Why join strategy affects Spark performance in Apache Spark - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a broadcast join in Spark.

Apache Spark

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

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

Abroadcast

Bshuffle

Csort

Dcache

Attempts:

3 left

💡 Hint

Common Mistakes

Using shuffle join when the smaller DataFrame should be broadcasted.

Trying to use 'cache' or 'sort' instead of broadcast.

Broadcast join sends the smaller DataFrame to all worker nodes, reducing shuffle and improving performance.

2fill in blank

medium

Complete the code to specify the join type as a shuffle hash join.

Apache Spark

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

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Abroadcast

Bshuffle_hash

Cinner

Dleft

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'broadcast' as a join type instead of a function.

Using 'shuffle_hash' which is not a valid join type string.

The 'inner' join type triggers a shuffle hash join by default in Spark when no broadcast is used.

3fill in blank

hard

Fix the error in the code to avoid a costly shuffle join by broadcasting the smaller DataFrame.

Apache Spark

from pyspark.sql.functions import [1]
joined = df1.join([1](df2), 'id')

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

Ashuffle

Bbroadcast

Ccache

Dpersist

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'shuffle' which is not a function.

Using 'cache' or 'persist' which do not affect join strategy.

Broadcasting the smaller DataFrame avoids shuffle and improves join performance.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters words longer than 4 characters and squares their lengths.

Apache Spark

lengths = {word: len(word)[1]2 for word in words if len(word) [2] 4}

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A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '**' for power.

Using '<' instead of '>' in the condition.

We square the length with '**2' and filter words with length greater than 4 using '>'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that uppercases keys, keeps values, and filters positive values.

Apache Spark

result = [1]: [2] for k, v in data.items() if v [3] 0}}

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Ak

Bv

C>

Dk.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' for keys.

Using '<' instead of '>' in the condition.

Keys are uppercased with 'k.upper()', values kept as 'v', and filtered where 'v > 0'.