1. Performance Forensics Uncovering the Mysteries of Performance and Scalability Incidents through Forensic Engineering Stephen Feldman Senior Director Performance Engineering and Architecture stephen.feldman@blackboard.com Uncovering the Mysteries of Performance and Scalability Incidents through Forensic Engineering Stephen Feldman Senior Director Performance Engineering and Architecture stephen.feldman@blackboard.com

2. Welcome to BbWold’08 Finishing my 5 th year at Blackboard. Brought in to build a Performance Engineering Practice. Team of 15 including myself –Half of the team are Performance Test Engineers –Half of the team are Software Developers Responsible for the performance and scalability of the BbLearn architecture.

3. Session Housekeeping Three hours of fun and excitement. Feel free to fire up your laptops. We will take 1 break at the half-way point –Take a break when ever you need to Questions are welcome at any time.

4. Our Session Schedule Part One: Introduction to Performance Forensics –1:00 to 2:25pm Break –2:25 to 2:35pm Part Two: Advanced Performance Forensics –2:35pm to 4:00pm

5. Sessions Goals The goals of today’s session are… Introduce you to the science of performance forensics. Present a methodology for performing forensics. Discuss techniques for arriving at root cause analysis. Familiarize the audience with tools that can be used to assist the forensics process.

6. Session Learning Objectives At the end of the session you should be able to… Write your own problem statements. Perform the process of evidence collection and interviewing. Apply techniques for using data and analysis to avoid diagnosis bias and value attribution. Perform root cause analysis as part of the performance forensics process. Begin using different tools for capturing key performance data

7. Part One: Introduction to Performance Forensics What is forensic engineering?

8. A Practical Definition The term forensics means “The science and practice of collection, analysis, and presentation of information relating to a crime in a manner suitable for use in a court of law.” –This definition is in the context of a crime. Forensic engineering is the application of accepted engineering practices and principles for discussion, debate, argumentative, or legal purposes.

9. Introduction to Performance Forensics

10. Definition of Performance Forensics The practice of collecting evidence, performing interviews and modeling for the purpose of root cause analysis of a performance or scalability problem. Performance problems can be classified in two main categories: –Response Time Latency –Queuing Latency

11. Cognition of Response Times

12. Queuing Model: Visual of a Bottleneck

13. Performance Forensics Methodology

15. Identify the Problem

16. Identifying the Problem Problems are not always easily identifiable. When they are easily apparent a simple problem statement should be declared so that the investigation can commence. –Calling out symptoms not diagnosing When the problem is not clear, narrowing down the possibilities of what the problem could be should be the appropriate course of action. Be willing to leave the problem statement open ended until a more formulated problem statement can be attained.

17. Problem Statements Example Weak Problem Statement: –Sally Simpleton is experiencing response time latency in the Grade Center. Why is it the statement weak? –Who is Sally Simpleton? –What defines response time latency? –What is she doing in the Grade Center? –When does it happen? Can it be reproduced?

18. Strengthen the Problem Statement Sand College is reporting response time latency of 90 to 120 seconds when course administrators edit Grade Center cells. –The problem is reproducible when using Sally Simpleton’s login credentials and accessing her course section (Introduction to Software Performance Engineering). –The problem has been reproduced at all times of days across different course sections and on different systems.

19. Evidence

20. Multiple types of gathered evidence used to solve performance problems. –Log artifacts –Monitoring/Measurement tools –Instrumentation/Sensors Interactive evidence gathering through interviews. Evidentiary support through discrete simulation Improving future evidentiary capabilities by improving Performance Maturity Model

21. Log Artifacts Understand what logs are in place and where they can be found. Know what they are used for and whether they provide the right information. Keep them slim and usable. Learn how to associate and correlate –Associate multiple log artifacts –Correlate events to the problem statement

22. Example Log Visualization

24. Putting Collectors/Sensors in Place When should this happen? –When a problem statement cannot be developed from the data you do have (evidence or interviews) and more data needs to be collected. How should you go about this? –Want to minimize disruption to the production environment. –Adaptive collection: Less Intensive to More Intensive over time. Basic SamplingContinuous Collection Profiling

25. Monitoring and Measurement Third party components whether commercial or open source deployed to measure responsiveness and resource utilization Excellent tools for trending and correlation Specialization of tools to solve different types of problems. Used in forensics for correlation for resource utilization to event occurrences.

