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How would you monitor and reduce disk-based queries (disk spilling)?

System Design/Architecturemedium0.5 min readPremium
Frequency
Low
Asked at 1 company
Category
179
questions in System Design/Architecture
Difficulty Split
15E|6M|158H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Capco
Interview Pro Tip

Red Flag: 'Just add more memory.' Pro-Move: 'We profiled with Spark UI, found skew on user_id—added salting and reduced spill by 90%; also tuned shuffle partitions from 200 to 400 for our data skew.'

Key Concepts Tested
joinpartitionsnowflakesparksqlwindow
Expert AnswerPremium
95 wordsInterview-ready
Disk spilling occurs when memory is exceeded—often 10–100x slower. WHY: Spilling indicates memory-pressure; it kills SLAs and increases I/O cost. MONITOR: Spark UI (spilled_bytes), Snowflake query_history (bytes_spilled_to_local_storage), Redshift SVL_QUERY_METRICS. REDUCE: (1) Increase executor memory or use memory-optimized instances. (2) Broadcast joins for small tables—tune spark.sql.autoBroadcastJoinThreshold. (3) Repartition before expensive ops to distribute load....
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 Capco. The answer also includes follow-up discussion points that interviewers commonly explore.

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