Apache Spark interfaces

Spark interfaces

There are three key Spark interfaces that you should know about.

Resilient Distributed Dataset (RDD)

Apache Spark’s first abstraction was the RDD. It is an interface to a sequence of data objects that consist of one or more types that are located across a collection of machines (a cluster). RDDs can be created in a variety of ways and are the “lowest level” API available. While this is the original data structure for Apache Spark, you should focus on the DataFrame API, which is a superset of the RDD functionality. The RDD API is available in the Java, Python, and Scala languages.

DataFrame

These are similar in concept to the DataFrame you may be familiar with in the pandas Python library and the R language. The DataFrame API is available in the Java, Python, R, and Scala languages.

 

Dataset

A combination of DataFrame and RDD. It provides the typed interface that is available in RDDs while providing the convenience of the DataFrame. The Dataset API is available in the Java and Scala languages.

In many scenarios, especially with the performance optimizations embedded in DataFrames and Datasets, it will not be necessary to work with RDDs. But it is important to understand the RDD abstraction because:

• The RDD is the underlying infrastructure that allows Spark to run so fast and provide data lineage.
• If you are diving into more advanced components of Spark, it may be necessary to use RDDs.
• The visualizations within the Spark UI reference RDDs.

When you develop Spark applications, you typically use DataFrames and Datasets.