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• ZFS for Database Log Files

  - by user12620111

  I've been troubled by drop outs in CPU usage in my application server, characterized by the CPUs suddenly going from close to 90% CPU busy to almost completely CPU idle for a few seconds. Here is an example of a drop out as shown by a snippet of vmstat data taken while the application server is under a heavy workload. # vmstat 1  kthr      memory            page            disk          faults      cpu  r b w   swap  free  re  mf pi po fr de sr s3 s4 s5 s6   in   sy   cs us sy id  1 0 0 130160176 116381952 0 16 0 0 0 0  0  0  0  0  0 207377 117715 203884 70 21 9  12 0 0 130160160 116381936 0 25 0 0 0 0 0  0  0  0  0 200413 117162 197250 70 20 9  11 0 0 130160176 116381920 0 16 0 0 0 0 0  0  1  0  0 203150 119365 200249 72 21 7  8 0 0 130160176 116377808 0 19 0 0 0 0  0  0  0  0  0 169826 96144 165194 56 17 27  0 0 0 130160176 116377800 0 16 0 0 0 0  0  0  0  0  1 10245 9376 9164 2  1 97  0 0 0 130160176 116377792 0 16 0 0 0 0  0  0  0  0  2 15742 12401 14784 4 1 95  0 0 0 130160176 116377776 2 16 0 0 0 0  0  0  1  0  0 19972 17703 19612 6 2 92  14 0 0 130160176 116377696 0 16 0 0 0 0 0  0  0  0  0 202794 116793 199807 71 21 8  9 0 0 130160160 116373584 0 30 0 0 0 0  0  0 18  0  0 203123 117857 198825 69 20 11 This behavior occurred consistently while the application server was processing synthetic transactions: HTTP requests from JMeter running on an external machine. I explored many theories trying to explain the drop outs, including: Unexpected JMeter behavior Network contention Java Garbage Collection Application Server thread pool problems Connection pool problems Database transaction processing Database I/O contention Graphing the CPU %idle led to a breakthrough: Several of the drop outs were 30 seconds apart. With that insight, I went digging through the data again and looking for other outliers that were 30 seconds apart. In the database server statistics, I found spikes in the iostat "asvc_t" (average response time of disk transactions, in milliseconds) for the disk drive that was being used for the database log files. Here is an example:                     extended device statistics     r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 2053.6    0.0 8234.3  0.0  0.2    0.0    0.1   0  24 c3t60080E5...F4F6d0s0     0.0 2162.2    0.0 8652.8  0.0  0.3    0.0    0.1   0  28 c3t60080E5...F4F6d0s0     0.0 1102.5    0.0 10012.8  0.0  4.5    0.0    4.1   0  69 c3t60080E5...F4F6d0s0     0.0   74.0    0.0 7920.6  0.0 10.0    0.0  135.1   0 100 c3t60080E5...F4F6d0s0     0.0  568.7    0.0 6674.0  0.0  6.4    0.0   11.2   0  90 c3t60080E5...F4F6d0s0     0.0 1358.0    0.0 5456.0  0.0  0.6    0.0    0.4   0  55 c3t60080E5...F4F6d0s0     0.0 1314.3    0.0 5285.2  0.0  0.7    0.0    0.5   0  70 c3t60080E5...F4F6d0s0 Here is a little more information about my database configuration: The database and application server were running on two different SPARC servers. Storage for the database was on a storage array connected via 8 gigabit Fibre Channel Data storage and log file were on different physical disk drives Reliable low latency I/O is provided by battery backed NVRAM Highly available: Two Fibre Channel links accessed via MPxIO Two Mirrored cache controllers The log file physical disks were mirrored in the storage device Database log files on a ZFS Filesystem with cutting-edge technologies, such as copy-on-write and end-to-end checksumming Why would I be getting service time spikes in my high-end storage? First, I wanted to verify that the database log disk service time spikes aligned with the application server CPU drop outs, and they did: At first, I guessed that the disk service time spikes might be related to flushing the write through cache on the storage device, but I was unable to validate that theory. After searching the WWW for a while, I decided to try using a separate log device: # zpool add ZFS-db-41 log c3t60080E500017D55C000015C150A9F8A7d0 The ZFS log device is configured in a similar manner as described above: two physical disks mirrored in the storage array. This change to the database storage configuration eliminated the application server CPU drop outs: Here is the zpool configuration: # zpool status ZFS-db-41   pool: ZFS-db-41  state: ONLINE  scan: none requested config:         NAME                                     STATE         ZFS-db-41                                ONLINE           c3t60080E5...F4F6d0  ONLINE         logs           c3t60080E5...F8A7d0  ONLINE Now, the I/O spikes look like this:                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1053.5    0.0 4234.1  0.0  0.8    0.0    0.7   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1131.8    0.0 4555.3  0.0  0.8    0.0    0.7   0  76 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1167.6    0.0 4682.2  0.0  0.7    0.0    0.6   0  74 c3t60080E5...F8A7d0s0     0.0  162.2    0.0 19153.9  0.0  0.7    0.0    4.2   0  12 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1247.2    0.0 4992.6  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0     0.0   41.0    0.0   70.0  0.0  0.1    0.0    1.6   0   2 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1241.3    0.0 4989.3  0.0  0.8    0.0    0.6   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1193.2    0.0 4772.9  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0 We can see the steady flow of 4k writes to the ZIL device from O_SYNC database log file writes. The spikes are from flushing the transaction group. Like almost all problems that I run into, once I thoroughly understand the problem, I find that other people have documented similar experiences. Thanks to all of you who have documented alternative approaches. Saved for another day: now that the problem is obvious, I should try "zfs:zfs_immediate_write_sz" as recommended in the ZFS Evil Tuning Guide. References: The ZFS Intent Log Solaris ZFS, Synchronous Writes and the ZIL Explained ZFS Evil Tuning Guide: Cache Flushes ZFS Evil Tuning Guide: Tuning ZFS for Database Performance

