Search Results

Search found 49453 results on 1979 pages for 'memory mapped files'.

Page 98/1979 | < Previous Page | 94 95 96 97 98 99 100 101 102 103 104 105  | Next Page >

  • Media center consumes all available memory when attempting to play music off of a server

    - by RCIX
    I have Windows 7 Ultimate, and recently, when i try to play a song off of my Twonky Media Server/Windows Media Connect (based on an HP WHS with an Atom), it plays choppily. When i open Resource Monitor, it shows that after ordering the music to play, memory usage rapidly spikes to consume most, if not all, of the available memory on my system (excluding a couple hundred megabytes in standby). Why does it do this and is there anything i can do to stop it? Edit: it happens when I attempt to browse the server's music, not just when i play music. Edit 2: the "ehshell" process is what consumes the memory, appears to me something specific to media center. Moreover, the ehshell process doesn't die in this case. Edit 3: It only happens when browsing my Twonky library, and not my Windows Media Connect.

    Read the article

  • Maximum memory allocation for 32bit linux kernel

    - by LedZeppelin
    I was reading this article that talks about how maximum amount of ram dedicated for kernel usage in 32 bit windows is 2GB even when the total amount of ram is 4GB. http://www.brianmadden.com/blogs/brianmadden/archive/2004/02/19/the-4gb-windows-memory-limit-what-does-it-really-mean.aspx\ Is this the same for 32bit linux environments like 32-bit ubuntu 10.04? IE is the max kernel allocation 2GB ram even if the total main memory 4GB? If you increase the total amount of memory to 64GB of ram by recompiling the kernel with the PAE option enabled, what is the maximum amount of ram you can dedicate for kernel usage? Is it still 2GB? Or can you increase it?

    Read the article

  • Tuning MySQL to consume less memory

    - by Alex
    I have a VM which has 2GB Ram, (full specs) And I am setting up a site which has one table in particular with over a million records. There's little or no usage of this particular database (perhaps once or twice a day) but simply running mysql grinds the whole server to a halt. I've looked through the top results but nothing is really denting the CPU however the memory seems to be the issue. The site isnt even live of taking requests yet. the memory situation looks like this: # free -m total used free shared buffers cached Mem: 2006 1880 126 0 3 53 -/+ buffers/cache: 1823 183 Swap: 2047 345 1702 Are there any good pointers to tune mysql to stop hogging the system memory? Thanks very much EDIT: (requested by 8bit): http://tny.cz/b41a0b12

    Read the article

  • innodb memory usage mysql

    - by Tiddo
    I have a small vps, with only 256mb of ram, with maximum burst up to 512mb. When I configure my vps without innodb, it only uses 130 mb of ram, so that is no problem for me. But when I turn on innodb, The memory usage grows to about 300-400 mb. Is it possible to run innodb such that I won't exceed the 256mb? preferably I don't want to use more than 100mb for innodb. I already came across some sites which said I could limit the memory usage, but if I limit it to only 100mb will the db run well enough? (compared to for example the MyISAM storage engine) If 100mb is to little memory for innodb, can you recommend me any other storage engine which supports transactions?

    Read the article

  • Asus P6X58-E WS motherboard memory limits

    - by Arsen Zahray
    I've just ordered motherboard Asus P6X58-E WS, and now I'm, looking for memory for it. It has 6 slots, but the strange thing is, that in specifications it says that it is limited to 24G of memory . I'm planning on using 8G Kingston KVR1333D3D4R9S/8G sticks with it. Using those sticks, I'll be teoretically limited to 48G. Does the 24G limitation mean, that even if I install all 6 sticks, I still will be limited by 24G of possible memory? Sorry if the question seems dumb, but I never faced such a limitation before

    Read the article

  • Clearing Windows file share "memory"

    - by Tom Shaw
    I'm currently upgrading a Samba file server (from 3.0.23d to 3.4.3). I have a problem on the Windows client side: if the client was accessing a UNC path or mapped drive from the Samba server before the upgrade, then after the upgrade those paths or drives are not accessible. However, I can consistently resolve the client side problem for good by rebooting the client and then re-mapping all of the mapped drives. The problem appears to be related to the client's "memory" of the pre-upgrade Samba server, which the reboot and re-map clears. I have the same issue and same fix on Windows XP SP3 and Windows Server 2003 SP2. This question is specifically: is it possible to reproduce the benefits of the Windows reboot without actually rebooting the client? I have tried restarting various Windows services, disabling and enabling the network, logging out and back in again, but nothing except a reboot appears to do the trick.

