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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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  • 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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  • Collaboration platforms

    - by Thomas
    Are there any good collaboration platforms for game development? This would include the following features: Easy way to find various people you need to build games (programmer, artist etc) and forming a team like for example codeplex Online portfolio for users where they can offer their services (either paid or free) Posibility to create a game specific blog or site with social media integration to show the world what's being created Easy way to manage game content / resources with sufficient online storage, version control and if possible source control Manage all phases of game development (startup, creating concept, finding a team, creating proof of concept, production phase etc) and publish specific information for each phase also on social media etc. Manage asset creation flow (request for specific content like a sound, production of sound, uploading the sound, notification to the requester, implementation of the file, retouching in several cycles etc)

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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Getting Started Plugging into the "Find in Projects" Dialog

    - by Geertjan
    In case you missed it amidst all the code in yesterday's blog entry, the "Find in Projects" dialog is now pluggable. I think that's really cool. The code yesterday gives you a complete example, but let's break it down a bit and deconstruct down to a very simple hello world scenario. We'll end up with as many extra tabs in the "Find in Projects" dialog as we need, for example, three in this case:  And clicking on any of those extra tabs will, in this simple example, simply show us this: Once we have that, we'll be able to continue adding small bits of code over the next few blog entries until we have something more useful. So, in this blog entry, you'll literally be able to display "Hello World" within a new tab in the "Find in Projects" dialog: import javax.swing.JComponent; import javax.swing.JLabel; import org.netbeans.spi.search.provider.SearchComposition; import org.netbeans.spi.search.provider.SearchProvider; import org.netbeans.spi.search.provider.SearchProvider.Presenter; import org.openide.NotificationLineSupport; import org.openide.util.lookup.ServiceProvider; @ServiceProvider(service = SearchProvider.class) public class ExampleSearchProvider1 extends SearchProvider { @Override public Presenter createPresenter(boolean replaceMode) { return new ExampleSearchPresenter(this); } @Override public boolean isReplaceSupported() { return false; } @Override public boolean isEnabled() { return true; } @Override public String getTitle() { return "Demo Extension 1"; } public class ExampleSearchPresenter extends SearchProvider.Presenter { private ExampleSearchPresenter(ExampleSearchProvider1 sp) { super(sp, true); } @Override public JComponent getForm() { return new JLabel("Hello World"); } @Override public SearchComposition composeSearch() { return null; } @Override public boolean isUsable(NotificationLineSupport nls) { return true; } } } That's it, not much code, works fine in NetBeans IDE 7.2 Beta, and is easier to digest than the big chunk from yesterday. If you make three classes like the above in a NetBeans module, and you install it, you'll have three new tabs in the "Find in Projects" dialog. The only required dependencies are Dialogs API, Lookup API, and Search in Projects API. Read the javadoc linked above and then in next blog entries we'll continue to build out something like the sample you saw in yesterday's blog entry.

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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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  • Smart Meter Management on the NetBeans Platform

    - by Geertjan
    Netinium® NCC is the operator console for the Netinium® AMM+ platform, a Head End system for multi-vendor smart meter and smart grid infrastructures. The role based NCC provides a uniform operations environment for grid operators and utilities to securely manage millions of smart meters, in-home displays and other smart devices using different types of communication networks such as IP, PLC, GPRS, CDMA and BPL. Based on the NetBeans Platform, the NCC offers the flexibility to easily extend the GUI with new functionality when new devices are added to the system.  For more information visit http://www.netinium.com.

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  • Managing Custom Series

    - by user702295
    Custom series that have been added should be done with client Defined Prefix, ex. ACME Final Forecast, so they are can be identified as non-standard series.  With that said, it is not always done, so beginning in v7.3.0 there is a new column called Application_Id in the Computed_Fields table.  This is the table that stores the Series information.  Standard Series will have have a prefix similar to COMPUTED_FIELD, while a custom series will have an Application_Id value similar to 9041128B99FC454DB8E8A289E5E8F0C5. So a SQL that will return the list of custom series in your database might look something like this: select computed_title Series_Name, application_id from computed_fields where application_id not like '%COMPUTED_FIELD%' order by 1;

