Search Results

Search found 23613 results on 945 pages for 'query parameters'.

Page 388/945 | < Previous Page | 384 385 386 387 388 389 390 391 392 393 394 395  | Next Page >

  • data is not inserting in my db table [closed]

    - by Sarojit Chakraborty
    Please see my below(SubjectDetailsDao.java) code of addZoneToDb method. My debugger is nicely running upto ** session.getTransaction().commit();** code. but after that debugger stops,I do not know why it stops after that line? .And because of this i am unable to insert my data into my database table. I don't know what to do.Why it is not inserting my data into my database table? Plz help me for this. H Now i am getting this Error: Struts Problem Report Struts has detected an unhandled exception: Messages: org.hibernate.event.PreInsertEvent.getSource()Lorg/hibernate/event/EventSource; File: org/hibernate/validator/event/ValidateEventListener.java Line number: 172 Stacktraces java.lang.reflect.InvocationTargetException sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) java.lang.reflect.Method.invoke(Method.java:601) com.opensymphony.xwork2.DefaultActionInvocation.invokeAction(DefaultActionInvocation.java:441) com.opensymphony.xwork2.DefaultActionInvocation.invokeActionOnly(DefaultActionInvocation.java:280) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:243) com.opensymphony.xwork2.interceptor.DefaultWorkflowInterceptor.doIntercept(DefaultWorkflowInterceptor.java:165) com.opensymphony.xwork2.interceptor.MethodFilterInterceptor.intercept(MethodFilterInterceptor.java:87) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.validator.ValidationInterceptor.doIntercept(ValidationInterceptor.java:252) org.apache.struts2.interceptor.validation.AnnotationValidationInterceptor.doIntercept(AnnotationValidationInterceptor.java:68) com.opensymphony.xwork2.interceptor.MethodFilterInterceptor.intercept(MethodFilterInterceptor.java:87) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.ConversionErrorInterceptor.intercept(ConversionErrorInterceptor.java:122) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.ParametersInterceptor.doIntercept(ParametersInterceptor.java:195) com.opensymphony.xwork2.interceptor.MethodFilterInterceptor.intercept(MethodFilterInterceptor.java:87) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.ParametersInterceptor.doIntercept(ParametersInterceptor.java:195) com.opensymphony.xwork2.interceptor.MethodFilterInterceptor.intercept(MethodFilterInterceptor.java:87) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.StaticParametersInterceptor.intercept(StaticParametersInterceptor.java:179) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) org.apache.struts2.interceptor.MultiselectInterceptor.intercept(MultiselectInterceptor.java:75) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) org.apache.struts2.interceptor.CheckboxInterceptor.intercept(CheckboxInterceptor.java:94) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) org.apache.struts2.interceptor.FileUploadInterceptor.intercept(FileUploadInterceptor.java:235) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.ModelDrivenInterceptor.intercept(ModelDrivenInterceptor.java:89) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.ScopedModelDrivenInterceptor.intercept(ScopedModelDrivenInterceptor.java:130) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) org.apache.struts2.interceptor.debugging.DebuggingInterceptor.intercept(DebuggingInterceptor.java:267) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.ChainingInterceptor.intercept(ChainingInterceptor.java:126) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.PrepareInterceptor.doIntercept(PrepareInterceptor.java:138) com.opensymphony.xwork2.interceptor.MethodFilterInterceptor.intercept(MethodFilterInterceptor.java:87) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.I18nInterceptor.intercept(I18nInterceptor.java:165) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) org.apache.struts2.interceptor.ServletConfigInterceptor.intercept(ServletConfigInterceptor.java:164) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.AliasInterceptor.intercept(AliasInterceptor.java:179) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.interceptor.ExceptionMappingInterceptor.intercept(ExceptionMappingInterceptor.java:176) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) org.apache.struts2.impl.StrutsActionProxy.execute(StrutsActionProxy.java:52) org.apache.struts2.dispatcher.Dispatcher.serviceAction(Dispatcher.java:488) org.apache.struts2.dispatcher.ng.ExecuteOperations.executeAction(ExecuteOperations.java:77) org.apache.struts2.dispatcher.ng.filter.StrutsPrepareAndExecuteFilter.doFilter(StrutsPrepareAndExecuteFilter.java:91) org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:243) org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:210) org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:240) org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:164) org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:498) org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:164) org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:100) org.apache.catalina.valves.AccessLogValve.invoke(AccessLogValve.java:562) org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:118) org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:394) org.apache.coyote.http11.Http11Processor.process(Http11Processor.java:243) org.apache.coyote.http11.Http11Protocol$Http11ConnectionHandler.process(Http11Protocol.java:188) org.apache.tomcat.util.net.JIoEndpoint$SocketProcessor.run(JIoEndpoint.java:302) java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1110) java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:603) java.lang.Thread.run(Thread.java:722) java.lang.NoSuchMethodError: org.hibernate.event.PreInsertEvent.getSource()Lorg/hibernate/event/EventSource; org.hibernate.validator.event.ValidateEventListener.onPreInsert(ValidateEventListener.java:172) org.hibernate.action.EntityInsertAction.preInsert(EntityInsertAction.java:156) org.hibernate.action.EntityInsertAction.execute(EntityInsertAction.java:49) org.hibernate.engine.ActionQueue.execute(ActionQueue.java:250) org.hibernate.engine.ActionQueue.executeActions(ActionQueue.java:234) org.hibernate.engine.ActionQueue.executeActions(ActionQueue.java:141) org.hibernate.event.def.AbstractFlushingEventListener.performExecutions(AbstractFlushingEventListener.java:298) org.hibernate.event.def.DefaultFlushEventListener.onFlush(DefaultFlushEventListener.java:27) org.hibernate.impl.SessionImpl.flush(SessionImpl.java:1000) org.hibernate.impl.SessionImpl.managedFlush(SessionImpl.java:338) org.hibernate.transaction.JDBCTransaction.commit(JDBCTransaction.java:106) v.esoft.dao.SubjectdetailsDAO.SubjectdetailsDAO.addZoneToDb(SubjectdetailsDAO.java:185) v.esoft.actions.LoginAction.datatobeinsert(LoginAction.java:53) sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) java.lang.reflect.Method.invoke(Method.java:601) com.opensymphony.xwork2.DefaultActionInvocation.invokeAction(DefaultActionInvocation.java:441) com.opensymphony.xwork2.DefaultActionInvocation.invokeActionOnly(DefaultActionInvocation.java:280) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:243) com.opensymphony.xwork2.interceptor.DefaultWorkflowInterceptor.doIntercept(DefaultWorkflowInterceptor.java:165) com.opensymphony.xwork2.interceptor.MethodFilterInterceptor.intercept(MethodFilterInterceptor.java:87) com.opensymphony.xwork2.DefaultActionInvocation.invoke(DefaultActionInvocation.java:237) com.opensymphony.xwork2.validator.ValidationInterceptor.doIntercept(ValidationInterceptor.java:252) org.apache.struts2.interceptor.validation.AnnotationValidationInterceptor.doIntercept(AnnotationValidationInterceptor.java:68) ............................... ............................... SubjectDetailsDao.java(I have problem in addZoneToDb) package v.esoft.dao.SubjectdetailsDAO; import java.text.SimpleDateFormat; import java.util.ArrayList; import java.util.Iterator; import java.util.List; import org.hibernate.HibernateException; import org.hibernate.Query; import org.hibernate.Session; import org.hibernate.SessionFactory; import org.hibernate.Transaction; import org.hibernate.criterion.Order; import com.opensymphony.xwork2.ActionSupport; import v.esoft.connection.HibernateUtil; import v.esoft.pojos.Subjectdetails; public class SubjectdetailsDAO extends ActionSupport { private static Session session = null; private static SessionFactory sessionFactory = null; static Transaction transaction = null; private String currentDate; SimpleDateFormat formatter1 = new SimpleDateFormat("yyyy-MM-dd"); private java.util.Date currentdate; public SubjectdetailsDAO() { sessionFactory = HibernateUtil.getSessionFactory(); SimpleDateFormat formatter = new SimpleDateFormat("yyyy-MM-dd"); currentdate = new java.util.Date(); currentDate = formatter.format(currentdate); } public List getAllCustomTempleteRoutinesForGrid() { List list = new ArrayList(); try { session = sessionFactory.openSession(); list = session.createCriteria(Subjectdetails.class).addOrder(Order.desc("subjectId")).list(); } catch (Exception e) { System.out.println("Exepetion in getAllCustomTempleteRoutines" + e); } finally { try { // HibernateUtil.shutdown(); } catch (Exception e) { System.out.println("Exception In getExerciseListByLoginId Resource closing :" + e); } } return list; } //**showing list on grid private static List<Subjectdetails> custLst=new ArrayList<Subjectdetails>(); static int total=50; static { SubjectdetailsDAO cts = new SubjectdetailsDAO(); Iterator iterator1 = cts.getAllCustomTempleteRoutinesForGrid().iterator(); while (iterator1.hasNext()) { Subjectdetails get = (Subjectdetails) iterator1.next(); custLst.add(get); } } /****************************************update Routines List by WorkId************************************/ public int updatesub(Subjectdetails s) { int updated = 0; try { session = sessionFactory.openSession(); transaction = session.beginTransaction(); Query query = session.createQuery("UPDATE Subjectdetails set subjectName = :routineName1 WHERE subjectId=:workoutId1"); query.setString("routineName1", s.getSubjectName()); query.setInteger("workoutId1", s.getSubjectId()); updated = query.executeUpdate(); if (updated != 0) { } transaction.commit(); } catch (Exception e) { if (transaction != null && transaction.isActive()) { try { transaction.rollback(); } catch (Exception e1) { System.out.println("Exception in addUser() Rollback :" + e1); } } } finally { try { session.flush(); session.close(); } catch (Exception e) { System.out.println("Exception In addUser Resource closing :" + e); } } return updated; } /****************************************update Routines List by WorkId************************************/ public int addSubjectt(Subjectdetails s) { int inserted = 0; Subjectdetails ss=new Subjectdetails(); try { session = sessionFactory.openSession(); transaction = session.beginTransaction(); ss. setSubjectName(s.getSubjectName()); session.save(ss); System.out.println("Successfully data insert in database"); inserted++; if (inserted != 0) { } transaction.commit(); } catch (Exception e) { if (transaction != null && transaction.isActive()) { try { transaction.rollback(); } catch (Exception e1) { System.out.println("Exception in addUser() Rollback :" + e1); } } } finally { try { session.flush(); session.close(); } catch (Exception e) { System.out.println("Exception In addUser Resource closing :" + e); } } return inserted; } /******************************************Get all Routines List by LoginID************************************/ public List getSubjects() { List list = null; try { session = sessionFactory.openSession(); list = session.createCriteria(Subjectdetails.class).list(); } catch (Exception e) { System.out.println("Exception in getRoutineList() :" + e); } finally { try { session.flush(); session.close(); } catch (Exception e) { System.out.println("Exception In getUserList Resource closing :" + e); } } return list; } //---\ public int addZoneToDb(String countryName, Integer loginId) { int inserted = 0; try { System.out.println("---------1--------"); Session session = HibernateUtil.getSessionFactory().openSession(); System.out.println("---------2------session--"+session); session.beginTransaction(); Subjectdetails country = new Subjectdetails(countryName, loginId, currentdate, loginId, currentdate); System.out.println("---------2------country--"+country); session.save(country); System.out.println("-------after save--"); inserted++; session.getTransaction().commit(); System.out.println("-------after commits--"); } catch (Exception e) { if (transaction != null && transaction.isActive()) { try { transaction.rollback(); } catch (Exception e1) { } } } finally { try { } catch (Exception e) { } } return inserted; } //-- public int nextId() { return total++; } public List<Subjectdetails> buildList() { return custLst; } public static int count() { return custLst.size(); } public static List<Subjectdetails> find(int o,int q) { return custLst.subList(o, q); } public void save(Subjectdetails c) { custLst.add(c); } public static Subjectdetails findById(Integer id) { try { for(Subjectdetails c:custLst) { if(c.getSubjectId()==id) { return c; } } } catch (Exception e) { // TODO Auto-generated catch block e.printStackTrace(); } return null; } public void update(Subjectdetails c) { for(Subjectdetails x:custLst) { if(x.getSubjectId()==c.getSubjectId()) { x.setSubjectName(c.getSubjectName()); } } } public void delete(Subjectdetails c) { custLst.remove(c); } public static List<Subjectdetails> findNotById(int id, int from,int to) { List<Subjectdetails> subLst=custLst.subList(from, to); List<Subjectdetails> temp=new ArrayList<Subjectdetails>(); for(Subjectdetails c:subLst) { if(c.getSubjectId()!=id) { temp.add(c); } } return temp; } public static List<Subjectdetails> findLesserAsId(int id, int from,int to) { List<Subjectdetails> subLst=custLst.subList(from, to); List<Subjectdetails> temp=new ArrayList<Subjectdetails>(); for(Subjectdetails c:subLst) { if(c.getSubjectId()<=id) { temp.add(c); } } return temp; } public static List<Subjectdetails> findGreaterAsId(int id, int from,int to) { List<Subjectdetails> subLst=custLst.subList(from, to); List<Subjectdetails> temp=new ArrayList<Subjectdetails>(); for(Subjectdetails c:subLst) { if(c.getSubjectId()>=id) { temp.add(c); } } return temp; } } Subjectdetails.hbm.xml <hibernate-mapping> <class name="vb.sofware.pojos.Subjectdetails" table="subjectdetails" catalog="vbsoftware"> <id name="subjectId" type="int"> <column name="subject_id" /> <generator class="increment"/> </id> <property name="subjectName" type="string"> <column name="subject_name" length="150" /> </property> <property name="createrId" type="java.lang.Integer"> <column name="creater_id" /> </property> <property name="createdDate" type="timestamp"> <column name="created_date" length="19" /> </property> <property name="updateId" type="java.lang.Integer"> <column name="update_id" /> </property> <property name="updatedDate" type="timestamp"> <column name="updated_date" length="19" /> </property> </class> </hibernate-mapping> My POJO - Subjectdetails.java package v.esoft.pojos; // Generated Oct 6, 2012 1:58:21 PM by Hibernate Tools 3.4.0.CR1 import java.util.Date; /** * Subjectdetails generated by hbm2java */ public class Subjectdetails implements java.io.Serializable { private int subjectId; private String subjectName; private Integer createrId; private Date createdDate; private Integer updateId; private Date updatedDate; public Subjectdetails( String subjectName) { //this.subjectId = subjectId; this.subjectName = subjectName; } public Subjectdetails() { } public Subjectdetails(int subjectId) { this.subjectId = subjectId; } public Subjectdetails(int subjectId, String subjectName, Integer createrId, Date createdDate, Integer updateId, Date updatedDate) { this.subjectId = subjectId; this.subjectName = subjectName; this.createrId = createrId; this.createdDate = createdDate; this.updateId = updateId; this.updatedDate = updatedDate; } public Subjectdetails( String subjectName, Integer createrId, Date createdDate, Integer updateId, Date updatedDate) { this.subjectName = subjectName; this.createrId = createrId; this.createdDate = createdDate; this.updateId = updateId; this.updatedDate = updatedDate; } public int getSubjectId() { return this.subjectId; } public void setSubjectId(int subjectId) { this.subjectId = subjectId; } public String getSubjectName() { return this.subjectName; } public void setSubjectName(String subjectName) { this.subjectName = subjectName; } public Integer getCreaterId() { return this.createrId; } public void setCreaterId(Integer createrId) { this.createrId = createrId; } public Date getCreatedDate() { return this.createdDate; } public void setCreatedDate(Date createdDate) { this.createdDate = createdDate; } public Integer getUpdateId() { return this.updateId; } public void setUpdateId(Integer updateId) { this.updateId = updateId; } public Date getUpdatedDate() { return this.updatedDate; } public void setUpdatedDate(Date updatedDate) { this.updatedDate = updatedDate; } } And my Sql query is CREATE TABLE IF NOT EXISTS `subjectdetails` ( `subject_id` int(3) NOT NULL, `subject_name` varchar(150) DEFAULT NULL, `creater_id` int(5) DEFAULT NULL, `created_date` datetime DEFAULT NULL, `update_id` int(5) DEFAULT NULL, `updated_date` datetime DEFAULT NULL, PRIMARY KEY (`subject_id`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1;

