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Home/Questions/SQL/Compare Glue partition discovery with Hive MSCK/ADD PARTITION. Explain the operational and cost implications of crawler-based vs. partition-projection approaches. When does partition projection become necessary, and what are its limitations?

Compare Glue partition discovery with Hive MSCK/ADD PARTITION. Explain the operational and cost implications of crawler-based vs. partition-projection approaches. When does partition projection become necessary, and what are its limitations?

SQLmedium0.5 min readPremium

Glue: Crawler infers partitions from S3 path (e.g., s3://bucket/table/year=2024/month=01/); catalogs in Glue Catalog. Hive: MSCK REPAIR TABLE or ADD PARTITION; expects Hive-style paths. Why Glue: AWS-native; integrates with Athena, Redshift Spectrum; no cluster to run. Partition...

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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
Capco
Key Concepts Tested
partition

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This medium-level SQL 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 (partition) will help you answer variations of this question confidently.

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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
109 words

Glue: Crawler infers partitions from S3 path (e.g., s3://bucket/table/year=2024/month=01/); catalogs in Glue Catalog. Hive: MSCK REPAIR TABLE or ADD PARTITION; expects Hive-style paths. Why Glue: AWS-native; integrates with Athena, Redshift Spectrum; no cluster to run. Partition projection: Define partition schema (type, range); no crawler needed; queries resolve partitions from path at query time. Operational: Crawler runs on schedule, costs DPU time; projection has no crawler cost. Cost: High-cardinality partitions (e.g., by hour, user_id) make crawler expensive; projection scales. Limitations: Projection requires predictable path pattern; no validation of actual data; complex patterns need custom projection. Best practice: Use projection for time-series or bounded cardinality; crawler for ad-hoc or evolving schema.

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