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  • What open source document-oriented database system is most mature for Windows usage?

    - by jdk
    After using relational databases as back-end storage all my Windows programming life (currently .NET), I want to experiment with a document-oriented database by this Wikipedia definition; it can be standalone or layered over an existing non-commercial database system. What open source document-oriented database solution would you recommend from your own experience and why? A nice to have would be a .NET provider. Admittedly this is somewhat subjective and potentially argumentative so keep it real folks and I'll do the same - also your answers will be invaluable to others looking into document-oriented databases for the first time on Windows. I'm sure the overall value of your answers will outweigh any biases. Thanks.

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  • Storing data with a stand-alone C++ application

    - by Mike
    I work with Apache, PHP, and MySQL for web development and local applications. For the past couple of years I have slowly been learning C++ and want to build an application this summer. Specifically, I want to make a "library" application in which I can store information about the books, CDs, and records that I own. I know this type of app exists, but I want to learn C++ and this seems like a good way to go about it. Here are a few questions: Is it possible to create a stand-alone application that does not require a database for storing data? If the answer to #1 above is "yes", is it a good idea to do this for an application that could potentially need to manage a lot of data? What data-storage options would you recommend for use with a C++ application? Thanks!

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  • Are there any portable Cloud APIs that allow you to easily change cloud hosts?

    - by MindJuice
    I am creating a web-based RESTful service and want to cloud-enable it for scalability. I don't want to get locked into one cloud provider though. I'd like to be able to switched between Go Grid or Amazon EC2, etc. as pricing and needs evolve. Is there a common API to control the launch, monitoring and shutdown of cloud resources? I've seen Right Scale, but their pricing is just from another planet. Similarly, is there a common API for cloud storage?

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  • An efficient way to store view counts for objects?

    - by Nick Brooks
    I maintain an application where users are able to store images, and then share them. The system is powered by MongoDB at the back end. Most of the image depiction pages are cached as flat HTML files, but I can run some code just before loading the file. I've decided to implement a view count for the system. I am wondering what is the best storage place for that. It should be like Memcached but it should save the viewcounts every hour or so, so even if our server has to be restarted we won't lose the view counts. What is the best solution for that (preferably with a PHP extension as a client)?

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  • How do I stop linux from trying to mount android phone as usb storage?

    - by user1160711
    When I plug in my Motorola Triumph to my fedora 17 linux box USB port, I get an endless series of errors on the linux box as it desperately attempts to mount the phone as a USB drive. Stuff like this: Jun 23 10:26:00 zooty kernel: [528926.714884] end_request: critical target error, dev sdg, sector 4 Jun 23 10:26:00 zooty kernel: [528926.715865] sd 16:0:0:1: [sdg] Result: hostbyte=DID_OK driverbyte=DRIVER_SENSE Jun 23 10:26:00 zooty kernel: [528926.715869] sd 16:0:0:1: [sdg] Sense Key : Illegal Request [current] Jun 23 10:26:00 zooty kernel: [528926.715872] sd 16:0:0:1: [sdg] Add. Sense: Invalid field in cdb Jun 23 10:26:00 zooty kernel: [528926.715876] sd 16:0:0:1: [sdg] CDB: Read(10): 28 20 00 00 00 00 00 00 04 00 If I go ahead and tell the phone to allow linux to mount the USB storage, the messages stop, and I get a mounted drive, but if all I want to do is use the debug bridge, my log on linux will continue to fill with this junk. Is there some udev magic I can do to make the system ignore this particular device as far as usb storage goes? I just noticed that if I tell the phone to enable USB storage, let linux recognize the new disk, then tell the phone to disable USB storage again, I get one additional log message about capacity changing to zero, but the endless spew of messages stops, so I guess one work around is to enable and disable USB right away.

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  • MongoDB and GrifFS. What are the best storage options in the range of 1 TB?

