When should I use RDD or DataFrame?

Usage. RDD- When you want low-level transformation and actions, we use RDDs. Also, when we need high-level abstractions we use RDDs. DataFrame- We use dataframe when we need a high level of abstraction and for unstructured data, such as media streams or streams of text.

Can we create RDD from DataFrame in Spark?

From existing DataFrames and DataSet To convert DataSet or DataFrame to RDD just use rdd() method on any of these data types.

Is Spark DataFrame smaller than RDD?

Incase of RDD whenever the data needs to be distributed within the cluster or written to the disk, it is done using Java serialization. Efficiency in case of RDD is less than DataFrame because serialization needs to be performed individually on the objects which takes more time. 5.

Why RDD is faster than DataFrame?

Aggregation. RDD – RDD API is slower to perform simple grouping and aggregation operations. DataFrame – DataFrame API is very easy to use. It is faster for exploratory analysis, creating aggregated statistics on large data sets.

What is the difference between list and DataFrame?

DataFrames are generic data objects of R which are used to store the tabular data. They are two-dimensional, heterogeneous data structures. A list in R, however, comprises of elements, vectors, data frames, variables, or lists that may belong to different data types.

What is the difference between RDD and pair RDD?

pairRDD operations are applied on each key/element in parallel. Operations on RDD (like flatMap) are applied to the whole collection. Spark provides special operations on RDDs containing key/value pairs. These RDDs are called pair RDDs.

Should I use DataFrame or Dataset?

If you want rich semantics, high-level abstractions, and domain specific APIs, use DataFrame or Dataset. If your processing demands high-level expressions, filters, maps, aggregation, averages, sum, SQL queries, columnar access and use of lambda functions on semi-structured data, use DataFrame or Dataset.

What is the RDD in Spark?

Resilient Distributed Dataset (RDD) is the fundamental data structure of Spark. They are immutable Distributed collections of objects of any type. As the name suggests is a Resilient (Fault-tolerant) records of data that resides on multiple nodes.

What is a DataFrame?

A DataFrame is a data structure that organizes data into a 2-dimensional table of rows and columns, much like a spreadsheet. Every DataFrame contains a blueprint, known as a schema, that defines the name and data type of each column.

What is the use of RDD in Spark?

The key idea of spark is Resilient Distributed Datasets (RDD); it supports in-memory processing computation. This means, it stores the state of memory as an object across the jobs and the object is sharable between those jobs. Data sharing in memory is 10 to 100 times faster than network and Disk.