How to Minimize Data Shuffling in PySpark Joins

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Answer Correct answer: A — Use transactions.join(F.broadcast(customers), transactions.customer_id == customers.customer_id) to broadcast the smaller DataFrame and minimize shuffling.

You are analyzing customer purchases in a Fabric notebook by using PySpark. You have the following DataFrames: transactions: Contains five columns named transaction_id, customer_id, product_id, amount, and date and has 10 million rows, with each row representing a transaction. customers: Contains customer details in 1,000 rows and three columns named customer_id, name, and country. You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code. from pyspark.sql import functions as F results = Which code should you run to populate the results DataFrame?

  1. transactions.join(F.broadcast(customers), transactions.customer_id == customers.customer_id) Correct Answer
  2. transactions.join(customers, transactions.customer_id == customers.customer_id).distinct()
  3. transactions.join(customers, transactions.customer_id == customers.customer_id)
  4. transactions.crossJoin(customers).where(transactions.customer_id == customers.customer_id)

Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

The question tests PySpark join optimization strategies, where the common trap is using a standard join which forces shuffling instead of broadcasting the smaller DataFrame.

This page explains how to minimize data shuffling when joining a large DataFrame with a small DataFrame in PySpark. It establishes that using the broadcast join optimization is the correct approach to avoid network shuffles.

Choosing a standard join (Option C) because it looks simpler, which fails to explicitly minimize shuffling and relies on Spark's potentially suboptimal auto-optimization.

Community Discussion (5 comments)

Momoanwar 👍 31 Selected: A
In Apache Spark, broadcasting refers to an optimization technique for join operations. When you join two DataFrames or RDDs and one of them is significantly smaller than the other, Spark can "broadcast" the smaller table to all nodes in the cluster. This approach avoids the need for network shuffles for each row of the larger table, significantly reducing the execution time of the join operation.
sraakesh95 👍 7 Selected: A
A - Broadcasting generates a copy of the data across all the nodes in the Spark cluster. Therefore, during a join operation, it won't require any I/Os from other nodes, thereby, reducing the shuffling requirement.
282b85d 👍 1 Selected: A
Broadcasting: The F.broadcast(customers) function is used to broadcast the smaller DataFrame (customers). This ensures that the smaller DataFrame is replicated across all nodes, and each node can perform the join locally with its partition of the larger DataFrame (transactions). This significantly reduces the data movement (shuffling) required during the join operation.
stilferx 👍 3 Selected: A
IMHO, "A" is correct! Broadcast joining copies the smaller table to each worker in Spark, which may significantly improve performance by reducing shuffling
SamuComqi 👍 2 Selected: A
A. transactions.join(F.broadcast(customers), transactions.customer_id == customers.customer_id) Optimized method to perform a join between a very large table and a smaller one. Source: https://sparkbyexamples.com/spark/broadcast-join-in-spark/"

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Expert Analysis

Why the Answer Is Correct

Option A uses F.broadcast(customers) to explicitly broadcast the smaller DataFrame (1,000 rows) to all worker nodes. This allows each node to perform the join locally with its partition of the larger DataFrame (10 million rows), completely avoiding the expensive network shuffle required by standard joins.

Why the Other Options Are Wrong

Option C performs a standard join, which defaults to a SortMergeJoin that requires shuffling both DataFrames across the network to ensure matching keys are on the same node. Option B adds a .distinct operation, which introduces even more shuffling to resolve duplicates. Option D uses a crossJoin, which creates a massive Cartesian product (10 billion rows) before filtering, causing extreme performance degradation and shuffling.

Community Comment Notes

Users correctly identified that broadcasting avoids shuffles by replicating the smaller table to all nodes, as Momoanwar noted: "avoids the need for network shuffles for each row of the larger table". sraakesh95 also pointed out that broadcasting "won't require any I/Os from other nodes, thereby, reducing the shuffling requirement".

Official Reference

https://spark.apache.org/docs/latest/sql-performance-tuning.html

Exam Strategy

When joining a very large DataFrame with a small DataFrame in PySpark, always look for the broadcast join option to minimize shuffling. Remember that explicit broadcast hints override Spark's default join strategies and guarantee shuffle avoidance.

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