Broadcast join sends the small table to every executor; join happens locally without shuffle. **Trigger**: broadcast(df) hint or spark.sql.autoBroadcastJoinThreshold (default 10MB). **Why use it**: Avoids shuffling the large table—the dominant cost in sort-merge join. **When**: One table fits in executor memory (typically < 100MB after serialization). **Scalability trade-off**: Driver fetches and distributes; oversized broadcast causes driver OOM....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Delivery Hero, Fragma Data Systems. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 2 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.