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• APEX-Berichte automatisch aktualisieren

  - by carstenczarski

  Einen Bericht auf einer Anwendungsseite in regelmäßigen Abständen zu aktualisieren, ist recht einfach: Seit APEX 4.0 muss man noch nicht einmal JavaScript-Code dafür programmieren; mit einem einfach zu nutzenden Plugin des APEX-Entwicklerteams setzt man das in kürzester Zeit um. In diesem Tipp gehen wir noch etwas weiter: Für eine Tabelle, die eine Spalte mit dem Zeitpunkt der letzten Änderung enthält, wollen wir die zuletzt geänderten Werte hervorheben, so dass man sie leichter erkennen kann.

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• sqlplus: Running "set lines" and "set pagesize" automatially

  - by katsumii

  This is a followup to my previous entry. Using the full tty real estate with sqlplus (INOUE Katsumi @ Tokyo) 'rlwrap' is widely used for adding 'sqlplus' the history function and command line editing. Here's another but again kludgy implementation. First this is the alias. alias sqlplus="rlwrap -z ~/sqlplus.filter sqlplus" And this is the file content. #!/usr/bin/env perl use lib ($ENV{RLWRAP_FILTERDIR} or "."); use RlwrapFilter; use POSIX qw(:signal_h); use strict; my $filter = new RlwrapFilter; $filter -> prompt_handler(\&prompt); sigprocmask(SIG_UNBLOCK, POSIX::SigSet->new(28)); $SIG{WINCH} = 'winchHandler'; $filter -> run; sub winchHandler { $filter -> input_handler(\&input); sigprocmask(SIG_UNBLOCK, POSIX::SigSet->new(28)); $SIG{WINCH} = 'winchHandler'; $filter -> run; } sub input { $filter -> input_handler(undef); return `resize |sed -n "1s/COLUMNS=/set linesize /p;2s/LINES=/set pagesize /p"` . $_; } sub prompt { if ($_ =~ "SQL> ") { $filter -> input_handler(\&input); $filter -> prompt_handler(undef); } return $_; } I hope I can compare these 2 implementations after testing more and getting some feedbacks.

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• Schema Based Code Completion for NetBeans Platform Applications

  - by Geertjan

  Toni's recent blog entry provides, among several other interesting things, instructions for something I've been wanting to cover for a long time, which is schema based code completion: The above is a sample I created via Toni's tutorial, using the schema described here: http://www.w3schools.com/schema/schema_example.asp The support for the Navigator ain't bad either, especially considering I didn't do any coding at all to get all this: And here's where you can find the whole sample: http://java.net/projects/nb-api-samples/sources/api-samples/show/versions/7.2/misc/ShipOrder

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• Cloud Odyssey: A Hero's Quest Wins Two Telly Awards!

  - by Sandra Cheevers

  Cloud Odyssey: A Hero's Quest is a sci-fi movie experience that shows you the key success factors for guiding your own journey to the cloud.   The movie shows the journey to a mysterious cloud planet, as a metaphor to YOUR journey to the cloud. And now, Cloud Odyssey: A Hero's Quest! receives 2 Telly awards in the categories 1) Motivational and 2) Use of Animation. This is truly an honor to be recognized in the company of so many outstanding entries from a wide range of major players, including Disney, Coca-Cola, NBC, Discovery...Kudos to the Cloud Odyssey team!

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• Integrating a Progress Bar into a Wizard

