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  • What IPC method should I use between Firefox extension and C# code running on the same machine?

    - by Rory
    I have a question about how to structure communication between a (new) Firefox extension and existing C# code. The firefox extension will use configuration data and will produce other data, so needs to get the config data from somewhere and save it's output somewhere. The data is produced/consumed by existing C# code, so I need to decide how the extension should interact with the C# code. Some pertinent factors: It's only running on windows, in a relatively controlled corporate environment. I have a windows service running on the machine, built in C#. Storing the data in a local datastore (like sqlite) would be useful for other reasons. The volume of data is low, e.g. 10kb of uncompressed xml every few minutes, and isn't very 'chatty'. The data exchange can be asynchronous for the most part if not completely. As with all projects, I have limited resources so want an option that's relatively easy. It doesn't have to be ultra-high performance, but shouldn't add significant overhead. I'm planning on building the extension in javascript (although could be convinced otherwise if really necessary) Some options I'm considering: use an XPCOM to .NET/COM bridge use a sqlite db: the extension would read from and save to it. The c# code would run in the service, populating the db and then processing data created by the service. use TCP sockets to communicate between the extension and the service. Let the service manage a local data store. My problem with (1) is I think this will be tricky and not so easy. But I could be completely wrong? The main problem I see with (2) is the locking of sqlite: only a single process can write data at a time so there'd be some blocking. However, it would be nice generally to have a local datastore so this is an attractive option if the performance impact isn't too great. I don't know whether (3) would be particularly easy or hard ... or what approach to take on the protocol: something custom or http. Any comments on these ideas or other suggestions? UPDATE: I was planning on building the extension in javascript rather than c++

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  • Architectural Design for a Data-Driven Silverlight WP7 app

    - by Rosarch
    I have a Silverlight Windows Phone 7 app that pulls data from a public API. I find myself doing much of the same thing over and over again: In the UI, set a loading message or loading progress bar in place of where the content is Get the content, which may be already in memory, cached in isolated file storage, or require an HTTP request If the content can not be acquired (no network connection, etc), display an error message If the content is acquired, display it in the UI Keep the content in main memory for subsequent queries The content that is displayed to the user can be taken directly from a data source, such as an ObservableCollection, or it may be a query on a data source. I would like to factor out this repetitive process into a framework where ideally only the following needs to be specified: Where to display the content in the UI The UI elements to show while loading, on failure, and on success The URI of the HTTP request How to parse the HTTP response into the data structure that will kept in memory The location of the file in isolated storage, if it exists How to parse the file contents into the data structure that will be kept in memory It may sound like a lot, but two strings, three FrameworkElements, and two methods is less than the overhead that I currently have. Also, this needs to work for however the data is maintained in memory, and needs to work for direct collections and queries on those collections. My questions are: Has something like this already been implemented? Are my thoughts about the topic above fundamentally wrong in some way? Here is a design I'm thinking of: There are two components, a View and a Model. The View is given the FrameworkElements for loading, failure, and success. It is also given a reference to the corresponding Model. The View is a UserControl that is placed somewhere in the UI. The Model a class that is given the URI for the data, a method of how to parse the data, and optionally a filename and how to parse the file. It is responsible for retrieving the data and notifying the View whenever the current status (loading/fail/success) changes. If the data downloaded from the network is different from the cache, the network data takes precedence. When the app closes or is tombstoned, the model writes the data to the cache. How does that sound?

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  • Using a large list of terms, search through page text and replace words with links

    - by dunc
    A while ago I posted this question asking if it's possible to convert text to HTML links if they match a list of terms from my database. I have a fairly huge list of terms - around 6000. The accepted answer on that question was superb, but having never used XPath, I was at a loss when problems started occurring. At one point, after fiddling with code, I somehow managed to add over 40,000 random characters to our database - the majority of which required manual removal. Since then I've lost faith in that idea and the more simple PHP solutions simply weren't efficient enough to deal with the amount of data and the quantity of terms. My next attempt at a solution is to write a JS script which, once the page has loaded, retrieves the terms and matches them against the text on a page. This answer has an idea which I'd like to attempt. I would use AJAX to retrieve the terms from the database, to build an object such as this: var words = [ { word: 'Something', link: 'http://www.something.com' }, { word: 'Something Else', link: 'http://www.something.com/else' } ]; When the object has been built, I'd use this kind of code: //for each array element $.each(words, function() { //store it ("this" is gonna become the dom element in the next function) var search = this; $('.message').each( function() { //if it's exactly the same if ($(this).text() === search.word) { //do your magic tricks $(this).html('<a href="' + search.link + '">' + search.link + '</a>'); } } ); } ); Now, at first sight, there is a major issue here: with 6,000 terms, will this code be in any way efficient enough to do what I'm trying to do?. One option would possibly be to perform some of the overhead within the PHP script that the AJAX communicates with. For instance, I could send the ID of the post and then the PHP script could use SQL statements to retrieve all of the information from the post and match it against all 6,000 terms.. then the return call to the JavaScript could simply be the matching terms, which would significantly reduce the number of matches the above jQuery would make (around 50 at most). I have no problem with the script taking a few seconds to "load" on the user's browser, as long as it isn't impacting their CPU usage or anything like that. So, two questions in one: Can I make this work? What steps can I take to make it as efficient as possible? Thanks in advance,

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  • Indexing on only part of a field in MongoDB

    - by Rob Hoare
    Is there a way to create an index on only part of a field in MongoDB, for example on the first 10 characters? I couldn't find it documented (or asked about on here). The MySQL equivalent would be CREATE INDEX part_of_name ON customer (name(10));. Reason: I have a collection with a single field that varies in length from a few characters up to over 1000 characters, average 50 characters. As there are a hundred million or so documents it's going to be hard to fit the full index in memory (testing with 8% of the data the index is already 400MB, according to stats). Indexing just the first part of the field would reduce the index size by about 75%. In most cases the search term is quite short, it's not a full-text search. A work-around would be to add a second field of 10 (lowercased) characters for each item, index that, then add logic to filter the results if the search term is over ten characters (and that extra field is probably needed anyway for case-insensitive searches, unless anybody has a better way). Seems like an ugly way to do it though. [added later] I tried adding the second field, containing the first 12 characters from the main field, lowercased. It wasn't a big success. Previously, the average object size was 50 bytes, but I forgot that includes the _id and other overheads, so my main field length (there was only one) averaged nearer to 30 bytes than 50. Then, the second field index contains the _id and other overheads. Net result (for my 8% sample) is the index on the main field is 415MB and on the 12 byte field is 330MB - only a 20% saving in space, not worthwhile. I could duplicate the entire field (to work around the case insensitive search problem) but realistically it looks like I should reconsider whether MongoDB is the right tool for the job (or just buy more memory and use twice as much disk space). [added even later] This is a typical document, with the source field, and the short lowercased field: { "_id" : ObjectId("505d0e89f56588f20f000041"), "q" : "Continental Airlines", "f" : "continental " } Indexes: db.test.ensureIndex({q:1}); db.test.ensureIndex({f:1}); The 'f" index, working on a shorter field, is 80% of the size of the "q" index. I didn't mean to imply I included the _id in the index, just that it needs to use that somewhere to show where the index will point to, so it's an overhead that probably helps explain why a shorter key makes so little difference. Access to the index will be essentially random, no part of it is more likely to be accessed than any other. Total index size for the full file will likely be 5GB, so it's not extreme for that one index. Adding some other fields for other search cases, and their associated indexes, and copies of data for lower case, does start to add up, which I why I started looking into a more concise index.

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  • Dynamic Types and DynamicObject References in C#

    - by Rick Strahl
    I've been working a bit with C# custom dynamic types for several customers recently and I've seen some confusion in understanding how dynamic types are referenced. This discussion specifically centers around types that implement IDynamicMetaObjectProvider or subclass from DynamicObject as opposed to arbitrary type casts of standard .NET types. IDynamicMetaObjectProvider types  are treated special when they are cast to the dynamic type. Assume for a second that I've created my own implementation of a custom dynamic type called DynamicFoo which is about as simple of a dynamic class that I can think of:public class DynamicFoo : DynamicObject { Dictionary<string, object> properties = new Dictionary<string, object>(); public string Bar { get; set; } public DateTime Entered { get; set; } public override bool TryGetMember(GetMemberBinder binder, out object result) { result = null; if (!properties.ContainsKey(binder.Name)) return false; result = properties[binder.Name]; return true; } public override bool TrySetMember(SetMemberBinder binder, object value) { properties[binder.Name] = value; return true; } } This class has an internal dictionary member and I'm exposing this dictionary member through a dynamic by implementing DynamicObject. This implementation exposes the properties dictionary so the dictionary keys can be referenced like properties (foo.NewProperty = "Cool!"). I override TryGetMember() and TrySetMember() which are fired at runtime every time you access a 'property' on a dynamic instance of this DynamicFoo type. Strong Typing and Dynamic Casting I now can instantiate and use DynamicFoo in a couple of different ways: Strong TypingDynamicFoo fooExplicit = new DynamicFoo(); var fooVar = new DynamicFoo(); These two commands are essentially identical and use strong typing. The compiler generates identical code for both of them. The var statement is merely a compiler directive to infer the type of fooVar at compile time and so the type of fooExplicit is DynamicFoo, just like fooExplicit. This is very static - nothing dynamic about it - and it completely ignores the IDynamicMetaObjectProvider implementation of my class above as it's never used. Using either of these I can access the native properties:DynamicFoo fooExplicit = new DynamicFoo();// static typing assignmentsfooVar.Bar = "Barred!"; fooExplicit.Entered = DateTime.Now; // echo back static values Console.WriteLine(fooVar.Bar); Console.WriteLine(fooExplicit.Entered); but I have no access whatsoever to the properties dictionary. Basically this creates a strongly typed instance of the type with access only to the strongly typed interface. You get no dynamic behavior at all. The IDynamicMetaObjectProvider features don't kick in until you cast the type to dynamic. If I try to access a non-existing property on fooExplicit I get a compilation error that tells me that the property doesn't exist. Again, it's clearly and utterly non-dynamic. Dynamicdynamic fooDynamic = new DynamicFoo(); fooDynamic on the other hand is created as a dynamic type and it's a completely different beast. I can also create a dynamic by simply casting any type to dynamic like this:DynamicFoo fooExplicit = new DynamicFoo(); dynamic fooDynamic = fooExplicit; Note that dynamic typically doesn't require an explicit cast as the compiler automatically performs the cast so there's no need to use as dynamic. Dynamic functionality works at runtime and allows for the dynamic wrapper to look up and call members dynamically. A dynamic type will look for members to access or call in two places: Using the strongly typed members of the object Using theIDynamicMetaObjectProvider Interface methods to access members So rather than statically linking and calling a method or retrieving a property, the dynamic type looks up - at runtime  - where the value actually comes from. It's essentially late-binding which allows runtime determination what action to take when a member is accessed at runtime *if* the member you are accessing does not exist on the object. Class members are checked first before IDynamicMetaObjectProvider interface methods are kick in. All of the following works with the dynamic type:dynamic fooDynamic = new DynamicFoo(); // dynamic typing assignments fooDynamic.NewProperty = "Something new!"; fooDynamic.LastAccess = DateTime.Now; // dynamic assigning static properties fooDynamic.Bar = "dynamic barred"; fooDynamic.Entered = DateTime.Now; // echo back dynamic values Console.WriteLine(fooDynamic.NewProperty); Console.WriteLine(fooDynamic.LastAccess); Console.WriteLine(fooDynamic.Bar); Console.WriteLine(fooDynamic.Entered); The dynamic type can access the native class properties (Bar and Entered) and create and read new ones (NewProperty,LastAccess) all using a single type instance which is pretty cool. As you can see it's pretty easy to create an extensible type this way that can dynamically add members at runtime dynamically. The Alter Ego of IDynamicObject The key point here is that all three statements - explicit, var and dynamic - declare a new DynamicFoo(), but the dynamic declaration results in completely different behavior than the first two simply because the type has been cast to dynamic. Dynamic binding means that the type loses its typical strong typing, compile time features. You can see this easily in the Visual Studio code editor. As soon as you assign a value to a dynamic you lose Intellisense and you see which means there's no Intellisense and no compiler type checking on any members you apply to this instance. If you're new to the dynamic type it might seem really confusing that a single type can behave differently depending on how it is cast, but that's exactly what happens when you use a type that implements IDynamicMetaObjectProvider. Declare the type as its strong type name and you only get to access the native instance members of the type. Declare or cast it to dynamic and you get dynamic behavior which accesses native members plus it uses IDynamicMetaObjectProvider implementation to handle any missing member definitions by running custom code. You can easily cast objects back and forth between dynamic and the original type:dynamic fooDynamic = new DynamicFoo(); fooDynamic.NewProperty = "New Property Value"; DynamicFoo foo = fooDynamic; foo.Bar = "Barred"; Here the code starts out with a dynamic cast and a dynamic assignment. The code then casts back the value to the DynamicFoo. Notice that when casting from dynamic to DynamicFoo and back we typically do not have to specify the cast explicitly - the compiler can induce the type so I don't need to specify as dynamic or as DynamicFoo. Moral of the Story This easy interchange between dynamic and the underlying type is actually super useful, because it allows you to create extensible objects that can expose non-member data stores and expose them as an object interface. You can create an object that hosts a number of strongly typed properties and then cast the object to dynamic and add additional dynamic properties to the same type at runtime. You can easily switch back and forth between the strongly typed instance to access the well-known strongly typed properties and to dynamic for the dynamic properties added at runtime. Keep in mind that dynamic object access has quite a bit of overhead and is definitely slower than strongly typed binding, so if you're accessing the strongly typed parts of your objects you definitely want to use a strongly typed reference. Reserve dynamic for the dynamic members to optimize your code. The real beauty of dynamic is that with very little effort you can build expandable objects or objects that expose different data stores to an object interface. I'll have more on this in my next post when I create a customized and extensible Expando object based on DynamicObject.© Rick Strahl, West Wind Technologies, 2005-2012Posted in CSharp  .NET   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • SQL Azure Reporting Limited CTP Arrived

