AI Query Analysis
Cursor/Claude can:
- Analyze query execution plans
- Suggest missing indexes
- Rewrite queries for performance
- Identify N+1 problems
- Generate query benchmarks
Leverage Cursor IDE and Claude Code for advanced database development. These patterns cover schema design, query optimization, migrations, performance tuning, and database-specific best practices across SQL and NoSQL systems.
claude "Create normalized database schema for SaaS application"
-- Cursor prompt for query optimization:"Optimize this slow query:SELECT * FROM orders oJOIN users u ON o.user_id = u.idWHERE o.created_at > '2024-01-01'AND u.country = 'USA'
Consider:- Index usage- Query execution plan- Covering indexes- Partitioning strategies"
AI Query Analysis
Cursor/Claude can:
-- Prompt: "Create analytics query using window functions for:-- - Running totals-- - Ranking within groups-- - Moving averages-- - Lag/lead comparisons-- - Percentile calculations"
-- Prompt: "Refactor complex query using CTEs for:-- - Recursive hierarchies-- - Multi-step calculations-- - Readable subqueries-- - Performance optimization"
-- PostgreSQL-specific prompt:"Implement PostgreSQL features:- JSONB columns with indexes- Full-text search with triggers- Table partitioning by date- Foreign data wrappers- Row-level security- Materialized views"
-- MySQL optimization prompt:"Optimize MySQL database for:- High write throughput- Proper character encoding- InnoDB buffer pool sizing- Query cache configuration- Replication setup- Partition pruning"
Read Replicas
-- Prompt: "Set up read replicas with:-- - Master-slave configuration-- - Read/write splitting-- - Lag monitoring-- - Failover strategy"
Sharding
-- Prompt: "Implement sharding for:-- - User-based partitioning-- - Geographic distribution-- - Cross-shard queries-- - Shard rebalancing"
// MongoDB schema design prompt:"Design MongoDB collections for social media app:- Denormalized for read performance- Embedded vs referenced documents- Compound indexes- Aggregation pipelines- Change streams setup- Sharding strategy"
// Embedding pattern:"Model one-to-many relationships with:- Embedded documents for under 100 items- Atomic updates- Single query retrieval- Size limitations considered"
// Reference pattern:"Model many-to-many with:- Document references- Populate queries- Consistency management- Join-like operations"
-- Redis implementation prompt:"Implement Redis caching for:- Session storage with TTL- Cache-aside pattern- Write-through caching- Pub/sub messaging- Sorted sets for leaderboards- HyperLogLog for counting"
Redis Use Cases
AI can implement:
-- Migration strategy prompt:"Create migration system with:- Versioned migrations- Rollback capabilities- Zero-downtime deployments- Data migrations- Schema validation- Migration testing"
-- Time-series prompt:"Design time-series database for IoT:- Efficient timestamp indexing- Data retention policies- Continuous aggregates- Downsampling strategies- Compression techniques- Query optimization"
// Graph database prompt:"Model social network in Neo4j:- User nodes and relationships- Friend recommendations query- Shortest path algorithms- Community detection- Performance optimization- Cypher query patterns"
-- Database testing prompt:"Create database tests for:- Schema validation- Constraint testing- Performance benchmarks- Data integrity checks- Migration testing- Backup/restore verification"
Synthetic Data
-- Prompt: "Generate test data:-- - Realistic distributions-- - Foreign key relationships-- - Edge cases-- - Performance test volumes"
Data Masking
-- Prompt: "Mask production data:-- - PII anonymization-- - Consistent masking-- - Referential integrity-- - Reversible for testing"
-- Monitoring setup prompt:"Implement query monitoring:- Slow query logging- Query performance metrics- Index usage statistics- Lock monitoring- Connection pool metrics- Automated alerting"
# Backup implementation prompt:"Create backup strategy with:- Automated daily backups- Point-in-time recovery- Backup testing- Offsite storage- Encryption at rest- Recovery procedures"
-- Security prompt:"Implement database security:- Role-based access control- Row-level security- Column encryption- Audit logging- SQL injection prevention- Connection encryption"
Security Best Practices
AI helps implement:
# Multi-database prompt:"Design polyglot architecture:- PostgreSQL for transactions- Redis for caching- MongoDB for documents- Elasticsearch for search- Data synchronization- Consistency strategies"
-- Structure prompts like:"Optimize [query/schema] for [use case] considering:- Current volume: [X] records- Growth rate: [Y] per month- Read/write ratio: [Z]- Consistency requirements- Performance SLAs"
-- Event sourcing implementation:"Design event store with:- Immutable event log- Aggregate snapshots- Event replay capability- Projection updates- Event versioning- CQRS integration"
# Proxy configuration:"Set up database proxy for:- Connection pooling- Query routing- Load balancing- Failover handling- Query caching- Security filtering"