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  • Why should you choose Oracle WebLogic 12c instead of JBoss EAP 6?

    - by Ricardo Ferreira
    In this post, I will cover some technical differences between Oracle WebLogic 12c and JBoss EAP 6, which was released a couple days ago from Red Hat. This article claims to help you in the evaluation of key points that you should consider when choosing for an Java EE application server. In the following sections, I will present to you some important aspects that most customers ask us when they are seriously evaluating for an middleware infrastructure, specially if you are considering JBoss for some reason. I would suggest that you keep the following question in mind while you are reading the points: "Why should I choose JBoss instead of WebLogic?" 1) Multi Datacenter Deployment and Clustering - D/R ("Disaster & Recovery") architecture support is embedded on the WebLogic Server 12c product. JBoss EAP 6 on the other hand has no direct D/R support included, Red Hat relies on third-part tools with higher prices. When you consider a middleware solution to host your business critical application, you should worry with every architectural aspect that are related with the solution. Fail-over support is one little aspect of a truly reliable solution. If you do not worry about D/R, your solution will not be reliable. Having said that, with Red Hat and JBoss EAP 6, you have this extra cost that will increase considerably the total cost of ownership of the solution. As we commonly hear from analysts, open-source are not so cheaper when you start seeing the big picture. - WebLogic Server 12c supports advanced LAN clustering, detection of death servers and have a common alert framework. JBoss EAP 6 on the other hand has limited LAN clustering support with no server death detection. They do not generate any alerts when servers goes down (only if you buy JBoss ON which is a separated technology, but until now does not support JBoss EAP 6) and manual intervention are required when servers goes down. In most cases, admin people must rely on "kill -9", "tail -f someFile.log" and "ps ax | grep java" commands to manage failures and clustering anomalies. - WebLogic Server 12c supports the concept of Node Manager, which is a separated process that runs on the physical | virtual servers that allows extend the administration of the cluster to WebLogic managed servers that are often distributed across multiple machines and geographic locations. JBoss EAP 6 on the other hand has no equivalent technology. Whole server instances must be managed individually. - WebLogic Server 12c Node Manager supports Coherence to boost performance when managing servers. JBoss EAP 6 on the other hand has no similar technology. There is no way to coordinate JBoss and infiniband instances provided by JBoss using high throughput and low latency protocols like InfiniBand. The Node Manager feature also allows another very important feature that JBoss EAP lacks: secure the administration. When using WebLogic Node Manager, all the administration tasks are sent to the managed servers in a secure tunel protected by a certificate, which means that the transport layer that separates the WebLogic administration console from the managed servers are secured by SSL. - WebLogic Server 12c are now integrated with OTD ("Oracle Traffic Director") which is a web server technology derived from the former Sun iPlanet Web Server. This software complements the web server support offered by OHS ("Oracle HTTP Server"). Using OTD, WebLogic instances are load-balanced by a high powerful software that knows how to handle SDP ("Socket Direct Protocol") over InfiniBand, which boost performance when used with engineered systems technologies like Oracle Exalogic Elastic Cloud. JBoss EAP 6 on the other hand only offers support to Apache Web Server with custom modules created to deal with JBoss clusters, but only across standard TCP/IP networks.  2) Application and Runtime Diagnostics - WebLogic Server 12c have diagnostics capabilities embedded on the server called WLDF ("WebLogic Diagnostic Framework") so there is no need to rely on third-part tools. JBoss EAP 6 on the other hand has no diagnostics capabilities. Their only diagnostics tool is the log generated by the application server. Admin people are encouraged to analyse thousands of log lines to find out what is going on. - WebLogic Server 12c complement WLDF with JRockit MC ("Mission Control"), which provides to administrators and developers a complete insight about the JVM performance, behavior and possible bottlenecks. WebLogic Server 12c also have an classloader analysis tool embedded, and even a log analyzer tool that enables administrators and developers to view logs of multiple servers at the same time. JBoss EAP 6 on the other hand relies on third-part tools to