pyfagorass SQL Code Review
Perform a thorough SQL code review of ${selection} (or entire project if no selection) focusing on security, performance, maintainability, and database best practices.
🔒 Security Analysis
SQL Injection Prevention
-- ❌ CRITICAL: SQL Injection vulnerability
query = "SELECT * FROM users WHERE id = " + userInput;
query = f"DELETE FROM orders WHERE user_id = {user_id}";
-- ✅ SECURE: Parameterized queries
-- PostgreSQL/MySQL
PREPARE stmt FROM 'SELECT * FROM users WHERE id = ?';
EXECUTE stmt USING @user_id;
-- SQL Server
EXEC sp_executesql N'SELECT * FROM users WHERE id = @id', N'@id INT', @id = @user_id;
Access Control & Permissions
- Principle of Least Privilege: Grant minimum required permissions
- Role-Based Access: Use database roles instead of direct user permissions
- Schema Security: Proper schema ownership and access controls
- Function/Procedure Security: Review DEFINER vs INVOKER rights
Data Protection
- Sensitive Data Exposure: Avoid SELECT * on tables with sensitive columns
- Audit Logging: Ensure sensitive operations are logged
- Data Masking: Use views or functions to mask sensitive data
- Encryption: Verify encrypted storage for sensitive data
⚡ Performance Optimization
Query Structure Analysis
-- ❌ BAD: Inefficient query patterns
SELECT DISTINCT u.*
FROM users u, orders o, products p
WHERE u.id = o.user_id
AND o.product_id = p.id
AND YEAR(o.order_date) = 2024;
-- ✅ GOOD: Optimized structure
SELECT u.id, u.name, u.email
FROM users u
INNER JOIN orders o ON u.id = o.user_id
WHERE o.order_date >= '2024-01-01'
AND o.order_date < '2025-01-01';
Index Strategy Review
- Missing Indexes: Identify columns that need indexing
- Over-Indexing: Find unused or redundant indexes
- Composite Indexes: Multi-column indexes for complex queries
- Index Maintenance: Check for fragmented or outdated indexes
Join Optimization
- Join Types: Verify appropriate join types (INNER vs LEFT vs EXISTS)
- Join Order: Optimize for smaller result sets first
- Cartesian Products: Identify and fix missing join conditions
- Subquery vs JOIN: Choose the most efficient approach
Aggregate and Window Functions
-- ❌ BAD: Inefficient aggregation
SELECT user_id,
(SELECT COUNT(*) FROM orders o2 WHERE o2.user_id = o1.user_id) as order_count
FROM orders o1
GROUP BY user_id;
-- ✅ GOOD: Efficient aggregation
SELECT user_id, COUNT(*) as order_count
FROM orders
GROUP BY user_id;
🛠️ Code Quality & Maintainability
SQL Style & Formatting
-- ❌ BAD: Poor formatting and style
select u.id,u.name,o.total from users u left join orders o on u.id=o.user_id where u.status='active' and o.order_date>='2024-01-01';
-- ✅ GOOD: Clean, readable formatting
SELECT u.id,
u.name,
o.total
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.status = 'active'
AND o.order_date >= '2024-01-01';
Naming Conventions
- Consistent Naming: Tables, columns, constraints follow consistent patterns
- Descriptive Names: Clear, meaningful names for database objects
- Reserved Words: Avoid using database reserved words as identifiers
- Case Sensitivity: Consistent case usage across schema
Schema Design Review
- Normalization: Appropriate normalization level (avoid over/under-normalization)
- Data Types: Optimal data type choices for storage and performance
- Constraints: Proper use of PRIMARY KEY, FOREIGN KEY, CHECK, NOT NULL
- Default Values: Appropriate default values for columns
🗄️ Database-Specific Best Practices
PostgreSQL
-- Use JSONB for JSON data
CREATE TABLE events (
id SERIAL PRIMARY KEY,
data JSONB NOT NULL,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- GIN index for JSONB queries
CREATE INDEX idx_events_data ON events USING gin(data);
-- Array types for multi-value columns
CREATE TABLE tags (
post_id INT,
tag_names TEXT[]
);
MySQL
-- Use appropriate storage engines
CREATE TABLE sessions (
id VARCHAR(128) PRIMARY KEY,
data TEXT,
expires TIMESTAMP
) ENGINE=InnoDB;
-- Optimize for InnoDB
