1. © 2012 Wellesley Information Services. All rights reserved. Preventing, Diagnosing, and Resolving the 20 Most Common Dashboard Performance Problems Dr. Bjarne Berg Comerit

2. Background In this session we will look at the application, design, interfaces from a front-end standpoint. We will also look at query design, connectivity impacts, in- memory processing options, BW design considerations, as well as dashboard performance monitoring options 1

3. Functionality vs. Performance — What Wins? 2

4. What We’ll Cover … Choosing the right connectivity and back end Exploring query performance Thinking about the dashboard design Increasing query performance with infrastructure and in-memory processing Leveraging pre-caching capabilities and aggregates Obtaining strategies for performance testing: Load and stress Looking at EarlyWatch Reports and the performance checklist Wrap-up 3

5. Problems #1 and #2: Connectivity and Performance As we covered in the earlier session, the type of connectivity matters for the performance Always pick the fastest interface available for the data source you are building dashboard on Source: SAP AG, 2011 BICS connectors perform well Avoid the MDX interface (it is slow) Avoid direct access to the InfoProviders since this bypasses the BI analytical engine in SAP NetWeaver ® BW

6. Problem #3: Data Connectivity — SAP Crystal Reports and SAP BusinessObjects Live Office You can use transient providers to create real-time dashboards on top of SAP ERP data You can also use SAP Crystal Reports for detailed drill-down analysis If you always use the “refresh on load” option for Live Office connections, your users will experience periodic slow performance By leveraging the aggregation in SAP Crystal Reports 2011, you can also get faster SAP BusinessObjects Dashboards (formerly Xcelsius ®) response time

7. Problem #4: Back End — Build on a Solid Performance Foundation Modularize the data and create sub-sets of data for really fast dashboarding Generic “metrics” data tables can be created for summarized KPI and scorecard dashboards The summary, or snapshot data can be accessed much faster than underlying data tables with millions of records Real example

8. Problem #5: Back End — Dashboard Performance Architecture In this example, the company uses snapshots for performance reasons 7  Dashboards for executive users  Pre-delivered SAP BusinessObjects Web Intelligence reports for casual users  Ad hoc SAP BusinessObjects Web Intelligence reports for power users The dashboards are only built on the low-volume daily snapshot cube (this is also placed in SAP NetWeaver BW Accelerator for very high performance) Real example

9. What We’ll Cover … Choosing the right connectivity and back end Exploring query performance Thinking about the dashboard design Increasing query performance with infrastructure and in-memory processing Leveraging pre-caching capabilities and aggregates Obtaining strategies for performance testing: Load and stress Looking at EarlyWatch Reports and the performance checklist Wrap-up 8

10. Problem #6: Query Read Modes There are three query read modes that determine the amount of data to be fetched from a database and sent to the application server 1. Read all data  All data is read from a database and stored in user memory space 2. Read data during navigation  Data is read from a database only on demand during navigation 3. Read data during navigation and when expanding the hierarchy  Data is read when requested by users in navigation 9 Reading data during navigation minimizes the impact on the application server resources because only data that the user requires will be retrieved

11. 10 Problem #7: Recommendation — Query Read Mode for Large Hierarchies For queries involving large hierarchies, it is smart to select “Read data during navigation” and when expanding this option to avoid reading data for the hierarchy nodes that are not expanded Reserve the Read all data mode for special queries  I.e., when a majority of the users need a given query to slice and dice against all dimensions, or data mining  This places heavy demand on database and memory resources and may impact other SAP NetWeaver BW processes  A query read mode can be defined on an individual query or as a default for new queries (transaction RSRT) Recommendations for OLAP universes and SAP BusinessObjects Web Intelligence analysis  Use of hierarchy variable is recommended  Hierarchy support in SAP BusinessObjects Web Intelligence for SAP NetWeaver BW is limited  The Use Query Drill option significantly improves drill-down performance  Look at the Query Stripping option for power users

12. Problem #8: Reduce the Use of Conditions and Exceptions Reporting Conditions and exceptions are usually processed by the application server  This generates additional data transfer between database and application servers If conditions and exceptions have to be used, the amount of data to be processed should be minimized with filters  When multiple drilldowns are required, separate the drill-down steps by using free characteristics rather than rows and columns BENEFIT: This results in a smaller initial result set, and therefore faster query processing and data transport as compared to a query where all characteristics are in rows This approach separates the drill-down steps. In addition to accelerating query processing, it provides the user more manageable portions of data.

13. Performance Settings for Query Execution This decides how many records are read during navigation 12 Examine the request status when reading the InfoProvider In SAP NetWeaver BW 7.x the BI Analytical engine can read deltas into the cache. Does not invalidate existing query cache. Displays the level of statistics collected Turn off/on parallel processing When will the query program be regenerated based on database statistics?

