Why is missing defined as singleton

General Usage
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for what i read here, when you have an x = Vector{Union{Missing,Float64}} a second array is created, of the same size, holding information of when the value at position x[i] is missing or not. using the approach would imply storing that information somewhere, with the subsequent cost.
The answer to your question is mencioned explicitly:

One of Julia’s strengths is that user-defined types are as powerful and fast as built-in types. To fully take advantage of this, missing values had to support not only standard types like Int , Float64 and String , but also any custom type. For this reason, Julia cannot use the so-called sentinel approach like R and Pandas to represent missingness, that is reserving special values within a type’s domain. For example, R represents missing values in integer and boolean vectors using the smallest representable 32-bit integer ( -2,147,483,648 ), and missing values in floating point vectors using a specific NaN payload ( 1954 , which rumour says refers to Ross Ihaka’s year of birth). Pandas only supports missing values in floating point vectors, and conflates them with NaN values.
In order to provide a consistent representation of missing values which can be combined with any type, Julia 0.7 will use missing , an object with no fields which is the only instance of the Missing singleton type. This is a normal Julia type for which a series of useful methods are implemented. Values which can be either of type T or missing can simply be declared as Union{Missing,T} . For example, a vector holding either integers or missing values is of type Array{Union{Missing,Int},1} :

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