PostgreSQL vs MongoDB: A Complete Comparison

Choosing between PostgreSQL and MongoDB is one of the most common database decisions. Both are excellent, but they excel in different scenarios. Here's everything you need to make an informed choice.

Quick Decision Matrix

Choose PostgreSQL if you:

• Need ACID transactions
• Have complex relationships between data
• Require complex queries and joins
• Value data consistency above all
• Have a relatively stable schema

Choose MongoDB if you:

• Have highly variable or evolving schema
• Need horizontal scaling out of the box
• Work with document-like data structures
• Prioritize developer velocity in early stages
• Have simple access patterns

Detailed Comparison

Data Model

PostgreSQL: Relational

-- Structured, normalized data
CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    name VARCHAR(255)
);

CREATE TABLE posts (
    id SERIAL PRIMARY KEY,
    user_id INTEGER REFERENCES users(id),
    title VARCHAR(255),
    content TEXT,
    created_at TIMESTAMP DEFAULT NOW()
);

Pros:

• Enforced relationships
• Data integrity
• Eliminates duplication through normalization

Cons:

• Schema changes require migrations
• Complex queries need joins
• Can be rigid for evolving requirements

MongoDB: Document-Oriented

// Flexible, denormalized documents
{
  _id: ObjectId("..."),
  email: "user@example.com",
  name: "John Doe",
  posts: [
    {
      title: "My First Post",
      content: "...",
      createdAt: ISODate("2025-10-01")
    }
  ],
  metadata: {
    // Any structure you want
  }
}

Pros:

• Schema flexibility
• Natural data grouping
• Easy to evolve

Cons:

• Can lead to data duplication
• No enforced relationships
• Potential for inconsistencies

Transactions and Consistency

PostgreSQL: ACID Guarantees

BEGIN;
UPDATE accounts SET balance = balance - 100 WHERE id = 1;
UPDATE accounts SET balance = balance + 100 WHERE id = 2;
COMMIT;

Strengths:

• Strong ACID compliance
• Multi-document transactions
• Perfect for financial data
• Serializable isolation

MongoDB: Eventual Consistency (with ACID support)

session.startTransaction();
await accounts.updateOne(
  { _id: 1 },
  { $inc: { balance: -100 } }
);
await accounts.updateOne(
  { _id: 2 },
  { $inc: { balance: 100 } }
);
await session.commitTransaction();

Strengths:

• ACID transactions since v4.0
• Tunable consistency levels
• Good for most use cases

Limitations:

• Transactions have performance overhead
• Historically weaker guarantees

Query Capabilities

PostgreSQL: SQL + Extensions

-- Complex joins
SELECT u.name, COUNT(p.id) as post_count
FROM users u
LEFT JOIN posts p ON u.id = p.user_id
WHERE u.created_at > '2025-01-01'
GROUP BY u.id, u.name
HAVING COUNT(p.id) > 5;

-- JSON support
SELECT data->>'name' as name
FROM json_table
WHERE data @> '{"status": "active"}';

-- Full-text search
SELECT title
FROM posts
WHERE to_tsvector(content) @@ to_tsquery('postgres & database');

Strengths:

• Extremely powerful query language
• Complex aggregations
• Window functions
• CTE (Common Table Expressions)
• Full-text search built-in

MongoDB: Aggregation Pipeline

db.users.aggregate([
  {
    $match: {
      createdAt: { $gt: new Date('2025-01-01') }
    }
  },
  {
    $lookup: {
      from: 'posts',
      localField: '_id',
      foreignField: 'userId',
      as: 'posts'
    }
  },
  {
    $project: {
      name: 1,
      postCount: { $size: '$posts' }
    }
  },
  {
    $match: {
      postCount: { $gt: 5 }
    }
  }
]);

Strengths:

• Intuitive for developers familiar with JSON
• Powerful aggregation framework
• Built-in geospatial queries
• Good for hierarchical data

Limitations:

• Less powerful than SQL for complex queries
• Joins are less efficient

Scaling

PostgreSQL: Vertical First, Horizontal Complex

Vertical Scaling:

• Easy: Increase server resources
• Can scale to very large sizes
• Single server simplicity

Horizontal Scaling:

• Read replicas: Easy
• Sharding: Complex, requires extensions (Citus) or application logic
• Write scaling: Challenging

Best for: Apps that can scale vertically to meet needs (most apps)

MongoDB: Horizontal Out of the Box

Sharding:

// Native sharding support
sh.shardCollection("mydb.mycollection", { userId: 1 })

Strengths:

• Built-in sharding
• Automatic data distribution
• Replica sets are first-class

Considerations:

• Sharding adds operational complexity
• Most apps don't need it
• Can over-engineer early

Best for: Apps that genuinely need horizontal write scaling

Performance

PostgreSQL:

• Excellent for complex queries
• Superior for analytical workloads
• Strong with large datasets
• Optimized for OLTP

Typical Performance:

• Simple queries: 10,000+ qps
• Complex joins: 1,000+ qps
• Large table scans: GBs per second

MongoDB:

• Fast for simple document lookups
• Good for write-heavy workloads
• Optimized for document-oriented access

Typical Performance:

• Document lookups: 50,000+ qps
• Aggregations: Varies widely
• Large collections: Can slow without proper indexing

Reality: For most apps, both are fast enough. Optimize your queries and indexes first.

