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• Mercurial says "nothing changed", but it did. Sometimes my software is too clever.

  - by user12608033

  It seems I have found a "bug" in Mercurial. It takes a shortcut when checking for differences in tracked files. If the file's size and modification time are unchanged, it assumes its contents are unchanged: $ hg init . $ cp -p .sccs2hg/2005-06-05_00\:00\:00\,nicstat.c nicstat.c $ ls -ogE nicstat.c -rw-r--r-- 1 14722 2012-08-24 11:22:48.819451726 -0700 nicstat.c $ hg add nicstat.c $ hg commit -m "added nicstat.c" $ cp -p .sccs2hg/2005-07-02_00\:00\:00\,nicstat.c nicstat.c $ ls -ogE nicstat.c -rw-r--r-- 1 14722 2012-08-24 11:22:48.819451726 -0700 nicstat.c $ hg diff $ hg commit nothing changed $ touch nicstat.c $ hg diff diff -r b49cf59d431d nicstat.c --- a/nicstat.c Fri Aug 24 11:21:27 2012 -0700 +++ b/nicstat.c Fri Aug 24 11:22:50 2012 -0700 @@ -2,7 +2,7 @@ * nicstat - print network traffic, Kb/s read and written. Solaris 8+. * "netstat -i" only gives a packet count, this program gives Kbytes. * - * 05-Jun-2005, ver 0.81 (check for new versions, http://www.brendangregg.com) + * 02-Jul-2005, ver 0.90 (check for new versions, http://www.brendangregg.com) * [...] Now, before you agree or disagree with me on whether this is a bug, I will also say that I believe it is a feature. Yes, I feel it is an acceptable shortcut because in "real" situations an edit to a file will change the modification time by at least one second (the resolution that hg diff or hg commit is looking for). The benefit of the shortcut is greatly improved performance of operations like "hg diff" and "hg status", particularly where your repository contains a lot of files. Why did I have no change in modification time? Well, my source file was generated by a script that I have written to convert SCCS change history to Mercurial commits. If my script can generate two revisions of a file within a second, and the files are the same size, then I run afoul of this shortcut. Solution - I will just change my script to apply the modification time from the SCCS history to the file prior to commit. A "touch -t " will do that easily.

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• Project OpenPTK Release 2.1 Available

  - by Scott Fehrman

  The OpenPTK owners are pleased to announce that release 2.1 is available.  It has been "tagged" in the svn repository. See the download page for details.   This release is an update to version 2.0.  This release contains bug fixes, enhancements to existing capabilities, and new features.  The most notable change in this release is the use of maven, instead of ant, for the build process.  The adoption of maven has made the project more modular, reduced its download size (less bundled jar files) and will enable the future support of Project OpenPTK in a maven repository. For full details, see the OpenPTK version 2.1 Release Notes

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• Identify high CPU consumed thread for Java app

  - by Vincent Ma

  Following java code to emulate busy and Idle thread and start it. import java.util.concurrent.*;import java.lang.*; public class ThreadTest {    public static void main(String[] args) {        new Thread(new Idle(), "Idle").start();        new Thread(new Busy(), "Busy").start();    }}class Idle implements Runnable {    @Override    public void run() {        try {            TimeUnit.HOURS.sleep(1);        } catch (InterruptedException e) {        }    }}class Busy implements Runnable {    @Override    public void run() {        while(true) {            "Test".matches("T.*");        }    }} Using Processor Explorer to get this busy java processor and get Thread id it cost lots of CPU see the following screenshot: Cover to 4044 to Hexadecimal is oxfcc. Using VistulVM to dump thread and get that thread. see the following screenshot In Linux you can use  top -H to get Thread information. That it! Any question let me know. Thanks

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• REMINDER : SPARC T4 Servers and ZFS Storage Appliance Demo Equipment Purchase Opportunity

  - by Cinzia Mascanzoni

  Please mark your calendars for the SPARC T4 Servers and ZFS Storage Appliance Demo Program webcast on November 22nd at 12 noon GMT/ 1pm CET and learn how you can take the maximum advantage from this unique opportunity. The objective of this call is to share value, details, guidelines and rules of this demo program with you. Go on the EMEA VAD Resource Center to find more info and the details to access the webcast.

