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• Enterprise Trade Compliance: Changing Trade Operations around the World

  - by John Murphy

  We live in a world of incredible bounty and speed where any product can be delivered anywhere on earth. However, our world is also filled with challenges for business – where volatility, uncertainty, risk, and chaos are our daily companions. To prosper amid the realities of this new world, organizations cannot rely on old strategies; they need new business models. Key trends within the global economy are mandating that companies fully integrate global trade management best practices within broader supply chain management strategies, rather than simply leaving it as a discrete event at the end of the order or procurement cycle. To explain, many companies face a complicated and changing compliance environment. This is directly linked to the speed and configuration of the supply chain, particularly with the explosion of new markets, shorter service cycles and ship times, accelerating rates of globalization and outsourcing, and increasing product complexity and regulation. Read More...

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• RPi and Java Embedded GPIO: Connecting LEDs

  - by hinkmond

  Next, we need some low-level peripherals to connect to the Raspberry Pi GPIO header. So, we'll do what's called a "Fry's Run" in Silicon Valley, which means we go shop at the local Fry's Electronics store for parts. In this case, we'll need some breadboard jumper wires (blue wires in photo), some LEDs, and some resistors (for the RPi GPIO, 150 ohms - 300 ohms would work for the 3.3V output of the GPIO ports). And, if you want to do other projects, you might as well by a breadboard, which is a development board with lots of holes in it. Ask a Fry's clerk for help. Or, better yet, ask the customer standing next to you in the electronics components aisle for help. (Might be faster) So, go to your local hobby electronics store, or go to Fry's if you have one close by, and come back here to the next blog post to see how to hook these parts up. Hinkmond

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• On-demand Webcast: Java in the Smart Grid

  - by Jacob Lehrbaum

  The Smart Grid is one of the most significant evolutions of our utility infrastructure in recent history. This innovative grid will soon revolutionize how utilities manage and control the energy in our homes--helping utilities reduce energy usage during peak hours, improve overall energy efficiency, and lower your energy bills. If you'd like to learn more about the Smart Grid and the role that Java is poised to play in this important initiative you can check out our on-demand webcast. We'll show you how Java solutions--including Java ME and Java SE for Embedded --can help build devices and infrastructure that take advantage of this new market. As the world's most popular developer language, Java enables you to work with a wide range of developers and provides access to tools and resources to build smarter devices, faster and more affordably.

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• CPU Usage in Very Large Coherence Clusters