    - by Nerian
    We are going to launch a service that will require between 1 and 2 GB for file storage per paid user. I am going to use GridFS for storing files. I am pondering the different options for storing the database. But since I am unexperienced at deployment and it is my first time with Mongodb I need your experience. Criteria: I want to spend my time developing my core business, that is, my own application. I am a Ruby on Rails developer. I do not like to mess with server configuration. Hence, I would like a fully managed hosting solution. But I would like to know about any other option, if you think it is worth it. It should be able to scale. Cloud style. Pay as you go. The lower the price, the better. So far I known of these services: https://mongohq.com/pricing https://mongomachine.com/pricing https://mongolab.com/about/pricing/ http://cloudcontrol.com/add-ons/mongodb/ And they seem to be OK for common needs, that is no file storage. But I am going to use GridFS, so the size matters. These services seems to scale, in price, quite poorly. MongoHQ: The larger plan max storage is 20 GB. Seems like a very little storage, for GridFS. MongoMachine: Flat price, 2.5$ per GB. I didn't found the limit. Seems like a good price, comparing the others. MongoLab: 3.984 GB max, which I don't think I will hit, so perfect. 8$ per GB, quite costly. CloudControl: The larger plan is 20 Gb. The custom service starts at 250€ plus some unspecified charge per GB. What is your experience with these services? Any downtimes? Other possibilities?

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  • Azure &ndash; Part 6 &ndash; Blob Storage Service

    - by Shaun
    When migrate your application onto the Azure one of the biggest concern would be the external files. In the original way we understood and ensure which machine and folder our application (website or web service) is located in. So that we can use the MapPath or some other methods to read and write the external files for example the images, text files or the xml files, etc. But things have been changed when we deploy them on Azure. Azure is not a server, or a single machine, it’s a set of virtual server machine running under the Azure OS. And even worse, your application might be moved between thses machines. So it’s impossible to read or write the external files on Azure. In order to resolve this issue the Windows Azure provides another storage serviec – Blob, for us. Different to the table service, the blob serivce is to be used to store text and binary data rather than the structured data. It provides two types of blobs: Block Blobs and Page Blobs. Block Blobs are optimized for streaming. They are comprised of blocks, each of which is identified by a block ID and each block can be a maximum of 4 MB in size. Page Blobs are are optimized for random read/write operations and provide the ability to write to a range of bytes in a blob. They are a collection of pages. The maximum size for a page blob is 1 TB.   In the managed library the Azure SDK allows us to communicate with the blobs through these classes CloudBlobClient, CloudBlobContainer, CloudBlockBlob and the CloudPageBlob. Similar with the table service managed library, the CloudBlobClient allows us to reach the blob service by passing our storage account information and also responsible for creating the blob container is not exist. Then from the CloudBlobContainer we can save or load the block blobs and page blobs into the CloudBlockBlob and the CloudPageBlob classes.   Let’s improve our exmaple in the previous posts – add a service method allows the user to upload the logo image. In the server side I created a method name UploadLogo with 2 parameters: email and image. Then I created the storage account from the config file. I also add the validation to ensure that the email passed in is valid. 1: var storageAccount = CloudStorageAccount.FromConfigurationSetting("DataConnectionString"); 2: var accountContext = new DynamicDataContext<Account>(storageAccount); 3:  4: // validation 5: var accountNumber = accountContext.Load() 6: .Where(a => a.Email == email) 7: .ToList() 8: .Count; 9: if (accountNumber <= 0) 10: { 11: throw new ApplicationException(string.Format("Cannot find the account with the email {0}.", email)); 12: } Then there are three steps for saving the image into the blob service. First alike the table service I created the container with a unique name and create it if it’s not exist. 1: // create the blob container for account logos if not exist 2: CloudBlobClient blobStorage = storageAccount.CreateCloudBlobClient(); 3: CloudBlobContainer container = blobStorage.GetContainerReference("account-logo"); 4: container.CreateIfNotExist(); Then, since in this example I will just send the blob access URL back to the client so I need to open the read permission on that container. 1: // configure blob container for public access 2: BlobContainerPermissions permissions = container.GetPermissions(); 3: permissions.PublicAccess = BlobContainerPublicAccessType.Container; 4: container.SetPermissions(permissions); And at the end I combine the blob resource name from the input file name and Guid, and then save it to the block blob by using the UploadByteArray method. Finally I returned the URL of this blob back to the client side. 1: // save the blob into the blob service 2: string uniqueBlobName = string.Format("{0}_{1}.jpg", email, Guid.NewGuid().ToString()); 3: CloudBlockBlob blob = container.GetBlockBlobReference(uniqueBlobName); 4: blob.UploadByteArray(image); 5:  6: return blob.Uri.ToString(); Let’s update a bit on the client side application and see the result. Here I just use my simple console application to let the user input the email and the file name of the image. If it’s OK it will show the URL of the blob on the server side so that we can see it through the web browser. Then we can see the logo I’ve just uploaded through the URL here. You may notice that the blob URL was based on the container name and the blob unique name. In the document of the Azure SDK there’s a page for the rule of naming them, but I think the simple rule would be – they must be valid as an URL address. So that you cannot name the container with dot or slash as it will break the ADO.Data Service routing rule. For exmaple if you named the blob container as Account.Logo then it will throw an exception says 400 Bad Request.   Summary In this short entity I covered the simple usage of the blob service to save the images onto Azure. Since the Azure platform does not support the file system we have to migrate our code for reading/writing files to the blob service before deploy it to Azure. In order to reducing this effort Microsoft provided a new approch named Drive, which allows us read and write the NTFS files just likes what we did before. It’s built up on the blob serivce but more properly for files accessing. I will discuss more about it in the next post.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • Announcing: Improvements to the Windows Azure Portal

