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  • Is it possible to read data that has been separately copied to the Android sd card without having ro

    - by icecream
    I am developing an application that needs to access data on the sd card. When I run on my development device (an odroid with Android 2.1) I have root access and can construct the path using: File sdcard = Environment.getExternalStorageDirectory(); String path = sdcard.getAbsolutePath() + File.separator + "mydata" File data = new File(path); File[] files = data.listFiles(new FilenameFilter() { @Override public boolean accept(File dir, String filename) { return filename.toLowerCase().endsWith(".xyz"); }}); However, when I install this on a phone (2.1) where I do not have root access I get files == null. I assume this is because I do not have the right permissions to read the data from the sd card. I also get files == null when just trying to list files on /sdcard. So the same applies without my constructed path. Also, this app is not intended to be distributed through the app store and is needs to use data copied separately to the sd card so this is a real use-case. It is too much data to put in res/raw (I have tried, it did not work). I have also tried adding: <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" /> to the manifest, even though I only want to read the sd card, but it did not help. I have not found a permission type for reading the storage. There is probably a correct way to do this, but I haven't been able to find it. Any hints would be useful.

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  • StorageClientException: The specified message does not exist?

    - by Aaron
    I have a simple video encoding worker role that pulls messages from a queue encodes a video then uploads the video to storage. Everything seems to be working but occasionally when deleting the message after I am done encoding and uploading I get a "StorageClientException: The specified message does not exist." Although the video is processed, I believe the message is reappearing in the queue because it's not being deleted correctly. Is it possible that another instance of the Worker role is processing and deleting the message? Doesn't the GetMessage() prevent other worker roles from picking up the same message? Am I doing something wrong in the setup of my queue? What could be causing this message to not be found on delete? some code... //onStart() queue setup var queueStorage = _storageAccount.CreateCloudQueueClient(); _queue = queueStorage.GetQueueReference(QueueReference); queueStorage.RetryPolicy = RetryPolicies.Retry(5, new TimeSpan(0, 5, 0)); _queue.CreateIfNotExist(); public override void Run() { while (true) { try { var msg = _queue.GetMessage(new TimeSpan(0, 5, 0)); if (msg != null) { EncodeIt(msg); PostIt(msg); _queue.DeleteMessage(msg); } else { Thread.Sleep(WaitTime); } } catch (StorageClientException exception) { BlobTrace.Write(exception.ToString()); Thread.Sleep(WaitTime); } } }

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  • Architecture for data layer that uses both localStorage and a REST remote server

    - by Zack
    Anybody has any ideas or references on how to implement a data persistence layer that uses both a localStorage and a REST remote storage: The data of a certain client is stored with localStorage (using an ember-data indexedDB adapter). The locally stored data is synced with the remote server (using ember-data RESTadapter). The server gathers all data from clients. Using mathematical sets notation: Server = Client1 ? Client2 ? ... ? ClientN where, in general, a record may not be unique to a certain client. Here are some scenarios: A client creates a record. The id of the record can not set on the client, since it may conflict with a record stored on the server. Therefore a newly created record needs to be committed to the server - receive the id - create the record in localStorage. A record is updated on the server, and as a consequence the data in localStorage and in the server go out of sync. Only the server knows that, so the architecture needs to implement a push architecture (?) Would you use 2 stores (one for localStorage, one for REST) and sync between them, or use a hybrid indexedDB/REST adapter and write the sync code within the adapter? Can you see any way to avoid implementing push (Web Sockets, ...)?

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  • Windows Azure - Automatic Load Balancing - partitioning

    - by veda
    I was going through some videos. I found that Windows Azure will group the blobs into partitions based on the partition key and will Automatically Load Balance these partitions on their servers. The partition key for a blob is blob name. Using the blob name, azure will automatically do partitions. Now, My question is that Can I able to make the azure to do partitions based on the Container Name. I wanted my partition key to be container name. For example, I have a storage account. In that I have 2 containers named container1 and container2. In container1, I have 1000 files named 1.txt, 2.txt, 3.txt, ......., 501.txt, 502.txt, ..... 999.txt, 1000.txt and in container2, I have another 1000 files named 1001.txt, 1002.txt, 1003.txt, ......., 1501.txt, 1502.txt, ..... 1999.txt, 2000.txt Now, Will Windows Azure will generate 2000 partitions based on the blob name and serve me through several servers??? Won't it be better if Azure partitions based on the Container name? container1 on one server and conatiner2 on another.

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  • How do I use HTML5's localStorage in a Google Chrome extension?

    - by davidkennedy85
    I am trying to develop an extension that will work with Awesome New Tab Page. I've followed the author's advice to the letter, but it doesn't seem like any of the script I add to my background page is being executed at all. Here's my background page: <script> var info = { poke: 1, width: 1, height: 1, path: "widget.html" } chrome.extension.onRequestExternal.addListener(function(request, sender, sendResponse) { if (request === "mgmiemnjjchgkmgbeljfocdjjnpjnmcg-poke") { chrome.extension.sendRequest( sender.id, { head: "mgmiemnjjchgkmgbeljfocdjjnpjnmcg-pokeback", body: info, } ); } }); function initSelectedTab() { localStorage.setItem("selectedTab", "Something"); } initSelectedTab(); </script> Here is manifest.json: { "update_url": "http://clients2.google.com/service/update2/crx", "background_page": "background.html", "name": "Test Widget", "description": "Test widget for mgmiemnjjchgkmgbeljfocdjjnpjnmcg.", "icons": { "128": "icon.png" }, "version": "0.0.1" } Here is the relevant part of widget.html: <script> var selectedTab = localStorage.getItem("selectedTab"); document.write(selectedTab); </script> Every time, the browser just displays null. The local storage isn't being set at all, which makes me think the background page is completely disconnected. Do I have something wired up incorrectly?

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  • How to use dd to make splitted ISO images from an storage device?