  - by Geertjan

  Normally, when you create a wizard, as described here, and you have your own iterator, you'll have a class signature like this: public final class MyWizardWizardIterator implements WizardDescriptor.InstantiatingIterator<WizardDescriptor> { Let's now imagine that you've got some kind of long running process your wizard needs to perform. Maybe the wizard needs to connect to something, which could take some time. Start by adding a new dependency on the Progress API, which gives you the classes that access the NetBeans Platform's progress functionality. Now all we need to do is change the class signature very slightly: public final class MyWizardWizardIterator implements WizardDescriptor.ProgressInstantiatingIterator<WizardDescriptor> { Take a look at the part of the signature above that is highlighted. I.e., use WizardDescriptor.ProgressInstantiatingIterator instead of WizardDescriptor.InstantiatingIterator. Now you will need to implement a new instantiate method, one that receives a ProgressHandle. The other instantiate method, i.e., the one that already existed, should never be accessed anymore, and so you can add an assert to that effect: @Override public Set<?> instantiate() throws IOException {     throw new AssertionError("instantiate(ProgressHandle) " //NOI18N             + "should have been called"); //NOI18N } @Override public Set instantiate(ProgressHandle ph) throws IOException {     return Collections.emptySet(); } OK. Let's now add some code to make our progress bar work: @Override public Set instantiate(ProgressHandle ph) throws IOException {     ph.start();     ph.progress("Processing...");     try {         //Simulate some long process:         Thread.sleep(2500);     } catch (InterruptedException ex) {         Exceptions.printStackTrace(ex);     }     ph.finish();     return Collections.emptySet(); } And, maybe even more impressive, you can also do this: @Override public Set instantiate(ProgressHandle ph) throws IOException {     ph.start(1000);     ph.progress("Processing...");     try {         //Simulate some long process:         ph.progress("1/4 complete...", 250);         Thread.sleep(2500);         ph.progress("1/2 complete...", 500);         Thread.sleep(5000);         ph.progress("3/4 complete...", 750);         Thread.sleep(7500);         ph.progress("Complete...", 1000);         Thread.sleep(1000);     } catch (InterruptedException ex) {         Exceptions.printStackTrace(ex);     }     ph.finish();     return Collections.emptySet(); } The screenshots above show you what you should see when the Finish button is clicked in each case.

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• Library order is important

  - by Darryl Gove

  I've written quite extensively about link ordering issues, but I've not discussed the interaction between archive libraries and shared libraries. So let's take a simple program that calls a maths library function: #include <math.h int main() { for (int i=0; i<10000000; i++) { sin(i); } } We compile and run it to get the following performance: bash-3.2$ cc -g -O fp.c -lm bash-3.2$ timex ./a.out real 6.06 user 6.04 sys 0.01 Now most people will have heard of the optimised maths library which is added by the flag -xlibmopt. This contains optimised versions of key mathematical functions, in this instance, using the library doubles performance: bash-3.2$ cc -g -O -xlibmopt fp.c -lm bash-3.2$ timex ./a.out real 2.70 user 2.69 sys 0.00 The optimised maths library is provided as an archive library (libmopt.a), and the driver adds it to the link line just before the maths library - this causes the linker to pick the definitions provided by the static library in preference to those provided by libm. We can see the processing by asking the compiler to print out the link line: bash-3.2$ cc -### -g -O -xlibmopt fp.c -lm /usr/ccs/bin/ld ... fp.o -lmopt -lm -o a.out... The flag to the linker is -lmopt, and this is placed before the -lm flag. So what happens when the -lm flag is in the wrong place on the command line: bash-3.2$ cc -g -O -xlibmopt -lm fp.c bash-3.2$ timex ./a.out real 6.02 user 6.01 sys 0.01 If the -lm flag is before the source file (or object file for that matter), we get the slower performance from the system maths library. Why's that? If we look at the link line we can see the following ordering: /usr/ccs/bin/ld ... -lmopt -lm fp.o -o a.out So the optimised maths library is still placed before the system maths library, but the object file is placed afterwards. This would be ok if the optimised maths library were a shared library, but it is not - instead it's an archive library, and archive library processing is different - as described in the linker and library guide: "The link-editor searches an archive only to resolve undefined or tentative external references that have previously been encountered." An archive library can only be used resolve symbols that are outstanding at that point in the link processing. When fp.o is placed before the libmopt.a archive library, then the linker has an unresolved symbol defined in fp.o, and it will search the archive library to resolve that symbol. If the archive library is placed before fp.o then there are no unresolved symbols at that point, and so the linker doesn't need to use the archive library. This is why libmopt needs to be placed after the object files on the link line. On the other hand if the linker has observed any shared libraries, then at any point these are checked for any unresolved symbols. The consequence of this is that once the linker "sees" libm it will resolve any symbols it can to that library, and it will not check the archive library to resolve them. This is why libmopt needs to be placed before libm on the link line. This leads to the following order for placing files on the link line: Object files Archive libraries Shared libraries If you use this order, then things will consistently get resolved to the archive libraries rather than to the shared libaries.

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• New Procurement Report for Transportation Sourcing

  - by John Murphy

  Welcome to our fourth annual transportation procurement benchmark report. American Shipper, in partnership with the Council of Supply Chain Management Professionals (CSCMP) and the Retail Industry Leaders Association (RILA), surveyed roughly 275 transportation buyers and sellers on procurement practices, processes, technologies and results. Some key findings: • Manual, spreadsheet-based procurement processes remain the most prevalent among transportation buyers, with 42 percent of the total • Another 25 percent of respondents use a hybrid platform, which presumably means these buyers are using spreadsheets for at least one mode and/or geography • Only 23 percent of buyers are using a completely systems-based approach of some kind • Shippers were in a holding pattern with regards to investment in procurement systems the past year • Roughly three-quarters of survey respondents report that transportation spend has increased in 2012, although the pace has declined slightly from last year’s increases • Nearly every survey respondent purchases multiple modes of transportation • The number of respondents with plans to address technology to support the procurement process has increased in 2012. About one quarter of respondents who do not have a system report they have a budget for this investment in the next two years.

