Predict with Exponential Smoothing• AutoCorrelation?• Seasonality aware monitoring

Questions?