    Read the article

  • Getting Started with Chart control in ASP.Net 4.0

    - by sreejukg
    In this article I am going to demonstrate the Chart control available in ASP.Net 4 and Visual Studio 2010. Most of the web applications need to generate reports for business users. The business users are happy to view the results in a graphical format more that seeing it in numbers. For the purpose of this demonstration, I have created a sales table. I am going to create charts from this sale data. The sale table looks as follows I have created an ASP.Net web application project in Visual Studio 2010. I have a default.aspx page that I am going to use for the demonstration. First I am going to add a chart control to the page. Visual Studio 2010 has a chart control. The Chart Control comes under the Data Tab in the toolbox. Drag and drop the Chart control to the default.aspx page. Visual Studio adds the below markup to the page. <asp:Chart ID="Chart1" runat="server"></asp:Chart> In the designer view, the Chart controls gives the following output. As you can see this is exactly similar to other server controls in ASP.Net, and similar to other controls under the data tab, Chart control is also a data bound control. So I am going to bind this with my sales data. From the design view, right click the chart control and select “show smart tag” Here you need so choose the Data source property and the chart type. From the choose data source drop down, select new data source. In the data source configuration wizard, select the SQL data base and write the query to retrieve the data. At first I am going to show the chart for amount of sales done by each sales person. I am going to use the following query inside sqldatasource select command. “SELECT SUM(SaleAmount) AS Expr1, salesperson FROM SalesData GROUP BY SalesPerson” This query will give me the amount of sales achieved by each sales person. The mark up of SQLDataSource is as follows. <asp:SqlDataSource ID="SqlDataSource1" runat="server" ConnectionString="<%$ ConnectionStrings:SampleConnectionString %>" SelectCommand="SELECT SUM(SaleAmount) as amount, SalesPerson FROM SalesData GROUP BY SalesPerson"></asp:SqlDataSource> Once you selected the data source for the chart control, you need to select the X and Y values for the columns. I have entered salesperson in the X Value member and amount in the Y value member. After modifications, the Chart control looks as follows Click F5 to run the application. The output of the page is as follows. Using ASP.Net it is much easier to represent your data in graphical format. To show this chart, I didn’t even write any single line of code. The chart control is a great tool that helps the developer to show the business intelligence in their applications without using third party products. I will write another blog that explore further possibilities that shows more reports by using the same sales data. If you want to get the Project in zipped format, post your email below.

