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Performance tuning the Spring Pet Clinic sample application
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Performance tuning the Spring Pet Clinic sample application



Slides from my talk at SpringOne 2GX 2013 in Santa Clara, CA.

Slides from my talk at SpringOne 2GX 2013 in Santa Clara, CA.




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Performance tuning the Spring Pet Clinic sample application Performance tuning the Spring Pet Clinic sample application Presentation Transcript

  • © 2013 SpringOne 2GX. All rights reserved. Do not distribute without permission. Performance-tuning the «Spring Pet Clinic» sample application By Julien Dubois
  • About me • Julien Dubois • From Paris, France • Co-authored «Spring par la pratique» (French book on Spring) • Former SpringSource France director • Director of Consulting at Ippon Technologies • Follow me @juliendubois 2
  • Goals of this presentation • Make a specific application run faster • Learn classic issues with Spring Web applications • Learn our methodology to solve performance issues 3
  • More information on this presentation • This presentation started with a series of blog posts: http://blog.ippon.fr/tag/spring-petclinic/ • You can run your own tests, every step is cleanly commited on Github: https://github.com/jdubois/spring-petclinic 4
  • What’s new since the blog entries? • Java 7 • I got a brand new MacBook :-) –Does a SSD really matters? –Does the new core i7 matters? 5
  • 6 Introducing the Spring Pet Clinic sample application
  • Do you remember the Sun Java Pet Store? • Used to explain how you could build an incredibly complex application • On a very expensive application server • With an horrible-looking result • When your customer only wanted a simple Web app 7
  • The Spring Pet Clinic • Is here to demonstrate Spring features and best practices • Is a very classical MVC-style application (no REST + funky JS library) • Is still a good excuse to use cute dogs in your presentation 8
  • More information on the Spring Pet Clinic • Code: https://github.com/SpringSource/spring-petclinic/ • Short presentation: https://speakerdeck.com/michaelisvy/spring-petclinic-sample- application 9
  • 10 #1 Putting the application in production
  • Moving to production • Be as close as possible to a «production environment» –Remove «debugging» logs [ Code ] –Use a «real» database [ Code ] • For this presentation our environment isn’t perfect –Everything runs on the same machine –Even the presentation itself :-) 11
  • Memory tuning • We’re running the application from the Maven Tomcat plugin –Of course Maven takes some resources • Still the application runs with a small memory footprint –export MAVEN_OPTS="-XX:MaxPermSize=64m -Xms128m - Xmx128m" • Why not use more memory? –It’s easier to spot memory problems –We don’t need it (we’re not using a big fat Java EE server!) 12
  • 13 #2 Creating a JMeter test
  • Simulate load • JMeter will simulate users –500 users –In a «realistic» way • Use a timer • Check «clear cookies each iteration?» 14
  • Results • 250 req/sec • Lots of errors • Quickly ends with a crash... 15
  • 16 #3 Profiling
  • Does profiling distort results? • Short answer : not a lot –We’ll see this when the application runs better • We have used JProfiler and VisualVM in production many times –Often caused less trouble than official «production monitoring tools» like HP Diagnostics –Only way to find problems and understand what’s going on –Easy to install 17
  • Choose your weapon • VisualVM –Free & lightweight –Included in the JDK • JProfiler –Best tool we’ve used • YourKit –Easy to use & cheap 18
  • First culprit : Dandelion • More specifically, Dandelion module for DataTables –https://github.com/dandelion/dandelion-datatables • Nice & fun JSP taglib for rendering JQuery DataTables –Has a memory leak in this version –Is now corrected (extremely quick response time from the Dandelion team) • So we switched to «plain old JQuery DataTables» [ Code ] 19
  • Second culprit : the HTTP Session • Using HTTP Sessions can be helpful –Developer productivity –Data security • Going stateless is often better –Uses less RAM –More scalable 20
  • Going stateless • Example : the PetController.processUpdateForm() method –Use Spring MVC REST to get more information from the URL –Fetch again the missing data • We said more scalability, not more performance (for the moment) • With some work, the whole application can become stateless [ Code ] 21
  • Results • 500 req/sec • A few errors • That’s a lot better than before! 22
  • 23 #4 Tomcat tuning
  • Tomcat tuning • This is the easiest step –Migrate to the latest version of the Tomcat plugin –Use the new NIO connector • org.apache.coyote.http11.Http11NioProtocol • [ Code ] 24
