Документ предоставляет руководство по профилированию Java-приложений, охватывая ключевые этапы диагностики проблем производительности и стратегий профилирования. Он описывает различные методы и инструменты, включая блоки кода, анализ потоков и использование статистики для выявления узких мест в производительности. Автор подчеркивает важность глубокого понимания системы и ее параметров производительности перед началом профилирования.

1. Java black box profiling
Alexey Ragozin
alexey.ragozin@gmail.com

2. The Problem
• There are application
• It doesn’t meat its SLA
• Users/client/stakeholders are unhappy
You are one to fix it!

3. What to do?
• Do not panic!
• Write out all moving parts in system
• Elaborate acceptance criteria
• Make sure you understand KPI and SLA
• Is problem in Java application?
 Yes - You are ready to start profiling

4. Moving parts
Server
Browser
Application
Database
Caching
JavaScript
HTTP
Network Network
SQL performance
CPU Memory / Swapping
Disk IO Virtualization

5. Understanding targets
• Business transaction ≠ Page
• Business transaction ≠ SQL transaction
• Page ≠ HTTP request
• HTTP request ≠ SQL transaction
Make sure you know how your KPI
are translating into milliseconds

6. Just be before you start profiling
 Take a deep breath
 Make sure problem is in Java part
 Make sure you understand end goal
These points are trivial so it is very easy to forget
them under stress.
Do not make that mistake!

7. Few more rule before start
Every system is a black box
 Do you think that know what is inside?
 You don’t!
There 3 kinds of data produced by profiling
 Lies
 Darn lies
 Statistics
Data incorrectly interpreted
Data incorrectly collected

8. Profiling

9. Reproducing performance issue
Stationary process
• Uniform load reasonable simulating real one
• Goal is optimizing average performance indicators
Transition process
• Goal is optimize prolonged process
• Load profile is changing over time systematically
Profiling single operation
• Goal is to optimize particular operation

10. Profiling in JVM
• Stack trace sampling
• Byte code instrumentation
• Thread MBeans
• GC logs
• Heap dumps / Heap histogram
More diagnostic sources
• JMX
• Performance counters
• Flight recorder / mission control

11. Stack trace sampling
• Work best with stationary process
• Uniformly impacts system under test
• Do not require “calibrating”
• Result from multiple runs could be combined
• You deal with probabilities, not hard numbers
• Measure wall clock, not CPU time

12. Byte code instrumentation
• Useful for all types of experiments
• Produce some absolute numbers
 Number of calls is accurate
 Time is predictably skewed
• Significantlyskewperformance ofsystemundertest
• Require careful targeting
• Could measure CPU time

13. Threading MBean
Standard JVM Threading MBean
 CPU usage per thread (user/sys)
 Memory allocation per thread
 Blocking / waiting statistics

14. Tools
SwissJavaKnife(SJK)-https://github.com/aragozin/jvm-tools
• Thread monitor
• Stack Trace Recorder/Analyzer
• Dead object histogram
• and more
BTrace - https://kenai.com/projects/btrace
• Incrementing profiler
• Used via CLI or API
• Scriptable in Java

15. Profiling strategy
Classify your bottleneck
 CPU
• Application CPU usage
• Excessive memory allocation / GC
 Disk IO
 Network latency
 Network bandwidth
 Inter thread contention

16. Recipes: CPU hogs
1. You sampling to identify suspects
• Frame frequency histogram + meditation
• Call tree + deep meditation
• Iterative classification
2. Instrument methods to get hard prove
• relative number of calls between methods
3. Iterate to pin point root cause

17. Recipes: CPU hogs
Iterative classification (JBoss + Seam)
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Base
Other
DefaultServlet.doGet
LifeCycleImpl (render)
LifeCycleImpl (execute)
Business logic
Seam interceptor (lock contention)
Seam interceptor (inject/outject)
Resource bundle (getObject)
Resource bundle (missing)
Faceletcompile
Hibernate (rest)
Hibernate (autoFlush)
JDBC

18. Recipes: CPU hogs
Iterative classification (JBoss + Seam)
[ROOT]
org.apache.catalina.connector.CoyoteAdapter.service
[JDBC]
org.jboss.jca.adapters.jdbc.
[Hibernate (autoFlush)]
org.hibernate.internal.SessionImpl.autoFlushIfRequired
[Hibernate (rest)]
org.hibernate.
[Facelets compile]
com.sun.faces.facelets.compiler.Compiler.compile
[XML]
javax.xml.
[ResourceBundle - missing resource]
java.util.ResourceBundle.getObject(ResourceBundle.java:395)
[ResourceBundle - getObject]
java.util.ResourceBundle.getObject
[Seam bean interceptor - inject/disinject/outject]
!REQUIRE
!LAST
org.jboss.seam.core.BijectionInterceptor.aroundInvoke
org.jboss.seam.core.SynchronizationInterceptor.aroundInvoke
!NOT FOLLOWED
**.proceed
!ANY
org.jboss.seam.Component.inject
org.jboss.seam.Component.disinject
org.jboss.seam.Component.outject
[Seam bean interceptor - lock contention]
!REQUIRE
!LAST
org.jboss.seam.core.BijectionInterceptor.aroundInvoke
org.jboss.seam.core.SynchronizationInterceptor.aroundInvoke
!NOT FOLLOWED
**.proceed
java.util.concurrent.locks.ReentrantLock
[Seam bean interceptor - other]
!REQUIRE
!LAST
org.jboss.seam.core.BijectionInterceptor.aroundInvoke
org.jboss.seam.core.SynchronizationInterceptor.aroundInvoke
!NOT FOLLOWED
**.proceed
[WorkItemController.doselect]
org.cp.shark.WorkItemController.doselect
[LifecycleImpl.execute]
com.sun.faces.lifecycle.LifecycleImpl.execute
[LifecycleImpl.render]
com.sun.faces.lifecycle.LifecycleImpl.render
[DefaultServlet.doGet]
org.apache.catalina.servlets.DefaultServlet.doGet
[other]

19. Recipes: Memory issues
1. Indentify thread producing garbage
• SJK: ttop command
2. Classify garbage
• Class histogram of dead object in heap
• SJK: hh --dead
3. Investigate suspect classes
• Heap dump
• Debugging / Instrumentation

20. Recipes: contention
• Stack trace sampling
 Good for “bad” contention cases
• Thread MBean
 Contention statistics could be enabled
• BTrace
 Analyze lock access pattern using your
codebase knowledge

21. Sub millisecond profiling
Low latency profiling requires
indirect measuring.
You should not measure sub ms directly!
• Make a hypothesis
• Arrange experiment to prove / disprove
• Collect aggregated metrics
• Compare, analyze and iterate

22. Flight record / Mission Control
Tight integration with JVM
• Low overhead
• Access to intimate JVM areas
Well balanced set of metrics
• Unobtrusive
• Indicative
Good user interface
May be one day I will abandon my SJK 

23. Thank you
Alexey Ragozin
alexey.ragozin@gmail.com
http://blog.ragozin.info
- my articles
http://aragozin.timepad.ru
- community events in Moscow