    Read the article

  • From NaN to Infinity...and Beyond!

    - by Tony Davis
    It is hard to believe that it was once possible to corrupt a SQL Server Database by storing perfectly normal data values into a table; but it is true. In SQL Server 2000 and before, one could inadvertently load invalid data values into certain data types via RPC calls or bulk insert methods rather than DML. In the particular case of the FLOAT data type, this meant that common 'special values' for this type, namely NaN (not-a-number) and +/- infinity, could be quite happily plugged into the database from an application and stored as 'out-of-range' values. This was like a time-bomb. When one then tried to query this data; the values were unsupported and so data pages containing them were flagged as being corrupt. Any query that needed to read a column containing the special value could fail or return unpredictable results. Microsoft even had to issue a hotfix to deal with failures in the automatic recovery process, caused by the presence of these NaN values, which rendered the whole database inaccessible! This problem is history for those of us on more current versions of SQL Server, but its ghost still haunts us. Recently, for example, a developer on Red Gate’s SQL Response team reported a strange problem when attempting to load historical monitoring data into a SQL Server 2005 database via the C# ADO.NET provider. The ratios used in some of their reporting calculations occasionally threw out NaN or infinity values, and the subsequent attempts to load these values resulted in a nasty error. It turns out to be a different manifestation of the same problem. SQL Server 2005 still does not fully support the IEEE 754 standard for floating point numbers, in that the FLOAT data type still cannot handle NaN or infinity values. Instead, they just added validation checks that prevent the 'invalid' values from being loaded in the first place. For people migrating from SQL Server 2000 databases that contained out-of-range FLOAT (or DATETIME etc.) data, to SQL Server 2005, Microsoft have added to the latter's version of the DBCC CHECKDB (or CHECKTABLE) command a DATA_PURITY clause. When enabled, this will seek out the corrupt data, but won’t fix it. You have to do this yourself in what can often be a slow, painful manual process. Our development team, after a quizzical shrug of the shoulders, simply decided to represent NaN and infinity values as NULL, and move on, accepting the minor inconvenience of not being able to tell them apart. However, what of scientific, engineering and other applications that really would like the luxury of being able to both store and access these perfectly-reasonable floating point data values? The sticking point seems to be the stipulation in the IEEE 754 standard that, when NaN is compared to any other value including itself, the answer is "unequal" (i.e. FALSE). This is clearly different from normal number comparisons and has repercussions for such things as indexing operations. Even so, this hardly applies to infinity values, which are single definite values. In fact, there is some encouraging talk in the Connect note on this issue that they might be supported 'in the SQL Server 2008 timeframe'. If didn't happen; SQL 2008 doesn't support NaN or infinity values, though one could be forgiven for thinking otherwise, based on the MSDN documentation for the FLOAT type, which states that "The behavior of float and real follows the IEEE 754 specification on approximate numeric data types". However, the truth is revealed in the XPath documentation, which states that "…float (53) is not exactly IEEE 754. For example, neither NaN (Not-a-Number) nor infinity is used…". Is it really so hard to fix this problem the right way, and properly support in SQL Server the IEEE 754 standard for the floating point data type, NaNs, infinities and all? Oracle seems to have managed it quite nicely with its BINARY_FLOAT and BINARY_DOUBLE types, so it is technically possible. We have an enterprise-class database that is marketed as being part of an 'integrated' Windows platform. Absurdly, we have .NET and XPath libraries that fully support the standard for floating point numbers, and we can't even properly store these values, let alone query them, in the SQL Server database! Cheers, Tony.

    Read the article

  • 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.

    Read the article

  • Archiving SQLHelp tweets

    - by jamiet
    #SQLHelp is a Twitter hashtag that can be used by any Twitter user to get help from the SQL Server community. I think its fair to say that in its first year of being it has proved to be a very useful resource however Kendra Little (@kendra_little) made a very salient point yesterday when she tweeted: Is there a way to search the archives of #sqlhelp Trying to remember answer to a question I know I saw a couple months ago http://twitter.com/#!/Kendra_Little/status/15538234184441856 This highlights an inherent problem with Twitter’s search capability – it simply does not reach far enough back in time. I have made steps to remedy that situation by putting into place two initiatives to archive Tweets that contain the #sqlhelp hashtag. The Archivist http://archivist.visitmix.com/ is a free service that, quite simply, archives a history of tweets that contain a given search term by periodically polling Twitter’s search service with that search term and subsequently displaying a dashboard providing an aggregate view of those tweets for things like tweet volume over time, top users and top words (Archivist FAQ). I have set up an archive on The Archivist for “sqlhelp” which you can view at http://archivist.visitmix.com/jamiet/7. Here is a screenshot of the SQLHelp dashboard 36 minutes after I set it up: There is lots of good information in there, including the fact that Jonathan Kehayias (@SQLSarg) is the most active SQLHelp tweeter (I suspect as an answerer rather than a questioner ) and that SSIS has proven to be a rather (ahem) popular subject!! Datasift The Archivist has its uses though for our purposes it has a couple of downsides. For starters you cannot search through an archive (which is what Kendra was after) and nor can you export the contents of the archive for offline analysis. For those functions we need something a bit more heavyweight and for that I present to you Datasift. Datasift is a tool (currently an alpha release) that allows you to search for tweets and provide them through an object called a Datasift stream. That sounds very similar to normal Twitter search though it has one distinct advantage that other Twitter search tools do not – Datasift has access to Twitter’s Streaming API (aka the Twitter Firehose). In addition it has access to a lot of other rather nice features: It provides the Datasift API that allows you to consume the output of a Datasift stream in your tool of choice (bring on my favourite ultimate mashup tool J ) It has a query language (called Filtered Stream Definition Language – FSDL for short) A Datasift stream can consume (and filter) other Datasift streams Datasift can (and does) consume services other than Twitter If I refer to Datasift as “ETL for tweets” then you may get some sort of idea what it is all about. Just as I did with The Archivist I have set up a publicly available Datasift stream for “sqlhelp” at http://datasift.net/stream/1581/sqlhelp. Here is the FSDL query that provides the data: twitter.text contains "sqlhelp" Pretty simple eh? At the current time it provides little more than a rudimentary dashboard but as Datasift is currently an alpha release I think this may be worth keeping an eye on. The real value though is the ability to consume the output of a stream via Datasift’s RESTful API, observe: http://api.datasift.net/stream.xml?stream_identifier=c7015255f07e982afdeebdf1ae6e3c0d&username=jamiet&api_key=XXXXXXX (Note that an api_key is required during the alpha period so, given that I’m not supplying my api_key, this URI will not work for you) Just to prove that a Datasift stream can indeed consume data from another stream I have set up a second stream that further filters the first one for tweets containing “SSIS”. That one is at http://datasift.net/stream/1586/ssis-sqlhelp and here is the FSDL query: rule "414c9845685ff8d2548999cf3162e897" and (interaction.content contains "ssis") When Datasift moves beyond alpha I’ll re-assess how useful this is going to be and post a follow-up blog. @Jamiet

