Broadcast joins for small tables in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to broadcast the small DataFrame before joining.

Apache Spark

from pyspark.sql.functions import broadcast
result = large_df.join([1](small_df), 'id')

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

Abroadcast

Bcache

Cpersist

Dcollect

Attempts:

3 left

💡 Hint

Common Mistakes

Using cache() or persist() instead of broadcast()

Trying to collect() the DataFrame before join

The broadcast() function tells Spark to send the small DataFrame to all worker nodes for efficient joining.

2fill in blank

medium

Complete the code to perform a broadcast join with a condition on 'id'.

Apache Spark

joined_df = large_df.join([1](small_df), large_df.id == small_df.id)

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

Abroadcast

Bcache

Cpersist

Dcollect

Attempts:

3 left

💡 Hint

Common Mistakes

Using cache() or persist() instead of broadcast()

Not wrapping the small DataFrame at all

broadcast() wraps the small DataFrame to optimize the join by sending it to all nodes.

3fill in blank

hard

Fix the error in the code to correctly broadcast the small DataFrame before join.

Apache Spark

from pyspark.sql.functions import broadcast
joined = large_df.join([1](small_df), 'id')

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

Apersist

Bcache

Cbroadcast

Dcollect

Attempts:

3 left

💡 Hint

Common Mistakes

Calling broadcast() after join() instead of before

Using cache() or persist() instead

broadcast() must wrap the small DataFrame before the join, not be called after join().

4fill in blank

hard

Fill both blanks to create a broadcast join and select columns from both DataFrames.

Apache Spark

from pyspark.sql.functions import [1]
result = large_df.join([2](small_df), 'id').select('large_col', 'small_col')

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

Abroadcast

Bcache

Cpersist

Dcollect

Attempts:

3 left

💡 Hint

Common Mistakes

Importing broadcast but not using it

Using different functions for import and usage

broadcast() is imported and used to wrap the small DataFrame for efficient join.

5fill in blank

hard

Fill all three blanks to broadcast the small DataFrame, join on 'id', and filter results.

Apache Spark

from pyspark.sql.functions import [1]
joined = large_df.join([2](small_df), 'id')
filtered = joined.filter(joined.[3] > 100)

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

Abroadcast

Bcache

Cvalue

Dpersist

Attempts:

3 left

💡 Hint

Common Mistakes

Using cache or persist instead of broadcast

Filtering on a non-existent column

broadcast() is imported and used to wrap small_df; 'value' is the column filtered on.

Practice

1.

What is the main benefit of using broadcast() in Spark when joining tables?

easy

A. It compresses the large table to save memory.

B. It sorts both tables before joining.

C. It sends the small table to all worker nodes to speed up the join.

D. It duplicates the large table across all nodes.

Solution

1. Step 1: Understand broadcast join purpose

   Broadcast join sends the small table to all worker nodes to avoid shuffling the large table.

2. Step 2: Identify the benefit

   This reduces network data movement and speeds up the join operation.

3. Final Answer:

   It sends the small table to all worker nodes to speed up the join. -> Option C

4. Quick Check:

   Broadcast join = send small table to all nodes [OK]

Hint: Broadcast small table to all nodes to avoid shuffle [OK]

Common Mistakes:

• Thinking broadcast compresses large table
• Assuming broadcast sorts tables
• Believing broadcast duplicates large table

2.

Which of the following is the correct syntax to broadcast a small DataFrame small_df in Spark before joining?

from pyspark.sql.functions import broadcast

result = large_df.join(____, 'id')

easy

A. small_df.join(broadcast())

B. small_df.broadcast()

C. broadcast(large_df)

D. broadcast(small_df)

Solution

1. Step 1: Recall broadcast syntax

   In Spark, you use broadcast() function around the small DataFrame to mark it for broadcasting.

2. Step 2: Apply to join

   The join should be large_df.join(broadcast(small_df), 'id') to broadcast the small table.

3. Final Answer:

   broadcast(small_df) -> Option D

4. Quick Check:

   Use broadcast() function on small_df [OK]

Hint: Use broadcast() around small DataFrame in join [OK]

Common Mistakes:

• Calling broadcast as a method on DataFrame
• Broadcasting the large DataFrame instead
• Incorrect join syntax with broadcast

3.

Given the following Spark code, what will be the output count?

from pyspark.sql import SparkSession
from pyspark.sql.functions import broadcast

spark = SparkSession.builder.getOrCreate()

large_data = [(1, 'A'), (2, 'B'), (3, 'C')]
small_data = [(1, 'X'), (2, 'Y')]

large_df = spark.createDataFrame(large_data, ['id', 'val1'])
small_df = spark.createDataFrame(small_data, ['id', 'val2'])

joined_df = large_df.join(broadcast(small_df), 'id')
print(joined_df.count())

medium

A. 2

B. 5

C. 3

D. 0

Solution

1. Step 1: Analyze join keys and data

   large_df has ids 1, 2, 3; small_df has ids 1, 2. Join on 'id' keeps only matching ids.

2. Step 2: Count matching rows

   Ids 1 and 2 match in both tables, so joined_df has 2 rows.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Join count = matching ids = 2 [OK]

Hint: Count matching keys in both tables after join [OK]

Common Mistakes:

• Counting all rows from large_df ignoring join
• Assuming broadcast changes join result count
• Counting unmatched rows

4.

Identify the error in the following Spark code that attempts a broadcast join:

from pyspark.sql.functions import broadcast

result = large_df.join(small_df.broadcast(), 'id')

medium

A. Missing import for broadcast function.

B. Using broadcast as a method on DataFrame is incorrect.

C. Join key 'id' does not exist in both DataFrames.

D. Broadcasting large_df instead of small_df.

Solution

1. Step 1: Check broadcast usage

   Broadcast is a function, not a method on DataFrame, so small_df.broadcast() is invalid syntax.

2. Step 2: Correct usage

   Use broadcast(small_df) instead to mark small_df for broadcasting.

3. Final Answer:

   Using broadcast as a method on DataFrame is incorrect. -> Option B

4. Quick Check:

   broadcast() is a function, not a DataFrame method [OK]

Hint: Use broadcast() function, not DataFrame.broadcast() [OK]

Common Mistakes:

• Calling broadcast as a DataFrame method
• Ignoring import of broadcast function
• Confusing which DataFrame to broadcast

5.

You have a large DataFrame orders with millions of rows and a small DataFrame countries with 10 rows. You want to join them on country_code. Which approach will give the best performance?

hard

A. Use orders.join(broadcast(countries), 'country_code') to broadcast the small table.

B. Use orders.join(countries, 'country_code') without broadcasting.

C. Broadcast the large orders DataFrame instead.

D. Collect both DataFrames to driver and join locally.

Solution

1. Step 1: Identify table sizes

   Since countries is small, broadcasting it avoids shuffling the large orders DataFrame.

2. Step 2: Choose best join method

   Broadcast join sends the small table to all nodes, speeding up the join and reducing network traffic.

3. Final Answer:

   Use orders.join(broadcast(countries), 'country_code') to broadcast the small table. -> Option A

4. Quick Check:

   Broadcast small table for best join performance [OK]

Hint: Broadcast the small table in large-small joins [OK]

Common Mistakes:

• Broadcasting the large DataFrame wastes memory
• Not broadcasting causes expensive shuffle
• Collecting large data to driver causes failure