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• Skynet Big Data Demo Using Hexbug Spider Robot, Raspberry Pi, and Java SE Embedded (Part 3)

  - by hinkmond

  In Part 2, I described what connections you need to make for this demo using a Hexbug Spider Robot, a Raspberry Pi, and Java SE Embedded for programming. Here are some photos of me doing the soldering. Software engineers should not be afraid of a little soldering work. It's all good. See: Skynet Big Data Demo (Part 2) One thing to watch out for when you open the remote is that there may be some glue covering the contact points. Make sure to use an Exacto knife or small screwdriver to scrape away any glue or non-conductive material covering each place where you need to solder. And after you are done with your soldering and you gave the solder enough time to cool, make sure all your connections are marked so that you know which wire goes where. Give each wire a very light tug to make sure it is soldered correctly and is making good contact. There are lots of videos on the Web to help you if this is your first time soldering. Check out Laday Ada's (from adafruit.com) links on how to solder if you need some additional help: http://www.ladyada.net/learn/soldering/thm.html If everything looks good, zip everything back up and meet back here for how to connect these wires to your Raspberry Pi. That will be it for the hardware part of this project. See, that wasn't so bad. Hinkmond

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• Performance triage

  - by Dave

  Folks often ask me how to approach a suspected performance issue. My personal strategy is informed by the fact that I work on concurrency issues. (When you have a hammer everything looks like a nail, but I'll try to keep this general). A good starting point is to ask yourself if the observed performance matches your expectations. Expectations might be derived from known system performance limits, prototypes, and other software or environments that are comparable to your particular system-under-test. Some simple comparisons and microbenchmarks can be useful at this stage. It's also useful to write some very simple programs to validate some of the reported or expected system limits. Can that disk controller really tolerate and sustain 500 reads per second? To reduce the number of confounding factors it's better to try to answer that question with a very simple targeted program. And finally, nothing beats having familiarity with the technologies that underlying your particular layer. On the topic of confounding factors, as our technology stacks become deeper and less transparent, we often find our own technology working against us in some unexpected way to choke performance rather than simply running into some fundamental system limit. A good example is the warm-up time needed by just-in-time compilers in Java Virtual Machines. I won't delve too far into that particular hole except to say that it's rare to find good benchmarks and methodology for java code. Another example is power management on x86. Power management is great, but it can take a while for the CPUs to throttle up from low(er) frequencies to full throttle. And while I love "turbo" mode, it makes benchmarking applications with multiple threads a chore as you have to remember to turn it off and then back on otherwise short single-threaded runs may look abnormally fast compared to runs with higher thread counts. In general for performance characterization I disable turbo mode and fix the power governor at "performance" state. Another source of complexity is the scheduler, which I've discussed in prior blog entries. Lets say I have a running application and I want to better understand its behavior and performance. We'll presume it's warmed up, is under load, and is an execution mode representative of what we think the norm would be. It should be in steady-state, if a steady-state mode even exists. On Solaris the very first thing I'll do is take a set of "pstack" samples. Pstack briefly stops the process and walks each of the stacks, reporting symbolic information (if available) for each frame. For Java, pstack has been augmented to understand java frames, and even report inlining. A few pstack samples can provide powerful insight into what's actually going on inside the program. You'll be able to see calling patterns, which threads are blocked on what system calls or synchronization constructs, memory allocation, etc. If your code is CPU-bound then you'll get a good sense where the cycles are being spent. (I should caution that normal C/C++ inlining can diffuse an otherwise "hot" method into other methods. This is a rare instance where pstack sampling might not immediately point to the key problem). At this point you'll need to reconcile what you're seeing with pstack and your mental model of what you think the program should be doing. They're often rather different. And generally if there's a key performance issue, you'll spot it with a moderate number of samples. I'll also use OS-level observability tools to lock for the existence of bottlenecks where threads contend for locks; other situations where threads are blocked; and the distribution of threads over the system. On Solaris some good tools are mpstat and too a lesser degree, vmstat. Try running "mpstat -a 5" in one window while the application program runs concurrently. One key measure is the voluntary context switch rate "vctx" or "csw" which reflects threads descheduling themselves. It's also good to look at the user; system; and idle CPU percentages. This can give a broad but useful understanding if your threads are mostly parked or mostly running. For instance if your program makes heavy use of malloc/free, then it might be the case you're contending on the central malloc lock in the default allocator. In that case you'd see malloc calling lock in the stack traces, observe a high csw/vctx rate as threads block for the malloc lock, and your "usr" time would be less than expected. Solaris dtrace is a wonderful and invaluable performance tool as well, but in a sense you have to frame and articulate a meaningful and specific question to get a useful answer, so I tend not to use it for first-order screening of problems. It's also most effective for OS and software-level performance issues as opposed to HW-level issues. For that reason I recommend mpstat & pstack as my the 1st step in performance triage. If some other OS-level issue is evident then it's good to switch to dtrace to drill more deeply into the problem. Only after I've ruled out OS-level issues do I switch to using hardware performance counters to look for architectural impediments.

