Arrays in Julia

In Julia, an Array is the built-in container for storing a collection of values of the same element type in a fixed-dimensional, ordered structure. The type is written Array{T,N}, where T is the element type and N is the number of dimensions — a Vector is just an alias for Array{T,1} and a Matrix for Array{T,2}. Unlike some languages that default to 0-based indexing, Julia arrays are 1-indexed by default, and Julia stores array elements in column-major order, meaning consecutive elements down a column sit next to each other in memory.

Arrays can be built with literal syntax like [1, 2, 3], with constructor functions such as zeros(3), ones(2, 3), or rand(4), or with comprehensions like [x^2 for x in 1:5]. Indexing uses square brackets — a[1] returns the first element and a[end] returns the last — and Julia supports range indexing such as a[2:4] to slice out a subarray. Accessing an index outside the array's bounds, such as a[10] on a three-element vector, throws a BoundsError rather than silently returning a garbage value or wrapping around.

Julia follows a naming convention where functions ending in ! mutate their arguments in place — push!(a, x) appends x to the end of a, pop!(a) removes and returns the last element, and append!(a, b) concatenates b's elements onto a, all without allocating a new array. Because arrays are reference types, assigning b = a does not copy the data; both variables point at the same underlying memory, so mutating b with push!(b, 4) also changes what a sees, which is why copy(a) or deepcopy(a) is used when an independent duplicate is actually needed.

Julia stores multi-dimensional arrays in column-major order, meaning that for a matrix A, the elements A[1,1], A[2,1], A[3,1] (an entire first column) are contiguous in memory before A[1,2] begins. This has real performance consequences: looping over columns as the outer loop and rows as the inner loop lets the CPU stream through memory sequentially and take advantage of cache locality, while the opposite loop order jumps around memory and can be dramatically slower on large arrays. Functions like reshape, eachcol, and eachrow let you reinterpret or iterate an array's shape without necessarily copying its underlying data.