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  • An Honest look at SharePoint Web Services

    - by juanlarios
    INTRODUCTION If you are a SharePoint developer you know that there are two basic ways to develop against SharePoint. 1) The object Model 2) Web services. SharePoint object model has the advantage of being quite rich. Anything you can do through the SharePoint UI as an administrator or end user, you can do through the object model. In fact everything that is done through the UI is done through the object model behind the scenes. The major disadvantage to getting at SharePoint this way is that the code needs to run on the server. This means that all web parts, event receivers, features, etc… all of this is code that is deployed to the server. The second way to get to SharePoint is through the built in web services. There are many articles on how to manipulate web services, how to authenticate to them and interact with them. The basic idea is that a remote application or process can contact SharePoint through a web service. Lots has been written about how great these web services are. This article is written to document the limitations, some of the issues and frustrations with working with SharePoint built in web services. Ultimately, for the tasks I was given to , SharePoint built in web services did not suffice. My evaluation of SharePoint built in services was compared against creating my own WCF Services to do what I needed. The current project I'm working on right now involved several "integration points". A remote application, installed on a separate server was to contact SharePoint and perform an task or operation. So I decided to start up Visual Studio and built a DLL and basically have 2 layers of logic. An integration layer and a data layer. A good friend of mine pointed me to SOLID principles and referred me to some videos and tutorials about it. I decided to implement the methodology (although a lot of the principles are common sense and I already incorporated in my coding practices). I was to deliver this dll to the application team and they would simply call the methods exposed by this dll and voila! it would do some task or operation in SharePoint. SOLUTION My integration layer implemented an interface that defined some of the basic integration tasks that I was to put together. My data layer was about the same, it implemented an interface with some of the tasks that I was going to develop. This gave me the opportunity to develop different data layers, ultimately different ways to get at SharePoint if I needed to. This is a classic SOLID principle. In this case it proved to be quite helpful because I wrote one data layer completely implementing SharePoint built in Web Services and another implementing my own WCF Service that I wrote. I should mention there is another layer underneath the data layer. In referencing SharePoint or WCF services in my visual studio project I created a class for every web service call. So for example, if I used List.asx. I created a class called "DocumentRetreival" this class would do the grunt work to connect to the correct URL, It would perform the basic operation of contacting the service and so on. If I used a view.asmx, I implemented a class called "ViewRetrieval" with the same idea as the last class but it would now interact with all he operations in view.asmx. This gave my data layer the ability to perform multiple calls without really worrying about some of the grunt work each class performs. This again, is a classic SOLID principle. So, in order to compare them side by side we can look at both data layers and with is involved in each. Lets take a look at the "Create Project" task or operation. The integration point is described as , "dll is to provide a way to create a project in SharePoint". Projects , in this case are basically document libraries. I am to implement a way in which a remote application can create a document library in SharePoint. Easy enough right? Use the list.asmx Web service in SharePoint. So here we go! Lets take a look at the code. I added the List.asmx web service reference to my project and this is the class that contacts it:  class DocumentRetrieval     {         private ListsSoapClient _service;      d   private bool _impersonation;         public DocumentRetrieval(bool impersonation, string endpt)         {             _service = new ListsSoapClient();             this.SetEndPoint(string.Format("{0}/{1}", endpt, ConfigurationManager.AppSettings["List"]));             _impersonation = impersonation;             if (_impersonation)             {                 _service.ClientCredentials.Windows.ClientCredential.Password = ConfigurationManager.AppSettings["password"];                 _service.ClientCredentials.Windows.ClientCredential.UserName = ConfigurationManager.AppSettings["username"];                 _service.ClientCredentials.Windows.AllowedImpersonationLevel =                     System.Security.Principal.TokenImpersonationLevel.Impersonation;             }     private void SetEndPoint(string p)          {             _service.Endpoint.Address = new EndpointAddress(p);          }          /// <summary>         /// Creates a document library with specific name and templateID         /// </summary>         /// <param name="listName">New list name</param>         /// <param name="templateID">Template ID</param>         /// <returns></returns>         public XmlElement CreateLibrary(string listName, int templateID, ref ExceptionContract exContract)         {             XmlDocument sample = new XmlDocument();             XmlElement viewCol = sample.CreateElement("Empty");             try             {                 _service.Open();                 viewCol = _service.AddList(listName, "", templateID);             }             catch (Exception ex)             {                 exContract = new ExceptionContract("DocumentRetrieval/CreateLibrary", ex.GetType(), "Connection Error", ex.StackTrace, ExceptionContract.ExceptionCode.error);                             }finally             {                 _service.Close();             }                                      return viewCol;         } } There was a lot more in this class (that I am not including) because i was reusing the grunt work and making other operations with LIst.asmx, For example, updating content types, changing or configuring lists or document libraries. One of the first things I noticed about working with the built in services is that you are really at the mercy of what is available to you. Before creating a document library (Project) I wanted to expose a IsProjectExisting method. This way the integration or data layer could recognize if a library already exists. Well there is no service call or method available to do that check. So this is what I wrote:   public bool DocLibExists(string listName, ref ExceptionContract exContract)         {             try             {                 var allLists = _service.GetListCollection();                                return allLists.ChildNodes.OfType<XmlElement>().ToList().Exists(x => x.Attributes["Title"].Value ==listName);             }             catch (Exception ex)             {                 exContract = new ExceptionContract("DocumentRetrieval/GetList/GetListWSCall", ex.GetType(), "Unable to Retrieve List Collection", ex.StackTrace, ExceptionContract.ExceptionCode.error);             }             return false;         } This really just gets an XMLElement with all the lists. It was then up to me to sift through the clutter and noise and see if Document library already existed. This took a little bit of getting used to. Now instead of working with code, you are working with XMLElement response format from web service. I wrote a LINQ query to go through and find if the attribute "Title" existed and had a value of the listname then it would return True, if not False. I didn't particularly like working this way. Dealing with XMLElement responses and then having to manipulate it to get at the exact data I was looking for. Once the check for the DocLibExists, was done, I would either create the document library or send back an error indicating the document library already existed. Now lets examine the code that actually creates the document library. It does what you are really after, it creates a document library. Notice how the template ID is really an integer. Every document library template in SharePoint has an ID associated with it. Document libraries, Image Library, Custom List, Project Tasks, etc… they all he a unique integer associated with it. Well, that's great but the client came back to me and gave me some specifics that each "project" or document library, should have. They specified they had 3 types of projects. Each project would have unique views, about 10 views for each project. Each Project specified unique configurations (auditing, versioning, content types, etc…) So what turned out to be a simple implementation of creating a document library as a repository for a project, turned out to be quite involved.  The first thing I thought of was to create a template for document library. There are other ways you can do this too. Using the web Service call, you could configure views, versioning, even content types, etc… the only catch is, you have to be working quite extensively with CAML. I am not fond of CAML. I can do it and work with it, I just don't like doing it. It is quite touchy and at times it is quite tough to understand where errors were made with CAML statements. Working with Web Services and CAML proved to be quite annoying. The service call would return a generic error message that did not particularly point me to a CAML statement syntax error, or even a CAML error. I was not sure if it was a security , performance or code based issue. It was quite tough to work with. At times it was difficult to work with because of the way SharePoint handles metadata. There are "Names", "Display Name", and "StaticName" fields. It was quite tough to understand at times, which one to use. So it took a lot of trial and error. There are tools that can help with CAML generation. There is also now intellisense for CAML statements in Visual Studio that might help but ultimately I'm not fond of CAML with Web Services.   So I decided on the template. So my plan was to create create a document library, configure it accordingly and then use The Template Builder that comes with the SharePoint SDK. This tool allows you to create site templates, list template etc… It is quite interesting because it does not generate an STP file, it actually generates an xml definition and a feature you can activate and make that template available on a site or site collection. The first issue I experienced with this is that one of the specifications to this template was that the "All Documents" view was to have 2 web parts on it. Well, it turns out that using the template builder , it did not include the web parts as part of the list template definition it generated. It backed up the settings, the views, the content types but not the custom web parts. I still decided to try this even without the web parts on the page. This new template defined a new Document library definition with a unique ID. The problem was that the service call accepts an int but it only has access to the built in library int definitions. Any new ones added or created will not be available to create. So this made it impossible for me to approach the problem this way.     I should also mention that one of the nice features about SharePoint is the ability to create list templates, back them up and then create lists based on that template. It can all be done by end user administrators. These templates are quite unique because they are saved as an STP file and not an xml definition. I also went this route and tried to see if there was another service call where I could create a document library based no given template name. Nope! none.      After some thinking I decide to implement a WCF service to do this creation for me. I was quite certain that the object model would allow me to create document libraries base on a template in which an ID was required and also templates saved as STP files. Now I don't want to bother with posting the code to contact WCF service because it's self explanatory, but I will post the code that I used to create a list with custom template. public ServiceResult CreateProject(string name, string templateName, string projectId)         {             string siteurl = SPContext.Current.Site.Url;             Guid webguid = SPContext.Current.Web.ID;                        using (SPSite site = new SPSite(siteurl))             {                 using (SPWeb rootweb = site.RootWeb)                 {                     SPListTemplateCollection temps = site.GetCustomListTemplates(rootweb);                     ProcessWeb(siteurl, webguid, web => Act_CreateProject(web, name, templateName, projectId, temps));                 }//SpWeb             }//SPSite              return _globalResult;                   }         private void Act_CreateProject(SPWeb targetsite, string name, string templateName, string projectId, SPListTemplateCollection temps) {                         var temp = temps.Cast<SPListTemplate>().FirstOrDefault(x => x.Name.Equals(templateName));             if (temp != null)             {                             try                 {                                         Guid listGuid = targetsite.Lists.Add(name, "", temp);                     SPList newList = targetsite.Lists[listGuid];                     _globalResult = new ServiceResult(true, "Success", "Success");                 }                 catch (Exception ex)                 {                     _globalResult = new ServiceResult(false, (string.IsNullOrEmpty(ex.Message) ? "None" : ex.Message + " " + templateName), ex.StackTrace.ToString());                 }                                       }        private void ProcessWeb(string siteurl, Guid webguid, Action<SPWeb> action) {                        using (SPSite sitecollection = new SPSite(siteurl)) {                 using (SPWeb web = sitecollection.AllWebs[webguid]) {                     action(web);                 }                     }                  } This code is actually some of the code I implemented for the service. there was a lot more I did on Project Creation which I will cover in my next blog post. I implemented an ACTION method to process the web. This allowed me to properly dispose the SPWEb and SPSite objects and not rewrite this code over and over again. So I implemented a WCF service to create projects for me, this allowed me to do a lot more than just create a document library with a template, it now gave me the flexibility to do just about anything the client wanted at project creation. Once this was implemented , the client came back to me and said, "we reference all our projects with ID's in our application. we want SharePoint to do the same". This has been something I have been doing for a little while now but I do hope that SharePoint 2010 can have more of an answer to this and address it properly. I have been adding metadata to SPWebs through property bag. I believe I have blogged about it before. This time it required metadata added to a document library. No problem!!! I also mentioned these web parts that were to go on the "All Documents" View. I took the opportunity to configure them to the appropriate settings. There were two settings that needed to be set on these web parts. One of them was a Project ID configured in the webpart properties. The following code enhances and replaces the "Act_CreateProject " method above:  private void Act_CreateProject(SPWeb targetsite, string name, string templateName, string projectId, SPListTemplateCollection temps) {                         var temp = temps.Cast<SPListTemplate>().FirstOrDefault(x => x.Name.Equals(templateName));             if (temp != null)             {                 SPLimitedWebPartManager wpmgr = null;                               try                 {                                         Guid listGuid = targetsite.Lists.Add(name, "", temp);                     SPList newList = targetsite.Lists[listGuid];                     SPFolder rootFolder = newList.RootFolder;                     rootFolder.Properties.Add(KEY, projectId);                     rootFolder.Update();                     if (rootFolder.ParentWeb != targetsite)                         rootFolder.ParentWeb.Dispose();                     if (!templateName.Contains("Natural"))                     {                         SPView alldocumentsview = newList.Views.Cast<SPView>().FirstOrDefault(x => x.Title.Equals(ALLDOCUMENTS));                         SPFile alldocfile = targetsite.GetFile(alldocumentsview.ServerRelativeUrl);                         wpmgr = alldocfile.GetLimitedWebPartManager(PersonalizationScope.Shared);                         ConfigureWebPart(wpmgr, projectId, CUSTOMWPNAME);                                              alldocfile.Update();                     }                                        if (newList.ParentWeb != targetsite)                         newList.ParentWeb.Dispose();                     _globalResult = new ServiceResult(true, "Success", "Success");                 }                 catch (Exception ex)                 {                     _globalResult = new ServiceResult(false, (string.IsNullOrEmpty(ex.Message) ? "None" : ex.Message + " " + templateName), ex.StackTrace.ToString());                 }                 finally                 {                     if (wpmgr != null)                     {                         wpmgr.Web.Dispose();                         wpmgr.Dispose();                     }                 }             }                         }       private void ConfigureWebPart(SPLimitedWebPartManager mgr, string prjId, string webpartname)         {             var wp = mgr.WebParts.Cast<System.Web.UI.WebControls.WebParts.WebPart>().FirstOrDefault(x => x.DisplayTitle.Equals(webpartname));             if (wp != null)             {                           (wp as ListRelationshipWebPart.ListRelationshipWebPart).ProjectID = prjId;                 mgr.SaveChanges(wp);             }         }   This Shows you how I was able to set metadata on the document library. It has to be added to the RootFolder of the document library, Unfortunately, the SPList does not have a Property bag that I can add a key\value pair to. It has to be done on the root folder. Now everything in the integration will reference projects by ID's and will not care about names. My, "DocLibExists" will now need to be changed because a web service is not set up to look at property bags.  I had to write another method on the Service to do the equivalent but with ID's instead of names.  The second thing you will notice about the code is the use of the Webpartmanager. I have seen several examples online, and also read a lot about memory leaks, The above code does not produce memory leaks. The web part manager creates an SPWeb, so just dispose it like I did. CONCLUSION This is a long long post so I will stop here for now, I will continue with more comparisons and limitations in my next post. My conclusion for this example is that Web Services will do the trick if you can suffer through CAML and if you are doing some simple operations. For Everything else, there's WCF! **** fireI apologize for the disorganization of this post, I was on a bus on a 12 hour trip to IOWA while I wrote it, I was half asleep and half awake, hopefully it makes enough sense to someone.