26. Ex 1: Thin-Slicing Monitoring Visualizations

27. Ex 2: Thin-Slicing Monitoring Visualizations

28. Ex 3: Thin-Slicing Monitoring Visualizations

29. Ex 4: Thin-Slicing Monitoring Visualizations

30. Interviewing Techniques –Lassie Question –Time Association –User experienced –Locality –Component/Feature Specific Gathering non-discrete clues Making use of method-R Avoiding diagnosis bias Eliminating value attribution Can a pattern be identified?

31. Diagnosis Bias It is human nature to label people, ideas or things based on our initial opinions of them. Not necessarily scientific, but rather a combination of gut feelings, irrational judgment or failure to process enough conclusive data. We often diagnose before we can get to root cause analysis based on a hunch or perception.

32. Value Attribution Humans have a tendency to imbue someone or something with certain qualities based on its perceived value rather than objective data. Example 1: The problem can’t be my SAN, I spent $250,000 on it. Example 2: It can’t be the network, my engineers are the best in the field. They won’t allow a network problem to happen.

33. Discrete Simulation as Evidentiary Support Performance testing is another technique for gathering evidence. Provides the opportunity to increase logging and watch for events or occurrences note seen originally. Also provides the opportunity to reproduce conditions that cause the performance issue.

34. Modeling and Visualizing

36. An Abstract Example Role of temperature in O-ring failures was difficult to determine by focusing on cases. Attention was focused on two key cases with O- ring failures: –SRM15 (cold launch) –SR22 (warm launch)

37. Missed Opportunities for Visualizing Data

39. Reshaping the Same Data

40. Hypothesis versus Diagnosis Hypothesis: A prediction or educated guess about a problem prior to proving scientifically or mathematically. Diagnosis: A scientific, empirical or measured conclusion about a problem. –Not necessarily the correct answer, but enough data has been gathered to propose a diagnosis. A problem statement needs to be in place for both to exist. Both need supporting data to develop either

41. Quick Comments About Method-R Method-R is a preferred methodology for problem statement development and problem diagnosis. While it was created for Oracle performance analysis, it can be applied to all aspects of software performance forensics. Identifying the most important user actions for the needs of the business in order to improve performance.

42. Correlation

43. What is Correlation? Correlation is a measure of the statistical relationship between two comparable data points. –Time associations are typically made. –Correlate to resource demand –Correlate to event or occurrence Correlation primarily a part of hypothesis and diagnosis.

44. Examples of Correlation

48. Getting to Root Cause Analysis

49. Performance Forensics Methodology

50. Getting to Root Cause Analysis Devising a strong problem statement –Foundation steps of Method-R Knowing where to collect evidence Formulating a data-driven hypothesis Appropriate use of correlation, modeling and visualizing Proving the hypothesis out (test-driven approach) Establishing a diagnosis –Avoid diagnosis bias and value attribution Treating the symptoms –A diagnosis is not always black and white

51. A Case for a Performance Maturity Model Reactive and Exploratory Monitor and Instrument Performance Optimizing Business Optimizing Process Optimized Level 5Level 4Level 3Level 2Level 1 Emphasis on Hardware Emphasis on Application Emphasis on Eco-System Emphasis on Process Emphasis on People

52. Part Two: Advanced Performance Forensics Applying Performance Forensics at Home

53. Resources vs. Interfaces One of the most critical data points to collect Interfaces are critical for understanding throughput and queuing models. –Queuing is another cause of latency –Also a cause of time-outs Resources are critical for understanding the cost of performing a transaction. –Core Resources: CPU, Memory and I/O Response Time = Service Time + Queue Time

54. Performance Forensics Tools

55. Categories of Tools HTTP and User Experience System Collectors: Not Going to Cover (Implied) JVM Instrumentation Java Profilers Database Instrumentation –Session and Wait Event –Profilers