    Read the article

  • Dropping Cached Memory on FreeBSD

    - by user1066698
    i use FreeNAS server which is built on OS version FreeBSD 8.2-RELEASE-p6. I use ZFS file system with 13TB HDD on my 8GB physical ram installed box. It almost uses all of RAM installed while proccessing some request. However, it still uses same amount of memory on idle times. So this is becoming a problem sometimes. On my centos web server; i use following command to drop cached memory with a cronjob; sync; echo 3 > /proc/sys/vm/drop_caches However, this command does not work on my Freenas server. How can i drop cached memory on my FreeNAS box which is built on FreeBSD 8.2 Thank you

    Read the article

  • debian out of memory error server crash

    - by user42700
    hi, the server keeps crashing due to apache, is there any way i can stop this, the server has 2GB swap space and 3GB ram May 25 03:33:41 server kernel: [ 3513.200719] [<c015959c>] out_of_memory+0x14e/0x17f May 25 03:33:41 server kernel: [ 3513.211491] Out of memory: kill process 2936 (apache2) score 87364 or a child May 25 04:35:30 server kernel: [ 7239.936995] [<c015959c>] out_of_memory+0x14e/0x17f May 25 04:35:30 server kernel: [ 7239.948878] Out of memory: kill process 2936 (apache2) score 88236 or a child May 25 05:42:57 server kernel: [11210.572510] [<c015959c>] out_of_memory+0x14e/0x17f May 25 08:13:23 server kernel: [ 0.000000] PM: Registered nosave memory: 00000000000a0000 - 0000000000100000

    Read the article

  • How can I split my conkeror-rc config over multiple files?

    - by Ryan Thompson
    Short version: can you help me fill in this code? var conkeror_settings_dir = ".conkeror.mozdev.org/settings"; function load_all_js_files_in_dir (dir) { var full_path = get_home_directory().appendRelativePath(dir); // YOUR CODE HERE } load_all_js_files_in_dir(conkeror_settings_dir); Background I'm trying out Conkeror for web browsing. It's an emacs-like browser running on Mozilla's rendering engine, using javascript as configuration language (filling the role that elisp plays for emacs). In my emacs config, I have split my customizations into a series of files, where each file is a single unit of related options (for example, all my perl-related settings might be in perl-settings.el. All these settings files are loaded automatically by a function in my .emacs that simply loads every elisp file under my "settings" directory. I am looking to structure my Conkeror config in the same way, with my main conkeror-rc file basically being a stub that loads all the js files under a certain directory relative to my home directory. Unfortunately, I am much less literate in javascript than I am in elisp, so I don't even know how to "source" a file.

    Read the article

  • Why does Perl's Devel::LeakTrace::Fast point to blank files and evals?

    - by kt
    I am using Devel::LeakTrace::Fast to debug a memory leak in a perl script designed as a daemon which runs an infinite loop with sleeps until interrupted. I am having some trouble both reading the output and finding documentation to help me understand the output. The perldoc doesn't contain much information on the output. Most of it makes sense, such as pointing to globals in DBI. Intermingled with the output, however, are several leaked SV(<LOCATION>) from (eval #) line # Where the numbers are numbers and <LOCATION> is a location in memory. The script itself is not using eval at any point - I have not investigated each used module to see if evals are present. Mostly what I want to know is how to find these evals (if possible). I also find the following entries repeated over and over again leaked SV(<LOCATION>) from line # Where line # is always the same #. Not very helpful in tracking down what file that line is in.

    Read the article

  • Where to create/keep secret files for license information/trials on Windows/Mac OS X/Linux?

    - by BastiBense
    I'm writing a commercial product which uses a simple registration mechanism and allows the user to use the application for a demo period before purchasing. My application must somewhere store the registration information (if entered) and/or the date of the first launch to calculate if the user is still within the demo/trail period. While I'm pretty much finished with the registration mechanism itself, I now have to find a good way to store the registration information on the user's disk. The most obvious idea would be to store the trial period in the preferences file, but since user tend to delete/tinker with those from time to time, it might be a good idea to keep the registration information in a separate, more hidden file. So here's my question: What is the best place/strategy to keep and create such hidden files on Windows, Mac OS X and Linux? Here is what came to my mind so far: Linux/Mac OS X Most Unix-like systems are rather locked down when it comes to places a user can write files to. In most cases this is only the /tmp directory and the user's home directory. I guess the easiest here is probably to create a file with a dot-prefix to make it less visible, then give it a name that won't make it obvious that it's associated with my application. Windows Probably much like Linux/Mac OS X - more recent Windows versions become more restrictive when it comes to file system permissions. Anyway, I'd like to hear your ideas and thoughs. Even better if you have already implemented something similar in the past. Thanks! Update For me the places for such files is more relevant than the discussion of the question if this way for copy protection is good or bad.

