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How to optimize join of large and small tables in Spark?

SQLmedium0.2 min read

**Broadcast**: df_large.join(broadcast(df_small), 'key'). Small table sent to all executors; no shuffle on large. **Threshold**: spark.sql.autoBroadcastJoinThreshold (default 10MB). **When**: Small table < threshold. **Hint**: broadcast() forces even if over threshold (use...

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Frequency
Low
Asked at 1 company
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Datametica
Key Concepts Tested
joinpartitionsparksql

Why This Question Matters

This medium-level SQL question appears frequently in data engineering interviews at companies like Datametica. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, spark) 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
42 words

Broadcast: df_large.join(broadcast(df_small), 'key'). Small table sent to all executors; no shuffle on large. Threshold: spark.sql.autoBroadcastJoinThreshold (default 10MB). When: Small table < threshold. Hint: broadcast() forces even if over threshold (use carefully). Moderate size: Repartition large on join key. Skew: Salt or AQE.

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