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• Reading a ZFS USB drive with Mac OS X Mountain Lion

  - by Karim Berrah

  The problem: I'm using a MacBook, mainly with Solaris 11, but something with Mac OS X (ML). The only missing thing is that Mac OS X can't read my external ZFS based USB drive, where I store all my data. So, I decided to look for a solution. Possible solution: I decided to use VirtualBox with a Solaris 11 VM as a passthrough to my data. Here are the required steps: Installing a Solaris 11 VM Install VirtualBox on your Mac OS X, add the extension pack (needed for USB) Plug your ZFS based USB drive on your Mac, ignore it when asked to initialize it. Create a VM for Solaris (bridged network), and before installing it, create a USB filter (in the settings of your Vbox VM, go to Ports, then USB, then add a new USB filter from the attached device "grey usb-connector logo with green plus sign")  Install a Solaris 11 VM, boot it, and install the Guest addition check with "ifconfg -a" the IP address of your Solaris VM Creating a path to your ZFS USB drive In MacOS X, use the "Disk Utility" to unmount the USB attached drive, and unplug the USB device. Switch back to VirtualBox, select the top of the window where your Solaris 11 is running plug your ZFS USB drive, select "ignore" if Mac OS invite you to initialize the disk In the VirtualBox VM menu, go to "Devices" then "USB Devices" and select from the dropping menu your "USB device" Connection your Solaris VM to the USB drive Inside Solaris, you might now check that your device is accessible by using the "format" cli command If not, repeat previous steps Now, with root privilege, force a zpool import -f myusbdevicepoolname because this pool was created on another system check that you see your new pool with "zpool status" share your pool with NFS: share -F NFS /myusbdevicepoolname Accessing the USB ZFS drive from Mac OS X This is the easiest step: access an NFS share from mac OS Create a "ZFSdrive" folder on your MacOS desktop from a terminal under mac OS: mount -t nfs IPadressofMySoalrisVM:/myusbdevicepoolname  /Users/yourusername/Desktop/ZFSdrive et voila ! you might access your data, on a ZFS USB drive, directly from your Mountain Lion Desktop. You might play with the share rights in order to alter any read/write rights as needed. You might activate compression, encryption inside the Solaris 11 VM ...

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• Parleys Testimonial at GlassFish Community Event, JavaOne 2012

  - by arungupta

  Parleys.com is an e-learning platform that provide a unique experience of online and offline viewing presentations, with integrated movies and chaptering, from the top notch developer conferences and about 40 JUGs all around the world. Stephan Janssen (the Devoxx man and Parleys webmaster) presented at the GlassFish Community Event at JavaOne 2012 and shared why they moved from Tomcat to GlassFish. The move paid off as GlassFish was able to handle 2000 concurrent users very easily. Now they are also running Devoxx CFP and registration on this updated infrastructure. The GlassFish clustering, the asadmin CLI, application versioning, and JMS implementation are some of the features that made them a happy user. Recently they migrated their application from Spring to Java EE 6. This allows them to get locked into proprietary frameworks and also avoid 40MB WAR file deployments. Stateless application, JAX-RS, MongoDB, and Elastic Search is their magical forumla for success there. Watch the video below showing him in full action: More details about their infrastructure is available here.

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• Inline template efficiency

  - by Darryl Gove

  I like inline templates, and use them quite extensively. Whenever I write code with them I'm always careful to check the disassembly to see that the resulting output is efficient. Here's a potential cause of inefficiency. Suppose we want to use the mis-named Leading Zero Detect (LZD) instruction on T4 (this instruction does a count of the number of leading zero bits in an integer register - so it should really be called leading zero count). So we put together an inline template called lzd.il looking like: .inline lzd lzd %o0,%o0 .end And we throw together some code that uses it: int lzd(int); int a; int c=0; int main() { for(a=0; a<1000; a++) { c=lzd(c); } return 0; } We compile the code with some amount of optimisation, and look at the resulting code: $ cc -O -xtarget=T4 -S lzd.c lzd.il $ more lzd.s .L77000018: /* 0x001c 11 */ lzd %o0,%o0 /* 0x0020 9 */ ld [%i1],%i3 /* 0x0024 11 */ st %o0,[%i2] /* 0x0028 9 */ add %i3,1,%i0 /* 0x002c */ cmp %i0,999 /* 0x0030 */ ble,pt %icc,.L77000018 /* 0x0034 */ st %i0,[%i1] What is surprising is that we're seeing a number of loads and stores in the code. Everything could be held in registers, so why is this happening? The problem is that the code is only inlined at the code generation stage - when the actual instructions are generated. Earlier compiler phases see a function call. The called functions can do all kinds of nastiness to global variables (like 'a' in this code) so we need to load them from memory after the function call, and store them to memory before the function call. Fortunately we can use a #pragma directive to tell the compiler that the routine lzd() has no side effects - meaning that it does not read or write to memory. The directive to do that is #pragma no_side_effect(<routine name), and it needs to be placed after the declaration of the function. The new code looks like: int lzd(int); #pragma no_side_effect(lzd) int a; int c=0; int main() { for(a=0; a<1000; a++) { c=lzd(c); } return 0; } Now the loop looks much neater: /* 0x0014 10 */ add %i1,1,%i1 ! 11 ! { ! 12 ! c=lzd(c); /* 0x0018 12 */ lzd %o0,%o0 /* 0x001c 10 */ cmp %i1,999 /* 0x0020 */ ble,pt %icc,.L77000018 /* 0x0024 */ nop

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• Kostenlose MySQL Seminare im Mai

  - by A&C Redaktion

  Im Mai führen wir für Sie zahlreiche MySQL Seminare mit unterschiedlichen Themenschwerpunkten durch. Vom „Skalierbarkeitstag“ über einen praxisorienterten MySQL Enterprise Workshop bis hin zum Überblick über die Hochverfügbarkeitslösungen für MySQL mit Anwendungsbeispiel aus der Praxis. Wir würden uns sehr freuen, Sie bei einem dieser Seminare begrüßen zu dürfen. Die einzelnen Termine und Anmeldungslinks finden Sie hier. Wir freuen uns auf Ihre Teilnahme!

