databases

---
name: databases
description: Design schemas, write queries for MongoDB and PostgreSQL. Use for database design, SQL/NoSQL queries, aggregation pipelines, indexes, migrations, replication, performance optimization, psql CLI.
license: MIT
---

# Databases Skill

Unified guide for working with MongoDB (document-oriented) and PostgreSQL (relational) databases. Choose the right database for your use case and master both systems.

## When to Use This Skill

Use when:
- Designing database schemas and data models
- Writing queries (SQL or MongoDB query language)
- Building aggregation pipelines or complex joins
- Optimizing indexes and query performance
- Implementing database migrations
- Setting up replication, sharding, or clustering
- Configuring backups and disaster recovery
- Managing database users and permissions
- Analyzing slow queries and performance issues
- Administering production database deployments

## Reference Navigation

### Database Design
- **[db-design.md](references/db-design.md)** - Activate when user requests: Database/table design for transactional (OLTP), analytics (OLAP), create or extend schema, design fact/dimension tables, analyze/review CSV/JSON/SQL files to create tables, or need advice on data storage structure.

### MongoDB References
- **[mongodb-crud.md](references/mongodb-crud.md)** - CRUD operations, query operators, atomic updates
- **[mongodb-aggregation.md](references/mongodb-aggregation.md)** - Aggregation pipeline, stages, operators, patterns
- **[mongodb-indexing.md](references/mongodb-indexing.md)** - Index types, compound indexes, performance optimization
- **[mongodb-atlas.md](references/mongodb-atlas.md)** - Atlas cloud setup, clusters, monitoring, search

### PostgreSQL References
- **[postgresql-queries.md](references/postgresql-queries.md)** - SELECT, JOINs, subqueries, CTEs, window functions
- **[postgresql-psql-cli.md](references/postgresql-psql-cli.md)** - psql commands, meta-commands, scripting
- **[postgresql-performance.md](references/postgresql-performance.md)** - EXPLAIN, query optimization, vacuum, indexes
- **[postgresql-administration.md](references/postgresql-administration.md)** - User management, backups, replication, maintenance

## Python Utilities

Database utility scripts in `scripts/`:
- **db_migrate.py** - Generate and apply migrations for both databases (MongoDB and PostgreSQL)
- **db_backup.py** - Backup and restore MongoDB and PostgreSQL
- **db_performance_check.py** - Analyze slow queries and recommend indexes

```bash
# Generate migration
python scripts/db_migrate.py --db mongodb --generate "add_user_index"

# Run backup
python scripts/db_backup.py --db postgres --output /backups/

# Check performance
python scripts/db_performance_check.py --db mongodb --threshold 100ms
```

## MongoDB vs PostgreSQL

| Criteria | MongoDB | PostgreSQL |
|----------|---------|------------|
| Data model | Document (JSON) | Relational (tables) |
| Schema | Flexible | Strict |
| Relationships | Embedded / $lookup | JOINs, foreign keys |
| Transactions | Multi-doc (4.0+) | Full ACID |
| Best for | Hierarchical, variable data | Structured, relational data |
| Scaling | Horizontal sharding | Vertical + read replicas |

## Workflow

1. **Design phase** — load `references/db-design.md`, define schema/ERD
2. **Query phase** — load relevant MongoDB or PostgreSQL reference
3. **Index phase** — analyze query patterns, add targeted indexes
4. **Optimize phase** — run EXPLAIN/explain(), identify bottlenecks
5. **Migrate phase** — use `scripts/db_migrate.py` to generate migration
6. **Backup phase** — use `scripts/db_backup.py` for backups/restores

## Common Mistakes

- Missing indexes on foreign keys and filter columns → slow JOINs
- Using `$where` in MongoDB → disables index usage
- `SELECT *` in production queries → transfers unnecessary data
- No connection pooling for web apps → connection exhaustion
- Skipping VACUUM/ANALYZE on PostgreSQL → bloat and stale stats
- Embedding unbounded arrays in MongoDB → document size limit hit

## Best Practices

**MongoDB:**
- Use embedded documents for 1-to-few relationships
- Reference documents for 1-to-many or many-to-many
- Index frequently queried fields
- Use aggregation pipeline for complex transformations
- Enable authentication and TLS in production
- Use Atlas for managed hosting

**PostgreSQL:**
- Normalize schema to 3NF, denormalize for performance
- Use foreign keys for referential integrity
- Index foreign keys and frequently filtered columns
- Use EXPLAIN ANALYZE to optimize queries
- Regular VACUUM and ANALYZE maintenance
- Connection pooling (pgBouncer) for web apps

## Resources

- MongoDB: https://www.mongodb.com/docs/
- PostgreSQL: https://www.postgresql.org/docs/
- MongoDB University: https://learn.mongodb.com/
- PostgreSQL Tutorial: https://www.postgresqltutorial.com/