Write a query to return the IDs of the Facebook pages which do not have any likes. The output should be sorted by page ID in ascending order.
This problem tests your understanding of set operations and NULL handling—critical skills for Meta's social graph queries. At Meta's scale, identifying inactive or underperforming content is essential for content recommendation algorithms and creator insights. The challenge is choosing the right approach: LEFT JOIN with NULL checks, NOT IN subqueries, or NOT EXISTS—each with different performance characteristics at scale.
Key concepts tested: LEFT JOIN with NULL filtering, NOT IN with subqueries, NOT EXISTS for anti-joins, DISTINCT to handle duplicates in the likes table, and ORDER BY for result sorting. Understanding when to use each approach based on data distribution and NULL handling is crucial for production queries.
Meta's data engineers use similar queries to: identify pages eligible for growth campaigns, detect content that needs promotion, generate creator analytics showing engagement gaps, feed recommendation systems with underperforming content for A/B testing, and audit data quality by finding orphaned records in distributed systems.
When tackling this Meta 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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