    - by ScottGu
    Earlier today we released a number of enhancements to the new Windows Azure Management Portal.  These new capabilities include: Service Bus Management and Monitoring Support for Managing Co-administrators Import/Export support for SQL Databases Virtual Machine Experience Enhancements Improved Cloud Service Status Notifications Media Services Monitoring Support Storage Container Creation and Access Control Support All of these improvements are now live in production and available to start using immediately.  Below are more details on them: Service Bus Management and Monitoring The new Windows Azure Management Portal now supports Service Bus management and monitoring. Service Bus provides rich messaging infrastructure that can sit between applications (or between cloud and on-premise environments) and allow them to communicate in a loosely coupled way for improved scale and resiliency. With the new Service Bus experience, you can now create and manage Service Bus Namespaces, Queues, Topics, Relays and Subscriptions. You can also get rich monitoring for Service Bus Queues, Topics and Subscriptions. To create a Service Bus namespace, you can now select the “Service Bus” tab in the Windows Azure portal and then simply select the CREATE command: Doing so will bring up a new “Create a Namespace” dialog that allows you to name and create a new Service Bus Namespace: Once created, you can obtain security credentials associated with the Namespace via the ACCESS KEY command. This gives you the ability to obtain the connection string associated with the service namespace. You can copy and paste these values into any application that requires these credentials: It is also now easy to create Service Bus Queues and Topics via the NEW experience in the portal drawer.  Simply click the NEW command and navigate to the “App Services” category to create a new Service Bus entity: Once you provision a new Queue or Topic it can be managed in the portal.  Clicking on a namespace will display all queues and topics within it: Clicking on an item in the list will allow you to drill down into a dashboard view that allows you to monitor the activity and traffic within it, as well as perform operations on it. For example, below is a view of an “orders” queue – note how we now surface both the incoming and outgoing message flow rate, as well as the total queue length and queue size: To monitor pub/sub subscriptions you can use the ADD METRICS command within a topic and select a specific subscription to monitor. Support for Managing Co-Administrators You can now add co-administrators for your Windows Azure subscription using the new Windows Azure Portal. This allows you to share management of your Windows Azure services with other users. Subscription co-administrators share the same administrative rights and permissions that service administrator have - except a co-administrator cannot change or view billing details about the account, nor remove the service administrator from a subscription. In the SETTINGS section, click on the ADMINISTRATORS tab, and select the ADD button to add a co-administrator to your subscription: To add a co-administrator, you specify the email address for a Microsoft account (formerly Windows Live ID) or an organizational account, and choose the subscription you want to add them to: You can later update the subscriptions that the co-administrator has access to by clicking on the EDIT button, and then select or deselect the subscriptions to which they belong. Import/Export Support for SQL Databases The Windows Azure administration portal now supports importing and exporting SQL Databases to/from Blob Storage.  Databases can be imported/exported to blob storage using the same BACPAC file format that is supported with SQL Server 2012.  Among other benefits, this makes it easy to copy and migrate databases between on-premise and cloud environments. SQL Databases now have an EXPORT command in the bottom drawer that when pressed will prompt you to save your database to a Windows Azure storage container: The UI allows you to choose an existing storage account or create a new one, as well as the name of the BACPAC file to persist in blob storage: You can also now import and create a new SQL Database by using the NEW command.  