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  • The penultimate audit trigger framework

    - by Piotr Rodak
    So, it’s time to see what I came up with after some time of playing with COLUMNS_UPDATED() and bitmasks. The first part of this miniseries describes the mechanics of the encoding which columns are updated within DML operation. The task I was faced with was to prepare an audit framework that will be fairly easy to use. The audited tables were to be the ones directly modified by user applications, not the ones heavily used by batch or ETL processes. The framework consists of several tables and procedures...(read more)

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  • Advanced TSQL Tuning: Why Internals Knowledge Matters

    - by Paul White
    There is much more to query tuning than reducing logical reads and adding covering nonclustered indexes.  Query tuning is not complete as soon as the query returns results quickly in the development or test environments.  In production, your query will compete for memory, CPU, locks, I/O and other resources on the server.  Today’s entry looks at some tuning considerations that are often overlooked, and shows how deep internals knowledge can help you write better TSQL. As always, we’ll need some example data.  In fact, we are going to use three tables today, each of which is structured like this: Each table has 50,000 rows made up of an INTEGER id column and a padding column containing 3,999 characters in every row.  The only difference between the three tables is in the type of the padding column: the first table uses CHAR(3999), the second uses VARCHAR(MAX), and the third uses the deprecated TEXT type.  A script to create a database with the three tables and load the sample data follows: USE master; GO IF DB_ID('SortTest') IS NOT NULL DROP DATABASE SortTest; GO CREATE DATABASE SortTest COLLATE LATIN1_GENERAL_BIN; GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest', SIZE = 3GB, MAXSIZE = 3GB ); GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest_log', SIZE = 256MB, MAXSIZE = 1GB, FILEGROWTH = 128MB ); GO ALTER DATABASE SortTest SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE SortTest SET AUTO_CLOSE OFF ; ALTER DATABASE SortTest SET AUTO_CREATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_SHRINK OFF ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS_ASYNC ON ; ALTER DATABASE SortTest SET PARAMETERIZATION SIMPLE ; ALTER DATABASE SortTest SET READ_COMMITTED_SNAPSHOT OFF ; ALTER DATABASE SortTest SET MULTI_USER ; ALTER DATABASE SortTest SET RECOVERY SIMPLE ; USE SortTest; GO CREATE TABLE dbo.TestCHAR ( id INTEGER IDENTITY (1,1) NOT NULL, padding CHAR(3999) NOT NULL,   CONSTRAINT [PK dbo.TestCHAR (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestMAX ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAX (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestTEXT ( id INTEGER IDENTITY (1,1) NOT NULL, padding TEXT NOT NULL,   CONSTRAINT [PK dbo.TestTEXT (id)] PRIMARY KEY CLUSTERED (id), ) ; -- ============= -- Load TestCHAR (about 3s) -- ============= INSERT INTO dbo.TestCHAR WITH (TABLOCKX) ( padding ) SELECT padding = REPLICATE(CHAR(65 + (Data.n % 26)), 3999) FROM ( SELECT TOP (50000) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) - 1 FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) AS Data ORDER BY Data.n ASC ; -- ============ -- Load TestMAX (about 3s) -- ============ INSERT INTO dbo.TestMAX WITH (TABLOCKX) ( padding ) SELECT CONVERT(VARCHAR(MAX), padding) FROM dbo.TestCHAR ORDER BY id ; -- ============= -- Load TestTEXT (about 5s) -- ============= INSERT INTO dbo.TestTEXT WITH (TABLOCKX) ( padding ) SELECT CONVERT(TEXT, padding) FROM dbo.TestCHAR ORDER BY id ; -- ========== -- Space used -- ========== -- EXECUTE sys.sp_spaceused @objname = 'dbo.TestCHAR'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAX'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestTEXT'; ; CHECKPOINT ; That takes around 15 seconds to run, and shows the space allocated to each table in its output: To illustrate the points I want to make today, the example task we are going to set ourselves is to return a random set of 150 rows from each table.  The basic shape of the test query is the same for each of the three test tables: SELECT TOP (150) T.id, T.padding FROM dbo.Test AS T ORDER BY NEWID() OPTION (MAXDOP 1) ; Test 1 – CHAR(3999) Running the template query shown above using the TestCHAR table as the target, we find that the query takes around 5 seconds to return its results.  This seems slow, considering that the table only has 50,000 rows.  Working on the assumption that generating a GUID for each row is a CPU-intensive operation, we might try enabling parallelism to see if that speeds up the response time.  Running the query again (but without the MAXDOP 1 hint) on a machine with eight logical processors, the query now takes 10 seconds to execute – twice as long as when run serially. Rather than attempting further guesses at the cause of the slowness, let’s go back to serial execution and add some monitoring.  The script below monitors STATISTICS IO output and the amount of tempdb used by the test query.  We will also run a Profiler trace to capture any warnings generated during query execution. DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TC.id, TC.padding FROM dbo.TestCHAR AS TC ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; Let’s take a closer look at the statistics and query plan generated from this: Following the flow of the data from right to left, we see the expected 50,000 rows emerging from the Clustered Index Scan, with a total estimated size of around 191MB.  The Compute Scalar adds a column containing a random GUID (generated from the NEWID() function call) for each row.  With this extra column in place, the size of the data arriving at the Sort operator is estimated to be 192MB. Sort is a blocking operator – it has to examine all of the rows on its input before it can produce its first row of output (the last row received might sort first).  This characteristic means that Sort requires a memory grant – memory allocated for the query’s use by SQL Server just before execution starts.  In this case, the Sort is the only memory-consuming operator in the plan, so it has access to the full 243MB (248,696KB) of memory reserved by SQL Server for this query execution. Notice that the memory grant is significantly larger than the expected size of the data to be sorted.  SQL Server uses a number of techniques to speed up sorting, some of which sacrifice size for comparison speed.  Sorts typically require a very large number of comparisons, so this is usually a very effective optimization.  One of the drawbacks is that it is not possible to exactly predict the sort space needed, as it depends on the data itself.  SQL Server takes an educated guess based on data types, sizes, and the number of rows expected, but the algorithm is not perfect. In spite of the large memory grant, the Profiler trace shows a Sort Warning event (indicating that the sort ran out of memory), and the tempdb usage monitor shows that 195MB of tempdb space was used – all of that for system use.  The 195MB represents physical write activity on tempdb, because SQL Server strictly enforces memory grants – a query cannot ‘cheat’ and effectively gain extra memory by spilling to tempdb pages that reside in memory.  Anyway, the key point here is that it takes a while to write 195MB to disk, and this is the main reason that the query takes 5 seconds overall. If you are wondering why using parallelism made the problem worse, consider that eight threads of execution result in eight concurrent partial sorts, each receiving one eighth of the memory grant.  The eight sorts all spilled to tempdb, resulting in inefficiencies as the spilled sorts competed for disk resources.  More importantly, there are specific problems at the point where the eight partial results are combined, but I’ll cover that in a future post. CHAR(3999) Performance Summary: 5 seconds elapsed time 243MB memory grant 195MB tempdb usage 192MB estimated sort set 25,043 logical reads Sort Warning Test 2 – VARCHAR(MAX) We’ll now run exactly the same test (with the additional monitoring) on the table using a VARCHAR(MAX) padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TM.id, TM.padding FROM dbo.TestMAX AS TM ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query takes around 8 seconds to complete (3 seconds longer than Test 1).  Notice that the estimated row and data sizes are very slightly larger, and the overall memory grant has also increased very slightly to 245MB.  The most marked difference is in the amount of tempdb space used – this query wrote almost 391MB of sort run data to the physical tempdb file.  Don’t draw any general conclusions about VARCHAR(MAX) versus CHAR from this – I chose the length of the data specifically to expose this edge case.  In most cases, VARCHAR(MAX) performs very similarly to CHAR – I just wanted to make test 2 a bit more exciting. MAX Performance Summary: 8 seconds elapsed time 245MB memory grant 391MB tempdb usage 193MB estimated sort set 25,043 logical reads Sort warning Test 3 – TEXT The same test again, but using the deprecated TEXT data type for the padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TT.id, TT.padding FROM dbo.TestTEXT AS TT ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query runs in 500ms.  If you look at the metrics we have been checking so far, it’s not hard to understand why: TEXT Performance Summary: 0.5 seconds elapsed time 9MB memory grant 5MB tempdb usage 5MB estimated sort set 207 logical reads 596 LOB logical reads Sort warning SQL Server’s memory grant algorithm still underestimates the memory needed to perform the sorting operation, but the size of the data to sort is so much smaller (5MB versus 193MB previously) that the spilled sort doesn’t matter very much.  Why is the data size so much smaller?  The query still produces the correct results – including the large amount of data held in the padding column – so what magic is being performed here? TEXT versus MAX Storage The answer lies in how columns of the TEXT data type are stored.  By default, TEXT data is stored off-row in separate LOB pages – which explains why this is the first query we have seen that records LOB logical reads in its STATISTICS IO output.  You may recall from my last post that LOB data leaves an in-row pointer to the separate storage structure holding the LOB data. SQL Server can see that the full LOB value is not required by the query plan until results are returned, so instead of passing the full LOB value down the plan from the Clustered Index Scan, it passes the small in-row structure instead.  SQL Server estimates that each row coming from the scan will be 79 bytes long – 11 bytes for row overhead, 4 bytes for the integer id column, and 64 bytes for the LOB pointer (in fact the pointer is rather smaller – usually 16 bytes – but the details of that don’t really matter right now). OK, so this query is much more efficient because it is sorting a very much smaller data set – SQL Server delays retrieving the LOB data itself until after the Sort starts producing its 150 rows.  The question that normally arises at this point is: Why doesn’t SQL Server use the same trick when the padding column is defined as VARCHAR(MAX)? The answer is connected with the fact that if the actual size of the VARCHAR(MAX) data is 8000 bytes or less, it is usually stored in-row in exactly the same way as for a VARCHAR(8000) column – MAX data only moves off-row into LOB storage when it exceeds 8000 bytes.  The default behaviour of the TEXT type is to be stored off-row by default, unless the ‘text in row’ table option is set suitably and there is room on the page.  There is an analogous (but opposite) setting to control the storage of MAX data – the ‘large value types out of row’ table option.  By enabling this option for a table, MAX data will be stored off-row (in a LOB structure) instead of in-row.  SQL Server Books Online has good coverage of both options in the topic In Row Data. The MAXOOR Table The essential difference, then, is that MAX defaults to in-row storage, and TEXT defaults to off-row (LOB) storage.  You might be thinking that we could get the same benefits seen for the TEXT data type by storing the VARCHAR(MAX) values off row – so let’s look at that option now.  This script creates a fourth table, with the VARCHAR(MAX) data stored off-row in LOB pages: CREATE TABLE dbo.TestMAXOOR ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAXOOR (id)] PRIMARY KEY CLUSTERED (id), ) ; EXECUTE sys.sp_tableoption @TableNamePattern = N'dbo.TestMAXOOR', @OptionName = 'large value types out of row', @OptionValue = 'true' ; SELECT large_value_types_out_of_row FROM sys.tables WHERE [schema_id] = SCHEMA_ID(N'dbo') AND name = N'TestMAXOOR' ; INSERT INTO dbo.TestMAXOOR WITH (TABLOCKX) ( padding ) SELECT SPACE(0) FROM dbo.TestCHAR ORDER BY id ; UPDATE TM WITH (TABLOCK) SET padding.WRITE (TC.padding, NULL, NULL) FROM dbo.TestMAXOOR AS TM JOIN dbo.TestCHAR AS TC ON TC.id = TM.id ; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAXOOR' ; CHECKPOINT ; Test 4 – MAXOOR We can now re-run our test on the MAXOOR (MAX out of row) table: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) MO.id, MO.padding FROM dbo.TestMAXOOR AS MO ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; TEXT Performance Summary: 0.3 seconds elapsed time 245MB memory grant 0MB tempdb usage 193MB estimated sort set 207 logical reads 446 LOB logical reads No sort warning The query runs very quickly – slightly faster than Test 3, and without spilling the sort to tempdb (there is no sort warning in the trace, and the monitoring query shows zero tempdb usage by this query).  SQL Server is passing the in-row pointer structure down the plan and only looking up the LOB value on the output side of the sort. The Hidden Problem There is still a huge problem with this query though – it requires a 245MB memory grant.  No wonder the sort doesn’t spill to tempdb now – 245MB is about 20 times more memory than this query actually requires to sort 50,000 records containing LOB data pointers.  Notice that the estimated row and data sizes in the plan are the same as in test 2 (where the MAX data was stored in-row). The optimizer assumes that MAX data is stored in-row, regardless of the sp_tableoption setting ‘large value types out of row’.  Why?  Because this option is dynamic – changing it does not immediately force all MAX data in the table in-row or off-row, only when data is added or actually changed.  SQL Server does not keep statistics to show how much MAX or TEXT data is currently in-row, and how much is stored in LOB pages.  This is an annoying limitation, and one which I hope will be addressed in a future version of the product. So why should we worry about this?  Excessive memory grants reduce concurrency and may result in queries waiting on the RESOURCE_SEMAPHORE wait type while they wait for memory they do not need.  245MB is an awful lot of memory, especially on 32-bit versions where memory grants cannot use AWE-mapped memory.  Even on a 64-bit server with plenty of memory, do you really want a single query to consume 0.25GB of memory unnecessarily?  That’s 32,000 8KB pages that might be put to much better use. The Solution The answer is not to use the TEXT data type for the padding column.  That solution happens to have better performance characteristics for this specific query, but it still results in a spilled sort, and it is hard to recommend the use of a data type which is scheduled for removal.  I hope it is clear to you that the fundamental problem here is that SQL Server sorts the whole set arriving at a Sort operator.  Clearly, it is not efficient to sort the whole table in memory just to return 150 rows in a random order. The TEXT example was more efficient because it dramatically reduced the size of the set that needed to be sorted.  We can do the same thing by selecting 150 unique keys from the table at random (sorting by NEWID() for example) and only then retrieving the large padding column values for just the 150 rows we need.  The following script implements that idea for all four tables: SET STATISTICS IO ON ; WITH TestTable AS ( SELECT * FROM dbo.TestCHAR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id = ANY (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAX ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestTEXT ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAXOOR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; All four queries now return results in much less than a second, with memory grants between 6 and 12MB, and without spilling to tempdb.  The small remaining inefficiency is in reading the id column values from the clustered primary key index.  As a clustered index, it contains all the in-row data at its leaf.  The CHAR and VARCHAR(MAX) tables store the padding column in-row, so id values are separated by a 3999-character column, plus row overhead.  The TEXT and MAXOOR tables store the padding values off-row, so id values in the clustered index leaf are separated by the much-smaller off-row pointer structure.  This difference is reflected in the number of logical page reads performed by the four queries: Table 'TestCHAR' logical reads 25511 lob logical reads 000 Table 'TestMAX'. logical reads 25511 lob logical reads 000 Table 'TestTEXT' logical reads 00412 lob logical reads 597 Table 'TestMAXOOR' logical reads 00413 lob logical reads 446 We can increase the density of the id values by creating a separate nonclustered index on the id column only.  This is the same key as the clustered index, of course, but the nonclustered index will not include the rest of the in-row column data. CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestCHAR (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAX (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestTEXT (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAXOOR (id); The four queries can now use the very dense nonclustered index to quickly scan the id values, sort them by NEWID(), select the 150 ids we want, and then look up the padding data.  The logical reads with the new indexes in place are: Table 'TestCHAR' logical reads 835 lob logical reads 0 Table 'TestMAX' logical reads 835 lob logical reads 0 Table 'TestTEXT' logical reads 686 lob logical reads 597 Table 'TestMAXOOR' logical reads 686 lob logical reads 448 With the new index, all four queries use the same query plan (click to enlarge): Performance Summary: 0.3 seconds elapsed time 6MB memory grant 0MB tempdb usage 1MB sort set 835 logical reads (CHAR, MAX) 686 logical reads (TEXT, MAXOOR) 597 LOB logical reads (TEXT) 448 LOB logical reads (MAXOOR) No sort warning I’ll leave it as an exercise for the reader to work out why trying to eliminate the Key Lookup by adding the padding column to the new nonclustered indexes would be a daft idea Conclusion This post is not about tuning queries that access columns containing big strings.  It isn’t about the internal differences between TEXT and MAX data types either.  It isn’t even about the cool use of UPDATE .WRITE used in the MAXOOR table load.  No, this post is about something else: Many developers might not have tuned our starting example query at all – 5 seconds isn’t that bad, and the original query plan looks reasonable at first glance.  Perhaps the NEWID() function would have been blamed for ‘just being slow’ – who knows.  5 seconds isn’t awful – unless your users expect sub-second responses – but using 250MB of memory and writing 200MB to tempdb certainly is!  If ten sessions ran that query at the same time in production that’s 2.5GB of memory usage and 2GB hitting tempdb.  Of course, not all queries can be rewritten to avoid large memory grants and sort spills using the key-lookup technique in this post, but that’s not the point either. The point of this post is that a basic understanding of execution plans is not enough.  Tuning for logical reads and adding covering indexes is not enough.  If you want to produce high-quality, scalable TSQL that won’t get you paged as soon as it hits production, you need a deep understanding of execution plans, and as much accurate, deep knowledge about SQL Server as you can lay your hands on.  The advanced database developer has a wide range of tools to use in writing queries that perform well in a range of circumstances. By the way, the examples in this post were written for SQL Server 2008.  They will run on 2005 and demonstrate the same principles, but you won’t get the same figures I did because 2005 had a rather nasty bug in the Top N Sort operator.  Fair warning: if you do decide to run the scripts on a 2005 instance (particularly the parallel query) do it before you head out for lunch… This post is dedicated to the people of Christchurch, New Zealand. © 2011 Paul White email: @[email protected] twitter: @SQL_Kiwi

