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objects such as reader that represent handles to transient, disposable resources, unless those objects are used in a way that respects the lifetime of that resource. The previous code can be corrected to avoid using laziness in combination with a transient resource: open System.IO let line1, line2 = let reader = new StreamReader(File.OpenRead("test.txt")) let firstLine = reader.ReadLine() let secondLine = reader.ReadLine() reader.Close() firstLine, secondLine Another technique uses language and/or library constructs that tie the lifetime of an object to some larger object. For example, you can use a use binding within a sequence expression, which augments the sequence object with the code needed to clean up the resource when iteration is finished or terminates. This technique is discussed further in 8 and shown by example here: let reader = seq { use reader = new StreamReader(File.OpenRead("test.txt")) while not reader.EndOfStream do yield reader.ReadLine() } The general lesson is to try to keep your core application pure. Use both delayed computations (laziness) and imperative programming (side effects) where appropriate, but be careful about using them together.

In this case and ones similar to it, the value added by the hardware should be weighed against the cost of the hardware and the expected gains of using the new hardware In other words, your server should perform some percentage faster, thereby saving you money (or increasing income)..

Summary

When there is only one argument, our favorite way of writing the extra arguments is as follows: let mapFirst inp = inp |> List.map (fun (x,y) -> x) The same problem arises when you try to define functions by composition. For example: let printFstElements = List.map fst >> List.iter (printf "res = %d") Once again the arguments here are implicit, which causes problems. This definition will not be automatically generalized since this is not a syntactic function. Again, just make the arguments explicit: let printFstElements inp = inp |> List.map fst |> List.iter (printf "res = %d")

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4. Some statisticians consider the statistical engine in Microsoft Excel to be inaccurate. However, for the values you are likely to see, the inaccuracies are not a problem.

In this chapter, you learned how to do imperative programming in F#, from some of the basic mutable data structures such as reference cells to working with side effects such as exceptions and I/O. You also looked at some general principles for avoiding the need for imperative programming and isolating your uses of side effects. The next chapter returns to some of the building blocks of both functional and imperative programming in F#, with a deeper look at types, type inference, and generics.

Let s take a look at this definition once again: let empties = Array.create 100 [] It s possible that you really did intend to define a function that generates arrays of different types on demand. In this case, just add a dummy argument: let empties () = Array.create 100 [] let intEmpties : int list [] = empties() let stringEmpties : string list [] = empties() The dummy argument ensures that empties is a function with generic arguments and can be automatically generalized.

If you estimate that you need to increase your network performance by 10 percent in order to meet a quarterly expense and income goal that will lead to a savings, use that value as your goal. If your benchmarks show that you have achieved the desired improvements (or better yet surpassed them), go ask your boss for a raise. If the benchmarks show performance metrics that don t meet the goal, you can tell your boss you can save him money by returning the hardware (and then ask for a raise). Either way, you can back up your claims with empirical data: your benchmarks!

F# constructs such as lists, tuples, and function values are all generic, which means they can be instantiated at multiple different types. For example, int list, string list, and (int * int) list are all instantiations of the generic family of F# list types. Likewise, int -> int and string -> int are both instantiations of the generic family of F# function types. The F# library and the .NET Framework have many other generic types and operations in addition to these. Closely related to generics is the notion of subtyping. Generics and subtyping combine to allow you to write code that is generic over families of types. This chapter covers .NET and F# types in general and generics and subtyping in particular. You show how F# uses automatic generalization to automatically infer generic types for your code, and the chapter covers some of the basic generic functions in the F# libraries, such as generic comparison, hashing, and binary serialization.

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