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Explain the concept of Broadcast Join in Spark. When should it be used?

Spark/Big Datamedium0.4 min read

Mechanism: Small table sent to all executors; join happens locally, no shuffle. Triggered by broadcast() hint or spark.sql.autoBroadcastJoinThreshold (default 10MB). Why: Shuffle of large table is expensive; broadcasting small table avoids it. When: One side fits in executor...

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Frequency
Low
Asked at 3 companies
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Delivery HeroDunnhumbyFragma Data Systems
Interview Pro Tip

Red Flag: Saying 'broadcast when small' without mentioning memory or threshold. Pro-Move: 'We broadcast our 8MB dim_product; sort-merge was shuffling 2TB fact—broadcast cut shuffle and runtime by 60%'—quantifies benefit.

Key Concepts Tested
joinsparksql

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Delivery Hero, Dunnhumby, Fragma Data Systems. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, spark, sql) will help you answer variations of this question confidently.

How to Approach This

Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
83 words

Mechanism: Small table sent to all executors; join happens locally, no shuffle. Triggered by broadcast() hint or spark.sql.autoBroadcastJoinThreshold (default 10MB). Why: Shuffle of large table is expensive; broadcasting small table avoids it. When: One side fits in executor memory (~broadcast threshold). Trade-off: Too large = driver/executor OOM; too small threshold = unnecessary shuffles. Cost: Broadcast data replicated per executor; acceptable for MB-scale. Scalability: Dimension tables (10–100MB) ideal; fact tables not. Best practice: Use for fact–dimension joins; monitor driver memory; tune threshold per workload.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 3 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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