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• JSF Template Designing in SUN RI_1.101

  - by Ram

  Hi everyone We are using JSF in UI.We need to develop Templates for designing.Can we develop templates in SUN RI1.1_01.If so,can any one tell me how to start?

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• Deploying EAR file in Sun App Server having problem with proxy server setttings

  - by Nick Long

  When I am deploying certain vendor EAR file to Sun App Server, I encountered a connection timeout errror. I thought the reason might be proxy settings need to be defined so I actually defined the following -Dhttp.proxyHost=hostname -Dhttp.proxyPassword=password -Dhttp.proxyPort=8080 -Dhttp.proxyUser=username After setting these and restart domain then redeploy I encountered 407 error. Anyone have any idea what could be the issue here?

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• Running a lot of jobs with sun grid engine

  - by R S

  I want to run a very large number (~30000) of jobs with Sun Grid Engine. I can theoretically, perform 30000 times the "qsub" command to submit jobs. However, I am afraid that will be too much. Is there a better way to do it? (i.e. from a file) Or otherwise, do you think it will work nonetheless? Thank you

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• Sun's JVM instruction speed table

  - by Pindatjuh

  Is there a benchmark available how much relative time each instruction costs in a single-thread, average-case scenario (either with or without JIT compiler), for the JVM (any version) by Sun? If there is not a benchmark already available, how can I get this information? E.g.: TIME iload_1 1 iadd 12 getfield 40 etc. Where getfield is equivalent to 40 iload_1 instructions.

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• Pysdm has disabled my ability to write to my storage partition

  - by Atlas

  I have a dual boot setup with Windows 7 and Mint 13 Cinnamon. As well as their respective partitions I also have a large one (NTFS) for storing all my music, videos, documents etc. I downloaded pysdm as I was told it would enable me to configure Linux to auto-mount my storage partition. It has indeed been helpful in auto-mounting my storage. However, since installing it I can no longer write to the partition which makes 500GB of my hard drive utterly useless! I've tried to unselect the "Mount file system in read only mode" option, but the program keeps re-checking it after I close that window (and even when I click apply). Why is it doing this and how can I get it to recognise that I need to read AND write on that partition?

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• Network Storage Bash

  - by NickTFried

  I was wondering how to connect to a network storage through the command line on my MacOSX computer, any tips?

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• Mountable online storage (no syncing)

  - by Sam

  I have a Linux VPS that I would like to turn into a media server. Like most cheap VPS's, it has a fairly small storage capacity. What I would like to do is attach the box to an online backup system such as SpiderOak or DropBox, where the files would reside and be directly accessible to either a webserver or media server software. Since the VPS hdd is small, I do not want the files to be synced to it. I would like a storage system that is online only. Ideally mountable like a network drive. Are there any services that suit my needs, or workarounds for services such as SpiderOak that do not require syncing?

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• Bacula Director and Storage in LAN

  - by B14D3

  I have two networks LAN and DMZ.. Machines in DMZ are accesible from internet ( only over http). In LAN I have servers that see all LAN and all DMZ machines but machinse from DMZ don't see any LAN servers. Machines in LAN have access only to all LAN and DMZ, no direct access to internet and no access from internet. DMZ <------ LAN DMZ ----X--->LAN I'm planning to configure Bacula as major backup system. My plan is to install Bacula Director and Storage deamon on the same server in LAN for safety reasons. So my question is: Will this configuration work, is it posible for bacula director and storage deamon installed on server in LAN to makes backup servers that are in my DMZ? Or in this network configuration Bacula should be in DMZ? (If yes will I can backup with it servers in LAN ?)

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• Have both domain and non-domain users use NetApp CIFS storage

  - by zladuric

  My specific use case is that I have a NetApp CIFS storage that's in the domain, say, intranet. But I also have one hyper-v host that's not in the domain. I can't allow it into the domain, but I need to create guests whose VHD is on the storage. How can I achieve this? When I simply connect to a CIFS share and create a VHD there, it works, but when I try to add that VHD to the virtual machine, I get a "Failed to set folder permissions" error - probably due to folder ownership being in the domain admin. Is there a way around this? So both domain and non-domain users use the same directory?

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• Intel Rapid Storage Technology service always crashes

  - by Massimo

  I'm running Windows 7 x64 on a system based on an Asus Z87-Deluxe motherboard; the storage is configured for RAID mode; there is a single SSD drive for the O.S. and two 4-TB disks in a RAID 1 setup for the data. I've installed the latest version of Intel's Rapid Storage Technology drivers, 12.8.0.1016. The program complains about its service not being running, and the service is actually stopped; if I try to start it, it crashes. I've already tried reinstalling the package, but nothing changed. All the disks work correctly, but the RST program is unusable. How can I fix this?

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• OS X server large scale storage and backup

  - by user135217

  I really hope this question doesn't come across as trolling or asking for buying advice. It's not intended. I've just started working for a small ad agency (40 employees). I actually quit being a system administrator a few years ago (too stressful!), but the company we're currently outsourcing our IT stuff to is doing such a bad job that I've felt compelled to get involved and do what I can to improve things. At the moment, all the company's data is stored on an 8TB external firewire drive attached to a Mac Mini running OS X Server 10.6, which provides filesharing (using AFP) for the whole company. There is a single backup drive, which is actually a caddy containing two 3TB hard drives arranged in RAID 0 (arrggghhhh!), which someone brings in as and when and copies over all the data using Carbon Copy Cloner. That's the entirety of the infrastructure, and the whole backup and restore strategy. I've been having sleepless nights. I've just started augmenting the backup process with FreeBSD, ZFS, sparse bundles and snapshot sends to get everything offsite. I think this is a workable behind the scenes solution, but for people's day to day use I'm struggling. Given the quantity and importance of the data, I think we should really be looking towards enterprise level storage solutions, high availability and so on, but the whole company is all Mac all the time, and I cannot find equipment that will do what we need. No more Xserve; no rack storage; no large scale storage at all apart from that Pegasus R6 that doesn't seem all that great; the Mac Pro has fibre channel, but it's not a real server and it's ludicrously expensive; Xsan looks like it's on the way out; things like heartbeatd and failoverd have apparently been removed from Lion Server; the new Mac Mini only has thunderbolt which severely limits our choices; the list goes on and on. I'm really, really not trying to troll here. I love Macs, but I just genuinely don't know where I'm supposed to look for server stuff. I have considered Linux or FreeBSD and netatalk for serving files with all the server-y goodness those OSes bring, but some the things I've read make me wonder if it's really the way to go. Also, in my own (admittedly quite cursory) experiments with it, I've struggled to get decent transfer speeds. I guess there's also the possibility of switching everyone off AFP and making them use SMB or NFS, but I understand that this can cause big problems with resource forks and file locks. I figure there must be plenty of all Mac companies out there. If you're the sysadmin at one, what do you use? Any suggestions very gratefully received.

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• Oracle’s New Memory-Optimized x86 Servers: Getting the Most Out of Oracle Database In-Memory

