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CHAPTER 4 FUN WITH FUNCTIONS, AND NEVER HAVING TO CLOSE THAT JDBC CONNECTION
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We can build expressions like 1 + 1, Add(Val(1), Val(1)), 3 * (1 + 1), Mul(Val(3), Add(Val(1), Val(1)), and a * 11, Mul(Var("a"), Val(11)). We can evaluate an expression by interpreting the expression:
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def calc(expr: Expr, vars: Map[String, Int]): Int = expr match { case Add(left, right) => calc(left, vars) + calc(right, vars) case Mul(left, right) => calc(left, vars) * calc(right, vars) case Val(v) => v case Var(name) => vars(name) }
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Let s look at how this method works. expr is the expression to evaluate, and vars is a Map that contains our variables. We use pattern matching to determine what to do based on the case class. If expr is an Add, we extract the left and right parameters, which are themselves Exprs. We call calc to calculate the value of the left and right parameters and add the results. If expr is Mul, we do the same thing (except we multiply things rather than adding them). If expr is Val, we simply extract the value and return it. If expr is Var, we extract the name and return the lookup of the name in the vars Map. We can turn this from a method call into a function. Having a function allows us to pass around the logic that the expression represents. It also means that we don t have to interpret the tree of Exprs each time. Let s see how we can compose a function based on the Expr.
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def buildCalc(expr: Expr): Map[String, Int] => Int = expr match { case Add(left, right) => val lf = buildCalc(left) val rf = buildCalc(right) m => lf(m) + rf(m) case Mul(left, right) => val lf = buildCalc(left) val rf = buildCalc(right) m => lf(m) * rf(m) case Val(v) => m => v case Var(name) => m => m(name) }
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CHAPTER 4 FUN WITH FUNCTIONS, AND NEVER HAVING TO CLOSE THAT JDBC CONNECTION
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The buildCalc method returns a function that can be passed to other functions. Also, the JVM can optimize the composed functions so that they perform better than the interpreted version. The performance of the composed function is better because there is no overhead associated with pattern matching each element. The function is evaluated by repeatedly calling the function s apply method. Thus, the cost of each node is one or two method dispatches rather than the cost of the pattern matching. Let s turn to other ways that functions can help us improve performance and readability.
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Call-by-Name, Call-by-Value, and General Laziness
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In Java programs, when you call a method with parameters, the value of the parameters are all calculated before the method is called. Thus, in
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foo(1 + 1, "A String".length());
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the expressions 1 + 1 and "A String".length() are both evaluated before the call to foo is made. This is usually what we want. However, there are some cases when we want to parameters to be optionally evaluated or repeatedly evaluated. In these cases, Scala provides the call-by-name mechanism. There s no syntactic difference to the caller for call-by-name parameters. The first example for call-by-name is the logging example. It s very computationally costly to calculate log messages simply to discard them if the message is not going to be logged. This is very common in Java code:
if (logger.level().intValue() >= INFO.intValue()) { logger.log(INFO, "The value is "+value); }
In this code, we have to push the decision to evaluate logger.log(INFO, "The value is "+value); into the place where we call logger. This means we need to wrap the call to
logger in an if statement. It would be much better from a coding perspective if the cost of evaluating the String to be logged were incurred only if the String is going to be logged and if the current log level is known to and tested by the code inside logger rather than in the call to logger. Call-by-name gives us the ability to delay the evaluation of the String to log only if that String will actually be logged. In Scala, we can define a log method that takes the thing to log as call-by-name: def log(level: Level, msg: => String) = if (logger.level.intValue >= level.intValue) logger.log(level, msg)
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