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Figure 5-3 Concurrent users vs transactions per second Initially, as you add concurrent users, the number of transactions increases At some point, however, adding additional users does not increase the number of transactions you can perform per second the graph tends to drop off The throughput has peaked and now response time starts to increase (you are doing the same number of transactions per second, but the end users are observing slower response times) As you continue adding users, you will find that the throughput will actually start to decline The concurrent user count before this drop-off is the maximum degree of concurrency you want to allow on the system Beyond this point, the system becomes flooded and queues begin forming to perform work Much like a backup at a tollbooth, the system can no longer keep up.

For example, if method Hello has two 4-byte parameters (either integer or floating point), the mangled name would be Hello@8 This kind of function name mangling is characteristic only of the stdcall functions, so if the calling convention is different and the name is mangled in some other way, the P/Invoke mechanism will not find the exported method You can see that the name digging methods employed by the P/Invoke mechanism are intended for Windows API naming conventions and name mangling schemes of the C/C++ compiler The thunk is perceived by the managed code as simply another method, and hence it must be declared as any method would be The presence of the pinvokeimpl flag in the respective Method record signals the runtime that this method is indeed a client thunk and not a true managed method You already encountered the following declaration of a P/Invoke thunk in 1: .

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Not only does response time rise dramatically at this point, but throughput from the system may fall as well as the overhead of simply context switching and sharing resources between too many consumers takes additional resources itself If we limit the maximum concurrency to the point right before this drop, we can sustain maximum throughput and minimize the increase in response time for most users Shared server allows us to limit the maximum degree of concurrency on our system to this number An analogy for this process could be a simple door The width of the door and the width of people limit the maximum people per minute throughput At low load, there is no problem; however, as more people approach, some forced waiting occurs (CPU time slice).

The final part of the script is the most basic. If the file to get and the command to run are the same, the script sees that there is no command to run.

If a lot of people want to get through the door, we get the fallback effect there are so many saying after you and false starts that the throughput falls Everybody gets delayed getting through Using a queue means the throughput increases, some people get through the door almost as fast as if there was no queue, while others (the ones put at the end of the queue) experience the greatest delay and might fret that this was a bad idea But when you measure how fast.

method public static pinvokeimpl("msvcrtdll" cdecl) vararg int32 sscanf(string,int8*) cil managed { } The parameters within the parentheses of the pinvokeimpl clause represent the implementation map data The string marshaling flag is not specified, and the marshaling defaults to ANSI The method name need not be specified because it is the same as the declared thunk name If you want to use sscanf but would rather call it Foo (sscanf is such a reptilian name!), you could declare the thunk as follows: method public static pinvokeimpl("msvcrtdll" as "sscanf" cdecl) vararg int32 Foo(string,int8*) cil managed { } The unmanaged method resides somewhere else and the thunk is generated by the runtime, so the Method record of a true P/Invoke thunk has its RVA entry set to 0..

As an example, we ll study the cost of latching the Shared pool. We ll compare a well-written program (one that uses bind variables) and a program that is not so well written (it uses literal

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