do something similar. Again, only log searching are offered to find out whats going on. - WebLogic Server 12c offers end-to-end traceability and monitoring available through Oracle EM ("Enterprise Manager"), including monitoring of business transactions that flows through web servers, ESBs, application servers and database servers, all of this with high deep JVM analysis and diagnostics. JBoss EAP 6 on the other hand, even using JBoss ON ("Operations Network"), which is a separated technology, does not support those features. Red Hat relies on third-part tools to provide direct Oracle database traceability across JVMs. One of those tools are Oracle EM for non-Oracle middleware that manage JBoss, Tomcat, Websphere and IIS transparently. - WebLogic Server 12c with their JRockit support offers a tool called JRockit Flight Recorder, which can give developers a complete visibility of a certain period of application production monitoring with zero extra overhead. This automatic recording allows you to deep analyse threads latency, memory leaks, thread contention, resource utilization, stack overflow damages and GC ("Garbage Collection") cycles, to observe in real time stop-the-world phenomenons, generational, reference count and parallel collects and mutator threads analysis. JBoss EAP 6 don't even dream to support something similar, even because they don't have their own JVM. 3) Application Server Administration - WebLogic Server 12c offers a complete administration console complemented with scripting and macro-like recording capabilities. A single WebLogic console can managed up to hundreds of WebLogic servers belonging to the same domain. JBoss EAP 6 on the other hand has a limited console and provides a XML centric administration. JBoss, after ten years, started the development of a rudimentary centralized administration that still leave a lot of administration tasks aside, so admin people and developers must touch scripts and XML configuration files for most advanced and even simple administration tasks. This lead applications to error prone and risky deployments. Even using JBoss ON, JBoss EAP are not able to offer decent administration features for admin people which must be high skilled in JBoss internal architecture and its managing capabilities. - Oracle EM is available to manage multiple domains, databases, application servers, operating systems and virtualization, with a complete end-to-end visibility. JBoss ON does not provide management capabilities across the complete architecture, only basic monitoring. Even deployment must be done aside JBoss ON which does no integrate well with others softwares than JBoss. Until now, JBoss ON does not supports JBoss EAP 6, so even their minimal support for JBoss are not available for JBoss EAP 6 leaving customers uncovered and subject to high skilled JBoss admin people. - WebLogic Server 12c has the same administration model whatever is the topology selected by the customer. JBoss EAP 6 on the other hand differentiates between two operational models: standalone-mode and domain-mode, that are not consistent with each other. Depending on the mode used, the administration skill is different. - WebLogic Server 12c has no point-of-failures processes, and it does not need to define any specialized server. Domain model in WebLogic is available for years (at least ten years or more) and is production proven. JBoss EAP 6 on the other hand needs special processes to garantee JBoss integrity, the PC ("Process-Controller") and the HC ("Host-Controller"). Different from WebLogic, the domain model in JBoss is quite new (one year at tops) of maturity, and need to mature considerably until start doing things like WebLogic domain model does. - WebLogic Server 12c supports parallel deployment model which enables some artifacts being deployed at the same time. JBoss EAP 6 on the other hand does not have any similar feature. Every deployment are done atomically in the containers. This means that if you have a huge EAR (an EAR of 120 MB of size for instance) and deploy onto JBoss EAP 6, this EAR will take some minutes in order to starting accept thread requests. The same EAR deployed onto WebLogic Server 12c will reduce the deployment time at least in 2X compared to JBoss. 