    - by Gustavo Bandeira
    This is a double question, I just hope it's valid. I need to know how to use dd to make splitted ISO images from some storage device, I'm doing it through SSH: the process is slow and the risk of faling at the mid of the operation (1) is high then I need to know how to make these splitted ISO images from my storage device. (2) I'm searching for some reference on dd, it could be a book or a good website about it for when any doubt arises. 1 - I'm doing it on a ~60GB storage device, it took me a whole day to copy ~10GB from this disk. 2 - For curious people, I'm trying to recover an accidentaly deleted file from an iPod, until now I've been able to make the whole process, I just need to improve it beucase I left it copying the disk yesterday: Today it gave me an error when it was at ~10GB.

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  • make a folder/partition on one computer appear as a mass storage device to another?

    - by user137560
    Is there anyway to make a folder or a partition on a computer (Linux or Windows) act like a mass storage device to other computers or devices when connected with a Male-Male USB cable? For example, I have a Windows 7 computer with 2 partitions, C and D. I would then connect that computer to another computer or a Smart TV using a Male-Male USB cable, and the other computer or device recognizes a folder/partition on current computer as a mass storage device. Is this possible? If not, is there any USB switch that can connect an external hard drive or flash drive to both a computer and TV without the need to manually switch them? (I know about some USB switches, but they only support automatic switching with some certain types of printers, not with mass storage)

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  • Combined Likelihood Models

    - by Lukas Vermeer
    In a series of posts on this blog we have already described a flexible approach to recording events, a technique to create analytical models for reporting, a method that uses the same principles to generate extremely powerful facet based predictions and a waterfall strategy that can be used to blend multiple (possibly facet based) models for increased accuracy. This latest, and also last, addition to this sequence of increasing modeling complexity will illustrate an advanced approach to amalgamate models, taking us to a whole new level of predictive modeling and analytical insights; combination models predicting likelihoods using multiple child models. The method described here is far from trivial. We therefore would not recommend you apply these techniques in an initial implementation of Oracle Real-Time Decisions. In most cases, basic RTD models or the approaches described before will provide more than enough predictive accuracy and analytical insight. The following is intended as an example of how more advanced models could be constructed if implementation results warrant the increased implementation and design effort. Keep implemented statistics simple! Combining likelihoods Because facet based predictions are based on metadata attributes of the choices selected, it is possible to generate such predictions for more than one attribute of a choice. We can predict the likelihood of acceptance for a particular product based on the product category (e.g. ‘toys’), as well as based on the color of the product (e.g. ‘pink’). Of course, these two predictions may be completely different (the customer may well prefer toys, but dislike pink products) and we will have to somehow combine these two separate predictions to determine an overall likelihood of acceptance for the choice. Perhaps the simplest way to combine multiple predicted likelihoods into one is to calculate the average (or perhaps maximum or minimum) likelihood. However, this would completely forgo the fact that some facets may have a far more pronounced effect on the overall likelihood than others (e.g. customers may consider the product category more important than its color). We could opt for calculating some sort of weighted average, but this would require us to specify up front the relative importance of the different facets involved. This approach would also be unresponsive to changing consumer behavior in these preferences (e.g. product price bracket may become more important to consumers as a result of economic shifts). Preferably, we would want Oracle Real-Time Decisions to learn, act upon and tell us about, the correlations between the different facet models and the overall likelihood of acceptance. This additional level of predictive modeling, where a single supermodel (no pun intended) combines the output of several (facet based) models into a single prediction, is what we call a combined likelihood model. Facet Based Scores As an example, we have implemented three different facet based models (as described earlier) in a simple RTD inline service. These models will allow us to generate predictions for likelihood of acceptance for each product based on three different metadata fields: Category, Price Bracket and Product Color. We will use an Analytical Scores entity to store these different scores so we can easily pass them between different functions. A simple function, creatively named Compute Analytical Scores, will compute for each choice the different facet scores and return an Analytical Scores entity that is stored on the choice itself. For each score, a choice attribute referring to this entity is also added to be returned to the client to facilitate testing. One Offer To Predict Them All In order to combine the different facet based predictions into one single likelihood for each product, we will need a supermodel which can predict the likelihood of acceptance, based on the outcomes of the facet models. This model will not need to consider any of the attributes of the session, because they are already represented in the outcomes of the underlying facet models. For the same reason, the supermodel will not need to learn separately for each product, because the specific combination of facets for this product are also already represented in the output of the underlying models. In other words, instead of learning how session attributes influence acceptance of a particular product, we will learn how the outcomes of facet based models for a particular product influence acceptance at a higher level. We will therefore be using a single All Offers choice to represent all offers in our combined likelihood predictions. This choice has no attribute values configured, no scores and not a single eligibility rule; nor is it ever intended to be returned to a client. The All Offers choice is to be used exclusively by the Combined Likelihood Acceptance model to predict the likelihood of acceptance for all choices; based solely on the output of the facet based models defined earlier. The Switcheroo In Oracle Real-Time Decisions, models can only learn based on attributes stored on the session. Therefore, just before generating a combined prediction for a given choice, we will temporarily copy the facet based scores—stored on the choice earlier as an Analytical Scores entity—to the session. The code for the Predict Combined Likelihood Event function is outlined below. // set session attribute to contain facet based scores. // (this is the only input for the combined model) session().setAnalyticalScores(choice.getAnalyticalScores); // predict likelihood of acceptance for All Offers choice. CombinedLikelihoodChoice c = CombinedLikelihood.getChoice("AllOffers"); Double la = CombinedLikelihoodAcceptance.getChoiceEventLikelihoods(c, "Accepted"); // clear session attribute of facet based scores. session().setAnalyticalScores(null); // return likelihood. return la; This sleight of hand will allow the Combined Likelihood Acceptance model to predict the likelihood of acceptance for the All Offers choice using these choice specific scores. After the prediction is made, we will clear the Analytical Scores session attribute to ensure it does not pollute any of the other (facet) models. To guarantee our combined likelihood model will learn based on the facet based scores—and is not distracted by the other session attributes—we will configure the model to exclude any other inputs, save for the instance of the Analytical Scores session attribute, on the model attributes tab. Recording Events In order for the combined likelihood model to learn correctly, we must ensure that the Analytical Scores session attribute is set correctly at the moment RTD records any events related to a particular choice. We apply essentially the same switching technique as before in a Record Combined Likelihood Event function. // set session attribute to contain facet based scores // (this is the only input for the combined model). session().setAnalyticalScores(choice.getAnalyticalScores); // record input event against All Offers choice. CombinedLikelihood.getChoice("AllOffers").recordEvent(event); // force learn at this moment using the Internal Dock entry point. Application.getPredictor().learn(InternalLearn.modelArray, session(), session(), Application.currentTimeMillis()); // clear session attribute of facet based scores. session().setAnalyticalScores(null); In this example, Internal Learn is a special informant configured as the learn location for the combined likelihood model. The informant itself has no particular configuration and does nothing in itself; it is used only to force the model to learn at the exact instant we have set the Analytical Scores session attribute to the correct values. Reporting Results After running a few thousand (artificially skewed) simulated sessions on our ILS, the Decision Center reporting shows some interesting results. In this case, these results reflect perfectly the bias we ourselves had introduced in our tests. In practice, we would obviously use a wider range of customer attributes and expect to see some more unexpected outcomes. The facetted model for categories has clearly picked up on the that fact our simulated youngsters have little interest in purchasing the one red-hot vehicle our ILS had on offer. Also, it would seem that customer age is an excellent predictor for the acceptance of pink products. Looking at the key drivers for the All Offers choice we can see the relative importance of the different facets to the prediction of overall likelihood. The comparative importance of the category facet for overall prediction might, in part, be explained by the clear preference of younger customers for toys over other product types; as evident from the report on the predictiveness of customer age for offer category acceptance. Conclusion Oracle Real-Time Decisions' flexible decisioning framework allows for the construction of exceptionally elaborate prediction models that facilitate powerful targeting, but nonetheless provide insightful reporting. Although few customers will have a direct need for such a sophisticated solution architecture, it is encouraging to see that this lies within the realm of the possible with RTD; and this with limited configuration and customization required. There are obviously numerous other ways in which the predictive and reporting capabilities of Oracle Real-Time Decisions can be expanded upon to tailor to individual customers needs. We will not be able to elaborate on them all on this blog; and finding the right approach for any given problem is often more difficult than implementing the solution. Nevertheless, we hope that these last few posts have given you enough of an understanding of the power of the RTD framework and its models; so that you can take some of these ideas and improve upon your own strategy. As always, if you have any questions about the above—or any Oracle Real-Time Decisions design challenges you might face—please do not hesitate to contact us; via the comments below, social media or directly at Oracle. We are completely multi-channel and would be more than glad to help. :-)

