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.

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

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

<example type="usage"> <code language="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" }) </code> </example>

PostgreSQL Setup

<example type="usage"> <code language="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'; </code> </example>

Common Operations

Create/Insert

<example type="usage"> <code language="javascript"> // MongoDB db.users.insertOne({ name: "Bob", email: "bob@example.com" }) db.users.insertMany([{ name: "Alice" }, { name: "Charlie" }]) </code> </example> <example type="usage"> <code language="sql"> -- PostgreSQL INSERT INTO users (name, email) VALUES ('Bob', 'bob@example.com'); INSERT INTO users (name, email) VALUES ('Alice', NULL), ('Charlie', NULL); </code> </example>

Read/Query

<example type="usage"> <code language="javascript"> // MongoDB db.users.find({ age: { $gte: 18 } }) db.users.findOne({ email: "bob@example.com" }) </code> </example> <example type="usage"> <code language="sql"> -- PostgreSQL SELECT * FROM users WHERE age >= 18; SELECT * FROM users WHERE email = 'bob@example.com' LIMIT 1; </code> </example>

Update

<example type="usage"> <code language="javascript"> // MongoDB db.users.updateOne({ name: "Bob" }, { $set: { age: 25 } }) db.users.updateMany({ status: "pending" }, { $set: { status: "active" } }) </code> </example> <example type="usage"> <code language="sql"> -- PostgreSQL UPDATE users SET age = 25 WHERE name = 'Bob'; UPDATE users SET status = 'active' WHERE status = 'pending'; </code> </example>

Delete

<example type="usage"> <code language="javascript"> // MongoDB db.users.deleteOne({ name: "Bob" }) db.users.deleteMany({ status: "deleted" }) </code> </example> <example type="usage"> <code language="sql"> -- PostgreSQL DELETE FROM users WHERE name = 'Bob'; DELETE FROM users WHERE status = 'deleted'; </code> </example>

Indexing

<example type="usage"> <code language="javascript"> // MongoDB db.users.createIndex({ email: 1 }) db.users.createIndex({ status: 1, createdAt: -1 }) </code> </example> <example type="usage"> <code language="sql"> -- PostgreSQL CREATE INDEX idx_users_email ON users(email); CREATE INDEX idx_users_status_created ON users(status, created_at DESC); </code> </example>

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

<example type="usage"> <code language="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 </code> </example>

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

<constraints> <constraint severity="critical">Always enable authentication in production databases</constraint> <constraint severity="critical">Never expose database ports directly to the internet</constraint> <constraint severity="high">Always use TLS/SSL for database connections in production</constraint> <constraint severity="high">Implement automated backups with tested restore procedures</constraint> <constraint severity="medium">Index foreign keys in PostgreSQL to prevent full table scans on joins</constraint> <constraint severity="medium">MongoDB $lookup has performance limitations - consider denormalization for frequent joins</constraint> </constraints>

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/