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Let s look at this signature first, because it represents the design of the type. The worker constructor is given a function of type 'State -> 'State to compute successive iterations of the computation, plus an initial state and the number of iterations to compute. For example, you can compute the Fibonacci numbers using the following iteration function: let fibOneStep (fibPrevPrev:bigint,fibPrev) = (fibPrev, fibPrevPrev+fibPrev);; The type of this function is as follows: val fibOneStep : bigint * bigint -> bigint * bigint The RunWorkerAsync and CancelAsync members follow the BackgroundWorker design pattern, as do the events, except that you expand the RunWorkerCompleted event into three events to correspond to the three termination conditions and modify the ProgressChanged to include the state. You can instantiate the type as follows: > let worker = new IterativeBackgroundWorker<_>( fibOneStep,(1I,1I),100);; val worker : IterativeBackgroundWorker<bigint * bigint> > worker.WorkerCompleted.Add(fun result -> MessageBox.Show(sprintf "Result = %A" result) |> ignore);; val it : unit = () > worker.ProgressChanged.Add(fun (percentage, state) -> printfn "%d%% complete, state = %A" percentage state);; val it : unit = () > worker.RunWorkerAsync();; 1% complete, state = (1I, 1I) 2% complete, state = (1I, 2I) 3% complete, state = (2I, 3I) 4% complete, state = (3I, 5I) ... 98% complete, state = (135301852344706746049I, 218922995834555169026I) 99% complete, state = (218922995834555169026I, 354224848179261915075I) 100% complete, state = (354224848179261915075I, 573147844013817084101I) val it : unit = () One difference here is that cancellation and percentage progress reporting are handled automatically based on the iterations of the computation. This is assuming each iteration takes roughly the same amount of time. Other variations on the BackgroundWorker design pattern are possible. For example, reporting percentage completion of fixed tasks such as installation is often performed by timing sample executions of the tasks and adjusting the percentage reports appropriately.

F# lists are represented in memory as linked lists, and each F# list value is a cons cell containing a value plus a pointer to the next chain in the list, or else it is a special nil object When you create a new list using the :: operator, then the tail of the new list will point to the old list, which ensures that the inner memory associated with lists is often reused as part of multiple list values You can decompose lists from the head downward by using pattern matching You saw some simple examples of pattern matching on tuples in 2, and we ll look at pattern matching in more detail in Getting Started with Pattern Matching later in this chapter.

2. Linux/Unix users can download it from the GNU web site (www.gnu.org/software/bison). Windows users can download a Win32 version from http://gnuwin32.sourceforge.net/packages/bison.htm.

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Note You implement IterativeBackgroundWorker via delegation rather than inheritance. This is because its

Here is an example of using pattern matching with lists: let printFirst primes = match primes with | h :: t -> printfn "The first prime in the list is %d" h | [] -> printfn "No primes found in the list" > printFirst oddPrimes;; The first prime in the list is 3 val it : unit = ().

external members are different from those of BackgroundWorker. The .NET documentation recommends that you use implementation inheritance for this, but we disagree. Implementation inheritance can only add complexity to the signature of an abstraction and never makes things simpler, whereas an IterativeBackgroundWorker is in many ways simpler than using a BackgroundWorker, despite that it uses an instance of the latter internally. Powerful, compositional, simple abstractions are the primary building blocks of functional programming.

Let s assume you want to add a command to the server to show the current disk usage of all of the databases in the server Although there are external tools that can retrieve this information, you desire a SQL equivalent function that you can easily use in your own databasedriven applications Let s also assume you want to add this as a SHOW command Specifically, you want to be able to execute the command SHOW DISK_USAGE and retrieve a result set that has each database listed as a row along with the total size of all of the files (tables) listed in kilobytes Adding a new SQL command involves adding symbols to the lexical analyzer and adding the SHOW DISK_USAGE command syntax to the YACC parser (sql_yaccyy).

> counter.Increment(2);; val it : unit = () > counter.Fetch();; val it : int = 3 > counter.Stop();; val it : unit = () Listing 13-11 shows several important aspects of message passing and processing using the mailbox-processing model: Internal messages protocols are often represented using discriminated unions. Here the type msg has cases Increment, Fetch, and Stop corresponding to the three methods accepted by the object that wraps the overall agent implementation. Pattern matching over discriminated unions gives a succinct way to process messages. A common pattern is a call to inbox.Receive() or inbox.TryReceive() followed by a match on the message contents. The PostSync on the MailboxProcessor type gives a way to post a message and wait for a reply. A temporary reply channel is created and should form part of the message. A reply channel is simply an object of type Microsoft.FSharp.Control.IChannel<'reply>, which in turn simply supports a Post method. This can be used by the MailboxProcessor to post a reply to the waiting caller. In Listing 13-11 the channel is sent to the underlying messageprocessing agent counter as part of the Fetch message. Table 13-6 summarizes the most important members available on the MailboxProcessor type.

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