56. HTTP and User Experience

57. Fiddler2 Fiddler 2 measures end-to-end client responsiveness of a web request. Captures requests in order to present http codes, size of objects, sequence of loading, time to process request, performance by bandwidth speed. –Rough estimation of User Experience based on locality. Inspects every detail of the http request –Detailed session inspection –Breakdown of http transformation Other Tools in Category: Y-slow/Firebug, Charlesproxy, liveHTTPheaders and IEInspector

59. Coradiant Truesight Commercial tool used for passive user experience monitoring. Captures page, object and session level data. Capable of defining Service Level Thresholds and Automatic Incident Management. Used to trace back session as if you were watching over the user’s shoulder. Exceptional tool for trend analysis. Primarily used in forensics as evidence for analysis. Other Tools in the Category: Quest User Experience and Citrix EdgeSight

60. Coradiant Truesight

62. Log Analyzers Both commercial and open source tools are available to parse and analyze http access logs. Provides trend data, client statistical data, http summary information. Recommend using this data to study request and bandwidth trends for correlation purposes with resource utilization graphs. –Such a large volume of data. –Recommend working within small time slices

63. JVM Instrumentation

64. -VerboseGC and -Xloggc

65. JSTAT

66. JHAT, JMAP and SAP Memory Analyzer Jhat: Java Heap Analysis Tool takes a heap dump and parses the data into useful and human-digestible information about what's in the JVM's memory. JMap: Java Memory Map is a JVM tool that provides information about what is in the heap at a given time. –Provides text and OQL views into JHat data SAP Memory Analyzer will visualize the JHat output

69. IBM Pattern Modeling Tool for Java GC

70. Database Wait Event Tools

71. The Importance of Wait Events Rise of Session Level Forensics –Underlying theme with all of these tools that “Session” is more important then “System” Wait event tuning used to account for latency –Exists in SQL Server (Waits and Queues) and Oracle (10046) Waits are statistical explanations of latency Each individual wait event might be deceiving, but looking at both aggregates and outliers can explain why a performance problem exists. When sampling directly, usually only have about 1 hour to act on the data.

72. The Importance of Wait Events

73. ASH ASH: Active Session History –Samples session activity in the system every second. –1 hour of history in memory for immediate access at your fingertips ASH in Memory –Collects active session data only –History v$session_wait + v$session + extras Circular Buffer - 1M to 128M (~2% of SGA) Flushed every hour to disk or when buffer 2/3 full (it protects itself so you can relax) Tools to Consider: SessPack and SessSnaper

74. SQL Server Performance Dashboard Feature of SQL Server 2005 SP2 Template report that take advantage of DMVs Provides views into wait events –Doesn’t link events to SQL IDs in the report –Provides aggregate views of wait events Complimentary Tools: SQL Server Health and History Tool and Quest Spotlight for SQL Server

76. Database Profilers and Utilities

77. RML and Profiler The RML utilities process SQL Server trace files and view reports showing how SQL Server is performing. –Which application, database or login is using the most resources, and which queries are responsible for that. –Whether there were any plan changes for a batch during the time when the trace was captured and how each of those plans performed. –What queries are running slower in today's data compared to a previous set of data Profiler captures statements, query counts/statistics, wait events –Can capture and correlate profile data to Perfmon data Heavy overhead with both Other Tools to Consider: Quest Performance Analysis for SQL Server

78. Oracle OEM and 10046 Oracle finally delivered with OEM with a web-based interface. –Performance dashboard provides great historical and present overview –Access to ADDM and ASH simplifies job of DBA –SQL History Problems –licensing somewhat cost prohibitive –Still doesn’t provide wait events For 10046 still need to consider profiling on your own and using a profiler reader like Hotsos P4. –Difficult to trace and capture sessions

80. References and Additional Places to Go

81. Want More? To view my resources and references for this presentation, visit www.scholar.com Simply click “Advanced Search” and search by sfeldman and tag: ‘bbworld08’ or ‘forensics’

82. Final Questions?