This will prompt you to select the storage container and file to import the database from: The Windows Azure Portal enables you to monitor the progress of import and export operations. If you choose to log out of the portal, you can come back later and check on the status of all of the operations in the new history tab of the SQL Database server – this shows your entire import and export history and the status (success/fail) of each: Enhancements to the Virtual Machine Experience One of the common pain-points we have heard from customers using the preview of our new Virtual Machine support has been the inability to delete the associated VHDs when a VM instance (or VM drive) gets deleted. Prior to today’s release the VHDs would continue to be in your storage account and accumulate storage charges. You can now navigate to the Disks tab within the Virtual Machine extension, select a VM disk to delete, and click the DELETE DISK command: When you click the DELETE DISK button you have the option to delete the disk + associated .VHD file (completely clearing it from storage).  Alternatively you can delete the disk but still retain a .VHD copy of it in storage. Improved Cloud Service Status Notifications The Windows Azure portal now exposes more information of the health status of role instances.  If any of the instances are in a non-running state, the status at the top of the dashboard will summarize the status (and update automatically as the role health changes): Clicking the instance hyperlink within this status summary view will navigate you to a detailed role instance view, and allow you to get more detailed health status of each of the instances.  The portal has been updated to provide more specific status information within this detailed view – giving you better visibility into the health of your app: Monitoring Support for Media Services Windows Azure Media Services allows you to create media processing jobs (for example: encoding media files) in your Windows Azure Media Services account. In the Windows Azure Portal, you can now monitor the number of encoding jobs that are queued up for processing as well as active, failed and queued tasks for encoding jobs. On your media services account dashboard, you can visualize the monitoring data for last 6 hours, 24 hours or 7 days. Storage Container Creation and Access Control Support You can now create Windows Azure Storage storage containers from within the Windows Azure Portal.  After selecting a storage account, you can navigate to the CONTAINERS tab and click the ADD CONTAINER command: This will display a dialog that lets you name the new container and control access to it: You can also update the access setting as well as container metadata of existing containers by selecting one and then using the new EDIT CONTAINER command: This will then bring up the edit container dialog that allows you to change and save its settings: In addition to creating and editing containers, you can click on them within the portal to drill-in and view blobs within them.  Summary The above features are all now live in production and available to use immediately.  If you don’t already have a Windows Azure account, you can sign-up for a free trial and start using them today.  Visit the Windows Azure Developer Center to learn more about how to build apps with it. We’ll have even more new features and enhancements coming later this month – including support for the recent Windows Server 2012 and .NET 4.5 releases (we will enable new web and worker role images with Windows Server 2012 and .NET 4.5, and support .NET 4.5 with Websites).  Keep an eye out on my blog for details as these new features become available. Hope this helps, Scott P.S. In addition to blogging, I am also now using Twitter for quick updates and to share links. Follow me at: twitter.com/scottgu