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• JavaOne Countdown, Are you ready?

  - by Angela Caicedo

  This is a great time of the year!  Not only does the weather start cooling down a bit, but it's time to get ready for JavaOne 2012.  It feels so long since my last JavaOne (last year I missed it because I was on a mom duty), so this year I couldn't be happier to be this close to the action again.  Have you ever been at JavaOne?  There are a million great reasons to love JavaOne, and the most important for me is the atmosphere of the conference: The Java community is there, and Java is in the air! This year we have more than 450 sessions, and there are HOLs (Hands on labs) to get your hands dirty with code.  In addition, there will be very cool demos, an exhibition hall. and a DEMOground.  During the whole time, you will have the opportunity to interact with the speakers, discuss topics and concerns, and even have a drink! Oh yes, I almost forgot, there will be lots of fun even apart from the technology!  For example there will be a Geek Bike Ride, a Thirsty Bear party, and the Appreciation Party with Pearl Jam and Kings of Leon.  How can this get any better! So, are you ready yet?  Have you registered?  If not, just follow this "Register for JavaOne" link and we'll see you there! P.S.  Little known fact: If you are a student you can get your pass for free!!!

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• Implementing a Custom Coherence PartitionAssignmentStrategy

  - by jpurdy

  A recent A-Team engagement required the development of a custom PartitionAssignmentStrategy (PAS). By way of background, a PAS is an implementation of a Java interface that controls how a Coherence partitioned cache service assigns partitions (primary and backup copies) across the available set of storage-enabled members. While seemingly straightforward, this is actually a very difficult problem to solve. Traditionally, Coherence used a distributed algorithm spread across the cache servers (and as of Coherence 3.7, this is still the default implementation). With the introduction of the PAS interface, the model of operation was changed so that the logic would run solely in the cache service senior member. Obviously, this makes the development of a custom PAS vastly less complex, and in practice does not introduce a significant single point of failure/bottleneck. Note that Coherence ships with a default PAS implementation but it is not used by default. Further, custom PAS implementations are uncommon (this engagement was the first custom implementation that we know of). The particular implementation mentioned above also faced challenges related to managing multiple backup copies but that won't be discussed here. There were a few challenges that arose during design and implementation: Naive algorithms had an unreasonable upper bound of computational cost. There was significant complexity associated with configurations where the member count varied significantly between physical machines. Most of the complexity of a PAS is related to rebalancing, not initial assignment (which is usually fairly simple). A custom PAS may need to solve several problems simultaneously, such as: Ensuring that each member has a similar number of primary and backup partitions (e.g. each member has the same number of primary and backup partitions) Ensuring that each member carries similar responsibility (e.g. the most heavily loaded member has no more than one partition more than the least loaded). Ensuring that each partition is on the same member as a corresponding local resource (e.g. for applications that use partitioning across message queues, to ensure that each partition is collocated with its corresponding message queue). Ensuring that a given member holds no more than a given number of partitions (e.g. no member has more than 10 partitions) Ensuring that backups are placed far enough away from the primaries (e.g. on a different physical machine or a different blade enclosure) Achieving the above goals while ensuring that partition movement is minimized. These objectives can be even more complicated when the topology of the cluster is irregular. For example, if multiple cluster members may exist on each physical machine, then clearly the possibility exists that at certain points (e.g. following a member failure), the number of members on each machine may vary, in certain cases significantly so. Consider the case where there are three physical machines, with 3, 3 and 9 members each (respectively). This introduces complexity since the backups for the 9 members on the the largest machine must be spread across the other 6 members (to ensure placement on different physical machines), preventing an even distribution. For any given problem like this, there are usually reasonable compromises available, but the key point is that objectives may conflict under extreme (but not at all unlikely) circumstances. The most obvious general purpose partition assignment algorithm (possibly the only general purpose one) is to define a scoring function for a given mapping of partitions to members, and then apply that function to each possible permutation, selecting the most optimal permutation. This would result in N! (factorial) evaluations of the scoring function. This is clearly impractical for all but the smallest values of N (e.g. a partition count in the single digits). It's difficult to prove that more efficient general purpose algorithms don't exist, but the key take away from this is that algorithms will tend to either have exorbitant worst case performance or may fail to find optimal solutions (or both) -- it is very important to be able to show that worst case performance is acceptable. This quickly leads to the conclusion that the problem must be further constrained, perhaps by limiting functionality or by using domain-specific optimizations. Unfortunately, it can be very difficult to design these more focused algorithms. In the specific case mentioned, we constrained the solution space to very small clusters (in terms of machine count) with small partition counts and supported exactly two backup copies, and accepted the fact that partition movement could potentially be significant (preferring to solve that issue through brute force). We then used the out-of-the-box PAS implementation as a fallback, delegating to it for configurations that were not supported by our algorithm. Our experience was that the PAS interface is quite usable, but there are intrinsic challenges to designing PAS implementations that should be very carefully evaluated before committing to that approach.