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• Take Two: Comparing JVMs on ARM/Linux

  - by user12608080

  Although the intent of the previous article, entitled Comparing JVMs on ARM/Linux, was to introduce and highlight the availability of the HotSpot server compiler (referred to as c2) for Java SE-Embedded ARM v7,  it seems, based on feedback, that everyone was more interested in the OpenJDK comparisons to Java SE-E.  In fact there were two main concerns: The fact that the previous article compared Java SE-E 7 against OpenJDK 6 might be construed as an unlevel playing field because version 7 is newer and therefore potentially more optimized. That the generic compiler settings chosen to build the OpenJDK implementations did not put those versions in a particularly favorable light. With those considerations in mind, we'll institute the following changes to this version of the benchmarking: In order to help alleviate an additional concern that there is some sort of benchmark bias, we'll use a different suite, called DaCapo.  Funded and supported by many prestigious organizations, DaCapo's aim is to benchmark real world applications.  Further information about DaCapo can be found at http://dacapobench.org. At the suggestion of Xerxes Ranby, who has been a great help through this entire exercise, a newer Linux distribution will be used to assure that the OpenJDK implementations were built with more optimal compiler settings.  The Linux distribution in this instance is Ubuntu 11.10 Oneiric Ocelot. Having experienced difficulties getting Ubuntu 11.10 to run on the original D2Plug ARMv7 platform, for these benchmarks, we'll switch to an embedded system that has a supported Ubuntu 11.10 release.  That platform is the Freescale i.MX53 Quick Start Board.  It has an ARMv7 Coretex-A8 processor running at 1GHz with 1GB RAM. We'll limit comparisons to 4 JVM implementations: Java SE-E 7 Update 2 c1 compiler (default) Java SE-E 6 Update 30 (c1 compiler is the only option) OpenJDK 6 IcedTea6 1.11pre 6b23~pre11-0ubuntu1.11.10.2 CACAO build 1.1.0pre2 OpenJDK 6 IcedTea6 1.11pre 6b23~pre11-0ubuntu1.11.10.2 JamVM build-1.6.0-devel Certain OpenJDK implementations were eliminated from this round of testing for the simple reason that their performance was not competitive.  The Java SE 7u2 c2 compiler was also removed because although quite respectable, it did not perform as well as the c1 compilers.  Recall that c2 works optimally in long-lived situations.  Many of these benchmarks completed in a relatively short period of time.  To get a feel for where c2 shines, take a look at the first chart in this blog. The first chart that follows includes performance of all benchmark runs on all platforms.  Later on we'll look more at individual tests.  In all runs, smaller means faster.  The DaCapo aficionado may notice that only 10 of the 14 DaCapo tests for this version were executed.  The reason for this is that these 10 tests represent the only ones successfully completed by all 4 JVMs.  Only the Java SE-E 6u30 could successfully run all of the tests.  Both OpenJDK instances not only failed to complete certain tests, but also experienced VM aborts too. One of the first observations that can be made between Java SE-E 6 and 7 is that, for all intents and purposes, they are on par with regards to performance.  While it is a fact that successive Java SE releases add additional optimizations, it is also true that Java SE 7 introduces additional complexity to the Java platform thus balancing out any potential performance gains at this point.  We are still early into Java SE 7.  We would expect further performance enhancements for Java SE-E 7 in future updates. In comparing Java SE-E to OpenJDK performance, among both OpenJDK VMs, Cacao results are respectable in 4 of the 10 tests.  The charts that follow show the individual results of those four tests.  Both Java SE-E versions do win every test and outperform Cacao in the range of 9% to 55%. For the remaining 6 tests, Java SE-E significantly outperforms Cacao in the range of 114% to 311% So it looks like OpenJDK results are mixed for this round of benchmarks.  In some cases, performance looks to have improved.  But in a majority of instances, OpenJDK still lags behind Java SE-Embedded considerably. Time to put on my asbestos suit.  Let the flames begin...