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  • 10 tape technology features that make you go hmm.

    - by Karoly Vegh
    A week ago an Oracle/StorageTek Tape Specialist, Christian Vanden Balck, visited Vienna, and agreed to visit customers to do techtalks and update them about the technology boom going around tape. I had the privilege to attend some of his sessions and noted the information and features that took the customers by surprise and made them think. Allow me to share the top 10: I. StorageTek as a brand: StorageTek is one of he strongest names in the Tape field. The brand itself was valued so much by customers that even after Sun Microsystems acquiring StorageTek and the Oracle acquiring Sun the brand lives on with all the Oracle tapelibraries are officially branded StorageTek.See http://www.oracle.com/us/products/servers-storage/storage/tape-storage/overview/index.html II. Disk information density limitations: Disk technology struggles with information density. You haven't seen the disk sizes exploding lately, have you? That's partly because there are physical limits on a disk platter. The size is given, the number of platters is limited, they just can't grow, and are running out of physical area to write to. Now, in a T10000C tape cartridge we have over 1000m long tape. There you go, you have got your physical space and don't need to stuff all that data crammed together. You can write in a reliable pattern, and have space to grow too. III. Oracle has a market share of 62% worldwide in recording head manufacturing. That's right. If you are running LTO drives, with a good chance you rely on StorageTek production. That's two out of three LTO recording heads produced worldwide.  IV. You can store 1 Exabyte data in a single tape library. Yes, an Exabyte. That is 1000 Petabytes. Or, a million Terabytes. A thousand million GigaBytes. You can store that in a stacked StorageTek SL8500 tapelibrary. In one SL8500 you can put 10.000 T10000C cartridges, that store 10TB data (compressed). You can stack 10 of these SL8500s together. Boom. 1000.000 TB.(n.b.: stacking means interconnecting the libraries. Yes, cartridges are moved between the stacked libraries automatically.)  V. EMC: 'Tape doesn't suck after all. We moved on.': Do you remember the infamous 'Tape sucks, move on' Datadomain slogan? Of course they had to put it that way, having only had disk products. But here's a fun fact: on the EMCWorld 2012 there was a major presence of a Tape-tech company - EMC, in a sudden burst of sanity is embracing tape again. VI. The miraculous T10000C: Oracle StorageTek has developed an enterprise-grade tapedrive and cartridge, the T10000C. With awesome numbers: The Cartridge: Native 5TB capacity, 10TB with compression Over a kilometer long tape within the cartridge. And it's locked when unmounted, no rattling of your data.  Replaced the metalparticles datalayer with BaFe (bariumferrite) - metalparticles lose around 7% of magnetism within 30 days. BaFe does not. Yes we employ solid-state physicists doing R&D on demagnetisation in our labs. Can be partitioned, storage tiering within the cartridge!  The Drive: 2GB Cache Encryption implemented in HW - no performance hit 252 MB/s native sustained data rate, beats disk technology by far. Not to mention peak throughput.  Leading the tape while never touching the data side of it, protecting your data physically too Data integritiy checking (CRC recalculation) on tape within the drive without having to read it back to the server reordering data from tape-order, delivering it back in application-order  writing 32 tracks at once, reading them back for CRC check at once VII. You only use 20% of your data on a regular basis. The rest 80% is just lying around for years. On continuously spinning disks. Doubly consuming energy (power+cooling), blocking diskstorage capacity. There is a solution called SAM (Storage Archive Manager) that provides you a filesystem unifying disk and tape, moving data on-demand and for clients transparently between the different storage tiers. You can share these filesystems with NFS or CIFS for clients, and enjoy the low TCO of tape. Tapes don't spin. They sit quietly in their slots, storing 10TB data, using no energy, producing no heat, automounted when a client accesses their data.See: http://www.oracle.com/us/products/servers-storage/storage/storage-software/storage-archive-manager/overview/index.html VIII. HW supported for three decades: Did you know that the original PowderHorn library was released in '87 and has been only discontinued in 2010? That is over two decades of supported operation. Tape libraries are - just like the data carrying on tapecartridges - built for longevity. Oh, and the T10000C cartridge has 30-year archival life for long-term retention.  IX. Tape is easy to manage: Have you heard of Tape Storage Analytics? It is a central graphical tool to summarize, monitor, analyze dataflow, health and performance of drives and libraries, see: http://www.oracle.com/us/products/servers-storage/storage/tape-storage/tape-analytics/overview/index.html X. The next generation: The T10000B drives were able to reuse the T10000A cartridges and write on them even more data. On the same cartridges. We call this investment protection, and this is very important for Oracle for the future too. We usually support two generations of cartridges together. The current drive is a T10000C. (...I know I promised to enlist 10, but I got still two more I really want to mention. Allow me to work around the problem: ) X++. The TallBots, the robots moving around the cartridges in the StorageTek library from tapeslots to the drives are cableless. Cables, belts, chains running to moving parts in a library cause maintenance downtimes. So StorageTek eliminated them. The TallBots get power, commands, even firmwareupgrades through the rails they are running on. Also, the TallBots don't just hook'n'pull the tapes out of their slots, they actually grip'n'lift them out. No friction, no scratches, no zillion little plastic particles floating around in the library, in the drives, on your data. (X++)++: Tape beats SSDs and Disks. In terms of throughput (252 MB/s), in terms of TCO: disks cause around 290x more power and cooling, in terms of capacity: 10TB on a single media and soon more.  So... do you need to store large amounts of data? Are you legally bound to archive it for dozens of years? Would you benefit from automatic storage tiering? Have you got large mediachunks to be streamed at times? Have you got power and cooling issues in the growing datacenters? Do you find EMC's 180° turn of tape attitude interesting, but appreciate it at the same time? With all that, you aren't alone. The most data on this planet is stored on tape. Tape is coming. Big time.