4) Support and Upgrades - WebLogic Server 12c has patch management available. JBoss EAP 6 on the other hand has no patch management available, each JBoss EAP instance should be patched manually. To achieve such feature, you need to buy a separated technology called JBoss ON ("Operations Network") that manage this type of stuff. But until now, JBoss ON does not support JBoss EAP 6 so, in practice, JBoss EAP 6 does not have this feature. - WebLogic Server 12c supports previuous WebLogic domains without any reconfiguration since its kernel is robust and mature since its creation in 1995. JBoss EAP 6 on the other hand has a proven lack of supportability between JBoss AS 4, 5, 6 and 7. Different kernels and messaging engines were implemented in JBoss stack in the last five years reveling their incapacity to create a well architected and proven middleware technology. - WebLogic Server 12c has patch prescription based on customer configuration. JBoss EAP 6 on the other hand has no such capability. People need to create ticket supports and have their installations revised by Red Hat support guys to gain some patch prescription from them. - Oracle WebLogic Server independent of the version has 8 years of support of new patches and has lifetime release of existing patches beyond that. JBoss EAP 6 on the other hand provides patches for a specific application server version up to 5 years after the release date. JBoss EAP 4 and previous versions had only 4 years. A good question that Red Hat will argue to answer is: "what happens when you find issues after year 5"?  5) RAC ("Real Application Clusters") Support - WebLogic Server 12c ships with a specific JDBC driver to leverage Oracle RAC clustering capabilities (Fast-Application-Notification, Transaction Affinity, Fast-Connection-Failover, etc). Oracle JDBC thin driver are also available. JBoss EAP 6 on the other hand ships only the standard Oracle JDBC thin driver. Load balancing with Oracle RAC are not supported. Manual intervention in case of planned or unplanned RAC downtime are necessary. In JBoss EAP 6, situation does not reestablish automatically after downtime. - WebLogic Server 12c has a feature called Active GridLink for Oracle RAC which provides up to 3X performance on OLTP applications. This seamless integration between WebLogic and Oracle database enable more value added to critical business applications leveraging their investments in Oracle database technology and Oracle middleware. JBoss EAP 6 on the other hand has no performance gains at all, even when admin people implement some kind of connection-pooling tuning. - WebLogic Server 12c also supports transaction and web session affinity to the Oracle RAC, which provides aditional gains of performance. This is particularly interesting if you are creating a reliable solution that are distributed not only in an LAN cluster, but into a different data center. JBoss EAP 6 on the other hand has no such support. 6) Standards and Technology Support - WebLogic Server 12c is fully Java EE 6 compatible and production ready since december of 2011. JBoss EAP 6 on the other hand became fully compatible with Java EE 6 only in the community version after three months, and production ready only in a few days considering that this article was written in June of 2012. Red Hat says that they are the masters of innovation and technology proliferation, but compared with Oracle and even other proprietary vendors like IBM, they historically speaking are lazy to deliver the most newest technologies and standards adherence. - Oracle is the steward of Java, driving innovation into the platform from commercial and open-source vendors. Red Hat on the other hand does not have its own JVM and relies on third-part JVMs to complete their application server offer. 95% of Red Hat customers are using Oracle HotSpot as JVM, which means that without Oracle involvement, their support are limited exclusively to the application server layer and we all know that most problems are happens in the JVM layer. - WebLogic Server 12c supports natively JDK 7, which empower developers to explore the maximum of the Java platform productivity when writing code. This feature differentiate WebLogic from others application servers (except GlassFish that are also managed by Oracle) because the usage of JDK 7 introduce such remarkable productivity features like the "try-with-resources" enhancement, catching multiple exceptions with one try block, Strings in the switch statements, JVM improvements in terms of JDBC, I/O, networking, security, concurrency and of course, the most important feature of Java 7: native support for multiple non-Java languages. More features regarding JDK 7 can be found here. JBoss EAP 6 on the other hand does not support JDK 7 officially, they comment in their community version that "Java SE 7 can be used with JBoss 7" which does not gives you any guarantees of enterprise support for JDK 7. - Oracle WebLogic Server 12c supports integration with Spring framework allowing Spring applications to use WebLogic special transaction manager, exposing bean interfaces to WebLogic MBeans to take advantage of all WebLogic monitoring and administration advantages. JBoss EAP 6 on the other hand has no special integration with Spring. In fact, Red Hat offers a suspicious package called "JBoss Web Platform" that in theory supports Spring, but in practice this package does not offers any special integration. It is just a facility for Red Hat customers to have support from both JBoss and Spring technology using the same customer support. 