  - by Josh Rosen, x86 Product Manager-Oracle

  With the launch of Oracle Database In-Memory, it is now possible to perform real-time analytics operations on your business data as it exists at that moment – in the DRAM of the server – and immediately return completely current and consistent data. The Oracle Database In-Memory option dramatically accelerates the performance of analytics queries by storing data in a highly optimized columnar in-memory format.  This is a truly exciting advance in database technology.As Larry Ellison mentioned in his recent webcast about Oracle Database In-Memory, queries run 100 times faster simply by throwing a switch.  But in order to get the most from the Oracle Database In-Memory option, the underlying server must also be memory-optimized. This week Oracle announced new 4-socket and 8-socket x86 servers, the Sun Server X4-4 and Sun Server X4-8, both of which have been designed specifically for Oracle Database In-Memory.  These new servers use the fastest Intel® Xeon® E7 v2 processors and each subsystem has been designed to be the best for Oracle Database, from the memory, I/O and flash technologies right down to the system firmware.Amongst these subsystems, one of the most important aspects we have optimized with the Sun Server X4-4 and Sun Server X4-8 are their memory subsystems.  The new In-Memory option makes it possible to select which parts of the database should be memory optimized.  You can choose to put a single column or table in memory or, if you can, put the whole database in memory.  The more, the better.  With 3 TB and 6 TB total memory capacity on the Sun Server X4-4 and Sun Server X4-8, respectively, you can memory-optimize more, if not your entire database.   Sun Server X4-8 CMOD with 24 DIMM slots per socket (up to 192 DIMM slots per server) But memory capacity is not the only important factor in selecting the best server platform for Oracle Database In-Memory.  As you put more of your database in memory, a critical performance metric known as memory bandwidth comes into play.  The total memory bandwidth for the server will dictate the rate in which data can be stored and retrieved from memory.  In order to achieve real-time analysis of your data using Oracle Database In-Memory, even under heavy load, the server must be able to handle extreme memory workloads.  With that in mind, the Sun Server X4-8 was designed with the maximum possible memory bandwidth, providing over a terabyte per second of total memory bandwidth.  Likewise, the Sun Server X4-4 also provides extreme memory bandwidth in an even more compact form factor with over half a terabyte per second, providing customers with scalability and choice depending on the size of the database.Beyond the memory subsystem, Oracle’s Sun Server X4-4 and Sun Server X4-8 systems provide other key technologies that enable Oracle Database to run at its best.  The Sun Server X4-4 allows for up 4.8 TB of internal, write-optimized PCIe flash while the Sun Server X4-8 allows for up to 6.4 TB of PCIe flash.  This enables dramatic acceleration of data inserts and updates to Oracle Database.  And with the new elastic computing capability of Oracle’s new x86 servers, server performance can be adapted to your specific Oracle Database workload to ensure that every last bit of processing power is utilized.Because Oracle designs and tests its x86 servers specifically for Oracle workloads, we provide the highest possible performance and reliability when running Oracle Database.  To learn more about Sun Server X4-4 and Sun Server X4-8, you can find more details including data sheets and white papers here. Josh Rosen is a Principal Product Manager for Oracle’s x86 servers, focusing on Oracle’s operating systems and software.  He previously spent more than a decade as a developer and architect of system management software. Josh has worked on system management for many of Oracle's hardware products ranging from the earliest blade systems to the latest Oracle x86 servers. 

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• Much Ado About Nothing: Stub Objects

