Lazy evaluation in Spark in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

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What is lazy evaluation in Spark?

Lazy evaluation means Spark delays running computations until an action is called. It builds a plan first, then runs all steps together.

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Why does Spark use lazy evaluation?

Spark uses lazy evaluation to optimize performance by combining steps and reducing data movement, saving time and resources.

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Which Spark operations trigger execution in lazy evaluation?

Actions like count(), collect(), saveAsTextFile(), and take() trigger Spark to run the planned computations.

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What is the difference between transformations and actions in Spark?

Transformations create a new dataset and are lazy (not executed immediately). Actions compute and return results, triggering execution.

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How does lazy evaluation help when working with large datasets?

It avoids unnecessary work by only computing what is needed, which saves memory and speeds up processing on big data.

What triggers Spark to execute computations in lazy evaluation?

A Calling an action like collect()

B Defining a transformation like map()

C Creating a SparkSession

D Loading data from a file

Only actions like collect() trigger execution; transformations are lazy and do not run immediately.

Which of these is an example of a Spark transformation?

A take()

B count()

C saveAsTextFile()

D filter()

filter() is a transformation and is lazy; count(), saveAsTextFile(), and take() are actions.

Why is lazy evaluation useful in Spark?

A It runs all code immediately

B It stores data permanently

C It helps optimize and reduce unnecessary work

D It disables caching

Lazy evaluation helps Spark optimize by delaying execution and combining steps.

Which Spark operation will NOT trigger execution?

A map()

B collect()

C count()

D reduce()

map() is a transformation and does not trigger execution; others are actions.

What happens if you define multiple transformations before an action?

A Spark runs each transformation immediately

B Spark runs all transformations together when the action is called

C Spark ignores the transformations

D Spark throws an error

Spark builds a plan with all transformations and runs them together when an action is called.

Explain lazy evaluation in Spark and why it is important.

Think about when Spark actually runs your code.

Describe the difference between transformations and actions in Spark with examples.

Focus on what causes Spark to run computations.