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  • How To - Guide to Importing Data from a MySQL Database to Excel using MySQL for Excel

    - by Javier Treviño
    Fetching data from a database to then get it into an Excel spreadsheet to do analysis, reporting, transforming, sharing, etc. is a very common task among users. There are several ways to extract data from a MySQL database to then import it to Excel; for example you can use the MySQL Connector/ODBC to configure an ODBC connection to a MySQL database, then in Excel use the Data Connection Wizard to select the database and table from which you want to extract data from, then specify what worksheet you want to put the data into.  Another way is to somehow dump a comma delimited text file with the data from a MySQL table (using the MySQL Command Line Client, MySQL Workbench, etc.) to then in Excel open the file using the Text Import Wizard to attempt to correctly split the data in columns. These methods are fine, but involve some degree of technical knowledge to make the magic happen and involve repeating several steps each time data needs to be imported from a MySQL table to an Excel spreadsheet. So, can this be done in an easier and faster way? With MySQL for Excel you can. MySQL for Excel features an Import MySQL Data action where you can import data from a MySQL Table, View or Stored Procedure literally with a few clicks within Excel.  Following is a quick guide describing how to import data using MySQL for Excel. This guide assumes you already have a working MySQL Server instance, Microsoft Office Excel 2007 or 2010 and MySQL for Excel installed. 1. Opening MySQL for Excel Being an Excel Add-In, MySQL for Excel is opened from within Excel, so to use it open Excel, go to the Data tab located in the Ribbon and click MySQL for Excel at the far right of the Ribbon. 2. Creating a MySQL Connection (may be optional) If you have MySQL Workbench installed you will automatically see the same connections that you can see in MySQL Workbench, so you can use any of those and there may be no need to create a new connection. If you want to create a new connection (which normally you will do only once), in the Welcome Panel click New Connection, which opens the Setup New Connection dialog. Here you only need to give your new connection a distinctive Connection Name, specify the Hostname (or IP address) where the MySQL Server instance is running on (if different than localhost), the Port to connect to and the Username for the login. If you wish to test if your setup is good to go, click Test Connection and an information dialog will pop-up stating if the connection is successful or errors were found. 3.Opening a connection to a MySQL Server To open a pre-configured connection to a MySQL Server you just need to double-click it, so the Connection Password dialog is displayed where you enter the password for the login. 4. Selecting a MySQL Schema After opening a connection to a MySQL Server, the Schema Selection Panel is shown, where you can select the Schema that contains the Tables, Views and Stored Procedures you want to work with. To do so, you just need to either double-click the desired Schema or select it and click Next >. 5. Importing data… All previous steps were really the basic minimum needed to drill-down to the DB Object Selection Panel  where you can see the Database Objects (grouped by type: Tables, Views and Procedures in that order) that you want to perform actions against; in the case of this guide, the action of importing data from them. a. From a MySQL Table To import from a Table you just need to select it from the list of Database Objects’ Tables group, after selecting it you will note actions below the list become available; then click Import MySQL Data. The Import Data dialog is displayed; you can see some basic information here like the name of the Excel worksheet the data will be imported to (in the window title), the Table Name, the total Row Count and a 10 row preview of the data meant for the user to see the columns that the table contains and to provide a way to select which columns to import. The Import Data dialog is designed with defaults in place so all data is imported (all rows and all columns) by just clicking Import; this is important to minimize the number of clicks needed to get the job done. After the import is performed you will have the data in the Excel worksheet formatted automatically. If you need to override the defaults in the Import Data dialog to change the columns selected for import or to change the number of imported rows you can easily do so before clicking Import. In the screenshot below the defaults are overridden to import only the first 3 columns and rows 10 – 60 (Limit to 50 Rows and Start with Row 10). If the number of rows to be imported exceeds the maximum number of rows Excel can hold in its worksheet, a warning will be displayed in the dialog, meaning the imported number of rows will be limited by that maximum number (65,535 rows if the worksheet is in Compatibility Mode).  In the screenshot below you can see the Table contains 80,559 rows, but only 65,534 rows will be imported since the first row is used for the column names if the Include Column Names as Headers checkbox is checked. b. From a MySQL View Similar to the way of importing from a Table, to import from a View you just need to select it from the list of Database Objects’ Views group, then click Import MySQL Data. The Import Data dialog is displayed; identically to the way everything looks when importing from a table, the dialog displays the View Name, the total Row Count and the data preview grid. Since Views are really a filtered way to display data from Tables, it is actually as if we are extracting data from a Table; so the Import Data dialog is actually identical for those 2 Database Objects. After the import is performed, the data in the Excel spreadsheet looks like the following screenshot. Note that you can override the defaults in the Import Data dialog in the same way described above for importing data from Tables. Also the Compatibility Mode warning will be displayed if data exceeds the maximum number of rows explained before. c. From a MySQL Procedure Too import from a Procedure you just need to select it from the list of Database Objects’ Procedures group (note you can see Procedures here but not Functions since these return a single value, so by design they are filtered out). After the selection is made, click Import MySQL Data. The Import Data dialog is displayed, but this time you can see it looks different to the one used for Tables and Views.  Given the nature of Store Procedures, they require first that values are supplied for its Parameters and also Procedures can return multiple Result Sets; so the Import Data dialog shows the Procedure Name and the Procedure Parameters in a grid where their values are input. After you supply the Parameter Values click Call. After calling the Procedure, the Result Sets returned by it are displayed at the bottom of the dialog; output parameters and the return value of the Procedure are appended as the last Result Set of the group. You can see each Result Set is displayed as a tab so you can see a preview of the returned data.  You can specify if you want to import the Selected Result Set (default), All Result Sets – Arranged Horizontally or All Result Sets – Arranged Vertically using the Import drop-down list; then click Import. After the import is performed, the data in the Excel spreadsheet looks like the following screenshot.  Note in this example all Result Sets were imported and arranged vertically. As you can see using MySQL for Excel importing data from a MySQL database becomes an easy task that requires very little technical knowledge, so it can be done by any type of user. Hope you enjoyed this guide! Remember that your feedback is very important for us, so drop us a message: MySQL on Windows (this) Blog - https://blogs.oracle.com/MySqlOnWindows/ Forum - http://forums.mysql.com/list.php?172 Facebook - http://www.facebook.com/mysql Cheers!

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  • How John Got 15x Improvement Without Really Trying

    - by rchrd
    The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here.  How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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  • SQL SERVER – SSMS: Disk Usage Report

