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  • Handling file upload in a non-blocking manner

    - by Kaliyug Antagonist
    The background thread is here Just to make objective clear - the user will upload a large file and must be redirected immediately to another page for proceeding different operations. But the file being large, will take time to be read from the controller's InputStream. So I unwillingly decided to fork a new Thread to handle this I/O. The code is as follows : The controller servlet /** * @see HttpServlet#doPost(HttpServletRequest request, HttpServletResponse * response) */ protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { // TODO Auto-generated method stub System.out.println("In Controller.doPost(...)"); TempModel tempModel = new TempModel(); tempModel.uploadSegYFile(request, response); System.out.println("Forwarding to Accepted.jsp"); /*try { Thread.sleep(1000 * 60); } catch (InterruptedException e) { // TODO Auto-generated catch block e.printStackTrace(); }*/ request.getRequestDispatcher("/jsp/Accepted.jsp").forward(request, response); } The model class package com.model; import java.io.IOException; import java.util.concurrent.ExecutionException; import java.util.concurrent.Future; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpServletResponse; import com.utils.ProcessUtils; public class TempModel { public void uploadSegYFile(HttpServletRequest request, HttpServletResponse response) { // TODO Auto-generated method stub System.out.println("In TempModel.uploadSegYFile(...)"); /* * Trigger the upload/processing code in a thread, return immediately * and notify when the thread completes */ try { FileUploaderRunnable fileUploadRunnable = new FileUploaderRunnable( request.getInputStream()); /* * Future<FileUploaderRunnable> future = ProcessUtils.submitTask( * fileUploadRunnable, fileUploadRunnable); * * FileUploaderRunnable processed = future.get(); * * System.out.println("Is file uploaded : " + * processed.isFileUploaded()); */ Thread uploadThread = new Thread(fileUploadRunnable); uploadThread.start(); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } /* * catch (InterruptedException e) { // TODO Auto-generated catch block * e.printStackTrace(); } catch (ExecutionException e) { // TODO * Auto-generated catch block e.printStackTrace(); } */ System.out.println("Returning from TempModel.uploadSegYFile(...)"); } } The Runnable package com.model; import java.io.File; import java.io.FileInputStream; import java.io.FileNotFoundException; import java.io.FileOutputStream; import java.io.IOException; import java.io.InputStream; import java.nio.ByteBuffer; import java.nio.channels.Channels; import java.nio.channels.ReadableByteChannel; public class FileUploaderRunnable implements Runnable { private boolean isFileUploaded = false; private InputStream inputStream = null; public FileUploaderRunnable(InputStream inputStream) { // TODO Auto-generated constructor stub this.inputStream = inputStream; } public void run() { // TODO Auto-generated method stub /* Read from InputStream. If success, set isFileUploaded = true */ System.out.println("Starting upload in a thread"); File outputFile = new File("D:/06c01_output.seg");/* * This will be changed * later */ FileOutputStream fos; ReadableByteChannel readable = Channels.newChannel(inputStream); ByteBuffer buffer = ByteBuffer.allocate(1000000); try { fos = new FileOutputStream(outputFile); while (readable.read(buffer) != -1) { fos.write(buffer.array()); buffer.clear(); } fos.flush(); fos.close(); readable.close(); } catch (FileNotFoundException e) { // TODO Auto-generated catch block e.printStackTrace(); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } System.out.println("File upload thread completed"); } public boolean isFileUploaded() { return isFileUploaded; } } My queries/doubts : Spawning threads manually from the Servlet makes sense to me logically but scares me coding wise - the container isn't aware of these threads after all(I think so!) The current code is giving an Exception which is quite obvious - the stream is inaccessible as the doPost(...) method returns before the run() method completes : In Controller.doPost(...) In TempModel.uploadSegYFile(...) Returning from TempModel.uploadSegYFile(...) Forwarding to Accepted.jsp Starting upload in a thread Exception in thread "Thread-4" java.lang.NullPointerException at org.apache.coyote.http11.InternalInputBuffer.fill(InternalInputBuffer.java:512) at org.apache.coyote.http11.InternalInputBuffer.fill(InternalInputBuffer.java:497) at org.apache.coyote.http11.InternalInputBuffer$InputStreamInputBuffer.doRead(InternalInputBuffer.java:559) at org.apache.coyote.http11.AbstractInputBuffer.doRead(AbstractInputBuffer.java:324) at org.apache.coyote.Request.doRead(Request.java:422) at org.apache.catalina.connector.InputBuffer.realReadBytes(InputBuffer.java:287) at org.apache.tomcat.util.buf.ByteChunk.substract(ByteChunk.java:407) at org.apache.catalina.connector.InputBuffer.read(InputBuffer.java:310) at org.apache.catalina.connector.CoyoteInputStream.read(CoyoteInputStream.java:202) at java.nio.channels.Channels$ReadableByteChannelImpl.read(Unknown Source) at com.model.FileUploaderRunnable.run(FileUploaderRunnable.java:39) at java.lang.Thread.run(Unknown Source) Keeping in mind the point 1., does the use of Executor framework help me in anyway ? package com.utils; import java.util.concurrent.Future; import java.util.concurrent.ScheduledThreadPoolExecutor; public final class ProcessUtils { /* Ensure that no more than 2 uploads,processing req. are allowed */ private static final ScheduledThreadPoolExecutor threadPoolExec = new ScheduledThreadPoolExecutor( 2); public static <T> Future<T> submitTask(Runnable task, T result) { return threadPoolExec.submit(task, result); } } So how should I ensure that the user doesn't block and the stream remains accessible so that the (uploaded)file can be read from it?

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  • Java concurrency - Should block or yield?

    - by teto
    Hi, I have multiple threads each one with its own private concurrent queue and all they do is run an infinite loop retrieving messages from it. It could happen that one of the queues doesn't receive messages for a period of time (maybe a couple seconds), and also they could come in big bursts and fast processing is necessary. I would like to know what would be the most appropriate to do in the first case: use a blocking queue and block the thread until I have more input or do a Thread.yield()? I want to have as much CPU resources available as possible at a given time, as the number of concurrent threads may increase with time, but also I don't want the message processing to fall behind, as there is no guarantee of when the thread will be reescheduled for execution when doing a yield(). I know that hardware, operating system and other factors play an important role here, but setting that aside and looking at it from a Java (JVM?) point of view, what would be the most optimal?

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  • Performance issues when using SSD for a developer notebook (WAMP/LAMP stack)?

    - by András Szepesházi
    I'm a web application developer using my notebook as a standalone development environment (WAMP stack). I just switched from a Core2-duo Vista 32 bit notebook with 2Gb RAM and SATA HDD, to an i5-2520M Win7 64 bit with 4Gb RAM and 128 GB SDD (Corsair P3 128). My initial experience was what I expected, fast boot, quick load of all the applications (Eclipse takes now 5 seconds as opposed to 30s on my old notebook), overall great experience. Then I started to build up my development stack, both as LAMP (using VirtualBox with a debian guest) and WAMP (windows native apache + mysql + php). I wanted to compare those two. This still all worked great out, then I started to pull in my projects to these stacks. And here came the nasty surprise, one of those projects produced a lot worse response times than on my old notebook (that was true for both the VirtualBox and WAMP stack). Apache, php and mysql configurations were practically identical in all environments. I started to do a lot of benchmarking and profiling, and here is what I've found: All general benchmarks (Performance Test 7.0, HDTune Pro, wPrime2 and some more) gave a big advantage to the new notebook. Nothing surprising here. Disc specific tests showed that read/write operations peaked around 380M/160M for the SSD, and all the different sized block operations also performed very well. Started apache performance benchmarking with Apache Benchmark for a small static html file (10 concurrent threads, 500 iterations). Old notebook: min 47ms, median 111ms, max 156ms New WAMP stack: min 71ms, median 135ms, max 296ms New LAMP stack (in VirtualBox): min 6ms, median 46ms, max 175ms Right here I don't get why the native WAMP stack performed so bad, but at least the LAMP environment brought the expected speed. Apache performance measurement for non-cached php content. The php runs a loop of 1000 and generates sha1(uniqid()) inisde. Again, 10 concurrent threads, 500 iterations were used for the benchmark. Old notebook: min 0ms, median 39ms, max 218ms New WAMP stack: min 20ms, median 61ms, max 186ms New LAMP stack (in VirtualBox): min 124ms, median 704ms, max 2463ms What the hell? The new LAMP performed miserably, and even the new native WAMP was outperformed by the old notebook. php + mysql test. The test consists of connecting to a database and reading a single record form a table using INNER JOIN on 3 more (indexed) tables, repeated 100 times within a loop. Databases were identical. 10 concurrent threads, 100 iterations were used for the benchmark. Old notebook: min 1201ms, median 1734ms, max 3728ms New WAMP stack: min 367ms, median 675ms, max 1893ms New LAMP stack (in VirtualBox): min 1410ms, median 3659ms, max 5045ms And the same test with concurrency set to 1 (instead of 10): Old notebook: min 1201ms, median 1261ms, max 1357ms New WAMP stack: min 399ms, median 483ms, max 539ms New LAMP stack (in VirtualBox): min 285ms, median 348ms, max 444ms Strictly for my purposes, as I'm using a self contained development environment (= low concurrency) I could be satisfied with the second test's result. Though I have no idea why the VirtualBox environment performed so bad with higher concurrency. Finally I performed a test of including many php files. The application that I mentioned at the beginning, the one that was performing so bad, has a heavy bootstrap, loads hundreds of small library and configuration files while initializing. So this test does nothing else just includes about 100 files. Concurrency set to 1, 100 iterations: Old notebook: min 140ms, median 168ms, max 406ms New WAMP stack: min 434ms, median 488ms, max 604ms New LAMP stack (in VirtualBox): min 413ms, median 1040ms, max 1921ms Even if I consider that VirtualBox reached those files via shared folders, and that slows things down a bit, I still don't see how could the old notebook outperform so heavily both new configurations. And I think this is the real root of the slow performance, as the application uses even more includes, and the whole bootstrap will occur several times within a page request (for each ajax call, for example). To sum it up, here I am with a brand new high-performance notebook that loads the same page in 20 seconds, that my old notebook can do in 5-7 seconds. Needless to say, I'm not a very happy person right now. Why do you think I experience these poor performance values? What are my options to remedy this situation?

