Why Datasets and DataFrames?
The simple answer is: speed and ease of use. DataFrames provide the benefits of optimization, speed, automatic schema discovery, working with multiple sources, and multiple languages support; they read less data, and provide inter-operability between RDDs. Let's delve into these concepts.
Optimization
Catalyst provides optimization for DataFrames. It provides two optimizations:
Predicate push down to sources, to read only the data needed
Creates a physical plan for execution and generates JVM bytecode that is better optimized than handwritten code
The DataFrame is not defining Directed Acyclic Graph (DAG) as in the case of RDDs. Abstract Syntax Trees (AST) are created, which the catalyst engine will parse, check, and improve using both rules-based optimization and cost-based optimization.
Figure 4.4 shows the phase of query planning in Spark Sql. So, any DataFrame, Dataset operation or SQL query will follow the same optimization path to create a physical plan and...