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  • I see no LOBs!

    - by Paul White
    Is it possible to see LOB (large object) logical reads from STATISTICS IO output on a table with no LOB columns? I was asked this question today by someone who had spent a good fraction of their afternoon trying to work out why this was occurring – even going so far as to re-run DBCC CHECKDB to see if any corruption had taken place.  The table in question wasn’t particularly pretty – it had grown somewhat organically over time, with new columns being added every so often as the need arose.  Nevertheless, it remained a simple structure with no LOB columns – no TEXT or IMAGE, no XML, no MAX types – nothing aside from ordinary INT, MONEY, VARCHAR, and DATETIME types.  To add to the air of mystery, not every query that ran against the table would report LOB logical reads – just sometimes – but when it did, the query often took much longer to execute. Ok, enough of the pre-amble.  I can’t reproduce the exact structure here, but the following script creates a table that will serve to demonstrate the effect: IF OBJECT_ID(N'dbo.Test', N'U') IS NOT NULL DROP TABLE dbo.Test GO CREATE TABLE dbo.Test ( row_id NUMERIC IDENTITY NOT NULL,   col01 NVARCHAR(450) NOT NULL, col02 NVARCHAR(450) NOT NULL, col03 NVARCHAR(450) NOT NULL, col04 NVARCHAR(450) NOT NULL, col05 NVARCHAR(450) NOT NULL, col06 NVARCHAR(450) NOT NULL, col07 NVARCHAR(450) NOT NULL, col08 NVARCHAR(450) NOT NULL, col09 NVARCHAR(450) NOT NULL, col10 NVARCHAR(450) NOT NULL, CONSTRAINT [PK dbo.Test row_id] PRIMARY KEY CLUSTERED (row_id) ) ; The next script loads the ten variable-length character columns with one-character strings in the first row, two-character strings in the second row, and so on down to the 450th row: WITH Numbers AS ( -- Generates numbers 1 - 450 inclusive SELECT TOP (450) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) INSERT dbo.Test WITH (TABLOCKX) SELECT REPLICATE(N'A', N.n), REPLICATE(N'B', N.n), REPLICATE(N'C', N.n), REPLICATE(N'D', N.n), REPLICATE(N'E', N.n), REPLICATE(N'F', N.n), REPLICATE(N'G', N.n), REPLICATE(N'H', N.n), REPLICATE(N'I', N.n), REPLICATE(N'J', N.n) FROM Numbers AS N ORDER BY N.n ASC ; Once those two scripts have run, the table contains 450 rows and 10 columns of data like this: Most of the time, when we query data from this table, we don’t see any LOB logical reads, for example: -- Find the maximum length of the data in -- column 5 for a range of rows SELECT result = MAX(DATALENGTH(T.col05)) FROM dbo.Test AS T WHERE row_id BETWEEN 50 AND 100 ; But with a different query… -- Read all the data in column 1 SELECT result = MAX(DATALENGTH(T.col01)) FROM dbo.Test AS T ; …suddenly we have 49 LOB logical reads, as well as the ‘normal’ logical reads we would expect. The Explanation If we had tried to create this table in SQL Server 2000, we would have received a warning message to say that future INSERT or UPDATE operations on the table might fail if the resulting row exceeded the in-row storage limit of 8060 bytes.  If we needed to store more data than would fit in an 8060 byte row (including internal overhead) we had to use a LOB column – TEXT, NTEXT, or IMAGE.  These special data types store the large data values in a separate structure, with just a small pointer left in the original row. Row Overflow SQL Server 2005 introduced a feature called row overflow, which allows one or more variable-length columns in a row to move to off-row storage if the data in a particular row would otherwise exceed 8060 bytes.  You no longer receive a warning when creating (or altering) a table that might need more than 8060 bytes of in-row storage; if SQL Server finds that it can no longer fit a variable-length column in a particular row, it will silently move one or more of these columns off the row into a separate allocation unit. Only variable-length columns can be moved in this way (for example the (N)VARCHAR, VARBINARY, and SQL_VARIANT types).  