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  • Cannot see the variable In my own JQuery plugin's function.

    - by qinHaiXiang
    I am writing one of my own JQuery plugin. And I got some strange which make me confused. I am using JQuery UI datepicker with my plugin. ;(function($){ var newMW = 1, mwZIndex = 0; // IgtoMW contructor Igtomw = function(elem , options){ var activePanel, lastPanel, daysWithRecords, sliding; // used to check the animation below is executed to the end. // used to access the plugin's default configuration this.opts = $.extend({}, $.fn.igtomw.defaults, options); // intial the model window this.intialMW(); }; $.extend(Igtomw.prototype, { // intial model window intialMW : function(){ this.sliding = false; //this.daysWithRecords = []; this.igtoMW = $('<div />',{'id':'igto'+newMW,'class':'igtoMW',}) .css({'z-index':mwZIndex}) // make it in front of all exist model window; .appendTo('body') .draggable({ containment: 'parent' , handle: '.dragHandle' , distance: 5 }); //var igtoWrapper = igtoMW.append($('<div />',{'class':'igtoWrapper'})); this.igtoWrapper = $('<div />',{'class':'igtoWrapper'}).appendTo(this.igtoMW); this.igtoOpacityBody = $('<div />',{'class':'igtoOpacityBody'}).appendTo(this.igtoMW); //var igtoHeaderInfo = igtoWrapper.append($('<div />',{'class':'igtoHeaderInfo dragHandle'})); this.igtoHeaderInfo = $('<div />',{'class':'igtoHeaderInfo dragHandle'}) .appendTo(this.igtoWrapper); this.igtoQuickNavigation = $('<div />',{'class':'igtoQuickNavigation'}) .css({'color':'#fff'}) .appendTo(this.igtoWrapper); this.igtoContentSlider = $('<div />',{'class':'igtoContentSlider'}) .appendTo(this.igtoWrapper); this.igtoQuickMenu = $('<div />',{'class':'igtoQuickMenu'}) .appendTo(this.igtoWrapper); this.igtoFooter = $('<div />',{'class':'igtoFooter dragHandle'}) .appendTo(this.igtoWrapper); // append to igtoHeaderInfo this.headTitle = this.igtoHeaderInfo.append($('<div />',{'class':'headTitle'})); // append to igtoQuickNavigation this.igQuickNav = $('<div />', {'class':'igQuickNav'}) .html('??') .appendTo(this.igtoQuickNavigation); // append to igtoContentSlider this.igInnerPanelTopMenu = $('<div />',{'class':'igInnerPanelTopMenu'}) .appendTo(this.igtoContentSlider); this.igInnerPanelTopMenu.append('<div class="igInnerPanelButtonPreWrapper"><a href="" class="igInnerPanelButton Pre" action="" style="background-image:url(images/igto/igInnerPanelTopMenu.bt.bg.png);"></a></div>'); this.igInnerPanelTopMenu.append('<div class="igInnerPanelSearch"><input type="text" name="igInnerSearch" /><a href="" class="igInnerSearch">??</a></div>' ); this.igInnerPanelTopMenu.append('<div class="igInnerPanelButtonNextWrapper"><a href="" class="igInnerPanelButton Next" action="sm" style="background-image:url(images/igto/igInnerPanelTopMenu.bt.bg.png); background-position:-272px"></a></div>' ); this.igInnerPanelBottomMenu = $('<div />',{'class':'igInnerPanelBottomMenu'}) .appendTo(this.igtoContentSlider); this.icWrapper = $('<div />',{'class':'icWrapper','id':'igto'+newMW+'Panel'}) .appendTo(this.igtoContentSlider); this.icWrapperCotentPre = $('<div class="slider pre"></div>').appendTo(this.icWrapper); this.icWrapperCotentShow = $('<div class="slider firstShow "></div>').appendTo(this.icWrapper); this.icWrapperCotentnext = $('<div class="slider next"></div>').appendTo(this.icWrapper); this.initialPanel(); this.initialQuickMenus(); console.log(this.leftPad(9)); newMW++; mwZIndex++; this.igtoMW.bind('mousedown',function(){ var $this = $(this); //alert($this.css('z-index') + ' '+mwZIndex); if( parseInt($this.css('z-index')) === (mwZIndex-1) ) return; $this.css({'z-index':mwZIndex}); mwZIndex++; //alert(mwZIndex); }); }, initialPanel : function(){ this.defaultPanelNum = this.opts.initialPanel; this.activePanel = this.defaultPanelNum; this.lastPanel = this.defaultPanelNum; this.defaultPanel = this.loadPanelContents(this.defaultPanelNum); $(this.defaultPanel).appendTo(this.icWrapperCotentShow); }, initialQuickMenus : function(){ // store the current element var obj = this; var defaultQM = this.opts.initialQuickMenu; var strMenu = ''; var marginFirstEle = '8'; $.each(defaultQM,function(key,value){ //alert(key+':'+value); if(marginFirstEle === '8'){ strMenu += '<a href="" class="btPanel" panel="'+key+'" style="margin-left: 8px;" >'+value+'</a>'; marginFirstEle = '4'; } else{ strMenu += '<a href="" class="btPanel" panel="'+key+'" style="margin-left: 4px;" >'+value+'</a>'; } }); // append to igtoQuickMenu this.igtoQMenu = $(strMenu).appendTo(this.igtoQuickMenu); this.igtoQMenu.bind('click',function(event){ event.preventDefault(); var element = $(this); if(element.is('.active')){ return; } else{ $(obj.igtoQMenu).removeClass('active'); element.addClass('active'); } var d = new Date(); var year = d.getFullYear(); var month = obj.leftPad( d.getMonth() ); var inst = null; if( obj.sliding === false){ console.log(obj.lastPanel); var currentPanelNum = parseInt(element.attr('panel')); obj.checkAvailability(); obj.getDays(year,month,inst,currentPanelNum); obj.slidePanel(currentPanelNum); obj.activePanel = currentPanelNum; console.log(obj.activePanel); obj.lastPanel = obj.activePanel; obj.icWrapper.find('input').val(obj.activePanel); } }); }, initialLoginPanel : function(){ var obj = this; this.igPanelLogin = $('<div />',{'class':"igPanelLogin"}); this.igEnterName = $('<div />',{'class':"igEnterName"}).appendTo(this.igPanelLogin); this.igInput = $('<input type="text" name="name" value="???" />').appendTo(this.igEnterName); this.igtoLoginBtWrap = $('<div />',{'class':"igButtons"}).appendTo(this.igPanelLogin); this.igtoLoginBt = $('<a href="" class="igtoLoginBt" action="OK" >??</a>\ <a href="" class="igtoLoginBt" action="CANCEL" >??</a>\ <a href="" class="igtoLoginBt" action="ADD" >????