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Under most normal circumstances, however, this will not happen If you have mostly small- to medium-sized transactions (OLTP), or you have a data warehouse that performs direct-path loads or uses DBMS_STATS to analyze tables after load operations, you ll find the blocks are generally cleaned for you If you recall from the earlier section titled What Does a COMMIT Do one of the steps of COMMIT-time processing is to revisit some blocks if they are still in the SGA and if they are accessible (no one else is modifying them), and then clean them out This activity is known as a commit clean out and is the activity that cleans out the transaction information on our modified block Optimally, our COMMIT can clean out the blocks so that a subsequent SELECT (read) will not have to clean it out.
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Table 1-1. UML class diagram notation (continued) Program element Composition Diagram element Meaning A has a B, and B depends on A.
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Solution to Exercise 5-3. Rewrite the program from Exercise 5-2 to do the same work with a switch statement. The switch statement makes a nice alternative to programs that have lots of nested if statements. To rewrite the program using a switch, you first have to define an enum that outlines the various possibilities. Then you ll need a series of (nonnested) if statements to determine which of the enum options applies. Once you ve done that, you can use a switch to output the appropriate statement, and even include a default that accounts for unexpected input. One solution is shown in Example A-11.
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Now create a new class named Consultant that will implement the INotifyDataErrorInfo interface. Make sure that you add this class to the previously created folder INotifyDataErrorInfoDemo in the chapter6 Silverlight project, to keep the same structure as described here. We need to add the following namespaces: using System.ComponentModel; using System.Collections.Generic; using System.Linq; For the ease of understanding, we will divide the code into regions (#region). So let us start with the region private members (#region private members). As you can see in the code snippet, there is a ValidationService soap client proxy vds created and some private string objects to use with public properties that we will develop next. #region private members private ValidationServiceReference.ValidationServiceSoapClient vds = new ValidationServiceReference.ValidationServiceSoapClient(); private string name; private string email; private string websiteurl; #endregion Our custom error class is called ErrorInfo, with public properties and an override version of the ToString method, as shown here. Here we override the default ToString() method to a more meaningful method that outputs ErrorMessage with ErrorCode.
Only an UPDATE of this block would truly clean out our residual transaction information, and since the UPDATE is already generating redo, the cleanout is not noticeable We can force a cleanout to not happen, and therefore observe its side effects, by understanding how the commit cleanout works In a commit list associated with our transaction, Oracle will record lists of blocks we have modified Each of these lists is 20 blocks long, and Oracle will allocate as many of these lists as it needs up to a point If the sum of the blocks we modify exceeds 10 percent of the block buffer cache size, Oracle will stop allocating new lists For example, if our buffer cache is set to cache 3,000 blocks, Oracle will maintain a list of up to 300 blocks (10 percent of 3,000).
<html> Viewport meta tag <head> <meta name="viewport" content="width=device-width" /> <script src="db.js" type="text/javascript" ></script> </head> <body> <h1>Unlocking Android Second Edition</h1> <h3>SQL database sample</h3> Output div <div id="outputarea"></div><br/>
If Session("Cart") IsNot Nothing Then Dim strCart = Session("Cart").ToString e.Command.CommandText &= "where ProductID in (" + _ strCart + ")" Else e.Cancel = True End If End Sub
The hash cluster query took the same amount of CPU for all intents and purposes, even though it went to the buffer cache one-third as many times. This, too, could be anticipated. The act of performing a hash is very CPU-intensive. The act of performing an index lookup is I/O-intensive. It was a tradeoff. However, as we scale up users, we would expect the hash cluster query to scale better, as it has to get in line to access the buffer cache less frequently.
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