Handling data skew at scale in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to repartition the DataFrame to reduce data skew.

Apache Spark

df = df.[1](numPartitions=10)

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

Aselect

Bfilter

Crepartition

DgroupBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using groupBy instead of repartition causes shuffles but not necessarily balanced partitions.

Using filter or select does not change data distribution.

Using repartition redistributes data across partitions to help reduce skew.

2fill in blank

medium

Complete the code to add a random salt column to the DataFrame to help with skew.

Apache Spark

from pyspark.sql.functions import rand

df = df.withColumn('salt', (rand() * [1]).cast('int'))

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

A1000

B10

C100

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few salt buckets may not reduce skew effectively.

Using too many salt buckets can increase shuffle overhead.

Adding a salt with 10 buckets helps distribute skewed keys into multiple groups.

3fill in blank

hard

Fix the error in the join to handle skew by joining on salted keys.

Apache Spark

from pyspark.sql.functions import rand

df1 = df1.withColumn('salt', (rand() * 10).cast('int'))
df2 = df2.withColumn('salt', (rand() * 10).cast('int'))

joined = df1.join(df2, ['key', [1]])

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

A'salt'

B'random'

C'salted_key'

D'skew_key'

Attempts:

3 left

💡 Hint

Common Mistakes

Joining only on 'key' ignores the salt and causes skew.

Using wrong column names causes join errors.

The join must include the 'salt' column to distribute skewed keys properly.

4fill in blank

hard

Fill both blanks to create a salted key and join on it to handle skew.

Apache Spark

from pyspark.sql.functions import concat, col, lit

df1 = df1.withColumn('salted_key', concat(col('key'), lit('_'), col([1]).cast('string')))
df2 = df2.withColumn('salted_key', concat(col('key'), lit('_'), col([2]).cast('string')))

joined = df1.join(df2, 'salted_key')

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

Asalt

Brandom

Dskew

Attempts:

3 left

💡 Hint

Common Mistakes

Using different column names for salt in each DataFrame.

Not casting salt to string before concatenation.

Both DataFrames use the 'salt' column to create a combined salted key for joining.

5fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Apache Spark

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

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

A:

Blen(word)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

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

Missing colon causes syntax error.

Using word instead of len(word) as value.

This dictionary comprehension maps each word to its length only if the word length is greater than 3.

Practice

1. What is the main problem caused by data skew in Apache Spark jobs?

easy

A. Some tasks become much larger and slower than others

B. All tasks run equally fast

C. Data is lost during processing

D. Spark automatically fixes skew without user action

Solution

1. Step 1: Understand data skew impact

   Data skew means some keys have many more records, causing uneven task sizes.

2. Step 2: Identify effect on tasks

   Tasks processing large keys take longer, slowing the whole job.

3. Final Answer:

   Some tasks become much larger and slower than others -> Option A

4. Quick Check:

   Data skew = uneven task sizes [OK]

Hint: Data skew means uneven task sizes slowing jobs [OK]

Common Mistakes:

• Thinking all tasks run equally fast
• Believing Spark fixes skew automatically
• Confusing data loss with skew

2. Which of the following is the correct way to add a salt key to a DataFrame column named user_id in PySpark?

easy

A. df.withColumn('salted_key', concat(col('user_id'), lit('_'), rand()))

B. df.withColumn('salted_key', col('user_id') + rand())

C. df.withColumn('salted_key', col('user_id').cast('int'))

D. df.withColumn('salted_key', col('user_id').substr(0, 3))

Solution

1. Step 1: Understand salting technique

   Salting adds a random value to the key to spread data across partitions.

2. Step 2: Check correct syntax for salting

   Concatenating user_id with a random number as string is correct.

3. Final Answer:

   df.withColumn('salted_key', concat(col('user_id'), lit('_'), rand())) -> Option A

4. Quick Check:

   Salting = concat key + random [OK]

Hint: Use concat with random value to salt keys [OK]

Common Mistakes:

• Adding random float directly to string key
• Casting key without salting
• Using substring instead of salting

3. Given the following PySpark code snippet to reduce skew by salting, what will be the output count of salted_df if df has 1000 rows?

from pyspark.sql.functions import col, concat, lit, rand
salted_df = df.withColumn('salted_key', concat(col('key'), lit('_'), (rand() * 10).cast('int')))
count = salted_df.count()

medium

A. 0

B. 1000

C. 10

D. 100

Solution

1. Step 1: Understand salting effect on row count

   Salting adds a new column but does not change the number of rows.

2. Step 2: Count rows after salting

   The count remains the same as original DataFrame, 1000 rows.

3. Final Answer:

   1000 -> Option B

4. Quick Check:

   Salting adds column, count unchanged [OK]

Hint: Salting adds column, row count stays same [OK]

Common Mistakes:

• Thinking salting duplicates rows
• Confusing salt range with row count
• Assuming count changes after withColumn

4. You have a join in Spark that is very slow due to data skew on the join key. Which of the following code snippets correctly applies salting to both DataFrames to fix the skew?

df1_salted = df1.withColumn('salt', (rand() * 5).cast('int'))
df2_salted = df2.withColumn('salt', (rand() * 5).cast('int'))
joined = df1_salted.join(df2_salted, ['key', 'salt'])

medium

A. Incorrect: salting with fixed value instead of random

B. Incorrect: salting only one DataFrame

C. Incorrect: joining without salt column

D. Correct: salting both sides and joining on key and salt

Solution

1. Step 1: Understand salting for joins

   To fix skew, salt both DataFrames on join key with same salt range.

2. Step 2: Verify join condition

   Joining on both original key and salt column ensures balanced partitions.

3. Final Answer:

   Correct: salting both sides and joining on key and salt -> Option D

4. Quick Check:

   Salt both sides + join on key & salt [OK]

Hint: Salt both DataFrames and join on key plus salt [OK]

Common Mistakes:

• Salting only one DataFrame
• Joining without salt column
• Using fixed salt value instead of random

5. You have a Spark job with a very large skewed key causing one partition to be huge. You want to reduce skew by salting and also control shuffle partitions. Which approach below best balances load and parallelism?

hard

A. Add salt column but keep shuffle partitions very low

B. Do not add salt, just increase shuffle partitions

C. Add a salt column with random values, join on salted keys, and increase shuffle partitions

D. Add salt column with fixed value and reduce shuffle partitions

Solution

1. Step 1: Apply salting to spread skewed key

   Adding a salt column with random values breaks large groups into smaller ones.

2. Step 2: Adjust shuffle partitions to increase parallelism

   Increasing shuffle partitions allows more tasks to run in parallel, reducing bottlenecks.

3. Step 3: Combine salting and partition tuning

   Both salting and increasing partitions together balance load and improve job speed.

4. Final Answer:

   Add a salt column with random values, join on salted keys, and increase shuffle partitions -> Option C

5. Quick Check:

   Salt + more partitions = balanced load [OK]

Hint: Salt keys and increase shuffle partitions for best balance [OK]

Common Mistakes:

• Keeping shuffle partitions too low after salting
• Not salting and only increasing partitions
• Using fixed salt value causing no skew fix