High-Performance/Capacity Planning: How to Fine Tune your Implementation for High-Performance Analytics. Presented by: Timothy Klawitter

Transcription

1 High-Performance/Capacity Planning: How to Fine Tune your Implementation for High-Performance Analytics Presented by: Timothy Klawitter

2 2 Value: Why Performance Matters High Performance is Essential for Successful BI Applications 18.1% Query Performance Too Slow % 32.5% 10.1% Unreliable Software (bugs) 10.3% 18.0% Missing Key Product Features Data Scalability Usability (too hard to use) Security Limitations in the Product 7.6% 8.4% 7.2% 8.4% 6.4% 5.7% 10.3% 3.8% 5.4% 8.3% 11.9% 12.6% Year after year, performance is cited as the leading cause of dissatisfaction with BI applications and the leading impediment to larger BI deployments User Scalability 2.3% 3.2% 6.3% Source: The BI Survey, BARC

3 Speed 3 Value: Why Performance Matters Mobile & Dashboard Apps Drive Need for Even Higher Performance Fast Mobile & Dashboard Apps (10,000s of users) Mobile users demand almost immediate response from their mobile devices The more diverse (and large) the user population, the lower the tolerance for slow performance Slow Web-based BI (1,000s of users) Desktop BI (100s of users) Low Capacity High

4 4 Agenda 1. Introduction to High Performance 2. Statistics Analysis with MicroStrategy Enterprise Manager 3. Infrastructure Recommendations & Capacity Planning 4. Configuration Settings 5. Caching Strategy 6. Dashboard Performance 7. Conclusion/Q&A

5 5 Introduction to High Performance A Proven Approach Repeatable Process Performance Analysis Performance Analysis Performance Tuning Performance Monitoring Performance Monitoring Performance Tuning

6 6 Performance Analysis Approach Overview Establish the Path to Improved Performance The performance analysis is a one week, database-to-end-user study of the business intelligence system s performance. Know where you are, where you want to be, and exactly how to get there. Establish configuration recommendations that can improve performance immediately. Understand the tools and metrics that can be leveraged to proactively monitor the performance of a growing BI system. Performance Optimization Roadmap Improve Upgrade Enhance Configuration Recommendations Recommended! Expected Results Performance Monitoring Plan 6

7 Meetings and Workshops Activities Customer Activity: Prepare for Analysis Delivery 7 Performance Analysis Approach Timeline A Proven Approach to High Performance Prep Information Gathering Review and Analysis Results A Establish performance profile B Analyze and interpret additional data C Identify and analyze potential solutions D Performance Roadmap Prep check-in Project Kick-off Infrastructure & Configuration Review Prioritization Workshop Final Presentation 1 Week

8 8 Performance Analysis Approach Preparation Preparation: Prepare for Analysis Decide on target Check on information 1 environment 2 3 availability Plan for resource availability R Q Target environment? Critical reports Architecture diagrams Logs, Enterprise Manager stats available Plan for Meetings Project Kick-off Configuration Review Prioritization Workshop Final Presentation 8

9 9 Performance Analysis Approach Information Gathering Step A: Establish Performance Profile Define the Gather data and 1 performance profile 2 3 information Integrate information into a repeatable performance baseline Today s Mean Goal Next Target Categorize and prioritize queries Predictability Frequency of use Importance For each query category define the Performance goal Next Target User experience as of today 9

10 0 Performance Analysis Approach Review and Analysis Step B: Analyze and interpret data 1 Interpretation of logs and statistics 2 Review configuration 3 Identify performance opportunities Recommend ed! Expecte d Results 10

11 Impact 1 Performance Analysis Approach Review and Analysis Step C: Identify and analyze potential solutions 1 Identify solutions 2 Define performance improving options for each solution 3 Evaluate option benefits and cost Employ In-Memory to Accelerate Performance Design High Performance Dashboards Optimize Query Efficiency Implement Effective Caching Strategies Server Memory Governing and Configuration Settings Option Option Option Option Option Effort Evaluate Effort Impact Priority 11

12 Impact 2 Performance Analysis Approach Monitoring and Results Step D: Select and plan solutions 1 Synthesize initiatives into a roadmap Develop a 2 performance 3 Share analysis and plans monitoring plan High Impact & Low Effort Time Effort Roadmap Prioritization Time Query Volume Memory? Capacity Data Volume Concurrency Speed End User Response Queue Time Monitor Range Value Concurrency < 60% 55% Memory usage < 60% 75% Query execution < 5 sec 4 Job queue < 3 sec 4 Analysis Recommendations Roadmap Monitoring Plan 12

13 3 Performance Analysis Approach Monitoring and Results Three Deliverables to Guide the High Performance Journey 1. Performance Optimization Roadmap Current performance profile Prioritized recommendations to achieve progress against the target 2. Configuration Recommendations Intelligence Server, Enterprise Manager, Project, and database instance, Web Administration, and more 3. System Monitoring Recommendations Identified metrics for monitoring to proactively manage the performance of your system Monitoring frequency and thresholds to trigger actions for each identified metric Performance Optimization Roadmap Improve Upgrade Enhance Configuration Recommendations Recommended! Expected Results Performance Monitoring Plan 13

