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:

Quick-Reference Examples

CTE Pattern

-- Isolate expensive subquery logic for reuse and readabilityWITH 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_amountFROM ranked_ordersWHERE rn = 1;                           -- latest completed order per customer

Window Function Pattern

-- Running total and rank within partition — no self-join requiredSELECT    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_rankFROM employees;

EXPLAIN ANALYZE Interpretation

-- PostgreSQL: always use ANALYZE to see actual row counts vs. estimatesEXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)SELECT *FROM orders oJOIN customers c ON c.id = o.customer_idWHERE 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

-- 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_countFROM orders o;
-- AFTER: single aggregation join (fast)SELECT o.order_id, COALESCE(agg.item_count, 0) AS item_countFROM orders oLEFT 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, Principal Consultant at Synergetic Solutions

Documentation