#Too many arguments for method map spark code#

Again, this is a very dangerous thing to do, as it makes it difficult to reason about the function. Map functions basically transform a dataset to a number of key/value pairs. This is what we call “side-effect”: the function doesn’t communicate only using its parameters and returns the value but instead modifies the environment it’s defined in. It does that by modifying the count variable, on which the lambda closes.

#Too many arguments for method map spark code#

In the code above, the lambda not only doubles the elements of the list but also counts them.

If the lambda closes on mutable variables, it can modify them, thus adding side effects. Nonetheless, this is very dangerous as well.

This is a powerful feature to avoid verbose lambda signatures. In this case, the lambda uses multiplier, which is a local variable we defined in the outer environment. This means that they can access variables that are not in their parameter list. Lambda expressions can “close” over the environment in which they are defined. Accepting a Lambda Expression as a Parameter So, for example, if we wanted to print the elements of ints above, we could just write ints.foreach(println). For example, if we wrote ints.map(_ + _) then the compilation would fail with the error: “ missing parameter type for expanded function ((x$1:, x$2) => x$1.$plus(x$2))“, which is a complex way to tell us that the compiler could not understand what we were trying to do.įinally, if the lambda consists of the one method call taking a single argument, we can omit the parameter entirely. However, we must use names if we reference the parameter more than once. Specifying that in the signature of the anonymous function is redundant, and therefore, we can omit it.Īlso, since the parameter x only appears at most once, in the body of the lambda, we can omit its name: val ints = List(1, 2, 3, 4) This is fine, as the compiler detects that ints are a List, so of course, each element of the list will be an Int.