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  • Java Thread - Memory consistency errors

    - by Yatendra Goel
    I was reading a Sun's tutorial on Concurrency. But I couldn't understand exactly what memory consistency errors are? I googled about that but didn't find any helpful tutorial or article about that. I know that this question is a subjective one, so you can provide me links to articles on the above topic. It would be great if you explain it with a simple example.

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  • Update thousands of records in a DataSet to SQL Server

    - by MSIL
    I have half a million records in a data set of which 50,000 are updated. Now I need to commit the updated records back to the SQL Server 2005 Database. What is the best and efficient way to do this considering the fact that such updates could be frequent (though concurrency is not an issue but performance is)

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  • BSoD when creating threads

    - by Behrooz
    I am trying to create +5 threads synchronously so there shouldn't be any concurrency error. Code: System.Threading.Thread t = new System.Threading.Thread(proc); t.Start();//==t.BlueScreen(); t.Join(); Is darkness a feature ? I am doing something wrong? OS:Microsoft windows vista(unfortunately) x64 Language:C# 3.0|4.0 .Net version:3.5|4

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  • ConcurrenctBag(Of MyType) Vs List(Of MyType)

    - by Ben
    What is the advantage of using a ConcurrentBag(Of MyType) against just using a List(Of MyType)? The MSDN page on the CB (http://msdn.microsoft.com/en-us/library/dd381779(v=VS.100).aspx) states that ConcurrentBag(Of T) is a thread-safe bag implementation, optimized for scenarios where the same thread will be both producing and consuming data stored in the bag So what is the advantage? I can understand the advantage of the other collection types in the Concurrency namespace, but this one puzzled me. Thanks.

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  • How solve consumer/producer task using semaphores

    - by user1074896
    I have SimpleProducerConsumer class that illustrate consumer/producer problem (I am not sure that it's correct). public class SimpleProducerConsumer { private Stack<Object> stack = new Stack<Object>(); private static final int STACK_MAX_SIZE = 10; public static void main(String[] args) { SimpleProducerConsumer pc = new SimpleProducerConsumer(); new Thread(pc.new Producer(), "p1").start(); new Thread(pc.new Producer(), "p2").start(); new Thread(pc.new Consumer(), "c1").start(); new Thread(pc.new Consumer(), "c2").start(); new Thread(pc.new Consumer(), "c3").start(); } public synchronized void push(Object d) { while (stack.size() >= STACK_MAX_SIZE) try { wait(); } catch (InterruptedException e) { e.printStackTrace(); } try { Thread.sleep(1000); } catch (InterruptedException e) { e.printStackTrace(); } stack.push(new Object()); System.out.println("push " + Thread.currentThread().getName() + " " + stack.size()); notify(); } public synchronized Object pop() { while (stack.size() == 0) try { wait(); } catch (InterruptedException e) { e.printStackTrace(); } try { Thread.sleep(50); } catch (InterruptedException e) { e.printStackTrace(); } stack.pop(); System.out.println("pop " + Thread.currentThread().getName() + " " + stack.size()); notify(); return null; } class Consumer implements Runnable { @Override public void run() { while (true) { pop(); } } } class Producer implements Runnable { @Override public void run() { while (true) { push(new Object()); } } } } I found simple realization of semaphore(here:http://docs.oracle.com/javase/tutorial/essential/concurrency/guardmeth.html I know that there is concurrency package) How I need to change code to exchange java objects monitors to my custom semaphore. (To illustrate C/P problem using semaphores) Semaphore: class Semaphore { private int counter; public Semaphore() { this(0); } public Semaphore(int i) { if (i < 0) throw new IllegalArgumentException(i + " < 0"); counter = i; } public synchronized void release() { if (counter == 0) { notify(); } counter++; } public synchronized void acquire() throws InterruptedException { while (counter == 0) { wait(); } counter--; } }

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  • App_offline.htm, CSS, images, and aspnet_isapi.dll

    - by LookitsPuck
    Hey all! So, the site I'm working on is using urlrewriting in coordination with aspnet_isapi.dll (everything is mapped to it). I put up my app_offline.htm file, and all the text shows, however, the CSS or images aren't being served. I'm guessing they're being processed by ASP.NET due to the wildcard mapping instead of IIS. Is this correct? If so, how can I allow IIS to serve these files? Furthermore, an issue I can see arising..in the web.config for the rewriter settings: <rewrite url="^~/images/network/(.*)/(.*).jpg$" to="~/services/ImageHandler.ashx?type=$1&amp;id=$2"/> <rewrite url="^~/image/view/(.*).jpg$" to="~/ServePRView.aspx?id=$1"/> <rewrite url="^~/asset/view/(.*).jpg$" to="~/services/ImageHandler.ashx?id=$1&amp;type=asset"/> Thanks for the help all, -Steve

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  • Spring MVC annotation config problem

