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

System Design/Architecturemedium0.5 min readPremium

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)...

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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

Why This Question Matters

This medium-level System Design/Architecture question appears frequently in data engineering interviews at companies like Capco. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, snowflake) 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
95 words

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. (4) Avoid wide transforms—reduce groupBy keys, limit window ranges. (5) Columnar formats (Parquet) reduce read volume. SCALABILITY: Spill scales poorly; more partitions = more parallelism but more overhead. COST: Memory-optimized instances cost more; spilled I/O increases egress cost; profile to find balance.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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According to DataEngPrep.tech, this is one of the most frequently asked System Design/Architecture 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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