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  • Managing Custom Series

    - by user702295
    Custom series that have been added should be done with client Defined Prefix, ex. ACME Final Forecast, so they are can be identified as non-standard series.  With that said, it is not always done, so beginning in v7.3.0 there is a new column called Application_Id in the Computed_Fields table.  This is the table that stores the Series information.  Standard Series will have have a prefix similar to COMPUTED_FIELD, while a custom series will have an Application_Id value similar to 9041128B99FC454DB8E8A289E5E8F0C5. So a SQL that will return the list of custom series in your database might look something like this: select computed_title Series_Name, application_id from computed_fields where application_id not like '%COMPUTED_FIELD%' order by 1;

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  • Real-Time Multi-User Gaming Platform

    - by Victor Engel
    I asked this question at Stack Overflow but was told it's more appropriate here, so I'm posting it again here. I'm considering developing a real-time multi-user game, and I want to gather some information about possibilities before I do some real development. I've thought about how best to ask the question, and for simplicity, the best way that occurred to me was to make an analogy to the field (or playground) game darebase. In the field game of darebase, there are two or more bases. To start, there is one team on each base. The game is a fancy game of tag. When two people meet out in the field, the person who left his base most recently timewise captures the other person. They then return to that person's base. Play continues until everyone is part of the same team. So, analogizing this to an online computer game, let's suppose there are an indefinite number of bases. When a person starts up the game, he has a team that is located at, for example, his current GPS coordinates. It could be a virtual world, but for sake of argument, let's suppose the virtual world corresponds to the player's actual GPS coordinates. The game software then consults the database to see where the closest other base is that is online, and the two teams play their game of virtual tag. Note that the user of the other base could have a different base than the one run by the current user as the closest base to him, in which case, he would be in two simultaneous battles, one with each base. When they go offline, the state of their players is saved on a server somewhere. Game logic calls for the players to have some automaton-logic of some sort, so they can fend for themselves in a limited way using basic rules, until their user goes online again. The user doesn't control the players' movements directly, but issues general directives that influence the players' movement logic. I think this analogy is good enough to frame my question. What sort of platforms are available to develop this sort of game? I've been looking at smartfoxserver, but I'm not convinced yet that it is the best option or even that it will work at all. One possibility, of course, would be to roll out my own web server, but I'd rather not do that if there is an existing service out there already that I could tap into. I will be developing for iOS devices at first. So any suggestions would be greatly appreciated. I think I need to establish the architecture first before proceeding with this project. Note that darbase is not the game I intend to implement, but, upon reflection, that might not be a bad idea either.

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  • New qeep app for Java ME feature phones: meet qeepy people

    - by hinkmond
    Is it "qeepy" if you meet people by using your cell phone instead of, you know, talking to them? Nah. Not if it's a Java ME cell phone! See: Use Qeep to Meet Peeps Here's a quote: Qeep is a free app, and compatible with over 1,000 Java-enabled feature phones... ... Qeep is one of the world's largest mobile gaming and social discovery platforms. Members of the mobile community can play live multiplayer games; blog photos; send sound attacks, text messages and virtual gifts; and meet new friends worldwide. So, go on. Go, use Qeep on your Java ME feature phone to play multiplayer games, blog photos, and meet new friends worldwide. No one will think that you're weird... Not much, at least. Hinkmond

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  • Join us for 2 JCP sessions today + beer

    - by heathervc
    Remember to join the 2 JCP sessions at JavaOne this afternoon in the Hilton.  First up the JCP.Next panel with JCP EC Members, followed by the 101 Ways to Participate BOF.  Stop in to learn what's new and how you can make the future Java and enjoy a beer or 2.  We will also be in the OTN Java Demogrounds in the Hilton Grand Ballroom from 4:00 - 4:30 PM.  Hope to see you there. JCP.Next: Reinvigorating Java Standards Session ID: BOF6272 Location: Hilton San Francisco - Plaza A/B Date and Time: 10/1/12, 4:30 PM - 5:15 PM 101 Ways to Improve Java: Why Developer Participation Matters Session ID: BOF6283 Location: Hilton San Francisco - Continental Ballroom 4 Date and Time: 10/1/12, 5:30 PM - 6:15 PM

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  • JavaOne Latin America Early Bird Discount: R$300,00 Off

    - by Tori Wieldt
    Learn how to code in Java more efficiently, pick up Java best practices, and participate in world-class networking at JavaOne Latin America—all for R$300,00 less if you register by 16 November. Have you ever wondered how to construct embedded Java applications for next-generation smart devices? Want to profit from client-side solutions using JavaFX, or simply build modern applications in Java 7? Techniques for these and much more are showcased at JavaOne Latin America—and you’re invited! Choose from more than 50 sessions, multiple demos, plus keynotes and hands-on labs. Topics include: Core Java Platform JavaFX and Rich User Experiences Java EE, Web Services, and the Cloud Java ME, Java Embedded, and Java Card Secure Your Place Now—Register now! Para mais informações ou inscrição ligue para (11) 2875-4163.

