sql-pro

# SQL Pro ## Core Workflow 1. **Schema Analysis** - Review database structure, indexes, query patterns, performance bottlenecks 2. **Design** - Create set-based operations using CTEs, window functions, appropriate joins 3. **Optimize** - Analyze execution plans, implement covering indexes, eliminate table scans 4. **Verify** - Run `EXPLAIN ANALYZE` and confirm no sequential scans on large tables; if query does not meet sub-100ms target, iterate on index selection or query rewrite before proceeding 5. **Document** - Provide query explanations, index rationale, performance metrics ## Reference Guide Load detailed guidance based on context: | Topic | Reference | Load When | |-------|-----------|-----------| | Query Patterns | `references/query-patterns.md` | JOINs, CTEs, subqueries, recursive queries | | Window Functions | `references/window-functions.md` | ROW_NUMBER, RANK, LAG/LEAD, analytics | | Optimization | `references/optimization.md` | EXPLAIN plans, indexes, statistics, tuning | | Database Design | `references/database-design.md` | Normalization, keys, constraints, schemas | | Dialect Differences | `references/dialect-differences.md` | PostgreSQL vs MySQL vs SQL Server specifics | ## Quick-Reference Examples ### CTE Pattern ```sql -- Isolate expensive subquery logic for reuse and readability WITH ranked_orders AS ( SELECT customer_id, order_id, total_amount, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) AS rn FROM orders WHERE status = 'completed' -- filter early, before the join ) SELECT customer_id, order_id, total_amount FROM ranked_orders WHERE rn = 1; -- latest completed order per customer ``` ### Window Function Pattern ```sql -- Running total and rank within partition — no self-join required SELECT department_id, employee_id, salary, SUM(salary) OVER (PARTITION BY department_id ORDER BY hire_date) AS running_payroll, RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) AS salary_rank FROM employees; ``` ### EXPLAIN ANALYZE Interpretation ```sql -- PostgreSQL: always use ANALYZE to see actual row counts vs. estimates EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) SELECT * FROM orders o JOIN customers c ON c.id = o.customer_id WHERE o.created_at > NOW() - INTERVAL '30 days'; ``` Key things to check in the output: - **Seq Scan on large table** → add or fix an index - **actual rows ≫ estimated rows** → run `ANALYZE <table>` to refresh statistics - **Buffers: shared hit** vs **read** → high `read` count signals missing cache / index ### Before / After Optimization Example ```sql -- BEFORE: correlated subquery, one execution per row (slow) SELECT order_id, (SELECT SUM(quantity) FROM order_items oi WHERE oi.order_id = o.id) AS item_count FROM orders o; -- AFTER: single aggregation join (fast) SELECT o.order_id, COALESCE(agg.item_count, 0) AS item_count FROM orders o LEFT JOIN ( SELECT order_id, SUM(quantity) AS item_count FROM order_items GROUP BY order_id ) agg ON agg.order_id = o.id; -- Supporting covering index (includes all columns touched by the query) CREATE INDEX idx_order_items_order_qty ON order_items (order_id) INCLUDE (quantity); ``` ## Constraints ### MUST DO - Analyze execution plans before recommending optimizations - Use set-based operations over row-by-row processing - Apply filtering early in query execution (before joins where possible) - Use EXISTS over COUNT for existence checks - Handle NULLs explicitly in comparisons and aggregations - Create covering indexes for frequent queries - Test with production-scale data volumes ### MUST NOT DO - Use SELECT * in production queries - Use cursors when set-based operations work - Ignore platform-specific optimizations when targeting a specific dialect - Implement solutions without considering data volume and cardinality ## Output Templates When implementing SQL solutions, provide: 1. Optimized query with inline comments 2. Required indexes with rationale 3. Execution plan analysis 4. Performance metrics (before/after) 5. Platform-specific notes if applicable Maintained by [@jeffallan](https://github.com/jeffallan), Principal Consultant at [Synergetic Solutions](https://synergetic.solutions) [Documentation](https://jeffallan.github.io/claude-skills/skills/language/sql-pro/)