Correlation is a statistical relationship between two variables such that when one variable changes, it leads to a change in the other variable. Correlation analysis measures the extent to which the two variables are correlated.
We see correlation in our daily life. The height of a person is correlated with the weight of a person, the load carrying capacity of a truck is correlated with the number of wheels it has, and so on. 

If an increase in one variable leads to an increase in another, it is called a positive correlation. If an increase in one variable leads to a decrease in the other, it is a negative correlation.

Spark supports two correlation algorithms: Pearson and Spearman. The Pearson algorithm works with two continuous variables, such as a person's height and weight or house size and house price. Spearman deals with one continuous and one categorical variable, for example, zip code and house price.