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• JCP.Next Progress Updates

  - by heathervc

  JSR 355, JCP Executive Committee Merge, is currently nearing the end of the Public Review period.  Review the current draft here and provide feedback here.  The review closes on 12 June 2012.  The JCP Executive Committee met face to face in Sao Paulo, Brazil earlier in May, and has published a revision (version 2.1) of the EC Standing Rules.  The EC Standing Rules were introduced in October 2011 with the launch of JCP version 2.8 (JSR 348).  Version 2.1 of the EC Standing Rules will modify rules for attendance at EC face-to-face meetings. Remote observers will be permitted in "read-only" mode but unless a member attends in person they will be counted as absent.  The review period for these changes will close on June 30 2012.  Please comment on the proposed changes by logging an issue in the JCP EC issue tracker.

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• New Java Tutorials Updated

  - by hinkmond

  The new Java Tutorials are here! The new Java Tutorials are here! So what? So, you can read them on your iPad thingie--if that's how you roll, that is... See: Read New Java Tutorials Here's a quote: What's New The Java Tutorials are continuously updated to keep up with changes to the Java Platform and to incorporate feedback from our readers. Recent updates include the following features: The Generics lesson has been completely reworked... The Java Tutorials are now available in two ebook formats: mobi ebook files for Kindle. ePub ebook files for iPad, Nook, and other eReaders that support the ePub format. Just kick back, open up your favorite tablet or eReader and learn all about the new things in the Java platform. Nice. All you need now is a cool drink and you're all set! Hinkmond

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• Employee Engagement: Drive Business Value

  - by Kellsey Ruppel

  As we’ve been discussing this week, employee engagement is extremely important and you’ve probably realized that effectively engaging your employees is essential to driving business value. Your employees are the ones responsible for executing on the business’ objectives. Your employees (in the sales & service departments) are the ones interacting with your customers the most, so delivering on customer expectations and attaining high levels of customer engagement are simply not possible without successfully empowering these this stakeholder group. High employee and partner engagement can have many benefits including: Higher levels of employee productivity Longer employee retention Stronger, more enduring and more successful relationships Serving as ambassadors for an organization’s brand More likely to deliver excellent customer service Referring others for hire Recommending the organization’s products and services Sharing feedback with their colleagues In a way, engagement is a measure of employee investment in an organization’s mission and brand. And then you have the enablement piece of this as well.  It’s hard to imagine a high level of engagement existing among employees who don’t feel that they’ve been enabled to do their jobs very efficiently or effectively. You’re just not going to find high engagement among people if the everyday processes and technologies  they work with make it a challenge for them to access, share and manage the information  they need do their jobs or if they’re unable to effectively collaborate around the projects they’re working on. How does your organization measure on the employee engagement spectrum? We’ve got a number of different resources to help you get started! Portal Resource Center Video: Got a minute? WebCenter in Action Webcast Series Portal Engagement Webcast 

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• MySQL Workbench 5.2.43 GA released