    Read the article

  • Local server updates for the network

    - by Brendon
    I have setup one computer on our network as the file server. Because Internet here in Tanzania is both slow and expensive I would like that one system to download all the updates and then the other 10 computers on the network to get those update files from the server. I'm a bit of a noobie to Ubuntu, but really want to learn how to get this working smoothly so as to help other NGOs and schools here in Tanzania. Brendon

    Read the article

  • How to clean and add options to the Open With list of apps

    - by Luis Alvarado
    After installing several PPAs (Wine, PoL) and opening several files with other apps (Like changing from Totem to VLC) I discovered that the Open With option had 2 problems: Many items on the list are duplicated (As seen on the image for "A Wine Program") Sometimes the app I want to use to open is not shown there (For example, Virtualbox or VLC) So how can I edit this list to clean the duplicates and add missing apps from the list.

    Read the article

  • "Ghost" output from locate?

    - by Hailwood
    I deleted some files, but they seem to still exist. Can anyone please explain the output of this: m@work:~$ locate cfx.css | xargs rm m@work:~$ locate cfx.css /var/www/wfox/hbr.co.nz/cfx/a/c/cfx.css /var/www/wfox/modules/gallery/cfx/a/c/cfx.css /var/www/wfox/phoenix/fp.co.nz/cfx/a/c/cfx.css /var/www/wfox/tmp.co.nz/cfx/a/c/cfx.css m@work:~$ cat /var/www/wfox/hbr.co.nz/cfx/a/c/cfx.css cat: /var/www/wfox/hbr.co.nz/cfx/a/c/cfx.css: No such file or directory

    Read the article

  • Application Crash cleared the content of the Folder

    - by Ameya
    Recently while working on the LinuxDC++ over the network the application crashed while downloading files. Now my Downloads folder which had at least 60-80GB of data is completely cleaned but the system is not reporting the available the correct free space. Is there way to restore the contents of the folder only as the solution available are for the whole partition. I just want to recover the contents from one folder.

    Read the article

  • MapViewOfFile shared between 32bit and 64bit processes

    - by MK
    Hi, I'm trying to use MapViewOfFile in a 64 bit process on a file that is already mapped to memory of another 32 bit process. It fails and gives me an "access denied" error. Is this a known Windows limitation or am I doing something wrong? Same code works fine with 2 32bit processes. The code sort of looks like this: hMapFile = OpenFileMapping(FILE_MAP_ALL_ACCESS, FALSE, szShmName); if (NULL == hMapFile) { /* failed to open - create new (this happens in the 32 bit app) */ SECURITY_ATTRIBUTES sa; sa.nLength = sizeof(SECURITY_ATTRIBUTES); sa.bInheritHandle = FALSE; /* give access to members of administrators group */ BOOL success = ConvertStringSecurityDescriptorToSecurityDescriptor( "D:(A;OICI;GA;;;BA)", SDDL_REVISION_1, &(sa.lpSecurityDescriptor), NULL); HANDLE hShmFile = CreateFile(FILE_FAXCOM_SHM, FILE_ALL_ACCESS, 0, &sa, OPEN_ALWAYS, 0, NULL); hMapFile = CreateFileMapping(hShmFile, &sa, PAGE_READWRITE, 0, SHM_SIZE, szShmName); CloseHandle(hShmFile); } // this one fails in 64 bit app pShm = MapViewOfFile(hMapFile, FILE_MAP_ALL_ACCESS, 0, 0, SHM_SIZE);

    Read the article

  • 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.

    Read the article

  • Memory leak when using Workflow 4.0 SqlWorkflowInstanceStore and PersistableIdleAction.Unload

    - by Rohland
    Hi, This particular problem is driving me nuts. I wonder if anyone has experienced a similar problem. If I load up a workflow then unload it and perform a memory snapshot then the result is predictable - my workflow is no longer in memory. However, if I load up a workflow and set the PersistableIdle action to PersistableIdleAction.Unload and let the workflow idle the workflow remains in memory even though the Unload action fires. I used ANTS Memory Profiler to debug this issue. This is the object retention graph outputted showing that an internal object is hanging onto my workflow instance. Can anyone else verify this problem? My code amounts to the following: Create SqlWorkflowInstanceStore and setup lock owner handle -- At this point I take a memory snapshot Create an instance of Workflow1 Set the PersistableIdle action Apply the instancestore to Workflow1 Setup action event handlers for Idle, Unload, UnhandledException etc. Persist the workflow instance Run the workflow instance Wait for instance to idle (caused by Delay activity) Ensure the Unload action is fired -- At this point I take a second memory snapshot From the above image, it is clear that the only object referencing Workflow1 is some internal event handlers result which I have no ability to dispose of. Any clues?

