Lazy evaluation in Spark in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Spark DataFrame from a list.

Apache Spark

data = [(1, 'Alice'), (2, 'Bob')]
spark_df = spark.createDataFrame([1], ['id', 'name'])

Drag options to blanks, or click blank then click option'

Adata

Bsc

Clist

Drdd

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sc' which is the Spark context, not the data.

Using 'rdd' without converting the list first.

The variable data holds the list of tuples to create the DataFrame.

2fill in blank

medium

Complete the code to apply a filter transformation on the DataFrame.

Apache Spark

filtered_df = spark_df.filter(spark_df['id'] [1] 1)

Drag options to blanks, or click blank then click option'

A<=

B==

C!=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' which filters only id equal to 1.

Using '<=' which includes 1 and smaller values.

The filter keeps rows where 'id' is greater than 1.

3fill in blank

hard

Fix the error in the code to trigger the lazy evaluation and show the results.

Apache Spark

result = filtered_df.[1]()

Drag options to blanks, or click blank then click option'

Ashow

Bfilter

Cselect

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'filter' which is a transformation, not an action.

Using 'map' which is not a DataFrame method.

The show() action triggers the lazy evaluation and displays the DataFrame.

4fill in blank

hard

Fill both blanks to create a new DataFrame with selected columns and trigger computation.

Apache Spark

selected_df = spark_df.[1]('name')
selected_df.[2]()

Drag options to blanks, or click blank then click option'

Aselect

Bshow

Cfilter

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'filter' instead of 'select' to pick columns.

Using 'count' which triggers computation but does not display data.

select('name') picks the 'name' column, and show() triggers the computation and displays the data.

5fill in blank

hard

Fill all three blanks to create a filtered DataFrame, select a column, and count the rows.

Apache Spark

filtered = spark_df.filter(spark_df['id'] [1] 1)
selected = filtered.[2]('name')
row_count = selected.[3]()

Drag options to blanks, or click blank then click option'

A>

Bselect

Ccount

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'filter' instead of 'select' for the second blank.

Using 'show' instead of 'count' for the last blank.

First, filter rows where 'id' is greater than 1, then select the 'name' column, and finally count the rows to trigger computation.

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