Google's marketing team is making a report on how many searches users handle. Write a query to report the median of searches made by users.
This is one of Google's most challenging SQL problems, testing your understanding of statistical calculations in SQL and recursive CTEs. Calculating medians in SQL is notoriously tricky because SQL is designed for set operations, not ordered statistics. Google uses this to assess candidates' ability to handle complex analytical queries that go beyond simple aggregations—essential for their massive-scale data analysis infrastructure.
Advanced concepts tested: recursive CTEs for data expansion, ROW_NUMBER() window function for ordering, COUNT() OVER() for total row calculation, FLOOR() and CEIL() for median position logic, and understanding frequency tables where one row represents multiple data points. This problem requires transforming aggregated data back into individual rows before calculating the median.
Google's data scientists use median calculations extensively to: analyze search query distributions while being robust to outliers, calculate typical page load times across billions of requests, measure user engagement metrics that aren't skewed by power users, generate percentile-based SLAs for infrastructure monitoring, and create fair comparison metrics across different user segments in A/B tests.
When tackling this Google problem, the key is to understand the grain of the result. Are you returning one row per user, or one row per category? Always start by identifying your unique join keys and consider if filtered aggregations (CASE WHEN) are more efficient than multiple subqueries.
Be careful with NULL values in your JOIN conditions or aggregate functions. In interview scenarios, datasets often include edge cases like zero-count categories or duplicate entries that can throw off a simple COUNT(*) if not handled with DISTINCT.
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