  - by jpurdy

  When sizing Coherence installations, one of the complicating factors is that these installations (by their very nature) tend to be application-specific, with some being large, memory-intensive caches, with others acting as I/O-intensive transaction-processing platforms, and still others performing CPU-intensive calculations across the data grid. Regardless of the primary resource requirements, Coherence sizing calculations are inherently empirical, in that there are so many permutations that a simple spreadsheet approach to sizing is rarely optimal (though it can provide a good starting estimate). So we typically recommend measuring actual resource usage (primarily CPU cycles, network bandwidth and memory) at a given load, and then extrapolating from those measurements. Of course there may be multiple types of load, and these may have varying degrees of correlation -- for example, an increased request rate may drive up the number of objects "pinned" in memory at any point, but the increase may be less than linear if those objects are naturally shared by concurrent requests. But for most reasonably-designed applications, a linear resource model will be reasonably accurate for most levels of scale. However, at extreme scale, sizing becomes a bit more complicated as certain cluster management operations -- while very infrequent -- become increasingly critical. This is because certain operations do not naturally tend to scale out. In a small cluster, sizing is primarily driven by the request rate, required cache size, or other application-driven metrics. In larger clusters (e.g. those with hundreds of cluster members), certain infrastructure tasks become intensive, in particular those related to members joining and leaving the cluster, such as introducing new cluster members to the rest of the cluster, or publishing the location of partitions during rebalancing. These tasks have a strong tendency to require all updates to be routed via a single member for the sake of cluster stability and data integrity. Fortunately that member is dynamically assigned in Coherence, so it is not a single point of failure, but it may still become a single point of bottleneck (until the cluster finishes its reconfiguration, at which point this member will have a similar load to the rest of the members). The most common cause of scaling issues in large clusters is disabling multicast (by configuring well-known addresses, aka WKA). This obviously impacts network usage, but it also has a large impact on CPU usage, primarily since the senior member must directly communicate certain messages with every other cluster member, and this communication requires significant CPU time. In particular, the need to notify the rest of the cluster about membership changes and corresponding partition reassignments adds stress to the senior member. Given that portions of the network stack may tend to be single-threaded (both in Coherence and the underlying OS), this may be even more problematic on servers with poor single-threaded performance. As a result of this, some extremely large clusters may be configured with a smaller number of partitions than ideal. This results in the size of each partition being increased. When a cache server fails, the other servers will use their fractional backups to recover the state of that server (and take over responsibility for their backed-up portion of that state). The finest granularity of this recovery is a single partition, and the single service thread can not accept new requests during this recovery. Ordinarily, recovery is practically instantaneous (it is roughly equivalent to the time required to iterate over a set of backup backing map entries and move them to the primary backing map in the same JVM). But certain factors can increase this duration drastically (to several seconds): large partitions, sufficiently slow single-threaded CPU performance, many or expensive indexes to rebuild, etc. The solution of course is to mitigate each of those factors but in many cases this may be challenging. Larger clusters also lead to the temptation to place more load on the available hardware resources, spreading CPU resources thin. As an example, while we've long been aware of how garbage collection can cause significant pauses, it usually isn't viewed as a major consumer of CPU (in terms of overall system throughput). Typically, the use of a concurrent collector allows greater responsiveness by minimizing pause times, at the cost of reducing system throughput. However, at a recent engagement, we were forced to turn off the concurrent collector and use a traditional parallel "stop the world" collector to reduce CPU usage to an acceptable level. In summary, there are some less obvious factors that may result in excessive CPU consumption in a larger cluster, so it is even more critical to test at full scale, even though allocating sufficient hardware may often be much more difficult for these large clusters.

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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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• SOA Composite Sensors : Good Practice

  - by angelo.santagata

  I was discussing a interesting design problem with a colleague of mine Niall (his blog) on the topic of how to cancel an inflight SOA Composite process.  Obviously one way to do this is to cancel the process from enterprise Manager ( http://hostort/em ) , however we were thinking this isnt a “user friendly” way of doing this.. If you look at Nialls blog you’ll see he’s highlighted a number of different APIs which enable you the ability to manipulate the SCA instance, e.g. Code Snippet to purge (delete) an instance How to determine the instanceId from a composite_sensor_value using the “composite_sensor_value” table How to determine a BPEL Process status using the cube_instance table   Now all of these require that you know the instanceId of your SOA Composite, how does one find this out? Well the easiest way of doing this is to create a composite sensor on the SCA component. A composite sensor is simply a way of publishing a piece of business data as part of your composite. The magic here is that you can later query composites based on this value. So a good best practice is that for any composites you create consider publishing a composite sensor value using a primary key of some sort , e.g. orderId, that way if you need to manipulate/query composites you can easily look up the instanceId using the sensorid.   For information on how to create a composite Sensor id see this documentation link  

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• A Patent for Workload Management Based on Service Level Objectives