    - by Pinal Dave
    Let us start with humor!  I think we the series on various reports, we come to a logical point. We covered all the reports at server level. This means the reports we saw were targeted towards activities that are related to instance level operations. These are mostly like how a doctor diagnoses a patient. At this point I am reminded of a dialog which I read somewhere: Patient: Doc, It hurts when I touch my head. Doc: Ok, go on. What else have you experienced? Patient: It hurts even when I touch my eye, it hurts when I touch my arms, it even hurts when I touch my feet, etc. Doc: Hmmm … Patient: I feel it hurts when I touch anywhere in my body. Doc: Ahh … now I get it. You need a plaster to your finger John. Sometimes the server level gives an indicator to what is happening in the system, but we need to get to the root cause for a specific database. So, this is the first blog in series where we would start discussing about database level reports. To launch database level reports, expand selected server in Object Explorer, expand the Databases folder, and then right-click any database for which we want to look at reports. From the menu, select Reports, then Standard Reports, and then any of database level reports. In this blog, we would talk about four “disk” reports because they are similar: Disk Usage Disk Usage by Top Tables Disk Usage by Table Disk Usage by Partition Disk Usage This report shows multiple information about the database. Let us discuss them one by one.  We have divided the output into 5 different sections. Section 1 shows the high level summary of the database. It shows the space used by database files (mdf and ldf). Under the hood, the report uses, various DMVs and DBCC Commands, it is using sys.data_spaces and DBCC SHOWFILESTATS. Section 2 and 3 are pie charts. One for data file allocation and another for the transaction log file. Pie chart for “Data Files Space Usage (%)” shows space consumed data, indexes, allocated to the SQL Server database, and unallocated space which is allocated to the SQL Server database but not yet filled with anything. “Transaction Log Space Usage (%)” used DBCC SQLPERF (LOGSPACE) and shows how much empty space we have in the physical transaction log file. Section 4 shows the data from Default Trace and looks at Event IDs 92, 93, 94, 95 which are for “Data File Auto Grow”, “Log File Auto Grow”, “Data File Auto Shrink” and “Log File Auto Shrink” respectively. Here is an expanded view for that section. If default trace is not enabled, then this section would be replaced by the message “Trace Log is disabled” as highlighted below. Section 5 of the report uses DBCC SHOWFILESTATS to get information. Here is the enhanced version of that section. This shows the physical layout of the file. In case you have In-Memory Objects in the database (from SQL Server 2014), then report would show information about those as well. Here is the screenshot taken for a different database, which has In-Memory table. I have highlighted new things which are only shown for in-memory database. The new sections which are highlighted above are using sys.dm_db_xtp_checkpoint_files, sys.database_files and sys.data_spaces. The new type for in-memory OLTP is ‘FX’ in sys.data_space. The next set of reports is targeted to get information about a table and its storage. These reports can answer questions like: Which is the biggest table in the database? How many rows we have in table? Is there any table which has a lot of reserved space but its unused? Which partition of the table is having more data? Disk Usage by Top Tables This report provides detailed data on the utilization of disk space by top 1000 tables within the Database. The report does not provide data for memory optimized tables. Disk Usage by Table This report is same as earlier report with few difference. First Report shows only 1000 rows First Report does order by values in DMV sys.dm_db_partition_stats whereas second one does it based on name of the table. Both of the reports have interactive sort facility. We can click on any column header and change the sorting order of data. Disk Usage by Partition This report shows the distribution of the data in table based on partition in the table. This is so similar to previous output with the partition details now. Here is the query taken from profiler. SELECT row_number() OVER (ORDER BY a1.used_page_count DESC, a1.index_id) AS row_number ,      (dense_rank() OVER (ORDER BY a5.name, a2.name))%2 AS l1 ,      a1.OBJECT_ID ,      a5.name AS [schema] ,       a2.name ,       a1.index_id ,       a3.name AS index_name ,       a3.type_desc ,       a1.partition_number ,       a1.used_page_count * 8 AS total_used_pages ,       a1.reserved_page_count * 8 AS total_reserved_pages ,       a1.row_count FROM sys.dm_db_partition_stats a1 INNER JOIN sys.all_objects a2  ON ( a1.OBJECT_ID = a2.OBJECT_ID) AND a1.OBJECT_ID NOT IN (SELECT OBJECT_ID FROM sys.tables WHERE is_memory_optimized = 1) INNER JOIN sys.schemas a5 ON (a5.schema_id = a2.schema_id) LEFT OUTER JOIN  sys.indexes a3  ON ( (a1.OBJECT_ID = a3.OBJECT_ID) AND (a1.index_id = a3.index_id) ) WHERE (SELECT MAX(DISTINCT partition_number) FROM sys.dm_db_partition_stats a4 WHERE (a4.OBJECT_ID = a1.OBJECT_ID)) >= 1 AND a2.TYPE <> N'S' AND  a2.TYPE <> N'IT' ORDER BY a5.name ASC, a2.name ASC, a1.index_id, a1.used_page_count DESC, a1.partition_number Using all of the above reports, you should be able to get the usage of database files and also space used by tables. I think this is too much disk information for a single blog and I hope you have used them in the past to get data. Do let me know if you found anything interesting using these reports in your environments. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

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  • SQL SERVER – Faster SQL Server Databases and Applications – Power and Control with SafePeak Caching Options

    - by Pinal Dave
    Update: This blog post is written based on the SafePeak, which is available for free download. Today, I’d like to examine more closely one of my preferred technologies for accelerating SQL Server databases, SafePeak. Safepeak’s software provides a variety of advanced data caching options, techniques and tools to accelerate the performance and scalability of SQL Server databases and applications. I’d like to look more closely at some of these options, as some of these capabilities could help you address lagging database and performance on your systems. To better understand the available options, it is best to start by understanding the difference between the usual “Basic Caching” vs. SafePeak’s “Dynamic Caching”. Basic Caching Basic Caching (or the stale and static cache) is an ability to put the results from a query into cache for a certain period of time. It is based on TTL, or Time-to-live, and is designed to stay in cache no matter what happens to the data. For example, although the actual data can be modified due to DML commands (update/insert/delete), the cache will still hold the same obsolete query data. Meaning that with the Basic Caching is really static / stale cache.  As you can tell, this approach has its limitations. Dynamic Caching Dynamic Caching (or the non-stale cache) is an ability to put the results from a query into cache while maintaining the cache transaction awareness looking for possible data modifications. The modifications can come as a result of: DML commands (update/insert/delete), indirect modifications due to triggers on other tables, executions of stored procedures with internal DML commands complex cases of stored procedures with multiple levels of internal stored procedures logic. When data modification commands arrive, the caching system identifies the related cache items and evicts them from cache immediately. In the dynamic caching option the TTL setting still exists, although its importance is reduced, since the main factor for cache invalidation (or cache eviction) become the actual data updates commands. Now that we have a basic understanding of the differences between “basic” and “dynamic” caching, let’s dive in deeper. SafePeak: A comprehensive and versatile caching platform SafePeak comes with a wide range of caching options. Some of SafePeak’s caching options are automated, while others require manual configuration. Together they provide a complete solution for IT and Data managers to reach excellent performance acceleration and application scalability for  a wide range of business cases and applications. Automated caching of SQL Queries: Fully/semi-automated caching of all “read” SQL queries, containing any types of data, including Blobs, XMLs, Texts as well as all other standard data types. SafePeak automatically analyzes the incoming queries, categorizes them into SQL Patterns, identifying directly and indirectly accessed tables, views, functions and stored procedures; Automated caching of Stored Procedures: Fully or semi-automated caching of all read” stored procedures, including procedures with complex sub-procedure logic as well as procedures with complex dynamic SQL code. All procedures are analyzed in advance by SafePeak’s  Metadata-Learning process, their SQL schemas are parsed – resulting with a full understanding of the underlying code, objects dependencies (tables, views, functions, sub-procedures) enabling automated or semi-automated (manually review and activate by a mouse-click) cache activation, with full understanding of the transaction logic for cache real-time invalidation; Transaction aware cache: Automated cache awareness for SQL transactions (SQL and in-procs); Dynamic SQL Caching: Procedures with dynamic SQL are pre-parsed, enabling easy cache configuration, eliminating SQL Server load for parsing time and delivering high response time value even in most complicated use-cases; Fully Automated Caching: SQL Patterns (including SQL queries and stored procedures) that are categorized by SafePeak as “read and deterministic” are automatically activated for caching; Semi-Automated Caching: SQL Patterns categorized as “Read and Non deterministic” are patterns of SQL queries and stored procedures that contain reference to non-deterministic functions, like getdate(). Such SQL Patterns are reviewed by the SafePeak administrator and in usually most of them are activated manually for caching (point and click activation); Fully Dynamic Caching: Automated detection of all dependent tables in each SQL Pattern, with automated real-time eviction of the relevant cache items in the event of “write” commands (a DML or a stored procedure) to one of relevant tables. A default setting; Semi Dynamic Caching: A manual cache configuration option enabling reducing the sensitivity of specific SQL Patterns to “write” commands to certain tables/views. An optimization technique relevant for cases when the query data is either known to be static (like archive order details), or when the application sensitivity to fresh data is not critical and can be stale for short period of time (gaining better performance and reduced load); Scheduled Cache Eviction: A manual cache configuration option enabling scheduling SQL Pattern cache eviction based on certain time(s) during a day. A very useful optimization technique when (for example) certain SQL Patterns can be cached but are time sensitive. Example: “select customers that today is their birthday”, an SQL with getdate() function, which can and should be cached, but the data stays relevant only until 00:00 (midnight); Parsing Exceptions Management: Stored procedures that were not fully parsed by SafePeak (due to too complex dynamic SQL or unfamiliar syntax), are signed as “Dynamic Objects” with highest transaction safety settings (such as: Full global cache eviction, DDL Check = lock cache and check for schema changes, and more). The SafePeak solution points the user to the Dynamic Objects that are important for cache effectiveness, provides easy configuration interface, allowing you to improve cache hits and reduce cache global evictions. Usually this is the first configuration in a deployment; Overriding Settings of Stored Procedures: Override the settings of stored procedures (or other object types) for cache optimization. For example, in case a stored procedure SP1 has an “insert” into table T1, it will not be allowed to be cached. However, it is possible that T1 is just a “logging or instrumentation” table left by developers. By overriding the settings a user can allow caching of the problematic stored procedure; Advanced Cache Warm-Up: Creating an XML-based list of queries and stored procedure (with lists of parameters) for periodically automated pre-fetching and caching. An advanced tool allowing you to handle more rare but very performance sensitive queries pre-fetch them into cache allowing high performance for users’ data access; Configuration Driven by Deep SQL Analytics: All SQL queries are continuously logged and analyzed, providing users with deep SQL Analytics and Performance Monitoring. Reduce troubleshooting from days to minutes with database objects and SQL Patterns heat-map. The performance driven configuration helps you to focus on the most important settings that bring you the highest performance gains. Use of SafePeak SQL Analytics allows continuous performance monitoring and analysis, easy identification of bottlenecks of both real-time and historical data; Cloud Ready: Available for instant deployment on Amazon Web Services (AWS). As you can see, there are many options to configure SafePeak’s SQL Server database and application acceleration caching technology to best fit a lot of situations. If you’re not familiar with their technology, they offer free-trial software you can download that comes with a free “help session” to help get you started. You can access the free trial here. Also, SafePeak is available to use on Amazon Cloud. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Voxel terrain rendering with marching cubes

    - by JavaJosh94
    I was working on making procedurally generated terrain using normal cubish voxels (like minecraft) But then I read about marching cubes and decided to convert to using those. I managed to create a working marching cubes class and cycle through the densities and everything in it seemed to be working so I went on to work on actual terrain generation. I'm using XNA (C#) and a ported libnoise library to generate noise for the terrain generator. But instead of rendering smooth terrain I get a 64x64 chunk (I specified 64 but can change it) of seemingly random marching cubes using different triangles. This is the code I'm using to generate a "chunk": public MarchingCube[, ,] getTerrainChunk(int size, float dMultiplyer, int stepsize) { MarchingCube[, ,] temp = new MarchingCube[size / stepsize, size / stepsize, size / stepsize]; for (int x = 0; x < size; x += stepsize) { for (int y = 0; y <size; y += stepsize) { for (int z = 0; z < size; z += stepsize) { float[] densities = {(float)terrain.GetValue(x, y, z)*dMultiplyer, (float)terrain.GetValue(x, y+stepsize, z)*dMultiplyer, (float)terrain.GetValue(x+stepsize, y+stepsize, z)*dMultiplyer, (float)terrain.GetValue(x+stepsize, y, z)*dMultiplyer, (float)terrain.GetValue(x,y,z+stepsize)*dMultiplyer,(float)terrain.GetValue(x,y+stepsize,z+stepsize)*dMultiplyer,(float)terrain.GetValue(x+stepsize,y+stepsize,z+stepsize)*dMultiplyer,(float)terrain.GetValue(x+stepsize,y,z+stepsize)*dMultiplyer }; Vector3[] corners = { new Vector3(x,y,z), new Vector3(x,y+stepsize,z),new Vector3(x+stepsize,y+stepsize,z),new Vector3(x+stepsize,y,z), new Vector3(x,y,z+stepsize), new Vector3(x,y+stepsize,z+stepsize), new Vector3(x+stepsize,y+stepsize,z+stepsize), new Vector3(x+stepsize,y,z+stepsize)}; if (x == 0 && y == 0 && z == 0) { temp[x / stepsize, y / stepsize, z / stepsize] = new MarchingCube(densities, corners, device); } temp[x / stepsize, y / stepsize, z / stepsize] = new MarchingCube(densities, corners); } } } (terrain is a Perlin Noise generated using libnoise) I'm sure there's probably an easy solution to this but I've been drawing a blank for the past hour. I'm just wondering if the problem is how I'm reading in the data from the noise or if I may be generating the noise wrong? Or maybe the noise is just not good for this kind of generation? If I'm reading it wrong does anyone know the right way? the answers on google were somewhat ambiguous but I'm going to keep searching. Thanks in advance!