Fixed-length columns (like INTEGER and DATETIME for example) never move into ‘row overflow’ storage.  The decision to move a column off-row is done on a row-by-row basis – so data in a particular column might be stored in-row for some table records, and off-row for others. In general, if SQL Server finds that it needs to move a column into row-overflow storage, it moves the largest variable-length column record for that row.  Note that in the case of an UPDATE statement that results in the 8060 byte limit being exceeded, it might not be the column that grew that is moved! Sneaky LOBs Anyway, that’s all very interesting but I don’t want to get too carried away with the intricacies of row-overflow storage internals.  The point is that it is now possible to define a table with non-LOB columns that will silently exceed the old row-size limit and result in ordinary variable-length columns being moved to off-row storage.  Adding new columns to a table, expanding an existing column definition, or simply storing more data in a column than you used to – all these things can result in one or more variable-length columns being moved off the row. Note that row-overflow storage is logically quite different from old-style LOB and new-style MAX data type storage – individual variable-length columns are still limited to 8000 bytes each – you can just have more of them now.  Having said that, the physical mechanisms involved are very similar to full LOB storage – a column moved to row-overflow leaves a 24-byte pointer record in the row, and the ‘separate storage’ I have been talking about is structured very similarly to both old-style LOBs and new-style MAX types.  The disadvantages are also the same: when SQL Server needs a row-overflow column value it needs to follow the in-row pointer a navigate another chain of pages, just like retrieving a traditional LOB. And Finally… In the example script presented above, the rows with row_id values from 402 to 450 inclusive all exceed the total in-row storage limit of 8060 bytes.  A SELECT that references a column in one of those rows that has moved to off-row storage will incur one or more lob logical reads as the storage engine locates the data.  The results on your system might vary slightly depending on your settings, of course; but in my tests only column 1 in rows 402-450 moved off-row.  You might like to play around with the script – updating columns, changing data type lengths, and so on – to see the effect on lob logical reads and which columns get moved when.  You might even see row-overflow columns moving back in-row if they are updated to be smaller (hint: reduce the size of a column entry by at least 1000 bytes if you hope to see this). Be aware that SQL Server will not warn you when it moves ‘ordinary’ variable-length columns into overflow storage, and it can have dramatic effects on performance.  It makes more sense than ever to choose column data types sensibly.  If you make every column a VARCHAR(8000) or NVARCHAR(4000), and someone stores data that results in a row needing more than 8060 bytes, SQL Server might turn some of your column data into pseudo-LOBs – all without saying a word. Finally, some people make a distinction between ordinary LOBs (those that can hold up to 2GB of data) and the LOB-like structures created by row-overflow (where columns are still limited to 8000 bytes) by referring to row-overflow LOBs as SLOBs.  I find that quite appealing, but the ‘S’ stands for ‘small’, which makes expanding the whole acronym a little daft-sounding…small large objects anyone? © Paul White 2011 email: [email protected] twitter: @SQL_Kiwi

