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Listing 5-27 illustrates the enhancements to the save action to set the values that the message tag will use.

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Another possible representation, which can have a bit less overhead in some cases, is to replace the adjacency sets with actual adjacency lists. For an example of this, see Listing 2-2. The same operations are now available, except that membership checking is now (n). This is a significant slowdown, but that is only a problem if you actually need it, of course. (If all your algorithm does is iterate over neighbors, using set objects would not only be pointless; the overhead would actually be detrimental to the constant factors of your implementation.) Listing 2-2. Adjacency Lists a, b, c, d, e, f, g, h = range(8) N = [ [b, c, d, e, f], # a [c, e], # b [d], # c [e], # d [f], # e [c, g, h], # f [f, h], # g [f, g] # h ] It might be argued that this representation is really a collection if adjacency arrays, rather than adjacency lists in the classical sense, because Python s list type is really a dynamic array behind the covers (see earlier black box sidebar about list). If you wanted, you could implement a linked list type and use that, rather than a Python list. That would allow you (asymptotically) cheaper inserts at arbitrary points in each list, but this is an operation you probably will not need, because you can just as easily append new neighbors at the end. The advantage of using list is that it is a well-tuned, very fast data structure (as opposed to any list structure you could implement in pure Python). A recurring theme when working with graphs is that the best representation depends on what you need to do with your graph. For example, using adjacency lists (or arrays) keeps the overhead low and

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... if(user.save()) { flash.message = "user.saved.message" flash.args = [user.firstName, user.lastName] flash.defaultMsg = "User Saved" redirect(action:show,id:user.id) } ...

It may be used in a broad sense where it describes any machine that has several network adapters connected to one or more networks, or in a narrow sense where it only applies to Internet access via more than a single network for higher reliability3 It may be the intention of a network administrator to allow clients on a certain LAN segment access to a DBMS instance on a multihomed system, but deny access to clients on another LAN segment To achieve this, the DBA needs to make sure that the TNS Listener can be reached from one LAN segment but not the other Technically speaking, the TNS Listener should bind to the IP address of one adapter, but not the other.

lets you efficiently iterate over N(v) for any node v. However, checking whether u and v are neighbors is (N(v)), which can be problematic if the graph is dense (that is, if it has many edges). In these cases, adjacency sets may be the way to go.

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The flash.message property is the message code to be looked up in the message bundle, flash.args are the arguments to be substituted into the message, and flash.defaultMsg is a default message to display in the event of a problem. Only one thing left to do: create an entry in the message bundle with the user.saved.message code and whatever you would like the text to be. See Listing 5-28 for an example.

Now it s time to finally fill you in on the rather awkward acronym INADDR ANY, that was mentioned in the status section at the beginning of this chapter It is a C language preprocessor macro used in the C language programming interface for sockets On POSIX-compliant systems, TCP/IP as well as UDP networking is implemented using sockets (see man socket) The socket programming paradigm for a server process such as the TNS Listener is to 1 create a socket 2 bind the socket to an address, potentially using INADDR ANY 3 listen for incoming connections 4 accept incoming connections (or maybe not as in valid node checking discussed in 30).

Tip We ve also seen that deleting objects from the middle of a Python list is costly. Deleting from the end of a list takes constant time, though. If you don t care about the order of the neighbors, you can delete arbitrary neighbors in constant time by overwriting them with the one that is currently last in the adjacency list, before calling the pop method.

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