</a>').appendTo(this.igtoLoginBtWrap); this.igtoLoginBt.bind('click',function(event){ event.preventDefault(); var elem = $(this); var action = elem.attr('action'); var userName = obj.igInput.val(); obj.loadRootMenu(); }); return this.igPanelLogin; }, initialWatchHistory : function(){ var obj = this; // for thirt part plugin used if(this.sliding === false){ this.watchHistory = $('<div />',{'class':'igInnerPanelSlider'}).append($('<div />',{'class':'igInnerPanel_pre'}).addClass('igInnerPanel')) .append($('<div />',{'class':'igInnerPanel'}).datepicker({ dateFormat: 'yy-mm-dd',defaultDate: '2010-12-01' ,showWeek: true,firstDay: 1, //beforeShow:setDateStatistics(), onChangeMonthYear:function(year, month, inst) { var panelNum = 1; month = obj.leftPad(month); obj.getDays(year,month,inst,panelNum); } , beforeShowDay: obj.checkAvailability, onSelect: function(dateText, inst) { obj.checkAvailability(); } }).append($('<div />',{'class':'extraMenu'})) ) .append($('<div />',{'class':'igInnerPanel_next'}).addClass('igInnerPanel')); return this.watchHistory; } }, loadPanelContents : function(panelNum){ switch(panelNum){ case 1: alert('inside loadPanelContents') return this.initialWatchHistory(); break; case 2: return this.initialWatchHistory(); break; case 3: return this.initialWatchHistory(); break; case 4: return this.initialWatchHistory(); break; case 5: return this.initialLoginPanel(); break; } }, loadRootMenu : function(){ var obj = this; var mainMenuPanel = $('<div />',{'class':'igRootMenu'}); var currentMWId = this.igtoMW.attr('id'); this.activePanel = 0; $('#'+currentMWId+'Panel .pre'). queue(function(next){ $(this). html(mainMenuPanel). addClass('panelShow'). removeClass('pre'). attr('panelNum',0); next(); }). queue(function(next){ $('<div style="width:0;" class="slider pre"></div>'). prependTo('#'+currentMWId+'Panel').animate({width:348}, function(){ $('#'+currentMWId+'Panel .slider:last').remove() $('#'+currentMWId+'Panel .slider:last').replaceWith('<div class="slider next"></div>'); $('.btMenu').remove(); // remove bottom quick menu obj.sliding = false; $(this).removeAttr('style'); }); $('.igtoQuickMenu .active').removeClass('active'); next(); }); }, slidePanel : function(currentPanelNum){ var currentMWId = this.igtoMW.attr('id'); var obj = this; //alert(obj.loadPanelContents(currentPanelNum)); if( this.activePanel > currentPanelNum){ $('#'+currentMWId+'Panel .pre'). queue(function(next){ alert('inside slidePanel') //var initialDate = getPanelDateStatus(panelNum); //console.log('intial day in bigger panel '+initialDate) $(this). html(obj.loadPanelContents(currentPanelNum)). addClass('panelShow'). removeClass('pre'). attr('panelNum',currentPanelNum); $('#'+currentMWId+'Panel .next').remove(); next(); }). queue(function(next){ $('<div style="width:0;" class="slider pre"></div>'). prependTo('#'+currentMWId+'Panel').animate({width:348}, function(){ //$('#igto1Panel .slider:last').find(setPanel(currentPanelNum)).datepicker('destroy'); $('#'+currentMWId+'Panel .slider:last').empty().removeClass('panelShow').addClass('next').removeAttr('panelNum'); $('#'+currentMWId+'Panel .slider:last').replaceWith('<div class="slider next"></div>') obj.sliding = false;console.log('inuse inside animation: '+obj.sliding); $(this).removeAttr('style'); }); next(); }); } else{ ///// current panel num smaller than next $('#'+currentMWId+'Panel .next'). queue(function(next){ $(this). html(obj.loadPanelContents(currentPanelNum)). addClass('panelShow'). removeClass('next'). attr('panelNum',currentPanelNum); $('<div class="slider next">empty</div>').appendTo('#'+currentMWId+'Panel'); next(); }). queue(function(next){ $('#'+currentMWId+'Panel .pre').animate({width:0}, function(){ $(this).remove(); //$('#igto1Panel .slider:first').find(setPanel(currentPanelNum)).datepicker('destroy'); $('#'+currentMWId+'Panel .slider:first').empty().removeClass('panelShow').addClass('pre').removeAttr('panelNum').removeAttr('style'); $('#'+currentMWId+'Panel .slider:first').replaceWith('<div class="slider pre"></div>') obj.sliding = false; console.log('inuse inside animation: '+obj.sliding); }); next(); }); } }, getDays : function(year,month,inst,panelNum){ var obj = this; // depand on the mysql qurey condition var table_of_record = 'moviewh';//getTable(panelNum); var date_of_record = 'watching_date';//getTableDateCol(panelNum); var date_to_find = year+'-'+month; var node_of_xml_date_list = 'whDateRecords';//getXMLDateNode(panelNum); var user_id = '1';//getLoginUserId(); //var daysWithRecords = []; // empty array before asigning this.daysWithRecords.length = 0; $.ajax({ type: "GET", url: "include/get.date.list.process.php", data:({ table_of_record : table_of_record,date_of_record:date_of_record,date_to_find:date_to_find,user_id:user_id,node_of_xml_date_list:node_of_xml_date_list }), dataType: "json", cache: false, // force broser don't cache the xml file async: false, // using this option to prevent datepicker refresh ??NO success:function(data){ // had no date records if(data === null) return; obj.daysWithRecords = data; } }); //setPanelDateStatus(year,month,panelNum); console.log('call from getdays() ' + this.daysWithRecords); }, checkAvailability : function(availableDays) { // var i; var checkdate = $.datepicker.formatDate('yy-mm-dd', availableDays); //console.log( checkdate); // for(var i = 0; i < this.daysWithRecords.length; i++) { // // if(this.daysWithRecords[i] == checkdate){ // // return [true, "available"]; // } // } //console.log('inside check availablility '+ this.daysWithRecords); //return [true, "available"]; console.log(typeof this.daysWithRecords) for(i in this.daysWithRecords){ //if(this.daysWithRecords[i] == checkdate){ console.log(typeof this.daysWithRecords[i]); //return [true, "available"]; //} } return [true, "available"]; //return [false, ""]; }, leftPad : function(num) { return (num < 10) ? '0' + num : num; } }); $.fn.igtomw = function(options){ // Merge options passed in with global defaults var opt = $.extend({}, $.fn.igtomw.defaults , options); return this.each(function() { new Igtomw(this,opt); }); }; $.fn.igtomw.defaults = { // 0:mainMenu 1:whatchHistor 2:requestHistory 3:userManager // 4:shoppingCart 5:loginPanel initialPanel : 5, // default panel is LoginPanel initialQuickMenu : {'1':'whatchHIstory','2':'????','3':'????','4':'????'} // defalut quick menu }; })(jQuery); usage: $('.openMW').click(function(event){ event.preventDefault(); $('<div class="">').igtomw(); }) HTML code: <div id="taskBarAndStartMenu"> <div class="taskBarAndStartMenuM"> <a href="" class="openMW" >??IGTO</a> </div> <div class="taskBarAndStartMenuO"></div> </div> In my work flow: when I click the "whatchHistory" button, my plugin would load a panel with JQuery UI datepicker applied which days had been set to be availabled or not. I am using the function "getDays()" to get the available days list and stored the data inside daysWithRecords, and final the UI datepicker's function "beforeShowDay()" called the function "checkAvailability()" to set the days. the variable "daysWithRecords" was declared inside Igtomw = function(elem , options) and was initialized inside the function getDays() I am using the function "initialWatchHistory()" to initialization and render the JQuery UI datepicker in the web. My problem is the function "checkAvailability()" cannot see the variable "daysWithRecords".The firebug prompts me that "daysWithRecords" is "undefined". this is the first time I write my first plugin. So .... Thank you very much for any help!!