    - by Seth
    I'm trying to improve my spring mvc configuration so as to not require a new config file for every servlet I add, but I'm running into problems. I've tried using this tutorial as a starting point, but I'm running into an issue that I can't figure out. The problem is that when I do a GET to my servlet, I get back a 404 error. Here's my config and a representative java snippet from a Controller: web.xml: <?xml version="1.0" encoding="UTF-8"?> <web-app version="2.5" xmlns="http://java.sun.com/xml/ns/javaee" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://java.sun.com/xml/ns/javaee http://java.sun.com/xml/ns/javaee/web-app_2_5.xsd"> <display-name>SightLogix Coordination System</display-name> <description>SightLogix Coordination System</description> <servlet> <servlet-name>Spring MVC Dispatcher Servlet</servlet-name> <servlet-class>org.springframework.web.servlet.DispatcherServlet</servlet-class> <init-param> <param-name>contextConfigLocation</param-name> <param-value> /WEB-INF/application-context.xml /WEB-INF/application-security.xml </param-value> </init-param> <load-on-startup>1</load-on-startup> </servlet> <servlet-mapping> <servlet-name>Spring MVC Dispatcher Servlet</servlet-name> <url-pattern>/slcs/*</url-pattern> </servlet-mapping> <context-param> <param-name>contextConfigLocation</param-name> <param-value> /WEB-INF/application-context.xml /WEB-INF/application-security.xml </param-value> </context-param> <listener> <listener-class> org.springframework.web.context.ContextLoaderListener </listener-class> </listener> <filter> <filter-name>springSecurityFilterChain</filter-name> <filter-class>org.springframework.web.filter.DelegatingFilterProxy</filter-class> </filter> <filter-mapping> <filter-name>springSecurityFilterChain</filter-name> <url-pattern>/*</url-pattern> </filter-mapping> </web-app> application-context.xml: <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:mvc="http://www.springframework.org/schema/mvc" xmlns:context="http://www.springframework.org/schema/context" xsi:schemaLocation=" http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-3.0.xsd http://www.springframework.org/schema/mvc http://www.springframework.org/schema/mvc/spring-mvc-3.0.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context-3.0.xsd" default-init-method="init" default-destroy-method="destroy"> <mvc:annotation-driven /> <context:component-scan base-package="top.level" /> </beans> application-security.xml: <beans:beans xmlns="http://www.springframework.org/schema/security" xmlns:beans="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-3.0.xsd http://www.springframework.org/schema/security http://www.springframework.org/schema/security/spring-security-3.0.xsd"> <http> <intercept-url pattern="/**" access="ROLE_MANAGER" requires-channel="https" /> <http-basic /> </http> <authentication-manager> <authentication-provider user-service-ref="myUserDetailsService"> <password-encoder hash="sha"/> </authentication-provider> </authentication-manager> <beans:bean id="myUserDetailsService" class="path.to.my.UserDetailsServiceImpl"> </beans:bean> </beans:beans> Snippet of a Controller class (one of many, but they all look essentially like this): @Controller @RequestMapping("/foo.xml") public class FooController { @RequestMapping(method=RequestMethod.GET) public void handleGET(HttpServletRequest request, HttpServletResponse response) throws IOException { ... Can anyone tell me what I'm doing incorrectly? Thanks!

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  • ZooKeeper and RabbitMQ/Qpid together - overkill or a good combination?

    - by Chris Sears
    Greetings, I'm evaluating some components for a multi-data center distributed system. We're going to be using message queues (via either RabbitMQ or Qpid) so agents can make asynchronous requests to other agents without worrying about addressing, routing, load balancing or retransmission. In many cases, the agents will be interacting with components that were not designed for highly concurrent access, so locking and cross-agent coordination will be needed to avoid race conditions. Also, we'd like the system to automatically respond to agent or data center failures. With the above use cases in mind, ZooKeeper seemed like it might be a good fit. But I'm wondering if trying to use both ZK and message queuing is overkill. It seems like what Zookeeper does could be accomplished by my own cluster manager using AMQP messaging, but that would be hard to get really right. On the other hand, I've seen some examples where ZooKeeper was used to implement message queuing, but I think RabbitMQ/Qpid are a more natural fit for that. Has anyone out there used a combination like this? Thanks in advance, -Chris

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  • What skills are needed to be successful at robotics?

    - by Click Upvote
    I'm considering studying to be a robotics engineer. The desire to work in that field is definitely there.. but I'm wondering what my chances of success would be. Generally I'm good with mental tasks of any sort such as programming, maths, physics, etc, and I think i'd be able to do electrical work such as soldering wires, changing batteries, etc. However, I'm not so good with things of a physical nature where I have to use my hands. For example, if you give me some vegetables to slice and dice, all of them will be diced un-evenly and some will look ugly. This might be because I have poor mind-hand coordination or because of a lack of concentration. Thoughts?

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  • design question for transportation agency/workflow system

    - by George2
    Hello everyone, I am designing a transportation agency/workflow system, and it including 3 types of people, customer who requests to transport some stuff, drivers who deliver the stuff, and truck manager who manages transport source/destination truck coordination and communicates/organizes drivers. The system is expected to be a web site, and 3 kinds of people could use the web site to submit request, accept request, monitor status of specific stuff transportation, etc. The web site is more like an open agency or a workflow system. I am wondering whether there are any existing technologies, tools or projects (better to be open source, but not a must) which I could build my application faster based on? I prefer to use .Net technologies, but not a must. thanks in advance, George

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  • Cocoa: how to implement a custom NSView with an editable text area?

    - by Rinzwind
    What's the minimum implementation needed to make a custom NSView with an editable text area? I assume NSTextFieldCell can be used for this. I've succeeded in drawing the cell in the view (which is straightforward), but making it editable seems to require a more complicated coordination between the view and the cell. Is there sample code available somewhere? Update. I should have made clear that my longer-term goal is to have many more editable text areas on the same view. AFAIU it is better to use cells in that case as they are more light-weight than full-blown views. My updated question is: What's the minimum implementation needed to make a custom NSView with an editable text area using an appropriate NSCell?

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  • Atoms and references

    - by StackedCrooked
    According to the book Programming Clojure refs manage coordinated, synchronous changes to shared state and atoms manage uncoordinated, synchronous changes to shared state. If I understood correctly "coordinated" implies multiple changes are encapsulated as one atomic operation. If that is the case then it seems to me that coordination only requires using a dosync call. For example what is the difference between: (def i (atom 0)) (def j (atom 0)) (dosync (swap! i inc) (swap! j dec)) and: (def i (ref 0)) (def j (ref 0)) (dosync (alter i inc) (alter j dec))

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  • Is Microsoft CCR gaining any traction?

    - by spender
    Microsoft's Concurrency and Coordination Runtime quite literally saved a project that was running into major issues with deadlocking. Since then I find that I use it more and more frequently for almost anything that requires asynchronous coding producing results that run lighter and faster than before. I can honestly state that it has transformed the way I think about multithread/multicore dev. Despite my personal love for CCR, there appears to be very little buzz surrounding it on the web and was wondering if anyone can offer any reason for this. Are there better alternatives, or is it a lack of promotion from MS, or are people simply happy with the existing tools?