  - by jsavit

  I'm very pleased to announce that after a tiny :-) wait of about 5 years, my patent application for a workload manager was finally approved. Background Many operating systems have a resource manager which lets you control machine resources. For example, Solaris provides controls for CPU with several options: shares for proportional CPU allocation. If you have twice as many shares as me, and we are competing for CPU, you'll get about twice as many CPU cycles), dedicated CPU allocation in which a number of CPUs are exclusively dedicated to an application's use. You can say that a zone or project "owns" 8 CPUs on a 32 CPU machine, for example. And, capped CPU in which you specify the upper bound, or cap, of how much CPU an application gets. For example, you can throttle an application to 0.125 of a CPU. (This isn't meant to be an exhaustive list of Solaris RM controls.) Workload management Useful as that is (and tragic that some other operating systems have little resource management and isolation, and frighten people into running only 1 app per OS instance - and wastefully size every server for the peak workload it might experience) that's not really workload management. With resource management one controls the resources, and hope that's enough to meet application service objectives. In fact, we hold resource distribution constant, see if that was good enough, and adjust resource distribution if that didn't meet service level objectives. Here's an example of what happens today: Let's try 30% dedicated CPU. Not enough? Let's try 80% Oh, that's too much, and we're achieving much better response time than the objective, but other workloads are starving. Let's back that off and try again. It's not the process I object to - it's that we to often do this manually. Worse, we sometimes identify and adjust the wrong resource and fiddle with that to no useful result. Back in my days as a customer managing large systems, one of my users would call me up to beg for a "CPU boost": Me: "it won't make any difference - there's plenty of spare CPU to be had, and your application is completely I/O bound." User: "Please do it anyway." Me: "oh, all right, but it won't do you any good." (I did, because he was a friend, but it didn't help.) Prior art There are some operating environments that take a stab about workload management (rather than resource management) but I find them lacking. I know of one that uses synthetic "service units" composed of the sum of CPU, I/O and memory allocations multiplied by weighting factors. A workload is set to make a target rate of service units consumed per second. But this seems to be missing a key point: what is the relationship between artificial 'service units' and actually meeting a throughput or response time objective? What if I get plenty of one of the components (so am getting enough service units), but not enough of the resource whose needed to remove the bottleneck? Actual workload management That's not really the answer either. What is needed is to specify a workload's service levels in terms of externally visible metrics that are meaningful to a business, such as response times or transactions per second, and have the workload manager figure out which resources are not being adequately provided, and then adjust it as needed. If an application is not meeting its service level objectives and the reason is that it's not getting enough CPU cycles, adjust its CPU resource accordingly. If the reason is that the application isn't getting enough RAM to keep its working set in memory, then adjust its RAM assignment appropriately so it stops swapping. Simple idea, but that's a task we keep dumping on system administrators. In other words - don't hold the number of CPU shares constant and watch the achievement of service level vary. Instead, hold the service level constant, and dynamically adjust the number of CPU shares (or amount of other resources like RAM or I/O bandwidth) in order to meet the objective. Instrumenting non-instrumented applications There's one little problem here: how do I measure application performance in a way relating to a service level. I don't want to do it based on internal resources like number of CPU seconds it received per minute - We need to make resource decisions based on externally visible and meaningful measures of performance, not synthetic items or internal resource counters. If I have a way of marking the beginning and end of a transaction, I can then measure whether or not the application is meeting an objective based on it. If I can observe the delay factors for an application, I can see which resource shortages are slowing an application enough to keep it from meeting its objectives. I can then adjust resource allocations to relieve those shortages. Fortunately, Solaris provides facilities for both marking application progress and determining what factors cause application latency. The Solaris DTrace facility let's me introspect on application behavior: in particular I can see events like "receive a web hit" and "respond to that web hit" so I can get transaction rate and response time. DTrace (and tools like prstat) let me see where latency is being added to an application, so I know which resource to adjust. Summary After a delay of a mere few years, I am the proud creator of a patent (advice to anyone interested in going through the process: don't hold your breath!). The fundamental idea is fairly simple: instead of holding resource constant and suffering variable levels of success meeting service level objectives, properly characterise the service level objective in meaningful terms, instrument the application to see if it's meeting the objective, and then have a workload manager change resource allocations to remove delays preventing service level attainment. I've done it by hand for a long time - I think that's what a computer should do for me.

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• Finding which activities will execute next in a process instance

  - by Mark Nelson

    We have had a few queries lately about how to find out what activity (or activities) will be the next to execute in a particular process instance.  It is possible to do this, however you will need to use a couple of undocumented APIs.  That means that they could (and probably will) change in some future release and break your code.  If you understand the risks of using undocumented APIs and are prepared to accept that risk, read on… READ MORE >>