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  • Rip and Convert DVD’s to an ISO Image

    - by Mysticgeek
    If you own a lot of DVD’s, you might want to convert them to an ISO image for backup and easily playing them on your media center. Today we take a look at ripping your discs using DVDFab, then using ImgBurn to create an ISO image of the ripped DVD files. Rip DVD with DVDFab6 DVDFab will remove copy protection and rip the DVD files for free. Other components in the suite require you to purchase a license after the 30 day trial, but you’ll still be able to rip DVD’s after the trial. Install DVDFab by accepting the defaults (link below)…a system restart is required to complete the install process. The first time you run it, a welcome screen is displayed. If you don’t want to see it again check the box Do not show again, then Start DVDFab.  Pop the DVD in your drive and click Next. Now select your region and check Do not show again, then OK. It will then open the DVD and begin to scan it. Under DVD to DVD you can select either Full Disc or Main Movie depending on what you want to rip. If you want to burn the DVD to a disc after it’s created select the Full Disc option. Now click the Start button to begin the ripping process. After the ripping process has completed, you’ll get a message telling you it’s waiting for you to put in a blank DVD. Since we aren’t burning the disc, just cancel the message. Click Finish and close out of DVDFab or just minimize it if you’re going to keep using it to rip another DVD. By default the temporary directory is in My Documents \ DVDFab \ Temp…however you can change it in settings. If you go to the Temp directory you’ll see the DVD files listed there… Convert Files to ISO with ImgBurn Now that we have the files ripped from the DVD, we need to convert them to an ISO image using ImgBurn (link below). Open it up and from the main menu click on Create image file from files/folders. Click on the folder icon to browse to the location of the ripped DVD files. Browse to the DVDFab temp directory and the VIDEO_TS folder for the source and click Ok. Then choose a destination directory, give the ISO a name, and click Save. In this case we ripped the Unbreakable DVD, so named it that.   So now in ImgBurn you have the source being the ripped DVD files, and the destination for the ISO…then click the Build button. If you don’t create a volume label, ImgBurn is kind enough to create on for you. If everything looks correct, click Ok. Now wait while ImgBurn goes through the process of converting the ripped DVD files to an ISO image. The process has successfully completed. The ISO image of the DVD will be in the output directory you selected earlier. Now you can burn the ISO image to a blank DVD or store it on an external hard drive for safe keeping. When you’re done, you’ll probably want to go into the temp DVDFab folder and delete the VOB and other files in the Video_TS folder as they will take up a lot of space on your hard drive.   Conclusion Although this method requires two programs to make an ISO out of a DVD, it’s extremely quick. When burning DVD’s of various lengths, it took less than 30 minutes to get the final ISO. Now, you’ll have your DVD movies backed up in case something were to happen to the discs and are no longer playable. If you use Windows Media Center to watch your movies, check out our article on how to automatically mount and view ISO files in Windows 7 Media Center. With DVDFab, you get a 30 day fully functional trial for all of its features. You’ll still be able rip DVD’s even after the 30 day trial has ended. The more we’ve been using DVDFab, the more impressed we are with its capabilities, so after the 30 day trial you should consider purchasing a license. We will have a full review of the of it to share with you soon.  Download DVDFab Download ImgBurn Similar Articles Productive Geek Tips How To Rip DVDs with VLCCalculate with Qalculate on LinuxConvert a Row to a Column in Excel the Easy WayEnjoy Quick & Easy Unit Conversion with Convert for WindowsConvert Older Excel Documents to Excel 2007 Format TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 PCmover Professional Beware Hover Kitties Test Drive Mobile Phones Online With TryPhone Ben & Jerry’s Free Cone Day, 3/23/10 New Stinger from McAfee Helps Remove ‘FakeAlert’ Threats Google Apps Marketplace: Tools & Services For Google Apps Users Get News Quick and Precise With Newser

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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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  • Oracle Database 12 c New Partition Maintenance Features by Gwen Lazenby

    - by hamsun
    One of my favourite new features in Oracle Database 12c is the ability to perform partition maintenance operations on multiple partitions. This means we can now add, drop, truncate and merge multiple partitions in one operation, and can split a single partition into more than two partitions also in just one command. This would certainly have made my life slightly easier had it been available when I administered a data warehouse at Oracle 9i. To demonstrate this new functionality and syntax, I am going to create two tables, ORDERS and ORDERS_ITEMS which have a parent-child relationship. ORDERS is to be partitioned using range partitioning on the ORDER_DATE column, and ORDER_ITEMS is going to partitioned using reference partitioning and its foreign key relationship with the ORDERS table. This form of partitioning was a new feature in 11g and means that any partition maintenance operations performed on the ORDERS table will also take place on the ORDER_ITEMS table as well. First create the ORDERS table - SQL CREATE TABLE orders ( order_id NUMBER(12), order_date TIMESTAMP, order_mode VARCHAR2(8), customer_id NUMBER(6), order_status NUMBER(2), order_total NUMBER(8,2), sales_rep_id NUMBER(6), promotion_id NUMBER(6), CONSTRAINT orders_pk PRIMARY KEY(order_id) ) PARTITION BY RANGE(order_date) (PARTITION Q1_2007 VALUES LESS THAN (TO_DATE('01-APR-2007','DD-MON-YYYY')), PARTITION Q2_2007 VALUES LESS THAN (TO_DATE('01-JUL-2007','DD-MON-YYYY')), PARTITION Q3_2007 VALUES LESS THAN (TO_DATE('01-OCT-2007','DD-MON-YYYY')), PARTITION Q4_2007 VALUES LESS THAN (TO_DATE('01-JAN-2008','DD-MON-YYYY')) ); Table created. Now the ORDER_ITEMS table SQL CREATE TABLE order_items ( order_id NUMBER(12) NOT NULL, line_item_id NUMBER(3) NOT NULL, product_id NUMBER(6) NOT NULL, unit_price NUMBER(8,2), quantity NUMBER(8), CONSTRAINT order_items_fk FOREIGN KEY(order_id) REFERENCES orders(order_id) on delete cascade) PARTITION BY REFERENCE(order_items_fk) tablespace example; Table created. Now look at DBA_TAB_PARTITIONS to get details of what partitions we have in the two tables – SQL select table_name,partition_name, partition_position position, high_value from dba_tab_partitions where table_owner='SH' and table_name like 'ORDER_%' order by partition_position, table_name; TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 Just as an aside it is also now possible in 12c to use interval partitioning on reference partitioned tables. In 11g it was not possible to combine these two new partitioning features. For our first example of the new 12cfunctionality, let us add all the partitions necessary for 2008 to the tables using one command. Notice that the partition specification part of the add command is identical in format to the partition specification part of the create command as shown above - SQL alter table orders add PARTITION Q1_2008 VALUES LESS THAN (TO_DATE('01-APR-2008','DD-MON-YYYY')), PARTITION Q2_2008 VALUES LESS THAN (TO_DATE('01-JUL-2008','DD-MON-YYYY')), PARTITION Q3_2008 VALUES LESS THAN (TO_DATE('01-OCT-2008','DD-MON-YYYY')), PARTITION Q4_2008 VALUES LESS THAN (TO_DATE('01-JAN-2009','DD-MON-YYYY')); Table altered. Now look at DBA_TAB_PARTITIONS and we can see that the 4 new partitions have been added to both tables – SQL select table_name,partition_name, partition_position position, high_value from dba_tab_partitions where table_owner='SH' and table_name like 'ORDER_%' order by partition_position, table_name; TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q1_2008 5 TIMESTAMP' 2008-04-01 00:00:00' ORDER_ITEMS Q1_2008 5 ORDERS Q2_2008 6 TIMESTAMP' 2008-07-01 00:00:00' ORDER_ITEM Q2_2008 6 ORDERS Q3_2008 7 TIMESTAMP' 2008-10-01 00:00:00' ORDER_ITEMS Q3_2008 7 ORDERS Q4_2008 8 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 8 Next, we can drop or truncate multiple partitions by giving a comma separated list in the alter table command. Note the use of the plural ‘partitions’ in the command as opposed to the singular ‘partition’ prior to 12c– SQL alter table orders drop partitions Q3_2008,Q2_2008,Q1_2008; Table altered. Now look at DBA_TAB_PARTITIONS and we can see that the 3 partitions have been dropped in both the two tables – TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q4_2008 5 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 5 Now let us merge all the 2007 partitions together to form one single partition – SQL alter table orders merge partitions Q1_2005, Q2_2005, Q3_2005, Q4_2005 into partition Y_2007; Table altered. TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Y_2007 1 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Y_2007 1 ORDERS Q4_2008 2 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 2 Splitting partitions is a slightly more involved. In the case of range partitioning one of the new partitions must have no high value defined, and in list partitioning one of the new partitions must have no list of values defined. I call these partitions the ‘everything else’ partitions, and will contain any rows contained in the original partition that are not contained in the any of the other new partitions. For example, let us split the Y_2007 partition back into 4 quarterly partitions – SQL alter table orders split partition Y_2007 into (PARTITION Q1_2007 VALUES LESS THAN (TO_DATE('01-APR-2007','DD-MON-YYYY')), PARTITION Q2_2007 VALUES LESS THAN (TO_DATE('01-JUL-2007','DD-MON-YYYY')), PARTITION Q3_2007 VALUES LESS THAN (TO_DATE('01-OCT-2007','DD-MON-YYYY')), PARTITION Q4_2007); Now look at DBA_TAB_PARTITIONS to get details of the new partitions – TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q4_2008 5 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 5 Partition Q4_2007 has a high value equal to the high value of the original Y_2007 partition, and so has inherited its upper boundary from the partition that was split. As for a list partitioning example let look at the following another table, SALES_PAR_LIST, which has 2 partitions, Americas and Europe and a partitioning key of country_name. SQL select table_name,partition_name, high_value from dba_tab_partitions where table_owner='SH' and table_name = 'SALES_PAR_LIST'; TABLE_NAME PARTITION_NAME HIGH_VALUE -------------- --------------- ----------------------------- SALES_PAR_LIST AMERICAS 'Argentina', 'Canada', 'Peru', 'USA', 'Honduras', 'Brazil', 'Nicaragua' SALES_PAR_LIST EUROPE 'France', 'Spain', 'Ireland', 'Germany', 'Belgium', 'Portugal', 'Denmark' Now split the Americas partition into 3 partitions – SQL alter table sales_par_list split partition americas into (partition south_america values ('Argentina','Peru','Brazil'), partition north_america values('Canada','USA'), partition central_america); Table altered. Note that no list of values was given for the ‘Central America’ partition. However it should have inherited any values in the original ‘Americas’ partition that were not assigned to either the ‘North America’ or ‘South America’ partitions. We can confirm this by looking at the DBA_TAB_PARTITIONS view. SQL select table_name,partition_name, high_value from dba_tab_partitions where table_owner='SH' and table_name = 'SALES_PAR_LIST'; TABLE_NAME PARTITION_NAME HIGH_VALUE --------------- --------------- -------------------------------- SALES_PAR_LIST SOUTH_AMERICA 'Argentina', 'Peru', 'Brazil' SALES_PAR_LIST NORTH_AMERICA 'Canada', 'USA' SALES_PAR_LIST CENTRAL_AMERICA 'Honduras', 'Nicaragua' SALES_PAR_LIST EUROPE 'France', 'Spain', 'Ireland', 'Germany', 'Belgium', 'Portugal', 'Denmark' In conclusion, I hope that DBA’s whose work involves maintaining partitions will find the operations a bit more straight forward to carry out once they have upgraded to Oracle Database 12c. Gwen Lazenby is a Principal Training Consultant at Oracle. She is part of Oracle University's Core Technology delivery team based in the UK, teaching Database Administration and Linux courses. Her specialist topics include using Oracle Partitioning and Parallelism in Data Warehouse environments, as well as Oracle Spatial and RMAN.

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  • Ball bouncing at a certain angle and efficiency computations