    Read the article

  • Scheme - Memory System

    - by Eric
    I am trying to make a memory system where you input something in a slot of memory. So what I am doing is making an Alist and the car of the pairs is the memory location and the cdr is the val. I need the program to understand two messages, Read and Write. Read just displaying the memory location selected and the val that is assigned to that location and write changes the val of the location or address. How do I make my code so it reads the location you want it to and write to the location you want it to? Feel free to test this yourself. Any help would be much appreciated. This is what I have: (define make-memory (lambda (n) (letrec ((mem '()) (dump (display mem))) (lambda () (if (= n 0) (cons (cons n 0) mem) mem) (cons (cons (- n 1) 0) mem)) (lambda (msg loc val) (cond ((equal? msg 'read) (display (cons n val))(set! n (- n 1))) ((equal? msg 'write) (set! mem (cons val loc)) (set! n (- n 1)) (display mem))))))) (define mymem (make-memory 100))

    Read the article

  • Using CGContextDrawTiledImage at different zooms causes massive memory growth

    - by Jacques
    I'm working on app an where there's a view in a zoomable UIScrollView. When the user zooms in or out, I redraw the view that's in the UIScrollView to be nice and sharp. That view has a background image that I draw with CGContextDrawTiledImage. I noticed that memory usage grows every time I switch to a new zoom level. It looks like CGContextDrawTiledImage keeps a cache somewhere of the image scaled to different sizes. So, If I go from 1.0 to 1.1x zoom, memory use grows. Going back to 1.0 doesn't cause it to grow, but then going to 1.05 and then 1.2 causes it to grow twice. Back to 1.1 and no growth. Of course, the zoom level is under user control so I don't have control over how many zoom levels happen. Right now my background image is kind of massive (512x512), so this causes memory usage to grow very quickly. It doesn't show up as a memory leak in Instruments, just additional allocations that never get freed. I've tried to find a way to free the cache that appears to be being created, but no luck. It doesn't seem to respond to low memory warnings, for example. I also tried setting the view's backgroundColor to a UIColor created with colorWithPatternImage, but that doesn't work because I'm doing the scaling by changing the graphics context's CTM, not by setting the view's transform. Any ideas on how to keep memory usage from blowing up?

    Read the article

  • What does an object look like in memory?

    - by NeilMonday
    This is probably a really dumb question, but I will ask anyway. I am curious what an object looks like in memory. Obviously it would have to have all of its member data in it. I assume that functions for an object would not be duplicated in memory (or maybe I am wrong?). It would seem wasteful to have 999 objects in memory all with the same function defined over and over. If there is only 1 function in memory for all 999 objects, then how does each function know who's member data to modify (I specifically want to know at the low level). Is there an object pointer that gets sent to the function behind the scenes? Perhaps it is different for every compiler? Also, how does the static keyword affect this? With static member data, I would think that all 999 objects would use the exact same memory location for their static member data. Where does this get stored? Static functions I guess would also just be one place in memory, and would not have to interact with instantiated objects, which I think I understand.

    Read the article

  • Why is my Internet connection randomly dropping?