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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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  • Groovy Refactoring in NetBeans

    - by Martin Janicek
    Hi guys, during the NetBeans 7.3 feature development, I spend quite a lot of time trying to get some basic Groovy refactoring to the game. I've implemented find usages and rename refactoring for some basic constructs (class types, fields, properties, variables and methods). It's certainly not perfect and it will definitely need a lot fixes and improvements to get it hundred percent reliable, but I need to start somehow :) I would like to ask all of you to test it as much as possible and file a new tickets to the cases where it doesn't work as expected (e.g. some occurrences which should be in usages isn't there etc.) ..it's really important for me because I don't have real Groovy project and thus I can test only some simple cases. I can promise, that with your help we can make it really useful for the next release. Also please be aware that the current version is focusing only on the .groovy files. That means it won't find any usages from the .java files (and the same applies for finding usages from java files - it won't find any groovy usages). I know it's not ideal, but as I said.. we have to start somehow and it wasn't possible to make it all-in-one, so only other option was to wait for the NetBeans 7.4. I'll focus on better Java-Groovy integration in the next release (not only in refactoring, but also in navigation, code completion etc.) BTW: I've created a new component with surprising name "Refactoring" in our bugzilla[1], so please put the reported issues into this category. [1] http://netbeans.org/bugzilla/buglist.cgi?product=groovy;component=Refactoring

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  • How to encourage version control adoption

    - by Man Wa kileleshwa
    I have recently started working in a team where there is no version control. Most of the team members are not used to any kind of version control. I've been using mercurial privately to track my work. I would like to encourage others to adopt it, and at the very least start to version their code as they develop changes. Can anyone give me advice on how I can encourage adoption of a distributed version control such as mercurial. Any advice on how to win people including managers to DVCS would be much appreciated.

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  • Notes - Part II - Play with JavaFX

    - by Silviu Turuga
    Open the project from last lesson Double click on NotesUI.fmxl, this will open the JavaFX Scene Builder On the left side you have a area called Hierarchy, from there press Del or Shift+Backspace on Mac to delete the Button and the Label. You'll receive a warning, that some components have been assigned an fx:id, click Delete as we don't need them anymore. Resize the AnchorPane to have enough room for our design, eg. 820x550px From the top left pick the Container called Accordion and drag over the AnchorPane design Chose then from Controls a List View and drag inside the Accordion. You'll notice that by default the Accordion has 2 TitledPane, and you can switch between them by clicking on their name. I'll let you the pleasure to do the rest in order to get the following result  Here is the list of objects used Save it and then return to NetBeans Run the application and it should be run without any issue. If you click on buttons they all are functional, but nothing happens as we didn't link them with any action. We'll see this in the next episode. Now, let's play a little bit with the application and try to resize it… Have you notice the behavior? If the form is too small, some objects aren't visible, if it is too large there is too much space . That's for sure something that your users won't like and you as a programmer have to care about this. From NetBeans double click NotesUI.fmxl so to return back to JavaFX Scene Builder Select the TextField from bottom left of Notes, the one where I put the text Category and then from the right part of JavaFX Scene Builder you'll notice a panel called Inspector. Chose Layout and then click on the dotted lines from left and bottom of the square, like you see in the below image This will make the textfield to have always the same distance from left and bottom no matter the size of the form. Save and run the application. Note that whenever the form is changing the Height, the Category TextField has the same distance from the bottom. Select Accordion and do the same steps but also check the top dotted line, because we want the Accordion to have the same height as the main form has. I'll let you the pleasure to do the same for the rest of components. It's very important to design an application that can be resize by user and in the same time, all the buttons are on place. Last step is to make sure our application is not getting smaller then a certain size, as this will hide parts of our layout. So select the AnchorPane and from Inspector go to Layout and note down the Width and Height. Go back to NetBeans and open the file Main.java and add the following code just after stage.setScene(scene); (around line 26) stage.setMinWidth(820); stage.setMinHeight(550); Use your own width and height. This will prevent user to reduce the width or height of your application to a value that will hide parts of your layout. So now you should have done most of the design part and next time we'll see how can we enter some data into our newly created application… Note: in case you miss something, here are the source files of the project till this point. 

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