14 4 Performance Analysis Get Started Today! Take the Next Step Towards High Performance Staff Involvement: BI Administrator System Administrator (s) Database Administrator Data Architect Next Steps: Select the BI environment Turn on statistics logging Identify critical reports Select a time period for the performance analysis 14

15 5 Real-World Examples of Analysis Conducted The following slides will show you real-world examples of data/issues encountered during performance analysis engagements This should demonstrate: What the purpose of the different analysis phases is How issues are identified What recommendations can be made How we arrive at these recommendations

16 6 Agenda 1. Introduction to High Performance 2. Statistics Analysis with MicroStrategy Enterprise Manager 3. Infrastructure Recommendations & Capacity Planning 4. Configuration Settings 5. Caching Strategy 6. Dashboard Performance 7. Conclusion/Q&A

17 7 User Concurrency Analysis Purpose: Establish realistic picture of usage patterns, system load, usage trend

18 8 Report/Document Run Time Analysis Report RP Number of Jobs RP Average Elapsed Duration per Job hh:mm:ss RP Average Exec Duration per Job hh:mm:ss Document DP Number of Jobs DP Average Execution Duration per Job hh:mm:ss TOTAL BY ACTIVE BY WATERFALL DAILY 33 0:29:33 0:26:49 PAGE BY DATE REG AND VISIT 66 0:25:32 0:22:18 PAGE BY PAGE BY DATE REG AND VISIT 56 0:19:19 0:19:13 TOPLINEREPORT BY LANDINGPAGE 57 0:18:25 0:18:22 DAILY CAMPAIGN REPORT 7 0:17:23 0:17:11 TOPLINEREPORT 56 0:15:06 0:14:59 BRADY 2 0:14:47 0:14:47 TOPLINEREPORT BY NAVIGATIONAL 87 0:14:46 0:13:35 BIG PAPA 1 0:11:27 0:11:06 RESUMEN CENTRO 139 0:02:59 RESUMEN DIVISIÓN 351 0:02:16 VENTAS CENTRO 532 0:01:52 VENTAS DIVISIÓN 2,928 0:01:44 CLIENTES CENTRO 76 0:01:31 DIVISIONES EMPRESA 16 0:01:10 DAILY REVENUE BY CLASS 91 0:10:25 0:09:37 DAILY TREND OF LAST 26 WEEKS 91 0:10:11 0:09:15 Purpose: Identify slowest reports/documents Correlate with job numbers Identify candidates for query optimization DESGLOSE GASTOS 139 0:01:06 PERSONAL DIVISIÓN 338 0:01:01 PERSONAL CENTRO 94 0:00:58

19 9 Queue Time Analysis Connection Source RP Exec Duration hh:mm:ss RP Queue Duration hh:mm:ss MICROSTRATEGY DESKTOP 40:41:16 7:22:50 MICROSTRATEGY WEB 219:29:33 191:31:20 Queue Time Analysis MICROSTRATEGY SCHEDULER 853:30: :58:55 MICROSTRATEGY NARROWCAST SERVER 2180:26: :09:10 Purpose: Find out whether significant queuing occurs Consider to change database instance settings: Priorities, thread numbers

20 0 Cache Hit Analysis Project Number of Report Jobs with Cache Hit Number of Report Jobs without Cache Hit Percentage of Jobs with Cache Hit KL_LIS 773 5, % KL_MIS 0 51, % KL_RPW 5 4, % KL_VIS 7,507 11, % KL_VIS_BG % KL_VIS_CZ % KL_VIS_HR 829 1, % KL_VIS_PL 1,729 2, % KL_VIS_RO % KL_VIS_SK 681 1, % KL_QS % KL_BDA2_RO % Purpose: find out whether caching is beneficial and where to improve it Consider to change caching strategy Use Intelligent Cubes if ratio caused by prompts/security filters

21 1 Agenda 1. Introduction to High Performance 2. Statistics Analysis with MicroStrategy Enterprise Manager 3. Infrastructure Recommendations & Capacity Planning 4. Configuration Settings 5. Caching Strategy 6. Dashboard Performance 7. Conclusion/Q&A

22 2 Powerful AIX System? Example: European Retailer Current: 2500 users Rollouts of major dashboards to all High concurrency peaks on Mondays Intelligence Server specifications: 2 nodes on AIX Each 1 Power6 CPU core, 8 GB of RAM Need to upgrade!