14. Problem #9: Filters in Queries Used in Dashboards Using filters contributes to reducing the number of database reads and the size of the result set  Thereby significantly improving query runtimes 13 Filters are especially valuable when associated with large dimensions, where there is a large number of characteristics such as customers and document numbers

15. Problem #10: The RSRT Transaction to Examine Slow Queries 14 P1 of 3 The RSRT transaction is one of the most beneficial transactions to examine the query performance and to conduct “diagnostics” on slow queries from the SAP NetWeaver BW system

16. Do You Need an Aggregate — Some Hints 15 This suggests that an Aggregate would have been beneficial P2 of 3

17. Get Database Info 16 In this example, the Basis team should be involved to research why the Oracle settings are not per SAP’s recommendation The RSRT and RSRV codes are key for debugging and analyzing slow queries P3 of 3 HINT: Track front-end data transfers and OLAP performance by using RSTT in SAP NetWeaver BW 7.0 (RSRTRACE in SAP BW 3.5)

18. Problem #11: Debug Queries Using the RSRT Transaction 17 Using RSRT you can execute the query and see each breakpoint, thereby debugging the query and seeing where the execution is slow Try running slow queries in debug mode with parallel processing deactivated to see if they run faster

19. After SAP BusinessObjects Enterprise XI 3.1 FP 1.1, the impact of large numbers of key figures was somewhat reduced by retrieving metadata information only when the unit/currency metadata info is selected However, this is still best practice Recommendation for Key Figures in OLAP Universes A large number of key figures (KFs) in the BEx query will incur a significant performance penalty when running queries, regardless of whether the key figures are included in the universe Only include key figures used for the dashboard in the BEx query (keep it small) This performance impact is due to time spent loading metadata for units, executed for all measures in the query 18

20. Problem #12: The Performance Killers — Restrictive Key Figures When Restrictive Key Figures (RKF) are included in a query, conditioning is done for each of them during query execution  This is very time consuming and a high number of RKFs can seriously hurt query performance My Recommendation: Reduce RKFs in the query to as few as possible  Also, define calculated key figures and RKFs on the InfoProvider level instead of locally within the query. Why? 19 Benefit: Formulas within an InfoProvider are returned at runtime and held in cache Drawback: Local formulas and selections are calculated with each navigation step

21. Dashboard Performance Killers — Calculated Key Figures Calculated Key Figures (CKF) are computed during runtime, and many CKFs can slow down the query performance How to fix this  Many of the CKF can be done during data loads and physically stored in the InfoProvider  This reduces the number of computations and the query can use simple table reads instead  Do not use total rows when not required (this requires additional processing on the OLAP side) 20 Recommendation for OLAP universes RKF and CKF should be built as part of the underlying BEx query to use the SAP NetWeaver BW back-end processing for better performance Queries with a larger set of such KFs should use the “Use Selection of Structure Members” option in the Query Monitor (RSRT) to leverage the OLAP engine

22. What We’ll Cover … Choosing the right connectivity and back end Exploring query performance Thinking about the dashboard design Increasing query performance with infrastructure and in-memory processing Leveraging pre-caching capabilities and aggregates Obtaining strategies for performance testing: Load and stress Looking at EarlyWatch Reports and the performance checklist Wrap-up 21

23. Problem #13: Dashboard Performance Hint — The Number of Rows in the Result Set 22 Limit the number of rows in your result set to between 100 – 500 Returning query result sets with few records of a numeric type or with keys and indicators provides for the best dashboard performance The Length of each record (# of columns) and the data type also impacts performance In exceptional cases when you have leveraged other performance- tuning methods, you may extend this to up to 1,000 rows

24. Divide and Get Performance Drill-down options Split your dashboards into logical units and get new data when drilldowns are executed This keeps the result set for each query small and also decreases the load time for each dashboard 23

25. Problem #14: Excel Performance Considerations — What to Avoid The logic you build into your Excel spreadsheet is also compiled into the Flash file when you export it Since some “daisy-chain” functions are very time consuming, you should be careful not to add too many conditions in the data  Lookup functions and conditioning that should be avoided include:  Lookups  Mid strings (MID)  Right and left strings (RIGHT/LEFT)  Horizontal Lookups (HLOOKUP)  Vertical Lookups (VLOOKUP)  Condition  General conditioning (IF)  Count if a condition is true (COUNTIF)  Sum if a condition is true (SUMIF) 24 Complex logic and nested logic create large SWF files and take a long time to open. Try to keep as much of the calculations and logic in the query instead of the spreadsheet.

