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Execution Plan ( instantiated view) ---------------------------------------------------------SELECT STATEMENT Optimizer=CHOOSE (Cost=15 Card=1 Bytes=95) HASH JOIN (Cost=15 Card=1 Bytes=95) TABLE ACCESS (FULL) OF 'T1' (Cost=2 Card=1 Bytes=69) VIEW OF 'AVG_VAL_VIEW' (Cost=12 Card=32 Bytes=832) SORT (GROUP BY) (Cost=12 Card=32 Bytes=224) TABLE ACCESS (FULL) OF 'T2' (Cost=5 Card=1024 Bytes=7168) Execution Plan ( merged view) ---------------------------------------------------------SELECT STATEMENT Optimizer=CHOOSE (Cost=14 Card=23 Bytes=1909) SORT (GROUP BY) (Cost=14 Card=23 Bytes=1909) HASH JOIN (Cost=8 Card=32 Bytes=2656) TABLE ACCESS (FULL) OF 'T1' (Cost=2 Card=1 Bytes=76) TABLE ACCESS (FULL) OF 'T2' (Cost=5 Card=1024 Bytes=7168) As you can see from the execution plans, my example allows Oracle to aggregate table t2 and then join it to t1, but it also allows Oracle to join the two tables and then do the aggregation. The equivalent code for the merged view would look something like this: select t1.vc1, avg(t2.val) from t1, t2 where t1.vc2 = lpad(18,32) and t2.id_par = t1.id_par group by t1.vc1, t1.id_par ; So which of the two paths is better and which execution plan will the optimizer choose The answer to the first part of the question depends on the data distribution. If there is an efficient way to get from t1 to t2, and if there are only a couple of rows in t2 for each row in t1, and if the extra volume of data added to each row by t2 is small, then joining before aggregating will probably be the better idea. If there is no efficient way to get from t1 to t2, and there are lots of rows in t2 for each row in t1, and the extra volume of data added to each row by t2 is large, then aggregating before joining will probably be the better idea. I can tell you about the extremes that make the options possible, but I can t give you an immediate answer about all the possible variations in between. But the optimizer can make a pretty good estimate.
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This book deals with F#, a language situated in the context of .NET-based software construction and engineering. As an expert F# programmer, you need more than knowledge of the F# language; you also need to use a range of software-engineering tools and methodologies wisely to build software that is truly valuable for the situation where it s deployed. The previous chapter touched on some important tools. This final chapter looks at some of the methodological issues related to F# library design: Designing vanilla .NET libraries that minimize the use of F#-specific constructs, according to existing .NET design conventions Elements of functional programming design methodology, which offers important and deep insights into programming but doesn t address several important aspects of the library or component design problems Specific suggestions for designing .NET and F# libraries, including naming conventions, how to design types and modules, and guidelines for using exceptions
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To log in from a client, select the name of the wireless network from the wireless networks list, and enter the username and password to the environment. The first time you do this, you will get a second dialog box asking you to enter the 802.1x username
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With the histograms in place, I achieved reasonable, though not fantastic, accuracy over a wide range of overlaps. With the histograms I chose initially, the computed cardinality went back to 1,000,000 when the low value on t1 went below 22 or above 77. You will notice that in the 99 row, I have shown a join cardinality of 10,615 in parentheses. When I changed my histogram definitions, the optimizer got accurate figures, as t1.join1 varied all the way from 99 to +99, before flipping back to a computed cardinality of 1,000,000 when t1.join1 hit +/ 100. Histograms look helpful although they do introduce a new problem when the data sets don t overlap at all. So what was special about my final choice of histograms, and why did one set of histogram definitions give better results than another After all, given the way I have been generating my data using the dbms_random.value() procedure, there aren t likely to be any extreme values. The answer is that one set of histograms was built with 85 buckets, the other was built with a nominal 254.
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Invoke.invokeApplication(Invoke.APP_TYPE_CAMERA, new CameraArguments(CameraArguments.ARG_VIDEO_RECORDER));
Figure 9-8. Save the Connection String window
Copy the output document into a text editor, and manually change the names of the OutputMessage nodes to InputMessage nodes so that the outcome is a valid instance of the input document for the true map. In this case, you want a document that can be used as the input for the map shown in Figure 3 66, as shown in Listing 3 45. The bold elements in the example are the only elements that need to be modified.
Listing 17-6. Running the xml2tbl Stored Procedure
In this chapter, you take a closer look at lexing and parsing, topics introduced briefly in s 9 and 11. In particular, the chapter introduces the lexer and parser generators, fslex and fsyacc, that come with the F# distribution. A typical scenario when these techniques and tools can come in handy is the following: You want to read user-readable input that has a well-defined syntax. You have a type (often an abstract syntax tree [AST] type) to represent this input.
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