A monoid is one of the simplest notions of algebra. Slightly oversimplifying, it consists of elements that have a multiplication operation

**a*b**defined on them, and a neutral element

**e**, such that

**e*a == a*e**. Multiplication should be associative

**(a*b)*c == a*(b*c)**, but not necessarily commutative (

**a*b != b*a**). If you can't imagine non-commutative multiplication, think of matrices... or of spatial rotations. The order counts.

A simpler example would consist of integer numbers and

*addition*playing the role of multiplication. 0 is the neutral element with respect to addition, so there.Now why would we need this structure? It turned out that if you have to add up a billion, or a quadrillion of integers, you may be interested in doing it by distributing the operation over several (or ~700 000, in the case of Google) machines. Adding up a quadrillion of integers may be not that interesting; but multiplying a billion of matrices is a different, and more challenging, story.

And why can we distribute it among the thousands of computers? We can do it all thanks to the

*associativity*.**(m**_{1}***m**_{2}***...*m**_{k1}***m**_{k1+1}***m**_{k1+2}**..*m**_{k2}***...*m**_{n}**)**is the same as**(m**

_{1}

***m**

_{2}

***...*m**

_{k1}

**)*(m**

_{k1+1}

***m**

_{k1+2}

**..*m**

_{k2}

**)*(...*m**

_{n}

**)**, and we can calculate intermediate results (shown in parentheses here) on different machines, then aggregating them together; we can do it in cascade, whatever we choose. The strategy does not influence the result.

The following Java class defines, or rather declares the necessary functionality:

public abstract class Monoid<X>

{

abstract X unit();

abstract X m(X x, X y);

public X fold(Iterable<X> source) {

X result = unit();

for (X x : source) {

result = m(result, x);

}

return result;

}

public X fold(X[] source) {

X result = unit();

for (X x : source) {

result = m(result, x);

}

return result;

}

}

`We can use it to add numbers, strings, matrices; to multiply numbers or matrices; to concatenate lists and join sets.`

Monoid<Set<T>> setMonoid = new Monoid<Set<T>> {Of course using all this for the data that we have in memory of one single machine. But, as I said before, we can actually delegate the monoid's fold operation to the computing cloud, and have the list deployed somewhere in the big file system, and let the cloud partition it. The result will be the same; and all this due to the monoidal properties of our concrete classes and operations.

public Set<T> unit() {

return Set<T>.EMPTY;

}

public Set<T> m(Set<T> a, Set<T> b) {

Set<T> c = a.clone();

c.addAll(b);

return c;

}