Handling skewed joins in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a join between two DataFrames on the 'id' column.

Apache Spark

result = df1.join(df2, on=[1], how='inner')

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

A'name'

B'id'

C'age'

D'date'

Attempts:

3 left

💡 Hint

Common Mistakes

Joining on a column that does not exist in both DataFrames.

Using the wrong join type.

We join on the 'id' column because it is the common key between the two DataFrames.

2fill in blank

medium

Complete the code to add a salt column with random integers between 0 and 9 to the DataFrame.

Apache Spark

from pyspark.sql.functions import rand
salted_df = df.withColumn('salt', (rand() * 10).cast([1]))

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

A'integer'

B'string'

C'float'

D'boolean'

Attempts:

3 left

💡 Hint

Common Mistakes

Casting to string or float instead of integer.

Not casting at all, resulting in float values.

We cast the salt column to 'integer' to get whole numbers for salting.

3fill in blank

hard

Fix the error in the code to perform a salted join by matching both 'id' and 'salt' columns.

Apache Spark

joined_df = df1.join(df2, on=['id', [1]], how='inner')

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

A'saltVal'

B'salted'

C'salt_column'

D'salt'

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect column names for salt.

Joining only on 'id' without salt.

The salt column is named 'salt' in both DataFrames and must be used in the join condition.

4fill in blank

hard

Fill both blanks to create a salted key by concatenating 'id' and 'salt' as strings.

Apache Spark

from pyspark.sql.functions import concat, col
salted_df = df.withColumn('salted_key', concat(col([1]), col([2])))

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

A'id'

B'salt'

C'key'

D'value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column names like 'key' or 'value'.

Concatenating only one column.

We concatenate 'id' and 'salt' columns to create a unique salted key for joining.

5fill in blank

hard

Fill all three blanks to filter the DataFrame for skewed keys where count is greater than 1000.

Apache Spark

skewed_keys = df.groupBy([1]).count().filter(col('count') [2] [3])

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

A'id'

B>

C1000

D'salt'

Attempts:

3 left

💡 Hint

Common Mistakes

Filtering with wrong operators like '<'.

Grouping by wrong columns like 'salt'.

We group by 'id', then filter where count is greater than 1000 to find skewed keys.

Practice

1.

What is the main problem caused by skewed joins in Apache Spark?

easy

A. Spark cannot read the input data format

B. Spark runs out of memory due to too many partitions

C. Some keys have many rows, causing slow join performance

D. Data gets duplicated across all nodes

Solution

1. Step 1: Understand skewed join definition

   Skewed joins happen when some keys have a lot more rows than others, causing uneven data distribution.

2. Step 2: Identify the impact on performance

   This uneven distribution causes some tasks to take much longer, slowing down the entire join operation.

3. Final Answer:

   Some keys have many rows, causing slow join performance -> Option C

4. Quick Check:

   Skewed joins = slow join due to uneven keys [OK]

Hint: Skewed join means uneven key distribution slows join [OK]

Common Mistakes:

• Thinking skewed joins cause data duplication
• Confusing skew with memory errors
• Assuming all keys have equal rows

2.

Which of the following is the correct way to add a salt column for handling skewed joins in Spark using PySpark?

from pyspark.sql.functions import rand, floor

# Add salt column
salted_df = df.withColumn('salt', floor(rand() * 10))

easy

A. salted_df = df.withColumn('salt', floor(rand() * 10))

B. salted_df = df.withColumn('salt', rand() + 10)

C. salted_df = df.withColumn('salt', floor(rand()))

D. salted_df = df.withColumn('salt', rand() * 10)

Solution

1. Step 1: Understand salt column creation

   Salt column should be an integer from 0 to 9 to spread keys evenly.

2. Step 2: Check the code correctness

   Using floor(rand() * 10) creates integers 0-9, which is correct for salting.

