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new AddToCollection<ListItem> { Collection = myList, Item = new LambdaValue<ListItem> (env => new ListItem("Ice Cream", 4, 5.75m, 5, "")) }, new WriteLine { Text = "Workflow ended" } } }; }
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// PlayType represents the direction of play: across, down, or pass. enum class PlayType { Across, Down, Pass }; // The types of spaces on the board. // DLS == Double Letter Score // DWS == Double Word Score // TLS == Triple Letter Score // TWS == Triple Word Score enum class SpaceType { Normal = 0, DLS = 1, DWS = 2, TLS = 3, TWS = 4, Center = 5 }; // A Scrabble Tile contains a letter and a fixed point value // that depends on the letter. We also include a property for the // letter that a blank tile represents once it is played. // Tiles are not the same as board spaces: tiles are placed into // board spaces as play goes on. ref struct Tile { property Letter LetterValue; property int PointValue; property Char BlankValue; // This array contains the static point values of each tile // in alphabetical order, starting with the blank. static array<int>^ point_values = {0, 1, 3, 3, 2, 1, 4, 2, 4, 1, 8, 5, 1, 2, 1, 1, 3, 10, 1, 1, 1, 1, 4, 3, 8, 4, 10}; // The Tile constructor initializes the tile from its letter // and the point value. Tile(Letter letter) { LetterValue = letter; PointValue = point_values[ safe_cast<int>( letter )]; } // Used when displaying the tile on the gameboard virtual String^ ToString() override { // Format(LetterValue) won't work because the compiler // won't be able to identify the right overload when the // type is an enum class. return String::Format("{0}", LetterValue); } };
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The accessed application will hold a label field and an instance of a class that enables the user to log in. Create a Flex application in FlashBuilder 4, and call it AccessedApplication.
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Note We focus here on the Internet Explorer browser. However, if you prefer using another browser,
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3. Once the hash table has been tidied up, Oracle starts to acquire rows from the second data set, applying the same hash function to the join column(s) of each row. The result of the hash function is used to check the relevant bit in the bitmap (a detail I chose to ignore in my description of the first example). 4. Oracle takes one of several possible actions, depending on the result of the test. 4a)Event: The bit is clear (0). Action: There is no match, and the row is discarded. 4b)Event: The bit is set (1), and the relevant hash bucket is in a partition that is in memory. Action: Check the hash bucket if the probe row matches the build row, report it; otherwise discard it. 4c) Event: The bit is set (1), but the relevant hash bucket is in a partition that is on disk. Action: Put the probe row to one side. It may match a build row that is on disk, but it would be too expensive to reread the relevant build partition at this point to check it. The probe rows that are put to one side are collected in sets that match the partitions of the hash table previously dumped to disk. Just as we used some spare clusters for accumulating and dumping the hash table to disk, we use some spare memory clusters for accumulating the probe rows that are possible matches, and dump them to disk as each cluster becomes full. As we get to the end of the probe table, we are left with matched pairs of partitions from the build table and probe table on disk. At this point, Oracle has a complete map showing where all the data is and how many rows there are in each partition, so it picks a matched pair of dumped partitions (one build, one probe), and performs a hash join between them. As an extra optimization detail, Oracle can choose to swap the roles of the two partitions at this point because it knows exactly how much data there is in each partition, and there may be some benefit in using whichever is the smaller one to build the new in-memory hash. So, in the case of a high-volume hash join, the hash table can spill to disk, with the probe table following it. The cost of the join ought to allow for the I/O performed in dumping the excess to disk and reloading it for the second phase of the join. This type of hash join is recorded as a onepass workarea execution because the probe dataset is reread from disk just once. To generate a reasonable calculation of the cost of the join, we need to know the extra volume of I/O, the form it takes (i.e., typical I/O size), and how the optimizer decides to cost each I/O. For example, in the worst possible case, Oracle might have to read the data sets from both tables, dump most of the data to disk, and then reread it. So the cost ought, perhaps, to go up by a factor of about three (corresponding to the fact that we have read, written, and reread virtually the entire volume of data). Of course, we have to allow for the fact that when we are dumping and rereading the partitions, we may be able to use more efficient I/O than we did when we first collected the data. We also have to allow for the fact that we won t dump every row of our data, as some rows will be joined and reported on the initial acquisition from the probe table. If we check the execution plan for the sample query, we can see the following data sizes:
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Figure 11-46. When severity is too low to set @@ERROR 6. By changing the severity to 11 or above, the @@ERROR setting will now be set to the message number within the RAISERROR. RAISERROR (50001,11,1,243) SELECT @@ERROR 7. The preceding code produces the same message as seen within our RAISERROR example, but as you can see in Figure 11-47, the error number setting now reflects that value placed in the msgnum parameter.
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In this chapter, we looked at the Silverlight printing API. We saw how to easily print content as it appears on the screen, as well as how to print custom content. As you saw, the new printing API lets you add rich printing functionality to your Silverlight applications. In the next chapter we will take a look at deploying Silverlight applications.
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