  - by user9154181

  The Solaris 11 link-editor (ld) contains support for a new type of object that we call a stub object. A stub object is a shared object, built entirely from mapfiles, that supplies the same linking interface as the real object, while containing no code or data. Stub objects cannot be executed — the runtime linker will kill any process that attempts to load one. However, you can link to a stub object as a dependency, allowing the stub to act as a proxy for the real version of the object. You may well wonder if there is a point to producing an object that contains nothing but linking interface. As it turns out, stub objects are very useful for building large bodies of code such as Solaris. In the last year, we've had considerable success in applying them to one of our oldest and thorniest build problems. In this discussion, I will describe how we came to invent these objects, and how we apply them to building Solaris. This posting explains where the idea for stub objects came from, and details our long and twisty journey from hallway idea to standard link-editor feature. I expect that these details are mainly of interest to those who work on Solaris and its makefiles, those who have done so in the past, and those who work with other similar bodies of code. A subsequent posting will omit the history and background details, and instead discuss how to build and use stub objects. If you are mainly interested in what stub objects are, and don't care about the underlying software war stories, I encourage you to skip ahead. The Long Road To Stubs This all started for me with an email discussion in May of 2008, regarding a change request that was filed in 2002, entitled: 4631488 lib/Makefile is too patient: .WAITs should be reduced This CR encapsulates a number of cronic issues with Solaris builds: We build Solaris with a parallel make (dmake) that tries to build as much of the code base in parallel as possible. There is a lot of code to build, and we've long made use of parallelized builds to get the job done quicker. This is even more important in today's world of massively multicore hardware. Solaris contains a large number of executables and shared objects. Executables depend on shared objects, and shared objects can depend on each other. Before you can build an object, you need to ensure that the objects it needs have been built. This implies a need for serialization, which is in direct opposition to the desire to build everying in parallel. To accurately build objects in the right order requires an accurate set of make rules defining the things that depend on each other. This sounds simple, but the reality is quite complex. In practice, having programmers explicitly specify these dependencies is a losing strategy: It's really hard to get right. It's really easy to get it wrong and never know it because things build anyway. Even if you get it right, it won't stay that way, because dependencies between objects can change over time, and make cannot help you detect such drifing. You won't know that you got it wrong until the builds break. That can be a long time after the change that triggered the breakage happened, making it hard to connect the cause and the effect. Usually this happens just before a release, when the pressure is on, its hard to think calmly, and there is no time for deep fixes. As a poor compromise, the libraries in core Solaris were built using a set of grossly incomplete hand written rules, supplemented with a number of dmake .WAIT directives used to group the libraries into sets of non-interacting groups that can be built in parallel because we think they don't depend on each other. From time to time, someone will suggest that we could analyze the built objects themselves to determine their dependencies and then generate make rules based on those relationships. This is possible, but but there are complications that limit the usefulness of that approach: To analyze an object, you have to build it first. This is a classic chicken and egg scenario. You could analyze the results of a previous build, but then you're not necessarily going to get accurate rules for the current code. It should be possible to build the code without having a built workspace available. The analysis will take time, and remember that we're constantly trying to make builds faster, not slower. By definition, such an approach will always be approximate, and therefore only incremantally more accurate than the hand written rules described above. The hand written rules are fast and cheap, while this idea is slow and complex, so we stayed with the hand written approach. Solaris was built that way, essentially forever, because these are genuinely difficult problems that had no easy answer. The makefiles were full of build races in which the right outcomes happened reliably for years until a new machine or a change in build server workload upset the accidental balance of things. After figuring out what had happened, you'd mutter "How did that ever work?", add another incomplete and soon to be inaccurate make dependency rule to the system, and move on. This was not a satisfying solution, as we tend to be perfectionists in the Solaris group, but we didn't have a better answer. It worked well enough, approximately. And so it went for years. We needed a different approach — a new idea to cut the Gordian Knot. In that discussion from May 2008, my fellow linker-alien Rod Evans had the initial spark that lead us to a game changing series of realizations: The link-editor is used to link objects together, but it only uses the ELF metadata in the object, consisting of symbol tables, ELF versioning sections, and similar data. Notably, it does not look at, or understand, the machine code that makes an object useful at runtime. If you had an object that only contained the ELF metadata for a dependency, but not the code or data, the link-editor would find it equally useful for linking, and would never know the difference. Call it a stub object. In the core Solaris OS, we require all objects to be built with a link-editor mapfile that describes all of its publically available functions and data. Could we build a stub object using the mapfile for the real object? It ought to be very fast to build stub objects, as there are no input objects to process. Unlike the real object, stub objects would not actually require any dependencies, and so, all of the stubs for the entire system could be built in parallel. When building the real objects, one could link against the stub objects instead of the real dependencies. This means that all the real objects can be built built in parallel too, without any serialization. We could replace a system that requires perfect makefile rules with a system that requires no ordering rules whatsoever. The results would be considerably more robust. We immediately realized that this idea had potential, but also that there were many details to sort out, lots of work to do, and that perhaps it wouldn't really pan out. As is often the case, it would be necessary to do the work and see how it turned out. Following that conversation, I set about trying to build a stub object. We determined that a faithful stub has to do the following: Present the same set of global symbols, with the same ELF versioning, as the real object. Functions are simple — it suffices to have a symbol of the right type, possibly, but not necessarily, referencing a null function in its text segment. Copy relocations make data more complicated to stub. The possibility of a copy relocation means that when you create a stub, the data symbols must have the actual size of the real data. Any error in this will go uncaught at link time, and will cause tragic failures at runtime that are very hard to diagnose. For reasons too obscure to go into here, involving tentative symbols, it is also important that the data reside in bss, or not, matching its placement in the real object. If the real object has more than one symbol pointing at the same data item, we call these aliased symbols. All data symbols in the stub object must exhibit the same aliasing as the real object. We imagined the stub library feature working as follows: A command line option to ld tells it to produce a stub rather than a real object. In this mode, only mapfiles are examined, and any object or shared libraries on the command line are are ignored. The extra information needed (function or data, size, and bss details) would be added to the mapfile. When building the real object instead of the stub, the extra information for building stubs would be validated against the resulting object to ensure that they match. In exploring these ideas, I immediately run headfirst into the reality of the original mapfile syntax, a subject that I would later write about as The Problem(s) With Solaris SVR4 Link-Editor Mapfiles. The idea of extending that poor language was a non-starter. Until a better mapfile syntax became available, which seemed unlikely in 2008, the solution could not involve extentions to the mapfile syntax. Instead, we cooked up the idea (hack) of augmenting mapfiles with stylized comments that would carry the necessary information. A typical definition might look like: # DATA(i386) __iob 0x3c0 # DATA(amd64,sparcv9) __iob 0xa00 # DATA(sparc) __iob 0x140 iob; A further problem then became clear: If we can't extend the mapfile syntax, then there's no good way to extend ld with an option to produce stub objects, and to validate them against the real objects. The idea of having ld read comments in a mapfile and parse them for content is an unacceptable hack. The entire point of comments is that they are strictly for the human reader, and explicitly ignored by the tool. Taking all of these speed bumps into account, I made a new plan: A perl script reads the mapfiles, generates some small C glue code to produce empty functions and data definitions, compiles and links the stub object from the generated glue code, and then deletes the generated glue code. Another perl script used after both objects have been built, to compare the real and stub objects, using data from elfdump, and validate that they present the same linking interface. By June 2008, I had written the above, and generated a stub object for libc. It was a useful prototype process to go through, and it allowed me to explore the ideas at a deep level. Ultimately though, the result was unsatisfactory as a basis for real product. There were so many issues: The use of stylized comments were fine for a prototype, but not close to professional enough for shipping product. The idea of having to document and support it was a large concern. The ideal solution for stub objects really does involve having the link-editor accept the same arguments used to build the real object, augmented with a single extra command line option. Any other solution, such as our prototype script, will require makefiles to be modified in deeper ways to support building stubs, and so, will raise barriers to converting existing code. A validation script that rederives what the linker knew when it built an object will always be at a disadvantage relative to the actual linker that did the work. A stub object should be identifyable as such. In the prototype, there was no tag or other metadata that would let you know that they weren't real objects. Being able to identify a stub object in this way means that the file command can tell you what it is, and that the runtime linker can refuse to try and run a program that loads one. At that point, we needed to apply this prototype to building Solaris. As you might imagine, the task of modifying all the makefiles in the core Solaris code base in order to do this is a massive task, and not something you'd enter into lightly. The quality of the prototype just wasn't good enough to justify that sort of time commitment, so I tabled the project, putting it on my list of long term things to think about, and moved on to other work. It would sit there for a couple of years. Semi-coincidentally, one of the projects I tacked after that was to create a new mapfile syntax for the Solaris link-editor. We had wanted to do something about the old mapfile syntax for many years. Others before me had done some paper designs, and a great deal of thought had already gone into the features it should, and should not have, but for various reasons things had never moved beyond the idea stage. When I joined Sun in late 2005, I got involved in reviewing those things and thinking about the problem. Now in 2008, fresh from relearning for the Nth time why the old mapfile syntax was a huge impediment to linker progress, it seemed like the right time to tackle the mapfile issue. Paving the way for proper stub object support was not the driving force behind that effort, but I certainly had them in mind as I moved forward. The new mapfile syntax, which we call version 2, integrated into Nevada build snv_135 in in February 2010: 6916788 ld version 2 mapfile syntax PSARC/2009/688 Human readable and extensible ld mapfile syntax In order to prove that the new mapfile syntax was adequate for general purpose use, I had also done an overhaul of the ON consolidation to convert all mapfiles to use the new syntax, and put checks in place that would ensure that no use of the old syntax would creep back in. That work went back into snv_144 in June 2010: 6916796 OSnet mapfiles should use version 2 link-editor syntax That was a big putback, modifying 517 files, adding 18 new files, and removing 110 old ones. I would have done this putback anyway, as the work was already done, and the benefits of human readable syntax are obvious. However, among the justifications listed in CR 6916796 was this We anticipate adding additional features to the new mapfile language that will be applicable to ON, and which will require all sharable object mapfiles to use the new syntax. I never explained what those additional features were, and no one asked. It was premature to say so, but this was a reference to stub objects. By that point, I had already put together a working prototype link-editor with the necessary support for stub objects. I was pleased to find that building stubs was indeed very fast. On my desktop system (Ultra 24), an amd64 stub for libc can can be built in a fraction of a second: % ptime ld -64 -z stub -o stubs/libc.so.1 -G -hlibc.so.1 \ -ztext -zdefs -Bdirect ... real 0.019708910 user 0.010101680 sys 0.008528431 In order to go from prototype to integrated link-editor feature, I knew that I would need to prove that stub objects were valuable. And to do that, I knew that I'd have to switch the Solaris ON consolidation to use stub objects and evaluate the outcome. And in order to do that experiment, ON would first need to be converted to version 2 mapfiles. Sub-mission accomplished. Normally when you design a new feature, you can devise reasonably small tests to show it works, and then deploy it incrementally, letting it prove its value as it goes. The entire point of stub objects however was to demonstrate that they could be successfully applied to an extremely large and complex code base, and specifically to solve the Solaris build issues detailed above. There was no way to finesse the matter — in order to move ahead, I would have to successfully use stub objects to build the entire ON consolidation and demonstrate their value. In software, the need to boil the ocean can often be a warning sign that things are trending in the wrong direction. Conversely, sometimes progress demands that you build something large and new all at once. A big win, or a big loss — sometimes all you can do is try it and see what happens. And so, I spent some time staring at ON makefiles trying to get a handle on how things work, and how they'd have to change. It's a big and messy world, full of complex interactions, unspecified dependencies, special cases, and knowledge of arcane makefile features... ...and so, I backed away, put it down for a few months and did other work... ...until the fall, when I felt like it was time to stop thinking and pondering (some would say stalling) and get on with it. Without stubs, the following gives a simplified high level view of how Solaris is built: An initially empty directory known as the proto, and referenced via the ROOT makefile macro is established to receive the files that make up the Solaris distribution. A top level setup rule creates the proto area, and performs operations needed to initialize the workspace so that the main build operations can be launched, such as copying needed header files into the proto area. Parallel builds are launched to build the kernel (usr/src/uts), libraries (usr/src/lib), and commands. The install makefile target builds each item and delivers a copy to the proto area. All libraries and executables link against the objects previously installed in the proto, implying the need to synchronize the order in which things are built. Subsequent passes run lint, and do packaging. Given this structure, the additions to use stub objects are: A new second proto area is established, known as the stub proto and referenced via the STUBROOT makefile macro. The stub proto has the same structure as the real proto, but is used to hold stub objects. All files in the real proto are delivered as part of the Solaris product. In contrast, the stub proto is used to build the product, and then thrown away. A new target is added to library Makefiles called stub. This rule builds the stub objects. The ld command is designed so that you can build a stub object using the same ld command line you'd use to build the real object, with the addition of a single -z stub option. This means that the makefile rules for building the stub objects are very similar to those used to build the real objects, and many existing makefile definitions can be shared between them. A new target is added to the Makefiles called stubinstall which delivers the stub objects built by the stub rule into the stub proto. These rules reuse much of existing plumbing used by the existing install rule. The setup rule runs stubinstall over the entire lib subtree as part of its initialization. All libraries and executables link against the objects in the stub proto rather than the main proto, and can therefore be built in parallel without any synchronization. There was no small way to try this that would yield meaningful results. I would have to take a leap of faith and edit approximately 1850 makefiles and 300 mapfiles first, trusting that it would all work out. Once the editing was done, I'd type make and see what happened. This took about 6 weeks to do, and there were many dark days when I'd question the entire project, or struggle to understand some of the many twisted and complex situations I'd uncover in the makefiles. I even found a couple of new issues that required changes to the new stub object related code I'd added to ld. With a substantial amount of encouragement and help from some key people in the Solaris group, I eventually got the editing done and stub objects for the entire workspace built. I found that my desktop system could build all the stub objects in the workspace in roughly a minute. This was great news, as it meant that use of the feature is effectively free — no one was likely to notice or care about the cost of building them. After another week of typing make, fixing whatever failed, and doing it again, I succeeded in getting a complete build! The next step was to remove all of the make rules and .WAIT statements dedicated to controlling the order in which libraries under usr/src/lib are built. This came together pretty quickly, and after a few more speed bumps, I had a workspace that built cleanly and looked like something you might actually be able to integrate someday. This was a significant milestone, but there was still much left to do. I turned to doing full nightly builds. Every type of build (open, closed, OpenSolaris, export, domestic) had to be tried. Each type failed in a new and unique way, requiring some thinking and rework. As things came together, I became aware of things that could have been done better, simpler, or cleaner, and those things also required some rethinking, the seeking of wisdom from others, and some rework. After another couple of weeks, it was in close to final form. My focus turned towards the end game and integration. This was a huge workspace, and needed to go back soon, before changes in the gate would made merging increasingly difficult. At this point, I knew that the stub objects had greatly simplified the makefile logic and uncovered a number of race conditions, some of which had been there for years. I assumed that the builds were faster too, so I did some builds intended to quantify the speedup in build time that resulted from this approach. It had never occurred to me that there might not be one. And so, I was very surprised to find that the wall clock build times for a stock ON workspace were essentially identical to the times for my stub library enabled version! This is why it is important to always measure, and not just to assume. One can tell from first principles, based on all those removed dependency rules in the library makefile, that the stub object version of ON gives dmake considerably more opportunities to overlap library construction. Some hypothesis were proposed, and shot down: Could we have disabled dmakes parallel feature? No, a quick check showed things being build in parallel. It was suggested that we might be I/O bound, and so, the threads would be mostly idle. That's a plausible explanation, but system stats didn't really support it. Plus, the timing between the stub and non-stub cases were just too suspiciously identical. Are our machines already handling as much parallelism as they are capable of, and unable to exploit these additional opportunities? Once again, we didn't see the evidence to back this up. Eventually, a more plausible and obvious reason emerged: We build the libraries and commands (usr/src/lib, usr/src/cmd) in parallel with the kernel (usr/src/uts). The kernel is the long leg in that race, and so, wall clock measurements of build time are essentially showing how long it takes to build uts. Although it would have been nice to post a huge speedup immediately, we can take solace in knowing that stub objects simplify the makefiles and reduce the possibility of race conditions. The next step in reducing build time should be to find ways to reduce or overlap the uts part of the builds. When that leg of the build becomes shorter, then the increased parallelism in the libs and commands will pay additional dividends. Until then, we'll just have to settle for simpler and more robust. And so, I integrated the link-editor support for creating stub objects into snv_153 (November 2010) with 6993877 ld should produce stub objects PSARC/2010/397 ELF Stub Objects followed by the work to convert the ON consolidation in snv_161 (February 2011) with 7009826 OSnet should use stub objects 4631488 lib/Makefile is too patient: .WAITs should be reduced This was a huge putback, with 2108 modified files, 8 new files, and 2 removed files. Due to the size, I was allowed a window after snv_160 closed in which to do the putback. It went pretty smoothly for something this big, a few more preexisting race conditions would be discovered and addressed over the next few weeks, and things have been quiet since then. Conclusions and Looking Forward Solaris has been built with stub objects since February. The fact that developers no longer specify the order in which libraries are built has been a big success, and we've eliminated an entire class of build error. That's not to say that there are no build races left in the ON makefiles, but we've taken a substantial bite out of the problem while generally simplifying and improving things. The introduction of a stub proto area has also opened some interesting new possibilities for other build improvements. As this article has become quite long, and as those uses do not involve stub objects, I will defer that discussion to a future article.