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• Mobile or the Science of Programming Languages

  - by user12652314

  Just two things to share today. First is some news in the mobile computing space and a pretty cool new relationship developing with DubLabs and AT&T to enable a student-centric mobile experience for our Campus Solution customers. And second, is an interesting article shared by a friend on Research in Programming Languages related to STEM education, a key story element to my project with Americas Cup and iED, but also to our national interest

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• Harnessing Business Events for Predictive Decision Making - part 1 / 3

  - by Sanjeev Sharma

  Businesses have long relied on data mining to elicit patterns and forecast future demand and supply trends. Improvements in computing hardware, specifically storage and compute capacity, have significantly enhanced the ability to store and analyze mountains of data in ever shrinking time-frames. Nevertheless, the reality is that data growth is outpacing storage capacity by a factor of two and computing power is still very much bounded by Moore's Law, doubling only every 18 months.Faced with this data explosion, businesses are exploring means to develop human brain-like capabilities in their decision systems (including BI and Analytics) to make sense of the data storm, in other words business events, in real-time and respond pro-actively rather than re-actively. It is more like having a little bit of the right information just a little bit before hand than having all of the right information after the fact. To appreciate this thought better let's first understand the workings of the human brain.Neuroscience research has revealed that the human brain is predictive in nature and that talent is nothing more than exceptional predictive ability. The cerebral-cortex, part of the human brain responsible for cognition, thought, language etc., comprises of five layers. The lowest layer in the hierarchy is responsible for sensory perception i.e. discrete, detail-oriented tasks whereas each of the above layers increasingly focused on assembling higher-order conceptual models. Information flows both up and down the layered memory hierarchy. This allows the conceptual mental-models to be refined over-time through experience and repetition. Secondly, and more importantly, the top-layers are able to prime the lower layers to anticipate certain events based on the existing mental-models thereby giving the brain a predictive ability. In a way the human brain develops a "memory of the future", some sort of an anticipatory thinking which let's it predict based on occurrence of events in real-time. A higher order of predictive ability stems from being able to recognize the lack of certain events. For instance, it is one thing to recognize the beats in a music track and another to detect beats that were missed, which involves a higher order predictive ability.Existing decision systems analyze historical data to identify patterns and use statistical forecasting techniques to drive planning. They are similar to the human-brain in that they employ business rules very much like mental-models to chunk and classify information. However unlike the human brain existing decision systems are unable to evolve these rules automatically (AI still best suited for highly specific tasks) and  predict the future based on real-time business events. Mistake me not,  existing decision systems remain vital to driving long-term and broader business planning. For instance, a telco will still rely on BI and Analytics software to plan promotions and optimize inventory but tap into business events enabled predictive insight to identify specifically which customers are likely to churn and engage with them pro-actively. In the next post, i will depict the technology components that enable businesses to harness real-time events and drive predictive decision making.

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• Exalogic 2.0.1 Tea Break Snippets - Creating and using Distribution Groups

  - by The Old Toxophilist

  By default running your Exalogic in a Virtual provides you with, what to Cloud Users, is a single large resource and they can just create vServers and not care about how they are laid down on the the underlying infrastructure. All the Cloud Users will know is that they can create vServers. For example if we have a Quarter Rack (8 Nodes) and our Cloud User creates 8 vServers those 8 vServers may run on 8 distinct nodes or may all run on the same node. Although in many cases we, as Cloud Users, may not be to worried how the Virtualisation Algorithm decides where to place our vServers there are cases where it is extremely important that vServers run on distinct physical compute nodes. For example if we have a Weblogic Cluster we will want the Servers with in the cluster to run on distinct physical node to cover for the situation where one physical node is lost. To achieve this the Exalogic Virtualised implementation provides Distribution Groups that define and anti-aliasing policy that the underlying Virtualisation Algorithm will take into account when placing vServers. It should be noted that Distribution Groups must be created before you create vServers because a vServer can only be added to a Distribution Group at creation time. Creating A Distribution Group To create a Distribution Groups we will first need to select the Account in which we want the Distribution Group to be created. Once we have selected the account we will see the Interface update and Account specific Actions will be displayed within the Action Panes. From the Action pane (or Right-Click on the Account) select the "Create Distribution Group" action. This will initiate the create wizard as follows. Distribution Group Details Within the first Step of the Wizard we can specify the name of the distribution group and this should be unique. In addition we can provide a detailed description of the group. Distribution Group Configuration The second step of the configuration wizard allows you to specify the number of elements that are required within this group and will specify a maximum of the number of nodes within you Exalogic. At this point it is always better to specify a group with spare capacity allowing for future expansion. As vServers are added to group the available slots decrease. Summary Finally the last step of the wizard display a summary of the information entered.

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• NetBeans IDE 7.3 Knows Null

  - by Geertjan

  What's the difference between these two methods, "test1" and "test2"? public int test1(String str) {     return str.length(); } public int test2(String str) {     if (str == null) {         System.err.println("Passed null!.");         //forgotten return;     }     return str.length(); } The difference, or at least, the difference that is relevant for this blog entry, is that whoever wrote "test2" apparently thinks that the variable "str" may be null, though did not provide a null check. In NetBeans IDE 7.3, you see this hint for "test2", but no hint for "test1", since in that case we don't know anything about the developer's intention for the variable and providing a hint in that case would flood the source code with too many false positives:  Annotations are supported in understanding how a piece of code is intended to be used. If method return types use @Nullable, @NullAllowed, @CheckForNull, the value is considered to be "strongly possible to be null", as well as if the variable is tested to be null, as shown above. When using @NotNull, @NonNull, @Nonnull, the value is considered to be non-null. (The exact FQNs of the annotations are ignored, only simple names are checked.) Here are examples showing where the hints are displayed for the non-null hints (the "strongly possible to be null" hints are not shown below, though you can see one of them in the screenshot above), together with a comment showing what is shown when you hover over the hint: There isn't a "one size fits all" refactoring for these various instances relating to null checks, hence you can't do an automated refactoring across your code base via tools in NetBeans IDE, as shown yesterday for class member reordering across code bases. However, you can, instead, go to Source | Inspect and then do a scan throughout a scope (e.g., current file/package/project or combinations of these or all open projects) for class elements that the IDE identifies as potentially having a problem in this area: Thanks to Jan Lahoda, who reports that this currently also works in NetBeans IDE 7.3 dev builds for fields but that may need to be disabled since right now too many false positives are returned, for help with the info above and any misunderstandings are my own fault!

