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Manipulate text, math, and dates with built-in functions, and implement conditional log mappings.
Writing efficient SQL queries requires understanding the relational algebra engine underlying SQL databases. Unlike imperative code where execution flows line-by-line, SQL is declarative: you specify what data you need, and the relational query optimizer determines how to retrieve it using indexes and memory join buffers.
When processing a query, SQL engines execute clauses in this exact order: FROM & JOIN → WHERE → GROUP BY → HAVING → SELECT → DISTINCT → ORDER BY → LIMIT. Knowing this sequence prevents attempting to filter aggregated values in a WHERE clause!
A classic interview trap is writing non-SARGable predicates (like WHERE YEAR(order_date) = 2026) which force full table scans. Always compare un-wrapped indexed columns (like WHERE order_date >= '2026-01-01') to enable B-Tree index range scans!
SQL engines evaluate clauses in a strict logical order: FROM & JOIN -> WHERE -> GROUP BY -> HAVING -> SELECT -> DISTINCT -> ORDER BY -> LIMIT. Understanding this order prevents scoping errors when referencing column aliases.
RDBMS query planners build execution graphs (using cost estimation algorithms) to pick between B-Tree Index Scans, Sequential Scans, Hash Joins, and Nested Loops. Writing SARGable predicates ensures indexes are fully utilized.
Top tech companies like Meta, Amazon, and Google test Built-in Functions & CASE WHEN by asking candidates to write queries against real e-commerce or HR schemas, optimize high-latency queries, and handle NULL edge cases accurately.