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Describe how you would optimize a join between two large tables where one is significantly smaller, using broadcast joins in PySpark.

Spark/Big Datahard0.3 min readPremium
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Asked at 1 company
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Dunnhumby
Key Concepts Tested
joinoptimizationsparksql
Expert AnswerPremium
65 wordsInterview-ready
**Why optimization matters**: Shuffle on both sides = expensive; broadcast eliminates shuffle for large table. **Approach**: `df1.join(broadcast(df2), "key")` or set `spark.sql.autoBroadcastJoinThreshold` (e.g., 100MB). Broadcast sends small table to all executors; large table stays put. **Scalability trade-offs**: Small table must fit executor memory; verify with EXPLAIN. **Cost implications**: No shuffle for large side = 5–50x faster....
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