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  • SQL SERVER – Signal Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28

    - by pinaldave
    In this post, let’s delve a bit more in depth regarding wait stats. The very first question: when do the wait stats occur? Here is the simple answer. When SQL Server is executing any task, and if for any reason it has to wait for resources to execute the task, this wait is recorded by SQL Server with the reason for the delay. Later on we can analyze these wait stats to understand the reason the task was delayed and maybe we can eliminate the wait for SQL Server. It is not always possible to remove the wait type 100%, but there are few suggestions that can help. Before we continue learning about wait types and wait stats, we need to understand three important milestones of the query life-cycle. Running - a query which is being executed on a CPU is called a running query. This query is responsible for CPU time. Runnable – a query which is ready to execute and waiting for its turn to run is called a runnable query. This query is responsible for Signal Wait time. (In other words, the query is ready to run but CPU is servicing another query). Suspended – a query which is waiting due to any reason (to know the reason, we are learning wait stats) to be converted to runnable is suspended query. This query is responsible for wait time. (In other words, this is the time we are trying to reduce). In simple words, query execution time is a summation of the query Executing CPU Time (Running) + Query Wait Time (Suspended) + Query Signal Wait Time (Runnable). Again, it may be possible a query goes to all these stats multiple times. Let us try to understand the whole thing with a simple analogy of a taxi and a passenger. Two friends, Tom and Danny, go to the mall together. When they leave the mall, they decide to take a taxi. Tom and Danny both stand in the line waiting for their turn to get into the taxi. This is the Signal Wait Time as they are ready to get into the taxi but the taxis are currently serving other customer and they have to wait for their turn. In other word they are in a runnable state. Now when it is their turn to get into the taxi, the taxi driver informs them he does not take credit cards and only cash is accepted. Neither Tom nor Danny have enough cash, they both cannot get into the vehicle. Tom waits outside in the queue and Danny goes to ATM to fetch the cash. During this time the taxi cannot wait, they have to let other passengers get into the taxi. As Tom and Danny both are outside in the queue, this is the Query Wait Time and they are in the suspended state. They cannot do anything till they get the cash. Once Danny gets the cash, they are both standing in the line again, creating one more Signal Wait Time. This time when their turn comes they can pay the taxi driver in cash and reach their destination. The time taken for the taxi to get from the mall to the destination is running time (CPU time) and the taxi is running. I hope this analogy is bit clear with the wait stats. You can check the Signalwait stats using following query of Glenn Berry. -- Signal Waits for instance SELECT CAST(100.0 * SUM(signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%signal (cpu) waits], CAST(100.0 * SUM(wait_time_ms - signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%resource waits] FROM sys.dm_os_wait_stats OPTION (RECOMPILE); Higher the Signal wait stats are not good for the system. Very high value indicates CPU pressure. In my experience, when systems are running smooth and without any glitch the Signal wait stat is lower than 20%. Again, this number can be debated (and it is from my experience and is not documented anywhere). In other words, lower is better and higher is not good for the system. In future articles we will discuss in detail the various wait types and wait stats and their resolution. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQL SERVER – Single Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28

    - by pinaldave
    In this post, let’s delve a bit more in depth regarding wait stats. The very first question: when do the wait stats occur? Here is the simple answer. When SQL Server is executing any task, and if for any reason it has to wait for resources to execute the task, this wait is recorded by SQL Server with the reason for the delay. Later on we can analyze these wait stats to understand the reason the task was delayed and maybe we can eliminate the wait for SQL Server. It is not always possible to remove the wait type 100%, but there are few suggestions that can help. Before we continue learning about wait types and wait stats, we need to understand three important milestones of the query life-cycle. Running - a query which is being executed on a CPU is called a running query. This query is responsible for CPU time. Runnable – a query which is ready to execute and waiting for its turn to run is called a runnable query. This query is responsible for Single Wait time. (In other words, the query is ready to run but CPU is servicing another query). Suspended – a query which is waiting due to any reason (to know the reason, we are learning wait stats) to be converted to runnable is suspended query. This query is responsible for wait time. (In other words, this is the time we are trying to reduce). In simple words, query execution time is a summation of the query Executing CPU Time (Running) + Query Wait Time (Suspended) + Query Single Wait Time (Runnable). Again, it may be possible a query goes to all these stats multiple times. Let us try to understand the whole thing with a simple analogy of a taxi and a passenger. Two friends, Tom and Danny, go to the mall together. When they leave the mall, they decide to take a taxi. Tom and Danny both stand in the line waiting for their turn to get into the taxi. This is the Signal Wait Time as they are ready to get into the taxi but the taxis are currently serving other customer and they have to wait for their turn. In other word they are in a runnable state. Now when it is their turn to get into the taxi, the taxi driver informs them he does not take credit cards and only cash is accepted. Neither Tom nor Danny have enough cash, they both cannot get into the vehicle. Tom waits outside in the queue and Danny goes to ATM to fetch the cash. During this time the taxi cannot wait, they have to let other passengers get into the taxi. As Tom and Danny both are outside in the queue, this is the Query Wait Time and they are in the suspended state. They cannot do anything till they get the cash. Once Danny gets the cash, they are both standing in the line again, creating one more Single Wait Time. This time when their turn comes they can pay the taxi driver in cash and reach their destination. The time taken for the taxi to get from the mall to the destination is running time (CPU time) and the taxi is running. I hope this analogy is bit clear with the wait stats. You can check the single wait stats using following query of Glenn Berry. -- Signal Waits for instance SELECT CAST(100.0 * SUM(signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%signal (cpu) waits], CAST(100.0 * SUM(wait_time_ms - signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%resource waits] FROM sys.dm_os_wait_stats OPTION (RECOMPILE); Higher the single wait stats are not good for the system. Very high value indicates CPU pressure. In my experience, when systems are running smooth and without any glitch the single wait stat is lower than 20%. Again, this number can be debated (and it is from my experience and is not documented anywhere). In other words, lower is better and higher is not good for the system. In future articles we will discuss in detail the various wait types and wait stats and their resolution. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Compress Large Video Files with DivX / Xvid and AutoGK

    - by DigitalGeekery
    Have you ever recorded home video on a camcorder only to find the video size is enormous? What if you wanted to share a video clip on YouTube or another video sharing site, but the file size was bigger than the maximum upload size? Today we’ll look at a way to compress certain video files, such as MPEG and AVI, with Auto Gordian Knot (AutoGK). AutoGK is a free application that runs on Windows. It supports Mpeg1, Mpeg2, Transport Streams, Vobs, and virtually any codec used for an .AVI file. AutoGK will accept as input the following file types: MPG, MPEG, VOB, VRO, M2V, DAT, IFO, TS, TP, TRP, M2T, and AVI. Files are output as .AVI files and are converted using the DivX or XviD codecs. Installing and Using AutoGK Download and install AutoGK (link below) Open the AutoGK. You’ll need to navigate a few wizard screens, but you can just accept the defaults.   Choose your video file by clicking on the folder to the right of the Input file text box.   Browse for and select your video file and click “Open.”   For this example, we’ll be working with an .AVI file that’s 167MB in size.   The output file is copied into the same directory as the input file by default, but you can change this if you choose. If the input file is also .AVI, AutoGK will append an _agk to the output file so that the original is not overwritten. Next, you’ll see any audio tracks listed. You can unselect the check box if you’d like to remove the audio track. You can choose one of the Predefined size options… Or, select a Custom size in MB or Target Quality in percentage. For our example, we’ll be compressing our 167MB file to 35MB. Click on Advanced Settings. Here you can choose your codec, if you have a preference, as well as output resolution and output audio. If you’d like to use the DivX codec, you’ll need to download and install it separately. (See link below) Typically you’ll want to keep the defaults. Click “OK.” Now you’re ready to add your file conversion job to the Job queue. Click Add Job to add it to the queue. You can add multiple files conversions to the job queue and  convert them in one batch. Click Start to begin the conversion process. The process will begin. You’ll be able to see the progress in the Log window on the bottom left. When the conversion is complete you’ll see a “Job finished” and the total time in the log window.   Check your output file to see it’s compressed size. Test your video just to make sure the output quality is satisfactory.   Note:  Conversion times can vary greatly depending on the size of the file and your computer hardware. Files that are several GBs in size may take several hours to compress. AutoGK is no longer being actively developed but is still a wonderful DivX/XviD conversion tool. It can also be used to compress and convert non-copy protected DVDs. Downloads AutoGordianKnot DivX (optional) Similar Articles Productive Geek Tips Use Your Mac Mini as a Media Server Part 2Make Disk Cleanup Compress Older(or Newer) Files on XPMysticgeek Blog: Exclusive Look Inside Vreel – Including Interview With Vreel Founder!Friday Fun: Watch HD Video Content with MeevidConvert a DVD Movie Directly to AVI with FairUse Wizard 2.9 TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Penolo Lets You Share Sketches On Twitter Visit Woolyss.com for Old School Games, Music and Videos Add a Custom Title in IE using Spybot or Spyware Blaster When You Need to Hail a Taxi in NYC Live Map of Marine Traffic NoSquint Remembers Site Specific Zoom Levels (Firefox)

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  • BizTalk host throttling &ndash; Singleton pattern and High database size