    - by Shaun
    It’s about 3 months later when I registered the SQL Azure Reporting CTP on the Microsoft Connect after TechED 2010 China. Today when I checked my mailbox I found that the SQL Azure team had just accepted my request and sent the activation code over to me. So let’s have a look on the new SQL Azure Reporting.   Concept The SQL Azure Reporting provides cloud-based reporting as a service, built on SQL Server Reporting Services and SQL Azure technologies. Cloud-based reporting solutions such as SQL Azure Reporting provide many benefits, including rapid provisioning, cost-effective scalability, high availability, and reduced management overhead for report servers; and secure access, viewing, and management of reports. By using the SQL Azure Reporting service, we can do: Embed the Visual Studio Report Viewer ADO.NET Ajax control or Windows Form control to view the reports deployed on SQL Azure Reporting Service in our web or desktop application. Leverage the SQL Azure Reporting SOAP API to manage and retrieve the report content from any kinds of application. Use the SQL Azure Reporting Service Portal to navigate and view the reports deployed on the cloud. Since the SQL Azure Reporting was built based on the SQL Server 2008 R2 Reporting Service, we can use any tools we are familiar with, such as the SQL Server Integration Studio, Visual Studio Report Viewer. The SQL Azure Reporting Service runs as a remote SQL Server Reporting Service just on the cloud rather than on a server besides us.   Establish a New SQL Azure Reporting Let’s move to the windows azure deveploer portal and click the Reporting item from the left side navigation bar. If you don’t have the activation code you can click the Sign Up button to send a requirement to the Microsoft Connect. Since I already recieved the received code mail I clicked the Provision button. Then after agree the terms of the service I will select the subscription for where my SQL Azure Reporting CTP should be provisioned. In this case I selected my free Windows Azure Pass subscription. Then the final step, paste the activation code and enter the password of our SQL Azure Reporting Service. The user name of the SQL Azure Reporting will be generated by SQL Azure automatically. After a while the new SQL Azure Reporting Server will be shown on our developer portal. The Reporting Service URL and the user name will be shown as well. We can reset the password from the toolbar button.   Deploy Report to SQL Azure Reporting If you are familiar with SQL Server Reporting Service you will find this part will be very similar with what you know and what you did before. Firstly we open the SQL Server Business Intelligence Development Studio and create a new Report Server Project. Then we will create a shared data source where the report data will be retrieved from. This data source can be SQL Azure but we can use local SQL Server or other database if it opens the port up. In this case we use a SQL Azure database located in the same data center of our reporting service. In the Credentials tab page we entered the user name and password to this SQL Azure database. The SQL Azure Reporting CTP only available at the North US Data Center now so that the related SQL Server and hosted service might be better to select the same data center to avoid the external data transfer fee. Then we create a very simple report, just retrieve all records from a table named Members and have a table in the report to list them. In the data source selection step we choose the shared data source we created before, then enter the T-SQL to select all records from the Member table, then put all fields into the table columns. The report will be like this as following In order to deploy the report onto the SQL Azure Reporting Service we need to update the project property. Right click the project node from the solution explorer and select the property item. In the Target Server URL item we will specify the reporting server URL of our SQL Azure Reporting. We can go back to the developer portal and select the reporting node from the left side, then copy the Web Service URL and paste here. But notice that we need to append “/reportserver” after pasted. Then just click the Deploy menu item in the context menu of the project, the Visual Studio will compile the report and then upload to the reporting service accordingly. In this step we will be prompted to input the user name and password of our SQL Azure Reporting Service. We can get the user name from the developer portal, just next to the Web Service URL in the SQL Azure Reporting page. And the password is the one we specified when created the reporting service. After about one minute the report will be deployed succeed.   View the Report in Browser SQL Azure Reporting allows us to view the reports which deployed on the cloud from a standard browser. We copied the Web Service URL from the reporting service main page and appended “/reportserver” in HTTPS protocol then we will have the SQL Azure Reporting Service login page. After entered the user name and password of the SQL Azure Reporting Service we can see the directories and reports listed. Click the report will launch the Report Viewer to render the report.   View Report in a Web Role with the Report Viewer The ASP.NET and Windows Form Report Viewer works well with the SQL Azure Reporting Service as well. We can create a ASP.NET Web Role and added the Report Viewer control in the default page. What we need to change to the report viewer are Change the Processing Mode to Remote. Specify the Report Server URL under the Server Remote category to the URL of the SQL Azure Reporting Web Service URL with “/reportserver” appended. Specify the Report Path to the report which we want to display. The report name should NOT include the extension name. For example my report was in the SqlAzureReportingTest project and named MemberList.rdl then the report path should be /SqlAzureReportingTest/MemberList. And the next one is to specify the SQL Azure Reporting Credentials. We can use the following class to wrap the report server credential. 1: private class ReportServerCredentials : IReportServerCredentials 2: { 3: private string _userName; 4: private string _password; 5: private string _domain; 6:  7: public ReportServerCredentials(string userName, string password, string domain) 8: { 9: _userName = userName; 10: _password = password; 11: _domain = domain; 12: } 13:  14: public WindowsIdentity ImpersonationUser 15: { 16: get 17: { 18: return null; 19: } 20: } 21:  22: public ICredentials NetworkCredentials 23: { 24: get 25: { 26: return null; 27: } 28: } 29:  30: public bool GetFormsCredentials(out Cookie authCookie, out string user, out string password, out string authority) 31: { 32: authCookie = null; 33: user = _userName; 34: password = _password; 35: authority = _domain; 36: return true; 37: } 38: } And then in the Page_Load method, pass it to the report viewer. 1: protected void Page_Load(object sender, EventArgs e) 2: { 3: ReportViewer1.ServerReport.ReportServerCredentials = new ReportServerCredentials( 4: "<user name>", 5: "<password>", 6: "<sql azure reporting web service url>"); 7: } Finally deploy it to Windows Azure and enjoy the report.   Summary In this post I introduced the SQL Azure Reporting CTP which had just available. Likes other features in Windows Azure, the SQL Azure Reporting is very similar with the SQL Server Reporting. As you can see in this post we can use the existing and familiar tools to build and deploy the reports and display them on a website. But the SQL Azure Reporting is just in the CTP stage which means It is free. There’s no support for it. Only available at the North US Data Center. You can get more information about the SQL Azure Reporting CTP from the links following SQL Azure Reporting Limited CTP at MSDN SQL Azure Reporting Samples at TechNet Wiki You can download the solutions and the projects used in this post here.   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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  • SQL SERVER – Extending SQL Azure with Azure worker role – Guest Post by Paras Doshi

    - by pinaldave
    This is guest post by Paras Doshi. Paras Doshi is a research Intern at SolidQ.com and a Microsoft student partner. He is currently working in the domain of SQL Azure. SQL Azure is nothing but a SQL server in the cloud. SQL Azure provides benefits such as on demand rapid provisioning, cost-effective scalability, high availability and reduced management overhead. To see an introduction on SQL Azure, check out the post by Pinal here In this article, we are going to discuss how to extend SQL Azure with the Azure worker role. In other words, we will attempt to write a custom code and host it in the Azure worker role; the aim is to add some features that are not available with SQL Azure currently or features that need to be customized for flexibility. This way we extend the SQL Azure capability by building some solutions that run on Azure as worker roles. To understand Azure worker role, think of it as a windows service in cloud. Azure worker role can perform background processes, and to handle processes such as synchronization and backup, it becomes our ideal tool. First, we will focus on writing a worker role code that synchronizes SQL Azure databases. Before we do so, let’s see some scenarios in which synchronization between SQL Azure databases is beneficial: scaling out access over multiple databases enables us to handle workload efficiently As of now, SQL Azure database can be hosted in one of any six datacenters. By synchronizing databases located in different data centers, one can extend the data by enabling access to geographically distributed data Let us see some scenarios in which SQL server to SQL Azure database synchronization is beneficial To backup SQL Azure database on local infrastructure Rather than investing in local infrastructure for increased workloads, such workloads could be handled by cloud Ability to extend data to different datacenters located across the world to enable efficient data access from remote locations Now, let us develop cloud-based app that synchronizes SQL Azure databases. For an Introduction to developing cloud based apps, click here Now, in this article, I aim to provide a bird’s eye view of how a code that synchronizes SQL Azure databases look like and then list resources that can help you develop the solution from scratch. Now, if you newly add a worker role to the cloud-based project, this is how the code will look like. (Note: I have added comments to the skeleton code to point out the modifications that will be required in the code to carry out the SQL Azure synchronization. Note the placement of Setup() and Sync() function.) Click here (http://parasdoshi1989.files.wordpress.com/2011/06/code-snippet-1-for-extending-sql-azure-with-azure-worker-role1.pdf ) Enabling SQL Azure databases synchronization through sync framework is a two-step process. In the first step, the database is provisioned and sync framework creates tracking tables, stored procedures, triggers, and tables to store metadata to enable synchronization. This is one time step. The code for the same is put in the setup() function which is called once when the worker role starts. Now, the second step is continuous (or on demand) synchronization of SQL Azure databases by propagating changes between databases. This is done on a continuous basis by calling the sync() function in the while loop. The code logic to synchronize changes between SQL Azure databases should be put in the sync() function. Discussing the coding part step by step is out of the scope of this article. Therefore, let me suggest you a resource, which is given here. Also, note that before you start developing the code, you will need to install SYNC framework 2.1 SDK (download here). Further, you will reference some libraries before you start coding. Details regarding the same are available in the article that I just pointed to. You will be charged for data transfers if the databases are not in the same datacenter. For pricing information, go here Currently, a tool named DATA SYNC, which is built on top of sync framework, is available in CTP that allows SQL Azure <-> SQL server and SQL Azure <-> SQL Azure synchronization (without writing single line of code); however, in some cases, the custom code shown in this blogpost provides flexibility that is not available with Data SYNC. For instance, filtering is not supported in the SQL Azure DATA SYNC CTP2; if you wish to have such a functionality now, then you have the option of developing a custom code using SYNC Framework. Now, this code can be easily extended to synchronize at some schedule. Let us say we want the databases to get synchronized every day at 10:00 pm. This is what the code will look like now: (http://parasdoshi1989.files.wordpress.com/2011/06/code-snippet-2-for-extending-sql-azure-with-azure-worker-role.pdf) Don’t you think that by writing such a code, we are imitating the functionality provided by the SQL server agent for a SQL server? Think about it. We are scheduling our administrative task by writing custom code – in other words, we have developed a “Light weight SQL server agent for SQL Azure!” Since the SQL server agent is not currently available in cloud, we have developed a solution that enables us to schedule tasks, and thus we have extended SQL Azure with the Azure worker role! Now if you wish to track jobs, you can do so by storing this data in SQL Azure (or Azure tables). The reason is that Windows Azure is a stateless platform, and we will need to store the state of the job ourselves and the choice that you have is SQL Azure or Azure tables. Note that this solution requires custom code and also it is not UI driven; however, for now, it can act as a temporary solution until SQL server agent is made available in the cloud. Moreover, this solution does not encompass functionalities that a SQL server agent provides, but it does open up an interesting avenue to schedule some of the tasks such as backup and synchronization of SQL Azure databases by writing some custom code in the Azure worker role. Now, let us see one more possibility – i.e., running BCP through a worker role in Azure-hosted services and then uploading the backup files either locally or on blobs. If you upload it locally, then consider the data transfer cost. If you upload it to blobs residing in the same datacenter, then no transfer cost applies but the cost on blob size applies. So, before choosing the option, you need to evaluate your preferences keeping the cost associated with each option in mind. In this article, I have shown that Azure worker role solution could be developed to synchronize SQL Azure databases. Moreover, a light-weight SQL server agent for SQL Azure can be developed. Also we discussed the possibility of running BCP through a worker role in Azure-hosted services for backing up our precious SQL Azure data. Thus, we can extend SQL Azure with the Azure worker role. But remember: you will be charged for running Azure worker roles. So at the end of the day, you need to ask – am I willing to build a custom code and pay money to achieve this functionality? I hope you found this blog post interesting. If you have any questions/feedback, you can comment below or you can mail me at Paras[at]student-partners[dot]com Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Azure, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Why can't I convert FLV to MP4 format using FFmpeg when MP3 works?

    - by hugemeow
    In fact I have succeeded to convert FLV to MP3: D:\tmp\ffmpeg-20121005-git-d9dfe9a-win64-static\ffmpeg-20121005-git-d9dfe9a-win 4-static\bin>ffmpeg.exe -i a.flv -acodec mp3 a.mp3 ffmpeg version N-45080-gd9dfe9a Copyright (c) 2000-2012 the FFmpeg developers built on Oct 5 2012 16:49:01 with gcc 4.7.1 (GCC) configuration: --enable-gpl --enable-version3 --disable-pthreads --enable-run ime-cpudetect --enable-avisynth --enable-bzlib --enable-frei0r --enable-libass -enable-libcelt --enable-libopencore-amrnb --enable-libopencore-amrwb --enable- ibfreetype --enable-libgsm --enable-libmp3lame --enable-libnut --enable-libopen peg --enable-librtmp --enable-libschroedinger --enable-libspeex --enable-libthe ra --enable-libutvideo --enable-libvo-aacenc --enable-libvo-amrwbenc --enable-l bvorbis --enable-libvpx --enable-libx264 --enable-libxavs --enable-libxvid --en ble-zlib libavutil 51. 73.102 / 51. 73.102 libavcodec 54. 63.100 / 54. 63.100 libavformat 54. 29.105 / 54. 29.105 libavdevice 54. 3.100 / 54. 3.100 libavfilter 3. 19.102 / 3. 19.102 libswscale 2. 1.101 / 2. 1.101 libswresample 0. 16.100 / 0. 16.100 libpostproc 52. 1.100 / 52. 1.100 Input #0, flv, from 'a.flv': Metadata: metadatacreator : iku hasKeyframes : true hasVideo : true hasAudio : true hasMetadata : true canSeekToEnd : false datasize : 16906383 videosize : 14558526 audiosize : 2270465 lasttimestamp : 530 lastkeyframetimestamp: 529 lastkeyframelocation: 16893721 Duration: 00:08:49.73, start: 0.000000, bitrate: 255 kb/s Stream #0:0: Video: h264 (Main), yuv420p, 448x336 [SAR 1:1 DAR 4:3], 218 kb s, 15 tbr, 1k tbn, 30 tbc Stream #0:1: Audio: aac, 44100 Hz, stereo, s16, 32 kb/s File 'a.mp3' already exists. Overwrite ? [y/N] y Output #0, mp3, to 'a.mp3': Metadata: metadatacreator : iku hasKeyframes : true hasVideo : true hasAudio : true hasMetadata : true canSeekToEnd : false datasize : 16906383 videosize : 14558526 audiosize : 2270465 lasttimestamp : 530 lastkeyframetimestamp: 529 lastkeyframelocation: 16893721 TSSE : Lavf54.29.105 Stream #0:0: Audio: mp3, 44100 Hz, stereo, s16 Stream mapping: Stream #0:1 -> #0:0 (aac -> libmp3lame) Press [q] to stop, [?] for help size= 8279kB time=00:08:49.78 bitrate= 128.0kbits/s video:0kB audio:8278kB subtitle:0 global headers:0kB muxing overhead 0.006842% But I failed to convert FLV to MP4. Why is the encoder 'mp4' unknown? What's more, how can I find the codecs which are already supported by my FFmpeg? D:\tmp\ffmpeg-20121005-git-d9dfe9a-win64-static\ffmpeg-20121005-git-d9dfe9a-win6 4-static\bin>ffmpeg.exe -i a.flv -acodec mp4 aa.mp4 ffmpeg version N-45080-gd9dfe9a Copyright (c) 2000-2012 the FFmpeg developers built on Oct 5 2012 16:49:01 with gcc 4.7.1 (GCC) configuration: --enable-gpl --enable-version3 --disable-pthreads --enable-runt ime-cpudetect --enable-avisynth --enable-bzlib --enable-frei0r --enable-libass - -enable-libcelt --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-l ibfreetype --enable-libgsm --enable-libmp3lame --enable-libnut --enable-libopenj peg --enable-librtmp --enable-libschroedinger --enable-libspeex --enable-libtheo ra --enable-libutvideo --enable-libvo-aacenc --enable-libvo-amrwbenc --enable-li bvorbis --enable-libvpx --enable-libx264 --enable-libxavs --enable-libxvid --ena ble-zlib libavutil 51. 73.102 / 51. 73.102 libavcodec 54. 63.100 / 54. 63.100 libavformat 54. 29.105 / 54. 29.105 libavdevice 54. 3.100 / 54. 3.100 libavfilter 3. 19.102 / 3. 19.102 libswscale 2. 1.101 / 2. 1.101 libswresample 0. 16.100 / 0. 16.100 libpostproc 52. 1.100 / 52. 1.100 Input #0, flv, from 'a.flv': Metadata: metadatacreator : iku hasKeyframes : true hasVideo : true hasAudio : true hasMetadata : true canSeekToEnd : false datasize : 16906383 videosize : 14558526 audiosize : 2270465 lasttimestamp : 530 lastkeyframetimestamp: 529 lastkeyframelocation: 16893721 Duration: 00:08:49.73, start: 0.000000, bitrate: 255 kb/s Stream #0:0: Video: h264 (Main), yuv420p, 448x336 [SAR 1:1 DAR 4:3], 218 kb/ s, 15 tbr, 1k tbn, 30 tbc Stream #0:1: Audio: aac, 44100 Hz, stereo, s16, 32 kb/s Unknown encoder 'mp4'

