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Optimizing Spark Program

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Questions and Answers

What is a recommended approach to improve performance when the same dataframe is being referred to in multiple places in a Spark program?

Increasing the size of the dataframe

Avoiding the use of dataframe caching

Running unnecessary Actions in the program

Using cache() or persist() functions (correct)

In a Spark program, what can be done to reduce shuffling when joining a big table with a small table?

Using additional Actions before the join

Partitioning the small table

Increasing the number of executors

Utilizing broadcast join (correct)

What will happen if unnecessary Actions are used in a Spark program?

Improves memory efficiency

Triggers unnecessary DAG execution (correct)

Optimizes the program automatically

Reduces CPU utilization

Which function in Spark is specifically used to store a dataframe at a user-defined storage level?

persist() function Signup and view all the answers

Why is it not recommended to run unnecessary Actions in Spark programs?

Because they trigger unnecessary DAG and execution from the beginning Signup and view all the answers

When does broadcast join tend to have less advantage over shuffle-based joins?

After crossing a certain threshold Signup and view all the answers

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 Signup and view all the answers

What technique can be used to eliminate data skewness issues in Spark?

Implementing the Salting Technique Signup and view all the answers

How does Spark optimize the logical plan internally for better performance?

By applying predicate pushdown for supported file formats Signup and view all the answers

When should you use the 'repartition()' function in Spark?

To increase the partitions for processing large data Signup and view all the answers

What is a common issue that can cause tasks to take longer in Spark?

Data skewness where data is unevenly distributed across partitions Signup and view all the answers

How does filtering data in earlier steps affect the performance of Spark applications?

It improves performance by reducing unnecessary processing Signup and view all the answers