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  • how to guarantee atomicity across two databases (the filesystem and your RDBMS)?

    - by Lock up
    i am working on a online file management project.In which we are storing references on the database(sql server) and files data on the on file system;.In which we are facing a problem of coordination between file system and database while we are uploading a file and also in case of deleting a file that first we create a reference in the data base or store files on file system;;the problem is that if create a reference in the database first and then storing a file on file system.bur while storing files on the file system any type of error occur.then reference for that file is created in the database but no file data on the file system;; please give me some solution how to deal with such situation;;i am badly in need of it;; and reason for that?

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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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  • Michael Crump&rsquo;s notes for 70-563 PRO &ndash; Designing and Developing Windows Applications usi

    - by mbcrump
    TIME TO GO PRO! This is my notes for 70-563 PRO – Designing and Developing Windows Applications using .NET Framework 3.5 I created it using several resources (various certification web sites, msdn, official ms 70-548 book). The reason that I created this review is because a) I am taking the exam. b) MS did not create a book for this exam. Use the(MS 70-548)book. c) To make sure I am familiar with each before the exam. I hope that it provides a good start for your own notes. I hope that someone finds this useful. At least, it will give you a starting point of what to expect to know on the PRO exam. Also, for those wondering, the PRO exam does contains very little code. It is basically all theory. 1. Validation Controls – How to prevent users from entering invalid data on forms. (MaskedTextBox control and RegEx) 2. ServiceController – used to start and control the behavior of existing services. 3. User Feedback (know winforms Status Bar, Tool Tips, Color, Error Provider, Context-Sensitive and Accessibility) 4. Specific (derived) exceptions must be handled before general (base class) exceptions. By moving the exception handling for the base type Exception to after exception handling of ArgumentNullException, all ArgumentNullException thrown by the Helper method will be caught and logged correctly. 5. A heartbeat method is a method exposed by a Web service that allows external applications to check on the status of the service. 6. New users must master key tasks quickly. Giving these tasks context and appropriate detail will help. However, advanced users will demand quicker paths. Shortcuts, accelerators, or toolbar buttons will speed things along for the advanced user. 7. MSBuild uses project files to instruct the build engine what to build and how to build it. MSBuild project files are XML files that adhere to the MSBuild XML schema. The MSBuild project files contain complete file, build action, and dependency information for each individual projects. 8. Evaluating whether or not to fix a bug involves a triage process. You must identify the bug's impact, set the priority, categorize it, and assign a developer. Many times the person doing the triage work will assign the bug to a developer for further investigation. In fact, the workflow for the bug work item inside of Team System supports this step. Developers are often asked to assess the impact of a given bug. This assessment helps the person doing the triage make a decision on how to proceed. When assessing the impact of a bug, you should consider time and resources to fix it, bug risk, and impacts of the bug. 9. In large projects it is generally impossible and unfeasible to fix all bugs because of the impact on schedule and budget. 10. Code reviews should be conducted by a technical lead or a technical peer. 11. Testing Applications 12. WCF Services – application state 13. SQL Server 2005 / 2008 Express Edition – reliable storage of data / Microsoft SQL Server 3.5 Compact Database– used for client computers to retrieve and save data from a shared location. 14. SQL Server 2008 Compact Edition – used for minimum possible memory and can synchronize data with a corporate SQL Server 2008 Database. Supports offline user and minimum dependency on external components. 15. MDI and SDI Forms (specifically IsMDIContainer) 16. GUID – in the case of data warehousing, it is important to define unique keys. 17. Encrypting / Security Data 18. Understanding of Isolated Storage/Proper location to store items 19. LINQ to SQL 20. Multithreaded access 21. ADO.NET Entity Framework model 22. Marshal.ReleaseComObject 23. Common User Interface Layout (ComboBox, ListBox, Listview, MaskedTextBox, TextBox, RichTextBox, SplitContainer, TableLayoutPanel, TabControl) 24. DataSets Class - http://msdn.microsoft.com/en-us/library/system.data.dataset%28VS.71%29.aspx 25. SQL Server 2008 Reporting Services (SSRS) 26. SystemIcons.Shield (Vista UAC) 27. Leverging stored procedures to perform data manipulation for a database schema that can change. 28. DataContext 29. Microsoft Windows Installer Packages, ClickOnce(bootstrapping features), XCopy. 30. Client Application Services – will authenticate users by using the same data source as a ASP.NET web application. 31. SQL Server 2008 Caching 32. StringBuilder 33. Accessibility Guidelines for Windows Applications http://msdn.microsoft.com/en-us/library/ms228004.aspx 34. Logging erros 35. Testing performance related issues. 36. Role Based Security, GenericIdentity and GenericPrincipal 37. System.Net.CookieContainer will store session data for webapps (see isolated storage for winforms) 38. .NET CLR Profiler tool will identify objects that cause performance issues. 39. ADO.NET Synchronization (SyncGroup) 40. Globalization - CultureInfo 41. IDisposable Interface- reports on several questions relating to this. 42. Adding timestamps to determine whether data has changed or not. 43. Converting applications to .NET Framework 3.5 44. MicrosoftReportViewer 45. Composite Controls 46. Windows Vista KNOWN folders. 47. Microsoft Sync Framework 48. TypeConverter -Provides a unified way of converting types of values to other types, as well as for accessing standard values and sub properties. http://msdn.microsoft.com/en-us/library/system.componentmodel.typeconverter.aspx 49. Concurrency control mechanisms The main categories of concurrency control mechanisms are: Optimistic - Delay the checking of whether a transaction meets the isolation rules (e.g., serializability and recoverability) until its end, without blocking any of its (read, write) operations, and then abort a transaction, if the desired rules are violated. Pessimistic - Block operations of a transaction, if they may cause violation of the rules. Semi-optimistic - Block operations in some situations, and do not block in other situations, while delaying rules checking to transaction's end, as done with optimistic. 50. AutoResetEvent 51. Microsoft Messaging Queue (MSMQ) 4.0 52. Bulk imports 53. KeyDown event of controls 54. WPF UI components 55. UI process layer 56. GAC (installing, removing and queuing) 57. Use a local database cache to reduce the network bandwidth used by applications. 58. Sound can easily be annoying and distracting to users, so use it judiciously. Always give users the option to turn sound off. Because a user might have sound off, never convey important information through sound alone.