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  • Oracle performance problem

    - by jreid42
    We are using an Oracle 11G machine that is very powerful; has redundant storage etc. It's a beast from what I have been told. We just got this DB for a tool that when I first came on as a coop had like 20 people using, now its upwards of 150 people. I am the only one working on it :( We currently have a system in place that distributes PERL scripts across our entire data center essentially giving us a sort of "grid" computing power. The Perl scripts run a sort of simulation and report back the results to the database. They do selects / inserts. The load is not very high for each script but it could be happening across 20-50 systems at the same time. We then have multiple data centers and users all hitting the same database with this same approach. Our main problem with this is that our database is getting overloaded with connections and having to drop some. We sometimes have upwards of 500 connections. These are old perl scripts and they do not handle this well. Essentially they fail and the results are lost. I would rather avoid having to rewrite a lot of these as they are poorly written, and are a headache to even look at. The database itself is not overloaded, just the connection overhead is too high. We open a connection, make a quick query and then drop the connection. Very short connections but many of them. The database team has basically said we need to lower the number of connections or they are going to ignore us. Because this is distributed across our farm we cant implement persistent connections. I do this with our webserver; but its on a fixed system. The other ones are perl scripts that get opened and closed by the distribution tool and thus arent always running. What would be my best approach to resolving this issue? The scripts themselves can wait for a connection to be open. They do not need to act immediately. Some sort of queing system? I've been suggested to set up a few instances of a tool called "SQL Relay". Maybe one in each data center. How reliable is this tool? How good is this approach? Would it work for what we need? We could have one for each data center and relay requests through it to our main database, keeping a pipeline of open persistent connections? Does this make sense? Is there any other suggestions you can make? Any ideas? Any help would be greatly appreciated. Sadly I am just a coop student working for a very big company and somehow all of this has landed all on my shoulders (there is literally nobody to ask for help; its a hardware company, everybody is hardware engineers, and the database team is useless and in India) and I am quite lost as what the best approach would be? I am extremely overworked and this problem is interfering with on going progress and basically needs to be resolved as quickly as possible; preferably without rewriting the whole system, purchasing hardware (not gonna happen), or shooting myself in the foot. HELP LOL!

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  • API Message Localization

    - by Jesse Taber
    In my post, “Keep Localizable Strings Close To Your Users” I talked about the internationalization and localization difficulties that can arise when you sprinkle static localizable strings throughout the different logical layers of an application. The main point of that post is that you should have your localizable strings reside as close to the user-facing modules of your application as possible. For example, if you’re developing an ASP .NET web forms application all of the localizable strings should be kept in .resx files that are associated with the .aspx views of the application. In this post I want to talk about how this same concept can be applied when designing and developing APIs. An API Facilitates Machine-to-Machine Interaction You can typically think about a web, desktop, or mobile application as a collection “views” or “screens” through which users interact with the underlying logic and data. The application can be designed based on the assumption that there will be a human being on the other end of the screen working the controls. You are designing a machine-to-person interaction and the application should be built in a way that facilitates the user’s clear understanding of what is going on. Dates should be be formatted in a way that the user will be familiar with, messages should be presented in the user’s preferred language, etc. When building an API, however, there are no screens and you can’t make assumptions about who or what is on the other end of each call. An API is, by definition, a machine-to-machine interaction. A machine-to-machine interaction should be built in a way that facilitates a clear and unambiguous understanding of what is going on. Dates and numbers should be formatted in predictable and standard ways (e.g. ISO 8601 dates) and messages should be presented in machine-parseable formats. For example, consider an API for a time tracking system that exposes a resource for creating a new time entry. The JSON for creating a new time entry for a user might look like: 1: { 2: "userId": 4532, 3: "startDateUtc": "2012-10-22T14:01:54.98432Z", 4: "endDateUtc": "2012-10-22T11:34:45.29321Z" 5: }   Note how the parameters for start and end date are both expressed as ISO 8601 compliant dates in UTC. Using a date format like this in our API leaves little room for ambiguity. It’s also important to note that using ISO 8601 dates is a much, much saner thing than the \/Date(<milliseconds since epoch>)\/ nonsense that is sometimes used in JSON serialization. Probably the most important thing to note about the JSON snippet above is the fact that the end date comes before the start date! The API should recognize that and disallow the time entry from being created, returning an error to the caller. You might inclined to send a response that looks something like this: 1: { 2: "errors": [ {"message" : "The end date must come after the start date"}] 3: }   While this may seem like an appropriate thing to do there are a few problems with this approach: What if there is a user somewhere on the other end of the API call that doesn’t speak English?  What if the message provided here won’t fit properly within the UI of the application that made the API call? What if the verbiage of the message isn’t consistent with the rest of the application that made the API call? What if there is no user directly on the other end of the API call (e.g. this is a batch job uploading time entries once per night unattended)? The API knows nothing about the context from which the call was made. There are steps you could take to given the API some context (e.g.allow the caller to send along a language code indicating the language that the end user speaks), but that will only get you so far. As the designer of the API you could make some assumptions about how the API will be called, but if we start making assumptions we could very easily make the wrong assumptions. In this situation it’s best to make no assumptions and simply design the API in such a way that the caller has the responsibility to convey error messages in a manner that is appropriate for the context in which the error was raised. You would work around some of these problems by allowing callers to add metadata to each request describing the context from which the call is being made (e.g. accepting a ‘locale’ parameter denoting the desired language), but that will add needless clutter and complexity. It’s better to keep the API simple and push those context-specific concerns down to the caller whenever possible. For our very simple time entry example, this can be done by simply changing our error message response to look like this: 1: { 2: "errors": [ {"code": 100}] 3: }   By changing our error error from exposing a string to a numeric code that is easily parseable by another application, we’ve placed all of the responsibility for conveying the actual meaning of the error message on the caller. It’s best to have the caller be responsible for conveying this meaning because the caller understands the context much better than the API does. Now the caller can see error code 100, know that it means that the end date submitted falls before the start date and take appropriate action. Now all of the problems listed out above are non-issues because the caller can simply translate the error code of ‘100’ into the proper action and message for the current context. The numeric code representation of the error is a much better way to facilitate the machine-to-machine interaction that the API is meant to facilitate. An API Does Have Human Users While APIs should be built for machine-to-machine interaction, people still need to wire these interactions together. As a programmer building a client application that will consume the time entry API I would find it frustrating to have to go dig through the API documentation every time I encounter a new error code (assuming the documentation exists and is accurate). The numeric error code approach hurts the discoverability of the API and makes it painful to integrate with. We can help ease this pain by merging our two approaches: 1: { 2: "errors": [ {"code": 100, "message" : "The end date must come after the start date"}] 3: }   Now we have an easily parseable numeric error code for the machine-to-machine interaction that the API is meant to facilitate and a human-readable message for programmers working with the API. The human-readable message here is not intended to be viewed by end-users of the API and as such is not really a “localizable string” in my opinion. We could opt to expose a locale parameter for all API methods and store translations for all error messages, but that’s a lot of extra effort and overhead that doesn’t add a lot real value to the API. I might be a bit of an “ugly American”, but I think it’s probably fine to have the API return English messages when the target for those messages is a programmer. When resources are limited (which they always are), I’d argue that you’re better off hard-coding these messages in English and putting more effort into building more useful features, improving security, tweaking performance, etc.

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  • Using Table-Valued Parameters in SQL Server

    - by Jesse
    I work with stored procedures in SQL Server pretty frequently and have often found myself with a need to pass in a list of values at run-time. Quite often this list contains a set of ids on which the stored procedure needs to operate the size and contents of which are not known at design time. In the past I’ve taken the collection of ids (which are usually integers), converted them to a string representation where each value is separated by a comma and passed that string into a VARCHAR parameter of a stored procedure. The body of the stored procedure would then need to parse that string into a table variable which could be easily consumed with set-based logic within the rest of the stored procedure. This approach works pretty well but the VARCHAR variable has always felt like an un-wanted “middle man” in this scenario. Of course, I could use a BULK INSERT operation to load the list of ids into a temporary table that the stored procedure could use, but that approach seems heavy-handed in situations where the list of values is usually going to contain only a few dozen values. Fortunately SQL Server 2008 introduced the concept of table-valued parameters which effectively eliminates the need for the clumsy middle man VARCHAR parameter. Example: Customer Transaction Summary Report Let’s say we have a report that can summarize the the transactions that we’ve conducted with customers over a period of time. The report returns a pretty simple dataset containing one row per customer with some key metrics about how much business that customer has conducted over the date range for which the report is being run. Sometimes the report is run for a single customer, sometimes it’s run for all customers, and sometimes it’s run for a handful of customers (i.e. a salesman runs it for the customers that fall into his sales territory). This report can be invoked from a website on-demand, or it can be scheduled for periodic delivery to certain users via SQL Server Reporting Services. Because the report can be created from different places and the query to generate the report is complex it’s been packed into a stored procedure that accepts three parameters: @startDate – The beginning of the date range for which the report should be run. @endDate – The end of the date range for which the report should be run. @customerIds – The customer Ids for which the report should be run. Obviously, the @startDate and @endDate parameters are DATETIME variables. The @customerIds parameter, however, needs to contain a list of the identity values (primary key) from the Customers table representing the customers that were selected for this particular run of the report. In prior versions of SQL Server we might have made this parameter a VARCHAR variable, but with SQL Server 2008 we can make it into a table-valued parameter. Defining And Using The Table Type In order to use a table-valued parameter, we first need to tell SQL Server about what the table will look like. We do this by creating a user defined type. For the purposes of this stored procedure we need a very simple type to model a table variable with a single integer column. We can create a generic type called ‘IntegerListTableType’ like this: CREATE TYPE IntegerListTableType AS TABLE (Value INT NOT NULL) Once defined, we can use this new type to define the @customerIds parameter in the signature of our stored procedure. The parameter list for the stored procedure definition might look like: 1: CREATE PROCEDURE dbo.rpt_CustomerTransactionSummary 2: @starDate datetime, 3: @endDate datetime, 4: @customerIds IntegerListTableTableType READONLY   Note the ‘READONLY’ statement following the declaration of the @customerIds parameter. SQL Server requires any table-valued parameter be marked as ‘READONLY’ and no DML (INSERT/UPDATE/DELETE) statements can be performed on a table-valued parameter within the routine in which it’s used. Aside from the DML restriction, however, you can do pretty much anything with a table-valued parameter as you could with a normal TABLE variable. With the user defined type and stored procedure defined as above, we could invoke like this: 1: DECLARE @cusomterIdList IntegerListTableType 2: INSERT @customerIdList VALUES (1) 3: INSERT @customerIdList VALUES (2) 4: INSERT @customerIdList VALUES (3) 5:  6: EXEC dbo.rpt_CustomerTransationSummary 7: @startDate = '2012-05-01', 8: @endDate = '2012-06-01' 9: @customerIds = @customerIdList   Note that we can simply declare a variable of type ‘IntegerListTableType’ just like any other normal variable and insert values into it just like a TABLE variable. We could also populate the variable with a SELECT … INTO or INSERT … SELECT statement if desired. Using The Table-Valued Parameter With ADO .NET Invoking a stored procedure with a table-valued parameter from ADO .NET is as simple as building a DataTable and passing it in as the Value of a SqlParameter. Here’s some example code for how we would construct the SqlParameter for the @customerIds parameter in our stored procedure: 1: var customerIdsParameter = new SqlParameter(); 2: customerIdParameter.Direction = ParameterDirection.Input; 3: customerIdParameter.TypeName = "IntegerListTableType"; 4: customerIdParameter.Value = selectedCustomerIds.ToIntegerListDataTable("Value");   All we’re doing here is new’ing up an instance of SqlParameter, setting the pamameters direction, specifying the name of the User Defined Type that this parameter uses, and setting its value. We’re assuming here that we have an IEnumerable<int> variable called ‘selectedCustomerIds’ containing all of the customer Ids for which the report should be run. The ‘ToIntegerListDataTable’ method is an extension method of the IEnumerable<int> type that looks like this: 1: public static DataTable ToIntegerListDataTable(this IEnumerable<int> intValues, string columnName) 2: { 3: var intergerListDataTable = new DataTable(); 4: intergerListDataTable.Columns.Add(columnName); 5: foreach(var intValue in intValues) 6: { 7: var nextRow = intergerListDataTable.NewRow(); 8: nextRow[columnName] = intValue; 9: intergerListDataTable.Rows.Add(nextRow); 10: } 11:  12: return intergerListDataTable; 13: }   Since the ‘IntegerListTableType’ has a single int column called ‘Value’, we pass that in for the ‘columnName’ parameter to the extension method. The method creates a new single-columned DataTable using the provided column name then iterates over the items in the IEnumerable<int> instance adding one row for each value. We can then use this SqlParameter instance when invoking the stored procedure just like we would use any other parameter. Advanced Functionality Using passing a list of integers into a stored procedure is a very simple usage scenario for the table-valued parameters feature, but I’ve found that it covers the majority of situations where I’ve needed to pass a collection of data for use in a query at run-time. I should note that BULK INSERT feature still makes sense for passing large amounts of data to SQL Server for processing. MSDN seems to suggest that 1000 rows of data is the tipping point where the overhead of a BULK INSERT operation can pay dividends. I should also note here that table-valued parameters can be used to deal with more complex data structures than single-columned tables of integers. A User Defined Type that backs a table-valued parameter can use things like identities and computed columns. That said, using some of these more advanced features might require the use the SqlDataRecord and SqlMetaData classes instead of a simple DataTable. Erland Sommarskog has a great article on his website that describes when and how to use these classes for table-valued parameters. What About Reporting Services? Earlier in the post I referenced the fact that our example stored procedure would be called from both a web application and a SQL Server Reporting Services report. Unfortunately, using table-valued parameters from SSRS reports can be a bit tricky and warrants its own blog post which I’ll be putting together and posting sometime in the near future.

