Question

What are the key differences between an RDD, a DataFrame, and a DataSet?

Answer 1

Following are the key differences between an RDD, a DataFrame, and a DataSet:

RDD:

1. RDD is an acronym that stands for Resilient Distributed Dataset. It is a core data structure of PySpark.
2. RDD is a low-level object that is highly efficient in performing distributed tasks.
3. RDD is best to do low-level transformations, operations, and control on a dataset.
4. RDD is mainly used to alter data with functional programming structures than with domain-specific expressions.
5. If you have a similar arrangement of data that needs to be calculated again, RDDs can be efficiently reserved.
6. RDD contains all datasets and DataFrames in PySpark.

DataFrame:

1. A DataFrame is equivalent to a relational table in Spark SQL. It facilitates the structure like lines and segments to be seen.
2. If you are working on Python, it is best to start with DataFrames and then switch to RDDs if you want more flexibility.
3. One of the biggest disadvantages of DataFrames is Compile Time Wellbeing. For example, if the information structure is unknown, you cannot control it.

DataSet:

1. A Dataset is a distributed collection of data. It is a subset of DataFrames.
2. Dataset is a newly added interface in Spark 1.6 to provide RDD benefits.
3. DataSet consists of the best encoding component. It provides time security in an organized manner, unlike information edges.
4. DataSet provides a greater level of type safety at compile-time. It can be used if you want typed JVM objects.
5. By using DataSet, you can take advantage of Catalyst optimization. You can also use it to benefit from Tungsten's fast code generation.