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  • What do YOU want to see in a SharePoint jQuery Session?

    - by Mark Rackley
    Hey party people. So, as you have probably realized by now, I’ve been using quite a bit of jQuery with SharePoint. It’s pretty amazing what you can actually accomplish with a little stubbornness and some guidance from the gurus. Well, it looks like I’ll be putting together a SharePoint jQuery session that I will be presenting at a few conferences. This is such a big and broad topic I could speak on it for hours! So, I need YOUR assistance to help me narrow down what I’ll be focusing on. Some ideas I have are: How to even get started; how to set up SharePoint to work with jQuery What third party libraries exist out there that integrate well with SharePoint How to interact with default SharePoint forms and jQuery (cascading dropdowns, disabling fields, etc..) What is SPServices and how can you use it When should you NOT use jQuery What do YOU want to see though? This session is for YOU guys, not for me. Please take a moment to leave a comment below and let me know what you would like to see and learn. Thanks, and I look forward to seeing you in my sessions!! Mark

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  • NINTENDO, EDCON and ALLEGIS GROUP @ Oracle Open World 2012 Conference Session (CON9418): The Business Case for Oracle Exalogic: A Customer Perspective

    - by Sanjeev Sharma
     Are you looking to deliver breakthrough performance for packaged and custom  applications? For many front-office applications such as Oracle WebCenter Sites, Oracle Transportation Management, and Oracle’s ATG and Siebel product families,  improved  performance leads directly to greater revenue or cost savings from the business - a  compelling  proposition. For back-office applications, improved performance has tangible benefits  in terms of  footprint reductions. For all applications, Oracle Exalogic and Oracle Exadata provide an engineered solution that provides shorter time to value and lower operational costs.  Edcon is a leading clothing, footwear and textiles (CFT) retailing group in southern Africa trading through a range of retail formats. The Company has grown from opening it's first store in 1929, to ten retail brands trading in over 1000 stores in South Africa, Botswana, Namibia, Swaziland and Lesotho. Edcon's retail business has, through recent acquisitions, added top stationery and houseware brands as well as general merchandise to its CFT portfolio. Edcon was looking to consolidate their existing middleware components (Weblogic and Oracle SOA) and retail applications (Retek, Siebel and E-Business Suite) on a common platform and turned to Oracle Exalogic. With Oracle Exalogic, Edcon is able to derive significant HW CAPEX savings, improve response-time of core business applications and mitigate operating risk. Hear senior business leaders from Nintendo, Edcon and Allegis Group discuss how the business value of  leveraging Oracle Exalogic at the following conference session at Oracle Open World 2012: Session:  CON9418 - The Business Case for Oracle Exalogic: A Customer PerspectiveDate: Monday, 1 Oct, 2012Time: 1:45 pm - 2:45 pm (PST)Venue: Moscone South (306)

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  • Wednesday at Oracle OpenWorld 2012 - Must See Session: “Cloud and On-Premises Applications Integration, Using Oracle Integration Adapters”

    - by Lionel Dubreuil
    Don’t miss this “CON8642 - Cloud and On-Premises Applications Integration, Using Oracle Integration Adapters“ with Ramkumar Menon - Senior Product Manager, Oracle: Date: Wednesday, Oct 3 Time: 1:15 PM - 2:15 PM Location: Moscone South – 310 Oracle integration adapters in Oracle Fusion Middleware offer organizations a service-oriented approach to unlocking the information assets that have evolved in most IT environments. This session provides a detailed overview of their features and product architecture and an update on the 11g release. It also examines the changing application and technology landscape and how the integration adapters will continue to provide connectivity and harness information from diverse enterprise applications and technologies—both on-premises and in the cloud. Objectives for this session are to: Present an Oracle integration adapters overview Describe key use cases Provide an update on the 11g release and future roadmap Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif";}

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  • Looking to Implement/Upgrade Your MDM Solution? OOW Has the Session For You

    - by Mala Narasimharajan
    By Bala Mahalingam  Hurray!  Oracle Open World next week.  Oh my God!  I need to plan my calendar for MDM focused sessions. The implementation/upgrade of Oracle Master Data Management solution is an art & science combined. This year at Open World, we have a dedicated session focused on sharing two great implementation stories of Oracle Customer Hub. Also hear from Oracle on the implementation/upgrade approach and methodology for Oracle Master Data Management and Data Quality applications. Here are some of the questions that you might be thinking around the implementation of Oracle MDM solution. If you are in the process of implementation / upgrade or evaluating the options for implementation of MDM solution and you would like to hear directly from T-Mobile and Sony on their roadmap and implementation experience, then I would highly recommend this session.     Hope to see you at Oracle Open World 2012 and stay in touch via our future blogs. Look here for a list of all the MDM sessions at OpenWorld.