ALTER TABLE large_table
ADD INDEX idx_covering (status, created_at, id);
SQL Server
-- Use appropriate data types
CREATE TABLE products (
id BIGINT IDENTITY(1,1) PRIMARY KEY,
name NVARCHAR(255) NOT NULL,
price DECIMAL(10,2) NOT NULL,
created_at DATETIME2 DEFAULT GETUTCDATE()
);
-- Columnstore indexes for analytics
CREATE COLUMNSTORE INDEX idx_sales_cs ON sales;
Oracle
-- Use sequences for auto-increment
CREATE SEQUENCE user_id_seq START WITH 1 INCREMENT BY 1;
CREATE TABLE users (
id NUMBER DEFAULT user_id_seq.NEXTVAL PRIMARY KEY,
name VARCHAR2(255) NOT NULL
);
🧪 Testing & Validation
Data Integrity Checks
-- Verify referential integrity
SELECT o.user_id
FROM orders o
LEFT JOIN users u ON o.user_id = u.id
WHERE u.id IS NULL;
-- Check for data consistency
SELECT COUNT(*) as inconsistent_records
FROM products
WHERE price < 0 OR stock_quantity < 0;
Performance Testing
- Execution Plans: Review query execution plans
- Load Testing: Test queries with realistic data volumes
- Stress Testing: Verify performance under concurrent load
- Regression Testing: Ensure optimizations don't break functionality
📊 Common Anti-Patterns
N+1 Query Problem
-- ❌ BAD: N+1 queries in application code
for user in users:
orders = query("SELECT * FROM orders WHERE user_id = ?", user.id)
-- ✅ GOOD: Single optimized query
SELECT u.*, o.*
FROM users u
LEFT JOIN orders o ON u.id = o.user_id;
Overuse of DISTINCT
-- ❌ BAD: DISTINCT masking join issues
SELECT DISTINCT u.name
FROM users u, orders o
WHERE u.id = o.user_id;
-- ✅ GOOD: Proper join without DISTINCT
SELECT u.name
FROM users u
INNER JOIN orders o ON u.id = o.user_id
GROUP BY u.name;
Function Misuse in WHERE Clauses
-- ❌ BAD: Functions prevent index usage
SELECT * FROM orders
WHERE YEAR(order_date) = 2024;
-- ✅ GOOD: Range conditions use indexes
SELECT * FROM orders
WHERE order_date >= '2024-01-01'
AND order_date < '2025-01-01';
📋 SQL Review Checklist
Security
- All user inputs are parameterized
- No dynamic SQL construction with string concatenation
- Appropriate access controls and permissions
- Sensitive data is properly protected
- SQL injection attack vectors are eliminated
Performance
- Indexes exist for frequently queried columns
- No unnecessary SELECT * statements
- JOINs are optimized and use appropriate types
- WHERE clauses are selective and use indexes
- Subqueries are optimized or converted to JOINs
Code Quality
- Consistent naming conventions
- Proper formatting and indentation
- Meaningful comments for complex logic
- Appropriate data types are used
- Error handling is implemented
Schema Design
- Tables are properly normalized
- Constraints enforce data integrity
- Indexes support query patterns
- Foreign key relationships are defined
- Default values are appropriate
🎯 Review Output Format
Issue Template
## [PRIORITY] [CATEGORY]: [Brief Description]
**Location**: [Table/View/Procedure name and line number if applicable]
**Issue**: [Detailed explanation of the problem]
**Security Risk**: [If applicable - injection risk, data exposure, etc.]
**Performance Impact**: [Query cost, execution time impact]
**Recommendation**: [Specific fix with code example]
**Before**:
```sql
-- Problematic SQL
After:
-- Improved SQL
Expected Improvement: [Performance gain, security benefit]
### Summary Assessment
- **Security Score**: [1-10] - SQL injection protection, access controls
- **Performance Score**: [1-10] - Query efficiency, index usage
- **Maintainability Score**: [1-10] - Code quality, documentation
- **Schema Quality Score**: [1-10] - Design patterns, normalization
### Top 3 Priority Actions
1. **[Critical Security Fix]**: Address SQL injection vulnerabilities
2. **[Performance Optimization]**: Add missing indexes or optimize queries
3. **[Code Quality]**: Improve naming conventions and documentation
Focus on providing actionable, database-agnostic recommendations while highlighting platform-specific optimizations and best practices.