databases

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

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

# Connection
mongodb+srv://user:[e-mail]/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

# 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

// MongoDB
db.users.insertOne({ name: "Bob", email: "[e-mail]" })
db.users.insertMany([{ name: "Alice" }, { name: "Charlie" }])

-- PostgreSQL
INSERT INTO users (name, email) VALUES ('Bob', '[e-mail]');
INSERT INTO users (name, email) VALUES ('Alice', NULL), ('Charlie', NULL);

Read/Query

// MongoDB
db.users.find({ age: { $gte: 18 } })
db.users.findOne({ email: "[e-mail]" })

-- PostgreSQL
SELECT * FROM users WHERE age >= 18;
SELECT * FROM users WHERE email = '[e-mail]' LIMIT 1;

Update

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

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

Delete

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

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

Indexing

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

-- 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 - CRUD operations, query operators, atomic updates
• mongodb-aggregation.md - Aggregation pipeline, stages, operators, patterns
• mongodb-indexing.md - Index types, compound indexes, performance optimization
• mongodb-atlas.md - Atlas cloud setup, clusters, monitoring, search

PostgreSQL References

• postgresql-queries.md - SELECT, JOINs, subqueries, CTEs, window functions
• postgresql-psql-cli.md - psql commands, meta-commands, scripting
• postgresql-performance.md - EXPLAIN, query optimization, vacuum, indexes
• 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

# 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/