PySpark and AWS: Master Big Data with PySpark and AWS - RDD (Partition)

The video tutorial covers the concepts of repartition and collapse transformations in Spark RDDs. It explains how repartitioning can increase or decrease the number of partitions to optimize parallel processing, while collapse is used solely for decreasing partitions. The tutorial includes practical examples demonstrating these transformations and discusses the importance of lazy evaluation in Spark. Additionally, it provides guidance on reading data from directories and highlights the impact of partitioning on performance.

What is the primary purpose of the repartition transformation in RDDs?

• To filter data based on a condition

• To sort the data within partitions

• To increase the number of partitions

• To decrease the number of partitions

Which transformation is used exclusively to decrease the number of partitions in an RDD?

• Map

• FlatMap

• Repartition

• Collapse

What is a key difference between repartition and collapse transformations?

• Both repartition and collapse can only decrease partitions

• Both repartition and collapse can only increase partitions

• Repartition can both increase and decrease partitions, while collapse can only decrease them

• Repartition can only increase partitions, while collapse can only decrease them

Why might increasing the number of partitions not always be beneficial?

• It can increase overhead and not improve performance

• It can decrease parallelization

• It can lead to data loss

• It can cause syntax errors

In the code example, what happens when the number of partitions is increased from 2 to 5?

• The data is filtered based on a condition

• The data is duplicated in each partition

• The data is equally distributed among the new partitions

• The data is sorted within each partition

What is the result of applying a flatMap transformation on an RDD?

• It sorts the data within each partition

• It increases the number of partitions

• It applies a function and flattens the results

• It filters out null values

What is the effect of lazy evaluation in Spark?

• It sorts data within each partition

• It delays data processing until an action is performed

• It processes data immediately as transformations are applied

• It automatically optimizes the number of partitions

What happens to the data when a collapse transformation is applied?

• The data is duplicated in each partition

• The data is combined into fewer partitions

• The data is filtered based on a condition

• The data is sorted within each partition

How does Spark handle data when reading from a directory containing multiple files?

• It reads data from all files and partitions it equally

• It reads data from all files but processes them sequentially

• It reads data from all files and combines them into a single partition

• It reads data from only the first file

What is the default number of partitions when reading data from a file in Spark?

• 2

• 4

• 1

• 3