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• Essbase - FormatString

  - by THE

  A look at the documentation for "Typed Measures" shows:"Using format strings, you can format the values (cell contents) of Essbase database members in numeric type measures so that they appear, for query purposes, as text, dates, or other types of predefined values. The resultant display value is the cell’s formatted value (FORMATTED_VALUE property in MDX). The underlying real value is numeric, and this value is unaffected by the associated formatted value."To actually switch ON the use of typed measures in general, you need to navigate to the outline properties: open outline select properties change "Typed Measures enable" to TRUE (click to enlarge) As an example, I created two additional members in the ASOSamp outline. - A member "delta Price" in the Measures (Accounts) Dimension with the Formula: ([Original Price],[Curr_year])-([Original Price],[Prev_year])This is equivalent to the Variance Formula used in the "Years" Dimension. - A member "Var_Quickview" in the "Years" Dimension with the same formula as the "Variance" Member.This will be used to simply display a second cell with the same underlying value as Variance - but formatted using Format String hence enabling BOTH in the same report. (click to enlarge) In the outline you now select the member you want the Format String associated with and change the "associated Format String" in the Member Properties.As you can see in this example an IIF statement reading:MdxFormat(IIF(CellValue()< 0,"Negative","Positive" ) ) has been chosen for both new members.After applying the Format String changes and running a report via SmartView, the result is: (click to enlarge) reference: Essbase Database Admin Guide ,Chapter 12 "Working with Typed Measures "

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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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• 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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• Welcome To The Nashorn Blog

  - by jlaskey

  Welcome to all.  Time to break the ice and instantiate The Nashorn Blog.  I hope to contribute routinely, but we are very busy, at this point, preparing for the next development milestone and, of course, getting ready for open source. So, if there are long gaps between postings please forgive. We're just coming back from JavaOne and are stoked by the positive response to all the Nashorn sessions. It was great for the team to have the front and centre slide from Georges Saab early in the keynote. It seems we have support coming from all directions. Most of the session videos are posted. Check out the links. Nashorn: Optimizing JavaScript and Dynamic Language Execution on the JVM. Unfortunately, Marcus - the code generation juggernaut,  got saddled with the first session of the first day. Still, he had a decent turnout. The talk focused on issues relating to optimizations we did to get good performance from the JVM. Much yet to be done but looking good. Nashorn: JavaScript on the JVM. This was the main talk about Nashorn. I delivered the little bit of this and a little bit of that session with an overview, a follow up on the open source announcement, a run through a few of the Nashorn features and some demos. The room was SRO, about 250±. High points: Sam Pullara, from Twitter, came forward to describe how painless it was to get Mustache.js up and running (20x over Rhino), and,  John Ceccarelli, from NetBeans came forward to describe how Nashorn has become an integral part of Netbeans. A healthy Q & A at the end was very encouraging. Meet the Nashorn JavaScript Team. Michel, Attila, Marcus and myself hosted a Q & A. There was only a handful of people in the room (we assume it was because of a conflicting session ;-) .) Most of the questions centred around Node.jar, which leads me to believe, Nashorn + Node.jar is what has the most interest. Akhil, Mr. Node.jar, sitting in the audience, fielded the Node.jar questions. Nashorn, Node, and Java Persistence. Doug Clarke, Akhil and myself, discussed the title topics, followed by a lengthy Q & A (security had to hustle us out.) 80 or so in the room. Lots of questions about Node.jar. It was great to see Doug's use of Nashorn + JPA. Nashorn in action, with such elegance and grace. Putting the Metaobject Protocol to Work: Nashorn’s Java Bindings. Attila discussed how he applied Dynalink to Nashorn. Good turn out for this session as well. I have a feeling that once people discover and embrace this hidden gem, great things will happen for all languages running on the JVM. Finally, there were quite a few JavaOne sessions that focused on non-Java languages and their impact on the JVM. I've always believed that one's tool belt should carry a variety of programming languages, not just for domain/task applicability, but also to enhance your thinking and approaches to problem solving. For the most part, future blog entries will focus on 'how to' in Nashorn, but if you have any suggestions for topics you want discussed, please drop a line.  Cheers. 