    - by X Y
    I would like to make a pong game with a small twist (for now). Every time the ball bounces off one of the paddles i want it to be under a certain angle (between a min and a max). I simply can't wrap my head around how to actually do it (i have some thoughts and such but i simply cannot implement them properly - i feel i'm overcomplicating things). Here's an image with a small explanation . One other problem would be that the conditions for bouncing have to be different for every edge. For example, in the picture, on the two small horizontal edges i do not want a perfectly vertical bounce when in the middle of the edge but rather a constant angle (pi/4 maybe) in either direction depending on the collision point (before the middle of the edge, or after). All of my collisions are done with the Separating Axes Theorem (and seem to work fine). I'm looking for something efficient because i want to add a lot of things later on (maybe polygons with many edges and such). So i need to keep to a minimum the amount of checking done every frame. The collision algorithm begins testing whenever the bounding boxes of the paddle and the ball intersect. Is there something better to test for possible collisions every frame? (more efficient in the long run,with many more objects etc, not necessarily easy to code). I'm going to post the code for my game: Paddle Class public class Paddle : Microsoft.Xna.Framework.DrawableGameComponent { #region Private Members private SpriteBatch spriteBatch; private ContentManager contentManager; private bool keybEnabled; private bool isLeftPaddle; private Texture2D paddleSprite; private Vector2 paddlePosition; private float paddleSpeedY; private Vector2 paddleScale = new Vector2(1f, 1f); private const float DEFAULT_Y_SPEED = 150; private Vector2[] Normals2Edges; private Vector2[] Vertices = new Vector2[4]; private List<Vector2> lst = new List<Vector2>(); private Vector2 Edge; #endregion #region Properties public float Speed { get {return paddleSpeedY; } set { paddleSpeedY = value; } } public Vector2[] Normal2EdgesVector { get { NormalsToEdges(this.isLeftPaddle); return Normals2Edges; } } public Vector2[] VertexVector { get { return Vertices; } } public Vector2 Scale { get { return paddleScale; } set { paddleScale = value; NormalsToEdges(this.isLeftPaddle); } } public float X { get { return paddlePosition.X; } set { paddlePosition.X = value; } } public float Y { get { return paddlePosition.Y; } set { paddlePosition.Y = value; } } public float Width { get { return (Scale.X == 1f ? (float)paddleSprite.Width : paddleSprite.Width * Scale.X); } } public float Height { get { return ( Scale.Y==1f ? (float)paddleSprite.Height : paddleSprite.Height*Scale.Y ); } } public Texture2D GetSprite { get { return paddleSprite; } } public Rectangle Boundary { get { return new Rectangle((int)paddlePosition.X, (int)paddlePosition.Y, (int)this.Width, (int)this.Height); } } public bool KeyboardEnabled { get { return keybEnabled; } } #endregion private void NormalsToEdges(bool isLeftPaddle) { Normals2Edges = null; Edge = Vector2.Zero; lst.Clear(); for (int i = 0; i < Vertices.Length; i++) { Edge = Vertices[i + 1 == Vertices.Length ? 0 : i + 1] - Vertices[i]; if (Edge != Vector2.Zero) { Edge.Normalize(); //outer normal to edge !! (origin in top-left) lst.Add(new Vector2(Edge.Y, -Edge.X)); } } Normals2Edges = lst.ToArray(); } public float[] ProjectPaddle(Vector2 axis) { if (Vertices.Length == 0 || axis == Vector2.Zero) return (new float[2] { 0, 0 }); float min, max; min = Vector2.Dot(axis, Vertices[0]); max = min; for (int i = 1; i < Vertices.Length; i++) { float p = Vector2.Dot(axis, Vertices[i]); if (p < min) min = p; else if (p > max) max = p; } return (new float[2] { min, max }); } public Paddle(Game game, bool isLeftPaddle, bool enableKeyboard = true) : base(game) { contentManager = new ContentManager(game.Services); keybEnabled = enableKeyboard; this.isLeftPaddle = isLeftPaddle; } public void setPosition(Vector2 newPos) { X = newPos.X; Y = newPos.Y; } public override void Initialize() { base.Initialize(); this.Speed = DEFAULT_Y_SPEED; X = 0; Y = 0; NormalsToEdges(this.isLeftPaddle); } protected override void LoadContent() { spriteBatch = new SpriteBatch(GraphicsDevice); paddleSprite = contentManager.Load<Texture2D>(@"Content\pongBar"); } public override void Update(GameTime gameTime) { //vertices array Vertices[0] = this.paddlePosition; Vertices[1] = this.paddlePosition + new Vector2(this.Width, 0); Vertices[2] = this.paddlePosition + new Vector2(this.Width, this.Height); Vertices[3] = this.paddlePosition + new Vector2(0, this.Height); // Move paddle, but don't allow movement off the screen if (KeyboardEnabled) { float moveDistance = Speed * (float)gameTime.ElapsedGameTime.TotalSeconds; KeyboardState newKeyState = Keyboard.GetState(); if (newKeyState.IsKeyDown(Keys.Down) && Y + paddleSprite.Height + moveDistance <= Game.GraphicsDevice.Viewport.Height) { Y += moveDistance; } else if (newKeyState.IsKeyDown(Keys.Up) && Y - moveDistance >= 0) { Y -= moveDistance; } } else { if (this.Y + this.Height > this.GraphicsDevice.Viewport.Height) { this.Y = this.Game.GraphicsDevice.Viewport.Height - this.Height - 1; } } base.Update(gameTime); } public override void Draw(GameTime gameTime) { spriteBatch.Begin(SpriteSortMode.Texture,null); spriteBatch.Draw(paddleSprite, paddlePosition, null, Color.White, 0f, Vector2.Zero, Scale, SpriteEffects.None, 0); spriteBatch.End(); base.Draw(gameTime); } } Ball Class public class Ball : Microsoft.Xna.Framework.DrawableGameComponent { #region Private Members private SpriteBatch spriteBatch; private ContentManager contentManager; private const float DEFAULT_SPEED = 50; private float speedIncrement = 0; private Vector2 ballScale = new Vector2(1f, 1f); private const float INCREASE_SPEED = 50; private Texture2D ballSprite; //initial texture private Vector2 ballPosition; //position private Vector2 centerOfBall; //center coords private Vector2 ballSpeed = new Vector2(DEFAULT_SPEED, DEFAULT_SPEED); //speed #endregion #region Properties public float DEFAULTSPEED { get { return DEFAULT_SPEED; } } public Vector2 ballCenter { get { return centerOfBall; } } public Vector2 Scale { get { return ballScale; } set { ballScale = value; } } public float SpeedX { get { return ballSpeed.X; } set { ballSpeed.X = value; } } public float SpeedY { get { return ballSpeed.Y; } set { ballSpeed.Y = value; } } public float X { get { return ballPosition.X; } set { ballPosition.X = value; } } public float Y { get { return ballPosition.Y; } set { ballPosition.Y = value; } } public Texture2D GetSprite { get { return ballSprite; } } public float Width { get { return (Scale.X == 1f ? (float)ballSprite.Width : ballSprite.Width * Scale.X); } } public float Height { get { return (Scale.Y == 1f ? (float)ballSprite.Height : ballSprite.Height * Scale.Y); } } public float SpeedIncreaseIncrement { get { return speedIncrement; } set { speedIncrement = value; } } public Rectangle Boundary { get { return new Rectangle((int)ballPosition.X, (int)ballPosition.Y, (int)this.Width, (int)this.Height); } } #endregion public Ball(Game game) : base(game) { contentManager = new ContentManager(game.Services); } public void Reset() { ballSpeed.X = DEFAULT_SPEED; ballSpeed.Y = DEFAULT_SPEED; ballPosition.X = Game.GraphicsDevice.Viewport.Width / 2 - ballSprite.Width / 2; ballPosition.Y = Game.GraphicsDevice.Viewport.Height / 2 - ballSprite.Height / 2; } public void SpeedUp() { if (ballSpeed.Y < 0) ballSpeed.Y -= (INCREASE_SPEED + speedIncrement); else ballSpeed.Y += (INCREASE_SPEED + speedIncrement); if (ballSpeed.X < 0) ballSpeed.X -= (INCREASE_SPEED + speedIncrement); else ballSpeed.X += (INCREASE_SPEED + speedIncrement); } public float[] ProjectBall(Vector2 axis) { if (axis == Vector2.Zero) return (new float[2] { 0, 0 }); float min, max; min = Vector2.Dot(axis, this.ballCenter) - this.Width/2; //center - radius max = min + this.Width; //center + radius return (new float[2] { min, max }); } public void ChangeHorzDirection() { ballSpeed.X *= -1; } public void ChangeVertDirection() { ballSpeed.Y *= -1; } public override void Initialize() { base.Initialize(); ballPosition.X = Game.GraphicsDevice.Viewport.Width / 2 - ballSprite.Width / 2; ballPosition.Y = Game.GraphicsDevice.Viewport.Height / 2 - ballSprite.Height / 2; } protected override void LoadContent() { spriteBatch = new SpriteBatch(GraphicsDevice); ballSprite = contentManager.Load<Texture2D>(@"Content\ball"); } public override void Update(GameTime gameTime) { if (this.Y < 1 || this.Y > GraphicsDevice.Viewport.Height - this.Height - 1) this.ChangeVertDirection(); centerOfBall = new Vector2(ballPosition.X + this.Width / 2, ballPosition.Y + this.Height / 2); base.Update(gameTime); } public override void Draw(GameTime gameTime) { spriteBatch.Begin(); spriteBatch.Draw(ballSprite, ballPosition, null, Color.White, 0f, Vector2.Zero, Scale, SpriteEffects.None, 0); spriteBatch.End(); base.Draw(gameTime); } } Main game class public class gameStart : Microsoft.Xna.Framework.Game { GraphicsDeviceManager graphics; SpriteBatch spriteBatch; public gameStart() { graphics = new GraphicsDeviceManager(this); Content.RootDirectory = "Content"; this.Window.Title = "Pong game"; } protected override void Initialize() { ball = new Ball(this); paddleLeft = new Paddle(this,true,false); paddleRight = new Paddle(this,false,true); Components.Add(ball); Components.Add(paddleLeft); Components.Add(paddleRight); this.Window.AllowUserResizing = false; this.IsMouseVisible = true; this.IsFixedTimeStep = false; this.isColliding = false; base.Initialize(); } #region MyPrivateStuff private Ball ball; private Paddle paddleLeft, paddleRight; private int[] bit = { -1, 1 }; private Random rnd = new Random(); private int updates = 0; enum nrPaddle { None, Left, Right }; private nrPaddle PongBar = nrPaddle.None; private ArrayList Axes = new ArrayList(); private Vector2 MTV; //minimum translation vector private bool isColliding; private float overlap; //smallest distance after projections private Vector2 overlapAxis; //axis of overlap #endregion protected override void LoadContent() { spriteBatch = new SpriteBatch(GraphicsDevice); paddleLeft.setPosition(new Vector2(0, this.GraphicsDevice.Viewport.Height / 2 - paddleLeft.Height / 2)); paddleRight.setPosition(new Vector2(this.GraphicsDevice.Viewport.Width - paddleRight.Width, this.GraphicsDevice.Viewport.Height / 2 - paddleRight.Height / 2)); paddleLeft.Scale = new Vector2(1f, 2f); //scale left paddle } private bool ShapesIntersect(Paddle paddle, Ball ball) { overlap = 1000000f; //large value overlapAxis = Vector2.Zero; MTV = Vector2.Zero; foreach (Vector2 ax in Axes) { float[] pad = paddle.ProjectPaddle(ax); //pad0 = min, pad1 = max float[] circle = ball.ProjectBall(ax); //circle0 = min, circle1 = max if (pad[1] <= circle[0] || circle[1] <= pad[0]) { return false; } if (pad[1] - circle[0] < circle[1] - pad[0]) { if (Math.Abs(overlap) > Math.Abs(-pad[1] + circle[0])) { overlap = -pad[1] + circle[0]; overlapAxis = ax; } } else { if (Math.Abs(overlap) > Math.Abs(circle[1] - pad[0])) { overlap = circle[1] - pad[0]; overlapAxis = ax; } } } if (overlapAxis != Vector2.Zero) { MTV = overlapAxis * overlap; } return true; } protected override void Update(GameTime gameTime) { updates += 1; float ftime = 5 * (float)gameTime.ElapsedGameTime.TotalSeconds; if (updates == 1) { isColliding = false; int Xrnd = bit[Convert.ToInt32(rnd.Next(0, 2))]; int Yrnd = bit[Convert.ToInt32(rnd.Next(0, 2))]; ball.SpeedX = Xrnd * ball.SpeedX; ball.SpeedY = Yrnd * ball.SpeedY; ball.X += ftime * ball.SpeedX; ball.Y += ftime * ball.SpeedY; } else { updates = 100; ball.X += ftime * ball.SpeedX; ball.Y += ftime * ball.SpeedY; } //autorun :) paddleLeft.Y = ball.Y; //collision detection PongBar = nrPaddle.None; if (ball.Boundary.Intersects(paddleLeft.Boundary)) { PongBar = nrPaddle.Left; if (!isColliding) { Axes.Clear(); Axes.AddRange(paddleLeft.Normal2EdgesVector); //axis from nearest vertex to ball's center Axes.Add(FORMULAS.NormAxisFromCircle2ClosestVertex(paddleLeft.VertexVector, ball.ballCenter)); } } else if (ball.Boundary.Intersects(paddleRight.Boundary)) { PongBar = nrPaddle.Right; if (!isColliding) { Axes.Clear(); Axes.AddRange(paddleRight.Normal2EdgesVector); //axis from nearest vertex to ball's center Axes.Add(FORMULAS.NormAxisFromCircle2ClosestVertex(paddleRight.VertexVector, ball.ballCenter)); } } if (PongBar != nrPaddle.None && !isColliding) switch (PongBar) { case nrPaddle.Left: if (ShapesIntersect(paddleLeft, ball)) { isColliding = true; if (MTV != Vector2.Zero) ball.X += MTV.X; ball.Y += MTV.Y; ball.ChangeHorzDirection(); } break; case nrPaddle.Right: if (ShapesIntersect(paddleRight, ball)) { isColliding = true; if (MTV != Vector2.Zero) ball.X += MTV.X; ball.Y += MTV.Y; ball.ChangeHorzDirection(); } break; default: break; } if (!ShapesIntersect(paddleRight, ball) && !ShapesIntersect(paddleLeft, ball)) isColliding = false; ball.X += ftime * ball.SpeedX; ball.Y += ftime * ball.SpeedY; //check ball movement if (ball.X > paddleRight.X + paddleRight.Width + 2) { //IncreaseScore(Left); ball.Reset(); updates = 0; return; } else if (ball.X < paddleLeft.X - 2) { //IncreaseScore(Right); ball.Reset(); updates = 0; return; } base.Update(gameTime); } protected override void Draw(GameTime gameTime) { GraphicsDevice.Clear(Color.Aquamarine); spriteBatch.Begin(SpriteSortMode.BackToFront, BlendState.AlphaBlend); spriteBatch.End(); base.Draw(gameTime); } } And one method i've used: public static Vector2 NormAxisFromCircle2ClosestVertex(Vector2[] vertices, Vector2 circle) { Vector2 temp = Vector2.Zero; if (vertices.Length > 0) { float dist = (circle.X - vertices[0].X) * (circle.X - vertices[0].X) + (circle.Y - vertices[0].Y) * (circle.Y - vertices[0].Y); for (int i = 1; i < vertices.Length;i++) { if (dist > (circle.X - vertices[i].X) * (circle.X - vertices[i].X) + (circle.Y - vertices[i].Y) * (circle.Y - vertices[i].Y)) { temp = vertices[i]; //memorize the closest vertex dist = (circle.X - vertices[i].X) * (circle.X - vertices[i].X) + (circle.Y - vertices[i].Y) * (circle.Y - vertices[i].Y); } } temp = circle - temp; temp.Normalize(); } return temp; } Thanks in advance for any tips on the 4 issues. EDIT1: Something isn't working properly. The collision axis doesn't come out right and the interpolation also seems to have no effect. I've changed the code a bit: private bool ShapesIntersect(Paddle paddle, Ball ball) { overlap = 1000000f; //large value overlapAxis = Vector2.Zero; MTV = Vector2.Zero; foreach (Vector2 ax in Axes) { float[] pad = paddle.ProjectPaddle(ax); //pad0 = min, pad1 = max float[] circle = ball.ProjectBall(ax); //circle0 = min, circle1 = max if (pad[1] < circle[0] || circle[1] < pad[0]) { return false; } if (Math.Abs(pad[1] - circle[0]) < Math.Abs(circle[1] - pad[0])) { if (Math.Abs(overlap) > Math.Abs(-pad[1] + circle[0])) { overlap = -pad[1] + circle[0]; overlapAxis = ax * (-1); } //to get the proper axis } else { if (Math.Abs(overlap) > Math.Abs(circle[1] - pad[0])) { overlap = circle[1] - pad[0]; overlapAxis = ax; } } } if (overlapAxis != Vector2.Zero) { MTV = overlapAxis * Math.Abs(overlap); } return true; } And part of the Update method: if (ShapesIntersect(paddleRight, ball)) { isColliding = true; if (MTV != Vector2.Zero) { ball.X += MTV.X; ball.Y += MTV.Y; } //test if (overlapAxis.X == 0) //collision with horizontal edge { } else if (overlapAxis.Y == 0) //collision with vertical edge { float factor = Math.Abs(ball.ballCenter.Y - paddleRight.Y) / paddleRight.Height; if (factor > 1) factor = 1f; if (overlapAxis.X < 0) //left edge? ball.Speed = ball.DEFAULTSPEED * Vector2.Normalize(Vector2.Reflect(ball.Speed, (Vector2.Lerp(new Vector2(-1, -3), new Vector2(-1, 3), factor)))); else //right edge? ball.Speed = ball.DEFAULTSPEED * Vector2.Normalize(Vector2.Reflect(ball.Speed, (Vector2.Lerp(new Vector2(1, -3), new Vector2(1, 3), factor)))); } else //vertex collision??? { ball.Speed = -ball.Speed; } } What seems to happen is that "overlapAxis" doesn't always return the right one. So instead of (-1,0) i get the (1,0) (this happened even before i multiplied with -1 there). Sometimes there isn't even a collision registered even though the ball passes through the paddle... The interpolation also seems to have no effect as the angles barely change (or the overlapAxis is almost never (-1,0) or (1,0) but something like (0.9783473, 0.02743843)... ). What am i missing here? :(