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  • ODI 12c - Parallel Table Load

    - by David Allan
    In this post we will look at the ODI 12c capability of parallel table load from the aspect of the mapping developer and the knowledge module developer - two quite different viewpoints. This is about parallel table loading which isn't to be confused with loading multiple targets per se. It supports the ability for ODI mappings to be executed concurrently especially if there is an overlap of the datastores that they access, so any temporary resources created may be uniquely constructed by ODI. Temporary objects can be anything basically - common examples are staging tables, indexes, views, directories - anything in the ETL to help the data integration flow do its job. In ODI 11g users found a few workarounds (such as changing the technology prefixes - see here) to build unique temporary names but it was more of a challenge in error cases. ODI 12c mappings by default operate exactly as they did in ODI 11g with respect to these temporary names (this is also true for upgraded interfaces and scenarios) but can be configured to support the uniqueness capabilities. We will look at this feature from two aspects; that of a mapping developer and that of a developer (of procedures or KMs). 1. Firstly as a Mapping Developer..... 1.1 Control when uniqueness is enabled A new property is available to set unique name generation on/off. When unique names have been enabled for a mapping, all temporary names used by the collection and integration objects will be generated using unique names. This property is presented as a check-box in the Property Inspector for a deployment specification. 1.2 Handle cleanup after successful execution Provided that all temporary objects that are created have a corresponding drop statement then all of the temporary objects should be removed during a successful execution. This should be the case with the KMs developed by Oracle. 1.3 Handle cleanup after unsuccessful execution If an execution failed in ODI 11g then temporary tables would have been left around and cleaned up in the subsequent run. In ODI 12c, KM tasks can now have a cleanup-type task which is executed even after a failure in the main tasks. These cleanup tasks will be executed even on failure if the property 'Remove Temporary Objects on Error' is set. If the agent was to crash and not be able to execute this task, then there is an ODI tool (OdiRemoveTemporaryObjects here) you can invoke to cleanup the tables - it supports date ranges and the like. That's all there is to it from the aspect of the mapping developer it's much, much simpler and straightforward. You can now execute the same mapping concurrently or execute many mappings using the same resource concurrently without worrying about conflict.  2. Secondly as a Procedure or KM Developer..... In the ODI Operator the executed code shows the actual name that is generated - you can also see the runtime code prior to execution (introduced in 11.1.1.7), for example below in the code type I selected 'Pre-executed Code' this lets you see the code about to be processed and you can also see the executed code (which is the default view). References to the collection (C$) and integration (I$) names will be automatically made unique by using the odiRef APIs - these objects will have unique names whenever concurrency has been enabled for a particular mapping deployment specification. It's also possible to use name uniqueness functions in procedures and your own KMs. 2.1 New uniqueness tags  You can also make your own temporary objects have unique names by explicitly including either %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG in the name passed to calls to the odiRef APIs. Such names would always include the unique tag regardless of the concurrency setting. To illustrate, let's look at the getObjectName() method. At <% expansion time, this API will append %UNIQUE_STEP_TAG to the object name for collection and integration tables. The name parameter passed to this API may contain  %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG. This API always generates to the <? version of getObjectName() At execution time this API will replace the unique tag macros with a string that is unique to the current execution scope. The returned name will conform to the name-length restriction for the target technology, and its pattern for the unique tag. Any necessary truncation will be performed against the initial name for the object and any other fixed text that may have been specified. Examples are:- <?=odiRef.getObjectName("L", "%COL_PRFEMP%UNIQUE_STEP_TAG", "D")?> SCOTT.C$_EABH7QI1BR1EQI3M76PG9SIMBQQ <?=odiRef.getObjectName("L", "EMP%UNIQUE_STEP_TAG_AE", "D")?> SCOTT.EMPAO96Q2JEKO0FTHQP77TMSAIOSR_ Methods which have this kind of support include getFrom, getTableName, getTable, getObjectShortName and getTemporaryIndex. There are APIs for retrieving this tag info also, the getInfo API has been extended with the following properties (the UNIQUE* properties can also be used in ODI procedures); UNIQUE_STEP_TAG - Returns the unique value for the current step scope, e.g. 5rvmd8hOIy7OU2o1FhsF61 Note that this will be a different value for each loop-iteration when the step is in a loop. UNIQUE_SESSION_TAG - Returns the unique value for the current session scope, e.g. 6N38vXLrgjwUwT5MseHHY9 IS_CONCURRENT - Returns info about the current mapping, will return 0 or 1 (only in % phase) GUID_SRC_SET - Returns the UUID for the current source set/execution unit (only in % phase) The getPop API has been extended with the IS_CONCURRENT property which returns info about an mapping, will return 0 or 1.  2.2 Additional APIs Some new APIs are provided including getFormattedName which will allow KM developers to construct a name from fixed-text or ODI symbols that can be optionally truncate to a max length and use a specific encoding for the unique tag. It has syntax getFormattedName(String pName[, String pTechnologyCode]) This API is available at both the % and the ? phase.  The format string can contain the ODI prefixes that are available for getObjectName(), e.g. %INT_PRF, %COL_PRF, %ERR_PRF, %IDX_PRF alongwith %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG. The latter tags will be expanded into a unique string according to the specified technology. Calls to this API within the same execution context are guaranteed to return the same unique name provided that the same parameters are passed to the call. e.g. <%=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG_AE", "ORACLE")%> <?=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG_AE", "ORACLE")?> C$_MY_TAB7wDiBe80vBog1auacS1xB_AE <?=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG.log", "FILE")?> C2_MY_TAB7wDiBe80vBog1auacS1xB.log 2.3 Name length generation  As part of name generation, the length of the generated name will be compared with the maximum length for the target technology and truncation may need to be applied. When a unique tag is included in the generated string it is important that uniqueness is not compromised by truncation of the unique tag. When a unique tag is NOT part of the generated name, the name will be truncated by removing characters from the end - this is the existing 11g algorithm. When a unique tag is included, the algorithm will first truncate the <postfix> and if necessary  the <prefix>. It is recommended that users will ensure there is sufficient uniqueness in the <prefix> section to ensure uniqueness of the final resultant name. SUMMARY To summarize, ODI 12c make it much simpler to utilize mappings in concurrent cases and provides APIs for helping developing any procedures or custom knowledge modules in such a way they can be used in highly concurrent, parallel scenarios. 