  - by Alfredo Kojima

  The MySQL developer tools team announces the availability of version 5.2.43 of the MySQL Workbench GUI tool. This version contains various fixes and minor enhancements and includes 53 resolved bugs. With this version, Fedora 15 packages are replaced with Fedora 17. Also, Gatekeeper in Mac OS X Mountain Lion is now properly handled. For a full list of issues fixed in this release, see http://dev.mysql.com/doc/workbench/en/changes-5.2.x.html Please get your copy from our Downloads site. In Windows, you can also use the MySQL Windows Installer to update Workbench. Sources and binary packages are available for several platforms, including Windows, Mac OS X and Linux. http://dev.mysql.com/downloads/workbench/ Workbench Documentation can be found here. http://dev.mysql.com/doc/workbench/en/index.html Utilities Documentation can be found here. http://dev.mysql.com/doc/workbench/en/mysql-utilities.html If you need any additional info or help please get in touch with us. Post in our forums or leave comments on our blog pages. - The MySQL Workbench Team

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• Dealing with Fine-Grained Cache Entries in Coherence

  - by jpurdy

  On occasion we have seen significant memory overhead when using very small cache entries. Consider the case where there is a small key (say a synthetic key stored in a long) and a small value (perhaps a number or short string). With most backing maps, each cache entry will require an instance of Map.Entry, and in the case of a LocalCache backing map (used for expiry and eviction), there is additional metadata stored (such as last access time). Given the size of this data (usually a few dozen bytes) and the granularity of Java memory allocation (often a minimum of 32 bytes per object, depending on the specific JVM implementation), it is easily possible to end up with the case where the cache entry appears to be a couple dozen bytes but ends up occupying several hundred bytes of actual heap, resulting in anywhere from a 5x to 10x increase in stated memory requirements. In most cases, this increase applies to only a few small NamedCaches, and is inconsequential -- but in some cases it might apply to one or more very large NamedCaches, in which case it may dominate memory sizing calculations. Ultimately, the requirement is to avoid the per-entry overhead, which can be done either at the application level by grouping multiple logical entries into single cache entries, or at the backing map level, again by combining multiple entries into a smaller number of larger heap objects. At the application level, it may be possible to combine objects based on parent-child or sibling relationships (basically the same requirements that would apply to using partition affinity). If there is no natural relationship, it may still be possible to combine objects, effectively using a Coherence NamedCache as a "map of maps". This forces the application to first find a collection of objects (by performing a partial hash) and then to look within that collection for the desired object. This is most naturally implemented as a collection of entry processors to avoid pulling unnecessary data back to the client (and also to encapsulate that logic within a service layer). At the backing map level, the NIO storage option keeps keys on heap, and so has limited benefit for this situation. The Elastic Data features of Coherence naturally combine entries into larger heap objects, with the caveat that only data -- and not indexes -- can be stored in Elastic Data.

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• PL/SQL Practices: "On Avoiding Termination"

  Continue PL/SQL execution beyond exceptions.

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• Best practices for Persona development

  - by user12277104

  Over the years, I have created a lot of Personas, I've co-authored a new method for creating them, and I've given talks about best practices for creating your own, so when I saw a call for participation in the OpenPersonas project, I was intrigued. While Jeremy and Steve were calling for persona content, that wasn't something I could contribute -- most of the personas I've created have been proprietary and specific to particular domains of my employers. However, I felt like there were a few things I could contribute: a process, a list of interview questions, and what information good personas should contain. The first item, my process for creating data-driven personas, I've posted as a list of best practices. My next post will be the list of 15 interview questions I use to guide the conversations with people whose data will become the personas. The last thing I'll share is a list of items that need to be part of any good persona artifact -- and if I have time, I'll mock them up in a template or two. 

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• Learn with New Demos and Tutorials

  Watch online demos and follow step-by-step instructions

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• Concurrency Utilities for Java EE 6: JSR 236 Rebooting

  - by arungupta

  JSR 166 added support for concurrency utilities in the Java platform. The JSR 236's, a.k.a Concurrency Utilities for Java EE, goal was to extend that support to the Java EE platform by adding asynchronous abilities to different application components. The EG was however stagnant since Dec 2003. Its coming back to life with the co-spec lead Anthony Lai's message to the JSR 236 EG (archived here). The JSR will be operating under JCP 2.8's transparency rules and can be tracked at concurrency-spec.java.net. All the mailing lists are archived here. The final release is expected in Q1 2013 and the APIs will live in the javax.enterprise.concurrent package. Please submit your nomination if you would like to join this EG.

