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How would you optimize a query fetching sales data across multiple countries with billions of rows?

SQLmedium0.6 min readPremium
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
Adidas
Key Concepts Tested
bigquerypartitionsnowflake
Expert AnswerPremium
124 wordsInterview-ready
For billions of rows across countries: (1) Partition by country and date—SELECT * FROM sales WHERE country IN ('US','UK') AND sale_date BETWEEN ... enables partition pruning. (2) Use columnar storage (Redshift, BigQuery, Snowflake)—only scan needed columns. (3) Aggregate at source—pre-aggregate by country/date in a summary table. (4) Use approximate queries (HyperLogLog, APPROX_COUNT_DISTINCT) when exact counts aren't needed. (5) Implement incremental processing—only process new/changed data....
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 Adidas. The answer also includes follow-up discussion points that interviewers commonly explore.

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