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  • Crash in audio resampler with some audio rates - FFMPEG PHP ( Solved! )

    - by Olaf Erlandsen
    i have a problem with this command( FFMPEG PHP ): Command: ffmpeg -i 62f76f050494f0ed6a5997967c00c0c0.wmv -ss 0 -t 99 -y -ar 44100 -async 44100 -r 29.970 -ac 2 -qscale 5 -f flv 62f76f050494f0ed6a5997967c00c0c0.flv Output: FFmpeg version 0.6.5, Copyright (c) 2000-2010 the FFmpeg developers built on Jan 29 2012 17:52:15 with gcc 4.4.5 20110214 (Red Hat 4.4.5-6) configuration: --prefix=/usr --libdir=/usr/lib64 --shlibdir=/usr/lib64 --mandir=/usr/share/man --incdir=/usr/include --disable-avisynth --extra-cflags='-O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector --param=ssp-buffer-size=4 -m64 -mtune=generic -fPIC' --enable-avfilter --enable-avfilter-lavf --enable-libdc1394 --enable-libdirac --enable-libfaac --enable-libfaad --enable-libfaadbin --enable-libgsm --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-librtmp --enable-libschroedinger --enable-libspeex --enable-libtheora --enable-libx264 --enable-gpl --enable-nonfree --enable-postproc --enable-pthreads --enable-shared --enable-swscale --enable-vdpau --enable-version3 --enable-x11grab libavutil 50.15. 1 / 50.15. 1 libavcodec 52.72. 2 / 52.72. 2 libavformat 52.64. 2 / 52.64. 2 libavdevice 52. 2. 0 / 52. 2. 0 libavfilter 1.19. 0 / 1.19. 0 libswscale 0.11. 0 / 0.11. 0 libpostproc 51. 2. 0 / 51. 2. 0 [asf @ 0xe81670]max_analyze_duration reached Input #0, asf, from '/var/www/resources/tmp/62f76f050494f0ed6a5997967c00c0c0.wmv': Metadata: WMFSDKVersion : 12.0.7601.17514 WMFSDKNeeded : 0.0.0.0000 IsVBR : 0 Duration: 00:00:50.87, bitrate: 2467 kb/s Stream #0.0: Audio: wmapro, 44100 Hz, stereo, flt, 256 kb/s Stream #0.1: Video: vc1, yuv420p, 950x460 [PAR 1:1 DAR 95:46], 25 fps, 25 tbr, 1k tbn, 25 tbc Output #0, flv, to '/var/www/resources/media/62f76f050494f0ed6a5997967c00c0c0.flv': Metadata: encoder : Lavf52.64.2 Stream #0.0: Video: flv, yuv420p, 950x460 [PAR 1:1 DAR 95:46], q=2-31, 200 kb/s, 1k tbn, 29.97 tbc Stream #0.1: Audio: libmp3lame, 11025 Hz, stereo, s16, 64 kb/s Stream mapping: Stream #0.1 -> #0.0 Stream #0.0 -> #0.1 Press [q] to stop encoding frame= 72 fps= 0 q=5.0 size= 0kB time=10.91 bitrate= 0.0kbits/s Multiple frames in a packet from stream 0 Warning, using s16 intermediate sample format for resampling frame= 141 fps=139 q=5.0 size= 103kB time=8.15 bitrate= 103.2kbits/s frame= 220 fps=144 q=5.0 size= 875kB time=10.92 bitrate= 656.6kbits/s frame= 290 fps=143 q=5.0 size= 1525kB time=13.74 bitrate= 909.1kbits/s frame= 356 fps=141 q=5.0 size= 2153kB time=15.99 bitrate=1103.1kbits/s frame= 427 fps=141 q=5.0 size= 2847kB time=18.70 bitrate=1247.0kbits/s frame= 497 fps=141 q=5.0 size= 3771kB time=21.16 bitrate=1460.0kbits/s frame= 575 fps=142 q=5.0 size= 4695kB time=24.61 bitrate=1563.0kbits/s frame= 639 fps=141 q=5.0 size= 5301kB time=26.80 bitrate=1620.2kbits/s frame= 703 fps=139 q=5.0 size= 5829kB time=29.36 bitrate=1626.2kbits/s frame= 774 fps=139 q=5.0 size= 6659kB time=32.39 bitrate=1684.0kbits/s frame= 842 fps=139 q=5.0 size= 7915kB time=35.27 bitrate=1838.6kbits/s frame= 911 fps=139 q=5.0 size= 9011kB time=37.98 bitrate=1943.4kbits/s frame= 975 fps=138 q=5.0 size= 9788kB time=40.59 bitrate=1975.3kbits/s frame= 1041 fps=138 q=5.0 size= 10904kB time=43.83 bitrate=2037.9kbits/s frame= 1115 fps=138 q=5.0 size= 11795kB time=46.24 bitrate=2089.8kbits/s frame= 1183 fps=138 q=5.0 size= 12678kB time=48.74 bitrate=2130.7kbits/s frame= 1247 fps=137 q=5.0 size= 13964kB time=51.36 bitrate=2227.5kbits/s frame= 1271 fps=136 q=5.0 Lsize= 15865kB time=58.86 bitrate=2208.1kbits/s video:15366kB audio:462kB global headers:0kB muxing overhead 0.238956% Problem: Warning, using s16 intermediate sample format for resampling I've also tried changing the parameter From -ar 44100 to -ar 11025 Thanks! Solution: Read this link: http://en.wikipedia.org/wiki/MP3#Bit_rate

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  • Linux not buffering block I/O when the device is not "in use" (i.e. mounted)

    - by Radek Hladík
    I am installing new server and I've found an interesting issue. The server is running Fedora 19 (3.11.7-200.fc19.x86_64 kernel) and is supposed to host a few KVM/Qemu virtual servers (mail server, file server, etc..). The HW is Intel(R) Xeon(R) CPU 5160 @ 3.00GHz with 16GB RAM. One of the most important features will be Samba server and we have decided to make it as virtual machine with almost direct access to the disks. So the real HDD is cached on SSD (via bcache) then raided with md and the final device is exported into the virtual machine via virtio. The virtual machine is again Fedora 19 with the same kernel. One important topic to find out is whether the virtualization layer will not introduce high overload into disk I/Os. So far I've been able to get up to 180MB/s in VM and up to 220MB/s on real HW (on the SSD disk). I am still not sure why the overhead is so big but it is more than the network can handle so I do not care so much. The interesting thing is that I've found that the disk reads are not buffered in the VM unless I create and mount FS on the disk or I use the disks somehow. Simply put: Lets do dd to read disk for the first time (the /dev/vdd is an old Raptor disk 70MB/s is its real speed): [root@localhost ~]# dd if=/dev/vdd of=/dev/null bs=256k count=10000 ; cat /proc/meminfo | grep Buffers 2621440000 bytes (2.6 GB) copied, 36.8038 s, 71.2 MB/s Buffers: 14444 kB Rereading the data shows that they are cached somewhere but not in buffers of the VM. Also the speed increased to "only" 500MB/s. The VM has 4GB of RAM (more that the test file) [root@localhost ~]# dd if=/dev/vdd of=/dev/null bs=256k count=10000 ; cat /proc/meminfo | grep Buffers 2621440000 bytes (2.6 GB) copied, 5.16016 s, 508 MB/s Buffers: 14444 kB [root@localhost ~]# dd if=/dev/vdd of=/dev/null bs=256k count=10000 ; cat /proc/meminfo | grep Buffers 2621440000 bytes (2.6 GB) copied, 5.05727 s, 518 MB/s Buffers: 14444 kB Now lets mount the FS on /dev/vdd and try the dd again: [root@localhost ~]# mount /dev/vdd /mnt/tmp [root@localhost ~]# dd if=/dev/vdd of=/dev/null bs=256k count=10000 ; cat /proc/meminfo | grep Buffers 2621440000 bytes (2.6 GB) copied, 4.68578 s, 559 MB/s Buffers: 2574592 kB [root@localhost ~]# dd if=/dev/vdd of=/dev/null bs=256k count=10000 ; cat /proc/meminfo | grep Buffers 2621440000 bytes (2.6 GB) copied, 1.50504 s, 1.7 GB/s Buffers: 2574592 kB While the first read was the same, all 2.6GB got buffered and the next read was at 1.7GB/s. And when I unmount the device: [root@localhost ~]# umount /mnt/tmp [root@localhost ~]# cat /proc/meminfo | grep Buffers Buffers: 14452 kB [root@localhost ~]# dd if=/dev/vdd of=/dev/null bs=256k count=10000 ; cat /proc/meminfo | grep Buffers 2621440000 bytes (2.6 GB) copied, 5.10499 s, 514 MB/s Buffers: 14468 kB The bcache was disabled while testing and the results are same on faster (newer) HDDs and on SSD (except for the initial read speed of course). To sum it up. When I read from the device via dd first time, it gets read from the disk. Next time I reread it gets cached in the host but not in the guest (thats actually the same issue, more on that later). When I mount the filesystem but try to read the device directly it gets cached in VM (via buffers). As soon as I stop "using" it, buffers are discarded and the device is not cached anymore in the VM. When I looked into buffers value on the host I realized that the situation is the same. The block I/O gets buffered only when the disk is in use, in this case it means "exported to a VM". On host, after all the measurement done: 3165552 buffers On the host, after the VM shutdown: 119176 buffers I know it is not important as the disks will be mounted all the time but I am curious and I would like to know why it is working like this.

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  • More Fun with C# Iterators and Generators

    - by James Michael Hare
    In my last post, I talked quite a bit about iterators and how they can be really powerful tools for filtering a list of items down to a subset of items.  This had both pros and cons over returning a full collection, which, in summary, were:   Pros: If traversal is only partial, does not have to visit rest of collection. If evaluation method is costly, only incurs that cost on elements visited. Adds little to no garbage collection pressure.    Cons: Very slight performance impact if you know caller will always consume all items in collection. And as we saw in the last post, that con for the cost was very, very small and only really became evident on very tight loops consuming very large lists completely.    One of the key items to note, though, is the garbage!  In the traditional (return a new collection) method, if you have a 1,000,000 element collection, and wish to transform or filter it in some way, you have to allocate space for that copy of the collection.  That is, say you have a collection of 1,000,000 items and you want to double every item in the collection.  Well, that means you have to allocate a collection to hold those 1,000,000 items to return, which is a lot especially if you are just going to use it once and toss it.   Iterators, though, don't have this problem.  Each time you visit the node, it would return the doubled value of the node (in this example) and not allocate a second collection of 1,000,000 doubled items.  Do you see the distinction?  In both cases, we're consuming 1,000,000 items.  But in one case we pass back each doubled item which is just an int (for example's sake) on the stack and in the other case, we allocate a list containing 1,000,000 items which then must be garbage collected.   So iterators in C# are pretty cool, eh?  Well, here's one more thing a C# iterator can do that a traditional "return a new collection" transformation can't!   It can return **unbounded** collections!   I know, I know, that smells a lot like an infinite loop, eh?  Yes and no.  Basically, you're relying on the caller to put the bounds on the list, and as long as the caller doesn't you keep going.  Consider this example:   public static class Fibonacci {     // returns the infinite fibonacci sequence     public static IEnumerable<int> Sequence()     {         int iteration = 0;         int first = 1;         int second = 1;         int current = 0;         while (true)         {             if (iteration++ < 2)             {                 current = 1;             }             else             {                 current = first + second;                 second = first;                 first = current;             }             yield return current;         }     } }   Whoa, you say!  Yes, that's an infinite loop!  What the heck is going on there?  Yes, that was intentional.  Would it be better to have a fibonacci sequence that returns only a specific number of items?  Perhaps, but that wouldn't give you the power to defer the execution to the caller.   The beauty of this function is it is as infinite as the sequence itself!  The fibonacci sequence is unbounded, and so is this method.  It will continue to return fibonacci numbers for as long as you ask for them.  Now that's not something you can do with a traditional method that would return a collection of ints representing each number.  In that case you would eventually run out of memory as you got to higher and higher numbers.  This method, though, never runs out of memory.   Now, that said, you do have to know when you use it that it is an infinite collection and bound it appropriately.  Fortunately, Linq provides a lot of these extension methods for you!   Let's say you only want the first 10 fibonacci numbers:       foreach(var fib in Fibonacci.Sequence().Take(10))     {         Console.WriteLine(fib);     }   Or let's say you only want the fibonacci numbers that are less than 100:       foreach(var fib in Fibonacci.Sequence().TakeWhile(f => f < 100))     {         Console.WriteLine(fib);     }   So, you see, one of the nice things about iterators is their power to work with virtually any size (even infinite) collections without adding the garbage collection overhead of making new collections.    You can also do fun things like this to make a more "fluent" interface for for loops:   // A set of integer generator extension methods public static class IntExtensions {     // Begins counting to inifity, use To() to range this.     public static IEnumerable<int> Every(this int start)     {         // deliberately avoiding condition because keeps going         // to infinity for as long as values are pulled.         for (var i = start; ; ++i)         {             yield return i;         }     }     // Begins counting to infinity by the given step value, use To() to     public static IEnumerable<int> Every(this int start, int byEvery)     {         // deliberately avoiding condition because keeps going         // to infinity for as long as values are pulled.         for (var i = start; ; i += byEvery)         {             yield return i;         }     }     // Begins counting to inifity, use To() to range this.     public static IEnumerable<int> To(this int start, int end)     {         for (var i = start; i <= end; ++i)         {             yield return i;         }     }     // Ranges the count by specifying the upper range of the count.     public static IEnumerable<int> To(this IEnumerable<int> collection, int end)     {         return collection.TakeWhile(item => item <= end);     } }   Note that there are two versions of each method.  One that starts with an int and one that starts with an IEnumerable<int>.  This is to allow more power in chaining from either an existing collection or from an int.  This lets you do things like:   // count from 1 to 30 foreach(var i in 1.To(30)) {     Console.WriteLine(i); }     // count from 1 to 10 by 2s foreach(var i in 0.Every(2).To(10)) {     Console.WriteLine(i); }     // or, if you want an infinite sequence counting by 5s until something inside breaks you out... foreach(var i in 0.Every(5)) {     if (someCondition)     {         break;     }     ... }     Yes, those are kinda play functions and not particularly useful, but they show some of the power of generators and extension methods to form a fluid interface.   So what do you think?  What are some of your favorite generators and iterators?