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• JDK bug migration milestone: JIRA now the system of record

  - by darcy

  I'm pleased to announce the OpenJDK bug database migration project has reached a significant milestone: the JDK has switched from the legacy Sun "bugtraq" system to a new internal JIRA instance as the system of record for our bug tracking. This completes the initial phase of the previously described plan of getting OpenJDK onto an externally visible and writable bug tracker. The identities contained in the current system include recognized OpenJDK contributors. The bug migration effort to date has been sizable in multiple dimensions. There are around 140,000 distinct issues imported into the JDK project of the JIRA instance, nearly 165,000 if backport issues to track multiple-release information are included. Separately, the Code Tools OpenJDK project has its own JIRA project populated with several thousands existing bugs. Once the OpenJDK JIRA instance is externalized, approved OpenJDK projects will be able to request the creation of a JIRA project for issue tracking. There are many differences in the schema used to model bugs between the legacy bug system and the schema for the new JIRA projects. We've favored simplifications to the existing system where possible and, after much discussion, we've settled on five main states for the OpenJDK JIRA projects: New Open In progress Resolved Closed The Open and In-progress states can have a substate Understanding field set to track whether the issues has its "Cause Known" or "Fix understood". In the closed state, a Verification field can indicate whether a fix has been verified, unverified, or if the fix has failed. At the moment, there will be very little externally visible difference between JIRA for OpenJDK and the legacy system it replaces. One difference is that bug numbers for newly filed issues in the JIRA JDK project will be 8000000 and above. If you are working with JDK Hg repositories, update any local copies of jcheck to the latest version which recognizes this expanded bug range. (The bug numbers of existing issues have been preserved on the import into JIRA). Relatively soon, we plan for the pages published on bugs.sun.com to be generated from information in JIRA rather than in the legacy system. When this occurs, there will be some differences in the page display and the terminology used will be revised to reflect JIRA usage, such as referring to the "component/subcomponent" of an issue rather than its "category". The exact timing of this transition will be announced when it is known. We don't currently have a firm timeline for externalization of the JIRA system. Updates will be provided as they become available. However, that is unlikely to happen before JavaOne next week!

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• Architect: "SOA Means Coming Together"

  Collaboration across a broad spectrum of stakeholders is key for SOA to succeed.

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• When to use each user research method

  - by user12277104

  There are a lot of user research methods out there, but sometimes we get stuck in a rut, conducting all formative usability testing before coding, or running surveys to gather satisfaction data. I'll be the first to admit that it happens to me, but to get out of a rut, it just takes a minute to look at where I am in the design & development cycle, what kind(s) of data I need, and what methods are available to me. We need reminders, or refreshers, every once in a while. One tool I've found useful is a graphic organizer that I created many years ago. It's been through several revisions, as I've adapted it to the product cycles of the places I've worked, changed my mind about how to categorize it, and added methods that I've used or created over time. I shared a version of this table at the 2012 International UPA conference, and I was contacted by someone yesterday who wanted to use it in a university course on user-center design. I was flattered at the the thought, but embarrassed, because I was sure it needed updating -- that was a year ago, after all. But I opened it today, and really, there's not much I'd change -- sure, I could add some nuance regarding what types of formative testing, such as modality (remote, unmoderated remote, or in-person) or flavor of testing (RITE, RITE-Krug, comparative, performance), but I think it's pretty much ok as is. Click on the image below, to get the full-size PDF. And whether it's entirely "right" or "wrong" isn't the whole value of looking at these methods across the product lifecycle. The real value lies in the reminder that I have options. And what those options are change as the field changes, so while I don't expect this graphic to have an eternal shelf life, it's still ok a year after I last updated it. That said, if you find something missing or out of place, let me know :) 

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• RPi and Java Embedded GPIO: Hooking Up Your Wires for Java