26. Hint: Reducing the text in the query will also speed up the query processing time User Sorts themselves Problem #15: The BI Analytical Engine and Sorting Sorting is done by the BI Analytical Engine  Like all computer systems, sorting data in a report with large result sets can be time consuming Reduce the number of sorts in the “default view”  This will provide the users with data faster. They can then choose to sort the data themselves. 25

27. These are dashboard objects that you need to carefully consider before employing Problem #16: Dashboard Objects That Can Cause Slow Performance 26

28. What We’ll Cover … Choosing the right connectivity and back end Exploring query performance Thinking about the dashboard design Increasing query performance with infrastructure and in-memory processing Leveraging pre-caching capabilities and aggregates Obtaining strategies for performance testing: Load and stress Looking at EarlyWatch Reports and the performance checklist Wrap-up 27

29. It is hard to build a fast dashboard with many queries and panels without SAP NetWeaver BW Accelerator  This provides in-memory processing of queries that is 10-100 faster Problem #17: It Is All About Performance, Performance, Performance What we simply do is place the data in-memory and retrieve it much faster  There is also some limited OLAP functionality that can be built into SAP NetWeaver BW Accelerator 7.3, but most data processing still occurs in the BI Analytical engine You can also place non-SAP data in-memory, using SAP BusinessObjects Data Services 28

30. The major improvement is to make query execution more predictable and faster overall Seconds Number of Queries Seconds SAP NetWeaver BW Accelerator Performance Increases — Real Example 29

31. BI Analytical Engine’s Query Executing Priorities 30 Query Execution Without SAP NetWeaver BW Accelerator Query Execution With SAP NetWeaver BW Accelerator Information Broadcasting / Precalculation Query Cache Aggregates InfoProvider Information Broadcasting / Precalculation Query Cache SAP NetWeaver BW Accelerator Aggregates can be replaced with SAP NetWeaver BW Accelerator, while the memory cache is still useful Source: SAP AG

32. What We’ll Cover … Choosing the right connectivity and back end Exploring query performance Thinking about the dashboard design Increasing query performance with infrastructure and in-memory processing Leveraging pre-caching capabilities and aggregates Obtaining strategies for performance testing: Load and stress Looking at EarlyWatch Reports and the performance checklist Wrap-up 31

33. Problem #18: Different Uses of the MDX and OLAP Cache The OLAP Cache is used by SAP NetWeaver BW as the core in-memory data set  It retrieves the data from the server if the data set is available The Cache is based on First in  Last out  This means that the query result set that was accessed by one user at 8:00 am may no longer be available in-memory when another user is accessing it at 1:00 pm  Therefore, queries may appear to run slower sometimes 32 The MDX cache is used by MDX-based interfaces, including the OLAP universe

34. Use the BEx Broadcaster to Pre-Fill the Cache 33 Distribution Types You can increase query speed by broadcasting the query result of commonly used queries to the cache Users do not need to execute the query from the database  Instead the result is already in the system memory (much faster)

35. The Memory Cache Size The OLAP Cache is, by default, 100MB for local and 200MB for global use  This may be too low... Look at available hardware and work with you Basis team to see if you can increase this If you decide to increase the cache, use the transaction code RSCUSTV14 The OLAP Cache is not used when a query contains a Virtual Key Figure or virtual characteristics, or when the query is accessing a transactional DSO or a virtual InfoProvider

36. Monitor Application Servers and Adjust Cache Size To monitor the usage of the cache on each of the application servers, use transaction code RSRCACHE and also periodically review the analysis of load distribution using ST03N – Expert Mode 35 P.S.! The size of the OLAP Cache is physically limited by the amount of memory set in system parameter rsdb/esm/buffersize_kb The settings are available in RSPFPAR and RZ11

37. The Four Options for OLAP Cache Persistence Settings CACHE OLAP Persistence Settings Note WhenWhatT-code DefaultFlat file Change the logical file BW_OLAP_CACHE when installing the system (not valid name)FILE OptionalCluster tableMedium and small result sets RSR_CACHE_DBS_IX RSR_CACHE_DB_IX Optional Binary Large Objects (blob)Best for large result sets RSR_CACHE_DBS_BL RSR_CACHE_DB_BL Available since SAP NetWeaver BW 7.0 SP 14 Blob/Cluster Enhanced No central cache directory or lock concept (enqueue). The mode is not available by default. Set RSR_CACHE_ACTIVATE_NEW RSADMIN VALUE=x 36

38. Problem #19: Correct Aggregates Are Easy to Build 37 We can create proposals from the query, last navigation by users, or by BW statistics Create aggregate proposals based on BW statistics. For example:  Select the runtime of queries to be analyzed  Select the time period to be analyzed  Only those queries executed in this time period will be reviewed to create the proposal Create aggregate proposals based on queries that are performing poorly