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  • SQL SERVER – Introduction to SQL Server 2014 In-Memory OLTP

    - by Pinal Dave
    In SQL Server 2014 Microsoft has introduced a new database engine component called In-Memory OLTP aka project “Hekaton” which is fully integrated into the SQL Server Database Engine. It is optimized for OLTP workloads accessing memory resident data. In-memory OLTP helps us create memory optimized tables which in turn offer significant performance improvement for our typical OLTP workload. The main objective of memory optimized table is to ensure that highly transactional tables could live in memory and remain in memory forever without even losing out a single record. The most significant part is that it still supports majority of our Transact-SQL statement. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. This engine is designed to ensure higher concurrency and minimal blocking. In-Memory OLTP alleviates the issue of locking, using a new type of multi-version optimistic concurrency control. It also substantially reduces waiting for log writes by generating far less log data and needing fewer log writes. Points to remember Memory-optimized tables refer to tables using the new data structures and key words added as part of In-Memory OLTP. Disk-based tables refer to your normal tables which we used to create in SQL Server since its inception. These tables use a fixed size 8 KB pages that need to be read from and written to disk as a unit. Natively compiled stored procedures refer to an object Type which is new and is supported by in-memory OLTP engine which convert it into machine code, which can further improve the data access performance for memory –optimized tables. Natively compiled stored procedures can only reference memory-optimized tables, they can’t be used to reference any disk –based table. Interpreted Transact-SQL stored procedures, which is what SQL Server has always used. Cross-container transactions refer to transactions that reference both memory-optimized tables and disk-based tables. Interop refers to interpreted Transact-SQL that references memory-optimized tables. Using In-Memory OLTP In-Memory OLTP engine has been available as part of SQL Server 2014 since June 2013 CTPs. Installation of In-Memory OLTP is part of the SQL Server setup application. The In-Memory OLTP components can only be installed with a 64-bit edition of SQL Server 2014 hence they are not available with 32-bit editions. Creating Databases Any database that will store memory-optimized tables must have a MEMORY_OPTIMIZED_DATA filegroup. This filegroup is specifically designed to store the checkpoint files needed by SQL Server to recover the memory-optimized tables, and although the syntax for creating the filegroup is almost the same as for creating a regular filestream filegroup, it must also specify the option CONTAINS MEMORY_OPTIMIZED_DATA. Here is an example of a CREATE DATABASE statement for a database that can support memory-optimized tables: CREATE DATABASE InMemoryDB ON PRIMARY(NAME = [InMemoryDB_data], FILENAME = 'D:\data\InMemoryDB_data.mdf', size=500MB), FILEGROUP [SampleDB_mod_fg] CONTAINS MEMORY_OPTIMIZED_DATA (NAME = [InMemoryDB_mod_dir], FILENAME = 'S:\data\InMemoryDB_mod_dir'), (NAME = [InMemoryDB_mod_dir], FILENAME = 'R:\data\InMemoryDB_mod_dir') LOG ON (name = [SampleDB_log], Filename='L:\log\InMemoryDB_log.ldf', size=500MB) COLLATE Latin1_General_100_BIN2; Above example code creates files on three different drives (D:  S: and R:) for the data files and in memory storage so if you would like to run this code kindly change the drive and folder locations as per your convenience. Also notice that binary collation was specified as Windows (non-SQL). BIN2 collation is the only collation support at this point for any indexes on memory optimized tables. It is also possible to add a MEMORY_OPTIMIZED_DATA file group to an existing database, use the below command to achieve the same. ALTER DATABASE AdventureWorks2012 ADD FILEGROUP hekaton_mod CONTAINS MEMORY_OPTIMIZED_DATA; GO ALTER DATABASE AdventureWorks2012 ADD FILE (NAME='hekaton_mod', FILENAME='S:\data\hekaton_mod') TO FILEGROUP hekaton_mod; GO Creating Tables There is no major syntactical difference between creating a disk based table or a memory –optimized table but yes there are a few restrictions and a few new essential extensions. Essentially any memory-optimized table should use the MEMORY_OPTIMIZED = ON clause as shown in the Create Table query example. DURABILITY clause (SCHEMA_AND_DATA or SCHEMA_ONLY) Memory-optimized table should always be defined with a DURABILITY value which can be either SCHEMA_AND_DATA or  SCHEMA_ONLY the former being the default. A memory-optimized table defined with DURABILITY=SCHEMA_ONLY will not persist the data to disk which means the data durability is compromised whereas DURABILITY= SCHEMA_AND_DATA ensures that data is also persisted along with the schema. Indexing Memory Optimized Table A memory-optimized table must always have an index for all tables created with DURABILITY= SCHEMA_AND_DATA and this can be achieved by declaring a PRIMARY KEY Constraint at the time of creating a table. The following example shows a PRIMARY KEY index created as a HASH index, for which a bucket count must also be specified. CREATE TABLE Mem_Table ( [Name] VARCHAR(32) NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 100000), [City] VARCHAR(32) NULL, [State_Province] VARCHAR(32) NULL, [LastModified] DATETIME NOT NULL, ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); Now as you can see in the above query example we have used the clause MEMORY_OPTIMIZED = ON to make sure that it is considered as a memory optimized table and not just a normal table and also used the DURABILITY Clause= SCHEMA_AND_DATA which means it will persist data along with metadata and also you can notice this table has a PRIMARY KEY mentioned upfront which is also a mandatory clause for memory-optimized tables. We will talk more about HASH Indexes and BUCKET_COUNT in later articles on this topic which will be focusing more on Row and Index storage on Memory-Optimized tables. So stay tuned for that as well. Now as we covered the basics of Memory Optimized tables and understood the key things to remember while using memory optimized tables, let’s explore more using examples to understand the Performance gains using memory-optimized tables. I will be using the database which i created earlier in this article i.e. InMemoryDB in the below Demo Exercise. USE InMemoryDB GO -- Creating a disk based table CREATE TABLE dbo.Disktable ( Id INT IDENTITY, Name CHAR(40) ) GO CREATE NONCLUSTERED INDEX IX_ID ON dbo.Disktable (Id) GO -- Creating a memory optimized table with similar structure and DURABILITY = SCHEMA_AND_DATA CREATE TABLE dbo.Memorytable_durable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO -- Creating an another memory optimized table with similar structure but DURABILITY = SCHEMA_Only CREATE TABLE dbo.Memorytable_nondurable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_only) GO -- Now insert 100000 records in dbo.Disktable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Disktable(Name) VALUES('sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Do the same inserts for Memory table dbo.Memorytable_durable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_durable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Now finally do the same inserts for Memory table dbo.Memorytable_nondurable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_nondurable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END The above 3 Inserts took 1.20 minutes, 54 secs, and 2 secs respectively to insert 100000 records on my machine with 8 Gb RAM. This proves the point that memory-optimized tables can definitely help businesses achieve better performance for their highly transactional business table and memory- optimized tables with Durability SCHEMA_ONLY is even faster as it does not bother persisting its data to disk which makes it supremely fast. Koenig Solutions is one of the few organizations which offer IT training on SQL Server 2014 and all its updates. Now, I leave the decision on using memory_Optimized tables on you, I hope you like this article and it helped you understand  the fundamentals of IN-Memory OLTP . Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Koenig