    - by Jeanno
    Ever since I have installed 12.04 (clean install not an upgrade), i have been having a drop in the Internet connection. The drop in the connection can be anything from 15 seconds to about 3 mins, and then the connection comes back. This behaviour happens while I am actively browsing the Internet, or if I wake up the computer and open Firefox (sometimes I have connection and sometimes I don't) . Please note that when the internet connection is on, it is not slow (as speedtest.net results show) In the beginning, I thought it was a problem with the driver r8169 for my RTL8111/8168B Ethernet card, so I downloaded the r8168 from Realtek website, followed the detailed instructions (blacklisted r8169, changed the file to '.bsh' ...), but still the same problem persisted. So I switched to a wireless connection, and I got the same problem with internet connection dropping randomly. Any ideas? Thanks in advance Output from 'lspci -v' Code: 00:00.0 Host bridge: Intel Corporation 2nd Generation Core Processor Family DRAM Controller (rev 09) Subsystem: Dell Device 04a7 Flags: bus master, fast devsel, latency 0 Capabilities: [e0] Vendor Specific Information: Len=0c <?> 00:01.0 PCI bridge: Intel Corporation Xeon E3-1200/2nd Generation Core Processor Family PCI Express Root Port (rev 09) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=01, subordinate=01, sec-latency=0 I/O behind bridge: 0000e000-0000efff Memory behind bridge: f8000000-fa0fffff Prefetchable memory behind bridge: 00000000d0000000-00000000dbffffff Capabilities: [88] Subsystem: Dell Device 04a7 Capabilities: [80] Power Management version 3 Capabilities: [90] MSI: Enable+ Count=1/1 Maskable- 64bit- Capabilities: [a0] Express Root Port (Slot+), MSI 00 Capabilities: [100] Virtual Channel Capabilities: [140] Root Complex Link Kernel driver in use: pcieport Kernel modules: shpchp 00:01.1 PCI bridge: Intel Corporation Xeon E3-1200/2nd Generation Core Processor Family PCI Express Root Port (rev 09) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=02, subordinate=02, sec-latency=0 I/O behind bridge: 0000d000-0000dfff Memory behind bridge: f4000000-f60fffff Prefetchable memory behind bridge: 00000000c0000000-00000000cbffffff Capabilities: [88] Subsystem: Dell Device 04a7 Capabilities: [80] Power Management version 3 Capabilities: [90] MSI: Enable+ Count=1/1 Maskable- 64bit- Capabilities: [a0] Express Root Port (Slot+), MSI 00 Capabilities: [100] Virtual Channel Capabilities: [140] Root Complex Link Kernel driver in use: pcieport Kernel modules: shpchp 00:16.0 Communication controller: Intel Corporation 6 Series/C200 Series Chipset Family MEI Controller #1 (rev 04) Subsystem: Dell Device 04a7 Flags: bus master, fast devsel, latency 0, IRQ 52 Memory at f6108000 (64-bit, non-prefetchable) [size=16] Capabilities: [50] Power Management version 3 Capabilities: [8c] MSI: Enable+ Count=1/1 Maskable- 64bit+ Kernel driver in use: mei Kernel modules: mei 00:1a.0 USB controller: Intel Corporation 6 Series/C200 Series Chipset Family USB Enhanced Host Controller #2 (rev 05) (prog-if 20 [EHCI]) Subsystem: Dell Device 04a7 Flags: bus master, medium devsel, latency 0, IRQ 16 Memory at f6107000 (32-bit, non-prefetchable) [size=1K] Capabilities: [50] Power Management version 2 Capabilities: [58] Debug port: BAR=1 offset=00a0 Capabilities: [98] PCI Advanced Features Kernel driver in use: ehci_hcd 00:1b.0 Audio device: Intel Corporation 6 Series/C200 Series Chipset Family High Definition Audio Controller (rev 05) Subsystem: Dell Device 04a7 Flags: bus master, fast devsel, latency 0, IRQ 53 Memory at f6100000 (64-bit, non-prefetchable) [size=16K] Capabilities: [50] Power Management version 2 Capabilities: [60] MSI: Enable+ Count=1/1 Maskable- 64bit+ Capabilities: [70] Express Root Complex Integrated Endpoint, MSI 00 Capabilities: [100] Virtual Channel Capabilities: [130] Root Complex Link Kernel driver in use: snd_hda_intel Kernel modules: snd-hda-intel 00:1c.0 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 1 (rev b5) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=03, subordinate=03, sec-latency=0 Memory behind bridge: fa400000-fa4fffff Capabilities: [40] Express Root Port (Slot+), MSI 00 Capabilities: [80] MSI: Enable- Count=1/1 Maskable- 64bit- Capabilities: [90] Subsystem: Dell Device 04a7 Capabilities: [a0] Power Management version 2 Kernel driver in use: pcieport Kernel modules: shpchp 00:1c.1 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 2 (rev b5) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=04, subordinate=04, sec-latency=0 I/O behind bridge: 0000c000-0000cfff Prefetchable memory behind bridge: 00000000dc100000-00000000dc1fffff Capabilities: [40] Express Root Port (Slot+), MSI 00 Capabilities: [80] MSI: Enable- Count=1/1 Maskable- 64bit- Capabilities: [90] Subsystem: Dell Device 04a7 Capabilities: [a0] Power Management version 2 Kernel driver in use: pcieport Kernel modules: shpchp 00:1c.2 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 3 (rev b5) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=05, subordinate=05, sec-latency=0 I/O behind bridge: 0000b000-0000bfff Memory behind bridge: fa300000-fa3fffff Capabilities: [40] Express Root Port (Slot+), MSI 00 Capabilities: [80] MSI: Enable- Count=1/1 Maskable- 64bit- Capabilities: [90] Subsystem: Dell Device 04a7 Capabilities: [a0] Power Management version 2 Kernel driver in use: pcieport Kernel modules: shpchp 00:1c.3 PCI bridge: Intel Corporation 6 Series/C200 Series Chipset Family PCI Express Root Port 4 (rev b5) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=06, subordinate=06, sec-latency=0 I/O behind bridge: 0000a000-0000afff Memory behind bridge: fa200000-fa2fffff Capabilities: [40] Express Root Port (Slot+), MSI 00 Capabilities: [80] MSI: Enable- Count=1/1 Maskable- 64bit- Capabilities: [90] Subsystem: Dell Device 04a7 Capabilities: [a0] Power Management version 2 Kernel driver in use: pcieport Kernel modules: shpchp 00:1d.0 USB controller: Intel Corporation 6 Series/C200 Series Chipset Family USB Enhanced Host Controller #1 (rev 05) (prog-if 20 [EHCI]) Subsystem: Dell Device 04a7 Flags: bus master, medium devsel, latency 0, IRQ 23 Memory at f6106000 (32-bit, non-prefetchable) [size=1K] Capabilities: [50] Power Management version 2 Capabilities: [58] Debug port: BAR=1 offset=00a0 Capabilities: [98] PCI Advanced Features Kernel driver in use: ehci_hcd 00:1f.0 ISA bridge: Intel Corporation P67 Express Chipset Family LPC Controller (rev 05) Subsystem: Dell Device 04a7 Flags: bus master, medium devsel, latency 0 Capabilities: [e0] Vendor Specific Information: Len=0c <?> Kernel modules: iTCO_wdt 00:1f.2 RAID bus controller: Intel Corporation 82801 SATA Controller [RAID mode] (rev 05) Subsystem: Dell Device 04a7 Flags: bus master, 66MHz, medium devsel, latency 0, IRQ 42 I/O ports at f070 [size=8] I/O ports at f060 [size=4] I/O ports at f050 [size=8] I/O ports at f040 [size=4] I/O ports at f020 [size=32] Memory at f6105000 (32-bit, non-prefetchable) [size=2K] Capabilities: [80] MSI: Enable+ Count=1/1 Maskable- 64bit- Capabilities: [70] Power Management version 3 Capabilities: [a8] SATA HBA v1.0 Capabilities: [b0] PCI Advanced Features Kernel driver in use: ahci 00:1f.3 SMBus: Intel Corporation 6 Series/C200 Series Chipset Family SMBus Controller (rev 05) Subsystem: Dell Device 04a7 Flags: medium devsel, IRQ 5 Memory at f6104000 (64-bit, non-prefetchable) [size=256] I/O ports at f000 [size=32] Kernel modules: i2c-i801 01:00.0 VGA compatible controller: NVIDIA Corporation Device 0dc5 (rev a1) (prog-if 00 [VGA controller]) Subsystem: NVIDIA Corporation Device 085b Flags: bus master, fast devsel, latency 0, IRQ 16 Memory at f8000000 (32-bit, non-prefetchable) [size=16M] Memory at d0000000 (64-bit, prefetchable) [size=128M] Memory at d8000000 (64-bit, prefetchable) [size=32M] I/O ports at e000 [size=128] Expansion ROM at fa000000 [disabled] [size=512K] Capabilities: [60] Power Management version 3 Capabilities: [68] MSI: Enable- Count=1/1 Maskable- 64bit+ Capabilities: [78] Express Endpoint, MSI 00 Capabilities: [b4] Vendor Specific Information: Len=14 <?> Capabilities: [100] Virtual Channel Capabilities: [128] Power Budgeting <?> Capabilities: [600] Vendor Specific Information: ID=0001 Rev=1 Len=024 <?