SQL Query

Overview

Generate production-ready SQL queries in under 60 seconds with built-in optimization, index recommendations, and performance estimates. Handles complex JOINs, subqueries, and aggregations across PostgreSQL, MySQL, SQL Server, Oracle, SQLite, and MariaDB.

Use Cases

• Debug slow queries eating up database resources during production incidents
• Generate complex JOINs for analytics dashboards without trial-and-error
• Write migration scripts that transform data across multiple tables
• Create reporting queries for stakeholder metrics during sprint planning
• Build data export queries for customer support requests
• Optimize existing queries causing timeout errors in production

Benefits

Save 20+ minutes per complex query by skipping manual optimization and index research. Get production-ready SQL with performance estimates instead of guessing which indexes to add or whether your JOIN strategy will scale.

Database optimization typically requires running EXPLAIN ANALYZE multiple times and researching index strategies. This template gives you optimized queries with index recommendations upfront, so you spend time validating solutions instead of building them from scratch.

Junior developers ship queries that perform well at scale. Senior developers cut query optimization time in half. DBAs get consistent index recommendations across the team.

Template

Write a SQL query for:

Database: {{database}}
Goal: {{goal}}

Tables involved:
{{tables}}

Conditions:
{{conditions}}

Include:
- Optimized query
- Index recommendations
- Explanation of query logic
- Expected performance
- Alternative approaches

Query type: {{queryType}}

Properties

• database: Single Selection (default: PostgreSQL)
  • Options: PostgreSQL, MySQL, SQL Server, Oracle, SQLite, MariaDB
• goal: Multi-line Text
• tables: Multi-line Text
• conditions (optional): Multi-line Text
• queryType: Single Selection (default: SELECT)
  • Options: SELECT, INSERT, UPDATE, DELETE, Complex JOIN

Example Output

Here’s what this template generates for a query that finds active users who purchased in the last 30 days:

SELECT
    u.id,
    u.email,
    COUNT(o.id) AS order_count,
    SUM(o.total_amount) AS total_spending
FROM users u
INNER JOIN orders o ON u.id = o.user_id
WHERE u.status = 'active'
    AND o.created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY u.id, u.email
ORDER BY total_spending DESC;

Index recommendations:

CREATE INDEX idx_users_status ON users(status) WHERE status = 'active';
CREATE INDEX idx_orders_user_created ON orders(user_id, created_at);

Performance estimate: 100-500ms for datasets with 100k-1M orders

Alternative approaches: The output includes 2-3 alternative query strategies with tradeoff analysis (CTEs for readability vs. subqueries for performance).

Common Mistakes

Forgetting to specify database dialect: PostgreSQL’s INTERVAL '30 days' syntax differs from MySQL’s DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY). Always specify your database to get correct syntax.

Vague table descriptions: Writing “user data” instead of listing actual table schemas leads to generic queries that won’t run. Include column names and data types for accurate JOIN conditions.

Skipping the conditions field: Queries without filtering conditions often produce unoptimized full table scans. Even simple WHERE clauses dramatically improve generated index recommendations.

Assuming indexes exist: Production databases rarely have perfect indexes. The template generates index recommendations, but you need to specify your current schema for accurate optimization advice.

Not testing with realistic data volumes: A query that works on 1,000 rows might timeout on 1 million. Always mention your expected data volume in the goal field for accurate performance estimates.

Frequently Used With

SQL queries rarely exist in isolation. After generating an optimized query, you’ll typically need:

• Performance Optimization - Profile query execution time and identify bottlenecks
• Code Review - Validate query logic and security (SQL injection risks)
• API Documentation - Document database endpoints that use these queries
• Unit Test - Write test cases for query edge cases and data validation

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