7) Lightweight Development - Oracle WebLogic Server 12c and Oracle GlassFish are completely integrated and can share applications without any modifications. Starting with the 12c version, WebLogic now understands natively GlassFish deployment descriptors and specific configurations in order to offer you a truly and reliable migration path from a community Java EE application server to a enterprise middleware product like WebLogic. JBoss EAP 6 on the other hand has no support to natively reuse an existing (or still in development) application from JBoss AS community server. Users of JBoss suffer of critical issues during deployment time that includes: changing the libraries and dependencies of the application, patching the DTD or XSD deployment descriptors, refactoring of the application layers due classloading issues and anomalies, rebuilding of persistence, business and web layers due issues with "usage of the certified version of an certain dependency" or "frameworks that Red Hat potentially does not recommend" etc. If you have the culture or enterprise IT directive of developing Java EE applications using community middleware to in a certain future, transition to enterprise (supported by a vendor) middleware, Oracle WebLogic plus Oracle GlassFish offers you a more sustainable solution. - WebLogic Server 12c has a very light ZIP distribution (less than 165 MB). JBoss EAP 6 ZIP size is around 130 MB, together with JBoss ON you have more 100 MB resulting in a higher download footprint. This is particularly interesting if you plan to use automated setup of application server instances (for example, to rapidly setup a development or staging environment) using Maven or Hudson. - WebLogic Server 12c has a complete integration with Maven allowing developers to setup WebLogic domains with few commands. Tasks like downloading WebLogic, installation, domain creation, data sources deployment are completely integrated. JBoss EAP 6 on the other hand has a limited offer integration with those tools.  - WebLogic Server 12c has a startup mode called WLX that turns-off EJB, JMS and JCA containers leaving enabled only the web container with Java EE 6 web profile. JBoss EAP 6 on the other hand has no such feature, you need to disable manually the containers that you do not want to use. - WebLogic Server 12c supports fastswap, which enables you to change classes without redeployment. This is particularly interesting if you are developing patches for the application that is already deployed and you do not want to redeploy the entire application. This is the same behavior that most application servers offers to JSP pages, but with WebLogic Server 12c, you have the same feature for Java classes in general. JBoss EAP 6 on the other hand has no such support. Even JBoss EAP 5 does not support this until now. 8) JMS and Messaging - WebLogic Server 12c has a proven and high scalable JMS implementation since its initial release in 1995. JBoss EAP 6 on the other hand has a still immature technology called HornetQ, which was introduced in JBoss EAP 5 replacing everything that was implemented in the previous versions. Red Hat loves to introduce new technologies across JBoss versions, playing around with customers and their investments. And when they are asked about why they have changed the implementation and caused such a mess, their answer is always: "the previous implementation was inadequate and not aligned with the community strategy so we are creating a new a improved one". This Red Hat practice leads to uncomfortable investments that in a near future (sometimes less than a year) will be affected in someway. - WebLogic Server 12c has troubleshooting and monitoring features included on the WebLogic console and WLDF. JBoss EAP 6 on the other hand has no direct monitoring on the console, activity is reflected only on the logs, no debug logs available in case of JMS issues. - WebLogic Server 12c has extremely good performance and scalability. JBoss EAP 6 on the other hand has a JMS storage mechanism relying on Oracle database or MySQL. This means that if an issue in production happens and Red Hat affirms that an performance issue is happening due to database problems, they will not support you on the performance issue. They will orient you to call Oracle instead. - WebLogic Server 12c supports messaging enterprise features like SAF ("Store and Forward"), Distributed Queues/Topics and Foreign JMS providers support that leverage JMS implementations without compromise developer code making things completely transparent. JBoss EAP 6 on the other hand do not even dream to support such features. 