    Read the article

  • Bind a Wijmo Grid to Salesforce.com Through the Salesforce OData Connector

    - by dataintegration
    This article will explain how to connect to any RSSBus OData Connector with Wijmo's data grid using JSONP. While the example will use the Salesforce Connector, the same process can be followed for any of the RSSBus OData Connectors. Step 1: Download and install both the Salesforce Connector from RSSBus and the Wijmo javascript library. Step 2: Next you will want to configure the Salesforce Connector to connect with your Salesforce account. If you browse to the Help tab in the Salesforce Connector application, there is a link to the Getting Started Guide which will walk you through setting up the Salesforce Connector. Step 3: Once you have successfully configured the Salesforce Connector application, you will want to open a Wijmo sample grid file to edit. This example will use the overview.html grid found in the Samples folder. Step 4: First, we will wrap the jQuery document ready function in a callback function for the JSONP service. In this example, we will wrap this in function called fnCallback which will take a single object args. <script id="scriptInit" type="text/javascript"> function fnCallback(args) { $(document).ready(function () { $("#demo").wijgrid({ ... }); }); }; </script> Step 5: Next, we need to format the columns object in a format that Wijmo's data grid expects. This is done by adding the headerText: element for each column. <script id="scriptInit" type="text/javascript"> function fnCallback(args) { var columns = []; for (var i = 0; i < args.columnnames.length; i++){ var col = { headerText: args.columnnames[i]}; columns.push(col); } $(document).ready(function () { $("#demo").wijgrid({ ... }); }); }; </script> Step 6: Now the wijgrid parameters are ready to be set. In this example, we will set the data input parameter to the args.data object and the columns input parameter to our newly created columns object. The resulting javascript function should look like this: <script id="scriptInit" type="text/javascript"> function fnCallback(args) { var columns = []; for (var i = 0; i < args.columnnames.length; i++){ var col = { headerText: args.columnnames[i]}; columns.push(col); } $(document).ready(function () { $("#demo").wijgrid({ allowSorting: true, allowPaging: true, pageSize: 10, data: args.data, columns: columns }); }); }; </script> Step 7: Finally, we need to add the JSONP reference to our Salesforce Connector's data table. You can find this by clicking on the Settings tab of the Salesforce Connector. Once you have found the JSONP URL, you will need to supply a valid table name that you want to connect with Wijmo. In this example, we will connect to the Lead table. You will also need to add authentication options in this step. In the example we will append the authtoken of the user who has access to the Salesforce Connector using the @authtoken query string parameter. IMPORTANT: This is not secure and will expose the authtoken of the user whose authtoken you supply in this step. There are other ways to secure the user's authtoken, but this example uses a query string parameter for simplicity. <script src="http://localhost:8181/sfconnector/data/conn/Lead.rsd?@jsonp=fnCallback&sql:query=SELECT%20*%20FROM%20Lead&@authtoken=<myAuthToken>" type="text/javascript"></script> Step 8: Now, we are done. If you point your browser to the URL of the sample, you should see your Salesforce.com leads in a Wijmo data grid.

    Read the article

  • Floating Panels and Describe Windows in Oracle SQL Developer

    - by thatjeffsmith
    One of the challenges I face as I try to share tips about our software is that I tend to assume there are features that you just ‘know about.’ Either they’re so intuitive that you MUST know about them, or it’s a feature that I’ve been using for so long I forget that others may have never even seen it before. I want to cover two of those today - Describe (DESC) – SHIFT+F4 Floating Panels My super-exciting desktop SQL Developer and Describe DESC or Describe is an Oracle SQL*Plus command. It shows what a table or view is composed of in terms of it’s column definition. Here’s an example: SQL*Plus: Release 11.2.0.3.0 Production on Fri Sep 21 14:25:37 2012 Copyright (c) 1982, 2011, Oracle. All rights reserved. Connected to: Oracle Database 11g Enterprise Edition Release 11.2.0.3.0 - Production With the Partitioning, OLAP, Data Mining and Real Application Testing options SQL> desc beer; Name Null? Type ----------------------------------------- -------- ---------------------------- BREWERY NOT NULL VARCHAR2(100) CITY VARCHAR2(100) STATE VARCHAR2(100) COUNTRY VARCHAR2(100) ID NUMBER SQL> You can get the same information – and a good bit more – in SQL Developer using the SQL Developer DESC command. You invoke it with SHIFT+F4. It will open a floating (non-modal!) window with the information you want. Here’s an example: I can see my column definitions, constratins, stats, privs, etc A few ‘cool’ things you should be aware of: I can open as many as I want, and still work in my worksheet, browser, etc. I can also DESC an index, user, or most any other database object I can of course move them off my primary desktop display The DESC panel’s are read-only. I can’t drop a constraint from within the DESC window of a given table. But for dragging columns into my worksheet, and checking out the stats for my objects as I query them – it’s very, very handy. Try This Right Now Type ‘scott.emp’ (or some other table you have), place your cursor on the text, and hit SHIFT+F4. You’ll see the EMP object open. Now click into a column name in the columns page. Drag it into your worksheet. It will paste that column name into your query. This is an alternative for those that don’t like our code insight feature or dragging columns off the connection tree (new for v3.2!) Got it? SQL Developer’s Floating Panels Ok, let’s talk about a similar feature. Did you know that any dockable panel from the View menu can also be ‘floated?’ One of my favorite features is the SQL History. Every query I run is recorded, and I can recall them later without having to remember what I ran and when. And I USUALLY use the keyboard shortcuts for this. Let your trouble float away…if only it were so easy as a right-click in the real world. But sometimes I still want to see my recall list without having to give up my screen real estate. So I just mouse-right click on the panel tab and select ‘Float.’ Then I move it over to my secondary display – see the poorly lit picture in the beginning of this post. And that’s it. Simple, I know. But I thought you should know about these two things!

    Read the article

  • MySql Connector/NET 6.7.4 GA has been released

    - by fernando
    MySQL Connector/Net 6.7.4, a new version of the all-managed .NET driver for MySQL has been released.  This is the GA, is feature complete. It is recommended for production environments.  It is appropriate for use with MySQL server versions 5.0-5.7.  It is now available in source and binary form from http://dev.mysql.com/downloads/connector/net/#downloads and mirror sites (note that not all mirror sites may be up to date at this point-if you can't find this version on some mirror, please try again later or choose another download site.) The 6.7 version of MySQL Connector/Net brings the following new features: -  WinRT Connector. -  Load Balancing support. -  Entity Framework 5.0 support. -  Memcached support for Innodb Memcached plugin. -  This version also splits the product in two: from now on, starting version 6.7, Connector/NET will include only the former Connector/NET ADO.NET driver, Entity Framework and ASP.NET providers (Core libraries of MySql.Data, MySql.Data.Entity & MySql.Web). While all the former product Visual Studio integration (Design support, Intellisense, Debugger) are available as part of MySql Windows Installer under the name "MySql for Visual Studio".  WinRT Connector  ------------------------------------------- Now you can write MySql data access apps in Windows Runtime (aka Store Apps) using the familiar API of Connector/NET for .NET.  Load Balancing Support  -------------------------------------------  Now you can setup a Replication or Cluster configuration in the backend, and Connector/NET will balance the load of queries among all servers making up the backend topology.  Entity Framework 5.0  -------------------------------------------  Connector/NET is now compatible with EF 5, including special features of EF 5 like spatial types.  Memcached  -------------------------------------------  Just setup Innodb memcached plugin and use Connector/NET new APIs to establish a client to MySql 5.6 server's memcached daemon.  Bug fixes included in this release: - Fix for Entity Framework when inserts data having Identity columns (Oracle bug #16494585). - Fix for Connector/NET cannot read data from a MySql table using UTF-16/UTF-32 (MySql bug #69169, Oracle bug #16776818). - Fix for Malformed query in Entity Framework when eager loading due to multiple projections (MySql bug #67183, Oracle bug #16872852). - Fix for database objects with 'dbo' prefix when using automatic migrations in Entity Framework 5.0 (Oracle bug #16909439). - Fix for bug IIS application pool reset worker process causes website to crash (Oracle bug #16909237, Mysql Bug #67665). - Fix for bug Error in LINQ to Entities query when using Distinct().Count() (MySql Bug #68513, Oracle bug #16950146). - Fix for occasionally return no data when socket connection is slow, interrupted or delayed (MySql bug #69039, Oracle bug #16950212). - Fix for ConstraintException when filling a datatable (MySql bug #65065, Oracle bug #16952323). - Fix for Data Provider is not found after uninstalling Mysql for visual studio (Oracle bug #16973456). - Fix for nested sql generated for LINQ to Entities query with Take and Order by (MySql bug #65723, Oracle bug #16973939). The documentation is available at http://dev.mysql.com/doc/refman/5.7/en/connector-net.html  Enjoy and thanks for the support!  --  Fernando Gonzalez Sanchez | Software Engineer |  Oracle MySQL Windows Experience Team, Connector/NET  Guadalajara | Jalisco | Mexico 

    Read the article

  • Pull Request Changes, Multi-Selection in Advanced View, and Advertisement Changes

