Partition tuning (repartition vs coalesce) in Apache Spark - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to increase the number of partitions of the DataFrame.

Apache Spark

df_repartitioned = df.[1](10)

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

Arepartition

Bcoalesce

CpartitionBy

DgroupBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using coalesce to increase partitions causes no change or error.

Using partitionBy or groupBy does not change partitions directly.

Use repartition to increase or change the number of partitions.

2fill in blank

medium

Complete the code to reduce the number of partitions without a full shuffle.

Apache Spark

df_coalesced = df.[1](5)

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

Arepartition

Bcoalesce

CgroupBy

DpartitionBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using repartition to reduce partitions causes unnecessary shuffle.

Using groupBy or partitionBy does not reduce partitions directly.

coalesce reduces partitions efficiently without a full shuffle.

3fill in blank

hard

Fix the error in the code to repartition the DataFrame by a column.

Apache Spark

df_partitioned = df.[1]('category')

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

Arepartition

Bcoalesce

CpartitionBy

DgroupBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using coalesce with a column causes an error.

Using partitionBy is for writing files, not repartitioning DataFrames.

Use repartition with a column name to shuffle and repartition by that column.

4fill in blank

hard

Fill both blanks to create a DataFrame with fewer partitions using the efficient method.

Apache Spark

df_small = df.[1]([2])

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

Acoalesce

Brepartition

C10

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using repartition to reduce partitions causes shuffle.

Choosing a larger number of partitions does not reduce partitions.

coalesce(5) reduces partitions to 5 efficiently without shuffle.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping words to their lengths for words longer than 3 letters.

Apache Spark

word_counts = {word: [1] for word in words if len(word) [2] 3 and word [3] stop_words}

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

Alen(word)

B>

Cnot in

Din

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'in' instead of 'not in' includes stop words.

Using '<' instead of '>' filters wrong words.

This dictionary comprehension maps words to their lengths for words longer than 3 letters and not in stop_words.

Practice

1. Which statement best describes the difference between repartition and coalesce in Apache Spark?

easy

A. coalesce only increases partitions with shuffle; repartition only decreases partitions without shuffle.

B. coalesce can increase or decrease partitions with shuffle; repartition only decreases partitions without shuffle.

C. repartition only increases partitions without shuffle; coalesce only decreases partitions with shuffle.

D. repartition can increase or decrease partitions with shuffle; coalesce only decreases partitions without shuffle.

Solution

1. Step 1: Understand repartition behavior

   repartition reshuffles data and can increase or decrease the number of partitions, causing a full shuffle.

2. Step 2: Understand coalesce behavior

   coalesce reduces the number of partitions without a full shuffle, making it faster but it cannot increase partitions.

3. Final Answer:

   repartition can increase or decrease partitions with shuffle; coalesce only decreases partitions without shuffle. -> Option D

4. Quick Check:

   repartition = shuffle & flexible, coalesce = no shuffle & reduce [OK]

Hint: Repartition reshuffles; coalesce only reduces partitions [OK]

Common Mistakes:

• Thinking coalesce can increase partitions
• Assuming repartition does not shuffle
• Confusing which method is faster

2. Which of the following is the correct syntax to reduce the number of partitions of a DataFrame df to 5 using coalesce?

easy

A. df.repartition(5)

B. df.coalesce(5)

C. df.coalesce().numPartitions(5)

D. df.reducePartitions(5)

Solution

1. Step 1: Recall coalesce syntax

   The correct method to reduce partitions is df.coalesce(numPartitions).

2. Step 2: Identify correct usage

   df.coalesce(5) uses df.coalesce(5), which correctly reduces partitions to 5.

3. Final Answer:

   df.coalesce(5) -> Option B

4. Quick Check:

   coalesce syntax = df.coalesce(number) [OK]

Hint: Use df.coalesce(number) to reduce partitions [OK]

Common Mistakes:

• Using repartition instead of coalesce to reduce partitions
• Calling non-existent methods like reducePartitions
• Incorrect method chaining syntax

3. Given the following Spark code, what will be the number of partitions after execution?

val df = spark.range(1000).repartition(10)
val df2 = df.coalesce(4)

medium

A. 4 partitions

B. 10 partitions

C. 14 partitions

D. 1000 partitions

Solution

1. Step 1: Analyze initial repartition

   df is repartitioned to 10 partitions explicitly.

2. Step 2: Apply coalesce

   df2 uses coalesce(4) which reduces partitions from 10 to 4 without shuffle.

3. Final Answer:

   4 partitions -> Option A

4. Quick Check:

   repartition to 10, then coalesce to 4 = 4 partitions [OK]

Hint: Coalesce reduces partitions to given number [OK]

Common Mistakes:

• Assuming partitions add up (10 + 4 = 14)
• Thinking coalesce increases partitions
• Confusing initial partition count with final

4. You want to increase the number of partitions of a DataFrame df from 4 to 8. You try df.coalesce(8) but it does not increase partitions. What is the issue?

medium

A. You need to call df.coalesce(8).shuffle() to increase partitions.

B. coalesce requires shuffle to increase partitions.

C. coalesce cannot increase partitions; use repartition instead.

D. The DataFrame must be cached before increasing partitions.

Solution

1. Step 1: Understand coalesce limitations

   coalesce only reduces partitions and does not increase them.

2. Step 2: Choose correct method to increase partitions

   To increase partitions, repartition must be used as it performs a shuffle.

3. Final Answer:

   coalesce cannot increase partitions; use repartition instead. -> Option C

4. Quick Check:

   coalesce only reduces, repartition reshuffles [OK]

Hint: Use repartition to increase partitions, not coalesce [OK]

Common Mistakes:

• Trying to increase partitions with coalesce
• Assuming coalesce triggers shuffle
• Believing caching affects partition count

5. You have a large DataFrame with 100 partitions. You want to reduce partitions to 10 for faster processing but want to avoid a full shuffle to save time. Which method should you use and why?

hard

A. Use df.coalesce(10) because it reduces partitions without a full shuffle.

B. Use df.repartition(10) because it reduces partitions without shuffle.

C. Use df.repartition(10) because it avoids shuffle and reduces partitions.

D. Use df.coalesce(10) because it increases partitions with shuffle.

Solution

1. Step 1: Identify partition reduction without shuffle

   coalesce reduces partitions without a full shuffle, making it faster.

2. Step 2: Compare with repartition

   repartition always causes a full shuffle, which is slower but can increase or decrease partitions.

3. Final Answer:

   Use df.coalesce(10) because it reduces partitions without a full shuffle. -> Option A

4. Quick Check:

   Reduce partitions fast = coalesce without shuffle [OK]

Hint: Coalesce reduces partitions fast without shuffle [OK]

Common Mistakes:

• Using repartition and expecting no shuffle
• Thinking coalesce increases partitions
• Confusing shuffle behavior of methods