  - by hinkmond

  So, you bought your blue jumper wires, your LEDs, your resistors, your breadboard, and your fill of Fry's for the day. How do you hook this cool stuff up to write Java code to blink them LEDs? I'll step you through it. First look at that pinout diagram of the GPIO header that's on your RPi. Find the pins in the corner of your RPi board and make sure to orient it the right way. The upper left corner pin should have the characters "P1" next to it on the board. That pin next to "P1" is your Pin #1 (in the diagram). Then, you can start counting left, right, next row, left, right, next row, left, right, and so on: Pins # 1, 2, next row, 3, 4, next row, 5, 6, and so on. Take one blue jumper wire and connect to Pin # 3 (GPIO0). Connect the other end to a resistor and then the other end of the resistor into the breadboard. Each row of grouped-together holes on a breadboard are connected, so plug in the short-end of a common cathode LED (long-end of a common anode LED) into a hole that is in the same grouping as where the resistor is plugged in. Then, connect the other end of the LED back to Pin # 6 (GND) on the RPi GPIO header. Now you have your first LED connected ready for you to write some Java code to turn it on and off. (As, extra credit you can connect 7 other LEDs the same way to with one lead to Pins # 5, 7, 11, 13, 15, 19 & 21). Whew! That wasn't so bad, was it? Next blog post on this thread will have some Java source code for you to try... Hinkmond

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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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• Community Forum at Openworld - Presentations available

  - by Javier Puerta

  On October 1st we held a new session of the Exadata & Manageability Partner Community in San Francisco. Thanks to all of you who participated in the event and very especially to the partner speakers who share their experiences with the rest of the community: Francisco Bermúdez (Capgemini Spain), Dmitry Krasilov (Nvision, Russia) and Miguel Alves (WeDo Technologies, Portugal)The slide decks used in the presentations are now available for download at the Manageability Partner Community Collaborative Workspace (for community members only - if you get an error message, please register for the Community first).In a few weeks we will be announcing the location for the next Community event in the spring timeframe.

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• Why are embedded device apps still written in C/C++? Why not Java programming language?

  - by hinkmond

  At the recent Black Hat 2014 conference in Sin City, the Black Hatters were focusing on Embedded Devices and IoT. You know? Make your networked-toaster burn your bread 10,000 miles away, over the Web for grins and giggles. Well, apparently the Black Hatters say it can be done pretty easily these days, which is scary. See: Securing Embedded Devices & IoT Here's a quote: All these devices are still written in C and C++. The challenges associated with developing securely in these languages have been fought for nearly two decades. "You often hear people say, 'Well, why don't we just get rid of the C and C++ language if it's so problematic. Why don't we just write everything in C# or Java, or something that is a little safer to develop in?'," DeMott says. Gah! Why are all these IoT devices still using C/C++? Of course they should be using Java SE Embedded technology! It's a natural fit to use for better security on embedded devices. Or, I guess, developers really don't mind if their networked-toasters do char their breakfast. If it can be burned, it will be... That's what I say. Unless they use Java. Hinkmond

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• NetBeans 7.2 RC1 is published

  - by Ondrej Brejla

  NetBeans 7.2 RC1 was today published. You can download it here. You could read about the PHP features added to the NetBeans 7.2 release here on the blog, but the main features added or improved are: Support for PHP 5.4 PHP editing: Fix Uses action, annotations support, editing of Neon and Apache Config files and more Support for Symfony2, Doctrine2 and ApiGen frameworks FTP remote synchronization Support for running PHP projects on Hudson For more information, just look at New and Noteworthy page for NetBeans 7.2. And as obvious you can help us to test the build. Just try it and if you find an issue / error, please report it. Thanks for your help.

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• JPRT: A Build & Test System

