12 Questions
What is a recommended approach to improve performance when the same dataframe is being referred to in multiple places in a Spark program?
Using cache() or persist() functions
In a Spark program, what can be done to reduce shuffling when joining a big table with a small table?
Utilizing broadcast join
What will happen if unnecessary Actions are used in a Spark program?
Triggers unnecessary DAG execution
Which function in Spark is specifically used to store a dataframe at a user-defined storage level?
persist() function
Why is it not recommended to run unnecessary Actions in Spark programs?
Because they trigger unnecessary DAG and execution from the beginning
When does broadcast join tend to have less advantage over shuffle-based joins?
After crossing a certain threshold
What is the major difference between the 'coalesce()' and 'repartition()' functions in Spark?
Coalesce() is used to decrease partitions while repartition() is used to increase partitions
What technique can be used to eliminate data skewness issues in Spark?
Implementing the Salting Technique
How does Spark optimize the logical plan internally for better performance?
By applying predicate pushdown for supported file formats
When should you use the 'repartition()' function in Spark?
To increase the partitions for processing large data
What is a common issue that can cause tasks to take longer in Spark?
Data skewness where data is unevenly distributed across partitions
How does filtering data in earlier steps affect the performance of Spark applications?
It improves performance by reducing unnecessary processing
Learn about optimizing Spark programs to make the most out of CPU power and resources. Explore techniques such as broadcast join for improving performance in big data operations.
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