Analyze - Six Sigma project - Database performance Improvement by Nirav Shah

1. 1.
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Nirav Shah (GB)
Improve UATIMNG
database performance
Dushyant Mohanty (TCS - sponsor)
V, Ms. Vijayalakshmi (MBB)
Nilesh Talele (BB)
Pierre Banroques (GE - sponsor)

2. 2.
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Data DisplayAnalyze
Step 4
Graphical display of data collected

3. 3.
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Data DisplayAnalyze
Step 4
Graphical display of data collected

4. 4.
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Findings from data plotting
•As the P-Value is greater than 0.05, the NULL Hypothesis H0 is accepted.
•Data is normal.
•The average query execution time is 192.7 seconds & needs to reduce.
Sample statistics
Mean: 192.7
Median: 192
Standard deviation: 5.47
Analyze
Step 4
Performance Variables

5. 5.
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Baseline Process CapabilityAnalyze
Step 4
Sigma Calculator
Baseline Process Capability Calculations

6. 6.
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Performance ObjectivesAnalyze
Step 5
PROBLEM STATEMENT (Statistical)
It has been noticed that the average time taking for reporting
queries is around 200 secs. This is causing the performance
degradation of reporting and the same has to be improved with
proper database tuning.
GOAL STATEMENT (Statistical)
Optimize the performance of UATIMNG database to
improve the response time for queries to 60 secs
with + 10 secs as tolerance.
Restate Problem and Goal statement
(Convert Business Problem -- Statistical Problem - Statistical goal )

7. 7.
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Query
Execution
Time
Physical database design
Improper Resource optimization
Database volume
Variation SourcesAnalyze
Step 6
Application design
Ishikawa Diagram
Row Chaining of
Data
Data
fragmentationQuery tuning
Unix Server
Capacity
Database
parameter
Large
Database
volumes
OFA
Wait time of db
sequential read
Storage parameter
Invalid server configuration
Invalid parameter setting

8. 8.
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Variation SourcesAnalyze
Step 6
Cause FrequencyCause Frequency
A meeting was conducted within the DBA team to find cause frequency
of UATIMNG database performance. The cause frequency are rated according
Meeting discussion.
Following are the findings,
 Database parameter are already tuned. The further tuning of database
parameter will not effect much.
 As UATIMNG Database is supporting data warehouse application volume of the
data will be high. It cannot be controlled.
 High data fragmentation on object is one of the major causes for the low
response of queries. Large wait time on db sequential read approves the
conclusion.
 Row chaining on Tables is one of the major causes for the low response of
queries.

9. 9.
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Variation SourcesAnalyze
Step 6
CausesCauses Frequency ofFrequency of
CausesCauses
Data Fragmentation 9
Row chaining of data 9
Row migration of data 8
Database parameter 3
Data Volume to be retrieved 3

10. 10.
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Variation SourcesAnalyze
Step 6
Pareto Chart
Vital Xs:1. Data FragmentationVital Xs:1. Data Fragmentation
2. Row Chaining of Data2. Row Chaining of Data
3. Row migration of Data3. Row migration of Data

11. 11.
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High Medium Low
In
Control
Data Fragmentation
Row chaining of data
Row migration of data Database parameter
Out of
Control
Data Volume to be
retrieved
C
O
N
T
R
O
L
IMPACT
Control Impact MatrixAnalyze
Step 6