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  • Scheduling thread tiles with C++ AMP

    - by Daniel Moth
    This post assumes you are totally comfortable with, what some of us call, the simple model of C++ AMP, i.e. you could write your own matrix multiplication. We are now ready to explore the tiled model, which builds on top of the non-tiled one. Tiling the extent We know that when we pass a grid (which is just an extent under the covers) to the parallel_for_each call, it determines the number of threads to schedule and their index values (including dimensionality). For the single-, two-, and three- dimensional cases you can go a step further and subdivide the threads into what we call tiles of threads (others may call them thread groups). So here is a single-dimensional example: extent<1> e(20); // 20 units in a single dimension with indices from 0-19 grid<1> g(e);      // same as extent tiled_grid<4> tg = g.tile<4>(); …on the 3rd line we subdivided the single-dimensional space into 5 single-dimensional tiles each having 4 elements, and we captured that result in a concurrency::tiled_grid (a new class in amp.h). Let's move on swiftly to another example, in pictures, this time 2-dimensional: So we start on the left with a grid of a 2-dimensional extent which has 8*6=48 threads. We then have two different examples of tiling. In the first case, in the middle, we subdivide the 48 threads into tiles where each has 4*3=12 threads, hence we have 2*2=4 tiles. In the second example, on the right, we subdivide the original input into tiles where each has 2*2=4 threads, hence we have 4*3=12 tiles. Notice how you can play with the tile size and achieve different number of tiles. The numbers you pick must be such that the original total number of threads (in our example 48), remains the same, and every tile must have the same size. Of course, you still have no clue why you would do that, but stick with me. First, we should see how we can use this tiled_grid, since the parallel_for_each function that we know expects a grid. Tiled parallel_for_each and tiled_index It turns out that we have additional overloads of parallel_for_each that accept a tiled_grid instead of a grid. However, those overloads, also expect that the lambda you pass in accepts a concurrency::tiled_index (new in amp.h), not an index<N>. So how is a tiled_index different to an index? A tiled_index object, can have only 1 or 2 or 3 dimensions (matching exactly the tiled_grid), and consists of 4 index objects that are accessible via properties: global, local, tile_origin, and tile. The global index is the same as the index we know and love: the global thread ID. The local index is the local thread ID within the tile. The tile_origin index returns the global index of the thread that is at position 0,0 of this tile, and the tile index is the position of the tile in relation to the overall grid. Confused? Here is an example accompanied by a picture that hopefully clarifies things: array_view<int, 2> data(8, 6, p_my_data); parallel_for_each(data.grid.tile<2,2>(), [=] (tiled_index<2,2> t_idx) restrict(direct3d) { /* todo */ }); Given the code above and the picture on the right, what are the values of each of the 4 index objects that the t_idx variables exposes, when the lambda is executed by T (highlighted in the picture on the right)? If you can't work it out yourselves, the solution follows: t_idx.global       = index<2> (6,3) t_idx.local          = index<2> (0,1) t_idx.tile_origin = index<2> (6,2) t_idx.tile             = index<2> (3,1) Don't move on until you are comfortable with this… the picture really helps, so use it. Tiled Matrix Multiplication Example – part 1 Let's paste here the C++ AMP matrix multiplication example, bolding the lines we are going to change (can you guess what the changes will be?) 01: void MatrixMultiplyTiled_Part1(vector<float>& vC, const vector<float>& vA, const vector<float>& vB, int M, int N, int W) 02: { 03: 04: array_view<const float,2> a(M, W, vA); 05: array_view<const float,2> b(W, N, vB); 06: array_view<writeonly<float>,2> c(M, N, vC); 07: parallel_for_each(c.grid, 08: [=](index<2> idx) restrict(direct3d) { 09: 10: int row = idx[0]; int col = idx[1]; 11: float sum = 0.0f; 12: for(int i = 0; i < W; i++) 13: sum += a(row, i) * b(i, col); 14: c[idx] = sum; 15: }); 16: } To turn this into a tiled example, first we need to decide our tile size. Let's say we want each tile to be 16*16 (which assumes that we'll have at least 256 threads to process, and that c.grid.extent.size() is divisible by 256, and moreover that c.grid.extent[0] and c.grid.extent[1] are divisible by 16). So we insert at line 03 the tile size (which must be a compile time constant). 03: static const int TS = 16; ...then we need to tile the grid to have tiles where each one has 16*16 threads, so we change line 07 to be as follows 07: parallel_for_each(c.grid.tile<TS,TS>(), ...that means that our index now has to be a tiled_index with the same characteristics as the tiled_grid, so we change line 08 08: [=](tiled_index<TS, TS> t_idx) restrict(direct3d) { ...which means, without changing our core algorithm, we need to be using the global index that the tiled_index gives us access to, so we insert line 09 as follows 09: index<2> idx = t_idx.global; ...and now this code just works and it is tiled! Closing thoughts on part 1 The process we followed just shows the mechanical transformation that can take place from the simple model to the tiled model (think of this as step 1). In fact, when we wrote the matrix multiplication example originally, the compiler was doing this mechanical transformation under the covers for us (and it has additional smarts to deal with the cases where the total number of threads scheduled cannot be divisible by the tile size). The point is that the thread scheduling is always tiled, even when you use the non-tiled model. But with this mechanical transformation, we haven't gained anything… Hint: our goal with explicitly using the tiled model is to gain even more performance. In the next post, we'll evolve this further (beyond what the compiler can automatically do for us, in this first release), so you can see the full usage of the tiled model and its benefits… Comments about this post by Daniel Moth welcome at the original blog.