    [Do you tweet? Follow us on Twitter @matthawley and @adacole_msft] We deployed a new version of the CodePlex website today. Pull Request Changes In this release, we have begun to re-focus on Pull Requests to ensure a productive experience between the project users and developers. We feel we made significant progress in this area for this release and look forward to using your feedback to drive future iterations. One of the biggest hurdles people have indicated is the inability to see what a pull request includes without pulling the source down from a Mercurial client. With today’s changes, any user has the ability to view a pull request, the changesets / changes included, and perform an inline diff of the file. When a pull request is made, the CodePlex website will query for all outgoing changes from the fork to the main repository for a point-in-time comparison. Because of this point-in-time comparison… All existing pull requests created prior to this release will not have changesets associated with them. If new commits are pushed to the fork while a pull request is active, they will not appear associated with the pull request. The pull request will need to be re-submitted for them to appear. Once a pull request is created, you can “View the Pull Request” which takes you to a page that looks like As you may notice, we now display a lot more detailed information regarding that pull request including who it was requested by and when, the associated changesets, the description, who it’s assigned to (we’ll come back to this) and the listing of summarized file changes. What you’ll also notice, is that each modified file has the ability to view a diff of all changes made. When you click “(view diff)” for a file, an inline diff experience appears. This new experience allows you to quickly navigate through all of the modified files as well as viewing the various change blocks for each file. You’ll also notice as you browse through each file’s changes, we update the URL to include the file path so you can quickly send a direct link to a pull request’s file. Clicking “(close diff)” will bring you back to the original pull request view. View this pull request live on WikiPlex. Pull Request Review Assignment Another new feature we added for pull requests is the ability for project members to assign pull requests for review. Any project member has the ability to assign (and re-assign if needed) a pull request to a project member. Once the assignment has been made, that project member will be notified via email of the assignment. Once they complete the review of the pull request, they can either accept or deny it similarly to the previous process. Multi-Selection in Advanced View Filters One of the more recent requests we have heard from users is the ability multi-select advanced view filters for work items. We are happy to announce this is now possible. Simply control-click the multiple options for each filter item and your work item query will be refined as such. Should you happen to unselect all options for a given filter, it will automatically reset to the default option for that filter. Furthermore, the “Direct Link” URL will be updated to include the multi-selected options for each filter. Note: The “Direct Link” feature was released in our previous deployment, just never written about. It allows you to capture the current state of your query and send it to other individuals. Advertisement Changes Very recently, the advertiser (The Lounge) we partnered to provide advertising revenue for projects, or donated to charity, was acquired by Lake Quincy Media. There has been no change in the advertising platform offering, and all projects have been converted over to using the new infrastructure. Project owners should note the new contact information for getting paid. The CodePlex team values your feedback, and is frequently monitoring Twitter, our Discussions and Issue Tracker for new features or problems. If you’ve not visited the Issue Tracker recently, please take a few moments to log an idea or vote for the features you would most like to see implemented on CodePlex.

    Read the article

  • NHibernate 3.0 and FluentNHibernate, how to get up and running&hellip;.

    - by DesigningCode
    First up. Its actually really easy. I’m not very religious about my DB tech, I don’t really care, I just want something that works.  So I’m happy to consider all options if they provide an advantage, and recently I was considering jumping from NHibernate to EF 4.0.  However before ditching NHibernate and jumping to EF 4.0 I thought I should try the head version of NHibernates trunk and the Head version of FluentNHibernate. I currently have a “Repository / Unit of Work” Framework built up around these two techs.  All up it makes my life pretty simple for dealing with databases.   The problem is the current release of NHibernate + the Linq provider wasn’t too hot for our purposes.  Especially trying to plug it into older VB.NET code.   The Linq provider spat the dummy with VB.NET lambdas.  Mainly because in C# Query().Where(l => l.Name.Contains("x") || l.Name.Contains("y")).ToList(); is not the same as the VB.NET Query().Where(Function(l) l.Name.Contains("x") Or l.Name.Contains("y")).ToList VB.NET seems to spit out … well…. something different :-) so anyways… Compiling your own version of NHibernate and FluentNHibernate.  It’s actually pretty easy! First you’ll need to install tortisesvn NAnt and Git if you don’t already have them.  NHibernate first step, get the subversion trunk https://nhibernate.svn.sourceforge.net/svnroot/nhibernate/trunk/ into a directory somewhere.  eg \thirdparty\nhibernate Then use NAnt to build it.   (if you open the .sln it will show errors in that  AssemblyInfo.cs doesn’t exist ) to build it, there is a .txt document with sample command line build instructions,  I simply used :- NAnt -D:project.config=release clean build >output-release-build.log *wait* *wait* *wait* and ta da, you will have a bin directory with all the release dlls. FluentNHibernate This was pretty simple. there’s instructions here :- http://wiki.fluentnhibernate.org/Getting_started#Installation basically, with git, create a directory, and you issue the command git clone git://github.com/jagregory/fluent-nhibernate.git and wait, and soon enough you have the source. Now, from the bin directory that NHibernate spit out, take everything and dump it into the subdirectory “fluent-nhibernate\tools\NHibernate” Now, to build, you can use rake….which a ruby build system, however you can also just open the solution and build.   Which is what I did.  I had a few problems with the references which I simply re-added using the new ones.  Once built, I just took all the NHibnerate dlls, and the fluent ones and replaced my existing NHibernate / Fluent and killed off the old linq project. All I had to change is the places that used  .Linq<T>  and replace them with .Query<T>  (which was easy as I had wrapped it already to isolate my code from such changes) and hey presto, everything worked.  Even the VB.NET linq calls. I need to do some more testing as I’ve only done basic smoke tests, but its all looking pretty good, so for now, I will stick to NHibernate!

    Read the article

  • Math with Timestamp

    - by Knut Vatsendvik
    table.sql { border-width: 1px; border-spacing: 2px; border-style: dashed; border-color: #0023ff; border-collapse: separate; background-color: white; } table.sql th { border-width: 1px; padding: 1px; border-style: none; border-color: gray; background-color: white; -moz-border-radius: 0px 0px 0px 0px; } table.sql td { border-width: 1px; padding: 3px; border-style: none; border-color: gray; background-color: white; -moz-border-radius: 0px 0px 0px 0px; } .sql-keyword { color: #0000cd; background-color: inherit; } .sql-result { color: #458b74; background-color: inherit; } Got this little SQL quiz from a colleague.  How to add or subtract exactly 1 second from a Timestamp?  Sounded simple enough at first blink, but was a bit trickier than expected. If the data type had been a Date, we knew that we could add or subtract days, minutes or seconds using + or – sysdate + 1 to add one day sysdate - (1 / 24) to subtract one hour sysdate + (1 / 86400) to add one second Would the same arithmetic work with Timestamp as with Date? Let’s test it out with the following query SELECT   systimestamp , systimestamp + (1 / 86400) FROM dual; ---------- 03.05.2010 22.11.50,240887 +02:00 03.05.2010 The first result line shows us the system time down to fractions of seconds. The second result line shows the result as Date (as used for date calculation) meaning now that the granularity is reduced down to a second.   By using the PL/SQL dump() function, we can confirm this with the following query SELECT   dump(systimestamp) , dump(systimestamp + (1 / 86400)) FROM dual; ---------- Typ=188 Len=20: 218,7,5,4,8,53,9,0,200,46,89,20,2,0,5,0,0,0,0,0 Typ=13 Len=8: 218,7,5,4,10,53,10,0 Where typ=13 is a runtime representation for Date. So how can we increase the precision to include fractions of second? After investigating it a bit, we found out that the interval data type INTERVAL DAY TO SECOND could be used with the result of addition or subtraction being a Timestamp. Let’s try again our first query again, now using the interval data type. SELECT systimestamp,    systimestamp + INTERVAL '0 00:00:01.0' DAY TO SECOND(1) FROM dual; ---------- 03.05.2010 22.58.32,723659000 +02:00 03.05.2010 22.58.33,723659000 +02:00 Yes, it worked! To finish the story, here is one example showing how to specify an interval of 2 days, 6 hours, 30 minutes, 4 seconds and 111 thousands of a second. INTERVAL ‘2 6:30:4.111’ DAY TO SECOND(3)