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• Fun tips with Analytics

  - by user12620172

  If you read this blog, I am assuming you are at least familiar with the Analytic functions in the ZFSSA. They are basically amazing, very powerful and deep. However, you may not be aware of some great, hidden functions inside the Analytic screen. Once you open a metric, the toolbar looks like this: Now, I’m not going over every tool, as we have done that before, and you can hover your mouse over them and they will tell you what they do. But…. Check this out. Open a metric (CPU Percent Utilization works fine), and click on the “Hour” button, which is the 2nd clock icon. That’s easy, you are now looking at the last hour of data. Now, hold down your ‘Shift’ key, and click it again. Now you are looking at 2 hours of data. Hold down Shift and click it again, and you are looking at 3 hours of data. Are you catching on yet? You can do this with not only the ‘Hour’ button, but also with the ‘Minute’, ‘Day’, ‘Week’, and the ‘Month’ buttons. Very cool. It also works with the ‘Show Minimum’ and ‘Show Maximum’ buttons, allowing you to go to the next iteration of either of those. One last button you can Shift-click is the handy ‘Drill’ button. This button usually drills down on one specific aspect of your metric. If you Shift-click it, it will display a “Rainbow Highlight” of the current metric. This works best if this metric has many ‘Range Average’ items in the left-hand window. Give it a shot. Also, one will sometimes click on a certain second of data in the graph, like this:  In this case, I clicked 4:57 and 21 seconds, and the 'Range Average' on the left went away, and was replaced by the time stamp. It seems at this point to some people that you are now stuck, and can not get back to an average for the whole chart. However, you can actually click on the actual time stamp of "4:57:21" right above the chart. Even though your mouse does not change into the typical browser finger that most links look like, you can click it, and it will change your range back to the full metric. Another trick you may like is to save a certain view or look of a group of graphs. Most of you know you can save a worksheet, but did you know you could Sync them, Pause them, and then Save it? This will save the paused state, allowing you to view it forever the way you see it now.  Heatmaps. Heatmaps are cool, and look like this:  Some metrics use them and some don't. If you have one, and wish to zoom it vertically, try this. Open a heatmap metric like my example above (I believe every metric that deals with latency will show as a heatmap). Select one or two of the ranges on the left. Click the "Change Outlier Elimination" button. Click it again and check out what it does.  Enjoy. Perhaps my next blog entry will be the best Analytic metrics to keep your eyes on, and how you can use the Alerts feature to watch them for you. Steve 

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• Choice Sessions: Java Champions at JavaOne

  - by Tori Wieldt

  There are so many reasons to attend JavaOne 2012 – great location, great networking opportunities but most importantly, great content! It’s tough to decide which sessions will be worth your while, but we advise you to start your decision making process by checking out sessions delivered by the 21 Java Champions attending and presenting at JavaOne. Java Champions are selected by their peers for their incredible contributions to the Java community and demonstration of their technical expertise in all aspects of Java. Our friend Markus Eisele @myfear has already kindly compiled a list in his blog entry Java Champions at JavaOne 2012 (thanks!). Happy schedule building!

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• VADs can now access their Partners Specialization Tracker

  - by Cinzia Mascanzoni

  Partners can now enable their VAD to help them in the process to become Specialized. It's as simple as the PRM Administrator to add their VAD in the OPN Competency Center preferences page. Invite your partners to add you! Click here for a 3 minute tutorial, and here for more information.