  • 25 • 950 req/sec • No errors • Victory :-) Results
  • 26 #5 Removing locks
  • Locks • Locks are blocked threads –Aka «there’s a synchronized somewhere» • They obviously cause scalability problems • Let’s launch our profiler again! 27
  • First culprit : commons DBCP • Commons DBCP does a lot of synchronization • We replaced it with Tomcat JDBC –New JDBC pool for Tomcat, for highly concurrent systems • But we have kept the pool size at 8 –So obviously we will still have locks –Making it bigger hinders performance • If the database was on a separate server, we would use a bigger number • The database is always the problem • [ Code ] 28
  • Second culprit : Webjars • WebJars are client-side libraries (JQuery, etc...) packaged as Jar files –http://www.webjars.org/ –Those libraries are loaded from the classloader • Which does a big lock each time it is accessed • So if you have a lot of libraries this can be a real problem • There are many solutions to this issue –Cache those resources with a proxy server –Use another solution for loading those libraries (Bower ?) –[ Code ] 29
  • Third culprit : the monitoring Aspect • This aspect was introduced on purpose • We should simply disable it [ Code ] 30
  • Results • 1020 req/sec, still without any error • Only 10% performance increase 31
  • 32 #6 JDBC vs JPA
  • 3 profiles are available • Do you use Spring profiles? • Choose between –JDBC –JPA –Spring Data 33
  • Switch to JPA • Change the profile in the web.xml file • The application is a little bit slower –904 req/sec –After an initial peak, we see the application hanging and then starting again -> looks like a GC issue 34
  • First problem : Memory • JPA uses more PermGen, and more memory in general than JDBC • Let’s boost the application memory –export MAVEN_OPTS="-XX:MaxPermSize=128m -Xms256m - Xmx256m" –1103 req/sec ! Better than JDBC ! • That’s a different result from our original blog entry –Looks like we need more memory with JDK 7... 35
  • Second problem : SQL requests • Run MySQL Workbench –With JDBC we had more than 30 000 SQL requests per second –With JPA we have 15 000 SQL requests per second • Why isn’t performance better when we have only half as many requests? 36
  • 37 #7 Lazy loading
  • Lazy loading & Open Session In View • Pet.visits is an EAGER collection –This is often a bad idea to eagerly fetch collections • Let’s configure it with the default, lazy-loaded mode • To prevent the dreaded LazyInitializationException we’ll use the Open Session In View pattern –Not the «perfect» solution, which would require more refactoring –Good enough in this situation, and easy to implement –[ Code ] 38
  • Results • 1165 req/sec, still without any error • 10 000 SQL req/sec • We have beaten the JDBC version! 39
  • 40 #8 Adding cache
  • Hibernate Caching • We have used Ehcache • We have put Hibernate’s @Cache annotation everywhere –Owners, Pets, Visits, etc... 41
  • Spring caching • For more complex queries, we used Spring’s cache abstraction –Adds a caching aspect around slow methods • Allowed us to remove the very slow «LIKE» in JpaOwnerRepositoryImpl.findByLastName() • We could also have used Hibernate’s query cache for the same result • [ Code ] 42
  • Results • 1240 req/sec, still without any error • But can we do better? 43
  • 44 #9 Extreme testing
  • Extreme testing • If we look at the JMeter report, we have reached JMeter’s limits –Adding more users (=threads) isn’t doing a big difference • The solution is to do more with the existing threads –Removed the counter –Decreased the ramp-up period to 5 seconds –Run without any listeners (or in command-line) 45
  • Results • We achieved 3000 req/sec • Memory stayed stable –We even let it run until we had 100 000 user sessions • We had to increase the JDBC pool size to 30 –Still a small number of connexions –Pushing it to 40 decreases performance 46
  • 47 Final notes
  • Does JDK 7 helps? • Tested the new G1 Garbage Collector –export MAVEN_OPTS="-XX:MaxPermSize=128m -Xms256m - Xmx256m -XX:+UseG1GC" –3% to 5% slower –Needs a bigger heap to work correctly –Doesn’t do any miracle 48
  • Does the new MacBook Pro helps? • Compared to my original blog posts, with my old MacBook –We still have the memory issues (until solved) –We still have the HTTP errors (until solved) –Results are 10% to 20% better until the last, «extreme testing» phase • If you write inefficient code, a faster CPU and a SSD won’t save you 49
  • 50 #1 Put the application in production #2 Create a JMeter test #3 Profile #4 Tune the application server #5 Remove locks #7 Lazy loading #6 JDBC vs JPA #8 Add cache
  • 51 Questions & Answers @juliendubois Late question?