Discover which object types cannot be contained within a Spark DataFrame column. Learn about the limitations and supported data types for efficient data processing in Apache Spark.
Table of Contents
Question
Which of the following object types cannot be contained within a column of a Spark DataFrame?
A. DataFrame
B. String
C. Array
D. null
E. Vector
Answer
A. DataFrame
Explanation
A Spark DataFrame column cannot contain another DataFrame within it. DataFrames are two-dimensional datasets consisting of rows and columns, where each column represents a specific data type or structure. While columns can hold various data types such as strings, arrays, nulls, and vectors, they cannot directly contain nested DataFrames.
If you need to work with nested or hierarchical data structures, you can use other data types like structs or arrays of structs to represent complex objects within a DataFrame column. However, directly embedding a DataFrame inside another DataFrame’s column is not supported in Spark.
Therefore, the correct answer is option A: DataFrame.
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