    - by S.E.R.
    Originally posted on: http://geekswithblogs.net/SERivas/archive/2013/06/30/biztalk-host-throttling-ndash-singleton-pattern-and-high-database-size.aspxI have worked for some days around the singleton pattern (for those unfamiliar with it, read this post by Victor Fehlberg) and have come across a few very interesting posts, among which one dealt with performance issues (here, also by Victor Fehlberg). Simply put: if you have an orchestration which implements the singleton pattern, then performances will continuously decrease as the orchestration receives and consumes messages, and that behavior is more obvious when the orchestration never ends (ie : it keeps looping and never terminates or completes). As I experienced the same kind of problem (actually I was alerted by SCOM, which told me that the host was being throttled because of High database size), I thought it would be a good idea to dig a little bit a see what happens deep inside BizTalk and thus understand the reasons for this behavior. NOTE: in this article, I will focus on this High database size throttling condition. I will try and work on the other conditions in some not too distant future… Test conditions The singleton orchestration For the purpose of this study, I have created the following orchestration, which is a very basic implementation of a singleton that piles up incoming messages, then does something else when a certain timeout has been reached without receiving another message: Throttling settings I have two distinct hosts : one that hosts the receive port (basic FILE port) : Ports_ReceiveHostone that hosts the orchestration : ProcessingHost In order to emphasize the throttling mechanism, I have modified the throttling settings for each of these hosts are as follows (all other parameters are set to the default value): [Throttling thresholds] Message count in database: 500 (default value : 50000) Evolution of performance counters when submitting messages Since we are investigating the High database size throttling condition, here are the performance counter that we should take a look at (all of them are in the BizTalk:Message Agent performance object): Database sizeHigh database sizeMessage delivery throttling stateMessage publishing throttling stateMessage delivery delay (ms)Message publishing delay (ms)Message delivery throttling state durationMessage publishing throttling state duration (If you are not used to Perfmon, I strongly recommend that you start using it right now: it is a wonderful tool that allows you to open the hood and see what is going on inside BizTalk – and other systems) Database size It is quite obvious that we will start by watching the database size and high database size counters, just to see when the first reaches the configured threshold (500) and when the second rings the alarm. NOTE : During this test I submitted 600 messages, one message at a time every 10ms to see the evolution of the counters we have previously selected. It might not show very well on this screenshot, but here is what happened: From 15:46:50 to 15:47:50, the database size for the Ports_ReceiveHost host (blue line) kept growing until it reached a maximum of 504.At 15:47:50, the high database size alert fires At first I was surprised by this result: why is it the database size of the receiving host that keeps growing since it is the processing host that piles up messages? Actually, it makes total sense. This counter measures the size of the database queue that is being filled by the host, not consumed. Therefore, the high database size alert is raised on the host that fills the queue: Ports_ReceiveHost. More information is available on the Public MPWiki page. Now, looking at the Message publishing throttling state for the receiving host (green line), we can see that a throttling condition has been reached at 15:47:50: We can also see that the Message publishing delay(ms) (blue line) has begun growing slowly from this point. All of this explains why performances keep decreasing when a singleton keeps processing new messages: the database size grows and when it has exceeded the Message count in database threshold, the host is throttled and the publishing delay keeps increasing. Digging further So, what happens to the database queue then? Is it flushed some day or does it keep growing and growing indefinitely? The real question being: will the host be throttled forever because of this singleton? To answer this question, I set the Message count in database threshold to 20 (this value is very low in order not to wait for too long, otherwise I certainly would have fallen asleep in front of my screen) and I submitted 30 messages. The test was started at 18:26. At 18:56 (ie : exactly 30min later) the throttling was stopped and the database size was divided by 2. 30 min later again, the database size had dropped to almost zero: I guess I’ll have to find some documentation and do some more testing before I sort this out! My guess is that some maintenance job is at work here, though I cannot tell which one Digging even further If we take a look at the Message delivery throttling state counter for the processing host, we can see that this host was also throttled during the submission of the 600 documents: The value for the counter was 1, meaning that Message delivery incoming rate for the host instance exceeds the Message delivery outgoing rate * the specified Rate overdrive factor (percent) value. We will see this another day… :) A last word Let’s end this article with a warning: DO NOT CHANGE THE THROTTLING SETTINGS LIGHTLY! The temptation can be great to just bypass throttling by setting very high values for each parameter (or zero in some cases, which simply disables throttling). Nevertheless, always keep in mind that this mechanism is here for a very good reason: prevent your BizTalk infrastructure from exploding!! So whatever you do with those settings, do a lot of testing and benchmarking!

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  • Le projet MonoDroid apporte .NET sur Android, Novell veut construire une passerelle entre le framewo

    Le projet MonoDroid apporte .NET sur Android Novell veut construire une passerelle entre le framework de Microsoft et l'OS de Google Ce n'est pas un scoop, .NET tend à se généraliser. Aujourd'hui, le framework de Microsoft pourrait bien toucher Android, la plateforme Java de son grand concurrent Google, grâce à un projet de Novell, l'éditeur de Mono. Petit retour sur le projet Mono. Mono est l'implantation open-source et portable du framework .Net. Certains vont même jusqu'à dire qu...

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  • The ugly evolution of running a background operation in the context of an ASP.NET app

    - by Jeff
    If you’re one of the two people who has followed my blog for many years, you know that I’ve been going at POP Forums now for over almost 15 years. Publishing it as an open source app has been a big help because it helps me understand how people want to use it, and having it translated to six languages is pretty sweet. Despite this warm and fuzzy group hug, there has been an ugly hack hiding in there for years. One of the things we find ourselves wanting to do is hide some kind of regular process inside of an ASP.NET application that runs periodically. The motivation for this has always been that a lot of people simply don’t have a choice, because they’re running the app on shared hosting, or don’t otherwise have access to a box that can run some kind of regular background service. In POP Forums, I “solved” this problem years ago by hiding some static timers in an HttpModule. Truthfully, this works well as long as you don’t run multiple instances of the app, which in the cloud world, is always a possibility. With the arrival of WebJobs in Azure, I’m going to solve this problem. This post isn’t about that. The other little hacky problem that I “solved” was spawning a background thread to queue emails to subscribed users of the forum. This evolved quite a bit over the years, starting with a long running page to mail users in real-time, when I had only a few hundred. By the time it got into the thousands, or tens of thousands, I needed a better way. What I did is launched a new thread that read all of the user data in, then wrote a queued email to the database (as in, the entire body of the email, every time), with the properly formatted opt-out link. It was super inefficient, but it worked. Then I moved my biggest site using it, CoasterBuzz, to an Azure Website, and it stopped working. So let’s start with the first stupid thing I was doing. The new thread was simply created with delegate code inline. As best I can tell, Azure Websites are more aggressive about garbage collection, because that thread didn’t queue even one message. When the calling server response went out of scope, so went the magic background thread. Duh, all I had to do was move the thread to a private static variable in the class. That’s the way I was able to keep stuff running from the HttpModule. (And yes, I know this is still prone to failure, particularly if the app recycles. For as infrequently as it’s used, I have not, however, experienced this.) It was still failing, but this time I wasn’t sure why. It would queue a few dozen messages, then die. Running in Azure, I had to turn on the application logging and FTP in to see what was going on. That led me to a helper method I was using as delegate to build the unsubscribe links. The idea here is that I didn’t want yet another config entry to describe the base URL, appended with the right path that would match the routing table. No, I wanted the app to figure it out for you, so I came up with this little thing: public static string FullUrlHelper(this Controller controller, string actionName, string controllerName, object routeValues = null) { var helper = new UrlHelper(controller.Request.RequestContext); var requestUrl = controller.Request.Url; if (requestUrl == null) return String.Empty; var url = requestUrl.Scheme + "://"; url += requestUrl.Host; url += (requestUrl.Port != 80 ? ":" + requestUrl.Port : ""); url += helper.Action(actionName, controllerName, routeValues); return url; } And yes, that should have been done with a string builder. This is useful for sending out the email verification messages, too. As clever as I thought I was with this, I was using a delegate in the admin controller to format these unsubscribe links for tens of thousands of users. I passed that delegate into a service class that did the email work: Func<User, string> unsubscribeLinkGenerator = user => this.FullUrlHelper("Unsubscribe", AccountController.Name, new { id = user.UserID, key = _profileService.GetUnsubscribeHash(user) }); _mailingListService.MailUsers(subject, body, htmlBody, unsubscribeLinkGenerator); Cool, right? Actually, not so much. If you look back at the helper, this delegate then will depend on the controller context to learn the routing and format for the URL. As you might have guessed, those things were turning null after a few dozen formatted links, when the original request to the admin controller went away. That this wasn’t already happening on my dedicated server is surprising, but again, I understand why the Azure environment might be eager to reclaim a thread after servicing the request. It’s already inefficient that I’m building the entire email for every user, but going back to check the routing table for the right link every time isn’t a win either. I put together a little hack to look up one generic URL, and use that as the basis for a string format. If you’re wondering why I didn’t just use the curly braces up front, it’s because they get URL formatted: var baseString = this.FullUrlHelper("Unsubscribe", AccountController.Name, new { id = "--id--", key = "--key--" }); baseString = baseString.Replace("--id--", "{0}").Replace("--key--", "{1}"); Func unsubscribeLinkGenerator = user => String.Format(baseString, user.UserID, _profileService.GetUnsubscribeHash(user)); _mailingListService.MailUsers(subject, body, htmlBody, unsubscribeLinkGenerator); And wouldn’t you know it, the new solution works just fine. It’s still kind of hacky and inefficient, but it will work until this somehow breaks too.

