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

Recall & Review

beginner

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.

beginner

Why does Spark use lazy evaluation?

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

beginner

Which Spark operations trigger execution in lazy evaluation?

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

beginner

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.

beginner

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.

Practice

1. What does lazy evaluation mean in Apache Spark?

easy

A. Spark runs all transformations immediately when defined

B. Spark delays running transformations until an action is called

C. Spark only runs actions if there are no errors

D. Spark saves all data permanently after each step

Solution

1. Step 1: Understand lazy evaluation concept

   Lazy evaluation means Spark does not run transformations right away but waits.

2. Step 2: Identify when Spark runs computations

   Spark runs the actual work only when an action like collect() or count() is called.

3. Final Answer:

   Spark delays running transformations until an action is called -> Option B

4. Quick Check:

   Lazy evaluation = Delay until action [OK]

Hint: Remember: transformations wait, actions run [OK]

Common Mistakes:

• Thinking transformations run immediately
• Confusing actions with transformations
• Believing Spark saves data after each step

2. Which of the following is the correct way to trigger execution in Spark?

easy

A. df.count()

B. df.select("name")

C. df.filter("age > 30")

D. df.withColumn("new", df["age"] + 1)

Solution

1. Step 1: Identify transformations vs actions

   filter(), select(), and withColumn() are transformations (lazy).

2. Step 2: Recognize actions that trigger execution

   count() is an action that forces Spark to run all prior transformations.

3. Final Answer:

   df.count() -> Option A

4. Quick Check:

   Action = count() triggers execution [OK]

Hint: Actions like count() run Spark jobs [OK]

Common Mistakes:

• Choosing transformations as actions
• Confusing withColumn as action
• Thinking select triggers execution

3. What will be the output of this Spark code snippet?

val data = spark.range(1, 5)
val filtered = data.filter(_ % 2 == 0)
filtered.show()

medium

A. No output, code has error

B. [1, 3, 5]

C. [1, 2, 3, 4]

D. [2, 4]

Solution

1. Step 1: Understand the data range

   spark.range(1, 5) creates numbers 1, 2, 3, 4 (end exclusive).

2. Step 2: Apply filter for even numbers

   filter(_ % 2 == 0) keeps only even numbers: 2 and 4.

3. Final Answer:

   [2, 4] -> Option D

4. Quick Check:

   Filter even numbers = [2, 4] [OK]

Hint: Filter keeps only matching rows, here even numbers [OK]

Common Mistakes:

• Including 5 in range output
• Confusing filter condition
• Assuming inclusive end in range

4. Identify the error in this Spark code:

val df = spark.read.csv("data.csv")
df.filter(col("age") > 30)

medium

A. csv() cannot read files

B. filter() is not a valid method

C. Missing import for col function

D. No error, code runs fine

Solution

1. Step 1: Check usage of col function

   col("age") requires import org.apache.spark.sql.functions.col.

2. Step 2: Verify other code parts

   filter() and csv() are valid methods; no syntax error there.

3. Final Answer:

   Missing import for col function -> Option C

4. Quick Check:

   col() needs import [OK]

Hint: Import col before using it [OK]

Common Mistakes:

• Assuming filter is invalid
• Thinking csv() can't read files
• Ignoring missing imports

5. You want to optimize a Spark job that chains multiple transformations but runs slowly. Which approach uses lazy evaluation to improve performance?

hard

A. Chain all transformations first, then call a single action at the end

B. Use collect() after every transformation to view data

C. Save intermediate results to disk after each transformation

D. Call an action after each transformation to run them step-by-step

Solution

1. Step 1: Understand lazy evaluation benefits

   Lazy evaluation lets Spark optimize by combining transformations before running.

2. Step 2: Identify best practice for chaining

   Chaining all transformations then calling one action lets Spark optimize execution plan.

3. Step 3: Evaluate other options

   Calling actions early or saving intermediate data breaks lazy evaluation and slows job.

4. Final Answer:

   Chain all transformations first, then call a single action at the end -> Option A

5. Quick Check:

   Chain transformations + one action = optimized [OK]

Hint: One action after all transformations optimizes Spark job [OK]

Common Mistakes:

• Calling actions too early
• Saving intermediate data unnecessarily
• Using collect() too often