Spark SQL was designed with an optimizer called Catalyst based on the functional programming of Scala. Its two main purposes are: first, to add new optimization techniques to solve some problems with “big data” and second, to allow developers to expand and customize the functions of the optimizer. Catalyst Spark SQL architecture and Catalyst optimizer integration Catalyst components Los componentes principales del optimizador de Catalyst son los siguientes: The main components of the Catalyst optimizer are as follows: Trees The main data type in Catalyst is the tree. Each tree is composed of nodes, and each node has a nodetype and zero or more children. These objects are immutable and can be manipulated with functional language. As an example, let me show you the use of the following nodes: Merge ( Attribute ( x ), Merge ( Literal ( 1 ), Literal ( 2 )) Where: Literal(value: Int) : a constant value Attribute(name: String) : an attribute as input row Merge(left: TreeNode, right: TreeNo...