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  • How can I get a gnome environment in my VNC session?

    - by adante
    When I start VNC I have an empty desktop without the ability to manage windows or start apps etc). I'd like to have a desktop environment to be able to basic desktop things (someone asked me why I wanted this - I can't really say except that I would like my computer to be useful). My focus at the moment is basically having a working environment with as little time/effort expenditure as possible, as opposed to spending a full-time week learning the most trivial and arcane details of x, vnc, gnome or whatever passes for the current desktop architecture standard of the hour. What command or series of hoops do I have to jump to to achieve this? I have tried running gnome-session but it looks like it is attempting to run compiz and fails spectacularly. I've also tried running metacity but this simply gives me a titlebars to my windows (this is great! But I'd also like the taskbar and other stuff). I considered trying to start gnome-session in a way that it uses metacity instead of compiz. But I don't know how to do this. Tutorials on the net exist for changing to metacity - once you already have compiz running. Not so useful if compiz does not run.

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  • Thread Local Memory, Using std::string's internal buffer for c-style Scratch Memory.

    - by Hassan Syed
    I am using Protocol Buffers and OpensSSL to generate, HMACs and then CBC encrypt the two fields to obfuscate the session cookies -- similar Kerberos tokens. Protocol Buffers' API communicates with std::strings and has a buffer caching mechanism; I exploit the caching mechanism, for successive calls in the the same thread, by placing it in thread local memory; additionally the OpenSSL HMAC and EVP CTX's are also placed in the same thread local memory structure ( see this question for some detail on why I use thread local memory and the massive amount of speedup it enables even with a single thread). The generation and deserialization, "my algorithms", of these cookie strings uses intermediary void *s and std::strings and since Protocol Buffers has an internal memory retention mechanism I want these characteristics for "my algorithms". So how do I implement a common scratch memory ? I don't know much about the rdbuf(streambuf - strinbuf ??) of the std::string object. I would presumeably need to grow it to the lowest common size ever encountered during the execution of "my algorithms". Thoughts ? My question I guess would be: " is the internal buffer of a string re-usable, and if so, how ?" Edit: See comments to Vlad's answer please.

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  • Is there a better way to keep track of session variable creation/access throughout different pages?

    - by Brandon
    Here's what I am working on. At my website I have multiple processes with each one containing multiple steps. Now in one of the processes, there is an error checking routine executed before proceeding to the next step of that process. A session var is set indicating the error status and it will either redirect back to the referrer or display the next page's contents. Now this kind of functionality, I believe, is common throughout web development. The issue that is occurring is that session vars are left around and are not being cleaned up properly. At times this introduces undesired behavior. My website is growing and I find that I am requiring more and more session vars to keep track of different system and error states. So I was thinking about creating a kind of "session variable keeper" to keep track of session var usage. The idea is fairly simple. It will have the notion of a context (e.g. registration process) and allow access to a predefined set of session vars within that context. In addition, the var and context will be paired with an action to proceed to some form of event handling. So if you haven't noticed I'm new to web development. Any thoughts or comments on the idea that I am proposing would be greatly appreciated. The back-end is written in PHP/MySQL.

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  • Very Urgent :How to start a new session if a user click on the new tab in IE or mozilla on websphere

    - by ha22109
    Hi, I have one "user search" portlet on the home page of one application running on websphere portal server.Which display the matching user records as per the search criteria filled in the search form.I have requirement to have a "back to search input" link on the results page which onclick should show the filled form on the input jsp. The issue which i am facing is if i open the application in two diff tab of same IE browser and start giving some search criteria and submit and same time search for some other input from other IE tab (in the same browser)and then go back to previous tab and click on "back to search input" link then instead of showing me the first input it will show me the imput which i entered in the next IE tab. I am setting and getting the bean(form bean) through portlet session.but in the two diff tab of same IE it will be the sae user session (and may be the same portlet session..) Please tell me solution for this. The one thing to be notice here is i can access this "user search" application even without doing login also.so it must be taking the default portlet session in this case. what wil happen once i login and then search,will it going to overwrite the portlet session and http session or howz is that?

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  • Scripting Language Sessions at Oracle OpenWorld and MySQL Connect, 2012