> Kernel driver in use: nouveau Kernel modules: nouveau, nvidiafb 01:00.1 Audio device: NVIDIA Corporation GF106 High Definition Audio Controller (rev a1) Subsystem: NVIDIA Corporation Device 085b Flags: bus master, fast devsel, latency 0, IRQ 17 Memory at fa080000 (32-bit, non-prefetchable) [size=16K] Capabilities: [60] Power Management version 3 Capabilities: [68] MSI: Enable- Count=1/1 Maskable- 64bit+ Capabilities: [78] Express Endpoint, MSI 00 Kernel driver in use: snd_hda_intel Kernel modules: snd-hda-intel 02:00.0 VGA compatible controller: NVIDIA Corporation Device 0dc5 (rev a1) (prog-if 00 [VGA controller]) Subsystem: NVIDIA Corporation Device 085b Flags: bus master, fast devsel, latency 0, IRQ 17 Memory at f4000000 (32-bit, non-prefetchable) [size=32M] Memory at c0000000 (64-bit, prefetchable) [size=128M] Memory at c8000000 (64-bit, prefetchable) [size=64M] I/O ports at d000 [size=128] Expansion ROM at f6000000 [disabled] [size=512K] Capabilities: [60] Power Management version 3 Capabilities: [68] MSI: Enable- Count=1/1 Maskable- 64bit+ Capabilities: [78] Express Endpoint, MSI 00 Capabilities: [b4] Vendor Specific Information: Len=14 <?> Capabilities: [100] Virtual Channel Capabilities: [128] Power Budgeting <?> Capabilities: [600] Vendor Specific Information: ID=0001 Rev=1 Len=024 <?> Kernel driver in use: nouveau Kernel modules: nouveau, nvidiafb 02:00.1 Audio device: NVIDIA Corporation GF106 High Definition Audio Controller (rev a1) Subsystem: NVIDIA Corporation Device 085b Flags: bus master, fast devsel, latency 0, IRQ 18 Memory at f6080000 (32-bit, non-prefetchable) [size=16K] Capabilities: [60] Power Management version 3 Capabilities: [68] MSI: Enable- Count=1/1 Maskable- 64bit+ Capabilities: [78] Express Endpoint, MSI 00 Kernel driver in use: snd_hda_intel Kernel modules: snd-hda-intel 03:00.0 USB controller: NEC Corporation uPD720200 USB 3.0 Host Controller (rev 03) (prog-if 30 [XHCI]) Subsystem: Dell Device 04a7 Flags: bus master, fast devsel, latency 0, IRQ 16 Memory at fa400000 (64-bit, non-prefetchable) [size=8K] Capabilities: [50] Power Management version 3 Capabilities: [70] MSI: Enable- Count=1/8 Maskable- 64bit+ Capabilities: [90] MSI-X: Enable+ Count=8 Masked- Capabilities: [a0] Express Endpoint, MSI 00 Capabilities: [100] Advanced Error Reporting Capabilities: [140] Device Serial Number ff-ff-ff-ff-ff-ff-ff-ff Capabilities: [150] Latency Tolerance Reporting Kernel driver in use: xhci_hcd 04:00.0 Ethernet controller: Realtek Semiconductor Co., Ltd. RTL8111/8168B PCI Express Gigabit Ethernet controller (rev 06) Subsystem: Dell Device 04a7 Flags: bus master, fast devsel, latency 0, IRQ 51 I/O ports at c000 [size=256] Memory at dc104000 (64-bit, prefetchable) [size=4K] Memory at dc100000 (64-bit, prefetchable) [size=16K] Capabilities: [40] Power Management version 3 Capabilities: [50] MSI: Enable+ Count=1/1 Maskable- 64bit+ Capabilities: [70] Express Endpoint, MSI 01 Capabilities: [b0] MSI-X: Enable- Count=4 Masked- Capabilities: [d0] Vital Product Data Capabilities: [100] Advanced Error Reporting Capabilities: [140] Virtual Channel Capabilities: [160] Device Serial Number 03-00-00-00-68-4c-e0-00 Kernel driver in use: r8168 Kernel modules: r8168 05:00.0 FireWire (IEEE 1394): VIA Technologies, Inc. VT6315 Series Firewire Controller (rev 01) (prog-if 10 [OHCI]) Subsystem: Dell Device 04a7 Flags: bus master, fast devsel, latency 0, IRQ 18 Memory at fa300000 (64-bit, non-prefetchable) [size=2K] I/O ports at b000 [size=256] Capabilities: [50] Power Management version 3 Capabilities: [80] MSI: Enable- Count=1/1 Maskable+ 64bit+ Capabilities: [98] Express Endpoint, MSI 00 Capabilities: [100] Advanced Error Reporting Capabilities: [130] Device Serial Number 00-10-dc-ff-ff-cf-56-1a Kernel driver in use: firewire_ohci Kernel modules: firewire-ohci 06:00.0 SATA controller: JMicron Technology Corp. JMB362 SATA Controller (rev 10) (prog-if 01 [AHCI 1.0]) Subsystem: Dell Device 04a7 Flags: bus master, fast devsel, latency 0, IRQ 19 I/O ports at a040 [size=8] I/O ports at a030 [size=4] I/O ports at a020 [size=8] I/O ports at a010 [size=4] I/O ports at a000 [size=16] Memory at fa210000 (32-bit, non-prefetchable) [size=512] Capabilities: [8c] Power Management version 3 Capabilities: [50] Express Legacy Endpoint, MSI 00 Kernel driver in use: ahci Note that my wireless card is not showing, I have the Ralink 3390 card (which apparently does not show up on Ubuntu for some reason), however I am able to connect to wireless network and connect to the internet (when it is working)

