1. Advanced QlikView Performance Tuning Techniques
Niklas Boman

2. Agenda Definition of ”Performance Tuning” End User Performance
Measuring Performance
Influencing Hardware Factors

3. When is Performance Tuning Necessary?

4. End User Performance

5. How to approach a Performance Tuning exercise?
Reduce QlikView file size
Improve QlikView file efficiency
Adjust hardware

6. What makes up the size of a QlikView document?
Number of rows
Number of columns
Number of distinct values / column
Data structure
Number of sheets / sheet objects

7. Number of Rows
Is it really necessary to have 10 years of data in the same QlikView document?
If there are too many detail rows, is it feasible to create a roll-up version at a higher aggregate level?
Applying security authorization to your QlikView document – to be discussed later in this session

8. Number of Columns
Be selective about what fields are included in the QlikView document
Focus on the larger fact tables first
2. But, do not ignore the dimension tables
Eliminate fields that are not currently being used
Do not leave fields that ”might” be used in the future
All fields ”might” be used in the future
The goal is to have a ”Lean” QlikView document
Provide the QlikView owner / developer with an efficient method of adding fields to their data model without having to manually change the code

9. Number of distinct values / column
Fields with highly distinct values use the most space
Many of them can be eliminated / truncated altogether
System keys
Timestamps with minutes / seconds
Others can be transformed by breaking them up into separate components
Phone Numbers
Timestamps

10. Data structure
Key Fields
Denormalization vs. Normalization

11. Normalization vs. Denormalization
A “traditional” QlikView data structure is most likely not ideal for a large QlikView document
“Normalization” Definition (Wikipedia):
Normalization splits up data to avoid redundancy (duplication) by moving commonly repeating groups of data into a new table. Normalization therefore tends to increase the number of tables that need to be joined in order to perform a given query, but reduces the space required to hold the data and the number of places where it needs to be updated if the data changes.
How does this apply to QlikView?

12. Improving a QlikView document’s efficiency
Avoid single-threaded processes
Avoid unnecessary expression complexity
Other general application design considerations

13. Avoid single-threaded QlikView processes
Macros
“Data Island” Fields vs. Variables

14. Expression Syntax / Strategy
Count Distinct
String Comparisons
Calculations
(CY-LY)/LY vs. CY/LY – 1
Use Flags
Target expressions that are frequently repeated

15. Application Design Avoid “Show Frequency”
Forcing Proper Object Display
Calculation Conditions
Show Conditional
Avoid too many active objects on the same sheet

16. QlikView Security
QlikView Publisher Security
Section Access

17. Publisher Security
Breaking up one large QlikView file into multiple smaller files based on row level security
Effective for memory management if security profiles do NOT contain much overlapping data

18. Section Access
Dynamically reduces the QlikView file at logon based on user authorization

19. Measuring Performance

20. How can I measure response times in QlikView?
QlikView log files
User / Selection simulation macro
Task Manager
Sometimes the simplest approach is the best. One way to identify memory consumption and performance is to use Task Manager on the server, open an application on the server using IE and then create more connections to the same application with IE. Click around and look at the Delta change for memory and processor utilization.

21. Influencing Hardware Factors

22. RAM RAM availability is a binary matter 75% Rule
32 vs 64 QV memory usage (contiguous vs. non-contiguous)
10% RAM overhead per additional user estimation

23. CPU Multi-Threading capabilities # of Cores vs. # of CPUs
Intel vs. AMD

24. Others Hyper-Threading Hard Disc Performance Network Capacity
No positive impact
Could actually have negative impact
Hard Disc Performance
Does impact how long it takes to load the QlikView file from disk into memory (i.e. initial QVS load)
Network Capacity
Data transfer between the server and the client during QlikView sessions is minimal so bandwidth is not typically a critical factor

25. Niklas Boman