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• UNESCO, J-ISIS, and the JavaFX 2.2 WebView

  - by Geertjan

  J-ISIS, which is the newly developed Java version of the UNESCO generalized information storage and retrieval system for bibliographic information, continues to be under heavy development and code refactoring in its open source repository. Read more about J-ISIS and its NetBeans Platform basis here. Soon a new version will be available for testing and it would be cool to see the application in action at that time. Currently, it looks as follows, though note that the menu bar is under development and many menus you see there will be replaced or removed soon: About one aspect of the application, the browser, which you can see above, Jean-Claude Dauphin, its project lead, wrote me the following: The DJ-Native Swing JWebBrowser has been a nice solution for getting a Java Web Browser for most popular platforms. But the Java integration has always produced from time to time some strange behavior (like losing the focus on the other components after clicking on the Browser window, overlapping of windows, etc.), most probably because of mixing heavyweight and lightweight components and also because of our incompetency in solving the issues. Thus, recently we changed for the JavaFX 2.2 WebWiew. The integration with Java is fine and we have got rid of all the DJ-Native Swing problems. However, we have lost some features which were given for free with the native browsers such as downloading resources in different formats and opening them in the right application. This is a pretty cool step forward, i.e., the JavaFX integration. It also confirms for me something I've heard other people saying too: the JavaFX WebView component is a perfect low threshold entry point for Swing developers feeling their way into the world of JavaFX.

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• WhatsApp Chat Messenger available for Java ME phones

  - by hinkmond

  If you like sending SMS text messages from your Java ME tech-enabled mobile phone without having to pay carrier charges, then WhatsApp Messenger is for you. See: Don't pay, Use Java ME WhatsApp Here's a quote: Free WhatsApp Messenger Download For S40 Java Phone now Available. The IM chat app whatsapp was earlier targeted on high end/cross-platform mobile phone with support for messaging exchange, SMS messages, send and receive pictures, exchange of videos and audios, share your location with your contacts etc. So, be a cheap-skate. It's OK. You're entitled. As long as you use WhatsApp and Java ME technology, that is. Hinkmond

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• Application Scope v's Static - Not Quite the same

  - by Duncan Mills

  An interesting question came up today which, innocent as it sounded, needed a second or two to consider. What's the difference between storing say a Map of reference information as a Static as opposed to storing the same map as an application scoped variable in JSF?  From the perspective of the web application itself there seems to be no functional difference, in both cases, the information is confined to the current JVM and potentially visible to your app code (note that Application Scope is not magically propagated across a cluster, you would need a separate instance on each VM). To my mind the primary consideration here is a matter of leakage. A static will be (potentially) visible to everything running within the same VM (OK this depends on which class-loader was used but let's keep this simple), and this includes your model code and indeed other web applications running in the same container. An Application Scoped object, in JSF terms, is much more ring-fenced and is only visible to the Web app itself, not other web apps running on the same server and not directly to the business model layer if that is running in the same VM. So given that I'm a big fan of coding applications to say what I mean, then using Application Scope appeals because it explicitly states how I expect the data to be used and a provides a more explicit statement about visibility and indeed dependency as I'd generally explicitly inject it where it is needed.  Alternative viewpoints / thoughts are, as ever, welcomed...

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• All Secure - "On the Security Evaluation Evolution"

  Looking at the past, present, and future of security evaluations.

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• Great Java EE Concurrency Write-up!

  - by reza_rahman

  As you are aware JSR-236, Concurrency Utilities for the Java EE platform, is now a candidate for addition into Java EE 7. While it is a critical enabling API it is not necessarily obvious why it is so important. This is especially true with existing features like EJB 3 @Asynchronous, Servlet 3 async and JAX-RS 2 async. On his blog DZone MVB Sander Mak does an excellent job of explaining the motivation and importance of JSR-236. Perhaps even more importantly, he discusses potential issues with the API such alignment with CDI and Java SE Fork/Join. Read the excellent write-up here!

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• Data Loading Issues? Try the new Demantra Data Load Guided Resolution

  - by user702295

  Hello!   Do you have data loading issues?  Perhaps you are trying the new partial schema export tool.   New to Demantra, the Data Load Guided Resolution, document 1461899.1.  This interactive guide will help you locate known solutions to previously discovered issues quickly.  From performance, ORA and ODPM errors to collections related issues that have no known hard number error.   This guide includes the diagnosis of data being imported into Demantra and data being exported from Demantra.  Contact me with any questions or suggestions.   Thank You!

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