A common use case for a for loop is to iterate over a predefined range or collection, and do the same task for all its elements. For instance, here we combine a for loop with a conditional if-elseif-else statement:

for i in 1:100
    if i % 15 == 0
        println("FizzBuzz")
    elseif i % 3 == 0
        println("Fizz")
    elseif i % 5 == 0
        println("Buzz")
    else
        println(i)
    end
end

This is the classic Fizz Buzz interview question. The (truncated) output is:

1
2
Fizz
4
Buzz
Fizz
7
8

In some situations, one might want to return from a function before finishing an entire loop. The return statement can be used for this.

function primefactor(n)
    for i in 2:n
        if n % i == 0
            return i
        end
    end
    @assert false  # unreachable
end

Usage:

julia> primefactor(100)
2

julia> primefactor(97)
97

Loops can also be terminated early with the break statement, which terminates just the enclosing loop instead of the entire function.

In Julia, a for loop can contain a comma (,) to specify iterating over multiple dimensions. This acts similarly to nesting a loop within another, but can be more compact. For instance, the below function generates elements of the Cartesian product of two iterables:

function cartesian(xs, ys)
    for x in xs, y in ys
        produce(x, y)
    end
end

Usage:

julia> collect(@task cartesian(1:2, 1:4))
8-element Array{Tuple{Int64,Int64},1}:
 (1,1)
 (1,2)
 (1,3)
 (1,4)
 (2,1)
 (2,2)
 (2,3)
 (2,4)

However, indexing over arrays of any dimension should be done with eachindex, not with a multidimensional loop (if possible):

s = zero(eltype(A))
for ind in eachindex(A)
    s += A[ind]
end

Julia provides macros to simplify distributing computation across multiple machines or workers. For instance, the following computes the sum of some number of squares, possibly in parallel.

function sumofsquares(A)
    @parallel (+) for i in A
        i ^ 2
    end
end

Usage:

julia> sumofsquares(1:10)
385

For more on this topic, see the example on @parallel within the Parallel Processesing topic.