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• Customer Experience Management for Retail 2.0 - part 2 / 2

  - by Sanjeev Sharma

  In the previous post, i discussed some of the key trends shaping up in the retail industry, their implications and the challenges facing retailers seeking to regain control of the buyer-seller relationship. Is Customer Experience Management the panacea for the ailing retailers who are now awakening to the power of the consumer? Quite honestly, customer acquisition, retention and satisfaction have been top of mind for retailers for quite some time now. The missing piece of this puzzle is bringing all those countless hours of strategy and planning to fruition. This is more of an execution gap than anything else. Although technology has made consumers more informed, more mobile and more social, customer experience is still largely defined by delivering on the following: Consistent experiences, whether shopping online or offline Personalize-able interaction ("mass market" sounds good as an internal strategy but not when you are a buyer!) Timely order fulfillment, if not pro-active notification of delays Below is a concept architecture for streamlining front-end, mid-office and back-end interfaces through shared process to achieve consistency and efficiency in managing the customer experience from order capture to order provisioning.

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• Impact of Server Failure on Coherence Request Processing

  - by jpurdy

  Requests against a given cache server may be temporarily blocked for several seconds following the failure of other cluster members. This may cause issues for applications that can not tolerate multi-second response times even during failover processing (ignoring for the moment that in practice there are a variety of issues that make such absolute guarantees challenging even when there are no server failures). In general, Coherence is designed around the principle that failures in one member should not affect the rest of the cluster if at all possible. However, it's obvious that if that failed member was managing a piece of state that another member depends on, the second member will need to wait until a new member assumes responsibility for managing that state. This transfer of responsibility is (as of Coherence 3.7) performed by the primary service thread for each cache service. The finest possible granularity for transferring responsibility is a single partition. So the question becomes how to minimize the time spent processing each partition. Here are some optimizations that may reduce this period: Reduce the size of each partition (by increasing the partition count) Increase the number of JVMs across the cluster (increasing the total number of primary service threads) Increase the number of CPUs across the cluster (making sure that each JVM has a CPU core when needed) Re-evaluate the set of configured indexes (as these will need to be rebuilt when a partition moves) Make sure that the backing map is as fast as possible (in most cases this means running on-heap) Make sure that the cluster is running on hardware with fast CPU cores (since the partition processing is single-threaded) As always, proper testing is required to make sure that configuration changes have the desired effect (and also to quantify that effect).

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• Clear Windows Extension for JDeveloper 11g

  Clear running server log, compiler log and message pane at one go

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• SPARC Architecture 2011

  - by Darryl Gove

  With what appears to be minimal fanfare, an update of the SPARC Architecture has been released. If you ever look at SPARC disassembly code, then this is the document that you need to bookmark. If you are not familiar with it, then it basically describes how a SPARC processor should behave - it doesn't describe a particular implementation, just the "generic" processor. As with all revisions, it supercedes the SPARC v9 book published back in the 90s, having both corrections, and definitions of new instructions. Anyway, should be an interesting read

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• Coherence Query Performance in Large Clusters

  - by jpurdy

  Large clusters (measured in terms of the number of storage-enabled members participating in the largest cache services) may introduce challenges when issuing queries. There is no particular cluster size threshold for this, rather a gradually increasing tendency for issues to arise. The most obvious challenges are that a client's perceived query latency will be determined by the slowest responder (more likely to be a factor in larger clusters) as well as the fact that adding additional cache servers will not increase query throughput if the query processing is not compute-bound (which would generally be the case for most indexed queries). If the data set can take advantage of the partition affinity features of Coherence, then the application can use a PartitionedFilter to target a query to a single server (using partition affinity to ensure that all data is in a single partition). If this can not be done, then avoiding an excessive number of cache server JVMs will help, as will ensuring that each cache server has sufficient CPU resources available and is also properly configured to minimize GC pauses (the most common cause of a slow-responding cache server).

