---
name: databases
description: "MongoDB and PostgreSQL database administration. Databases: MongoDB (document store, aggregation, Atlas), PostgreSQL (relational, SQL, psql). Capabilities: schema design, query optimization, indexing, migrations, replication, sharding, backup/restore, user management, performance analysis. Actions: design, query, optimize, migrate, backup, restore, index, shard databases. Keywords: MongoDB, PostgreSQL, SQL, NoSQL, BSON, aggregation pipeline, Atlas, psql, pgAdmin, schema design, index, query optimization, EXPLAIN, replication, sharding, backup, restore, migration, ORM, Prisma, Mongoose, connection pooling, transactions, ACID. Use when: designing database schemas, writing complex queries, optimizing query performance, creating indexes, performing migrations, setting up replication, implementing backup strategies, managing database permissions, troubleshooting slow queries."
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

## Database Selection Guide

### Choose MongoDB When:
- Schema flexibility: frequent structure changes, heterogeneous data
- Document-centric: natural JSON/BSON data model
- Horizontal scaling: need to shard across multiple servers
- High write throughput: IoT, logging, real-time analytics
- Nested/hierarchical data: embedded documents preferred
- Rapid prototyping: schema evolution without migrations

**Best for:** Content management, catalogs, IoT time series, real-time analytics, mobile apps, user profiles

### Choose PostgreSQL When:
- Strong consistency: ACID transactions critical
- Complex relationships: many-to-many joins, referential integrity
- SQL requirement: team expertise, reporting tools, BI systems
- Data integrity: strict schema validation, constraints
- Mature ecosystem: extensive tooling, extensions
- Complex queries: window functions, CTEs, analytical workloads

**Best for:** Financial systems, e-commerce transactions, ERP, CRM, data warehousing, analytics

### Both Support:
- JSON/JSONB storage and querying
- Full-text search capabilities
- Geospatial queries and indexing
- Replication and high availability
- ACID transactions (MongoDB 4.0+)
- Strong security features

## Quick Start

### MongoDB Setup

```bash
# Atlas (Cloud) - Recommended
# 1. Sign up at mongodb.com/atlas
# 2. Create M0 free cluster
# 3. Get connection string

# Connection
mongodb+srv://user:pass@cluster.mongodb.net/db

# Shell
mongosh "mongodb+srv://cluster.mongodb.net/mydb"

# Basic operations
db.users.insertOne({ name: "Alice", age: 30 })
db.users.find({ age: { $gte: 18 } })
db.users.updateOne({ name: "Alice" }, { $set: { age: 31 } })
db.users.deleteOne({ name: "Alice" })
```

### PostgreSQL Setup

```bash
# Ubuntu/Debian
sudo apt-get install postgresql postgresql-contrib

# Start service
sudo systemctl start postgresql

# Connect
psql -U postgres -d mydb

# Basic operations
CREATE TABLE users (id SERIAL PRIMARY KEY, name TEXT, age INT);
INSERT INTO users (name, age) VALUES ('Alice', 30);
SELECT * FROM users WHERE age >= 18;
UPDATE users SET age = 31 WHERE name = 'Alice';
DELETE FROM users WHERE name = 'Alice';
```

## Common Operations

### Create/Insert
```javascript
// MongoDB
db.users.insertOne({ name: "Bob", email: "bob@example.com" })
db.users.insertMany([{ name: "Alice" }, { name: "Charlie" }])
```

```sql
-- PostgreSQL
INSERT INTO users (name, email) VALUES ('Bob', 'bob@example.com');
INSERT INTO users (name, email) VALUES ('Alice', NULL), ('Charlie', NULL);
```

### Read/Query
```javascript
// MongoDB
db.users.find({ age: { $gte: 18 } })
db.users.findOne({ email: "bob@example.com" })
```

```sql
-- PostgreSQL
SELECT * FROM users WHERE age >= 18;
SELECT * FROM users WHERE email = 'bob@example.com' LIMIT 1;
```

### Update
```javascript
// MongoDB
db.users.updateOne({ name: "Bob" }, { $set: { age: 25 } })
db.users.updateMany({ status: "pending" }, { $set: { status: "active" } })
```

```sql
-- PostgreSQL
UPDATE users SET age = 25 WHERE name = 'Bob';
UPDATE users SET status = 'active' WHERE status = 'pending';
```

### Delete
```javascript
// MongoDB
db.users.deleteOne({ name: "Bob" })
db.users.deleteMany({ status: "deleted" })
```

```sql
-- PostgreSQL
DELETE FROM users WHERE name = 'Bob';
DELETE FROM users WHERE status = 'deleted';
```

### Indexing
```javascript
// MongoDB
db.users.createIndex({ email: 1 })
db.users.createIndex({ status: 1, createdAt: -1 })
```

```sql
-- PostgreSQL
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_status_created ON users(status, created_at DESC);
```

## Reference Navigation

### 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
- **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
```

## Key Differences Summary

| Feature | MongoDB | PostgreSQL |
|---------|---------|------------|
| Data Model | Document (JSON/BSON) | Relational (Tables/Rows) |
| Schema | Flexible, dynamic | Strict, predefined |
| Query Language | MongoDB Query Language | SQL |
| Joins | $lookup (limited) | Native, optimized |
| Transactions | Multi-document (4.0+) | Native ACID |
| Scaling | Horizontal (sharding) | Vertical (primary), Horizontal (extensions) |
| Indexes | Single, compound, text, geo, etc | B-tree, hash, GiST, GIN, etc |

## 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/