9) Caching and Grid - Coherence, which is the leading and most mature data grid technology from Oracle, is available since early 2000 and was integrated with WebLogic in 2009. Coherence and WebLogic clusters can be both managed from WebLogic administrative console. Even Node Manager supports Coherence. JBoss on the other hand discontinued JBoss Cache, which was their caching implementation just like they did with the messaging implementation (JBossMQ) which was a issue for long term customers. JBoss EAP 6 ships InfiniSpan version 1.0 which is immature and lack a proven record of successful cases and reliability. - WebLogic Server 12c has a feature called ActiveCache which uses Coherence to, without any code changes, replicate HTTP sessions from both WebLogic and other application servers like JBoss, Tomcat, Websphere, GlassFish and even Microsoft IIS. JBoss EAP 6 on the other hand does have such support and even when they do in the future, they probably will support only their own application server. - Coherence can be used to manage both L1 and L2 cache levels, providing support to Oracle TopLink and others JPA compliant implementations, even Hibernate. JBoss EAP 6 and Infinispan on the other hand supports only Hibernate. And most important of all: Infinispan does not have any successful case of L1 or L2 caching level support using Hibernate, which lead us to reflect about its viability. 10) Performance - WebLogic Server 12c is certified with Oracle Exalogic Elastic Cloud and can run unchanged applications at this engineered system. This approach can benefit customers from Exalogic optimization's of both kernel and JVM layers to boost performance in terms of 10X for web, OLTP, JMS and grid applications. JBoss EAP 6 on the other hand has no investment on engineered systems: customers do not have the choice to deploy on a Java ultra fast system if their project becomes relevant and performance issues are detected. - WebLogic Server 12c maintains a performance gain across each new release: starting on WebLogic 5.1, the overall performance gain has been close to 4X, which close to a 20% gain release by release. JBoss on the other hand does not provide SPECJAppServer or SPECJEnterprise performance benchmarks. Their so called "performance gains" remains hidden in their customer environments, which lead us to think if it is true or not since we will never get access to those environments. - WebLogic Server 12c has industry performance benchmarks with submissions across platforms and configurations leading SPECJ. Oracle WebLogic leads SPECJAppServer performance in multiple categories, fitting all customer topologies like: dual-node, single-node, multi-node and multi-node with RAC. JBoss... again, does not provide any SPECJAppServer performance benchmarks. - WebLogic Server 12c has a feature called work manager which allows your application to embrace new performance levels based on critical resource utilization of the CPUs usage. Work managers prioritizes work and allocates threads based on an execution model that takes into account administrator-defined parameters and actual run-time performance and throughput. JBoss EAP 6 on the other hand has no compared feature and probably they never will. Not supporting such feature like work managers, JBoss EAP 6 forces admin people and specially developers to uncover performance gains in a intrusive way, rewriting the code and doing performance refactorings. 11) Professional Services Support - WebLogic Server 12c and any other technology sold by Oracle give customers the possibility of hire OCS ("Oracle Consulting Services") to manage critical scenarios, deployment assistance of new applications, high skilled consultancy of architecture, best practices and people allocation together with customer teams. All OCS services are available without any restrictions, having the customer bought software from Oracle or just starting their implementation before any acquisition. JBoss EAP 6 or Red Hat to be more specifically, only offers professional services if you buy subscriptions from them. If you are developing a new critical application for your business and need the help of Red Hat for a serious issue or architecture decision, they will probably say: "OK... I can help you but after you buy subscriptions from me". Red Hat also does not allows their professional services consultants to manage environments that uses community based software. They will probably force you to first buy a subscription, download their "enterprise" version and them, optionally hire their consultants. - Oracle provides you our university to educate your team into our technologies, including of course specialized trainings of WebLogic application server. At any time and location, you can hire Oracle to train your team so you get trustful knowledge according to your specific needs. Certifications for the products are also available if your technical people desire to differentiate themselves as professionals. Red Hat on the other