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  • SQL SERVER – SSIS Look Up Component – Cache Mode – Notes from the Field #028

    - by Pinal Dave
    [Notes from Pinal]: Lots of people think that SSIS is all about arranging various operations together in one logical flow. Well, the understanding is absolutely correct, but the implementation of the same is not as easy as it seems. Similarly most of the people think lookup component is just component which does look up for additional information and does not pay much attention to it. Due to the same reason they do not pay attention to the same and eventually get very bad performance. Linchpin People are database coaches and wellness experts for a data driven world. In this 28th episode of the Notes from the Fields series database expert Tim Mitchell (partner at Linchpin People) shares very interesting conversation related to how to write a good lookup component with Cache Mode. In SQL Server Integration Services, the lookup component is one of the most frequently used tools for data validation and completion.  The lookup component is provided as a means to virtually join one set of data to another to validate and/or retrieve missing values.  Properly configured, it is reliable and reasonably fast. Among the many settings available on the lookup component, one of the most critical is the cache mode.  This selection will determine whether and how the distinct lookup values are cached during package execution.  It is critical to know how cache modes affect the result of the lookup and the performance of the package, as choosing the wrong setting can lead to poorly performing packages, and in some cases, incorrect results. Full Cache The full cache mode setting is the default cache mode selection in the SSIS lookup transformation.  Like the name implies, full cache mode will cause the lookup transformation to retrieve and store in SSIS cache the entire set of data from the specified lookup location.  As a result, the data flow in which the lookup transformation resides will not start processing any data buffers until all of the rows from the lookup query have been cached in SSIS. The most commonly used cache mode is the full cache setting, and for good reason.  The full cache setting has the most practical applications, and should be considered the go-to cache setting when dealing with an untested set of data. With a moderately sized set of reference data, a lookup transformation using full cache mode usually performs well.  Full cache mode does not require multiple round trips to the database, since the entire reference result set is cached prior to data flow execution. There are a few potential gotchas to be aware of when using full cache mode.  First, you can see some performance issues – memory pressure in particular – when using full cache mode against large sets of reference data.  If the table you use for the lookup is very large (either deep or wide, or perhaps both), there’s going to be a performance cost associated with retrieving and caching all of that data.  Also, keep in mind that when doing a lookup on character data, full cache mode will always do a case-sensitive (and in some cases, space-sensitive) string comparison even if your database is set to a case-insensitive collation.  This is because the in-memory lookup uses a .NET string comparison (which is case- and space-sensitive) as opposed to a database string comparison (which may be case sensitive, depending on collation).  There’s a relatively easy workaround in which you can use the UPPER() or LOWER() function in the pipeline data and the reference data to ensure that case differences do not impact the success of your lookup operation.  Again, neither of these present a reason to avoid full cache mode, but should be used to determine whether full cache mode should be used in a given situation. Full cache mode is ideally useful when one or all of the following conditions exist: The size of the reference data set is small to moderately sized The size of the pipeline data set (the data you are comparing to the lookup table) is large, is unknown at design time, or is unpredictable Each distinct key value(s) in the pipeline data set is expected to be found multiple times in that set of data Partial Cache When using the partial cache setting, lookup values will still be cached, but only as each distinct value is encountered in the data flow.  Initially, each distinct value will be retrieved individually from the specified source, and then cached.  To be clear, this is a row-by-row lookup for each distinct key value(s). This is a less frequently used cache setting because it addresses a narrower set of scenarios.  Because each distinct key value(s) combination requires a relational round trip to the lookup source, performance can be an issue, especially with a large pipeline data set to be compared to the lookup data set.  If you have, for example, a million records from your pipeline data source, you have the potential for doing a million lookup queries against your lookup data source (depending on the number of distinct values in the key column(s)).  Therefore, one has to be keenly aware of the expected row count and value distribution of the pipeline data to safely use partial cache mode. Using partial cache mode is ideally suited for the conditions below: The size of the data in the pipeline (more specifically, the number of distinct key column) is relatively small The size of the lookup data is too large to effectively store in cache The lookup source is well indexed to allow for fast retrieval of row-by-row values No Cache As you might guess, selecting no cache mode will not add any values to the lookup cache in SSIS.  As a result, every single row in the pipeline data set will require a query against the lookup source.  Since no data is cached, it is possible to save a small amount of overhead in SSIS memory in cases where key values are not reused.  In the real world, I don’t see a lot of use of the no cache setting, but I can imagine some edge cases where it might be useful. As such, it’s critical to know your data before choosing this option.  Obviously, performance will be an issue with anything other than small sets of data, as the no cache setting requires row-by-row processing of all of the data in the pipeline. I would recommend considering the no cache mode only when all of the below conditions are true: The reference data set is too large to reasonably be loaded into SSIS memory The pipeline data set is small and is not expected to grow There are expected to be very few or no duplicates of the key values(s) in the pipeline data set (i.e., there would be no benefit from caching these values) Conclusion The cache mode, an often-overlooked setting on the SSIS lookup component, represents an important design decision in your SSIS data flow.  Choosing the right lookup cache mode directly impacts the fidelity of your results and the performance of package execution.  Know how this selection impacts your ETL loads, and you’ll end up with more reliable, faster packages. If you want me to take a look at your server and its settings, or if your server is facing any issue we can Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SSIS

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  • Building dynamic OLAP data marts on-the-fly

    - by DrJohn
    At the forthcoming SQLBits conference, I will be presenting a session on how to dynamically build an OLAP data mart on-the-fly. This blog entry is intended to clarify exactly what I mean by an OLAP data mart, why you may need to build them on-the-fly and finally outline the steps needed to build them dynamically. In subsequent blog entries, I will present exactly how to implement some of the techniques involved. What is an OLAP data mart? In data warehousing parlance, a data mart is a subset of the overall corporate data provided to business users to meet specific business needs. Of course, the term does not specify the technology involved, so I coined the term "OLAP data mart" to identify a subset of data which is delivered in the form of an OLAP cube which may be accompanied by the relational database upon which it was built. To clarify, the relational database is specifically create and loaded with the subset of data and then the OLAP cube is built and processed to make the data available to the end-users via standard OLAP client tools. Why build OLAP data marts? Market research companies sell data to their clients to make money. To gain competitive advantage, market research providers like to "add value" to their data by providing systems that enhance analytics, thereby allowing clients to make best use of the data. As such, OLAP cubes have become a standard way of delivering added value to clients. They can be built on-the-fly to hold specific data sets and meet particular needs and then hosted on a secure intranet site for remote access, or shipped to clients' own infrastructure for hosting. Even better, they support a wide range of different tools for analytical purposes, including the ever popular Microsoft Excel. Extension Attributes: The Challenge One of the key challenges in building multiple OLAP data marts based on the same 'template' is handling extension attributes. These are attributes that meet the client's specific reporting needs, but do not form part of the standard template. Now clearly, these extension attributes have to come into the system via additional files and ultimately be added to relational tables so they can end up in the OLAP cube. However, processing these files and filling dynamically altered tables with SSIS is a challenge as SSIS packages tend to break as soon as the database schema changes. There are two approaches to this: (1) dynamically build an SSIS package in memory to match the new database schema using C#, or (2) have the extension attributes provided as name/value pairs so the file's schema does not change and can easily be loaded using SSIS. The problem with the first approach is the complexity of writing an awful lot of complex C# code. The problem of the second approach is that name/value pairs are useless to an OLAP cube; so they have to be pivoted back into a proper relational table somewhere in the data load process WITHOUT breaking SSIS. How this can be done will be part of future blog entry. What is involved in building an OLAP data mart? There are a great many steps involved in building OLAP data marts on-the-fly. The key point is that all the steps must be automated to allow for the production of multiple OLAP data marts per day (i.e. many thousands, each with its own specific data set and attributes). Now most of these steps have a great deal in common with standard data warehouse practices. The key difference is that the databases are all built to order. The only permanent database is the metadata database (shown in orange) which holds all the metadata needed to build everything else (i.e. client orders, configuration information, connection strings, client specific requirements and attributes etc.). The staging database (shown in red) has a short life: it is built, populated and then ripped down as soon as the OLAP Data Mart has been populated. In the diagram below, the OLAP data mart comprises the two blue components: the Data Mart which is a relational database and the OLAP Cube which is an OLAP database implemented using Microsoft Analysis Services (SSAS). The client may receive just the OLAP cube or both components together depending on their reporting requirements.  So, in broad terms the steps required to fulfil a client order are as follows: Step 1: Prepare metadata Create a set of database names unique to the client's order Modify all package connection strings to be used by SSIS to point to new databases and file locations. Step 2: Create relational databases Create the staging and data mart relational databases using dynamic SQL and set the database recovery mode to SIMPLE as we do not need the overhead of logging anything Execute SQL scripts to build all database objects (tables, views, functions and stored procedures) in the two databases Step 3: Load staging database Use SSIS to load all data files into the staging database in a parallel operation Load extension files containing name/value pairs. These will provide client-specific attributes in the OLAP cube. Step 4: Load data mart relational database Load the data from staging into the data mart relational database, again in parallel where possible Allocate surrogate keys and use SSIS to perform surrogate key lookup during the load of fact tables Step 5: Load extension tables & attributes Pivot the extension attributes from their native name/value pairs into proper relational tables Add the extension attributes to the views used by OLAP cube Step 6: Deploy & Process OLAP cube Deploy the OLAP database directly to the server using a C# script task in SSIS Modify the connection string used by the OLAP cube to point to the data mart relational database Modify the cube structure to add the extension attributes to both the data source view and the relevant dimensions Remove any standard attributes that not required Process the OLAP cube Step 7: Backup and drop databases Drop staging database as it is no longer required Backup data mart relational and OLAP database and ship these to the client's infrastructure Drop data mart relational and OLAP database from the build server Mark order complete Start processing the next order, ad infinitum. So my future blog posts and my forthcoming session at the SQLBits conference will all focus on some of the more interesting aspects of building OLAP data marts on-the-fly such as handling the load of extension attributes and how to dynamically alter the structure of an OLAP cube using C#.

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  • PostSharp, Obfuscation, and IL

    - by Simon Cooper
    Aspect-oriented programming (AOP) is a relatively new programming paradigm. Originating at Xerox PARC in 1994, the paradigm was first made available for general-purpose development as an extension to Java in 2001. From there, it has quickly been adapted for use in all the common languages used today. In the .NET world, one of the primary AOP toolkits is PostSharp. Attributes and AOP Normally, attributes in .NET are entirely a metadata construct. Apart from a few special attributes in the .NET framework, they have no effect whatsoever on how a class or method executes within the CLR. Only by using reflection at runtime can you access any attributes declared on a type or type member. PostSharp changes this. By declaring a custom attribute that derives from PostSharp.Aspects.Aspect, applying it to types and type members, and running the resulting assembly through the PostSharp postprocessor, you can essentially declare 'clever' attributes that change the behaviour of whatever the aspect has been applied to at runtime. A simple example of this is logging. By declaring a TraceAttribute that derives from OnMethodBoundaryAspect, you can automatically log when a method has been executed: public class TraceAttribute : PostSharp.Aspects.OnMethodBoundaryAspect { public override void OnEntry(MethodExecutionArgs args) { MethodBase method = args.Method; System.Diagnostics.Trace.WriteLine( String.Format( "Entering {0}.{1}.", method.DeclaringType.FullName, method.Name)); } public override void OnExit(MethodExecutionArgs args) { MethodBase method = args.Method; System.Diagnostics.Trace.WriteLine( String.Format( "Leaving {0}.{1}.", method.DeclaringType.FullName, method.Name)); } } [Trace] public void MethodToLog() { ... } Now, whenever MethodToLog is executed, the aspect will automatically log entry and exit, without having to add the logging code to MethodToLog itself. PostSharp Performance Now this does introduce a performance overhead - as you can see, the aspect allows access to the MethodBase of the method the aspect has been applied to. If you were limited to C#, you would be forced to retrieve each MethodBase instance using Type.GetMethod(), matching on the method name and signature. This is slow. Fortunately, PostSharp is not limited to C#. It can use any instruction available in IL. And in IL, you can do some very neat things. Ldtoken C# allows you to get the Type object corresponding to a specific type name using the typeof operator: Type t = typeof(Random); The C# compiler compiles this operator to the following IL: ldtoken [mscorlib]System.Random call class [mscorlib]System.Type [mscorlib]System.Type::GetTypeFromHandle( valuetype [mscorlib]System.RuntimeTypeHandle) The ldtoken instruction obtains a special handle to a type called a RuntimeTypeHandle, and from that, the Type object can be obtained using GetTypeFromHandle. These are both relatively fast operations - no string lookup is required, only direct assembly and CLR constructs are used. However, a little-known feature is that ldtoken is not just limited to types; it can also get information on methods and fields, encapsulated in a RuntimeMethodHandle or RuntimeFieldHandle: // get a MethodBase for String.EndsWith(string) ldtoken method instance bool [mscorlib]System.String::EndsWith(string) call class [mscorlib]System.Reflection.MethodBase [mscorlib]System.Reflection.MethodBase::GetMethodFromHandle( valuetype [mscorlib]System.RuntimeMethodHandle) // get a FieldInfo for the String.Empty field ldtoken field string [mscorlib]System.String::Empty call class [mscorlib]System.Reflection.FieldInfo [mscorlib]System.Reflection.FieldInfo::GetFieldFromHandle( valuetype [mscorlib]System.RuntimeFieldHandle) These usages of ldtoken aren't usable from C# or VB, and aren't likely to be added anytime soon (Eric Lippert's done a blog post on the possibility of adding infoof, methodof or fieldof operators to C#). However, PostSharp deals directly with IL, and so can use ldtoken to get MethodBase objects quickly and cheaply, without having to resort to string lookups. The kicker However, there are problems. Because ldtoken for methods or fields isn't accessible from C# or VB, it hasn't been as well-tested as ldtoken for types. This has resulted in various obscure bugs in most versions of the CLR when dealing with ldtoken and methods, and specifically, generic methods and methods of generic types. This means that PostSharp was behaving incorrectly, or just plain crashing, when aspects were applied to methods that were generic in some way. So, PostSharp has to work around this. Without using the metadata tokens directly, the only way to get the MethodBase of generic methods is to use reflection: Type.GetMethod(), passing in the method name as a string along with information on the signature. Now, this works fine. It's slower than using ldtoken directly, but it works, and this only has to be done for generic methods. Unfortunately, this poses problems when the assembly is obfuscated. PostSharp and Obfuscation When using ldtoken, obfuscators don't affect how PostSharp operates. Because the ldtoken instruction directly references the type, method or field within the assembly, it is unaffected if the name of the object is changed by an obfuscator. However, the indirect loading used for generic methods was breaking, because that uses the name of the method when the assembly is put through the PostSharp postprocessor to lookup the MethodBase at runtime. If the name then changes, PostSharp can't find it anymore, and the assembly breaks. So, PostSharp needs to know about any changes an obfuscator does to an assembly. The way PostSharp does this is by adding another layer of indirection. When PostSharp obfuscation support is enabled, it includes an extra 'name table' resource in the assembly, consisting of a series of method & type names. When PostSharp needs to lookup a method using reflection, instead of encoding the method name directly, it looks up the method name at a fixed offset inside that name table: MethodBase genericMethod = typeof(ContainingClass).GetMethod(GetNameAtIndex(22)); PostSharp.NameTable resource: ... 20: get_Prop1 21: set_Prop1 22: DoFoo 23: GetWibble When the assembly is later processed by an obfuscator, the obfuscator can replace all the method and type names within the name table with their new name. That way, the reflection lookups performed by PostSharp will now use the new names, and everything will work as expected: MethodBase genericMethod = typeof(#kGy).GetMethod(GetNameAtIndex(22)); PostSharp.NameTable resource: ... 20: #kkA 21: #zAb 22: #EF5a 23: #2tg As you can see, this requires direct support by an obfuscator in order to perform these rewrites. Dotfuscator supports it, and now, starting with SmartAssembly 6.6.4, SmartAssembly does too. So, a relatively simple solution to a tricky problem, with some CLR bugs thrown in for good measure. You don't see those every day!

