distinguishing barcode scanners from the keyboard in winforms

distinguishing barcode scanners from the keyboard in winforms

distinguishing barcode scanners from the keyboard in winforms

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Provided that a covering index is in place defined with the cursor ordering columns as the index keys and the measure you re aggregating as an included column, the cursor solution scans the data only once without the need for sorting . If you have p partitions with an average of r rows per partition, and a relatively uniform distribution of rows per partition, you can express the cost of this solution as p r + p r o, where o, as before, represents the cursor overhead . Therefore, the complexity of this solution is linearly proportional to the number of partitions and linearly proportional to the partition size . Assuming you have the aforementioned index in place, the cost of the set-based solution can be expressed as p r + p (r + r 2)/2. With respect to the number of partitions, the complexity of the set-based solution is linear, but with respect to the partition size, it s quadratic, or O(n2) . If the partition size grows by a factor of f, the cost increases by almost f 2 . Without the aforementioned index in place, of course, things are even worse . When the partition size is small, the set-based solution will typically be faster than the cursor solution . That s when the cursor s overhead exceeds the set-based solution s extra work of

scanning more data . However, as the partition size increases, the cursor solution s cost grows linearly while the set-based solution s cost grows much more rapidly quadratically, and at some point the cursor solution becomes faster . Tests that I ve done show that up to a partition size of about 500 rows the set-based solution tends to perform better, whereas beyond 500 rows the cursor solution performs better . I ll provide actual benchmark results later in this section . So now you can see that in certain cases a T-SQL cursor-based solution can be faster than a set-based solution . If you are willing to consider an iterative approach based on cursors, you should consider a CLR-based solution . Using a CLR procedure, you can use a data reader to iterate through the records one at a time, then pipe the rows in the result back to the caller . Think of a .NET data reader as a faster cursor than the T-SQL one . You still pay some overhead to manipulate each row, but the overhead is much lower than that of the T-SQL cursor . Here s the definition of a CLR stored procedure called SalesRunningSum that implements our task in C#:

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using using using using using System; System.Data; System.Data.SqlClient; System.Data.SqlTypes; Microsoft.SqlServer.Server;

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BugAccessorBean ComponentLocator ctx : Context updateBugList (String) : org.jdom.document ejbActivate() ejbPassivate() ejbRemove() setSessionContext(setSessionContext) ejbCreate() ctx : Context cl : ComponentLocator ComponentLocator() getInstance() : ComponentLocator getBeanHome(String) : EJBHome getWebService(String) : EJBObject Attribute Name: Type

public partial class StoredProcedures { [Microsoft.SqlServer.Server.SqlProcedure] public static void SalesRunningSum() { using (SqlConnection conn = new SqlConnection("context connection=true;")) { SqlCommand comm = new SqlCommand(); comm.Connection = conn; comm.CommandText = "" + "SELECT empid, dt, qty " + "FROM dbo.Sales " + "ORDER BY empid, dt;"; SqlMetaData[] columns = new SqlMetaData[4]; columns[0] = new SqlMetaData("empid", SqlDbType.Int); columns[1] = new SqlMetaData("dt", SqlDbType.DateTime); columns[2] = new SqlMetaData("qty", SqlDbType.Int); columns[3] = new SqlMetaData("sumqty", SqlDbType.BigInt); SqlDataRecord record = new SqlDataRecord(columns); SqlContext.Pipe.SendResultsStart(record); conn.Open(); SqlDataReader reader = comm.ExecuteReader(); SqlInt32 prvempid = 0; SqlInt64 sumqty = 0;

while (reader.Read()) { SqlInt32 empid = reader.GetSqlInt32(0); SqlInt32 qty = reader.GetSqlInt32(2); if (empid == prvempid) { sumqty += qty; } else { sumqty = qty; } prvempid = empid; record.SetSqlInt32(0, reader.GetSqlInt32(0)); record.SetSqlDateTime(1, reader.GetSqlDateTime(1)); record.SetSqlInt32(2, qty); record.SetSqlInt64(3, sumqty); SqlContext.Pipe.SendResultsRow(record); } SqlContext.Pipe.SendResultsEnd(); } } };

And here s the definition of the procedure using Microsoft Visual Basic:

The ApplicationMenu component The ApplicationMenu is a class that is responsible for building the initial menu for our system. The information necessary to build the menu is stored in a file named menu.xml. This was designed intentionally, so that new branches and functions can be added to the system with relative ease. Each time a user request is received, it is impractical to reload this information from a file. This will undoubtedly cause performance problems. In order to alleviate this problem, we employ the Singleton pattern once again and load the data into our ApplicationMenu class. Once we have initially loaded the menu, the getMenu method merely returns the JDOM containing the menu information to the caller. This is a caching method often used when the data is accessed frequently and is not frequently modified. The reason that we have chosen this architecture over using a bean is that the remote calls necessary to look up a bean require too much overhead. One instance of this class in each JVM serves our needs. In a different situation in which our class performed more intense logic, we would reevaluate our design. The next issue we must consider is the process for updating the menu. Unlike the ComponentLocator class, the ApplicationMenu must refresh itself each time the menu is updated. The solution to this problem is the use of the Service Activator pattern discussed in appendix A. In this pattern, a client subscribes to a JMS queue to receive asynchronous messages. The client listens for a message and performs some logic when a message is received. In our case, we configure a JMS queue and add the ApplicationMenu as a subscriber. Any

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