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  • Ensure your view and function meta data is upto date.

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    Last week, I have received a very interesting question and I find in email and I really liked the question as I had to play around with SQL Script for a while to come up with the answer he was looking for. Please read the question and I believe that all of us face this kind of situation. “Pinal, In our database we have recently introduced ModifiedDate column in all of the tables. Now onwards any update happens in the row, we are updating current date and time to that field. Now here is the issue, when we added that field we did not update it with a default value because we were not sure when we will go live with the system so we let it be NULL. Now modification to the application went live yesterday and we are now updating this field. Here is where I need your help. We need to update all the tables in our database where we have column created ModifiedDate and now want to update with current datetime. As our system is already live since yesterday there are several thousands of the rows which are already updated with real world value so we do not want to update those values. Essentially, in our entire database where ever there is a ModifiedDate column and if it is NULL we want to update that with current date time?  Do you have a script for it?” Honestly I did not have such a script. This is very specific required but I was able to come up with two different methods how he can use this method. Method 1 : Using INFORMATION_SCHEMA SELECT 'UPDATE ' + T.TABLE_SCHEMA + '.' + T.TABLE_NAME + ' SET ModifiedDate = GETDATE() WHERE ModifiedDate IS NULL;' FROM INFORMATION_SCHEMA.TABLES T INNER JOIN INFORMATION_SCHEMA.COLUMNS C ON T.TABLE_NAME = C.TABLE_NAME AND c.COLUMN_NAME ='ModifiedDate' WHERE T.TABLE_TYPE = 'BASE TABLE' ORDER BY T.TABLE_SCHEMA, T.TABLE_NAME; Method 2: Using DMV SELECT 'UPDATE ' + SCHEMA_NAME(t.schema_id) + '.' + t.name + ' SET ModifiedDate = GETDATE() WHERE ModifiedDate IS NULL;' FROM sys.tables AS t INNER JOIN sys.columns c ON t.OBJECT_ID = c.OBJECT_ID WHERE c.name ='ModifiedDate' ORDER BY SCHEMA_NAME(t.schema_id), t.name; Above scripts will create an UPDATE script which will do the task which is asked. We can pretty much the update script to any other SELECT statement and retrieve any other data as well. Click to Download Scripts Reference: Pinal Dave (http://blog.sqlauthority.com)  Filed under: PostADay, SQL, SQL Authority, SQL Joins, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Retrieving model position after applying modeltransforms in XNA

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  • Wubi 12.04 installation error executing command command

    - by Erik Lau
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  • 2D metaball liquid effect - how to feed output of one rendering pass as input to another shader

    - by Guye Incognito
    I'm attempting to make a shader for unity3d web project. I want to implement something like in the great answer by DMGregory in this question. in order to achieve a final look something like this.. Its metaballs with specular and shading. The steps to make this shader are. 1. Convert the feathered blobs into a heightmap. 2. Generate a normalmap from the heightmap 3. Feed the normal map and height map into a standard unity shader, for instance transparent parallax specular. I pretty much have all the pieces I need assembled but I am new to shaders and need help putting them together I can generate a heightmap from the blobs using some fragment shader code I wrote (I'm just using the red channel here cus i dont know if you can access the brightness) half4 frag (v2f i) : COLOR{ half4 texcol,finalColor; texcol = tex2D (_MainTex, i.uv); finalColor=_MyColor; if(texcol.r<_botmcut) { finalColor.r= 0; } else if((texcol.r>_topcut)) { finalColor.r= 0; } else { float r = _topcut-_botmcut; float xpos = _topcut - texcol.r; finalColor.r= (_botmcut + sqrt((xpos*xpos)-(r*r)))/_constant; } return finalColor; } turns these blobs.. into this heightmap Also I've found some CG code that generates a normal map from a height map. The bit of code that makes the normal map from finite differences is here void surf (Input IN, inout SurfaceOutput o) { o.Albedo = fixed3(0.5); float3 normal = UnpackNormal(tex2D(_BumpMap, IN.uv_MainTex)); float me = tex2D(_HeightMap,IN.uv_MainTex).x; float n = tex2D(_HeightMap,float2(IN.uv_MainTex.x,IN.uv_MainTex.y+1.0/_HeightmapDimY)).x; float s = tex2D(_HeightMap,float2(IN.uv_MainTex.x,IN.uv_MainTex.y-1.0/_HeightmapDimY)).x; float e = tex2D(_HeightMap,float2(IN.uv_MainTex.x-1.0/_HeightmapDimX,IN.uv_MainTex.y)).x; float w = tex2D(_HeightMap,float2(IN.uv_MainTex.x+1.0/_HeightmapDimX,IN.uv_MainTex.y)).x; float3 norm = normal; float3 temp = norm; //a temporary vector that is not parallel to norm if(norm.x==1) temp.y+=0.5; else temp.x+=0.5; //form a basis with norm being one of the axes: float3 perp1 = normalize(cross(norm,temp)); float3 perp2 = normalize(cross(norm,perp1)); //use the basis to move the normal in its own space by the offset float3 normalOffset = -_HeightmapStrength * ( ( (n-me) - (s-me) ) * perp1 + ( ( e - me ) - ( w - me ) ) * perp2 ); norm += normalOffset; norm = normalize(norm); o.Normal = norm; } Also here is the built-in transparent parallax specular shader for unity. Shader "Transparent/Parallax Specular" { Properties { _Color ("Main Color", Color) = (1,1,1,1) _SpecColor ("Specular Color", Color) = (0.5, 0.5, 0.5, 0) _Shininess ("Shininess", Range (0.01, 1)) = 0.078125 _Parallax ("Height", Range (0.005, 0.08)) = 0.02 _MainTex ("Base (RGB) TransGloss (A)", 2D) = "white" {} _BumpMap ("Normalmap", 2D) = "bump" {} _ParallaxMap ("Heightmap (A)", 2D) = "black" {} } SubShader { Tags {"Queue"="Transparent" "IgnoreProjector"="True" "RenderType"="Transparent"} LOD 600 CGPROGRAM #pragma surface surf BlinnPhong alpha #pragma exclude_renderers flash sampler2D _MainTex; sampler2D _BumpMap; sampler2D _ParallaxMap; fixed4 _Color; half _Shininess; float _Parallax; struct Input { float2 uv_MainTex; float2 uv_BumpMap; float3 viewDir; }; void surf (Input IN, inout SurfaceOutput o) { half h = tex2D (_ParallaxMap, IN.uv_BumpMap).w; float2 offset = ParallaxOffset (h, _Parallax, IN.viewDir); IN.uv_MainTex += offset; IN.uv_BumpMap += offset; fixed4 tex = tex2D(_MainTex, IN.uv_MainTex); o.Albedo = tex.rgb * _Color.rgb; o.Gloss = tex.a; o.Alpha = tex.a * _Color.a; o.Specular = _Shininess; o.Normal = UnpackNormal(tex2D(_BumpMap, IN.uv_BumpMap)); } ENDCG } FallBack "Transparent/Bumped Specular" }

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  • More on PHP and Oracle 11gR2 Improvements to Client Result Caching

    - by christopher.jones
    Oracle 11.2 brought several improvements to Client Result Caching. CRC is way for the results of queries to be cached in the database client process for reuse.  In an Oracle OpenWorld presentation "Best Practices for Developing Performant Application" my colleague Luxi Chidambaran had a (non-PHP generated) graph for the Niles benchmark that shows a DB CPU reduction up to 600% and response times up to 22% faster when using CRC. Sometimes CRC is called the "Consistent Client Cache" because Oracle automatically invalidates the cache if table data is changed.  This makes it easy to use without needing application logic rewrites. There are a few simple database settings to turn on and tune CRC, so management is also easy. PHP OCI8 as a "client" of the database can use CRC.  The cache is per-process, so plan carefully before caching large data sets.  Tables that are candidates for caching are look-up tables where the network transfer cost dominates. CRC is really easy in 11.2 - I'll get to that in a moment.  It was also pretty easy in Oracle 11.1 but it needed some tiny application changes.  In PHP it was used like: $s = oci_parse($c, "select /*+ result_cache */ * from employees"); oci_execute($s, OCI_NO_AUTO_COMMIT); // Use OCI_DEFAULT in OCI8 <= 1.3 oci_fetch_all($s, $res); I blogged about this in the past.  The query had to include a specific hint that you wanted the results cached, and you needed to turn off auto committing during execution either with the OCI_DEFAULT flag or its new, better-named alias OCI_NO_AUTO_COMMIT.  The no-commit flag rule didn't seem reasonable to me because most people wouldn't be specific about the commit state for a query. Now in Oracle 11.2, DBAs can now nominate tables for caching, either with CREATE TABLE or ALTER TABLE.  That means you don't need the query hint anymore.  As well, the no-commit flag requirement has been lifted.  Your code can now look like: $s = oci_parse($c, "select * from employees"); oci_execute($s); oci_fetch_all($s, $res); Since your code probably already looks like this, your DBA can find the top queries in the database and simply tune the system by turning on CRC in the database and issuing an ALTER TABLE statement for candidate tables.  Voila. Another CRC improvement in Oracle 11.2 is that it works with DRCP connection pooling. There is some fine print about what is and isn't cached, check the Oracle manuals for details.  If you're using 11.1 or non-DRCP "dedicated servers" then make sure you use oci_pconnect() persistent connections.  Also in PHP don't bind strings in the query, although binding as SQLT_INT is OK.

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  • Difference between DISTINCT and VALUES in DAX

    - by Marco Russo (SQLBI)
    I recently got a question about differences between DISTINCT and VALUES in DAX and thanks to Jeffrey Wang I created a simple example to describe the difference. Consider the two tables below: Fact and Dim tables, having a single column with the same name of the table. A relationship exists between Fact[Fact] and Dim[Dim]. This relationship generates a referential integrity violations in table Fact for rows containing C, which doesn’t exist in table Dim. In this case, an empty row is virtually inserted...(read more)

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  • Can't update scala on Gentoo