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  • Master-slave vs. peer-to-peer archictecture: benefits and problems

    - by Ashok_Ora
    Normal 0 false false false EN-US X-NONE X-NONE Almost two decades ago, I was a member of a database development team that introduced adaptive locking. Locking, the most popular concurrency control technique in database systems, is pessimistic. Locking ensures that two or more conflicting operations on the same data item don’t “trample” on each other’s toes, resulting in data corruption. In a nutshell, here’s the issue we were trying to address. In everyday life, traffic lights serve the same purpose. They ensure that traffic flows smoothly and when everyone follows the rules, there are no accidents at intersections. As I mentioned earlier, the problem with typical locking protocols is that they are pessimistic. Regardless of whether there is another conflicting operation in the system or not, you have to hold a lock! Acquiring and releasing locks can be quite expensive, depending on how many objects the transaction touches. Every transaction has to pay this penalty. To use the earlier traffic light analogy, if you have ever waited at a red light in the middle of nowhere with no one on the road, wondering why you need to wait when there’s clearly no danger of a collision, you know what I mean. The adaptive locking scheme that we invented was able to minimize the number of locks that a transaction held, by detecting whether there were one or more transactions that needed conflicting eyou could get by without holding any lock at all. In many “well-behaved” workloads, there are few conflicts, so this optimization is a huge win. If, on the other hand, there are many concurrent, conflicting requests, the algorithm gracefully degrades to the “normal” behavior with minimal cost. We were able to reduce the number of lock requests per TPC-B transaction from 178 requests down to 2! Wow! This is a dramatic improvement in concurrency as well as transaction latency. The lesson from this exercise was that if you can identify the common scenario and optimize for that case so that only the uncommon scenarios are more expensive, you can make dramatic improvements in performance without sacrificing correctness. So how does this relate to the architecture and design of some of the modern NoSQL systems? NoSQL systems can be broadly classified as master-slave sharded, or peer-to-peer sharded systems. NoSQL systems with a peer-to-peer architecture have an interesting way of handling changes. Whenever an item is changed, the client (or an intermediary) propagates the changes synchronously or asynchronously to multiple copies (for availability) of the data. Since the change can be propagated asynchronously, during some interval in time, it will be the case that some copies have received the update, and others haven’t. What happens if someone tries to read the item during this interval? The client in a peer-to-peer system will fetch the same item from multiple copies and compare them to each other. If they’re all the same, then every copy that was queried has the same (and up-to-date) value of the data item, so all’s good. If not, then the system provides a mechanism to reconcile the discrepancy and to update stale copies. So what’s the problem with this? There are two major issues: First, IT’S HORRIBLY PESSIMISTIC because, in the common case, it is unlikely that the same data item will be updated and read from different locations at around the same time! For every read operation, you have to read from multiple copies. That’s a pretty expensive, especially if the data are stored in multiple geographically separate locations and network latencies are high. Second, if the copies are not all the same, the application has to reconcile the differences and propagate the correct value to the out-dated copies. This means that the application program has to handle discrepancies in the different versions of the data item and resolve the issue (which can further add to cost and operation latency). Resolving discrepancies is only one part of the problem. What if the same data item was updated independently on two different nodes (copies)? In that case, due to the asynchronous nature of change propagation, you might land up with different versions of the data item in different copies. In this case, the application program also has to resolve conflicts and then propagate the correct value to the copies that are out-dated or have incorrect versions. This can get really complicated. My hunch is that there are many peer-to-peer-based applications that don’t handle this correctly, and worse, don’t even know it. Imagine have 100s of millions of records in your database – how can you tell whether a particular data item is incorrect or out of date? And what price are you willing to pay for ensuring that the data can be trusted? Multiple network messages per read request? Discrepancy and conflict resolution logic in the application, and potentially, additional messages? All this overhead, when all you were trying to do was to read a data item. Wouldn’t it be simpler to avoid this problem in the first place? Master-slave architectures like the Oracle NoSQL Database handles this very elegantly. A change to a data item is always sent to the master copy. Consequently, the master copy always has the most current and authoritative version of the data item. The master is also responsible for propagating the change to the other copies (for availability and read scalability). Client drivers are aware of master copies and replicas, and client drivers are also aware of the “currency” of a replica. In other words, each NoSQL Database client knows how stale a replica is. This vastly simplifies the job of the application developer. If the application needs the most current version of the data item, the client driver will automatically route the request to the master copy. If the application is willing to tolerate some staleness of data (e.g. a version that is no more than 1 second out of date), the client can easily determine which replica (or set of replicas) can satisfy the request, and route the request to the most efficient copy. This results in a dramatic simplification in application logic and also minimizes network requests (the driver will only send the request to exactl the right replica, not many). So, back to my original point. A well designed and well architected system minimizes or eliminates unnecessary overhead and avoids pessimistic algorithms wherever possible in order to deliver a highly efficient and high performance system. If you’ve every programmed an Oracle NoSQL Database application, you’ll know the difference! /* 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:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}

