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Static Analysis in Search of Performance Anti-Patterns

Static Analysis in Search of Performance Anti-Patterns. Patrycja Wegrzynowicz Founder and CTO, Yonita Inc. Founder and CTO, Yon Labs and Yon Consulting. About me. Past 10+ years of pr ofessional experience as software developer, architect, and head of software R&D

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Static Analysis in Search of Performance Anti-Patterns

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  1. Static Analysis in Search of Performance Anti-Patterns Patrycja Wegrzynowicz Founder and CTO, Yonita Inc. Founder and CTO, Yon Labs and Yon Consulting

  2. About me • Past • 10+ years of professionalexperience as software developer, architect, and head of software R&D • PhD in Computer Science (automated code analysis) • Speaker at JavaOne, JavaZone, ASE, OOPSLA, Jazoon, and others • Present • Founder and CTO of Yonita Inc., Yon Labs, and Yon Consulting • Bridge the gap between the industry and the academia • Future • Who cares? Seize the day!

  3. Agenda • Performance issues • Performance bottlenecks and how to deal with them • Automated code analysis • Semantic code query system • Performance anti-patterns • Code samples • Code queries

  4. Performance Issues

  5. This is Java.

  6. Is Java slow? • On most Intels Java is as fast or faster than C • The Gaia Satellie and Data Processing • William O’Mullane from European Space Astronomy Centre • Jazoon 2010

  7. Limited Resources Locks Pools Application FD Memory OS Threads Network Processors Hardware RAM HD

  8. Sun’s SDK, Arrays.binarySearch, Joshua Bloch

  9. The Way We Write Software • Business Requirements • Technology Stack • Software Teams struts JSP awt GWT Tapestry Swing JSF RMI JNDI JMS EJB JTA JPA iBatis hibernate JDO

  10. Performance Issues • Root causes • Bad architecture, design, or code • Wrong configuration (app/db server, OS, RDBMS) • Rarely insufficient hardware • Bottlenecks usually unnoticed until happen • Special conditions to happen: size of data, number of users • The later discovered the more expensive to solve

  11. Performance Tuning • MIF cycle • Dynamic analysis • Log/traces • Profiles • Environments • Testing • Production

  12. Our Code

  13. A Single Spaghetti is Hard to See

  14. (Anti-)Patterns in Our Spaghetti

  15. Automated Code Performance Tuning + Automated code analysis Definions of performance anti-patterns

  16. Detection of Performance Anti-Patterns • Benefits • Hints on performance on higher level of abstraction (architecture, design, programming constructs) instead of in terms of lower level concepts (CPU usage, memory consumptions etc.) • Supports developers and development phase • Promotes best practices (refactored solutions) • Problems • Lack of formalization, high-level concepts, textual description • Implementation variants • Size of code

  17. Think? Why think! We have computers to do that for us. – Jean Rostand Automated Code Analysis

  18. Semantic Code Query System • Code query systems • Explore relationships among program elements • Structural and call-related predicates • CodeQuest, JQuery • Semantic code query systems • Focus on behavior (in-depth data flow analysis) • Behavioral predicates in addition to structural ones • D-CUBED – my PhD thesis (focus on design patterns) • Yonita – rewrite + queries

  19. How does Yonita work? Bytecode or Sources SQL or Prolog (anti-)patterns bugs vulnerabilities ... Asm/Recoder Parser Query Analyses analyses: structural, call flow, data flow transitive closures Store relational (MySQL) or deductive (XSB) database

  20. Metamodel • Structural elements • Very similar to CodeQuest and Jquery • Statements • Instances • Symbolic instances • Behavioral predicates supporting call and data flows • Output and input values • Assignments

  21. Instances • Null • New • This • Parameter • Exception • Multiple Return Instance This Instance Param Instance Except. Instance New Instance Null Instance Instance Instance … Instance … Instance …

  22. Behavioral Predicates • Calls • makesCallConditionally(Callable, Callable) • makesCallAlways(Callable, Callable) • Input and output values • hasInput(Callable, Instance) • hasInput(Statement, Instance) • hasOutput(Callable, Instance) • hasOutput(Statement, Instance) • Assignments • assigns(Field, Instance)

  23. Running Yonita Prototype • Run Yonita to parse an application • yonita [classes] [libs] –prolog –mysql • MySQL configuration • hibernate.cfg.xml • Run MySQL console and ask queries (SQL) • Prolog configuration • -Dprolog.file=output.P -prolog=output.P • Run XSB Prolog console and ask queries (Prolog) • Yonita on Google App Engine • In progress

  24. Performance Anti-Patterns

  25. Redundant Work – Example 1 (Web)

  26. Redundant Work – Example 1 (Web) • Multiple calls to database (7 calls!) • Multiple security check • Each call to getUser checks permissions • What if getUser were a remote method?!

  27. Redundant Work – Example 2 (agilefant)

  28. Redundant Work – Example 2 (agilefant)

  29. Redundant Work – Example 2 (agilefant – sourceforge project) Thank you hibernate! Open Session in View Session cache

  30. Redundant Work – Example 3 (agilefant) AJAX Calls

  31. Redundant Work – Example 3 (agilefant) AJAX Calls

  32. Redundant Work – Example 3 (agilefant) AJAX Calls

  33. Redundant Work • Description • A time-consuming method is called many times on a single execution path • Consequences • A slower execution time • Refactored solution • Call the heavy method only once and store the result for further re-use Heavy Heavy Heavy

  34. Redundant Work – Code Query redundantWork(X, Heavy) :- // method Heavy called at least twice... method(X), method(Heavy), isHeavy(Heavy), hasChild(X, Invocation1), methodInvocation(Invocation1, Heavy), hasChild(X, Invocation2), methodInvocation(Invocation2, Heavy), // ...with the same parameters setof(I1, (hasInput(Invocation1, newInstance(I1)), All), setof(I2, (hasInput(Invocation2, newInstance(I2)), All). isHeavy(X) :- isWebService(X). isHeavy(X) :- isDatabaseService(X).

  35. Fine Grained Remote Calls • Entity Beans • EJB1 • EJB2 • Remote Calls • Remote calls have significant impact on application performance Bean getX() Client getY() getZ()

  36. One by One Processing – Example 1 • Add users to a group • Performed in the web layer one by one • Multiple calls to the service layer • Multiple loads and updates each in a separate transaction! • Multiple authorization!

  37. One by One Processing – Example 2 • Delete a group • Detach a group from every user in a group one by one

  38. One by One Processing • Description • The objects in a collection are processed one by one and a single processing requires a call to a time-consuming method (e.g. database access, remote call) • Consequences • A significant increase in time with the increase of collection size • Refactored solution • Consider batch processing Heavy Heavy Heavy Heavy

  39. Unused Object – Example (richfaces 3.2.2)

  40. Unused Object • Description • An object created (usually retrieved from a database), passed to further methods (usually a long chain of method calls), and than not used • Consequences • Higher memory consumption that may lead to performance issues • Refactored solution • Create objects only if you need them.

  41. Unused Objects – Hibernate Mapping

  42. Unused Objects - Standard Collection Mapping

  43. Unused Objects - Immutable Collection

  44. Summary • We need to write better code! • Performance anti-patterns • High-level concepts • Many implenentation variants • Static analysis can help in discovery of performance issues • Useful tool but it does not replace a traditional approach to performance tuning (measure, identify, fix)

  45. Contact • Patrycja • patrycja@YonLabs.com • twitter.com/YonLabs • Yonita • http://www.Yonita.com • http://www.YonLabs.com • http://www.YonConsulting.com

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