    - by cj
    This posts highlights some great scripting language sessions coming up at the Oracle OpenWorld and MySQL Connect conferences. These events are happening in San Francisco from the end of September. You can search for other interesting conference sessions in the Content Catalog. Also check out what is happening at JavaOne in that event's Content Catalog (I haven't included sessions from it in this post.) To find the timeslots and locations of each session, click their respective link and check the "Session Schedule" box on the top right. GEN8431 - General Session: What’s New in Oracle Database Application Development This general session takes a look at what’s been new in the last year in Oracle Database application development tools using the latest generation of database technology. Topics range from Oracle SQL Developer and Oracle Application Express to Java and PHP. (Thomas Kyte - Architect, Oracle) BOF9858 - Meet the Developers of Database Access Services (OCI, ODBC, DRCP, PHP, Python) This session is your opportunity to meet in person the Oracle developers who have built Oracle Database access tools and products such as the Oracle Call Interface (OCI), Oracle C++ Call Interface (OCCI), and Open Database Connectivity (ODBC) drivers; Transparent Application Failover (TAF); Oracle Database Instant Client; Database Resident Connection Pool (DRCP); Oracle Net Services, and so on. The team also works with those who develop the PHP, Ruby, Python, and Perl adapters for Oracle Database. Come discuss with them the features you like, your pains, and new product enhancements in the latest database technology. CON8506 - Syndication and Consolidation: Oracle Database Driver for MySQL Applications This technical session presents a new Oracle Database driver that enables you to run MySQL applications (written in PHP, Perl, C, C++, and so on) against Oracle Database with almost no code change. Use cases for such a driver include application syndication such as interoperability across a relationship database management system, application migration, and database consolidation. In addition, the session covers enhancements in database technology that enable and simplify the migration of third-party databases and applications to and consolidation with Oracle Database. Attend this session to learn more and see a live demo. (Srinath Krishnaswamy - Director, Software Development, Oracle. Kuassi Mensah - Director Product Management, Oracle. Mohammad Lari - Principal Technical Staff, Oracle ) CON9167 - Current State of PHP and MySQL Together, PHP and MySQL power large parts of the Web. The developers of both technologies continue to enhance their software to ensure that developers can be satisfied despite all their changing and growing needs. This session presents an overview of changes in PHP 5.4, which was released earlier this year and shows you various new MySQL-related features available for PHP, from transparent client-side caching to direct support for scaling and high-availability needs. (Johannes Schlüter - SoftwareDeveloper, Oracle) CON8983 - Sharding with PHP and MySQL In deploying MySQL, scale-out techniques can be used to scale out reads, but for scaling out writes, other techniques have to be used. To distribute writes over a cluster, it is necessary to shard the database and store the shards on separate servers. This session provides a brief introduction to traditional MySQL scale-out techniques in preparation for a discussion on the different sharding techniques that can be used with MySQL server and how they can be implemented with PHP. You will learn about static and dynamic sharding schemes, their advantages and drawbacks, techniques for locating and moving shards, and techniques for resharding. (Mats Kindahl - Senior Principal Software Developer, Oracle) CON9268 - Developing Python Applications with MySQL Utilities and MySQL Connector/Python This session discusses MySQL Connector/Python and the MySQL Utilities component of MySQL Workbench and explains how to write MySQL applications in Python. It includes in-depth explanations of the features of MySQL Connector/Python and the MySQL Utilities library, along with example code to illustrate the concepts. Those interested in learning how to expand or build their own utilities and connector features will benefit from the tips and tricks from the experts. This session also provides an opportunity to meet directly with the engineers and provide feedback on your issues and priorities. You can learn what exists today and influence future developments. (Geert Vanderkelen - Software Developer, Oracle) BOF9141 - MySQL Utilities and MySQL Connector/Python: Python Developers, Unite! Come to this lively discussion of the MySQL Utilities component of MySQL Workbench and MySQL Connector/Python. It includes in-depth explanations of the features and dives into the code for those interested in learning how to expand or build their own utilities and connector features. This is an audience-driven session, so put on your best Python shirt and let’s talk about MySQL Utilities and MySQL Connector/Python. (Geert Vanderkelen - Software Developer, Oracle. Charles Bell - Senior Software Developer, Oracle) CON3290 - Integrating Oracle Database with a Social Network Facebook, Flickr, YouTube, Google Maps. There are many social network sites, each with their own APIs for sharing data with them. Most developers do not realize that Oracle Database has base tools for communicating with these sites, enabling all manner of information, including multimedia, to be passed back and forth between the sites. This technical presentation goes through the methods in PL/SQL for connecting to, and then sending and retrieving, all types of data between these sites. (Marcelle Kratochvil - CTO, Piction) CON3291 - Storing and Tuning Unstructured Data and Multimedia in Oracle Database Database administrators need to learn new skills and techniques when the decision is made in their organization to let Oracle Database manage its unstructured data. They will face new scalability challenges. A single row in a table can become larger than a whole database. This presentation covers the techniques a DBA needs for managing the large volume of data in a standard Oracle Database instance. (Marcelle Kratochvil - CTO, Piction) CON3292 - Using PHP, Perl, Visual Basic, Ruby, and Python for Multimedia in Oracle Database These five programming languages are just some of the most popular ones in use at the moment in the marketplace. This presentation details how you can use them to access and retrieve multimedia from Oracle Database. It covers programming techniques and methods for achieving faster development against Oracle Database. (Marcelle Kratochvil - CTO, Piction) UGF5181 - Building Real-World Oracle DBA Tools in Perl Perl is not normally associated with building mission-critical application or DBA tools. Learn why Perl could be a good choice for building your next killer DBA app. This session draws on real-world experience of building DBA tools in Perl, showing the framework and architecture needed to deal with portability, efficiency, and maintainability. Topics include Perl frameworks; Which Comprehensive Perl Archive Network (CPAN) modules are good to use; Perl and CPAN module licensing; Perl and Oracle connectivity; Compiling and deploying your app; An example of what is possible with Perl. (Arjen Visser - CEO & CTO, Dbvisit Software Limited) CON3153 - Perl: A DBA’s and Developer’s Best (Forgotten) Friend This session reintroduces Perl as a language of choice for many solutions for DBAs and developers. Discover what makes Perl so successful and why it is so versatile in our day-to-day lives. Perl can automate all those manual tasks and is truly platform-independent. Perl may not be in the limelight the way other languages are, but it is a remarkable language, it is still very current with ongoing development, and it has amazing online resources. Learn what makes Perl so great (including CPAN), get an introduction to Perl language syntax, find out what you can use Perl for, hear how Oracle uses Perl, discover the best way to learn Perl, and take away a small Perl project challenge. (Arjen Visser - CEO & CTO, Dbvisit Software Limited) CON10332 - Oracle RightNow CX Cloud Service’s Connect PHP API: Intro, What’s New, and Roadmap Connect PHP is a public API that enables developers to build solutions with the Oracle RightNow CX Cloud Service platform. This API is used primarily by developers working within the Oracle RightNow Customer Portal Cloud Service framework who are looking to gain access to data and services hosted by the Oracle RightNow CX Cloud Service platform through a backward-compatible API. Connect for PHP leverages the same data model and services as the Connect Web Services for SOAP API. Come to this session to get an introduction and learn what’s new and what’s coming up. (Mark Rhoads - Senior Principal Applications Engineer, Oracle. Mark Ericson - Sr. Principle Product Manager, Oracle) CON10330 - Oracle RightNow CX Cloud Service APIs and Frameworks Overview Oracle RightNow CX Cloud Service APIs are available in the following areas: desktop UI, Web services, customer portal, PHP, and knowledge. These frameworks provide access to Oracle RightNow CX Cloud Service’s Connect Common Object Model and custom objects. This session provides a broad overview of capabilities in all these areas. (Mark Ericson - Sr. Principle Product Manager, Oracle)

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  • Is RTD Stateless or Stateful?