    Read the article

  • Multiple sites with the same codebase in Python

    - by Jimmy
    I am trying to run a large amount of sites which share about 90% of their code. They are simply designed to query an API and return the results. They will have a common userbase / database but will be configured slightly different and will have different CSS (perhaps even different templating). My initial idea was to run them as separate applications with a common library but I have read about the sites framework which would allow them to run from a single instance of Django which may help to reduce memory usage. https://docs.djangoproject.com/en/dev/ref/contrib/sites/ Is the site framework the right approach to a problem like this, and does it have real benefits over running separate applications? Initially I thought it was, but now I think otherwise. I have heard the following: Your SITE_ID is set in settings.py, so in order to have multiple sites, you need multiple settings.py configurations, which means multiple distinct processes/instances. You can of course share the code base between them, but each site will need a dedicated worker / WSGIDaemon to serve the site. This effectively removes any benefit of running multiple sites under one hood, if each site needs a UWSGI instance running. Alternative ideas of systems: https://github.com/iivvoo/django_layers https://github.com/shestera/django-multisite I don't know what route to be taking with this.

    Read the article

  • Problems with Maverick upgrade

    - by altenuta
    I upgraded to Maverick 10.10 from Lucid. I have an old Toshiba Satellite with a 1.1 MHz and 256MB RAM. Initially I couldn't get my wireless to work. That solved itself after installing various updates and programs. The problems that remain are: I have to authorize at least 2 times at start-up. This machine is Ubuntu only. No boot load screen. I have a ton of programs and system directories that are in my home folder. Is this normal? It is difficult to wake the computer from sleep. Usually I just shut it down and restart. Tonight I waited and got a message about corrupt memory. The computer takes forever to do just about everything. Slow to start programs or doing things on the web. I am a longtime Mac user (since 1986). I also manage a network of several windoze machines. I am definitely a GUI guy and do very little in the terminal so I really need to know where to begin to get things straightened out. Can I rescue this machine without wiping it and doing a fresh install? This is basically a hobby machine. Aside from all the programs and upgrades I've installed, I have almost no files or documents to worry about saving. Anyone have any ideas about the problems I'm having and the best way to proceed? Thanks, Al

    Read the article

  • How do I throttle a command in a terminal window?

    - by To Do
    I needed to run convert with a lot of images at the same time. The command took quite a while but this doesn't bother me. The issue is that this command rendered my computer unusable while the command was running (for about 15 minutes). So is it possible to throttle the command by limiting resources (processor and memory) to the command, directly from the command line? This can only work if I add something to the same line before pressing Enter because once I start the process the computer slows so much that it is impossible for example to switch to "System monitor" and reduce priority. Edit: top and iotop results I managed to run top and sudo iotop >iotop.txt while doing one of these convert operations. (The iotop.txt file produced is difficult to read) Results of top: PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 14275 username 20 0 4043m 3.0g 1448 D 7.0 80.4 0:16.45 convert Results of iotop: [?1049h[1;24r(B[m[4l[?7h[?1h=[39;49m[?25l[39;49m(B[m[H[2JTotal DISK READ: 1269.04 K/s | Total DISK WRITE:[59G0.00 B/s (B[0;7m TID PRIO USER DISK READ DISK WRITE SWAPIN(B[0;1;7m IO(B[0;7m COMMAND [3;2H(B[m2516 be/4 username 350.08 K/s 0.00 B/s 0.00 % 0.00 % zeitgeist-datahub 7394 be/4 username 568.88 K/s 0.00 B/s 77.41 % 0.00 % --rendere~.530483991[5;1H14275 idle username 350.08 K/s 0.00 B/s 37.49 % 0.00 % convert S~f test.pdf[6;2H2048 be/4 root[6;24H0.00 B/s 0.00 B/s 0.00 % 0.00 % [kworker/3:2] [5G1 be/4 root[7;24H0.00 B/s 0.00 B/s 0.00 % 0.00 % init Furthermore, even after the process ends, the computer does not return to the previous performance. I found a way around this by running sudo swapoff -a followed by sudo swapon -a

    Read the article

< Previous Page | 94 95 96 97 98 99 100 101 102 103 104 105  | Next Page >