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  • Stackify Aims to Put More ‘Dev’ in ‘DevOps’

    - by Matt Watson
    Originally published on VisualStudioMagazine.com on 8/22/2012 by Keith Ward.The Kansas City-based startup wants to make it easier for developers to examine the network stack and find problems in code.The first part of “DevOps” is “Dev”. But according to Matt Watson, Devs aren’t connected enough with Ops, and it’s time that changed.He founded the startup company Stackify earlier this year to do something about it. Stackify gives developers unprecedented access to the IT side of the equation, Watson says, without putting additional burden on the system and network administrators who ultimately ensure the health of the environment.“We need a product designed for developers, with the goal of getting them more involved in operations and app support. Now, there’s next to nothing designed for developers,” Watson says. Stackify allows developers to search the network stack to troubleshoot problems in their software that might otherwise take days of coordination between development and IT teams to solve.Stackify allows developers to search log files, configuration files, databases and other infrastructure to locate errors. A key to this is that the developers are normally granted read-only access, soothing admin fears that developers will upload bad code to their servers.Implementation starts with data collection on the servers. Among the information gleaned is application discovery, server monitoring, file access, and other data collection, according to Stackify’s Web site. Watson confirmed that Stackify works seamlessly with virtualized environments as well.Although the data collection software must be installed on Windows servers, it can monitor both Windows and Linux servers. Once collection’s finished, developers have the kind of information they need, without causing heartburn for the IT staff.Stackify is a 100 percent cloud-based service. The company uses Windows Azure for hosting, a decision Watson’s happy with. With Azure, he says, “It’s nice to have all the dev tools like cache and table storage.” Although there have been a few glitches here and there with the service, it’s run very smoothly for the most part, he adds.Stackify is currently in a closed beta, with a public release scheduled for October. Watson says that pricing is expected to be $25 per month, per server, with volume discounts available. He adds that the target audience is companies with at least five developers.Watson founded Stackify after selling his last company, VinSolutions, to AutoTrader.com for “close to $150 million”, according to press accounts. Watson has since  founded the Watson Technology Group, which focuses on angel investing.About the Author: Keith Ward is the editor in chief of Visual Studio Magazine.

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  • Writing or extending existing emacs packages: is it worth or should I move to Netbeans/Eclipse?

    - by Andrea
    I'm finishing my master degree course in CS and I've almost become addicted to Emacs. I've used it to write in C, Latex, Java, JSP,XML, CommonLisp, Ada and other languages no other editor supported, like AMPL. I'd like to improve the packages I've been using the most or create new ones, but, in practice, I find that the implementation of Emacs leaves a lot to be desired. There are a lot of poorly-featured/poorly-maintained packages with either overlapping functionalities or obscure incompatibilities, and Elisp just seems to foster the situation by lacking the common features modern lisps have. In contrast Eclipse and Netbeans are actively improved and it does seem they can be effective for non-mainstream languages. I tried Hibachi for Ada in Eclipse and it worked well, there's CUPS for Lisp in Eclipse and LambdaBeans built using NetBeans components. On the other hand those plugins seem to be less active than their Emacs' counterparts, for example Hibachi was archived last year. What's your opinion on this? Which editor should I write extension for? EDIT: To answer Larry Coleman (see comment below): I like Emacs as a user because it is efficient both for me and the computer I'm using. It's fast and the textual interface (i.e. minibuffer) allows for quick interaction. It's solid and packages are usually small and easy to manage. If I need to correct or remove something I usually just have to change a row in my .emacs or an elisp file, or delete a directory. Eclipse plugins rely on a more complicated process that screwed my Eclipse configuration a couple of times, forcing me to do a clean reinstall. Emacs works as long as I use the basic packages. If I need something more complicated the situation gets pretty hairy. As a "power user" I think that the best I can hope for is to write a severely crippled version of the extensions I'd actually like to have; in other words, that it's not worth the trouble. I'd like to write extensions for the things I'd like to have automated in Emacs, for example project support with automated tag-table update on file writing. There are a few projects on this that lack integration, documentation, extensibility and so forth. The best one is probably CEDET, for which I believe the Greenspun's 10th rule can be applied. EDIT: To comment Larry Coleman's answer I'm pretty sure I can pick elisp programming but the extensions I have in mind don't exist yet despite their relative simplicity and the effort more knowledgeable people poured into related projects.This makes me wonder whether it is so because of the way emacs is developed, i.e. people tend to write their own little extensions without coordination, or its implementation, its extension language not being able to keep up with the growing complexity.

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  • An Unstoppable Force!

    - by TammyBednar
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* 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:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Building a high-availability database platform presents unique challenges. Combining servers, storage, networking, OS, firmware, and database is complicated and raises important concerns: Will coordination between multiple SME’s delay deployment? Will it be reliable? Will it scale? Will routine maintenance consume precious IT-staff time? Ultimately, will it work? Enter the Oracle Database Appliance, a complete package of software, server, storage, and networking that’s engineered for simplicity. It saves time and money by simplifying deployment, maintenance, and support of database workloads. Plus, it’s based on Intel Xeon processors to ensure a high level of performance and scalability. Take a look at this video to compare Heather and Ted’s approach to building a server for their Oracle database! http://www.youtube.com/watch?v=os4RDVclWS8 If you missed the “Compare Database Platforms: Build vs. Buy” webcast or want to listen again to find out how Jeff Schulte - Vice President at Yodlee uses Oracle Database Appliance.

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