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  • Google I/O 2011: Memory management for Android Apps

    Google I/O 2011: Memory management for Android Apps Patrick Dubroy Android apps have more memory available to them than ever before, but are you sure you're using it wisely? This talk will cover the memory management changes in Gingerbread and Honeycomb (concurrent GC, heap-allocated bitmaps, "largeHeap" option) and explore tools and techniques for profiling the memory usage of Android apps. From: GoogleDevelopers Views: 5698 45 ratings Time: 58:42 More in Science & Technology

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  • Thread placement policies on NUMA systems - update

    - by Dave
    In a prior blog entry I noted that Solaris used a "maximum dispersal" placement policy to assign nascent threads to their initial processors. The general idea is that threads should be placed as far away from each other as possible in the resource topology in order to reduce resource contention between concurrently running threads. This policy assumes that resource contention -- pipelines, memory channel contention, destructive interference in the shared caches, etc -- will likely outweigh (a) any potential communication benefits we might achieve by packing our threads more densely onto a subset of the NUMA nodes, and (b) benefits of NUMA affinity between memory allocated by one thread and accessed by other threads. We want our threads spread widely over the system and not packed together. Conceptually, when placing a new thread, the kernel picks the least loaded node NUMA node (the node with lowest aggregate load average), and then the least loaded core on that node, etc. Furthermore, the kernel places threads onto resources -- sockets, cores, pipelines, etc -- without regard to the thread's process membership. That is, initial placement is process-agnostic. Keep reading, though. This description is incorrect. On Solaris 10 on a SPARC T5440 with 4 x T2+ NUMA nodes, if the system is otherwise unloaded and we launch a process that creates 20 compute-bound concurrent threads, then typically we'll see a perfect balance with 5 threads on each node. We see similar behavior on an 8-node x86 x4800 system, where each node has 8 cores and each core is 2-way hyperthreaded. So far so good; this behavior seems in agreement with the policy I described in the 1st paragraph. I recently tried the same experiment on a 4-node T4-4 running Solaris 11. Both the T5440 and T4-4 are 4-node systems that expose 256 logical thread contexts. To my surprise, all 20 threads were placed onto just one NUMA node while the other 3 nodes remained completely idle. I checked the usual suspects such as processor sets inadvertently left around by colleagues, processors left offline, and power management policies, but the system was configured normally. I then launched multiple concurrent instances of the process, and, interestingly, all the threads from the 1st process landed on one node, all the threads from the 2nd process landed on another node, and so on. This happened even if I interleaved thread creating between the processes, so I was relatively sure the effect didn't related to thread creation time, but rather that placement was a function of process membership. I this point I consulted the Solaris sources and talked with folks in the Solaris group. The new Solaris 11 behavior is intentional. The kernel is no longer using a simple maximum dispersal policy, and thread placement is process membership-aware. Now, even if other nodes are completely unloaded, the kernel will still try to pack new threads onto the home lgroup (socket) of the primordial thread until the load average of that node reaches 50%, after which it will pick the next least loaded node as the process's new favorite node for placement. On the T4-4 we have 64 logical thread contexts (strands) per socket (lgroup), so if we launch 48 concurrent threads we will find 32 placed on one node and 16 on some other node. If we launch 64 threads we'll find 32 and 32. That means we can end up with our threads clustered on a small subset of the nodes in a way that's quite different that what we've seen on Solaris 10. So we have a policy that allows process-aware packing but reverts to spreading threads onto other nodes if a node becomes too saturated. It turns out this policy was enabled in Solaris 10, but certain bugs suppressed the mixed packing/spreading behavior. There are configuration variables in /etc/system that allow us to dial the affinity between nascent threads and their primordial thread up and down: see lgrp_expand_proc_thresh, specifically. In the OpenSolaris source code the key routine is mpo_update_tunables(). This method reads the /etc/system variables and sets up some global variables that will subsequently be used by the dispatcher, which calls lgrp_choose() in lgrp.c to place nascent threads. Lgrp_expand_proc_thresh controls how loaded an lgroup must be before we'll consider homing a process's threads to another lgroup. Tune this value lower to have it spread your process's threads out more. To recap, the 'new' policy is as follows. Threads from the same process are packed onto a subset of the strands of a socket (50% for T-series). Once that socket reaches the 50% threshold the kernel then picks another preferred socket for that process. Threads from unrelated processes are spread across sockets. More precisely, different processes may have different preferred sockets (lgroups). Beware that I've simplified and elided details for the purposes of explication. The truth is in the code. Remarks: It's worth noting that initial thread placement is just that. If there's a gross imbalance between the load on different nodes then the kernel will migrate threads to achieve a better and more even distribution over the set of available nodes. Once a thread runs and gains some affinity for a node, however, it becomes "stickier" under the assumption that the thread has residual cache residency on that node, and that memory allocated by that thread resides on that node given the default "first-touch" page-level NUMA allocation policy. Exactly how the various policies interact and which have precedence under what circumstances could the topic of a future blog entry. The scheduler is work-conserving. The x4800 mentioned above is an interesting system. Each of the 8 sockets houses an Intel 7500-series processor. Each processor has 3 coherent QPI links and the system is arranged as a glueless 8-socket twisted ladder "mobius" topology. Nodes are either 1 or 2 hops distant over the QPI links. As an aside the mapping of logical CPUIDs to physical resources is rather interesting on Solaris/x4800. On SPARC/Solaris the CPUID layout is strictly geographic, with the highest order bits identifying the socket, the next lower bits identifying the core within that socket, following by the pipeline (if present) and finally the logical thread context ("strand") on the core. But on Solaris on the x4800 the CPUID layout is as follows. [6:6] identifies the hyperthread on a core; bits [5:3] identify the socket, or package in Intel terminology; bits [2:0] identify the core within a socket. Such low-level details should be of interest only if you're binding threads -- a bad idea, the kernel typically handles placement best -- or if you're writing NUMA-aware code that's aware of the ambient placement and makes decisions accordingly. Solaris introduced the so-called critical-threads mechanism, which is expressed by putting a thread into the FX scheduling class at priority 60. The critical-threads mechanism applies to placement on cores, not on sockets, however. That is, it's an intra-socket policy, not an inter-socket policy. Solaris 11 introduces the Power Aware Dispatcher (PAD) which packs threads instead of spreading them out in an attempt to be able to keep sockets or cores at lower power levels. Maximum dispersal may be good for performance but is anathema to power management. PAD is off by default, but power management polices constitute yet another confounding factor with respect to scheduling and dispatching. If your threads communicate heavily -- one thread reads cache lines last written by some other thread -- then the new dense packing policy may improve performance by reducing traffic on the coherent interconnect. On the other hand if your threads in your process communicate rarely, then it's possible the new packing policy might result on contention on shared computing resources. Unfortunately there's no simple litmus test that says whether packing or spreading is optimal in a given situation. The answer varies by system load, application, number of threads, and platform hardware characteristics. Currently we don't have the necessary tools and sensoria to decide at runtime, so we're reduced to an empirical approach where we run trials and try to decide on a placement policy. The situation is quite frustrating. Relatedly, it's often hard to determine just the right level of concurrency to optimize throughput. (Understanding constructive vs destructive interference in the shared caches would be a good start. We could augment the lines with a small tag field indicating which strand last installed or accessed a line. Given that, we could augment the CPU with performance counters for misses where a thread evicts a line it installed vs misses where a thread displaces a line installed by some other thread.)