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  • Das T5-4 TPC-H Ergebnis naeher betrachtet

    - by Stefan Hinker
    Inzwischen haben vermutlich viele das neue TPC-H Ergebnis der SPARC T5-4 gesehen, das am 7. Juni bei der TPC eingereicht wurde.  Die wesentlichen Punkte dieses Benchmarks wurden wie gewohnt bereits von unserer Benchmark-Truppe auf  "BestPerf" zusammengefasst.  Es gibt aber noch einiges mehr, das eine naehere Betrachtung lohnt. Skalierbarkeit Das TPC raet von einem Vergleich von TPC-H Ergebnissen in unterschiedlichen Groessenklassen ab.  Aber auch innerhalb der 3000GB-Klasse ist es interessant: SPARC T4-4 mit 4 CPUs (32 Cores mit 3.0 GHz) liefert 205,792 QphH. SPARC T5-4 mit 4 CPUs (64 Cores mit 3.6 GHz) liefert 409,721 QphH. Das heisst, es fehlen lediglich 1863 QphH oder 0.45% zu 100% Skalierbarkeit, wenn man davon ausgeht, dass die doppelte Anzahl Kerne das doppelte Ergebnis liefern sollte.  Etwas anspruchsvoller, koennte man natuerlich auch einen Faktor von 2.4 erwarten, wenn man die hoehere Taktrate mit beruecksichtigt.  Das wuerde die Latte auf 493901 QphH legen.  Dann waere die SPARC T5-4 bei 83%.  Damit stellt sich die Frage: Was hat hier nicht skaliert?  Vermutlich der Plattenspeicher!  Auch hier lohnt sich eine naehere Betrachtung: Plattenspeicher Im Bericht auf BestPerf und auch im Full Disclosure Report der TPC stehen einige interessante Details zum Plattenspeicher und der Konfiguration.   In der Konfiguration der SPARC T4-4 wurden 12 2540-M2 Arrays verwendet, die jeweils ca. 1.5 GB/s Durchsatz liefert, insgesamt also eta 18 GB/s.  Dabei waren die Arrays offensichtlich mit jeweils 2 Kabeln pro Array direkt an die 24 8GBit FC-Ports des Servers angeschlossen.  Mit den 2x 8GBit Ports pro Array koennte man so ein theoretisches Maximum von 2GB/s erreichen.  Tatsaechlich wurden 1.5GB/s geliefert, was so ziemlich dem realistischen Maximum entsprechen duerfte. Fuer den Lauf mit der SPARC T5-4 wurden doppelt so viele Platten verwendet.  Dafuer wurden die 2540-M2 Arrays mit je einem zusaetzlichen Plattentray erweitert.  Mit dieser Konfiguration wurde dann (laut BestPerf) ein Maximaldurchsatz von 33 GB/s erreicht - nicht ganz das doppelte des SPARC T4-4 Laufs.  Um tatsaechlich den doppelten Durchsatz (36 GB/s) zu liefern, haette jedes der 12 Arrays 3 GB/s ueber seine 4 8GBit Ports liefern muessen.  Im FDR stehen nur 12 dual-port FC HBAs, was die Verwendung der Brocade FC Switches erklaert: Es wurden alle 4 8GBit ports jedes Arrays an die Switches angeschlossen, die die Datenstroeme dann in die 24 16GBit HBA ports des Servers buendelten.  Das theoretische Maximum jedes Storage-Arrays waere nun 4 GB/s.  Wenn man jedoch den Protokoll- und "Realitaets"-Overhead mit einrechnet, sind die tatsaechlich gelieferten 2.75 GB/s gar nicht schlecht.  Mit diesen Zahlen im Hinterkopf ist die Verdopplung des SPARC T4-4 Ergebnisses eine gute Leistung - und gleichzeitig eine gute Erklaerung, warum nicht bis zum 2.4-fachen skaliert wurde. Nebenbei bemerkt: Weder die SPARC T4-4 noch die SPARC T5-4 hatten in der gemessenen Konfiguration irgendwelche Flash-Devices. Mitbewerb Seit die T4 Systeme auf dem Markt sind, bemuehen sich unsere Mitbewerber redlich darum, ueberall den Eindruck zu hinterlassen, die Leistung des SPARC CPU-Kerns waere weiterhin mangelhaft.  Auch scheinen sie ueberzeugt zu sein, dass (ueber)grosse Caches und hohe Taktraten die einzigen Schluessel zu echter Server Performance seien.  Wenn ich mir nun jedoch die oeffentlichen TPC-H Ergebnisse ansehe, sehe ich dies: TPC-H @3000GB, Non-Clustered Systems System QphH SPARC T5-4 3.6 GHz SPARC T5 4/64 – 2048 GB 409,721.8 SPARC T4-4 3.0 GHz SPARC T4 4/32 – 1024 GB 205,792.0 IBM Power 780 4.1 GHz POWER7 8/32 – 1024 GB 192,001.1 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64 – 512 GB 162,601.7 Kurz zusammengefasst: Mit 32 Kernen (mit 3 GHz und 4MB L3 Cache), liefert die SPARC T4-4 mehr QphH@3000GB ab als IBM mit ihrer 32 Kern Power7 (bei 4.1 GHz und 32MB L3 Cache) und auch mehr als HP mit einem 64 Kern Intel Xeon System (2.27 GHz und 24MB L3 Cache).  Ich frage mich, wo genau SPARC hier mangelhaft ist? Nun koennte man natuerlich argumentieren, dass beide Ergebnisse nicht gerade neu sind.  Nun, in Ermangelung neuerer Ergebnisse kann man ja mal ein wenig spekulieren: IBMs aktueller Performance Report listet die o.g. IBM Power 780 mit einem rPerf Wert von 425.5.  Ein passendes Nachfolgesystem mit Power7+ CPUs waere die Power 780+ mit 64 Kernen, verfuegbar mit 3.72 GHz.  Sie wird mit einem rPerf Wert von  690.1 angegeben, also 1.62x mehr.  Wenn man also annimmt, dass Plattenspeicher nicht der limitierende Faktor ist (IBM hat mit 177 SSDs getestet, sie duerfen das gerne auf 400 erhoehen) und IBMs eigene Leistungsabschaetzung zugrunde legt, darf man ein theoretisches Ergebnis von 311398 QphH@3000GB erwarten.  Das waere dann allerdings immer noch weit von dem Ergebnis der SPARC T5-4 entfernt, und gerade in der von IBM so geschaetzen "per core" Metric noch weniger vorteilhaft. In der x86-Welt sieht es nicht besser aus.  Leider gibt es von Intel keine so praktischen rPerf-Tabellen.  Daher muss ich hier fuer eine Schaetzung auf SPECint_rate2006 zurueckgreifen.  (Ich bin kein grosser Fan von solchen Kreuz- und Querschaetzungen.  Insb. SPECcpu ist nicht besonders geeignet, um Datenbank-Leistung abzuschaetzen, da fast kein IO im Spiel ist.)  Das o.g. HP System wird bei SPEC mit 1580 CINT2006_rate gelistet.  Das bis einschl. 2013-06-14 beste Resultat fuer den neuen Intel Xeon E7-4870 mit 8 CPUs ist 2180 CINT2006_rate.  Das ist immerhin 1.38x besser.  (Wenn man nur die Taktrate beruecksichtigen wuerde, waere man bei 1.32x.)  Hier weiter zu rechnen, ist muessig, aber fuer die ungeduldigen Leser hier eine kleine tabellarische Zusammenfassung: TPC-H @3000GB Performance Spekulationen System QphH* Verbesserung gegenueber der frueheren Generation SPARC T4-4 32 cores SPARC T4 205,792 2x SPARC T5-464 cores SPARC T5 409,721 IBM Power 780 32 cores Power7 192,001 1.62x IBM Power 780+ 64 cores Power7+  311,398* HP ProLiant DL980 G764 cores Intel Xeon X7560 162,601 1.38x HP ProLiant DL980 G780 cores Intel Xeon E7-4870    224,348* * Keine echten Resultate  - spekulative Werte auf der Grundlage von rPerf (Power7+) oder SPECint_rate2006 (HP) Natuerlich sind IBM oder HP herzlich eingeladen, diese Werte zu widerlegen.  Aber stand heute warte ich noch auf aktuelle Benchmark Veroffentlichungen in diesem Datensegment. Was koennen wir also zusammenfassen? Es gibt einige Hinweise, dass der Plattenspeicher der begrenzende Faktor war, der die SPARC T5-4 daran hinderte, auf jenseits von 2x zu skalieren Der Mythos, dass SPARC Kerne keine Leistung bringen, ist genau das - ein Mythos.  Wie sieht es umgekehrt eigentlich mit einem TPC-H Ergebnis fuer die Power7+ aus? Cache ist nicht der magische Performance-Schalter, fuer den ihn manche Leute offenbar halten. Ein System, eine CPU-Architektur und ein Betriebsystem jenseits einer gewissen Grenze zu skalieren ist schwer.  In der x86-Welt scheint es noch ein wenig schwerer zu sein. Was fehlt?  Nun, das Thema Preis/Leistung ueberlasse ich gerne den Verkaeufern ;-) Und zu guter Letzt: Nein, ich habe mich nicht ins Marketing versetzen lassen.  Aber manchmal kann ich mich einfach nicht zurueckhalten... Disclosure Statements The views expressed on this blog are my own and do not necessarily reflect the views of Oracle. TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org, results as of 6/7/13. Prices are in USD. SPARC T5-4 409,721.8 QphH@3000GB, $3.94/QphH@3000GB, available 9/24/13, 4 processors, 64 cores, 512 threads; SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads. SPEC and the benchmark names SPECfp and SPECint are registered trademarks of the Standard Performance Evaluation Corporation. Results as of June 18, 2013 from www.spec.org. HP ProLiant DL980 G7 (2.27 GHz, Intel Xeon X7560): 1580 SPECint_rate2006; HP ProLiant DL980 G7 (2.4 GHz, Intel Xeon E7-4870): 2180 SPECint_rate2006,

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  • Evaluating Solutions to Manage Product Compliance? Don't Wait Much Longer

    - by Kerrie Foy
    Depending on severity, product compliance issues can cause all sorts of problems from run-away budgets to business closures. But effective policies and safeguards can create a strong foundation for innovation, productivity, market penetration and competitive advantage. If you’ve been putting off a systematic approach to product compliance, it is time to reconsider that decision, or indecision. Why now?  No matter what industry, companies face a litany of worldwide and regional regulations that require proof of product compliance and environmental friendliness for market access.  For example, Restriction of Hazardous Substances (RoHS) is a regulation that restricts the use of six dangerous materials used in the manufacture of electronic and electrical equipment.  ROHS was originally adopted by the European Union in 2003 for implementation in 2006, and it has evolved over time through various regional versions for North America, China, Japan, Korea, Norway and Turkey.  In addition, the RoHS directive allowed for material exemptions used in Medical Devices, but that exemption ends in 2014.   Additional regulations worth watching are the Battery Directive, Waste Electrical and Electronic Equipment (WEEE), and Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) directives.  Additional evolving regulations are coming from governing bodies like the Food and Drug Administration (FDA) and the International Organization for Standardization (ISO). Corporate sustainability initiatives are also gaining urgency and influencing product design. In a survey of 405 corporations in the Global 500 by Carbon Disclosure Project, co-written by PwC (CDP Global 500 Climate Change Report 2012 entitled Business Resilience in an Uncertain, Resource-Constrained World), 48% of the respondents indicated they saw potential to create new products and business services as a response to climate change. Just 21% reported a dedicated budget for the research. However, the report goes on to explain that those few companies are winning over new customers and driving additional profits by exploiting their abilities to adapt to environmental needs. The article cites Dell as an example – Dell has invested in research to develop new products designed to reduce its customers’ emissions by more than 10 million metric tons of CO2e per year. This reduction in emissions should save Dell’s customers over $1billion per year as a result! Over time we expect to see many additional companies prove that eco-design provides marketplace benefits through differentiation and direct customer value. How do you meet compliance requirements and also successfully invest in eco-friendly designs? No doubt companies struggle to answer this question. After all, the journey to get there may involve transforming business models, go-to-market strategies, supply networks, quality assurance policies and compliance processes per the rapidly evolving global and regional directives. There may be limited executive focus on the initiative, inability to quantify noncompliance, or not enough resources to justify investment. To make things even more difficult to address, compliance responsibility can be a passionate topic within an organization, making the prospect of change on an enterprise scale problematic and time-consuming. Without a single source of truth for product data and without proper processes in place, ensuring product compliance burgeons into a crushing task that is cost-prohibitive and overwhelming to an organization. With all the overhead, certain markets or demographics become simply inaccessible. Therefore, the risk to consumer goodwill and satisfaction, revenue, business continuity, and market potential is too great not to solve the compliance challenge. Companies are beginning to adapt and even thrive in today’s highly regulated and transparent environment by implementing systematic approaches to product compliance that are more than functional bandages but revenue-generating engines. Consider partnering with Oracle to help you address your compliance needs. Many of the world’s most innovative leaders and pioneers are leveraging Oracle’s Agile Product Lifecycle Management (PLM) portfolio of enterprise applications to manage the product value chain, centralize product data, automate processes, and launch more eco-friendly products to market faster.   Particularly, the Agile Product Governance & Compliance (PG&C) solution provides out-of-the-box functionality to integrate actionable regulatory information into the enterprise product record from the ideation to the disposal/recycling phase. Agile PG&C makes it possible to efficiently manage compliance per corporate green initiatives as well as regional and global directives. Options are critical, but so is ease-of-use. Anyone who’s grappled with compliance policy knows legal interpretation plays a major role in determining how an organization responds to regulation. Agile PG&C gives you the freedom to configure product compliance per your needs, while maintaining rigorous control over the product record in an easy-to-use interface that facilitates adoption efforts. It allows you to assign regulations as specifications for a part or BOM roll-up. Each specification has a threshold value that alerts you to a non-compliance issue if the threshold value is exceeded. Set however many regulations as specifications you need to make sure a product can be sold in your target countries. Another option is to implement like one of our leading consumer electronics customers and define your own “catch-all” specification to ensure compliance in all markets. You can give your suppliers secure access to enter their component data or integrate a third party’s data. With Agile PG&C you are able to design compliance earlier into your products to reduce cost and improve quality downstream when stakes are higher. Agile PG&C is a comprehensive solution that makes product compliance more reliable and efficient. Throughout product lifecycles, use the solution to support full material disclosures, efficiently manage declarations with your suppliers, feed compliance data into a corrective action if a product must be changed, and swiftly satisfy audits by showing all due diligence tracked in one solution. Given the compounding regulation and consumer focus on urgent environmental issues, now is the time to act. Implementing an enterprise, systematic approach to product compliance is a competitive investment. From the start, Agile Product Governance & Compliance enables companies to confidently design for compliance and sustainability, reduce the cost of compliance, minimize the risk of business interruption, deliver responsible products, and inspire new innovation.  Don’t wait any longer! To find out more about Agile Product Governance & Compliance download the data sheet, contact your sales representative, or call Oracle at 1-800-633-0738. Many thanks to Shane Goodwin, Senior Manager, Oracle Agile PLM Product Management, for contributions to this article. 