    - by [email protected]
    Yes.   A stateless service is one where each request is an independent transaction that can be processed by any of the servers in a cluster.  A stateful service is one where state is kept in a server's memory from transaction to transaction, thus necessitating the proper routing of requests to the right server. The main advantage of stateless systems is simplicity of design. The main advantage of stateful systems is performance. I'm often asked whether RTD is a stateless or stateful service, so I wanted to clarify this issue in depth so that RTD's architecture will be properly understood. The short answer is: "RTD can be configured as a stateless or stateful service." The performance difference between stateless and stateful systems can be very significant, and while in a call center implementation it may be reasonable to use a pure stateless configuration, a web implementation that produces thousands of requests per second is practically impossible with a stateless configuration. RTD's performance is orders of magnitude better than most competing systems. RTD was architected from the ground up to achieve this performance. Features like automatic and dynamic compression of prediction models, automatic translation of metadata to machine code, lack of interpreted languages, and separation of model building from decisioning contribute to achieving this performance level. Because  of this focus on performance we decided to have RTD's default configuration work in a stateful manner. By being stateful RTD requests are typically handled in a few milliseconds when repeated requests come to the same session. Now, those readers that have participated in implementations of RTD know that RTD's architecture is also focused on reducing Total Cost of Ownership (TCO) with features like automatic model building, automatic time windows, automatic maintenance of database tables, automatic evaluation of data mining models, automatic management of models partitioned by channel, geography, etcetera, and hot swapping of configurations. How do you reconcile the need for a low TCO and the need for performance? How do you get the performance of a stateful system with the simplicity of a stateless system? The answer is that you make the system behave like a stateless system to the exterior, but you let it automatically take advantage of situations where being stateful is better. For example, one of the advantages of stateless systems is that you can route a message to any server in a cluster, without worrying about sending it to the same server that was handling the session in previous messages. With an RTD stateful configuration you can still route the message to any server in the cluster, so from the point of view of the configuration of other systems, it is the same as a stateless service. The difference though comes in performance, because if the message arrives to the right server, RTD can serve it without any external access to the session's state, thus tremendously reducing processing time. In typical implementations it is not rare to have high percentages of messages routed directly to the right server, while those that are not, are easily handled by forwarding the messages to the right server. This architecture usually provides the best of both worlds with performance and simplicity of configuration.   Configuring RTD as a pure stateless service A pure stateless configuration requires session data to be persisted at the end of handling each and every message and reloading that data at the beginning of handling any new message. This is of course, the root of the inefficiency of these configurations. This is also the reason why many "stateless" implementations actually do keep state to take advantage of a request coming back to the same server. Nevertheless, if the implementation requires a pure stateless decision service, this is easy to configure in RTD. The way to do it is: Mark every Integration Point to Close the session at the end of processing the message In the Session entity persist the session data on closing the session In the session entity check if a persisted version exists and load it An excellent solution for persisting the session data is Oracle Coherence, which provides a high performance, distributed cache that minimizes the performance impact of persisting and reloading the session. Alternatively, the session can be persisted to a local database. An interesting feature of the RTD stateless configuration is that it can cope with serializing concurrent requests for the same session. For example, if a web page produces two requests to the decision service, these requests could come concurrently to the decision services and be handled by different servers. Most stateless implementation would have the two requests step onto each other when saving the state, or fail one of the messages. When properly configured, RTD will make one message wait for the other before processing.   A Word on Context Using the context of a customer interaction typically significantly increases lift. For example, offer success in a call center could double if the context of the call is taken into account. For this reason, it is important to utilize the contextual information in decision making. To make the contextual information available throughout a session it needs to be persisted. When there is a well defined owner for the information then there is no problem because in case of a session restart, the information can be easily retrieved. If there is no official owner of the information, then RTD can be configured to persist this information.   Once again, RTD provides flexibility to ensure high performance when it is adequate to allow for some loss of state in the rare cases of server failure. For example, in a heavy use web site that serves 1000 pages per second the navigation history may be stored in the in memory session. In such sites it is typical that there is no OLTP that stores all the navigation events, therefore if an RTD server were to fail, it would be possible for the navigation to that point to be lost (note that a new session would be immediately established in one of the other servers). In most cases the loss of this navigation information would be acceptable as it would happen rarely. If it is desired to save this information, RTD would persist it every time the visitor navigates to a new page. Note that this practice is preferred whether RTD is configured in a stateless or stateful manner.  

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  • Generic class for performing mass-parallel queries. Feedback?

    - by Aaron
    I don't understand why, but there appears to be no mechanism in the client library for performing many queries in parallel for Windows Azure Table Storage. I've created a template class that can be used to save considerable time, and you're welcome to use it however you wish. I would appreciate however, if you could pick it apart, and provide feedback on how to improve this class. public class AsyncDataQuery<T> where T: new() { public AsyncDataQuery(bool preserve_order) { m_preserve_order = preserve_order; this.Queries = new List<CloudTableQuery<T>>(1000); } public void AddQuery(IQueryable<T> query) { var data_query = (DataServiceQuery<T>)query; var uri = data_query.RequestUri; // required this.Queries.Add(new CloudTableQuery<T>(data_query)); } /// <summary> /// Blocking but still optimized. /// </summary> public List<T> Execute() { this.BeginAsync(); return this.EndAsync(); } public void BeginAsync() { if (m_preserve_order == true) { this.Items = new List<T>(Queries.Count); for (var i = 0; i < Queries.Count; i++) { this.Items.Add(new T()); } } else { this.Items = new List<T>(Queries.Count * 2); } m_wait = new ManualResetEvent(false); for (var i = 0; i < Queries.Count; i++) { var query = Queries[i]; query.BeginExecuteSegmented(callback, i); } } public List<T> EndAsync() { m_wait.WaitOne(); return this.Items; } private List<T> Items { get; set; } private List<CloudTableQuery<T>> Queries { get; set; } private bool m_preserve_order; private ManualResetEvent m_wait; private int m_completed = 0; private void callback(IAsyncResult ar) { int i = (int)ar.AsyncState; CloudTableQuery<T> query = Queries[i]; var response = query.EndExecuteSegmented(ar); if (m_preserve_order == true) { // preserve ordering only supports one result per query this.Items[i] = response.Results.First(); } else { // add any number of items this.Items.AddRange(response.Results); } if (response.HasMoreResults == true) { // more data to pull query.BeginExecuteSegmented(response.ContinuationToken, callback, i); return; } m_completed = Interlocked.Increment(ref m_completed); if (m_completed == Queries.Count) { m_wait.Set(); } } }