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  • Autoscaling in a modern world&hellip;. Part 2

    - by Steve Loethen
    When we last left off, we had a web application spinning away in the cloud, and a local console application watching it and reacting to changes in demand.  Reactions that were specified by a set of rules.  Let’s talk about those rules. Constraints.  The first set of rules this application answered to were the constraints. Here is what they looked like: <constraintRules> <rule name="default" enabled="true" rank="1" description="The default constraint rule"> <actions> <range min="1" max="4" target="AutoscalingApplicationRole"/> </actions> </rule> </constraintRules> Pretty basic.  We have one role, the “AutoscalingApplicationRole”, and we have decided to have it live within a range of 1 to 4.  This rule does not adjust, but instead, set’s limits on what other rules can do.  It has a rank, so you can have you can specify other sets of constraints, perhaps based on time or date, to allow for deviations from this set.  But for now, let’s keep it simple.  In the real world, you would probably use the minimum to set a lower end SLA.  A common value might be a 2, to prevent the reactive rules from ever taking you down to 1 role.  The maximum is often used to keep a rule from driving the cost up, setting an upper limit to prevent you waking up one morning and find a bill for hundreds of instances you didn’t expect.  So, here we have the range we want our application to live inside.  This is good for our investigation and testing.  Next, let’s take a look at the reactive rules.  These rules are what you use to react (hence reactive rules) to changing demands on your application.  The HOL has two simple rules.  One that looks at a queue depth, and one that looks at a performance counter that reports cpu utilization.  the XML in the rules file looks like this: <reactiveRules> <rule name="ScaleUp" rank="10" description="Scale Up the web role" enabled="true"> <when> <any> <greaterOrEqual operand="Length_05_holqueue" than="10"/> <greaterOrEqual operand="CPU_05_holwebrole" than="65"/> </any> </when> <actions> <scale target="AutoscalingApplicationRole" by="1"/> </actions> </rule> <rule name="ScaleDown" rank="10" description="Scale down the web role" enabled="true"> <when> <all> <less operand="Length_05_holqueue" than="5"/> <less operand="CPU_05_holwebrole" than="40"/> </all> </when> <actions> <scale target="AutoscalingApplicationRole" by="-1"/> </actions> </rule> </reactiveRules> <operands> <performanceCounter alias="CPU_05_holwebrole" performanceCounterName="\Processor(_Total)\% Processor Time" source="AutoscalingApplicationRole" timespan="00:05:00" aggregate="Average" /> <queueLength alias="Length_05_holqueue" queue="hol-queue" timespan="00:05:00" aggregate="Average"/> </operands> These rules are currently contained in a file called rules.xml, that is in the root of the console application.  The console app, starts up, grabs the rules and starts watching the 2 operands.  When it detects a rule has been satisfied, it performs the desired action.  (here, scale up or down my 1). But I want to host the autoscaler  in the cloud.  For my first trick, I will move the rules (and another file called services.xml) to azure blob storage.  Look for part 3.

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  • Victor Grazi, Java Champion!

    - by Tori Wieldt
    Congratulations to Victor Grazi, who has been made a Java Champion! He was nominated by his peers and selected as a Java Champion for his experience as a developer, and his work in the Java and Open Source communities. Grazi is a Java evangelist and serves on the Executive Committee of the Java Community Process, representing Credit Suisse - the first non-technology vendor on the JCP. He also arranges the NY Java SIG meetings at Credit Suisse's New York campus each month, and he says it has been a valuable networking opportunity. He also is the spec lead for JSR 354, the Java Money and Currency API. Grazi has been building real time financial systems in Java since JDK version 1.02! In 1996, the internet was just starting to happen, Grazi started a dot com called Supermarkets to Go, that provided an on-line shopping presence to supermarkets and grocers. Grazi wrote most of the code, which was a great opportunity for him to learn Java and UI development, as well as database management. Next, he went to work at Bank of NY building a trading system. He studied for Java certification, and he noted that getting his certification was a game changer because it helped him started to learn the nuances of the Java language. He has held other development positions, "You may have noticed that you don't get as much junk mail from Citibank as you used to - that is thanks to one of my projects!" he told us. Grazi joined Credit Suisse in 2005 and is currently Vice President on the central architecture team. Grazi is proud of his open source project, Java Concurrent Animated, a series of animations that visualize the functionality of the components in the java.util.concurrent library. "It has afforded me the opportunity to speak around the globe" and because of it, has discovered that he really enjoys doing public presentations. He is a fine addition to the Java Champions program. The Java Champions are an exclusive group of passionate Java technology and community leaders who are community-nominated and selected under a project sponsored by Oracle. Nominees are named and selected through a peer review process. Java Champions get the opportunity to provide feedback, ideas, and direction that will help Oracle grow the Java Platform. This interchange may be in the form of technical discussions and/or community-building activities with Oracle's Java Development and Developer Program teams.

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  • ATG Live Webcast March 29: Diagnosing E-Business Suite JVM and Forms Performance Issues (Performance Series Part 4 of 4)

    - by BillSawyer
    The next webcast in our popular EBS series on performance management is going to be a showstopper.  Dave Suri, Project Lead, Applications Performance and Gustavo Jimenez, Senior Development Manager will discuss some of the steps involved in triaging and diagnosing E-Business Suite systems related to JVM and Forms components. Please join us for our next ATG Live Webcast on Mar. 29, 2012: Triage and Diagnostics for E-Business Suite JVM and Forms The topics covered in this webcast will be: Overall Menu/Sections Architecture Patches/Certified browsers/jdk versions JVM Tuning JVM Tools (jstat,eclipse mat, ibm tda) Forms Tools (strace/FRD) Java Concurrent Program options location Case studies Case Studies JVM Thread dump case for Oracle Advanced Product Catalog Forms FRD trace relating to Saving an SR Java Concurrent Program for BT Date:               Thursday, March 29, 2012Time:              8:00 AM - 9:00 AM Pacific Standard TimePresenters:  Dave Suri, Project Lead, Applications Performance                        Gustavo Jimenez, Senior Development ManagerWebcast Registration Link (Preregistration is optional but encouraged)To hear the audio feed:   Domestic Participant Dial-In Number:            877-697-8128    International Participant Dial-In Number:      706-634-9568    Additional International Dial-In Numbers Link:    Dial-In Passcode:                                              99342To see the presentation:    The Direct Access Web Conference details are:    Website URL: https://ouweb.webex.com    Meeting Number:  597073984 If you miss the webcast, or you have missed any webcast, don't worry -- we'll post links to the recording as soon as it's available from Oracle University.  You can monitor this blog for pointers to the replay. And, you can find our archive of our past webcasts and training here.If you have any questions or comments, feel free to email Bill Sawyer (Senior Manager, Applications Technology Curriculum) at BilldotSawyer-AT-Oracle-DOT-com. 

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  • C# async and actors

    - by Alex.Davies
    If you read my last post about async, you might be wondering what drove me to write such odd code in the first place. The short answer is that .NET Demon is written using NAct Actors. Actors are an old idea, which I believe deserve a renaissance under C# 5. The idea is to isolate each stateful object so that only one thread has access to its state at any point in time. That much should be familiar, it's equivalent to traditional lock-based synchronization. The different part is that actors pass "messages" to each other rather than calling a method and waiting for it to return. By doing that, each thread can only ever be holding one lock. This completely eliminates deadlocks, my least favourite concurrency problem. Most people who use actors take this quite literally, and there are plenty of frameworks which help you to create message classes and loops which can receive the messages, inspect what type of message they are, and process them accordingly. But I write C# for a reason. Do I really have to choose between using actors and everything I love about object orientation in C#? Type safety Interfaces Inheritance Generics As it turns out, no. You don't need to choose between messages and method calls. A method call makes a perfectly good message, as long as you don't wait for it to return. This is where asynchonous methods come in. I have used NAct for a while to wrap my objects in a proxy layer. As long as I followed the rule that methods must always return void, NAct queued up the call for later, and immediately released my thread. When I needed to get information out of other actors, I could use EventHandlers and callbacks (continuation passing style, for any CS geeks reading), and NAct would call me back in my isolated thread without blocking the actor that raised the event. Using callbacks looks horrible though. To remind you: m_BuildControl.FilterEnabledForBuilding(    projects,    enabledProjects = m_OutOfDateProjectFinder.FilterNeedsBuilding(        enabledProjects,             newDirtyProjects =             {                 ....... Which is why I'm really happy that NAct now supports async methods. Now, methods are allowed to return Task rather than just void. I can await those methods, and C# 5 will turn the rest of my method into a continuation for me. NAct will run the other method in the other actor's context, but will make sure that when my method resumes, we're back in my context. Neither actor was ever blocked waiting for the other one. Apart from when they were actually busy doing something, they were responsive to concurrent messages from other sources. To be fair, you could use async methods with lock statements to achieve exactly the same thing, but it's ugly. Here's a realistic example of an object that has a queue of data that gets passed to another object to be processed: class QueueProcessor {    private readonly ItemProcessor m_ItemProcessor = ...     private readonly object m_Sync = new object();    private Queue<object> m_DataQueue = ...    private List<object> m_Results = ...     public async Task ProcessOne() {         object data = null;         lock (m_Sync)         {             data = m_DataQueue.Dequeue();         }         var processedData = await m_ItemProcessor.ProcessData(data); lock (m_Sync)         {             m_Results.Add(processedData);         }     } } We needed to write two lock blocks, one to get the data to process, one to store the result. The worrying part is how easily we could have forgotten one of the locks. Compare that to the version using NAct: class QueueProcessorActor : IActor { private readonly ItemProcessor m_ItemProcessor = ... private Queue<object> m_DataQueue = ... private List<object> m_Results = ... public async Task ProcessOne()     {         // We are an actor, it's always thread-safe to access our private fields         var data = m_DataQueue.Dequeue();         var processedData = await m_ItemProcessor.ProcessData(data);         m_Results.Add(processedData);     } } You don't have to explicitly lock anywhere, NAct ensures that your code will only ever run on one thread, because it's an actor. Either way, async is definitely better than traditional synchronous code. Here's a diagram of what a typical synchronous implementation might do: The left side shows what is running on the thread that has the lock required to access the QueueProcessor's data. The red section is where that lock is held, but doesn't need to be. Contrast that with the async version we wrote above: Here, the lock is released in the middle. The QueueProcessor is free to do something else. Most importantly, even if the ItemProcessor sometimes calls the QueueProcessor, they can never deadlock waiting for each other. So I thoroughly recommend you use async for all code that has to wait a while for things. And if you find yourself writing lots of lock statements, think about using actors as well. Using actors and async together really takes the misery out of concurrent programming.