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• Processing Text and Binary (Blob, ArrayBuffer, ArrayBufferView) Payload in WebSocket - (TOTD #185)

  - by arungupta

  The WebSocket API defines different send(xxx) methods that can be used to send text and binary data. This Tip Of The Day (TOTD) will show how to send and receive text and binary data using WebSocket. TOTD #183 explains how to get started with a WebSocket endpoint using GlassFish 4. A simple endpoint from that blog looks like: @WebSocketEndpoint("/endpoint") public class MyEndpoint { public void receiveTextMessage(String message) { . . . } } A message with the first parameter of the type String is invoked when a text payload is received. The payload of the incoming WebSocket frame is mapped to this first parameter. An optional second parameter, Session, can be specified to map to the "other end" of this conversation. For example: public void receiveTextMessage(String message, Session session) {     . . . } The return type is void and that means no response is returned to the client that invoked this endpoint. A response may be returned to the client in two different ways. First, set the return type to the expected type, such as: public String receiveTextMessage(String message) { String response = . . . . . . return response; } In this case a text payload is returned back to the invoking endpoint. The second way to send a response back is to use the mapped session to send response using one of the sendXXX methods in Session, when and if needed. public void receiveTextMessage(String message, Session session) {     . . .     RemoteEndpoint remote = session.getRemote();     remote.sendString(...);     . . .     remote.sendString(...);    . . .    remote.sendString(...); } This shows how duplex and asynchronous communication between the two endpoints can be achieved. This can be used to define different message exchange patterns between the client and server. The WebSocket client can send the message as: websocket.send(myTextField.value); where myTextField is a text field in the web page. Binary payload in the incoming WebSocket frame can be received if ByteBuffer is used as the first parameter of the method signature. The endpoint method signature in that case would look like: public void receiveBinaryMessage(ByteBuffer message) {     . . . } From the client side, the binary data can be sent using Blob, ArrayBuffer, and ArrayBufferView. Blob is a just raw data and the actual interpretation is left to the application. ArrayBuffer and ArrayBufferView are defined in the TypedArray specification and are designed to send binary data using WebSocket. In short, ArrayBuffer is a fixed-length binary buffer with no format and no mechanism for accessing its contents. These buffers are manipulated using one of the views defined by one of the subclasses of ArrayBufferView listed below: Int8Array (signed 8-bit integer or char) Uint8Array (unsigned 8-bit integer or unsigned char) Int16Array (signed 16-bit integer or short) Uint16Array (unsigned 16-bit integer or unsigned short) Int32Array (signed 32-bit integer or int) Uint32Array (unsigned 16-bit integer or unsigned int) Float32Array (signed 32-bit float or float) Float64Array (signed 64-bit float or double) WebSocket can send binary data using ArrayBuffer with a view defined by a subclass of ArrayBufferView or a subclass of ArrayBufferView itself. The WebSocket client can send the message using Blob as: blob = new Blob([myField2.value]);websocket.send(blob); where myField2 is a text field in the web page. The WebSocket client can send the message using ArrayBuffer as: var buffer = new ArrayBuffer(10);var bytes = new Uint8Array(buffer);for (var i=0; i<bytes.length; i++) { bytes[i] = i;}websocket.send(buffer); A concrete implementation of receiving the binary message may look like: @WebSocketMessagepublic void echoBinary(ByteBuffer data, Session session) throws IOException {    System.out.println("echoBinary: " + data);    for (byte b : data.array()) {        System.out.print(b);    }    session.getRemote().sendBytes(data);} This method is just printing the binary data for verification but you may actually be storing it in a database or converting to an image or something more meaningful. Be aware of TYRUS-51 if you are trying to send binary data from server to client using method return type. Here are some references for you: JSR 356: Java API for WebSocket - Specification (Early Draft) and Implementation (already integrated in GlassFish 4 promoted builds) TOTD #183 - Getting Started with WebSocket in GlassFish TOTD #184 - Logging WebSocket Frames using Chrome Developer Tools, Net-internals and Wireshark Subsequent blogs will discuss the following topics (not necessary in that order) ... Error handling Custom payloads using encoder/decoder Interface-driven WebSocket endpoint Java client API Client and Server configuration Security Subprotocols Extensions Other topics from the API

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• OSB, Service Callouts and OQL - Part 3

  - by Sabha

  In the previous sections of the "OSB, Service Callouts and OQL" series, we analyzed the threading model used by OSB for Service Callouts and analysis of OSB Server threads hung in Service callouts and identifying  the Proxies and Remote services involved in the hang using OQL. This final section of the series will focus on the corrective action to avoid Service Callout related OSB Server hangs. Please refer to the blog post for more details.

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