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  • Objective-C memory management issue

    - by Toby Wilson
    I've created a graphing application that calls a web service. The user can zoom & move around the graph, and the program occasionally makes a decision to call the web service for more data accordingly. This is achieved by the following process: The graph has a render loop which constantly renders the graph, and some decision logic which adds web service call information to a stack. A seperate thread takes the most recent web service call information from the stack, and uses it to make the web service call. The other objects on the stack get binned. The idea of this is to reduce the number of web service calls to only those appropriate, and only one at a time. Right, with the long story out of the way (for which I apologise), here is my memory management problem: The graph has persistant (and suitably locked) NSDate* objects for the currently displayed start & end times of the graph. These are passed into the initialisers for my web service request objects. The web service call objects then retain the dates. After the web service calls have been made (or binned if they were out of date), they release the NSDate*. The graph itself releases and reallocates new NSDates* on the 'touches ended' event. If there is only one web service call object on the stack when removeAllObjects is called, EXC_BAD_ACCESS occurs in the web service call object's deallocation method when it attempts to release the date objects (even though they appear to exist and are in scope in the debugger). If, however, I comment out the release messages from the destructor, no memory leak occurs for one object on the stack being released, but memory leaks occur if there are more than one object on the stack. I have absolutely no idea what is going wrong. It doesn't make a difference what storage symantics I use for the web service call objects dates as they are assigned in the initialiser and then only read (so for correctness' sake are set to readonly). It also doesn't seem to make a difference if I retain or copy the dates in the initialiser (though anything else obviously falls out of scope or is unwantedly released elsewhere and causes a crash). I'm sorry this explanation is long winded, I hope it's sufficiently clear but I'm not gambling on that either I'm afraid. Major big thanks to anyone that can help, even suggest anything I may have missed?

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  • Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • SQLAuthority News – Great Time Spent at Great Indian Developers Summit 2014

    - by Pinal Dave
    The Great Indian Developer Conference (GIDS) is one of the most popular annual event held in Bangalore. This year GIDS is scheduled on April 22, 25. I will be presented total four sessions at this event and each session is very different from each other. Here are the details of four of my sessions, which I presented there. Pluralsight Shades This event was a great event and I had fantastic fun presenting a technology over here. I was indeed very excited that along with me, I had many of my friends presenting at the event as well. I want to thank all of you to attend my session and having standing room every single time. I have already sent resources in my newsletter. You can sign up for the newsletter over here. Indexing is an Art I was amazed with the crowd present in the sessions at GIDS. There was a great interest in the subject of SQL Server and Performance Tuning. Audience at GIDS I believe event like such provides a great platform to meet and share knowledge. Pinal at Pluralsight Booth Here are the abstract of the sessions which I had presented. They were recorded so at some point in time they will be available, but if you want the content of all the courses immediately, I suggest you check out my video courses on the same subject on Pluralsight. Indexes, the Unsung Hero Relevant Pluralsight Course Slow Running Queries are the most common problem that developers face while working with SQL Server. While it is easy to blame SQL Server for unsatisfactory performance, the issue often persists with the way queries have been written, and how Indexes has been set up. The session will focus on the ways of identifying problems that slow down SQL Server, and Indexing tricks to fix them. Developers will walk out with scripts and knowledge that can be applied to their servers, immediately post the session. Indexes are the most crucial objects of the database. They are the first stop for any DBA and Developer when it is about performance tuning. There is a good side as well evil side to indexes. To master the art of performance tuning one has to understand the fundamentals of indexes and the best practices associated with the same. We will cover various aspects of Indexing such as Duplicate Index, Redundant Index, Missing Index as well as best practices around Indexes. SQL Server Performance Troubleshooting: Ancient Problems and Modern Solutions Relevant Pluralsight Course Many believe Performance Tuning and Troubleshooting is an art which has been lost in time. However, truth is that art has evolved with time and there are more tools and techniques to overcome ancient troublesome scenarios. There are three major resources that when bottlenecked creates performance problems: CPU, IO, and Memory. In this session we will focus on High CPU scenarios detection and their resolutions. If time permits we will cover other performance related tips and tricks. At the end of this session, attendees will have a clear idea as well as action items regarding what to do when facing any of the above resource intensive scenarios. Developers will walk out with scripts and knowledge that can be applied to their servers, immediately post the session. To master the art of performance tuning one has to understand the fundamentals of performance, tuning and the best practices associated with the same. We will discuss about performance tuning in this session with the help of Demos. Pinal Dave at GIDS MySQL Performance Tuning – Unexplored Territory Relevant Pluralsight Course Performance is one of the most essential aspects of any application. Everyone wants their server to perform optimally and at the best efficiency. However, not many people talk about MySQL and Performance Tuning as it is an extremely unexplored territory. In this session, we will talk about how we can tune MySQL Performance. We will also try and cover other performance related tips and tricks. At the end of this session, attendees will not only have a clear idea, but also carry home action items regarding what to do when facing any of the above resource intensive scenarios. Developers will walk out with scripts and knowledge that can be applied to their servers, immediately post the session. To master the art of performance tuning one has to understand the fundamentals of performance, tuning and the best practices associated with the same. You will also witness some impressive performance tuning demos in this session. Hidden Secrets and Gems of SQL Server We Bet You Never Knew Relevant Pluralsight Course SQL Trio Session! It really amazes us every time when someone says SQL Server is an easy tool to handle and work with. Microsoft has done an amazing work in making working with complex relational database a breeze for developers and administrators alike. Though it looks like child’s play for some, the realities are far away from this notion. The basics and fundamentals though are simple and uniform across databases, the behavior and understanding the nuts and bolts of SQL Server is something we need to master over a period of time. With a collective experience of more than 30+ years amongst the speakers on databases, we will try to take a unique tour of various aspects of SQL Server and bring to you life lessons learnt from working with SQL Server. We will share some of the trade secrets of performance, configuration, new features, tuning, behaviors, T-SQL practices, common pitfalls, productivity tips on tools and more. This is a highly demo filled session for practical use if you are a SQL Server developer or an Administrator. The speakers will be able to stump you and give you answers on almost everything inside the Relational database called SQL Server. I personally attended the session of Vinod Kumar, Balmukund Lakhani, Abhishek Kumar and my favorite Govind Kanshi. Summary If you have missed this event here are two action items 1) Sign up for Resource Newsletter 2) Watch my video courses on Pluralsight Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Author Visit, SQLAuthority News, T SQL Tagged: GIDS