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  • Tips On Using The Service Contracts Import Program

    - by LuciaC
    Prior to release 12.1 there was no supported way to import contracts into the EBS Service Contracts application - there were no public APIs nor contract load programs provided.  From release 12.1 onwards the 'Service Contracts Import Program' is provided to load service contracts into the application. The Service Contracts Import functionality is explained in How to Use the Service Contracts Import Program - Scope and Limitations (Doc ID 1057242.1).  This note includes an attached document which explains the program architecture, shows the Entity Relationship Diagram and details the interface table definitions. The Import program takes data from the interface tables listed below and populates the contracts schema tables:  OKS_USAGE_COUNTERS_INTERFACE OKS_SALES_CREDITS_INTERFACEOKS_NOTES_INTERFACEOKS_LINES_INTERFACEOKS_HEADERS_INTERFACEOKS_COVERED_LEVELS_INTERFACEThese interface tables must be loaded via a custom load program.The Service Contracts Import concurrent request is then submitted to create contracts from this legacy data. The parameters to run the Import program are:  Parameter Description  Mode Validate only, Import  Batch Number Batch_Id (unique id populated into the OKS_HEADERS_INTERFACE table)  Number of Workers Number of workers required (these are spawned as separate sub-requests)  Commit size Represents number of successfully processed contracts commited to database The program spawns sub-requests for the import worker(s) and the 'Service Contracts Import Report'.  The data is validated prior to import and into the Contracts tables and will report errors in the Service Contracts Import Report program output file (Import Execution Report).  Troubleshooting tips are provided in R12.1 - Common Service Contract Import Errors (Doc ID 762545.1); this document lists some, but not all, import errors.  The document will be updated over time.  Additional help is given in Debugging Tip for Service Contracts Import Errors (Doc ID 971426.1).After you successfully import contracts, you can purge the records from the interface tables by running the Service Contracts Import Purge concurrent program. Note that there is no supported way to mass delete data from the Contracts schema tables once they are populated, so data loaded by the Import program must be fully tested and verified before the program is run to load data into a Production system.A Service Contracts Import Test program has been provided which will take an existing contract in the application and load the interface tables using the data from that contract.  This can be used as an example for guidance on how to load the interface tables.  The Test program functionality is explained in How to Use the Service Contracts Test Import Program Provided in Release 12.1 (Doc ID 761209.1).  Note that the Test program has some limitations which do not apply to the full Import program and is not a supported program, it is simply a testing tool.  

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  • L'alternative d'Apple au Flash s'appelle Gianduia, écrite en JavaScript elle s'appuierait sur Cocoa

    Mise à jour du 10/05/10 L'alternative d'Apple au Flash s'appelle Gianduia Elle est écrite en JavaScript Critiquer c'est bien. Proposer c'est mieux. C'est ce que Apple serait sur le point de faire avec sa propre solution pour remplacer Flash (et par la même occasion Silverlight, le concurrent de chez Microsoft). Baptisée Gianduia, cette technologie RIA aurait déjà été testée par Apple dans plusieurs de ses services de distribution comme le programme One-to-One, (formation individuelle dans les magasins de la marque), le système de réservation de l'iPhone ou les applications des Concierges (ses vendeurs spécialisés).

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  • Is there any way to test how will the site perform under load

    - by Pankaj Upadhyay
    I have made an Asp.net MVC website and hosted it on a shared hosting provider. Since my website surrounds a very generic idea, it might have number of concurrent users sometime in future. So, I was thinking of a way to test my website for on-load performance. Like how will the site perform when 100 or 1000 users are online at the same time and surfing the website. This will also make me understand whether my LINQ queries are well written or not.

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  • Internet Explorer 9 ne soutiendra que le H.264 : vers un nouveau coup dur pour Flash ?

    Mise à jour du 30/04/10 Internet Explorer 9 ne supportera que le H.264 Vers un nouveau coup dur pour Flash ? Microsoft vient de réitérer son implication dans la future norme du HTML 5. « Le futur du Web c'est le HTML5 », a même écrit hier sur son blog le General Manager d'Internet Explorer, qui explique que « la spécification HTML 5 permet de décrire le support d'une vidéo sans spécifier un format particulier ». Jusqu'ici, rien de très nouveau, même si cette implication pose la question de son articulation avec Silverlight, le concurrent maison de Flash (lire par ailleurs :

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  • Basic Defensive Database Programming Techniques

    We can all recognise good-quality database code: It doesn't break with every change in the server's configuration, or on upgrade. It isn't affected by concurrent usage, or high workload. In an extract from his forthcoming book, Alex explains just how to go about producing resilient TSQL code that works, and carries on working.

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  • If immutable objects are good, why do people keep creating mutable objects?

    - by Vinoth Kumar
    If immutable objects are good,simple and offers benefits in concurrent programming why do programmers keep creating mutable objects? I have four years of experience in Java programming and as I see it, the first thing people do after creating a class is generate getters and setters in the IDE (thus making it mutable). Is there a lack of awareness or can we get away with using mutable objects in most scenarios?

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  • Basic Defensive Database Programming Techniques

    We can all recognize good-quality database code: It doesn't break with every change in the server's configuration, or on upgrade. It isn't affected by concurrent usage, or high workload. In an extract from his forthcoming book, Alex explains just how to go about producing resilient TSQL code that works, and carries on working.

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  • L'ancien cadre de Microsoft interdit d'embauche chez Salesforce par la justice, après le vol de données confidentielles

    L'ancien cadre de Microsoft interdit d'embauche chez Salesforce Par la justice, suite au vol de données confidentielles du CRM de Microsoft Mise à jour du 24/02/11, par Hinault Romaric Matt Miszewski, ancien directeur de la division des activités grands comptes de Microsoft, ne pourra pas occuper un poste similaire chez Saleforces.com, concurrent de Microsoft dans le domaine des CRM en mode Cloud Computing. C'est ce qui ressort de la décision de justice après l'étude de la requête déposée par Microsoft auprès de la cour de Washington. Pour mémoire Microsoft accusait Miszewski d'avoir emporté avec lui 600 Mo de données confidenti...

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  • WebM : la justice américaine enquête sur le groupe MPEG-LA et des actions potentiellement anticoncurrentielles contre le format de Google

    WebM : la justice américaine enquête sur le groupe MPEG-LA Et des actions potentiellement anticoncurrentielles contre le format de Google Mise à jour du 07/03/2011 par Idelways D'après un rapport rendu public par le Wall Street Journal, le département de la justice américaine aurait lancé une enquête antitrust sur le groupe de gestion de brevets MPEG-LA, le soupçonnant de vouloir attenter injustement à un rival technologique open-source supporté par Google (VP8). Le groupe MPEG-LA avait lancé mi-février un appel à tous les industriels qui estiment détenir des brevets potentiellement utilisés par le codec concurrent « VP...

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