Cross joins and when to avoid them in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a cross join between two DataFrames df1 and df2.

Apache Spark

result = df1.[1](df2)

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Aunion

Bjoin

CcrossJoin

Dselect

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'join' instead of 'crossJoin' which performs other types of joins.

Using 'union' which combines rows but does not join.

The crossJoin method performs a cross join between two DataFrames in Apache Spark.

2fill in blank

medium

Complete the code to count the number of rows in the cross join result.

Apache Spark

count = df1.crossJoin(df2).[1]()

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Acount

Bcollect

Cprint

Dshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'show' which displays rows but does not return count.

Using 'collect' which gathers data but does not count.

The count() method returns the number of rows in a DataFrame.

3fill in blank

hard

Complete the code to enable cross joins explicitly (required before using crossJoin).

Apache Spark

spark.conf.set('spark.sql.crossJoin.enabled', [1])

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A'true'

B'false'

CTrue

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using boolean True or False instead of string 'true' or 'false'.

Setting the value to 'false' which disables cross joins.

To allow cross joins in Spark, set the configuration spark.sql.crossJoin.enabled to the string 'true'.

4fill in blank

hard

Complete the code to calculate the expected number of rows in a cross join result (product of individual row counts).

Apache Spark

expected_rows = df1.[1]() * df2.[1]()

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Acollect

Bcount

Cshow

Dsize

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'show' or 'collect', which do not return row counts.

There is no 'size()' method on DataFrame.

A cross join (cartesian product) results in df1.count() * df2.count() rows, which grows very large for big DataFrames—avoid unless necessary.

5fill in blank

hard

Complete the code to disable cross joins to prevent accidental performance issues with large datasets.

Apache Spark

spark.conf.set('spark.sql.crossJoin.enabled', [1])

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

Atrue

B'true'

Cfalse

D'false'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'true' or true which enables cross joins.

Using boolean false instead of string 'false'.

Setting spark.sql.crossJoin.enabled to 'false' (default) disables cross joins, avoiding massive cartesian products and OOM errors.