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The Profiler library can be wrapped around sections of code to yield data on how often particular methods are visited and how long those visits last. Anyone familiar with profiling C applications should be right at home with what follows. Let s start with the easiest way to use this library as exhibited in Listing 3-4. Listing 3-4. Simple Profiling (Recursive Method) require "profile" def factorial(n) n > 1 n * factorial(n - 1) : 1 end factorial(627) You may notice a distinct lack of any instructions to profile anything. Thus it is kind of magical that running this listing on my machine gives the following: % cumulative time seconds 45.42 1.34 15.93 1.81 15.59 2.27 10.51 2.58 6.78 2.78 5.76 2.95 0.00 2.95 0.00 2.95 self seconds 1.34 0.47 0.46 0.31 0.20 0.17 0.00 0.00 self ms/call 2.14 0.75 0.75 0.49 0.32 0.28 0.00 0.00 total ms/call 1930.22 1.76 0.75 0.49 0.32 0.28 0.00 2950.00

Size (quantity) precedence varies by market. In some markets, small orders have precedence over large ones, and in some markets the opposite is true. Most exchanges allow traders to issue orders with size restrictions. Traders can specify that their entire order must be filled at once, or they can specify a minimum size for partial execution. Orders with quantity restriction usually have lower precedence than unrestricted orders because they are harder to fill.

With these changes, we can use editGadget() as the action listener for the Edit links on the search results screen:

Assume that some traders enter the orders shown in Table 2-4 for a particular security. Table 2-4. Buy and Sell Orders Sorted Based on Order Arrival Time

name Object#factorial Fixnum#* Bignum#* Fixnum#> Fixnum#Bignum#coerce Module#method_added #toplevel

Time (a.m.)

. . . <h:dataTable value="#{gadgetMatches}" var="selGadget"> . . . <h:column> <s:link linkStyle="button" value="Edit" action="#{gadgetAdmin.editGadget}" /> </h:column> </h:dataTable> . . .

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10:01 10:05 10:08 10:09 10:10 10:15 10:18 10:20 10:29

The very act of require profile in Listing 3-4 places the entire script under the watchful eye of the Profiler library, which automatically spits out its results upon completion. The whole thing took about three seconds, of which nearly 50% of the time was spent calling our factorial method in accordance with its frantically recursive nature. Although optimization is the subject of the next section, this is a good opportunity to try some. The profile identifies that the most ravenous part of our code is in the 627 calls to factorial. One of those almost-truisms from computer science is never to choose a recursive algorithm when an iterative one is available (to avoid enormous stacks, memory usage, object allocation, etc.). Let s try Listing 3-5 instead.

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Now both the add new gadget and edit gadget paths in our pageflow are being handled by the same editGadget() action listener method. When the user clicks an Add a new gadget link on the home page, editGadget() is invoked as before. There is no selected gadget set, so the logic to set the active gadget is bypassed, and the method returns successfully. The @Begin annotation causes a new explicit conversation to be started, because there is only an implicit conversation active at this point. When the user performs a search from the home page, the conversationPropagation parameter on the search link causes a new explicit conversation to be created, as before. When the user clicks an Edit link, the selected gadget is injected into the mSelGadget member, as before, and the editGadget() method is called. In this case, the activeGadget property is set to the selected gadget, causing the gadget component to be outjected at the end of the method call. The @Begin annotation is checked as well, but since an explicit conversation is already active, and the join=true option has been used, the conversation context is left in place, and the gadget edit session is subsumed into the current conversation.

The exchange will send an order acknowledgment to the traders trading terminals and fill the order book as shown in Table 2-5. Table 2-5. Order Book (Pre-Match)

Listing 3-5. Simple Profiling (Iterative Method) require "profile" def factorial(n) (2..n).to_a.inject(1) { |product, i| product * i } end factorial(627) Running this listing on my machine shows that switching to an iterative form does the trick, bringing the execution time down from the previous 3 seconds to a more respectable 80 milliseconds: % cumulative time seconds 50.00 0.04 50.00 0.08 0.00 0.08 0.00 0.08 0.00 0.08 0.00 0.08 0.00 0.08 0.00 0.08 0.00 0.08 self seconds 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 self ms/call 40.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 total ms/call 80.00 0.07 80.00 80.00 0.00 0.00 0.00 0.00 80.00

10:15 10:18 10:01 10:08 10:29

Until now, we ve only dealt with a flat conversation model. In all our examples, a conversation is a singular context, with no child conversation contexts, and no concurrent conversations. This is more than sufficient for most situations. But Seam also supports both nested conversations, where a conversation contains one or more child conversations, and concurrent conversations, where multiple conversations are active in a user s session. This section will cover nested conversations, and the next section will cover the broader topic of concurrent conversations, also called workspaces, in the Seam framework.

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