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Of course, as you have just seen, within the limitations of the semi-join, you can have nested loop, merge, or hash semi-joins but if you look carefully at the 9i version of the hash semi-join, you will see that the join has operated the wrong way round It has built the in-memory hash from the larger data set, and then probed it with the smaller data set Semi-joins and antijoins (like traditional outer joins) are constrained in 9i to operate in a very specific order the table from the subquery has to appear after the table in the main query However, when we move to 10g, where an enhancement to the hash join code allows an outer hash join to operate in either direction, we find an interesting little change The execution plan (in fact the default execution plan in my test case) is as follows: Execution Plan (101.

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Figure 5-13. The interface for editing places, with a DataList control Now the user can stop editing simply by navigating to another item. This is handled in the SelectedIndexChanged event handler, which resets any previous EditItemIndex value to 1 and calls BindPlaces() to refresh the display.

04 autotrace) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=38 Card=10000 Bytes=770000) 1 0 HASH JOIN (RIGHT SEMI) (Cost=38 Card=10000 Bytes=770000) 2 1 TABLE ACCESS (FULL) OF 'DEPT' (TABLE) (Cost=2 Card=3 Bytes=15) 3 1 TABLE ACCESS (FULL) OF 'EMP' (TABLE) (Cost=35 Card=20000 Bytes=1440000) Note the (right semi) in line one, compared to the (semi) in the 9i execution plan Semijoins and anti-joins, like outer joins, are no longer constrained to operate in a specific order in 10g if they are executed as hash joins, so the smaller dataset can always be the one chosen to.

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The timePeriod attribute, available inside the <timeSeriesDataset> tag, is for specifying the type of the data series inside the dataset. Time series can contain numeric values associated with days, months, years, or other predefined time periods.

On the other hand, the user can click the Save or the Cancel button If the user chooses the latter, the CancelCommand event is raised, in response to which you reset the EditItemIndex and set the SelectedIndex to the current element The user will then be positioned in the element he or she was just editing If the user clicks the Save button, the UpdateCommand event is raised The handler for this event first locates the controls with the data to be used for the update, as in: Dim addr As TextBox = CType(eItemFindControl("txtAddress"), TextBox) Then it gets a reference to the original row To achieve this, first reload the dataset: adPlacesFill(dsPlaces) Then build a filtering expression with the PlaceID found in the corresponding (hidden) label: Dim sql As String = "PlaceID = '" + place.

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Execution Plan (9.2.0.6 - autotrace) ---------------------------------------------------------SELECT STATEMENT Optimizer=ALL_ROWS (Cost=2149 Card=1 Bytes=9) SORT (AGGREGATE) TABLE ACCESS (BY INDEX ROWID) OF 'FACT_TABLE' (Cost=2149 Card=10000 Bytes=90000) BITMAP CONVERSION (TO ROWIDS) BITMAP INDEX (SINGLE VALUE) OF 'FCT_DIM_PAR' As you can see, Oracle has decided that this query will return 10,000 rows (card=10000). Taking the usual 80/20 split, this gives us 2,000 rows assumed to be widely scattered, and 8,000 rows tightly packed. The table in question consists of 1,000,000 rows and 30,303 blocks so 33 rows per blocks. The data therefore requires 2,000 + 8,000/33 = 2,242 block visits which is a little bit over the top for the cost of 2,149 that has been reported, especially when you add in roughly another 7 for the index block visits. However, it falls inside the range of values you can get by adjusting the db_file_multiblock_read_count, so I m not too worried by the difference. In case you re wondering, even in 9i the optimizer will calculate the cost of the two-table join before examining the cost of this single table access path.

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