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• Do your filesystems have un-owned files ?

  - by darrenm

  As part of our work for integrated compliance reporting in Solaris we plan to provide a check for determining if the system has "un-owned files", ie those which are owned by a uid that does not exist in our configured nameservice.  Tests such as this already exist in the Solaris CIS Benchmark (9.24 Find Un-owned Files and Directories) and other security benchmarks. The obvious method of doing this would be using find(1) with the -nouser flag.  However that requires we bring into memory the metadata for every single file and directory in every local file system we have mounted.  That is probaby not an acceptable thing to do on a production system that has a large amount of storage and it is potentially going to take a long time. Just as I went to bed last night an idea for a much faster way of listing file systems that have un-owned files came to me. I've now implemented it and I'm happy to report it works very well and peforms many orders of magnatude better than using find(1) ever will.   ZFS (since pool version 15) has per user space accounting and quotas.  We can report very quickly and without actually reading any files at all how much space any given user id is using on a ZFS filesystem.  Using that information we can implement a check to very quickly list which filesystems contain un-owned files. First a few caveats because the output data won't be exactly the same as what you get with find but it answers the same basic question.  This only works for ZFS and it will only tell you which filesystems have files owned by unknown users not the actual files.  If you really want to know what the files are (ie to give them an owner) you still have to run find(1).  However it has the huge advantage that it doesn't use find(1) so it won't be dragging the metadata for every single file and directory on the system into memory. It also has the advantage that it can check filesystems that are not mounted currently (which find(1) can't do). It ran in about 4 seconds on a system with 300 ZFS datasets from 2 pools totalling about 3.2T of allocated space, and that includes the uid lookups and output. #!/bin/sh for fs in $(zfs list -H -o name -t filesystem -r rpool) ; do unknowns="" for uid in $(zfs userspace -Hipn -o name,used $fs | cut -f1); do if [ -z "$(getent passwd $uid)" ]; then unknowns="$unknowns$uid " fi done if [ ! -z "$unknowns" ]; then mountpoint=$(zfs list -H -o mountpoint $fs) mounted=$(zfs list -H -o mounted $fs) echo "ZFS File system $fs mounted ($mounted) on $mountpoint \c" echo "has files owned by unknown user ids: $unknowns"; fi done Sample output: ZFS File system rpool/ROOT/solaris-30/var mounted (no) on /var has files owned by unknown user ids: 6435 33667 101 ZFS File system rpool/ROOT/solaris-32/var mounted (yes) on /var has files owned by unknown user ids: 6435 33667ZFS File system builds/bob mounted (yes) on /builds/bob has files owned by unknown user ids: 101 Note that the above might not actually appear exactly like that in any future Solaris product or feature, it is provided just as an example of what you can do with ZFS user space accounting to answer questions like the above.

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• Advantages of SQL Backup Pro

  - by Grant Fritchey

  Getting backups of your databases in place is a fundamental issue for protection of the business. Yes, I said business, not data, not databases, but business. Because of a lack of good, tested, backups, companies have gone completely out of business or suffered traumatic financial loss. That’s just a simple fact (outlined with a few examples here). So you want to get backups right. That’s a big part of why we make Red Gate SQL Backup Pro work the way it does. Yes, you could just use native backups, but you’ll be missing a few advantages that we provide over and above what you get out of the box from Microsoft. Let’s talk about them. Guidance If you’re a hard-core DBA with 20+ years of experience on every version of SQL Server and several other data platforms besides, you may already know what you need in order to get a set of tested backups in place. But, if you’re not, maybe a little help would be a good thing. To set up backups for your servers, we supply a wizard that will step you through the entire process. It will also act to guide you down good paths. For example, if your databases are in Full Recovery, you should set up transaction log backups to run on a regular basis. When you choose a transaction log backup from the Backup Type you’ll see that only those databases that are in Full Recovery will be listed: This makes it very easy to be sure you have a log backup set up for all the databases you should and none of the databases where you won’t be able to. There are other examples of guidance throughout the product. If you have the responsibility of managing backups but very little knowledge or time, we can help you out. Throughout the software you’ll notice little green question marks. You can see two in the screen above and more in each of the screens in other topics below this one. Clicking on these will open a window with additional information about the topic in question which should help to guide you through some of the tougher decisions you may have to make while setting up your backup jobs. Here’s an example: Backup Copies As a part of the wizard you can choose to make a copy of your backup on your network. This process runs as part of the Red Gate SQL Backup engine. It will copy your backup, after completing the backup so it doesn’t cause any additional blocking or resource use within the backup process, to the network location you define. Creating a copy acts as a mechanism of protection for your backups. You can then backup that copy or do other things with it, all without affecting the original backup file. This requires either an additional backup or additional scripting to get it done within the native Microsoft backup engine. Offsite Storage Red Gate offers you the ability to immediately copy your backup to the cloud as a further, off-site, protection of your backups. It’s a service we provide and expose through the Backup wizard. Your backup will complete first, just like with the network backup copy, then an asynchronous process will copy that backup to cloud storage. Again, this is built right into the wizard or even the command line calls to SQL Backup, so it’s part a single process within your system. With native backup you would need to write additional scripts, possibly outside of T-SQL, to make this happen. Before you can use this with your backups you’ll need to do a little setup, but it’s built right into the product to get this done. You’ll be directed to the web site for our hosted storage where you can set up an account. Compression If you have SQL Server 2008 Enterprise, or you’re on SQL Server 2008R2 or greater and you have a Standard or Enterprise license, then you have backup compression. It’s built right in and works well. But, if you need even more compression then you might want to consider Red Gate SQL Backup Pro. We offer four levels of compression within the product. This means you can get a little compression faster, or you can just sacrifice some CPU time and get even more compression. You decide. For just a simple example I backed up AdventureWorks2012 using both methods of compression. The resulting file from native was 53mb. Our file was 33mb. That’s a file that is smaller by 38%, not a small number when we start talking gigabytes. We even provide guidance here to help you determine which level of compression would be right for you and your system: So for this test, if you wanted maximum compression with minimum CPU use you’d probably want to go with Level 2 which gets you almost as much compression as Level 3 but will use fewer resources. And that compression is still better than the native one by 10%. Restore Testing Backups are vital. But, a backup is just a file until you restore it. How do you know that you can restore that backup? Of course, you’ll use CHECKSUM to validate that what was read from disk during the backup process is what gets written to the backup file. You’ll also use VERIFYONLY to check that the backup header and the checksums on the backup file are valid. But, this doesn’t do a complete test of the backup. The only complete test is a restore. So, what you really need is a process that tests your backups. This is something you’ll have to schedule separately from your backups, but we provide a couple of mechanisms to help you out here. First, when you create a backup schedule, all done through our wizard which gives you as much guidance as you get when running backups, you get the option of creating a reminder to create a job to test your restores. You can enable this or disable it as you choose when creating your scheduled backups. Once you’re ready to schedule test restores for your databases, we have a wizard for this as well. After you choose the databases and restores you want to test, all configurable for automation, you get to decide if you’re going to restore to a specified copy or to the original database: If you’re doing your tests on a new server (probably the best choice) you can just overwrite the original database if it’s there. If not, you may want to create a new database each time you test your restores. Another part of validating your backups is ensuring that they can pass consistency checks. So we have DBCC built right into the process. You can even decide how you want DBCC run, which error messages to include, limit or add to the checks being run. With this you could offload some DBCC checks from your production system so that you only run the physical checks on your production box, but run the full check on this backup. That makes backup testing not just a general safety process, but a performance enhancer as well: Finally, assuming the tests pass, you can delete the database, leave it in place, or delete it regardless of the tests passing. All this is automated and scheduled through the SQL Agent job on your servers. Running your databases through this process will ensure that you don’t just have backups, but that you have tested backups. Single Point of Management If you have more than one server to maintain, getting backups setup could be a tedious process. But, with Red Gate SQL Backup Pro you can connect to multiple servers and then manage all your databases and all your servers backups from a single location. You’ll be able to see what is scheduled, what has run successfully and what has failed, all from a single interface without having to connect to different servers. Log Shipping Wizard If you want to set up log shipping as part of a disaster recovery process, it can frequently be a pain to get configured correctly. We supply a wizard that will walk you through every step of the process including setting up alerts so you’ll know should your log shipping fail. Summary You want to get your backups right. As outlined above, Red Gate SQL Backup Pro will absolutely help you there. We supply a number of processes and functionalities above and beyond what you get with SQL Server native. Plus, with our guidance, hints and reminders, you will get your backups set up in a way that protects your business.

