What is the difference between an RDD and a DataFrame in Apache Spark?

Vaibhav Kothia May 26, 2023 0 Comments

RDDs (Resilient Distributed Datasets) are the primary data abstraction in Apache Spark. RDDs are immutable collections of objects that can be split across multiple machines in a cluster. They can be created from files, databases, or other RDDs. RDDs are resilient because they can be reconstructed if a node fails.

DataFrames are a higher-level abstraction built on top of RDDs. They are similar to tables in a relational database and provide a schema that describes the data. DataFrames provide a domain-specific language for structured data manipulation and can be constructed from a wide array of sources such as CSV files, JSON files, and existing RDDs.

Example:

RDD:

val rdd = sc.textFile(“data.txt”)

DataFrame:

val df = spark.read.csv(“data.csv”)

What is the difference between an RDD and a DataFrame in Apache Spark?

Vaibhav Kothia May 26, 2023 0 Comments

RDDs (Resilient Distributed Datasets) are the basic data structures of Apache Spark. RDDs are immutable, distributed collections of objects that can be operated on in parallel. RDDs are the fundamental data structure of Spark and are built on top of the distributed filesystems. RDDs are fault tolerant and can be recomputed on failure.

A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as structured data files, tables in Hive, external databases, or existing RDDs.

Example:

RDD:
val rdd = sc.parallelize(List((1, “John”), (2, “Jane”), (3, “Bob”)))

DataFrame:
val df = rdd.toDF(“id”, “name”)