    Read the article

  • Crime Scene Investigation: SQL Server

    - by Rodney Landrum
    “The packages are running slower in Prod than they are in Dev” My week began with this simple declaration from one of our lead BI developers, quickly followed by an emailed spreadsheet demonstrating that, over 5 executions, an extensive ETL process was running average 630 seconds faster on Dev than on Prod. The situation needed some scientific investigation to determine why the same code, the same data, the same schema would yield consistently slower results on a more powerful server. Prod had yet to be officially christened with a “Go Live” date so I had the time, and having recently been binge watching CSI: New York, I also had the inclination. An inspection of the two systems, Prod and Dev, revealed the first surprise: although Prod was indeed a “bigger” system, with double the amount of RAM of Dev, the latter actually had twice as many processor cores. On neither system did I see much sign of resources being heavily taxed, while the ETL process was running. Without any real supporting evidence, I jumped to a conclusion that my years of performance tuning should have helped me avoid, and that was that the hardware differences explained the better performance on Dev. We spent time setting up a Test system, similarly scoped to Prod except with 4 times the cores, and ported everything across. The results of our careful benchmarks left us truly bemused; the ETL process on the new server was slower than on both other systems. We burned more time tweaking server configurations, monitoring IO and network latency, several times believing we’d uncovered the smoking gun, until the results of subsequent test runs pitched us back into confusion. Finally, I decided, enough was enough. Hadn’t I learned very early in my DBA career that almost all bottlenecks were caused by code and database design, not hardware? It was time to get back to basics. With over 100 SSIS packages and hundreds of queries, each handling specific tasks such as file loads, bulk inserts, transforms, logging, and so on, the task seemed formidable. And yet, after barely an hour spent with Profiler, Extended Events, and wait statistics DMVs, I had a lead in the shape of a query that joined three tables, containing millions of rows, returned 3279 results, but performed 239K logical reads. As soon as I looked at the execution plans for the query in Dev and Test I saw the culprit, an implicit conversion warning on a join predicate field that was numeric in one table and a varchar(50) in another! I turned this information over to the BI developers who quickly resolved the data type mismatches and found and fixed “several” others as well. After the schema changes the same query with the same databases ran in under 1 second on all systems and reduced the logical reads down to fewer than 300. The analysis also revealed that on Dev, the ETL task was pulling data across a LAN, whereas Prod and Test were connected across slower WAN, in large part explaining why the same process ran slower on the latter two systems. Loading the data locally on Prod delivered a further 20% gain in performance. As we progress through our DBA careers we learn valuable lessons. Sometimes, with a project deadline looming and pressure mounting, we choose to forget them. I was close to giving into the temptation to throw more hardware at the problem. I’m pleased at least that I resisted, though I still kick myself for not looking at the code on day one. It can seem a daunting prospect to return to the fundamentals of the code so close to roll out, but with the right tools, and surprisingly little time, you can collect the evidence that reveals the true problem. It is a lesson I trust I will remember for my next 20 years as a DBA, if I’m ever again tempted to bypass the evidence.

    Read the article

  • Fetching Partition Information

    - by Mike Femenella
    For a recent SSIS package at work I needed to determine the distinct values in a partition, the number of rows in each partition and the file group name on which each partition resided in order to come up with a grouping mechanism. Of course sys.partitions comes to mind for some of that but there are a few other tables you need to link to in order to grab the information required. The table I’m working on contains 8.8 billion rows. Finding the distinct partition keys from this table was not a fast operation. My original solution was to create  a temporary table, grab the distinct values for the partitioned column, then update via sys.partitions for the rows and the $partition function for the partitionid and finally look back to the sys.filegroups table for the filegroup names. It wasn’t pretty, it could take up to 15 minutes to return the results. The primary issue is pulling distinct values from the table. Queries for distinct against 8.8 billion rows don’t go quickly. A few beers into a conversation with a friend and we ended up talking about work which led to a conversation about the task described above. The solution was already built in SQL Server, just needed to pull it together. The first table I needed was sys.partition_range_values. This contains one row for each range boundary value for a partition function. In my case I have a partition function which uses dayid values. For example July 4th would be represented as an int, 20130704. This table lists out all of the dayid values which were defined in the function. This eliminated the need to query my source table for distinct dayid values, everything I needed was already built in here for me. The only caveat was that in my SSIS package I needed to create a bucket for any dayid values that were out of bounds for my function. For example if my function handled 20130501 through 20130704 and I had day values of 20130401 or 20130705 in my table, these would not be listed in sys.partition_range_values. I just created an “everything else” bucket in my ssis package just in case I had any dayid values unaccounted for. To get the number of rows for a partition is very easy. The sys.partitions table contains values for each partition. Easy enough to achieve by querying for the object_id and index value of 1 (the clustered index) The final piece of information was the filegroup name. There are 2 options available to get the filegroup name, sys.data_spaces or sys.filegroups. For my query I chose sys.filegroups but really it’s a matter of preference and data needs. In order to bridge between sys.partitions table and either sys.data_spaces or sys.filegroups you need to get the container_id. This can be done by joining sys.allocation_units.container_id to the sys.partitions.hobt_id. sys.allocation_units contains the field data_space_id which then lets you join in either sys.data_spaces or sys.file_groups. The end result is the query below, which typically executes for me in under 1 second. I’ve included the join to sys.filegroups and to sys.dataspaces, and I’ve  just commented out the join sys.filegroups. As I mentioned above, this shaves a good 10-15 minutes off of my original ssis package and is a really easy tweak to get a boost in my ETL time. Enjoy.

    Read the article

  • Investigating on xVelocity (VertiPaq) column size

    - by Marco Russo (SQLBI)
      In January I published an article about how to optimize high cardinality columns in VertiPaq. In the meantime, VertiPaq has been rebranded to xVelocity: the official name is now “xVelocity in-memory analytics engine (VertiPaq)” but using xVelocity and VertiPaq when we talk about Analysis Services has the same meaning. In this post I’ll show how to investigate on columns size of an existing Tabular database so that you can find the most important columns to be optimized. A first approach can be looking in the DataDir of Analysis Services and look for the folder containing the database. Then, look for the biggest files in all subfolders and you will find the name of a file that contains the name of the most expensive column. However, this heuristic process is not very optimized. A better approach is using a DMV that provides the exact information. For example, by using the following query (open SSMS, open an MDX query on the database you are interested to and execute it) you will see all database objects sorted by used size in a descending way. SELECT * FROM $SYSTEM.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS ORDER BY used_size DESC You can look at the first rows in order to understand what are the most expensive columns in your tabular model. The interesting data provided are: TABLE_ID: it is the name of the object – it can be also a dictionary or an index COLUMN_ID: it is the column name the object belongs to – you can also see ID_TO_POS and POS_TO_ID in case they refer to internal indexes RECORDS_COUNT: it is the number of rows in the column USED_SIZE: it is the used memory for the object By looking at the ration between USED_SIZE and RECORDS_COUNT you can understand what you can do in order to optimize your tabular model. Your options are: Remove the column. Yes, if it contains data you will never use in a query, simply remove the column from the tabular model Change granularity. If you are tracking time and you included milliseconds but seconds would be enough, round the data source column to the nearest second. If you have a floating point number but two decimals are good enough (i.e. the temperature), round the number to the nearest decimal is relevant to you. Split the column. Create two or more columns that have to be combined together in order to produce the original value. This technique is described in VertiPaq optimization article. Sort the table by that column. When you read the data source, you might consider sorting data by this column, so that the compression will be more efficient. However, this technique works better on columns that don’t have too many distinct values and you will probably move the problem to another column. Sorting data starting from the lower density columns (those with a few number of distinct values) and going to higher density columns (those with high cardinality) is the technique that provides the best compression ratio. After the optimization you should be able to reduce the used size and improve the count/size ration you measured before. If you are interested in a longer discussion about internal storage in VertiPaq and you want understand why this approach can save you space (and time), you can attend my 24 Hours of PASS session “VertiPaq Under the Hood” on March 21 at 08:00 GMT.

    Read the article

  • Designing An ACL Based Permission System

    - by ryanzec
    I am trying to create a permissions system where everything is going to be stored in MySQL (or some database) and pulled using PHP for a project management system I am building.  I am right now trying to do it is an ACL kind of way.  There are a number key features I want to be able to support: 1.  Being able to assign permissions without being tied to a specific object. The reason for this is that I want to be able to selectively show/hide elements of the UI based on permissions at a point where I am not directly looking at a domain object instance.  For instance, a button to create a new project should only should only be shown to users that have the pm.project.create permission but obviously you can assign a create permission to an domain object instance (as it is already created). 2.  Not have to assign permissions for every single object. Obviously creating permissions entries for every single object (projects, tickets, comments, etc…) would become a nightmare to maintain so I want to have some level of permission inheritance. *3.  Be able to filter queries based on permissions. This would be a really nice to have but I am not sure if it is possible.  What I mean by this is say I have a page that list all projects.  I want the query that pulls all projects to incorporate the ACL so that it would not show projects that the current user does not have pm.project.read access to.  This would have to be incorporated into the main query as if it is a process that is done after that main query (which I know I could do) certain features like pagination become much more difficult. Right now this is my basic design for the tables: AclEntities id - the primary key key - the unique identifier for the domain object (usually the primary key of that object) parentId - the parent of the domain object (like the project object if this was a ticket object) aclDomainObjectId - metadata about the domain object AclDomainObjects id - primary key title - simple string to unique identify the domain object(ie. project, ticket, comment, etc…) fullyQualifiedClassName - the fully qualified class name for use in code (I am using namespaces) There would also be tables mapping AclEntities to Users and UserGroups. I also have this interface that all acl entity based object have to implement: IAclEntity getAclKey() - to the the unique key for this specific instance of the acl domain object (generally return the primary key or a concatenated string of a composite primary key) getAclTitle() - to get the unique title for the domain object (generally just returning a static string) getAclDisplayString() - get the string that represents this entity (generally one or more field on the object) getAclParentEntity() - get the parent acl entity object (or null if no parent) getAclEntity() - get the acl enitty object for this instance of the domain object (or null if one has not been created yet) hasPermission($permissionString, $user = null) - whether or not the user has the permission for this instance of the domain object static getFromAclEntityId($aclEntityId) - get a specific instance of the domain object from an acl entity id. Do any of these features I am looking for seems hard to support or are just way off base? Am I missing or not taking in account anything in my implementation? Is performance something I should keep in mind?