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• The new Auto Scaling Service in Windows Azure

  - by shiju

  One of the key features of the Cloud is the on-demand scalability, which lets the cloud application developers to scale up or scale down the number of compute resources hosted on the Cloud. Auto Scaling provides the capability to dynamically scale up and scale down your compute resources based on user-defined policies, Key Performance Indicators (KPI), health status checks, and schedules, without any manual intervention. Auto Scaling is an important feature to consider when designing and architecting cloud based solutions, which can unleash the real power of Cloud to the apps for providing truly on-demand scalability and can also guard the organizational budget for cloud based application deployment. In the past, you have had to leverage the the Microsoft Enterprise Library Autoscaling Application Block (WASABi) or a services like  MetricsHub for implementing Automatic Scaling for your cloud apps hosted on the Windows Azure. The WASABi required to host your auto scaling block in a Windows Azure Worker Role for effectively implementing the auto scaling behaviour to your Windows Azure apps. The newly announced Auto Scaling service in Windows Azure lets you add automatic scaling capability to your Windows Azure Compute Services such as Cloud Services, Web Sites and Virtual Machine. Unlike WASABi hosted on a Worker Role, you don’t need to host any monitoring service for using the new Auto Scaling service and the Auto Scaling service will be available to individual Windows Azure Compute Services as part of the Scaling. Configure Auto Scaling for a Windows Azure Cloud Service Currently the Auto Scaling service supports Cloud Services, Web Sites and Virtual Machine. In this demo, I will be used a Cloud Services app with a Web Role and a Worker Role. To enable the Auto Scaling, select t your Windows Azure app in the Windows Azure management portal, and choose “SCLALE” tab. The Scale tab will show the all information regards with Auto Scaling. The below image shows that we have currently disabled the AutoScale service. To enable Auto Scaling, you need to choose either CPU or QUEUE. The QUEUE option is not available for Web Sites. The image below demonstrates how to configure Auto Scaling for a Web Role based on the utilization of CPU. We have configured the web role app for running with 1 to 5 Virtual Machine instances based on the CPU utilization with a range of 50 to 80%. If the aggregate utilization is becoming above above 80%, it will scale up instances and it will scale down instances when utilization is becoming below 50%. The image below demonstrates how to configure Auto Scaling for a Worker Role app based on the messages added into the Windows Azure storage Queue. We configured the worker role app for running with 1 to 3 Virtual Machine instances based on the Queue messages added into the Windows Azure storage Queue. Here we have specified the number of messages target per machine is 2000. The image below shows the summary of the Auto Scaling for the Cloud Service after configuring auto scaling service. Summary Auto Scaling is an extremely important behaviour of the Cloud applications for providing on-demand scalability without any manual intervention. Windows Azure provides greater support for enabling Auto Scaling for the apps deployed on the Windows Azure cloud platform. The new Auto Scaling service in Windows Azure lets you add automatic scaling capability to your Windows Azure Compute Services such as Cloud Services, Web Sites and Virtual Machine. In the new Auto Scaling service, you don’t have to host any monitor service like you have had in WASABi block. The Auto Scaling service is an excellent alternative to the manually hosting WASABi block in a Worker Role app.

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• How do I remove SUN Java and use OpenJDK instead?

  - by Adel Ramadan

  As a programmer I use java for learning to code in Netbeans. I installed Sun java 6 long time ago over openJDK that came with my ubuntu just cause it seemed more responsive... Now that oracle left the repos I wanted something easy to handle to install and uninstall, so I want to Remove completely sun java 6 from my computer and set as default OPENjdk....and openjre. I already have installed OpenJDK and OPENjre...but not marked as default. Besides I want to clean Sun java from here, dont wanna get messy ^^. Running ubuntu 11.10

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• Using R to Analyze G1GC Log Files

