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When would you choose a broadcast join over a shuffle join? Any memory risks?

Spark/Big Datamedium0.4 min readPremium

**Choose Broadcast**: When one table is small (<~100MB). Avoids shuffle of large table. 10–100x faster. **Choose Shuffle**: When both tables large. Broadcast would OOM. **Memory Risk**: Broadcast table sent to every executor. Must fit in executor memory. Oversized = driver or...

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Frequency
Low
Asked at 1 company
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
Microsoft
Key Concepts Tested
joinsparksql

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This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Microsoft. 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.

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Expert Answer
85 words

Choose Broadcast: When one table is small (<~100MB). Avoids shuffle of large table. 10–100x faster.

Choose Shuffle: When both tables large. Broadcast would OOM.

Memory Risk: Broadcast table sent to every executor. Must fit in executor memory. Oversized = driver or executor OOM.

Mitigation: Monitor broadcast size. spark.sql.autoBroadcastJoinThreshold (default 10MB). Manual broadcast() when appropriate. If table grows, remove broadcast.

Scalability Trade-offs: Threshold too high = OOM. Too low = unnecessary shuffle.

Cost Implications: Broadcast when safe = huge savings. OOM = job fail, wasted run.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. 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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