  - by kto

  DRAFT A while back I did a little blogging on a system called JPRT, the hardware used and a summary on my java.net weblog. This is an update on the JPRT system. JPRT ("JDK Putback Reliablity Testing", but ignore what the letters stand for, I change what they mean every day, just to annoy people :\^) is a build and test system for the JDK, or any source base that has been configured for JPRT. As I mentioned in the above blog, JPRT is a major modification to a system called PRT that the HotSpot VM development team has been using for many years, very successfully I might add. Keeping the source base always buildable and reliable is the first step in the 12 steps of dealing with your product quality... or was the 12 steps from Alcoholics Anonymous... oh well, anyway, it's the first of many steps. ;\^) Internally when we make changes to any part of the JDK, there are certain procedures we are required to perform prior to any putback or commit of the changes. The procedures often vary from team to team, depending on many factors, such as whether native code is changed, or if the change could impact other areas of the JDK. But a common requirement is a verification that the source base with the changes (and merged with the very latest source base) will build on many of not all 8 platforms, and a full 'from scratch' build, not an incremental build, which can hide full build problems. The testing needed varies, depending on what has been changed. Anyone that was worked on a project where multiple engineers or groups are submitting changes to a shared source base knows how disruptive a 'bad commit' can be on everyone. How many times have you heard: "So And So made a bunch of changes and now I can't build!". But multiply the number of platforms by 8, and make all the platforms old and antiquated OS versions with bizarre system setup requirements and you have a pretty complicated situation (see http://download.java.net/jdk6/docs/build/README-builds.html). We don't tolerate bad commits, but our enforcement is somewhat lacking, usually it's an 'after the fact' correction. Luckily the Source Code Management system we use (another antique called TeamWare) allows for a tree of repositories and 'bad commits' are usually isolated to a small team. Punishment to date has been pretty drastic, the Queen of Hearts in 'Alice in Wonderland' said 'Off With Their Heads', well trust me, you don't want to be the engineer doing a 'bad commit' to the JDK. With JPRT, hopefully this will become a thing of the past, not that we have had many 'bad commits' to the master source base, in general the teams doing the integrations know how important their jobs are and they rarely make 'bad commits'. So for these JDK integrators, maybe what JPRT does is keep them from chewing their finger nails at night. ;\^) Over the years each of the teams have accumulated sets of machines they use for building, or they use some of the shared machines available to all of us. But the hunt for build machines is just part of the job, or has been. And although the issues with consistency of the build machines hasn't been a horrible problem, often you never know if the Solaris build machine you are using has all the right patches, or if the Linux machine has the right service pack, or if the Windows machine has it's latest updates. Hopefully the JPRT system can solve this problem. When we ship the binary JDK bits, it is SO very important that the build machines are correct, and we know how difficult it is to get them setup. Sure, if you need to debug a JDK problem that only shows up on Windows XP or Solaris 9, you'll still need to hunt down a machine, but not as a regular everyday occurance. I'm a big fan of a regular nightly build and test system, constantly verifying that a source base builds and tests out. There are many examples of automated build/tests, some that trigger on any change to the source base, some that just run every night. Some provide a protection gateway to the 'golden' source base which only gets changes that the nightly process has verified are good. The JPRT (and PRT) system is meant to guard the source base before anything is sent to it, guarding all source bases from the evil developer, well maybe 'evil' isn't the right word, I haven't met many 'evil' developers, more like 'error prone' developers. ;\^) Humm, come to think about it, I may be one from time to time. :\^{ But the point is that by spreading the build up over a set of machines, and getting the turnaround down to under an hour, it becomes realistic to completely build on all platforms and test it, on every putback. We have the technology, we can build and rebuild and rebuild, and it will be better than it was before, ha ha... Anybody remember the Six Million Dollar Man? Man, I gotta get out more often.. Anyway, now the nightly build and test can become a 'fetch the latest JPRT build bits' and start extensive testing (the testing not done by JPRT, or the platforms not tested by JPRT). Is it Open Source? No, not yet. Would you like to be? Let me know. Or is it more important that you have the ability to use such a system for JDK changes? So enough blabbering on about this JPRT system, tell me what you think. And let me know if you want to hear more about it or not. Stay tuned for the next episode, same Bloody Bat time, same Bloody Bat channel. ;\^) -kto

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• Video Whiteboard on Exadata Smart Flash Cache

  - by jean-pierre.dijcks

  Personally, I like this stuff. It places the unique features of Exadata in perspective with the overall business value. Enjoy the presentation!

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• Analyst's Corner: "In Perfect Harmony"

  Data integration tools improve movement of information among systems.

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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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• The Benefits of Upgrading to PeopleSoft 9.0

  Doris Wong, Vice President and General Manager of PeopleSoft Enterprise speaks with Fred about how PeopleSoft 9.0 fits into Applications Unlimited, what the key enhancements are in release 9.0 and why PeopleSoft customers should consider upgrading to this new release.

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