Let’s assume our object can be identified by an integer number, we can use an integer Array as data structure for our solution (id[]) . In Quick-Find we consider two sites p and q are in the same component if id[p] is equal do id[q].

The initQuickFindUF function allocates the zeroed array and assign the slice that refers to that array to Elements; then go through it and set the value corresponding to each index ( *Elements[i] = i *).

The connected function simply checks whether given two index (p, q) their entries are equal and returns.

The union function is a little more complicated: given two index  *(p, q)* it retrieves their entries in the array, then loop through the whole array looking for entries equal to the p entry and set those to q entry.

``````
package dyncon
type QuickFindUF struct {
Elements []int
}
func initQuickFindUF(size int) *QuickFindUF {
qfUF := QuickFindUF{Elements: make([]int, size)}
for i := range qfUF.Elements {
qfUF.Elements[i] = i
}
return &qfUF
}
func (qfUF QuickFindUF) connected(p, q int) bool {
return qfUF.Elements[p] == qfUF.Elements[q]
}
func (qfUF *QuickFindUF) union(p, q int) {
pid := qfUF.Elements[p]
qid := qfUF.Elements[q]
for i := range qfUF.Elements {
if qfUF.Elements[i] == pid {
qfUF.Elements[i] = qid
}
}
}
```
```

By evaluating this algorithm by the number of times it access the array we can assume the following measurements:

Algorithm initQuickFindUF connected union
Quick-Find N 1 N

The union function costs N accesses to the array; if we have N union operations this algorithm will take quadratic time complexity.

So the Quick-find algorithm is too slow for big N.