3. Final Answer:

   salted_df = df.withColumn('salt', floor(rand() * 10)) -> Option A

4. Quick Check:

   floor(rand()*10) creates salt 0-9 [OK]

Hint: Use floor(rand()*N) for integer salt column [OK]

Common Mistakes:

• Using rand() without floor gives float salt
• Adding 10 instead of multiplying
• Not converting to integer with floor

3.

Given the following PySpark code snippet, what will be the output count of joined_df?

df1 = spark.createDataFrame([(1, 'A'), (1, 'B'), (2, 'C')], ['id', 'val1'])
df2 = spark.createDataFrame([(1, 'X'), (1, 'Y'), (3, 'Z')], ['id', 'val2'])

joined_df = df1.join(df2, 'id')
print(joined_df.count())

medium

A. 2

B. 4

C. 3

D. 5

Solution

1. Step 1: Analyze join keys and rows

   df1 has id=1 twice, df2 has id=1 twice, so join on id=1 produces 2*2=4 rows. id=2 and id=3 do not match both sides.

2. Step 2: Calculate total joined rows

   Only id=1 matches, producing 4 rows in joined_df.

3. Final Answer:

   4 -> Option B

4. Quick Check:

   2x2 matches for id=1 = 4 rows [OK]

Hint: Multiply matching rows per key for join count [OK]

Common Mistakes:

• Counting only unique keys, ignoring duplicates
• Assuming join count equals smaller table size
• Ignoring multiple matches per key

4.

Identify the error in the following PySpark code snippet intended to handle skewed joins by salting:

from pyspark.sql.functions import rand, floor

salted_df1 = df1.withColumn('salt', floor(rand() * 5))
salted_df2 = df2.withColumn('salt', floor(rand() * 5))

joined_df = salted_df1.join(salted_df2, ['id', 'salt'])

medium

A. rand() function is not imported correctly

B. floor(rand() * 5) produces values outside 0-4 range

C. Join should be on 'id' only, not on 'salt'

D. Salt columns are added independently, so keys may not match on salt

Solution

1. Step 1: Understand salting logic

   Salt must be added consistently to both dataframes so matching keys have the same salt value.

2. Step 2: Identify error in salt generation

   Here, salt is generated independently with rand(), so salt values won't match for same keys, causing join to fail.

3. Final Answer:

   Salt columns are added independently, so keys may not match on salt -> Option D

4. Quick Check:

   Independent salt columns cause join mismatch [OK]

Hint: Salt must be consistent on both sides for join [OK]

Common Mistakes:

• Adding salt independently without coordination
• Joining only on 'id' when salting is used
• Misunderstanding rand() output range

5.

You have two large datasets with a highly skewed join key. Which approach best handles the skew to improve join performance?

1. Add a random salt column to the skewed dataset and replicate the smaller dataset across all salt values, then join on both key and salt.
2. Disable broadcast joins to force shuffle join.
3. Use map-side join without salting.
4. Increase executor memory without changing join logic.

hard

A. Add random salt to skewed dataset and replicate smaller dataset across salt values, join on key and salt

B. Disable broadcast joins to force shuffle join

C. Use map-side join without salting

D. Increase executor memory without changing join logic

Solution

1. Step 1: Understand skewed join handling

   Salting spreads skewed keys across partitions by adding a random salt column to the skewed dataset and replicating the smaller dataset across all possible salt values.

2. Step 2: Evaluate options

   Disabling broadcast or increasing memory alone won't fix skew. Map-side join without salting won't handle skew properly.

3. Final Answer:

   Add random salt to skewed dataset and replicate smaller dataset across salt values, join on key and salt -> Option A

4. Quick Check:

   Random salt skewed + replicate small fixes skew [OK]

Hint: Random salt skewed + replicate small fixes skew [OK]

Common Mistakes:

• Disabling broadcast join alone fixes skew
• Not replicating the smaller dataset when salting
• Relying only on memory increase