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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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  • 5 Best Practices - Laying the Foundation for WebCenter Projects

    - by Kellsey Ruppel
    Today’s guest post comes from Oracle WebCenter expert John Brunswick. John specializes in enterprise portal and content management solutions and actively contributes to the enterprise software business community and has authored a series of articles about optimal business involvement in portal, business process management and SOA development, examining ways of helping organizations move away from monolithic application development. We’re happy to have John join us today! Maximizing success with Oracle WebCenter portal requires a strategic understanding of Oracle WebCenter capabilities.  The following best practices enable the creation of portal solutions with minimal resource overhead, while offering the greatest flexibility for progressive elaboration. They are inherently project agnostic, enabling a strong foundation for future growth and an expedient return on your investment in the platform.  If you are able to embrace even only a few of these practices, you will materially improve your deployment capability with WebCenter. 1. Segment Duties Around 3Cs - Content, Collaboration and Contextual Data "Agility" is one of the most common business benefits touted by modern web platforms.  It sounds good - who doesn't want to be Agile, right?  How exactly IT organizations go about supplying agility to their business counterparts often lacks definition - hamstrung by ambiguity. Ultimately, businesses want to benefit from reduced development time to deliver a solution to a particular constituent, which is augmented by as much self-service as possible to develop and manage the solution directly. All done in the absence of direct IT involvement. With Oracle WebCenter's depth in the areas of content management, pallet of native collaborative services, enterprise mashup capability and delegated administration, it is very possible to execute on this business vision at a technical level. To realize the benefits of the platform depth we can think of Oracle WebCenter's segmentation of duties along the lines of the 3 Cs - Content, Collaboration and Contextual Data.  All three of which can have their foundations developed by IT, then provisioned to the business on a per role basis. Content – Oracle WebCenter benefits from an extremely mature content repository.  Work flow, audit, notification, office integration and conversion capabilities for documents (HTML & PDF) make this a haven for business users to take control of content within external and internal portals, custom applications and web sites.  When deploying WebCenter portal take time to think of areas in which IT can provide the "harness" for content to reside, then allow the business to manage any content items within the site, using the content foundation to ensure compliance with business rules and process.  This frees IT to work on more mission critical challenges and allows the business to respond in short order to emerging market needs. Collaboration – Native collaborative services and WebCenter spaces are a perfect match for business users who are looking to enable document sharing, discussions and social networking.  The ability to deploy the services is granular and on the basis of roles scoped to given areas of the system - much like the first C “content”.  This enables business analysts to design the roles required and IT to provision with peace of mind that users leveraging the collaborative services are only able to do so in explicitly designated areas of a site. Bottom line - business will not need to wait for IT, but cannot go outside of the scope that has been defined based on their roles. Contextual Data – Collaborative capabilities are most powerful when included within the context of business data.  The ability to supply business users with decision shaping data that they can include in various parts of a portal or portals, just as they would with content items, is one of the most powerful aspects of Oracle WebCenter.  Imagine a discussion about new store selection for a retail chain that re-purposes existing information from business intelligence services about various potential locations and or custom backend systems - presenting it directly in the context of the discussion.  If there are some data sources that are preexisting in your enterprise take a look at how they can be made into discrete offerings within the portal, then scoped to given business user roles for inclusion within collaborative activities. 2. Think Generically, Execute Specifically Constructs.  Anyone who has spent much time around me knows that I am obsessed with this word.  Why? Because Constructs offer immense power - more than APIs, Web Services or other technical capability. Constructs offer organizations the ability to leverage a platform's native characteristics to offer substantial business functionality - without writing code.  This concept becomes more powerful with the additional understanding of the concepts from the platform that an organization learns over time.  Let's take a look at an example of where an Oracle WebCenter construct can substantially reduce the time to get a subscription-based site out the door and into the hands of the end consumer. Imagine a site that allows members to subscribe to specific disciplines to access information and application data around that various discipline.  A space is a collection of secured pages within Oracle WebCenter.  Spaces are not only secured, but also default content stored within it to be scoped automatically to that space. Taking this a step further, Oracle WebCenter’s Activity Stream surfaces events, discussions and other activities that are scoped to the given user on the basis of their space affiliations.  In order to have a portal that would allow users to "subscribe" to information around various disciplines - spaces could be used out of the box to achieve this capability and without using any APIs or low level technical work to achieve this. 3. Make Governance Work for You Imagine driving down the street without the painted lines on the road.  The rules of the road are so ingrained in our minds, we often do not think about the process, but seemingly mundane lane markers are critical enablers. Lane markers allow us to travel at speeds that would be impossible if not for the agreed upon direction of flow. Additionally and more importantly, it allows people to act autonomously - going where they please at any given time. The return on the investment for mobility is high enough for people to buy into globally agreed up governance processes. In Oracle WebCenter we can use similar enablers to lane markers.  Our goal should be to enable the flow of information and provide end users with the ability to arrive at business solutions as needed, not on the basis of cumbersome processes that cannot meet the business needs in a timely fashion. How do we do this? Just as with "Segmentation of Duties" Oracle WebCenter technologies offer the opportunity to compartmentalize various business initiatives from each other within the system due to constructs and security that are available to use within the platform. For instance, when a WebCenter space is created, any content added within that space by default will be secured to that particular space and inherits meta data that is associated with a folder created for the space. Oracle WebCenter content uses meta data to support a broad range of rich ECM functionality and can automatically impart retention, workflow and other policies automatically on the basis of what has been defaulted for that space. Depending on your business needs, this paradigm will also extend to sub sections of a space, offering some interesting possibilities to enable automated management around content. An example may be press releases within a particular area of an extranet that require a five year retention period and need to the reviewed by marketing and legal before release.  The underlying content system will transparently take care of this process on the basis of the above rules, enabling peace of mind over unstructured data - which could otherwise become overwhelming. 4. Make Your First Project Your Second Imagine if Michael Phelps was competing in a swimming championship, but told right before his race that he had to use a brand new stroke.  There is no doubt that Michael is an outstanding swimmer, but chances are that he would like to have some time to get acquainted with the new stroke. New technologies should not be treated any differently.  Before jumping into the deep end it helps to take time to get to know the new approach - even though you may have been swimming thousands of times before. To quickly get a handle on Oracle WebCenter capabilities it can be helpful to deploy a sandbox for the team to use to share project documents, discussions and announcements in an effort to help the actual deployment get under way, while increasing everyone’s knowledge of the platform and its functionality that may be helpful down the road. Oracle Technology Network has made a pre-configured virtual machine available for download that can be a great starting point for this exercise. 5. Get to Know the Community If you are reading this blog post you have most certainly faced a software decision or challenge that was solved on the basis of a small piece of missing critical information - which took substantial research to discover.  Chances were also good that somewhere, someone had already come across this information and would have been excited to share it. There is no denying the power of passionate, connected users, sharing key tips around technology.  The Oracle WebCenter brand has a rich heritage that includes industry-leading technology and practitioners.  With the new Oracle WebCenter brand, opportunities to connect with these experts has become easier. Oracle WebCenter Blog Oracle Social Enterprise LinkedIn WebCenter Group Oracle WebCenter Twitter Oracle WebCenter Facebook Oracle User Groups Additionally, there are various Oracle WebCenter related blogs by an excellent grouping of services partners.

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  • Benchmarking MySQL Replication with Multi-Threaded Slaves

    - by Mat Keep
    0 0 1 1145 6530 Homework 54 15 7660 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The objective of this benchmark is to measure the performance improvement achieved when enabling the Multi-Threaded Slave enhancement delivered as a part MySQL 5.6. As the results demonstrate, Multi-Threaded Slaves delivers 5x higher replication performance based on a configuration with 10 databases/schemas. For real-world deployments, higher replication performance directly translates to: · Improved consistency of reads from slaves (i.e. reduced risk of reading "stale" data) · Reduced risk of data loss should the master fail before replicating all events in its binary log (binlog) The multi-threaded slave splits processing between worker threads based on schema, allowing updates to be applied in parallel, rather than sequentially. This delivers benefits to those workloads that isolate application data using databases - e.g. multi-tenant systems deployed in cloud environments. Multi-Threaded Slaves are just one of many enhancements to replication previewed as part of the MySQL 5.6 Development Release, which include: · Global Transaction Identifiers coupled with MySQL utilities for automatic failover / switchover and slave promotion · Crash Safe Slaves and Binlog · Optimized Row Based Replication · Replication Event Checksums · Time Delayed Replication These and many more are discussed in the “MySQL 5.6 Replication: Enabling the Next Generation of Web & Cloud Services” Developer Zone article  Back to the benchmark - details are as follows. Environment The test environment consisted of two Linux servers: · one running the replication master · one running the replication slave. Only the slave was involved in the actual measurements, and was based on the following configuration: - Hardware: Oracle Sun Fire X4170 M2 Server - CPU: 2 sockets, 6 cores with hyper-threading, 2930 MHz. - OS: 64-bit Oracle Enterprise Linux 6.1 - Memory: 48 GB Test Procedure Initial Setup: Two MySQL servers were started on two different hosts, configured as replication master and slave. 10 sysbench schemas were created, each with a single table: CREATE TABLE `sbtest` (    `id` int(10) unsigned NOT NULL AUTO_INCREMENT,    `k` int(10) unsigned NOT NULL DEFAULT '0',    `c` char(120) NOT NULL DEFAULT '',    `pad` char(60) NOT NULL DEFAULT '',    PRIMARY KEY (`id`),    KEY `k` (`k`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1 10,000 rows were inserted in each of the 10 tables, for a total of 100,000 rows. When the inserts had replicated to the slave, the slave threads were stopped. The slave data directory was copied to a backup location and the slave threads position in the master binlog noted. 10 sysbench clients, each configured with 10 threads, were spawned at the same time to generate a random schema load against each of the 10 schemas on the master. Each sysbench client executed 10,000 "update key" statements: UPDATE sbtest set k=k+1 WHERE id = <random row> In total, this generated 100,000 update statements to later replicate during the test itself. Test Methodology: The number of slave workers to test with was configured using: SET GLOBAL slave_parallel_workers=<workers> Then the slave IO thread was started and the test waited for all the update queries to be copied over to the relay log on the slave. The benchmark clock was started and then the slave SQL thread was started. The test waited for the slave SQL thread to finish executing the 100k update queries, doing "select master_pos_wait()". When master_pos_wait() returned, the benchmark clock was stopped and the duration calculated. The calculated duration from the benchmark clock should be close to the time it took for the SQL thread to execute the 100,000 update queries. The 100k queries divided by this duration gave the benchmark metric, reported as Queries Per Second (QPS). Test Reset: The test-reset cycle was implemented as follows: · the slave was stopped · the slave data directory replaced with the previous backup · the slave restarted with the slave threads replication pointer repositioned to the point before the update queries in the binlog. The test could then be repeated with identical set of queries but a different number of slave worker threads, enabling a fair comparison. The Test-Reset cycle was repeated 3 times for 0-24 number of workers and the QPS metric calculated and averaged for each worker count. MySQL Configuration The relevant configuration settings used for MySQL are as follows: binlog-format=STATEMENT relay-log-info-repository=TABLE master-info-repository=TABLE As described in the test procedure, the slave_parallel_workers setting was modified as part of the test logic. The consequence of changing this setting is: 0 worker threads:    - current (i.e. single threaded) sequential mode    - 1 x IO thread and 1 x SQL thread    - SQL thread both reads and executes the events 1 worker thread:    - sequential mode    - 1 x IO thread, 1 x Coordinator SQL thread and 1 x Worker thread    - coordinator reads the event and hands it to the worker who executes 2+ worker threads:    - parallel execution    - 1 x IO thread, 1 x Coordinator SQL thread and 2+ Worker threads    - coordinator reads events and hands them to the workers who execute them Results Figure 1 below shows that Multi-Threaded Slaves deliver ~5x higher replication performance when configured with 10 worker threads, with the load evenly distributed across our 10 x schemas. This result is compared to the current replication implementation which is based on a single SQL thread only (i.e. zero worker threads). Figure 1: 5x Higher Performance with Multi-Threaded Slaves The following figure shows more detailed results, with QPS sampled and reported as the worker threads are incremented. The raw numbers behind this graph are reported in the Appendix section of this post. Figure 2: Detailed Results As the results above show, the configuration does not scale noticably from 5 to 9 worker threads. When configured with 10 worker threads however, scalability increases significantly. The conclusion therefore is that it is desirable to configure the same number of worker threads as schemas. Other conclusions from the results: · Running with 1 worker compared to zero workers just introduces overhead without the benefit of parallel execution. · As expected, having more workers than schemas adds no visible benefit. Aside from what is shown in the results above, testing also demonstrated that the following settings had a very positive effect on slave performance: relay-log-info-repository=TABLE master-info-repository=TABLE For 5+ workers, it was up to 2.3 times as fast to run with TABLE compared to FILE. Conclusion As the results demonstrate, Multi-Threaded Slaves deliver significant performance increases to MySQL replication when handling multiple schemas. This, and the other replication enhancements introduced in MySQL 5.6 are fully available for you to download and evaluate now from the MySQL Developer site (select Development Release tab). You can learn more about MySQL 5.6 from the documentation  Please don’t hesitate to comment on this or other replication blogs with feedback and questions. Appendix – Detailed Results

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  • PostSharp, Obfuscation, and IL