hand have a limited pool of resources to train your team in their technologies. Basically they are selling training and certification for RHEL ("Red Hat Enterprise Linux") but if you demand more specialized training in JBoss middleware, they will probably connect you to some "certified" partner localized training since they are apparently discontinuing their education center, at least here in Brazil. They were not able to reproduce their success with RHEL education to their middleware division since they need first sell the subscriptions to after gives you specialized training. And again, they only offer you specialized training based on their enterprise version (EAP in the case of JBoss) which means that the courses will be a quite outdated. There are reports of developers that took official training's from Red Hat at this year (2012) and in a certain JBoss advanced course, Red Hat supposedly covered JBossMQ as the messaging subsystem, and even the printed material provided was based on JBossMQ since the training was created for JBoss EAP 4.3. 12) Encouraging Transparency without Ulterior Motives - WebLogic Server 12c like any other software from Oracle can be downloaded any time from anywhere, you should only possess an OTN ("Oracle Technology Network") credential and you can download any enterprise software how many times you want. And is not some kind of "trial" version. It is the official binaries that will be running for ever in your data center. Oracle does not encourages the usage of "specific versions" of our software. The binaries you buy from Oracle are the same binaries anyone in the world could download and use for testing and personal education. JBoss EAP 6 on the other hand are not available for download unless you buy a subscription and get access to the Red Hat enterprise repositories. If you need to test, learn or just start creating your application using Red Hat's middleware software, you should download it from the community website. You are not allowed to download the enterprise version that, according to Red Hat are more secure, reliable and robust. But no one of us want to start the development of a software with an unsecured, unreliable and not scalable middleware right? So what you do? You are "invited" by Red Hat to buy subscriptions from them to get access to the "cool" version of the software. - WebLogic Server 12c prices are publicly available in the Oracle website. If you want to know right now how much WebLogic will cost to your organization, just click here and get access to our price list. In the case of WebLogic, check out the "US Oracle Technology Commercial Price List". Oracle also encourages you to get in touch with a sales representative to discuss discounts that would make possible the investment into our technology. But you are not required to do this, only if you are interested in buying our technology or maybe you want to discuss some discount scenarios. JBoss EAP 6 on the other hand does not have its cost publicly available in Red Hat's website or in any other media, at least is not so easy to get such information. The only link you will possibly find in their website is a "Contact a Sales Representative" link. This is not a very good relationship between an customer and an vendor. This is not an example of transparency, mainly when the software are sold as open. In this situations, customers expects to see the software prices publicly available, so they can have the chance to decide, based on the existing features of the software, if the cost is fair or not. Conclusion Oracle WebLogic is the most mature, secure, reliable and scalable Java EE application server of the market, and have a proven record of success around the globe to prove it's majority. Don't lose the chance to discover today how WebLogic could fit your needs and sustain your global IT middleware strategy, no matter if your strategy are completely based on the Cloud or not.

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  • Using R to Analyze G1GC Log Files

    - by user12620111
    Using R to Analyze G1GC Log Files body, td { font-family: sans-serif; background-color: white; font-size: 12px; margin: 8px; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1 { font-size:2.2em; } h2 { font-size:1.8em; } h3 { font-size:1.4em; } h4 { font-size:1.0em; } h5 { font-size:0.9em; } h6 { font-size:0.8em; } a:visited { color: rgb(50%, 0%, 50%); } pre { margin-top: 0; max-width: 95%; border: 1px solid #ccc; white-space: pre-wrap; } pre code { display: block; padding: 0.5em; } code.r, code.cpp { background-color: #F8F8F8; } table, td, th { border: none; } blockquote { color:#666666; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { 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  Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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