  - by user12620111

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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. 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[ERROR] Line 13: Unexpected exception while processing element 'inherits' com.google.gwt.core.ext.UnableToCompleteException: (see previous log entries) at com.google.gwt.dev.cfg.ModuleDefLoader.nestedLoad(ModuleDefLoader.java:239) at com.google.gwt.dev.cfg.ModuleDefSchema$BodySchema.__inherits_begin(ModuleDefSchema.java:354) at sun.reflect.GeneratedMethodAccessor1.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at com.google.gwt.dev.util.xml.HandlerMethod.invokeBegin(HandlerMethod.java:223) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.startElement(ReflectiveParser.java:270) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.startElement(AbstractSAXParser.java:501) at com.sun.org.apache.xerces.internal.parsers.AbstractXMLDocumentParser.emptyElement(AbstractXMLDocumentParser.java:179) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanStartElement(XMLDocumentFragmentScannerImpl.java:1339) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl$FragmentContentDriver.next(XMLDocumentFragmentScannerImpl.java:2747) at com.sun.org.apache.xerces.internal.impl.XMLDocumentScannerImpl.next(XMLDocumentScannerImpl.java:648) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanDocument(XMLDocumentFragmentScannerImpl.java:510) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:807) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:737) at com.sun.org.apache.xerces.internal.parsers.XMLParser.parse(XMLParser.java:107) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.parse(AbstractSAXParser.java:1205) at com.sun.org.apache.xerces.internal.jaxp.SAXParserImpl$JAXPSAXParser.parse(SAXParserImpl.java:522) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.parse(ReflectiveParser.java:327) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.access$100(ReflectiveParser.java:48) at com.google.gwt.dev.util.xml.ReflectiveParser.parse(ReflectiveParser.java:398) at com.google.gwt.dev.cfg.ModuleDefLoader.nestedLoad(ModuleDefLoader.java:257) at com.google.gwt.dev.cfg.ModuleDefLoader$1.load(ModuleDefLoader.java:169) at com.google.gwt.dev.cfg.ModuleDefLoader.doLoadModule(ModuleDefLoader.java:283) at com.google.gwt.dev.cfg.ModuleDefLoader.loadFromClassPath(ModuleDefLoader.java:141) at com.google.gwt.dev.Compiler.run(Compiler.java:184) at com.google.gwt.dev.Compiler$1.run(Compiler.java:152) at com.google.gwt.dev.CompileTaskRunner.doRun(CompileTaskRunner.java:87) at com.google.gwt.dev.CompileTaskRunner.runWithAppropriateLogger(CompileTaskRunner.java:81) at com.google.gwt.dev.Compiler.main(Compiler.java:159) [ERROR] Failure while parsing XML com.google.gwt.core.ext.UnableToCompleteException: (see previous log entries) at com.google.gwt.dev.util.xml.DefaultSchema.onHandlerException(DefaultSchema.java:56) at com.google.gwt.dev.util.xml.Schema.onHandlerException(Schema.java:66) at com.google.gwt.dev.util.xml.Schema.onHandlerException(Schema.java:66) at com.google.gwt.dev.util.xml.HandlerMethod.invokeBegin(HandlerMethod.java:233) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.startElement(ReflectiveParser.java:270) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.startElement(AbstractSAXParser.java:501) at com.sun.org.apache.xerces.internal.parsers.AbstractXMLDocumentParser.emptyElement(AbstractXMLDocumentParser.java:179) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanStartElement(XMLDocumentFragmentScannerImpl.java:1339) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl$FragmentContentDriver.next(XMLDocumentFragmentScannerImpl.java:2747) at com.sun.org.apache.xerces.internal.impl.XMLDocumentScannerImpl.next(XMLDocumentScannerImpl.java:648) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanDocument(XMLDocumentFragmentScannerImpl.java:510) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:807) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:737) at com.sun.org.apache.xerces.internal.parsers.XMLParser.parse(XMLParser.java:107) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.parse(AbstractSAXParser.java:1205) at com.sun.org.apache.xerces.internal.jaxp.SAXParserImpl$JAXPSAXParser.parse(SAXParserImpl.java:522) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.parse(ReflectiveParser.java:327) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.access$100(ReflectiveParser.java:48) at com.google.gwt.dev.util.xml.ReflectiveParser.parse(ReflectiveParser.java:398) at com.google.gwt.dev.cfg.ModuleDefLoader.nestedLoad(ModuleDefLoader.java:257) at com.google.gwt.dev.cfg.ModuleDefLoader$1.load(ModuleDefLoader.java:169) at com.google.gwt.dev.cfg.ModuleDefLoader.doLoadModule(ModuleDefLoader.java:283) at com.google.gwt.dev.cfg.ModuleDefLoader.loadFromClassPath(ModuleDefLoader.java:141) at com.google.gwt.dev.Compiler.run(Compiler.java:184) at com.google.gwt.dev.Compiler$1.run(Compiler.java:152) at com.google.gwt.dev.CompileTaskRunner.doRun(CompileTaskRunner.java:87) at com.google.gwt.dev.CompileTaskRunner.runWithAppropriateLogger(CompileTaskRunner.java:81) at com.google.gwt.dev.Compiler.main(Compiler.java:159) [ERROR] Unexpected error while processing XML com.google.gwt.core.ext.UnableToCompleteException: (see previous log entries) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.parse(ReflectiveParser.java:351) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.access$100(ReflectiveParser.java:48) at com.google.gwt.dev.util.xml.ReflectiveParser.parse(ReflectiveParser.java:398) at com.google.gwt.dev.cfg.ModuleDefLoader.nestedLoad(ModuleDefLoader.java:257) at com.google.gwt.dev.cfg.ModuleDefLoader$1.load(ModuleDefLoader.java:169) at com.google.gwt.dev.cfg.ModuleDefLoader.doLoadModule(ModuleDefLoader.java:283) at com.google.gwt.dev.cfg.ModuleDefLoader.loadFromClassPath(ModuleDefLoader.java:141) at com.google.gwt.dev.Compiler.run(Compiler.java:184) at com.google.gwt.dev.Compiler$1.run(Compiler.java:152) at com.google.gwt.dev.CompileTaskRunner.doRun(CompileTaskRunner.java:87) at com.google.gwt.dev.CompileTaskRunner.runWithAppropriateLogger(CompileTaskRunner.java:81) at com.google.gwt.dev.Compiler.main(Compiler.java:159) Anyone knows how it works?

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• How to include an external jar in a GWT module?

  - by Sergio del Amo

  I would like to use the org.apache.commons.validator.GenericValidator class in a view class of my GWT web app. I have read that I have to implicitely tell that I intend to use this external library. I thought adding the next line into my App.gwt.xml would work. <inherits name='org.apache.commons.validator.GenericValidator'/> I get the next error: Loading inherited module 'org.apache.commons.validator.GenericValidator' [ERROR] Unable to find 'org/apache/commons/validator/GenericValidator.gwt.xml' on your classpath; could be a typo, or maybe you forgot to include a classpath entry for source? [ERROR] Line 13: Unexpected exception while processing element 'inherits' com.google.gwt.core.ext.UnableToCompleteException: (see previous log entries) at com.google.gwt.dev.cfg.ModuleDefLoader.nestedLoad(ModuleDefLoader.java:239) at com.google.gwt.dev.cfg.ModuleDefSchema$BodySchema.__inherits_begin(ModuleDefSchema.java:354) at sun.reflect.GeneratedMethodAccessor1.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at com.google.gwt.dev.util.xml.HandlerMethod.invokeBegin(HandlerMethod.java:223) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.startElement(ReflectiveParser.java:270) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.startElement(AbstractSAXParser.java:501) at com.sun.org.apache.xerces.internal.parsers.AbstractXMLDocumentParser.emptyElement(AbstractXMLDocumentParser.java:179) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanStartElement(XMLDocumentFragmentScannerImpl.java:1339) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl$FragmentContentDriver.next(XMLDocumentFragmentScannerImpl.java:2747) at com.sun.org.apache.xerces.internal.impl.XMLDocumentScannerImpl.next(XMLDocumentScannerImpl.java:648) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanDocument(XMLDocumentFragmentScannerImpl.java:510) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:807) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:737) at com.sun.org.apache.xerces.internal.parsers.XMLParser.parse(XMLParser.java:107) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.parse(AbstractSAXParser.java:1205) at com.sun.org.apache.xerces.internal.jaxp.SAXParserImpl$JAXPSAXParser.parse(SAXParserImpl.java:522) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.parse(ReflectiveParser.java:327) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.access$100(ReflectiveParser.java:48) at com.google.gwt.dev.util.xml.ReflectiveParser.parse(ReflectiveParser.java:398) at com.google.gwt.dev.cfg.ModuleDefLoader.nestedLoad(ModuleDefLoader.java:257) at com.google.gwt.dev.cfg.ModuleDefLoader$1.load(ModuleDefLoader.java:169) at com.google.gwt.dev.cfg.ModuleDefLoader.doLoadModule(ModuleDefLoader.java:283) at com.google.gwt.dev.cfg.ModuleDefLoader.loadFromClassPath(ModuleDefLoader.java:141) at com.google.gwt.dev.Compiler.run(Compiler.java:184) at com.google.gwt.dev.Compiler$1.run(Compiler.java:152) at com.google.gwt.dev.CompileTaskRunner.doRun(CompileTaskRunner.java:87) at com.google.gwt.dev.CompileTaskRunner.runWithAppropriateLogger(CompileTaskRunner.java:81) at com.google.gwt.dev.Compiler.main(Compiler.java:159) [ERROR] Failure while parsing XML com.google.gwt.core.ext.UnableToCompleteException: (see previous log entries) at com.google.gwt.dev.util.xml.DefaultSchema.onHandlerException(DefaultSchema.java:56) at com.google.gwt.dev.util.xml.Schema.onHandlerException(Schema.java:66) at com.google.gwt.dev.util.xml.Schema.onHandlerException(Schema.java:66) at com.google.gwt.dev.util.xml.HandlerMethod.invokeBegin(HandlerMethod.java:233) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.startElement(ReflectiveParser.java:270) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.startElement(AbstractSAXParser.java:501) at com.sun.org.apache.xerces.internal.parsers.AbstractXMLDocumentParser.emptyElement(AbstractXMLDocumentParser.java:179) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanStartElement(XMLDocumentFragmentScannerImpl.java:1339) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl$FragmentContentDriver.next(XMLDocumentFragmentScannerImpl.java:2747) at com.sun.org.apache.xerces.internal.impl.XMLDocumentScannerImpl.next(XMLDocumentScannerImpl.java:648) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanDocument(XMLDocumentFragmentScannerImpl.java:510) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:807) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:737) at com.sun.org.apache.xerces.internal.parsers.XMLParser.parse(XMLParser.java:107) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.parse(AbstractSAXParser.java:1205) at com.sun.org.apache.xerces.internal.jaxp.SAXParserImpl$JAXPSAXParser.parse(SAXParserImpl.java:522) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.parse(ReflectiveParser.java:327) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.access$100(ReflectiveParser.java:48) at com.google.gwt.dev.util.xml.ReflectiveParser.parse(ReflectiveParser.java:398) at com.google.gwt.dev.cfg.ModuleDefLoader.nestedLoad(ModuleDefLoader.java:257) at com.google.gwt.dev.cfg.ModuleDefLoader$1.load(ModuleDefLoader.java:169) at com.google.gwt.dev.cfg.ModuleDefLoader.doLoadModule(ModuleDefLoader.java:283) at com.google.gwt.dev.cfg.ModuleDefLoader.loadFromClassPath(ModuleDefLoader.java:141) at com.google.gwt.dev.Compiler.run(Compiler.java:184) at com.google.gwt.dev.Compiler$1.run(Compiler.java:152) at com.google.gwt.dev.CompileTaskRunner.doRun(CompileTaskRunner.java:87) at com.google.gwt.dev.CompileTaskRunner.runWithAppropriateLogger(CompileTaskRunner.java:81) at com.google.gwt.dev.Compiler.main(Compiler.java:159) [ERROR] Unexpected error while processing XML com.google.gwt.core.ext.UnableToCompleteException: (see previous log entries) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.parse(ReflectiveParser.java:351) at com.google.gwt.dev.util.xml.ReflectiveParser$Impl.access$100(ReflectiveParser.java:48) at com.google.gwt.dev.util.xml.ReflectiveParser.parse(ReflectiveParser.java:398) at com.google.gwt.dev.cfg.ModuleDefLoader.nestedLoad(ModuleDefLoader.java:257) at com.google.gwt.dev.cfg.ModuleDefLoader$1.load(ModuleDefLoader.java:169) at com.google.gwt.dev.cfg.ModuleDefLoader.doLoadModule(ModuleDefLoader.java:283) at com.google.gwt.dev.cfg.ModuleDefLoader.loadFromClassPath(ModuleDefLoader.java:141) at com.google.gwt.dev.Compiler.run(Compiler.java:184) at com.google.gwt.dev.Compiler$1.run(Compiler.java:152) at com.google.gwt.dev.CompileTaskRunner.doRun(CompileTaskRunner.java:87) at com.google.gwt.dev.CompileTaskRunner.runWithAppropriateLogger(CompileTaskRunner.java:81) at com.google.gwt.dev.Compiler.main(Compiler.java:159) I have commons.validator-1.3.1.jar in war/WEB-INF/lib I am using eclipse with Google Plugin. Anyone knows how it works?

