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Database query problems involving joins, filtering, and aggregation are common in technical interviews, including FAANG-style assessments. While this exact problem may not appear, similar SQL patterns are frequently tested.
The optimal approach uses a LEFT JOIN between the Books and Orders tables combined with aggregation. By summing order quantities within a specific date range and filtering with HAVING, you can identify books whose total sales fall below the required threshold.
Key SQL concepts include LEFT JOIN, GROUP BY, SUM aggregation, and date filtering with WHERE clauses. Understanding how to handle NULL values for books without orders is also important.
In SQL-based problems like this, relational joins and aggregation functions act as the main tools rather than traditional data structures. Indexes on book_id and date columns can significantly improve query performance.