    - by xhochy
    As I wanted to test Scala 2.9.2 on my gentoo system I tried updated the package but ended up with this error. I can't figure out where the problem may be: Calculating dependencies ...... done! >>> Verifying ebuild manifests >>> Jobs: 0 of 1 complete, 1 running Load avg: 0.23, 0.16, 0.20 >>> Emerging (1 of 1) dev-lang/scala-2.9.2 >>> Jobs: 0 of 1 complete, 1 running Load avg: 0.23, 0.16, 0.20 >>> Failed to emerge dev-lang/scala-2.9.2, Log file: >>> Jobs: 0 of 1 complete, 1 running Load avg: 0.23, 0.16, 0.20 >>> '/var/tmp/portage/dev-lang/scala-2.9.2/temp/build.log' >>> Jobs: 0 of 1 complete, 1 running Load avg: 0.23, 0.16, 0.20 >>> Jobs: 0 of 1 complete, 1 running, 1 failed Load avg: 0.23, 0.16, 0.20 >>> Jobs: 0 of 1 complete, 1 failed Load avg: 0.23, 0.16, 0.20 * Package: dev-lang/scala-2.9.2 * Repository: gentoo * Maintainer: [email protected] * USE: amd64 elibc_glibc kernel_linux multilib userland_GNU * FEATURES: sandbox [01m[31;06m!!! ERROR: Couldn't find suitable VM. Possible invalid dependency string. Due to jdk-with-com-sun requiring a target of 1.7 but the virtual machines constrained by virtual/jdk-1.6 and/or this package requiring virtual(s) jdk-with-com-sun[0m * Unable to determine VM for building from dependencies: NV_DEPEND: virtual/jdk:1.6 java-virtuals/jdk-with-com-sun !binary? ( dev-java/ant-contrib:0 ) app-arch/xz-utils >=dev-java/java-config-2.1.9-r1 source? ( app-arch/zip ) >=dev-java/ant-core-1.7.0 dev-java/ant-nodeps >=dev-java/javatoolkit-0.3.0-r2 >=dev-lang/python-2.4 * ERROR: dev-lang/scala-2.9.2 failed (setup phase): * Failed to determine VM for building. * * Call stack: * ebuild.sh, line 93: Called pkg_setup * scala-2.9.2.ebuild, line 43: Called java-pkg-2_pkg_setup * java-pkg-2.eclass, line 53: Called java-pkg_init * java-utils-2.eclass, line 2187: Called java-pkg_switch-vm * java-utils-2.eclass, line 2674: Called die * The specific snippet of code: * die "Failed to determine VM for building." * * If you need support, post the output of `emerge --info '=dev-lang/scala-2.9.2'`, * the complete build log and the output of `emerge -pqv '=dev-lang/scala-2.9.2'`. !!! When you file a bug report, please include the following information: GENTOO_VM= CLASSPATH="" JAVA_HOME="" JAVACFLAGS="" COMPILER="" and of course, the output of emerge --info * The complete build log is located at '/var/tmp/portage/dev-lang/scala-2.9.2/temp/build.log'. * The ebuild environment file is located at '/var/tmp/portage/dev-lang/scala-2.9.2/temp/die.env'. * Working directory: '/var/tmp/portage/dev-lang/scala-2.9.2' * S: '/var/tmp/portage/dev-lang/scala-2.9.2/work/scala-2.9.2-sources' * Messages for package dev-lang/scala-2.9.2: * Unable to determine VM for building from dependencies: * ERROR: dev-lang/scala-2.9.2 failed (setup phase): * Failed to determine VM for building. * * Call stack: * ebuild.sh, line 93: Called pkg_setup * scala-2.9.2.ebuild, line 43: Called java-pkg-2_pkg_setup * java-pkg-2.eclass, line 53: Called java-pkg_init * java-utils-2.eclass, line 2187: Called java-pkg_switch-vm * java-utils-2.eclass, line 2674: Called die * The specific snippet of code: * die "Failed to determine VM for building." * * If you need support, post the output of `emerge --info '=dev-lang/scala-2.9.2'`, * the complete build log and the output of `emerge -pqv '=dev-lang/scala-2.9.2'`. * The complete build log is located at '/var/tmp/portage/dev-lang/scala-2.9.2/temp/build.log'. * The ebuild environment file is located at '/var/tmp/portage/dev-lang/scala-2.9.2/temp/die.env'. * Working directory: '/var/tmp/portage/dev-lang/scala-2.9.2' * S: '/var/tmp/portage/dev-lang/scala-2.9.2/work/scala-2.9.2-sources' The following eix output may help: % eix java-virtuals/jdk-with-com-sun [I] java-virtuals/jdk-with-com-sun Available versions: 20111111 {{ELIBC="FreeBSD"}} Installed versions: 20111111(16:08:51 18/04/12)(ELIBC="-FreeBSD") Homepage: http://www.gentoo.org Description: Virtual ebuilds that require internal com.sun classes from a JDK Both virtual jdks 1.6 and 1.7 are installed: % eix virtual/jdk [I] virtual/jdk Available versions: (1.4) ~1.4.2-r1[1] (1.5) 1.5.0 ~1.5.0-r3[1] (1.6) 1.6.0 1.6.0-r1 (1.7) (~)1.7.0 Installed versions: 1.6.0-r1(1.6)(23:22:48 10/11/12) 1.7.0(1.7)(23:21:09 10/11/12) Description: Virtual for JDK [1] "java-overlay" /var/lib/layman/java-overlay

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  • Need help identiying a nasty rootkit in Windows

    - by goofrider
    I have a nasty rootkit that not tools seem to be able to idenity. I know for sure it's a rootkit, but I can figure out which rootkit it is. Here's what I gathered so far: It creates multiple copies of itself in %HOME%\Local Settings\Temp with names like Q.EXE, IAJARZ.exe, etc., and install them as hidden services. These EXE have SysInternals identifiers in them so they're definitely rootkits. It hooked very deep in the system, including file read/write, security policies, registry read/write, and possibly WinSock/TCP/IP. When going to Sophos.com to download their software, the rootkit inject something called Microsoft Ajax Tootkit into the page, which injects code into the email submission form in order to redirect it. (EDIT: I might have panicked. Looks like Sophos does use an AJAZ email form, their form is just broken on Chrome so it looked like a mail form injection attack, the link is http://www.sophos.com/en-us/products/free-tools/virus-removal-tool/download.aspx ) Super-Antispyware found a lot of spyware cookies, in the name of .kaspersky.2o7.net, etc. (just chedk 2o7.net, looks like it's a legit ad company) I tried comparing DNS lookup from the infected systems and from system in other physical locations, no DNS redirections it seems. I used dd to copy the MBR and compared it with the MBR provided by ms-sys package, no differences so it's not infecting MBR. No antivirus or rootkit scanner be able to identify it. Most of them can't even find it. I tried scanning, in-situ (normal mode), in safe mode, and boot to linux live CD. Scanners used: Avast, Sophos anti rootkit, Kasersky TDSSKiller, GMER, RootkitRevealer, and many others. Kaspersky reported some unsigned system files that ought to be signed (e.g. tcpip.sys), and reported a number of MD5 mismatches. But otherwise couldn't identify anything based on signature. When running Sysinternal RootkitRevealer and Sophos AntiRootkit, CPU usage goes up to 100% and gets stucked. The Rootkit is blocking them. When trying running/installing HiJackThis, RootkitRevealer and some other scanners, it tells me system security policy prevent running/installing it. The list of malicious acitivities go on and on. here's a sample of logs from all my scans. In particular, aswSnx.SYS, apnenfno.sys and PROCMON20.SYS has a huge number of hooks. It's hard to tell if the rootkit replaced legit program files like aswSnx.SYS (from Avast) and PROCMON20.SYS (from Sysinternal Process Monitor). I can't find whether apnenfno.sys is from a legit program. Help to identify it is appreciated. Trend Micro RootkitBuster ------ [HIDDEN_REGISTRY][Hidden Reg Value]: KeyPath : HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\sptd\Cfg Root : 586bfc0 SubKey : Cfg ValueName : g0 Data : 38 23 E8 D0 BF F2 2D 6F ... ValueType : 3 AccessType: 0 FullLength: 61 DataSize : 32 [HOOKED_SERVICE_API]: Service API : ZwCreateMutant Image Path : C:\WINDOWS\System32\Drivers\aswSnx.SYS OriginalHandler : 0x8061758e CurrentHandler : 0xaa66cce8 ServiceNumber : 0x2b ModuleName : aswSnx.SYS SDTType : 0x0 [HOOKED_SERVICE_API]: Service API : ZwCreateThread Image Path : c:\windows\system32\drivers\apnenfno.sys OriginalHandler : 0x805d1038 CurrentHandler : 0xaa5f118c ServiceNumber : 0x35 ModuleName : apnenfno.sys SDTType : 0x0 [HOOKED_SERVICE_API]: Service API : ZwDeleteKey Image Path : C:\WINDOWS\system32\Drivers\PROCMON20.SYS OriginalHandler : 0x80624472 CurrentHandler : 0xa709b0f8 ServiceNumber : 0x3f ModuleName : PROCMON20.SYS SDTType : 0x0 HiJackThis ------ O23 - Service: JWAHQAGZ - Sysinternals - www.sysinternals.com - C:\DOCUME~1\jeff\LOCALS~1\Temp\JWAHQAGZ.exe O23 - Service: LHIJ - Sysinternals - www.sysinternals.com - C:\DOCUME~1\jeff\LOCALS~1\Temp\LHIJ.exe Kaspersky TDSSKiller ------ 21:05:58.0375 3936 C:\WINDOWS\system32\ati2sgag.exe - copied to quarantine 21:05:59.0217 3936 ATI Smart ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:05:59.0342 3936 C:\WINDOWS\system32\BUFADPT.SYS - copied to quarantine 21:05:59.0856 3936 BUFADPT ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:05:59.0965 3936 C:\Program Files\CrashPlan\CrashPlanService.exe - copied to quarantine 21:06:00.0152 3936 CrashPlanService ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:06:00.0246 3936 C:\WINDOWS\system32\epmntdrv.sys - copied to quarantine 21:06:00.0433 3936 epmntdrv ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:06:00.0464 3936 C:\WINDOWS\system32\EuGdiDrv.sys - copied to quarantine 21:06:00.0526 3936 EuGdiDrv ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:06:00.0604 3936 C:\Program Files\Common Files\Macrovision Shared\FLEXnet Publisher\FNPLicensingService.exe - copied to quarantine 21:06:01.0181 3936 FLEXnet Licensing Service ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:06:01.0321 3936 C:\Program Files\AddinForUNCFAT\UNCFATDMS.exe - copied to quarantine 21:06:01.0430 3936 OTFSDMS ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:06:01.0492 3936 C:\WINDOWS\system32\DRIVERS\tcpip.sys - copied to quarantine 21:06:01.0539 3936 Tcpip ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:06:01.0601 3936 C:\DOCUME~1\jeff\LOCALS~1\Temp\TULPUWOX.exe - copied to quarantine 21:06:01.0664 3936 HKLM\SYSTEM\ControlSet003\services\TULPUWOX - will be deleted on reboot 21:06:01.0664 3936 C:\DOCUME~1\jeff\LOCALS~1\Temp\TULPUWOX.exe - will be deleted on reboot 21:06:01.0664 3936 TULPUWOX ( UnsignedFile.Multi.Generic ) - User select action: Delete 21:06:01.0757 3936 C:\WINDOWS\system32\Drivers\usbaapl.sys - copied to quarantine 21:06:01.0866 3936 USBAAPL ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:06:01.0913 3936 C:\Program Files\VMware\VMware Player\vmware-authd.exe - copied to quarantine 21:06:02.0443 3936 VMAuthdService ( UnsignedFile.Multi.Generic ) - User select action: Quarantine 21:06:02.0443 3936 vmount2 ( UnsignedFile.Multi.Generic ) - skipped by user 21:06:02.0443 3936 vmount2 ( UnsignedFile.Multi.Generic ) - User select action: Skip 21:06:02.0459 3936 vstor2 ( UnsignedFile.Multi.Generic ) - skipped by user 21:06:02.0459 3936 vstor2 ( UnsignedFile.Multi.Generic ) - User select action: Skip

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  • How to suggest using an ORM instead of stored procedures?

    - by Wayne M
    I work at a company that only uses stored procedures for all data access, which makes it very annoying to keep our local databases in sync as every commit we have to run new procs. I have used some basic ORMs in the past and I find the experience much better and cleaner. I'd like to suggest to the development manager and rest of the team that we look into using an ORM Of some kind for future development (the rest of the team are only familiar with stored procedures and have never used anything else). The current architecture is .NET 3.5 written like .NET 1.1, with "god classes" that use a strange implementation of ActiveRecord and return untyped DataSets which are looped over in code-behind files - the classes work something like this: class Foo { public bool LoadFoo() { bool blnResult = false; if (this.FooID == 0) { throw new Exception("FooID must be set before calling this method."); } DataSet ds = // ... call to Sproc if (ds.Tables[0].Rows.Count > 0) { foo.FooName = ds.Tables[0].Rows[0]["FooName"].ToString(); // other properties set blnResult = true; } return blnResult; } } // Consumer Foo foo = new Foo(); foo.FooID = 1234; foo.LoadFoo(); // do stuff with foo... There is pretty much no application of any design patterns. There are no tests whatsoever (nobody else knows how to write unit tests, and testing is done through manually loading up the website and poking around). Looking through our database we have: 199 tables, 13 views, a whopping 926 stored procedures and 93 functions. About 30 or so tables are used for batch jobs or external things, the remainder are used in our core application. Is it even worth pursuing a different approach in this scenario? I'm talking about moving forward only since we aren't allowed to refactor the existing code since "it works" so we cannot change the existing classes to use an ORM, but I don't know how often we add brand new modules instead of adding to/fixing current modules so I'm not sure if an ORM is the right approach (too much invested in stored procedures and DataSets). If it is the right choice, how should I present the case for using one? Off the top of my head the only benefits I can think of is having cleaner code (although it might not be, since the current architecture isn't built with ORMs in mind so we would basically be jury-rigging ORMs on to future modules but the old ones would still be using the DataSets) and less hassle to have to remember what procedure scripts have been run and which need to be run, etc. but that's it, and I don't know how compelling an argument that would be. Maintainability is another concern but one that nobody except me seems to be concerned about.

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  • pwmconfig: "There are no pwm-capable sensor modules installed"

    - by Sman789
    I'm trying to reduce my fan speed with fancontrol and pwmanager because, despite the temperatures being the same, they are much louder on Linux (Ubuntu Gnome 14.04) than on Windows. I've followed the instructions in the first answer here but when running pwmanager I get pwmconfig: "There are no pwm-capable sensor modules installed" I know that my system has working thermal sensors because PSensor has no trouble telling me my CPU temp and GPU temp. I would appreciate any help you can give in helping me reduce my fan speed to that of Windows (which uses the ASUS AI Suite 3 software which came with the Z87-A motherboard, if that's relevant).

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