23 3 Projection of Future Growth Customer Example: Established baseline for capacity planning through Enterprise Manager Analysis, monitoring data Discussed different growth scenarios with business Result: Number of users Number of Ivy Bridge cores per Intelligence Server Total number of Ivy Bridge cores for cluster Up to 20, Up to 16, Up to 13, Up to 10, Up to Up to

24 4 Web Server Overload Example: Large Spanish company Web servers shared with 40 other applications Web performance is bad especially early in the day Administrators do not see need to change Assumption: Intelligence Server overloaded Idea: Compare Intelligence Server versus web server response times Web Statistics: Total Web Server + IServer processing time (milliseconds) : 609 Web server processing time (milliseconds, %) : Transmission Time (milliseconds) : 1 IServer processing time (milliseconds, %) : IServer API time (milliseconds, %) : IServer polling time (milliseconds, %) : Number of Web API polling calls : 1 Number of Web API calls : 6 Bytes sent/received : Start Time (millis) : End Time (millis) :

25 5 Web Server Overload Ratio goes up under load: Series1 30 per. Mov. Avg. (Series1) Although no details are known (time, action?) the logged statistics show that web becomes bottleneck Needs to be addressed

26 6 Capacity Planning Approach Work Tasks 1 Collection of requirements/statistics 2 Analysis of collected requirements/statistics Creation of specific architecture & hardware recommendations Creation of initial system configuration recommendations Stress testing 26

27 7 Capacity Planning Take the Next Step Towards High Performance Choosing the most efficient architecture: Operating system Number of nodes & clustering CPU model Number of CPUs Amount of RAM Disk space Adequately sizing a new MicroStrategy environment s servers is critical to ensure performance and stability in a new installation or rollout. 27

28 8 Capacity Planning CPU Model Considerations Picking a CPU CPU benchmarks can be used: SPEC ratings available at CINT2006 Rating Single core performance in the rating Machine rate is a benchmark for the whole machine The best CPU models available currently are the Ivy Bridge and Haswell processors from Intel. This is helpful in order to not overspend on hardware that will not necessarily improve performance.

29 9 Agenda 1. Introduction to High Performance 2. Statistics Analysis with MicroStrategy Enterprise Manager 3. Infrastructure Recommendations & Capacity Planning 4. Configuration Settings 5. Caching Strategy 6. Dashboard Performance 7. Conclusion/Q&A

30 0 Database Instance Level Define prioritization Increase thread numbers to appropriate level Conduct optimization study for thread numbers

31 1 Database Connection Level Enable extended fetch Enable parallel query execution where applicable Adjust connection timeout

32 2 Memory-relevant Settings Working Set maximum RAM usage examples: RAM available: 16 GB, Working Set: 16 GB RAM available: 32 GB, 4000 users, Working Set: 50 MB Object, element caches: On systems with sufficient RAM, increase maximum RAM usage settings from default values Maximum number of XML cells : Maximum number per data fetch! Large influence on exports Increase on 64-bit systems with sufficient RAM

33 3 Agenda 1. Introduction to High Performance 2. Statistics Analysis with MicroStrategy Enterprise Manager 3. Infrastructure Recommendations & Capacity Planning 4. Configuration Settings 5. Caching Strategy 6. Dashboard Performance 7. Conclusion/Q&A

34 4 Power of Intelligent Cubes Example: Large Spanish company Daily ~300 Narrowcast services Large time window required Overlap with interactive usage -> frustration Analysis shows: only 25 attributes, 90 metrics required Creation of one big cube Issues with cross joins slow execution (2 hours) Identification of helper reports can be cached separately Several attributes removed from cube Cube publication now 15 minutes Clearing path to successful implementation

35 5 Agenda 1. Introduction to High Performance 2. Statistics Analysis with MicroStrategy Enterprise Manager 3. Infrastructure Recommendations & Capacity Planning 4. Configuration Settings 5. Caching Strategy 6. Dashboard Performance 7. Conclusion/Q&A

36 6 9.X Flash Dashboard Rendering Time Example: US Financial Institution Need to display large dashboards in less than 15 seconds Analysis shows: Rendering itself requires ~20 seconds Definition XML is up to 2 MB much bigger than Data XML Many objects on dashboards, heavy formatting Used in analysis: Web statistics, Flash debugger, stopwatch

37 7 Flash Rendering Dashboard Document Caching Mode Intelligence Server Processing Web Server Processing Flash Processing Total Network Transfers (Difference) Total Rendering Time Landing Dashboard Non-Cached Landing Dashboard Non-Cached Landing Dashboard Non-Cached Landing Dashboard Non-Cached Landing Dashboard Cached Intelligence Server processing of documents, database executions are fast Web server is not a problem Network is not a problem Need to redesign to meet SLA

38 8 Agenda 1. Introduction to High Performance 2. Statistics Analysis with MicroStrategy Enterprise Manager 3. Infrastructure Recommendations & Capacity Planning 4. Configuration Settings 5. Caching Strategy 6. Dashboard Performance 7. Conclusion/Q&A

39 9 Performance Optimization Roadmap Task Example: Immediate changes of configuration settings Upgrading current Intelligence Servers (memory and CPU) Further changes of configuration settings after upgrade (incl. document caching) Replacement of current hardware with modern machines Performance Impact Estimated Work Effort Intelligent Cubes Prototype High Four FTE weeks Proposed Timeline Required Resources Minor 1 day Short-term BI Administrators Medium 1 day Mid-term System Administrators Medium 1 day Mid-term BI Administrators High Several weeks Long-term System Administrators, BI Administrators, Testers Mid-term MicroStrategy Consultant(s)

40 0 Q&A