    - by Simon Cooper
    Aspect-oriented programming (AOP) is a relatively new programming paradigm. Originating at Xerox PARC in 1994, the paradigm was first made available for general-purpose development as an extension to Java in 2001. From there, it has quickly been adapted for use in all the common languages used today. In the .NET world, one of the primary AOP toolkits is PostSharp. Attributes and AOP Normally, attributes in .NET are entirely a metadata construct. Apart from a few special attributes in the .NET framework, they have no effect whatsoever on how a class or method executes within the CLR. Only by using reflection at runtime can you access any attributes declared on a type or type member. PostSharp changes this. By declaring a custom attribute that derives from PostSharp.Aspects.Aspect, applying it to types and type members, and running the resulting assembly through the PostSharp postprocessor, you can essentially declare 'clever' attributes that change the behaviour of whatever the aspect has been applied to at runtime. A simple example of this is logging. By declaring a TraceAttribute that derives from OnMethodBoundaryAspect, you can automatically log when a method has been executed: public class TraceAttribute : PostSharp.Aspects.OnMethodBoundaryAspect { public override void OnEntry(MethodExecutionArgs args) { MethodBase method = args.Method; System.Diagnostics.Trace.WriteLine( String.Format( "Entering {0}.{1}.", method.DeclaringType.FullName, method.Name)); } public override void OnExit(MethodExecutionArgs args) { MethodBase method = args.Method; System.Diagnostics.Trace.WriteLine( String.Format( "Leaving {0}.{1}.", method.DeclaringType.FullName, method.Name)); } } [Trace] public void MethodToLog() { ... } Now, whenever MethodToLog is executed, the aspect will automatically log entry and exit, without having to add the logging code to MethodToLog itself. PostSharp Performance Now this does introduce a performance overhead - as you can see, the aspect allows access to the MethodBase of the method the aspect has been applied to. If you were limited to C#, you would be forced to retrieve each MethodBase instance using Type.GetMethod(), matching on the method name and signature. This is slow. Fortunately, PostSharp is not limited to C#. It can use any instruction available in IL. And in IL, you can do some very neat things. Ldtoken C# allows you to get the Type object corresponding to a specific type name using the typeof operator: Type t = typeof(Random); The C# compiler compiles this operator to the following IL: ldtoken [mscorlib]System.Random call class [mscorlib]System.Type [mscorlib]System.Type::GetTypeFromHandle( valuetype [mscorlib]System.RuntimeTypeHandle) The ldtoken instruction obtains a special handle to a type called a RuntimeTypeHandle, and from that, the Type object can be obtained using GetTypeFromHandle. These are both relatively fast operations - no string lookup is required, only direct assembly and CLR constructs are used. However, a little-known feature is that ldtoken is not just limited to types; it can also get information on methods and fields, encapsulated in a RuntimeMethodHandle or RuntimeFieldHandle: // get a MethodBase for String.EndsWith(string) ldtoken method instance bool [mscorlib]System.String::EndsWith(string) call class [mscorlib]System.Reflection.MethodBase [mscorlib]System.Reflection.MethodBase::GetMethodFromHandle( valuetype [mscorlib]System.RuntimeMethodHandle) // get a FieldInfo for the String.Empty field ldtoken field string [mscorlib]System.String::Empty call class [mscorlib]System.Reflection.FieldInfo [mscorlib]System.Reflection.FieldInfo::GetFieldFromHandle( valuetype [mscorlib]System.RuntimeFieldHandle) These usages of ldtoken aren't usable from C# or VB, and aren't likely to be added anytime soon (Eric Lippert's done a blog post on the possibility of adding infoof, methodof or fieldof operators to C#). However, PostSharp deals directly with IL, and so can use ldtoken to get MethodBase objects quickly and cheaply, without having to resort to string lookups. The kicker However, there are problems. Because ldtoken for methods or fields isn't accessible from C# or VB, it hasn't been as well-tested as ldtoken for types. This has resulted in various obscure bugs in most versions of the CLR when dealing with ldtoken and methods, and specifically, generic methods and methods of generic types. This means that PostSharp was behaving incorrectly, or just plain crashing, when aspects were applied to methods that were generic in some way. So, PostSharp has to work around this. Without using the metadata tokens directly, the only way to get the MethodBase of generic methods is to use reflection: Type.GetMethod(), passing in the method name as a string along with information on the signature. Now, this works fine. It's slower than using ldtoken directly, but it works, and this only has to be done for generic methods. Unfortunately, this poses problems when the assembly is obfuscated. PostSharp and Obfuscation When using ldtoken, obfuscators don't affect how PostSharp operates. Because the ldtoken instruction directly references the type, method or field within the assembly, it is unaffected if the name of the object is changed by an obfuscator. However, the indirect loading used for generic methods was breaking, because that uses the name of the method when the assembly is put through the PostSharp postprocessor to lookup the MethodBase at runtime. If the name then changes, PostSharp can't find it anymore, and the assembly breaks. So, PostSharp needs to know about any changes an obfuscator does to an assembly. The way PostSharp does this is by adding another layer of indirection. When PostSharp obfuscation support is enabled, it includes an extra 'name table' resource in the assembly, consisting of a series of method & type names. When PostSharp needs to lookup a method using reflection, instead of encoding the method name directly, it looks up the method name at a fixed offset inside that name table: MethodBase genericMethod = typeof(ContainingClass).GetMethod(GetNameAtIndex(22)); PostSharp.NameTable resource: ... 20: get_Prop1 21: set_Prop1 22: DoFoo 23: GetWibble When the assembly is later processed by an obfuscator, the obfuscator can replace all the method and type names within the name table with their new name. That way, the reflection lookups performed by PostSharp will now use the new names, and everything will work as expected: MethodBase genericMethod = typeof(#kGy).GetMethod(GetNameAtIndex(22)); PostSharp.NameTable resource: ... 20: #kkA 21: #zAb 22: #EF5a 23: #2tg As you can see, this requires direct support by an obfuscator in order to perform these rewrites. Dotfuscator supports it, and now, starting with SmartAssembly 6.6.4, SmartAssembly does too. So, a relatively simple solution to a tricky problem, with some CLR bugs thrown in for good measure. You don't see those every day!

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  • SQL SERVER – Weekly Series – Memory Lane – #031

    - by Pinal Dave
    Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 Find Table without Clustered Index – Find Table with no Primary Key Clustered index is very important concept for any table. They impact the performance very heavily. Here is a quick script to find tables without a clustered index. Replace TEXT with VARCHAR(MAX) – Stop using TEXT, NTEXT, IMAGE Data Types Question: “Is VARCHAR (MAX) big enough to store the TEXT field?” Answer: “Yes, VARCHAR(MAX) is big enough to accommodate TEXT field. TEXT, NTEXT and IMAGE data types of SQL Server 2000 will be deprecated in a future version of SQL Server, SQL Server 2005 provides backward compatibility to data types but it is recommended to use new data types which are VARHCAR (MAX), NVARCHAR (MAX) and VARBINARY (MAX).” Limiting Result Sets by Using TABLESAMPLE – Examples Introduced in SQL Server 2005, TABLESAMPLE allows you to extract a sampling of rows from a table in the FROM clause. The rows retrieved are random and they are are not in any order. This sampling can be based on a percentage of number of rows. You can use TABLESAMPLE when only a sampling of rows is necessary for the application instead of a full result set. User Defined Functions (UDF) Limitations UDF have its own advantage and usage but in this article we will see the limitation of UDF. Things UDF can not do and why Stored Procedure are considered as more flexible then UDFs. Stored Procedure are more flexibility then User Defined Functions(UDF). However, this blog post is a good read to know what are the limitations of UDF. Change Database Compatible Level – Backward Compatibility For a long time SQL Server stayed on the compatibility level of 80 which is of SQL Server 2000. However, as soon as SQL Server 2005 introduced the issue of compatibility was quite a major issue. Since that time MS has been releasing the versions at every 2-3 years, changing compatibility is a ever popular topic. In this blog post, we learn how we can do the same using T-SQL. We can also do the same using SSMS and here is the blog post for the same: Change Database Compatible Level – Backward Compatibility – Part 2 – Management Studio. Constraint on VARCHAR(MAX) Field To Limit It Certain Length How can I limit the VARCHAR(MAX) field with maximum length of 12500 characters only. His Question was valid as our application was allowed 12500 characters. First of all – this requirement is bit strange but if someone wants to do the same, they can do it as described in this blog post. 2008 UNPIVOT Table Example Understanding UNPIVOT can be very complicated at times. In this blog post, I have attempted to explain the same concept in very simple words. Create Default Constraint Over Table Column A simple straight to script blog post – I still use this blog quite many times for my own reference. UDF – Get the Day of the Week Function It took me 4 iteration to find this very simple function which can immediately get the day of the week in a single line. 2009 Find Hostname and Current Logged In User Name There are two tricks listed in this blog post where users can find out the hostname and current logged user name immediately and very easily. Interesting Observation of Logon Trigger On All Servers When I was doing a project, I made an interesting observation of executing a logon trigger multiple times. It was absolutely unexpected for me! As I was logging only once, naturally, I was expecting the entry only once. However, it did it multiple times on different threads – indeed an eccentric phenomenon at first sight! Difference Between Candidate Keys and Primary Key One needs to be very careful in selecting the Primary Key as an incorrect selection can adversely impact the database architect and future normalization. For a Candidate Key to qualify as a Primary Key, it should be Non-NULL and unique in any domain. I have observed quite often that Primary Keys are seldom changed. I would like to have your feedback on not changing a Primary Key. Create Multiple Filegroup For Single Database Why should one create multiple file group for any database and what are the advantages of the same. In this blog post, I explain the same in detail. List All Objects Created on All Filegroups in Database In this blog post we discuss the essential question – “How can I find which object belongs to which filegroup. Is there any way to know this?” 2010 DATE and TIME in SQL Server 2008 When DATE is converted to DATETIME it adds the of midnight. When TIME is converted to DATETIME it adds the date of 1900 and it is something one wants to consider if you are going to run scripts from SQL Server 2008 to earlier version with CONVERT. Disabled Index and Update Statistics If you do not need a nonclustered index, I suggest you to drop it as keeping them disabled is an overhead on your system. This is because every time the statistics are updated for system all the statistics for disabled indexes are also updated. Precision of SMALLDATETIME – A 1 Minute Precision The precision of the datatype SMALLDATETIME is 1 minute. It discards the seconds by rounding up or rounding down any seconds greater than zero. 2011 Getting Columns Headers without Result Data – SET FMTONLY ON SET FMTONLY ON returns only metadata to the client. It can be used to test the format of the response without actually running the query. When this setting is ON the resultset only have headers of the results but no data. Copy Database from Instance to Another Instance – Copy Paste in SQL Server SQL Server has a feature which copy database from one database to another database and it can be automated as well using SSIS. Make sure you have SQL Server Agent Turned on as this feature will create a job. Puzzle – SELECT * vs SELECT COUNT(*) If you have ever wondered SELECT * gives error when executed alone but SELECT COUNT(*) does not. Why? in that case, you should read this blog post. Creating All New Database with Full Recovery Model This blog post is very based on very interesting story where the user wants to do something by default for every single new database created. Model database is a secret weapon which should be used very carefully and with proper evalution. If used carefully this can be a very much beneficiary when we need a newly created database behave in certain fashion. 2012 In year 2012 I had two interesting series ran on the blog. If there is no fun in learning, the learning becomes a burden. For the same reason, I had decided to build a three part quiz around SEQUENCE. The quiz was to identify the next value of the sequence. I encourage all of you to take part in this fun quiz. Guess the Next Value – Puzzle 1 Guess the Next Value – Puzzle 2 Guess the Next Value – Puzzle 3 Can anyone remember their final day of schooling?  This is probably a silly question because – of course you can!  Many people mark this as the most exciting, happiest day of their life.  It marks the end of testing, the end of following rules set by teachers, and the beginning of finally being able to earn money and work in your chosen field. Read five part series on developer training subject Developer Training - Importance and Significance - Part 1 Developer Training – Employee Morals and Ethics – Part 2 Developer Training – Difficult Questions and Alternative Perspective - Part 3 Developer Training – Various Options for Developer Training – Part 4 Developer Training – A Conclusive Summary- Part 5 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Checking who is connected to your server, with PowerShell.

    - by Fatherjack
    There are many occasions when, as a DBA, you want to see who is connected to your SQL Server, along with how they are connecting and what sort of activities they are carrying out. I’m going to look at a couple of ways of getting this information and compare the effort required and the results achieved of each. SQL Server comes with a couple of stored procedures to help with this sort of task – sp_who and its undocumented counterpart sp_who2. There is also the pumped up version of these called sp_whoisactive, written by Adam Machanic which does way more than these procedures. I wholly recommend you try it out if you don’t already know how it works. When it comes to serious interrogation of your SQL Server activity then it is absolutely indispensable. Anyway, back to the point of this blog, we are going to look at getting the information from sp_who2 for a remote server. I wrote this Powershell script a week or so ago and was quietly happy with it for a while. I’m relatively new to Powershell so forgive both my rather low threshold for entertainment and the fact that something so simple is a moderate achievement for me. $Server = 'SERVERNAME' $SMOServer = New-Object Microsoft.SQLServer.Management.SMO.Server $Server # connection and query stuff         $ConnectionStr = "Server=$Server;Database=Master;Integrated Security=True" $Query = "EXEC sp_who2" $Connection = new-object system.Data.SQLClient.SQLConnection $Table = new-object "System.Data.DataTable" $Connection.connectionstring = $ConnectionStr try{ $Connection.open() $Command = $Connection.CreateCommand() $Command.commandtext = $Query $result = $Command.ExecuteReader() $Table.Load($result) } catch{ # Show error $error[0] | format-list -Force } $Title = "Data access processes (" + $Table.Rows.Count + ")" $Table | Out-GridView -Title $Title $Connection.close() So this is pretty straightforward, create an SMO object that represents our chosen server, define a connection to the database and a table object for the results when we get them, execute our query over the connection, load the results into our table object and then, if everything is error free display these results to the PowerShell grid viewer. The query simply gets the results of ‘EXEC sp_who2′ for us. Depending on how many connections there are will influence how long the query runs. The grid viewer lets me sort and search the results so it can be a pretty handy way to locate troublesome connections. Like I say, I was quite pleased with this, it seems a pretty simple script and was working well for me, I have added a few parameters to control the output and give me more specific details but then I see a script that uses the $SMOServer object itself to provide the process information and saves having to define the connection object and query specifications. $Server = 'SERVERNAME' $SMOServer = New-Object Microsoft.SQLServer.Management.SMO.Server $Server $Processes = $SMOServer.EnumProcesses() $Title = "SMO processes (" + $Processes.Rows.Count + ")" $Processes | Out-GridView -Title $Title Create the SMO object of our server and then call the EnumProcesses method to get all the process information from the server. Staggeringly simple! The results are a little different though. Some columns are the same and we can see the same basic information so my first thought was to which runs faster – so that I can get my results more quickly and also so that I place less stress on my server(s). PowerShell comes with a great way of testing this – the Measure-Command function. All you have to do is wrap your piece of code in Measure-Command {[your code here]} and it will spit out the time taken to execute the code. So, I placed both of the above methods of getting SQL Server process connections in two Measure-Command wrappers and pressed F5! The Powershell console goes blank for a while as the code is executed internally when Measure-Command is used but the grid viewer windows appear and the console shows this. You can take the output from Measure-Command and format it for easier reading but in a simple comparison like this we can simply cross refer the TotalMilliseconds values from the two result sets to see how the two methods performed. The query execution method (running EXEC sp_who2 ) is the first set of timings and the SMO EnumProcesses is the second. I have run these on a variety of servers and while the results vary from execution to execution I have never seen the SMO version slower than the other. The difference has varied and the time for both has ranged from sub-second as we see above to almost 5 seconds on other systems. This difference, I would suggest is partly due to the cost overhead of having to construct the data connection and so on where as the SMO EnumProcesses method has the connection to the server already in place and just needs to call back the process information. There is also the difference in the data sets to consider. Let’s take a look at what we get and where the two methods differ Query execution method (sp_who2) SMO EnumProcesses Description - Urn What looks like an XML or JSON representation of the server name and the process ID SPID Spid The process ID Status Status The status of the process Login Login The login name of the user executing the command HostName Host The name of the computer where the  process originated BlkBy BlockingSpid The SPID of a process that is blocking this one DBName Database The database that this process is connected to Command Command The type of command that is executing CPUTime Cpu The CPU activity related to this process DiskIO - The Disk IO activity related to this process LastBatch - The time the last batch was executed from this process. ProgramName Program The application that is facilitating the process connection to the SQL Server. SPID1 - In my experience this is always the same value as SPID. REQUESTID - In my experience this is always 0 - Name In my experience this is always the same value as SPID and so could be seen as analogous to SPID1 from sp_who2 - MemUsage An indication of the memory used by this process but I don’t know what it is measured in (bytes, Kb, Mb…) - IsSystem True or False depending on whether the process is internal to the SQL Server instance or has been created by an external connection requesting data. - ExecutionContextID In my experience this is always 0 so could be analogous to REQUESTID from sp_who2. Please note, these are my own very brief descriptions of these columns, detail can be found from MSDN for columns in the sp_who results here http://msdn.microsoft.com/en-GB/library/ms174313.aspx. Where the columns are common then I would use that description, in other cases then the information returned is purely for interpretation by the reader. Rather annoyingly both result sets have useful information that the other doesn’t. sp_who2 returns Disk IO and LastBatch information which is really useful but the SMO processes method give you IsSystem and MemUsage which have their place in fault diagnosis methods too. So which is better? On reflection I think I prefer to use the sp_who2 method primarily but knowing that the SMO Enumprocesses method is there when I need it is really useful and I’m sure I’ll use it regularly. I’m OK with the fact that it is the slower method because Measure-Command has shown me how close it is to the other option and that it really isn’t a large enough margin to matter.

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