    Read the article

  • Using Recursive SQL and XML trick to PIVOT(OK, concat) a "Document Folder Structure Relationship" table, works like MySQL GROUP_CONCAT

    - by Kevin Shyr
    I'm in the process of building out a Data Warehouse and encountered this issue along the way.In the environment, there is a table that stores all the folders with the individual level.  For example, if a document is created here:{App Path}\Level 1\Level 2\Level 3\{document}, then the DocumentFolder table would look like this:IDID_ParentFolderName1NULLLevel 121Level 232Level 3To my understanding, the table was built so that:Each proposal can have multiple documents stored at various locationsDifferent users working on the proposal will have different access level to the folder; if one user is assigned access to a folder level, she/he can see all the sub folders and their content.Now we understand from an application point of view why this table was built this way.  But you can quickly see the pain this causes the report writer to show a document link on the report.  I wasn't surprised to find the report query had 5 self outer joins, which is at the mercy of nobody creating a document that is buried 6 levels deep, and not to mention the degradation in performance.With the help of 2 posts (at the end of this post), I was able to come up with this solution:Use recursive SQL to build out the folder pathUse SQL XML trick to concat the strings.Code (a reminder, I built this code in a stored procedure.  If you copy the syntax into a simple query window and execute, you'll get an incorrect syntax error) Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} -- Get all folders and group them by the original DocumentFolderID in PTSDocument table;WITH DocFoldersByDocFolderID(PTSDocumentFolderID_Original, PTSDocumentFolderID_Parent, sDocumentFolder, nLevel)AS (-- first member      SELECT 'PTSDocumentFolderID_Original' = d1.PTSDocumentFolderID            , PTSDocumentFolderID_Parent            , 'sDocumentFolder' = sName            , 'nLevel' = CONVERT(INT, 1000000)      FROM (SELECT DISTINCT PTSDocumentFolderID                  FROM dbo.PTSDocument_DY WITH(READPAST)            ) AS d1            INNER JOIN dbo.PTSDocumentFolder_DY AS df1 WITH(READPAST)                  ON d1.PTSDocumentFolderID = df1.PTSDocumentFolderID      UNION ALL      -- recursive      SELECT ddf1.PTSDocumentFolderID_Original            , df1.PTSDocumentFolderID_Parent            , 'sDocumentFolder' = df1.sName            , 'nLevel' = ddf1.nLevel - 1      FROM dbo.PTSDocumentFolder_DY AS df1 WITH(READPAST)            INNER JOIN DocFoldersByDocFolderID AS ddf1                  ON df1.PTSDocumentFolderID = ddf1.PTSDocumentFolderID_Parent)-- Flatten out folder path, DocFolderSingleByDocFolderID(PTSDocumentFolderID_Original, sDocumentFolder)AS (SELECT dfbdf.PTSDocumentFolderID_Original            , 'sDocumentFolder' = STUFF((SELECT '\' + sDocumentFolder                                         FROM DocFoldersByDocFolderID                                         WHERE (PTSDocumentFolderID_Original = dfbdf.PTSDocumentFolderID_Original)                                         ORDER BY PTSDocumentFolderID_Original, nLevel                                         FOR XML PATH ('')),1,1,'')      FROM DocFoldersByDocFolderID AS dfbdf      GROUP BY dfbdf.PTSDocumentFolderID_Original) And voila, I use the second CTE to join back to my original query (which is now a CTE for Source as we can now use MERGE to do INSERT and UPDATE at the same time).Each part of this solution would not solve the problem by itself because:If I don't use recursion, I cannot build out the path properly.  If I use the XML trick only, then I don't have the originating folder ID info that I need to link to the document.If I don't use the XML trick, then I don't have one row per document to show in the report.I could conceivably do this in the report function, but I'd rather not deal with the beginning or ending backslash and how to attach the document name.PIVOT doesn't do strings and UNPIVOT runs into the same problem as the above.I'm excited that each version of SQL server provides us new tools to solve old problems and/or enables us to solve problems in a more elegant wayThe 2 posts that helped me along:Recursive Queries Using Common Table ExpressionHow to use GROUP BY to concatenate strings in SQL server?

    Read the article

  • We have our standards, and we need them

    - by Tony Davis
    The presenter suddenly broke off. He was midway through his section on how to apply to the relational database the Continuous Delivery techniques that allowed for rapid-fire rounds of development and refactoring, while always retaining a “production-ready” state. He sighed deeply and then launched into an astonishing diatribe against Database Administrators, much of his frustration directed toward Oracle DBAs, in particular. In broad strokes, he painted the picture of a brave new deployment philosophy being frustratingly shackled by the relational database, and by especially by the attitudes of the guardians of these databases. DBAs, he said, shunned change and “still favored tools I’d have been embarrassed to use in the ’80′s“. DBAs, Oracle DBAs especially, were more attached to their vendor than to their employer, since the former was the primary source of their career longevity and spectacular remuneration. He contended that someone could produce the best IDE or tool in the world for Oracle DBAs and yet none of them would give a stuff, unless it happened to come from the “mother ship”. I sat blinking in astonishment at the speaker’s vehemence, and glanced around nervously. Nobody in the audience disagreed, and a few nodded in assent. Although the primary target of the outburst was the Oracle DBA, it made me wonder. Are we who work with SQL Server, database professionals or merely SQL Server fanbois? Do DBAs, in general, have an image problem? Is it a good career-move to be seen to be holding onto a particular product by the whites of our knuckles, to the exclusion of all else? If we seek a broad, open-minded, knowledge of our chosen technology, the database, and are blessed with merely mortal powers of learning, then we like standards. Vendors of RDBMSs generally don’t conform to standards by instinct, but by customer demand. Microsoft has made great strides to adopt the international SQL Standards, where possible, thanks to considerable lobbying by the community. The implementation of Window functions is a great example. There is still work to do, though. SQL Server, for example, has an unusable version of the Information Schema. One cast-iron rule of any RDBMS is that we must be able to query the metadata using the same language that we use to query the data, i.e. SQL, and we do this by running queries against the INFORMATION_SCHEMA views. Developers who’ve attempted to apply a standard query that works on MySQL, or some other database, but doesn’t produce the expected results on SQL Server are advised to shun the Standards-based approach in favor of the vendor-specific one, using the catalog views. The argument behind this is sound and well-documented, and of course we all use those catalog views, out of necessity. And yet, as database professionals, committed to supporting the best databases for the business, whatever they are now and in the future, surely our heart should sink somewhat when we advocate a vendor specific approach, to a developer struggling with something as simple as writing a guard clause. And when we read messages on the Microsoft documentation informing us that we shouldn’t rely on INFORMATION_SCHEMA to identify reliably the schema of an object, in SQL Server!

    Read the article

  • Investigating on xVelocity (VertiPaq) column size

    - by Marco Russo (SQLBI)
      In January I published an article about how to optimize high cardinality columns in VertiPaq. In the meantime, VertiPaq has been rebranded to xVelocity: the official name is now “xVelocity in-memory analytics engine (VertiPaq)” but using xVelocity and VertiPaq when we talk about Analysis Services has the same meaning. In this post I’ll show how to investigate on columns size of an existing Tabular database so that you can find the most important columns to be optimized. A first approach can be looking in the DataDir of Analysis Services and look for the folder containing the database. Then, look for the biggest files in all subfolders and you will find the name of a file that contains the name of the most expensive column. However, this heuristic process is not very optimized. A better approach is using a DMV that provides the exact information. For example, by using the following query (open SSMS, open an MDX query on the database you are interested to and execute it) you will see all database objects sorted by used size in a descending way. SELECT * FROM $SYSTEM.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS ORDER BY used_size DESC You can look at the first rows in order to understand what are the most expensive columns in your tabular model. The interesting data provided are: TABLE_ID: it is the name of the object – it can be also a dictionary or an index COLUMN_ID: it is the column name the object belongs to – you can also see ID_TO_POS and POS_TO_ID in case they refer to internal indexes RECORDS_COUNT: it is the number of rows in the column USED_SIZE: it is the used memory for the object By looking at the ration between USED_SIZE and RECORDS_COUNT you can understand what you can do in order to optimize your tabular model. Your options are: Remove the column. Yes, if it contains data you will never use in a query, simply remove the column from the tabular model Change granularity. If you are tracking time and you included milliseconds but seconds would be enough, round the data source column to the nearest second. If you have a floating point number but two decimals are good enough (i.e. the temperature), round the number to the nearest decimal is relevant to you. Split the column. Create two or more columns that have to be combined together in order to produce the original value. This technique is described in VertiPaq optimization article. Sort the table by that column. When you read the data source, you might consider sorting data by this column, so that the compression will be more efficient. However, this technique works better on columns that don’t have too many distinct values and you will probably move the problem to another column. Sorting data starting from the lower density columns (those with a few number of distinct values) and going to higher density columns (those with high cardinality) is the technique that provides the best compression ratio. After the optimization you should be able to reduce the used size and improve the count/size ration you measured before. If you are interested in a longer discussion about internal storage in VertiPaq and you want understand why this approach can save you space (and time), you can attend my 24 Hours of PASS session “VertiPaq Under the Hood” on March 21 at 08:00 GMT.

