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Preventing, Diagnosing, and Resolving the 20 Most Common Dashboard Performance Problems

Preventing, Diagnosing, and Resolving the 20 Most Common Dashboard Performance Problems

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Preventing, Diagnosing, and Resolving the 20 Most Common Dashboard Performance Problems

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  1. 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

  3. Functionality vs. Performance — What Wins?

  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

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

  6. Problem #3: Data Connectivity — SAP Crystal Reports and SAP BusinessObjects Live Office By leveraging the aggregation in SAP Crystal Reports 2011, you can also get faster SAP BusinessObjects Dashboards (formerly Xcelsius®) response time 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

  7. Problem #4: Back End — Build on a Solid Performance Foundation Real example 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

  8. Problem #5: Back End — Dashboard Performance Architecture • In this example, the company uses snapshots for performance reasons Real example • Dashboards for executive users • Pre-delivered SAPBusinessObjects 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)

  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

  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 • Read all data • All data is read from a database and stored in user memory space • Read data during navigation • Data is read from a database only on demand during navigation • Read data during navigation and when expanding the hierarchy • Data is read when requested by users in navigation Reading data during navigation minimizes the impact on the application server resources because only data that the user requires will be retrieved

  11. 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 Intelligencefor SAP NetWeaver BW is limited • The Use Query Drill option significantly improves drill-down performance • Look at the Query Stripping option for power users 10

  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 In SAP NetWeaver BW 7.x the BI Analytical engine can read deltas into the cache. Does not invalidate existing query cache. Examine the request status when reading the InfoProvider Turn off/on parallel processing When will the query program be regenerated based on database statistics? Displays the level of statistics collected

  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 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 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 P2 of 3 This suggests that an Aggregate would have been beneficial

  17. Get Database Info P3 of 3 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 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 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. 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 • 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

  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? 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) 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

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

  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

  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) 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. Problem #15: The BI Analytical Engine and Sorting • Sorting is done by the BI Analytical Engine • Like all computer systems, sorting data in areport 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. User Sorts themselves Hint: Reducing the text in the query will also speed up the query processing time

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

  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

  29. Problem #17: It Is All About Performance, Performance, Performance • 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 • What we simply do is place the data in-memory and retrieve itmuch 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

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

  31. BI Analytical Engine’s Query Executing Priorities Query ExecutionWithout SAP NetWeaverBW Accelerator Query ExecutionWith SAP NetWeaver BW Accelerator Information Broadcasting /Precalculation Information Broadcasting /Precalculation Query Cache Query Cache Aggregates SAP NetWeaver BW Accelerator InfoProvider Source: SAP AG Aggregates can be replaced with SAP NetWeaver BW Accelerator, while the memory cache is still useful

  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

  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 The MDX cache is used by MDX-based interfaces, including the OLAP universe

  34. Use the BEx Broadcaster to Pre-Fill the Cache 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 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

  38. Problem #19: Correct Aggregates Are Easy to Build Create aggregate proposals based on queries that are performing poorly • 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 We can create proposals from the query, last navigation by users, or by BW statistics

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

  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

  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 All dashboard systems should be load testedto 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 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

  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”) 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” 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 • The hardware servers — Check sizing • The server locations and networks — Check loads • Query review — Look at database time, calculation time, and design • Interface review — Make sure you are using the best for the data source • Dashboard review — Look at Excel logic, container usage, number of Flash objects, sorts, size of result set, and simplification opportunities • In-memory review — Look at cache usage, hit rations, and SAP NetWeaver BW Accelerator usage • Review data sources — Examine if snapshots can be leveraged and look for possibilities to create aggregates • Examine compatibilities between browsers, Flash, and Microsoft office versions • Review PC performance issues — Memory, disk, and processors 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

  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

  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