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• Error accessing a Web Service with SSL

  - by Elie

  I have a program that is supposed to send a file to a web service, which requires an SSL connection. I run the program as follows: SET JAVA_HOME=C:\Program Files\Java\jre1.6.0_07 SET com.ibm.SSL.ConfigURL=ssl.client.props "%JAVA_HOME%\bin\java" -cp ".;Test.jar" ca.mypackage.Main This was works fine, but when I change the first line to SET JAVA_HOME=C:\Program Files\IBM\SDP\runtimes\base_v7\java\jre I get the following error: com.sun.xml.internal.ws.client.ClientTransportException: HTTP transport error: java.net.SocketException: java.lang.ClassNotFoundException: Cannot find the specified class com.ibm.websphere.ssl.protocol.SSLSocketFactory at com.sun.xml.internal.ws.transport.http.client.HttpClientTransport.getOutput(HttpClientTransport.java:119) at com.sun.xml.internal.ws.transport.http.client.HttpTransportPipe.process(HttpTransportPipe.java:140) at com.sun.xml.internal.ws.transport.http.client.HttpTransportPipe.processRequest(HttpTransportPipe.java:86) at com.sun.xml.internal.ws.api.pipe.Fiber.__doRun(Fiber.java:593) at com.sun.xml.internal.ws.api.pipe.Fiber._doRun(Fiber.java:552) at com.sun.xml.internal.ws.api.pipe.Fiber.doRun(Fiber.java:537) at com.sun.xml.internal.ws.api.pipe.Fiber.runSync(Fiber.java:434) at com.sun.xml.internal.ws.client.Stub.process(Stub.java:247) at com.sun.xml.internal.ws.client.sei.SEIStub.doProcess(SEIStub.java:132) at com.sun.xml.internal.ws.client.sei.SyncMethodHandler.invoke(SyncMethodHandler.java:242) at com.sun.xml.internal.ws.client.sei.SyncMethodHandler.invoke(SyncMethodHandler.java:222) at com.sun.xml.internal.ws.client.sei.SEIStub.invoke(SEIStub.java:115) at $Proxy26.fileSubmit(Unknown Source) at com.testing.TestingSoapProxy.fileSubmit(TestingSoapProxy.java:81) at ca.mypackage.Main.main(Main.java:63) Caused by: java.net.SocketException: java.lang.ClassNotFoundException: Cannot find the specified class com.ibm.websphere.ssl.protocol.SSLSocketFactory at javax.net.ssl.DefaultSSLSocketFactory.a(SSLSocketFactory.java:7) at javax.net.ssl.DefaultSSLSocketFactory.createSocket(SSLSocketFactory.java:1) at com.ibm.net.ssl.www2.protocol.https.c.afterConnect(c.java:110) at com.ibm.net.ssl.www2.protocol.https.d.connect(d.java:14) at sun.net.www.protocol.http.HttpURLConnection.getOutputStream(HttpURLConnection.java:902) at com.ibm.net.ssl.www2.protocol.https.b.getOutputStream(b.java:86) at com.sun.xml.internal.ws.transport.http.client.HttpClientTransport.getOutput(HttpClientTransport.java:107) ... 14 more Caused by: java.lang.ClassNotFoundException: Cannot find the specified class com.ibm.websphere.ssl.protocol.SSLSocketFactory at javax.net.ssl.SSLJsseUtil.b(SSLJsseUtil.java:20) at javax.net.ssl.SSLSocketFactory.getDefault(SSLSocketFactory.java:36) at javax.net.ssl.HttpsURLConnection.getDefaultSSLSocketFactory(HttpsURLConnection.java:16) at javax.net.ssl.HttpsURLConnection.<init>(HttpsURLConnection.java:36) at com.ibm.net.ssl.www2.protocol.https.b.<init>(b.java:1) at com.ibm.net.ssl.www2.protocol.https.Handler.openConnection(Handler.java:11) at java.net.URL.openConnection(URL.java:995) at com.sun.xml.internal.ws.api.EndpointAddress.openConnection(EndpointAddress.java:206) at com.sun.xml.internal.ws.transport.http.client.HttpClientTransport.createHttpConnection(HttpClientTransport.java:277) at com.sun.xml.internal.ws.transport.http.client.HttpClientTransport.getOutput(HttpClientTransport.java:103) ... 14 more So it seems that this problem would be related to the JRE I'm using, but what doesn't seem to make sense is that the non-IBM JRE works fine, but the IBM JRE does not. Any ideas, or suggestions?