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  • Persistance JDO - How to query a property of a collection with JDOQL?

    - by Sergio del Amo
    I want to build an application where a user identified by an email address can have several application accounts. Each account can have one o more users. I am trying to use the JDO Storage capabilities with Google App Engine Java. Here is my attempt: @PersistenceCapable @Inheritance(strategy = InheritanceStrategy.NEW_TABLE) public class AppAccount { @PrimaryKey @Persistent(valueStrategy = IdGeneratorStrategy.IDENTITY) private Long id; @Persistent private String companyName; @Persistent List<Invoices> invoices = new ArrayList<Invoices>(); @Persistent List<AppUser> users = new ArrayList<AppUser>(); // Getter Setters and Other Fields } @PersistenceCapable @EmbeddedOnly public class AppUser { @Persistent private String username; @Persistent private String firstName; @Persistent private String lastName; // Getter Setters and Other Fields } When a user logs in, I want to check how many accounts does he belongs to. If he belongs to more than one he will be presented with a dashboard where he can click which account he wants to load. This is my code to retrieve a list of app accounts where he is registered. public static List<AppAccount> getUserAppAccounts(String username) { PersistenceManager pm = JdoUtil.getPm(); Query q = pm.newQuery(AppAccount.class); q.setFilter("users.username == usernameParam"); q.declareParameters("String usernameParam"); return (List<AppAccount>) q.execute(username); } But I get the next error: SELECT FROM invoices.server.AppAccount WHERE users.username == usernameParam PARAMETERS String usernameParam: Encountered a variable expression that isn't part of a join. Maybe you're referencing a non-existent field of an embedded class. org.datanucleus.store.appengine.FatalNucleusUserException: SELECT FROM com.softamo.pelicamo.invoices.server.AppAccount WHERE users.username == usernameParam PARAMETERS String usernameParam: Encountered a variable expression that isn't part of a join. Maybe you're referencing a non-existent field of an embedded class. at org.datanucleus.store.appengine.query.DatastoreQuery.getJoinClassMetaData(DatastoreQuery.java:1154) at org.datanucleus.store.appengine.query.DatastoreQuery.addLeftPrimaryExpression(DatastoreQuery.java:1066) at org.datanucleus.store.appengine.query.DatastoreQuery.addExpression(DatastoreQuery.java:846) at org.datanucleus.store.appengine.query.DatastoreQuery.addFilters(DatastoreQuery.java:807) at org.datanucleus.store.appengine.query.DatastoreQuery.performExecute(DatastoreQuery.java:226) at org.datanucleus.store.appengine.query.JDOQLQuery.performExecute(JDOQLQuery.java:85) at org.datanucleus.store.query.Query.executeQuery(Query.java:1489) at org.datanucleus.store.query.Query.executeWithArray(Query.java:1371) at org.datanucleus.jdo.JDOQuery.execute(JDOQuery.java:243) at com.softamo.pelicamo.invoices.server.Store.getUserAppAccounts(Store.java:82) at com.softamo.pelicamo.invoices.test.server.StoreTest.testgetUserAppAccounts(StoreTest.java:39) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:44) at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:15) at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:41) at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:20) at org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:28) at org.junit.internal.runners.statements.RunAfters.evaluate(RunAfters.java:31) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:76) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:50) at org.junit.runners.ParentRunner$3.run(ParentRunner.java:193) at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:52) at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:191) at org.junit.runners.ParentRunner.access$000(ParentRunner.java:42) at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:184) at org.junit.runners.ParentRunner.run(ParentRunner.java:236) at org.eclipse.jdt.internal.junit4.runner.JUnit4TestReference.run(JUnit4TestReference.java:46) at org.eclipse.jdt.internal.junit.runner.TestExecution.run(TestExecution.java:38) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:467) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:683) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.run(RemoteTestRunner.java:390) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.main(RemoteTestRunner.java:197) Any idea? I am getting JDO persistance totally wrong?

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  • Fast distributed filesystem for a large amounts of data with metadata in database

    - by undefined hero
    My project uses several processing machines and one storage machine. Currently storage organized with a MSSQL filetable shared folder. Every file in storage have some metadata in database. Processing machines executes tasks for which they needed files from storage and their metadata. After completing task, processing machine puts resulting data back in storage. From there its taken by another processing machine, which also generates some file and put it back in storage. And etc. Everything was fine, but as number of processing machines increases, I found myself bottlenecked myself with storage machines hard drive performance. So I want processing machines to put files in distributed FS. to lift load from storage machines, from which they can take data from each other, not only storage machine. Can You suggest a particular distributed FS which meets my needs? Or there is another way to solve this problem, without it? Amounts of data in FS in one time are like several terabytes. (storage can handle this, but processors cannot). Data consistence is critical. Read write policy is: once file is written - its constant and may be only removed, but not modified. My current platform is Windows, but I'm ready to switch it, if there is a substantially more convenient solution on another one.

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