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  • AWS EC2 Oracle RDB - Storing and managing my data

    - by llaszews
    When create an Oracle Database on the Amazon cloud you will need to store you database files somewhere on the EC2 cloud. There are basically three places where database files can be stored: 1. Local drive - This is the local drive that is part of the virtual server EC2 instance. 2. Elastic Block Storage (EBS) - Network attached storage that appears as a local drive. 3. Simple Storage Server (S3) - 'Storage for the Internet'. S3 is not high speed and intended for store static document type files. S3 can also be used for storing static web page files. Local drives are ephemeral so not appropriate to be used as a database storage device. The leaves EBS which is the best place to store database files. EBS volumes appear as local disk drives. They are actually network-attached to an Amazon EC2 instance. In addition, EBS persists independently from the running life of a single Amazon EC2 instance. If you use an EBS backed instance for your database data, it will remain available after reboot but not after terminate. In many cases you would not need to terminate your instance but only stop it, which is equivalent of shutdown. In order to save your database data before you terminate an instance, you can snapshot the EBS to S3. Using EBS as a data store you can move your Oracle data files from one instance to another. This allows you to move your database from one region or or zone to another. Unfortunately, to scale out your Oracle RDS on AWS you can not have read only replicas. This is only possible with the other Oracle relational database - MySQL. The free micro instances use EBS as its storage. This is a very good white paper that has more details: AWS Storage Options This white paper also discusses: SQS, SimpleDB, and Amazon RDS in the context of storage devices. However, these are not storage devices you would use to store an Oracle database. This slide deck discusses a lot of information that is in the white paper: AWS Storage Options slideshow

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  • How can I build something like Amazon S3 in Perl?

    - by Joel G
    I am looking to code a file storage application in perl similar to amazon s3. I already have a amazon s3 clone that I found online called parkplace but its in ruby and is old also isn't built for high loads. I am not really sure what modules and programs I should use so id like some help picking them out. My requirements are listed below (yes I know there are lots but I could start simple then add more once I get it going): Easy API implementation for client side apps. (maybe REST (?) Centralized database server for the USERDB (maybe PostgreSQL (?). Logging of all connections, bandwidth used, well pretty much everything to a centralized server (maybe PostgreSQL again (?). Easy server side configuration (config file(s) stored on the servers). Web based control panel for admin(s) and user(s) to show logs. (could work just running queries from the databases) Fast High Uptime Low memory usage Some sort of load distribution/load balancer (maybe a dns based or pound or perlbal or something else (?). Maybe a cache of some sort (memcached or parlbal or something else (?). Thanks in advance

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

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

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  • StorageClientException: The specified message does not exist?

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

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

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

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  • Windows Azure - Automatic Load Balancing - partitioning

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

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

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

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

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

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

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

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

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

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  • Objective-C memory management issue

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

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

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

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

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

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

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

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