39. Activate the Aggregate The process of turning 'on' the aggregates is simple 38

40. Fill the Aggregate with Summary Data

41. What We’ll Cover … Choosing the right connectivity and back end Exploring query performance Thinking about the dashboard design Increasing query performance with infrastructure and in-memory processing Leveraging pre-caching capabilities and aggregates Obtaining strategies for performance testing: Load and stress Looking at EarlyWatch Reports and the performance checklist Wrap-up 40

42. Problem #20: Performance Testing — Load and Stress Load testing is done to 20% of the named user base  Turn off the cache (we assume all hits “new data”)  Execute the dashboard URLs using a tool or a simple JavaScript  Monitor database, portal, and BI system load  Log response time and have multiple browsers and PCs hitting the data from multiple locations (network testing) Stress testing is done at 40% of named user base  The test is done the same way as on the load testing, just with more “users”  The system may not be able to pass at this level, but the break- points are identified 41 All dashboard systems should be load tested to 20% of user base prior to Go-Live

43. Bonus Problem #1: Server Locations and Network Capacity Having a central global install of SAP BusinessObjects BI 4.x with many users can cause significant network load and performance issues 42 Consider the network topology, capacity, and the user locations before implementing global dashboards

44. What We’ll Cover … Choosing the right connectivity and back end Exploring query performance Thinking about the dashboard design Increasing query performance with infrastructure and in-memory processing Leveraging pre-caching capabilities and aggregates Obtaining strategies for performance testing: Load and stress Looking at EarlyWatch Reports and the performance checklist Wrap-up 43

45. Bonus Problem #2: EarlyWatch Reports in SAP Solution Manager EarlyWatch reports provide a simple way to confirm how your system is running and to catch problems  A “goldmine” for system recommendations EarlyWatch Reports have been available since SAP Solution Manager version 3.2 SP8 The more statistics cubes you have activated in SAP NetWeaver BW, the better usage information you will get  Depending on your version of SAP NetWeaver BW, you can activate 11-13 InfoCubes  Also, make sure you capture statistics at the query level (set it to “all”) 44 System issues can be hard to pin-down without access to EarlyWatch Reports. Monitoring reports allow you to tune the system before a user complains.

46. Information About a Pending “Disaster” 45 This system is about to crash The system is growing by 400+ GB per month, the app server is 100% utilized, and the DB server is at 92% This customer needed to improve the hardware to get the query performance to an acceptable level

47. Bonus Problem #3: The Dashboard Performance Checklist 1. The hardware servers — Check sizing 2. The server locations and networks — Check loads 3. Query review — Look at database time, calculation time, and design 4. Interface review — Make sure you are using the best for the data source 5. Dashboard review — Look at Excel logic, container usage, number of Flash objects, sorts, size of result set, and simplification opportunities 6. In-memory review — Look at cache usage, hit rations, and SAP NetWeaver BW Accelerator usage 7. Review data sources — Examine if snapshots can be leveraged and look for possibilities to create aggregates 8. Examine compatibilities between browsers, Flash, and Microsoft office versions 9. Review PC performance issues — Memory, disk, and processors 46 Performance is complex, look at more than one area (e.g., Web portal bottlenecks and LDAP servers)

48. What We’ll Cover … Choosing the right connectivity and back end Exploring query performance Thinking about the dashboard design Increasing query performance with infrastructure and in-memory processing Leveraging pre-caching capabilities and aggregates Obtaining strategies for performance testing: Load and stress Looking at EarlyWatch Reports and the performance checklist Wrap-up 47

49. Resources Tuning SAP BusinessObjects Solutions for Optimal Performance: Tips from the Trenches by Chris Dinkel  The SAP BusinessObjects seminar (SAPinsider, 2011) SAP Business Objects Tuning by Steve Bickerton  wp.broadstreetdata.com/wp-content/uploads/BOCX-Speaker- Performance-Tuning_-Steve-Bickerton.pdf SAP Service Marketplace for sizing guidelines  https://websmp104.sap-ag.de/sizing and follow the Sizing Guidelines menu path  SBO_BI_4_0_Dashboard_designer.pdf  Need to be a customer to access this  Requires login credentials to the SAP Service Marketplace 48

50. 7 Key Points to Take Home Dashboards are all about performance, performance, and performance You have to spend time on the back end performance tuning Avoid direct querying of high data volumes, create summaries instead Consider in-memory processing for all critical dashboards Your interface to the data will impact the performance — Avoid MDX Size your hardware one size “too big” — It is hard to make a second first impression Use a gradual rollout of your dashboards, monitor the performance, and conduct load and stress tests before any major go-lives 49

51. Your Turn! 50 How to contact me: Dr. Bjarne Berg bberg@comerit.com

52. Disclaimer SAP, R/3, mySAP, mySAP.com, SAP NetWeaver ®, Duet ®, PartnerEdge, and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and in several other countries all over the world. All other product and service names mentioned are the trademarks of their respective companies. Wellesley Information Services is neither owned nor controlled by SAP. 51