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• XMI format error loading project on argouml

  - by Tom Brito

  Have anyone experienced this (org.argouml.model.)XmiException opening a project lastest version of argouml? XMI format error : org.argouml.model.XmiException: XMI parsing error at line: 18: Cannot set a multi-value to a non-multivalued reference:namespace If this file was produced by a tool other than ArgoUML, please check to make sure that the file is in a supported format, including both UML and XMI versions. If you believe that the file is legal UML/XMI and should have loaded or if it was produced by any version of ArgoUML, please report the problem as a bug by going to http://argouml.tigris.org/project_bugs.html. System Info: ArgoUML version : 0.30 Java Version : 1.6.0_15 Java Vendor : Sun Microsystems Inc. Java Vendor URL : http://java.sun.com/ Java Home Directory : /usr/lib/jvm/java-6-sun-1.6.0.15/jre Java Classpath : /usr/lib/jvm/java-6-sun-1.6.0.15/jre/lib/deploy.jar Operation System : Linux, Version 2.6.31-20-generic Architecture : i386 User Name : wellington User Home Directory : /home/wellington Current Directory : /home/wellington JVM Total Memory : 34271232 JVM Free Memory : 10512336 Error occurred at : Thu Apr 01 11:21:10 BRT 2010 Cause : org.argouml.model.XmiException: XMI parsing error at line: 18: Cannot set a multi-value to a non-multivalued reference:namespace at org.argouml.model.mdr.XmiReaderImpl.parse(XmiReaderImpl.java:307) at org.argouml.persistence.ModelMemberFilePersister.readModels(ModelMemberFilePersister.java:273) at org.argouml.persistence.XmiFilePersister.doLoad(XmiFilePersister.java:261) at org.argouml.ui.ProjectBrowser.loadProject(ProjectBrowser.java:1597) at org.argouml.ui.LoadSwingWorker.construct(LoadSwingWorker.java:89) at org.argouml.ui.SwingWorker.doConstruct(SwingWorker.java:153) at org.argouml.ui.SwingWorker$2.run(SwingWorker.java:281) at java.lang.Thread.run(Thread.java:619) Caused by: org.netbeans.lib.jmi.util.DebugException: Cannot set a multi-value to a non-multivalued reference:namespace at org.netbeans.lib.jmi.xmi.XmiSAXReader.startElement(XmiSAXReader.java:232) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.startElement(AbstractSAXParser.java:501) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanStartElement(XMLDocumentFragmentScannerImpl.java:1359) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl$FragmentContentDriver.next(XMLDocumentFragmentScannerImpl.java:2747) at com.sun.org.apache.xerces.internal.impl.XMLDocumentScannerImpl.next(XMLDocumentScannerImpl.java:648) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanDocument(XMLDocumentFragmentScannerImpl.java:510) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:807) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:737) at com.sun.org.apache.xerces.internal.parsers.XMLParser.parse(XMLParser.java:107) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.parse(AbstractSAXParser.java:1205) at com.sun.org.apache.xerces.internal.jaxp.SAXParserImpl$JAXPSAXParser.parse(SAXParserImpl.java:522) at javax.xml.parsers.SAXParser.parse(SAXParser.java:395) at org.netbeans.lib.jmi.xmi.XmiSAXReader.read(XmiSAXReader.java:136) at org.netbeans.lib.jmi.xmi.XmiSAXReader.read(XmiSAXReader.java:98) at org.netbeans.lib.jmi.xmi.SAXReader.read(SAXReader.java:56) at org.argouml.model.mdr.XmiReaderImpl.parse(XmiReaderImpl.java:233) ... 7 more Caused by: org.netbeans.lib.jmi.util.DebugException: Cannot set a multi-value to a non-multivalued reference:namespace at org.netbeans.lib.jmi.xmi.XmiElement$Instance.setReferenceValues(XmiElement.java:699) at org.netbeans.lib.jmi.xmi.XmiElement$Instance.resolveAttributeValue(XmiElement.java:772) at org.netbeans.lib.jmi.xmi.XmiElement$Instance. (XmiElement.java:496) at org.netbeans.lib.jmi.xmi.XmiContext.resolveInstanceOrReference(XmiContext.java:688) at org.netbeans.lib.jmi.xmi.XmiElement$ObjectValues.startSubElement(XmiElement.java:1460) at org.netbeans.lib.jmi.xmi.XmiSAXReader.startElement(XmiSAXReader.java:219) ... 22 more ------- Full exception : org.argouml.persistence.XmiFormatException: org.argouml.model.XmiException: XMI parsing error at line: 18: Cannot set a multi-value to a non-multivalued reference:namespace at org.argouml.persistence.ModelMemberFilePersister.readModels(ModelMemberFilePersister.java:298) at org.argouml.persistence.XmiFilePersister.doLoad(XmiFilePersister.java:261) at org.argouml.ui.ProjectBrowser.loadProject(ProjectBrowser.java:1597) at org.argouml.ui.LoadSwingWorker.construct(LoadSwingWorker.java:89) at org.argouml.ui.SwingWorker.doConstruct(SwingWorker.java:153) at org.argouml.ui.SwingWorker$2.run(SwingWorker.java:281) at java.lang.Thread.run(Thread.java:619) Caused by: org.argouml.model.XmiException: XMI parsing error at line: 18: Cannot set a multi-value to a non-multivalued reference:namespace at org.argouml.model.mdr.XmiReaderImpl.parse(XmiReaderImpl.java:307) at org.argouml.persistence.ModelMemberFilePersister.readModels(ModelMemberFilePersister.java:273) ... 6 more Caused by: org.netbeans.lib.jmi.util.DebugException: Cannot set a multi-value to a non-multivalued reference:namespace at org.netbeans.lib.jmi.xmi.XmiSAXReader.startElement(XmiSAXReader.java:232) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.startElement(AbstractSAXParser.java:501) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanStartElement(XMLDocumentFragmentScannerImpl.java:1359) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl$FragmentContentDriver.next(XMLDocumentFragmentScannerImpl.java:2747) at com.sun.org.apache.xerces.internal.impl.XMLDocumentScannerImpl.next(XMLDocumentScannerImpl.java:648) at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanDocument(XMLDocumentFragmentScannerImpl.java:510) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:807) at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:737) at com.sun.org.apache.xerces.internal.parsers.XMLParser.parse(XMLParser.java:107) at com.sun.org.apache.xerces.internal.parsers.AbstractSAXParser.parse(AbstractSAXParser.java:1205) at com.sun.org.apache.xerces.internal.jaxp.SAXParserImpl$JAXPSAXParser.parse(SAXParserImpl.java:522) at javax.xml.parsers.SAXParser.parse(SAXParser.java:395) at org.netbeans.lib.jmi.xmi.XmiSAXReader.read(XmiSAXReader.java:136) at org.netbeans.lib.jmi.xmi.XmiSAXReader.read(XmiSAXReader.java:98) at org.netbeans.lib.jmi.xmi.SAXReader.read(SAXReader.java:56) at org.argouml.model.mdr.XmiReaderImpl.parse(XmiReaderImpl.java:233) ... 7 more Caused by: org.netbeans.lib.jmi.util.DebugException: Cannot set a multi-value to a non-multivalued reference:namespace at org.netbeans.lib.jmi.xmi.XmiElement$Instance.setReferenceValues(XmiElement.java:699) at org.netbeans.lib.jmi.xmi.XmiElement$Instance.resolveAttributeValue(XmiElement.java:772) at org.netbeans.lib.jmi.xmi.XmiElement$Instance. (XmiElement.java:496) at org.netbeans.lib.jmi.xmi.XmiContext.resolveInstanceOrReference(XmiContext.java:688) at org.netbeans.lib.jmi.xmi.XmiElement$ObjectValues.startSubElement(XmiElement.java:1460) at org.netbeans.lib.jmi.xmi.XmiSAXReader.startElement(XmiSAXReader.java:219) ... 22 more the original project was created on argo v0.28.1, and (as I remember) have only use case diagrams. and yes, I'll report at the specified argo website either.. :) But anyone know anything about this exception?

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• Can VMWare Workstation 7.x and Sun VirtualBox 3.1.x co-exist on the same Windows7 64bit HOST togethe

  - by Heston T. Holtmann

  BACKGROUND INFO: My Old Workstation Host: 32bit Ubuntu 9.04 running Sun Virtual Box 3.x hosting Windows-XP VM Guest for Windows Software app development (VS2008, etc) My New Workstation Host: 64bit Windows7 running VMWare Workstation 7 to host 32bit Ubuntu 9.10 for linux project work. NEEDS: I need to get my original Sun-VBox Windows-XP Guest running on my new Windows7 Workstation either imported into VMWare or running on the Windows version of Sun-Virtual box (I have the VM-Guest Backed up and copied to the new computer data drive. PROBLEM: I don't need to run VM's from Both Virtual-Machine Software packages at the same time... but I do need to run some older Virtual-Machines from Sun-Virtualbox on the same 64bit Windows7 host until I can migrate those VM's to VMWare. Before switching from Linux HOST to Windows HOST, I ensured to export the VirtualBox VM to an OVF "appliance" with intentions of importing into VMWare Workstation 7.. but VMWare gives me an error stating it can't import it QUESTION: Will installing Sun Virtual Box bash or interfere with my VMWare installtion?

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• How to list my harddrives in OpenSolaris?

  - by Sanoj

  I have a machine with two harddrives. I have installed OpenSolaris on one of them and now I want to add the other one as a mirror-drive in my zpool. But to do that I have to know the name of my drive, something like c7d0s0. Using the command zpool status can I see the harddrives already in use in my zpool but not the hard-drives that are not in use. How can I list the names of the hard-drives that are not in use in any zpool or list the name of all my hard-drives?

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