The query was doing COUNT(DISTINCT e.user_id) grouped by u.user_id which gives you either 0 or 1 per user, not a site-wide DAU count at all.
Start by systematically reviewing the query for common Hive pitfalls: partition pruning, join keys, and aggregation logic. Then rewrite the query with explicit partition filters, correct join conditions, and efficient aggregation. Finally, outline a validation plan that includes sanity checks, cross-verification with another method, and edge case testing.
Pro tip: Always mention the importance of checking the query plan (EXPLAIN) to ensure partition pruning and avoid full table scans. Also, highlight that DAU should count distinct users, not events, and that timezone considerations can affect the date boundary.
Scan the query for missing partition filters, incorrect join keys (e.g., joining on non-unique columns), and improper aggregation (e.g., counting events instead of distinct users). Also look for unnecessary columns, lack of predicate pushdown, and potential data skew.
Add a WHERE clause to filter on the partition column (e.g., dt='2023-01-01') to enable partition pruning. Ensure the join is on the correct user identifier and that you use COUNT(DISTINCT user_id) for DAU. Remove any redundant operations.
Consider using techniques like map-side joins if one table is small, or bucketing if joining on a skewed key. Use appropriate file formats (ORC/Parquet) and ensure statistics are gathered. Avoid SELECT * and only select needed columns.
Run sanity checks: compare DAU with previous days, ensure it's less than total users and greater than zero. Cross-validate with a different query (e.g., using a subquery or a different join order). Check for duplicates and nulls in user_id.
Clearly articulate each bug, the fix, and why it matters. Discuss trade-offs (e.g., performance vs. accuracy) and how you would monitor the query in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.