.NET/Java PDF, Tiff, Barcode SDK Library

connections[i] = occi.getConnection(); if( connections[i] != null ) System.out.println( " Successful." ); else System.out.println( " Failed." ); } // close all connections for( int i=0; i < connections.length; i++ ) { JDBCUtil.close( connections[i] ); } }// end of getOneMoreThanMaxConnections }// end of program The following is the output of the program when I ran it on my machine: B:\>java DemoOracleConnectionCache By default, the cache scheme is: 1 Max Limit: 3 Demo of dynamic cache scheme - the default Getting connection no 1 ... Successful. Getting connection no 2 ... Successful. Getting connection no 3 ... Successful. Getting connection no 4 ... Successful. Demo of Getting Getting Getting Getting fixed return null cache scheme connection no 1 ... Successful. connection no 2 ... Successful. connection no 3 ... Successful. connection no 4 ... Failed.

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Not consuming any server-side resources State information pinned to client that the information is about Lives closest to process servicing request Does not increase request or response sizes

In 3 you saw the different forms of string literals (strings with escape characters, verbatim strings, and byte arrays) and the most typical operations such as concatenation using string builders. You may also remember that string values are immutable and that string operations that change their input return a new string that represents the result. In the following sections, we cover further ways to work with strings and text.

As shown, even though we hit the maximum limit, we still got a connection successfully when the cache scheme was dynamic (which is the default). When the cache scheme was fixed return null, we got a null object when we tried to get a fourth connection. The fixed wait cache scheme isn t shown, but if you modify the program to use it, the program will wait forever when you try to get the fourth connection. This concludes our discussion of Oracle9i connection pooling and caching. It s time now to look at the implicit connection caching of Oracle 10g.

Increases request and response sizes Page specific (hidden inputs only) Consumes web server resources Can be problematic in a Web Farm Must still have clientside mechanism to tie state to requestor Process hop can cause a performance hit Network hop can cause a performance hit Must still have clientside mechanism to tie state to requestor

One of the most popular ways of working with strings as data is through the use of regular expressions. This is done using the functionality from the .NET System.Text.RegularExpressions namespace. To get started, first note that the F# library includes the following definition: open System.Text.RegularExpressions let regex s = new Regex(s) To this you can add the following Perl-like operators: let (=~) s (re:Regex) = re.IsMatch(s) let (<>~) s (re:Regex) = not (s =~ re) Here the inferred types are as follows: val regex : string -> Regex val ( =~ ) : string -> Regex -> bool val ( <>~ ) : string -> Regex -> bool The infix operators allow you to test for matches: > let samplestring = "This is a string";; val samplestring : string > if samplestring =~ regex "his" then printfn "A Match! ";; A Match! val it : unit = () Regular expressions can include *, +, and symbols for zero or more occurrences, one or more occurrences, and zero or one occurrences of the immediately preceding regular expression and can include parentheses to group regular expressions. For example: > "This is a string" =~ regex "(is )+";; val it : bool = true