    Read the article

  • DevExpress AspxGridView filter in ObjectDataSource

    - by Constantin Baciu
    Yet another problem with DevExpress AspxGridView :) The context: One Page In the Page, a custom control In the custom Control, a AspxDropDown The AspxDropDown, has a DropDownWindowTemplate In the DropDownItemTemplate, I add a GridView and a paging/sorting/filtering enabled ObjectDataSource When handling the Selecting event of the ObjectDataSource, I should set filter parameters for the datasource. There filter parameters should come from the FilterRow of the AspxGridView (preferably using the AspxGriedView.FilterExpression property). The problem: the AspxGriedView.FilterExpression property is not set to the proper values (set by the user). Did anyone find a good implementation of what I'm trying to do here? Thanks a bunch. :)

    Read the article

  • Using Perl WWW::Facebook::API to Publish To Facebook Newsfeed

    - by Russell C.
    We use Facebook Connect on our site in conjunction with the WWW::Facebook::API CPAN module to publish to our users newsfeed when requested by the user. So far we've been able to successfully update the user's status using the following code: use WWW::Facebook::API; my $facebook = WWW::Facebook::API->new( desktop => 0, api_key => $fb_api_key, secret => $fb_secret, session_key => $query->cookie($fb_api_key.'_session_key'), session_expires => $query->cookie($fb_api_key.'_expires'), session_uid => $query->cookie($fb_api_key.'_user') ); my $response = $facebook->stream->publish( message => qq|Test status message|, ); However, when we try to update the code above so we can publish newsfeed stories that include attachments and action links as specified in the Facebook API documentation for Stream.Publish, we have tried about 100 different ways without any success. According to the CPAN documentation all we should have to do is update our code to something like the following and pass the attachments & action links appropriately which doesn't seem to work: my $response = $facebook->stream->publish( message => qq|Test status message|, attachment => $json, action_links => [@links], ); For example, we are passing the above arguments as follows: $json = qq|{ 'name': 'i\'m bursting with joy', 'href': ' http://bit.ly/187gO1', 'caption': '{*actor*} rated the lolcat 5 stars', 'description': 'a funny looking cat', 'properties': { 'category': { 'text': 'humor', 'href': 'http://bit.ly/KYbaN'}, 'ratings': '5 stars' }, 'media': [{ 'type': 'image', 'src': 'http://icanhascheezburger.files.wordpress.com/2009/03/funny-pictures-your-cat-is-bursting-with-joy1.jpg', 'href': 'http://bit.ly/187gO1'}] }|; @links = ["{'text':'Link 1', 'href':'http://www.link1.com'}","{'text':'Link 2', 'href':'http://www.link2.com'}"]; The above, nor any of the other representations we tried seem to work. I'm hoping some other perl developer out there has this working and can explain how to create the attachment and action_links variables appropriately in Perl for posting to the Facebook news feed through WWW::Facebook::API. Thanks in advance for your help!

    Read the article

  • JUnit Parameterized Runner and mvn Surefire Report integration

    - by fraido
    I'm using the Junit Parameterized Runner and the Maven Plugin Surefire Report to generate detailed reports during the mvn site phase. I've something like this @RunWith(Parameterized.class) public class MyTest { private String string1; private String string2; @Parameterized.Parameters public static Collection params() { return Arrays.asList(new String[][] { { "1", "2"}, { "3", "4"}, { "5", "6"} }); } public MyTest(String string1, String string2) { this.string1 = string1; this.string2 = string2; } @Test public void myTestMethod() { ... } @Test public void myOtherTestMethod() { ... } The report shows something like myTestMethod[0] 0.018 myTestMethod[1] 0.009 myTestMethod[2] 0.009 ... myOtherTestMethod[0] 0.018 myOtherTestMethod[1] 0.009 myOtherTestMethod[2] 0.009 ... Is there a way to display something else rather than the iteration number [0]..[1]..etc.. The constructor parameters would be a much better information. For example myTestMethod["1", "2"] 0.018 ...

    Read the article

  • Fatal error encountered during command execution with a mySQL INSERT

    - by Brian
    I am trying to execute a INSERT statement on a mySQL DB in C#: MySqlConnection connection = new MySqlConnection("SERVER=" + _dbConnection + ";" + "DATABASE=" + _dbName + ";" + "PORT=" + _dbPort + ";" + "UID=" + _dbUsername + ";" + "PASSWORD=" + _dbPassword + ";"); MySqlDataAdapter adapter; DataSet dataset = new DataSet(); command = new MySqlCommand(); command.Connection = connection; command.CommandText = "INSERT INTO plugins (pluginName, enabled) VALUES (@plugin,@enabled)"; command.Parameters.AddWithValue("@name", "pluginName"); command.Parameters.AddWithValue("@enabled", false); adapter = new MySqlDataAdapter(command); adapter.Fill(dataset); The plugin table consists of two columns: pluginName(varchar(50)) and enabled(boolean). This fails with the error: mysql Fatal error encountered during command execution. Any ideas on why this would fail?

    Read the article

  • Jqgrid + CodeIgniter

    - by Ivan
    I tried to make jqgrid work with codeigniter, but I could not do it, I only want to show the data from the table in json format... but nothing happens.. but i dont know what i am doing wrong, i cant see the table with the content i am calling. my controller class Grid extends Controller { public function f() { $this->load->model('dbgrid'); $var['grid'] = $this->dbgrid->getcontentfromtable(); foreach($var['grid'] as $row) { $responce->rows[$i]['id']=$row->id; $responce->rows[$i]['cell']=array($row->id,$row->id_catalogo); } $json = json_encode($responce); $this->load->view('vgrid',$json); } function load_view_grid() { $this->load->view('vgrid'); } } my model class Dbgrid extends Model{ function getcontentfromtable() { $sql = 'SELECT * FROM anuncios'; $query = $this->db->query($sql); $result = $query->result(); return $result; } } my view(script) $(document).ready(function() { jQuery("#list27").jqGrid({ url:'http://localhost/sitio/index.php/grid/f', datatype: "json", mtype: "post", height: 255, width: 600, colNames:['ID','ID_CATALOGO'], colModel:[ {name:'id',index:'id', width:65, sorttype:'int'}, {name:'id_catalogo',index:'id_catalogo', sorttype:'int'} ], rowNum:50, rowTotal: 2000, rowList : [20,30,50], loadonce:true, rownumbers: true, rownumWidth: 40, gridview: true, pager: '#pager27', sortname: 'item_id', viewrecords: true, sortorder: "asc", caption: "Loading data from server at once" }); }); hope someone help me

    Read the article

  • Dealing with huge SQL resultset

    - by Dave McClelland
    I am working with a rather large mysql database (several million rows) with a column storing blob images. The application attempts to grab a subset of the images and runs some processing algorithms on them. The problem I'm running into is that, due to the rather large dataset that I have, the dataset that my query is returning is too large to store in memory. For the time being, I have changed the query to not return the images. While iterating over the resultset, I run another select which grabs the individual image that relates to the current record. This works, but the tens of thousands of extra queries have resulted in a performance decrease that is unacceptable. My next idea is to limit the original query to 10,000 results or so, and then keep querying over spans of 10,000 rows. This seems like the middle of the road compromise between the two approaches. I feel that there is probably a better solution that I am not aware of. Is there another way to only have portions of a gigantic resultset in memory at a time? Cheers, Dave McClelland

    Read the article

  • Spring - adding BindingResult to newly created model attribute

    - by Max
    My task is - to create a model attribute by given request parameters, to validate it (in same method) and to give it whole to the View. I was given this code: //Create the model attribute by request parameters Promotion promotion = Promotions.get(someRequestParam); //Add the attribute to the model modelMap.addAttribute("promotion", promotion); if (!promotion.validate()) { BindingResult errors = new BeanPropertyBindingResult(promotion, "promotion"); errors.reject("promotion.invalid"); //TODO: This is the part I don't like model.put(BindingResult.MODEL_KEY_PREFIX + "promotion", errors); } This thing sure works, but that part with creating key with MODEL_KEY_PREFIX and attribute name looks very hackish and not a Spring style to me. Is there a way to make the same thing prettier?

    Read the article

< Previous Page | 384 385 386 387 388 389 390 391 392 393 394 395  | Next Page >