Developer Experience

PostgreSQL:

Pros:

• Industry-standard SQL
• Huge ecosystem of tools
• Great ORMs (Prisma, TypeORM, SQLAlchemy)
• Predictable behavior

Cons:

• Migrations can be tedious
• Schema changes require planning
• Learning curve for advanced features

MongoDB:

Pros:

• Quick to start
• No migrations needed
• Natural for JavaScript developers
• Flexible iterations

Cons:

• Easy to create bad data models
• Less tooling maturity
• Can paint yourself into corners

Operational Considerations

PostgreSQL:

Deployment:

• Simpler (usually single server + replicas)
• Well-understood operational patterns
• Excellent managed services (AWS RDS, GCP CloudSQL)

Maintenance:

• VACUUM process (automatic in modern versions)
• Connection pooling considerations
• Backup/restore well-understood

MongoDB:

Deployment:

• Replica sets as minimum
• Sharding adds complexity
• Good managed services (Atlas)

Maintenance:

• Compaction may be needed
• Index management crucial
• Backup of distributed data

Cost Comparison

PostgreSQL:

• Lower infrastructure costs (vertical scaling)
• Mature managed services
• Predictable pricing

Typical AWS RDS Costs:

• Small (db.t3.medium): $60/month
• Medium (db.m5.xlarge): $300/month
• Large (db.m5.4xlarge): $1,200/month

MongoDB:

• Higher costs for sharded clusters
• Atlas pricing can be expensive
• Need more servers for HA

Typical MongoDB Atlas Costs:

• Small (M10): $60/month
• Medium (M30): $580/month
• Large (M50): $1,600/month

For most startups: PostgreSQL is more cost-effective

Use Case Recommendations

PostgreSQL is Better For:

1. Financial Applications

• Banking, payments, e-commerce
• Need: Strong ACID guarantees

2. Complex Data Relationships

• CRM systems, ERPs
• Need: Joins and relational integrity

3. Reporting and Analytics

• Data warehousing, BI tools
• Need: Complex aggregations

4. Audit and Compliance

• Healthcare, legal
• Need: Data consistency and history

MongoDB is Better For:

1. Content Management

• Blogs, news sites, wikis
• Need: Flexible content structures

2. Real-time Analytics

• IoT data, time-series data
• Need: High write throughput

3. Product Catalogs

• E-commerce with varying attributes
• Need: Schema flexibility

4. Mobile Apps

• User profiles, preferences
• Need: Rapid iteration

Hybrid Approach: Use Both

Many companies use both databases for different purposes:

Example Architecture:

PostgreSQL:
- User accounts and authentication
- Financial transactions
- Order management
- Audit logs

MongoDB:
- Product catalog
- User activity logs
- Session storage
- Real-time analytics

When to use both:

• Large, mature systems
• Different teams, different needs
• Specific performance requirements

When not to:

• Small teams
• Early stage products
• Operational complexity concerns

Migration Stories

PostgreSQL → MongoDB:

• Reason: Need for horizontal scaling
• Risk: Losing ACID guarantees
• Mitigation: Careful transaction design

MongoDB → PostgreSQL:

• Reason: Need for relational integrity
• Risk: Schema migration effort
• Mitigation: Gradual migration

Reality: Both migrations are painful. Choose carefully upfront.

The Decision Framework

Answer These Questions:

1. Do you have complex relationships in your data?

• Yes → PostgreSQL
• No → Either

2. Do you need strict ACID transactions?

• Yes → PostgreSQL
• No → Either

3. Is your schema likely to change frequently?

• Yes → MongoDB
• No → PostgreSQL

4. Do you need to scale writes horizontally?

• Yes → MongoDB
• No → PostgreSQL

5. What does your team know?

• SQL → PostgreSQL
• NoSQL/JavaScript → MongoDB

The Default Choice

For most startups and new projects: PostgreSQL

Why:

• Solves 80% of use cases excellently
• JSONB gives schema flexibility when needed
• Easier to operate
• More cost-effective
• Larger talent pool

When to choose MongoDB:

• Genuinely need horizontal write scaling
• Schema is highly variable
• Team has strong MongoDB expertise
• Document-oriented data is natural fit

Real-World Examples

Companies Using PostgreSQL:

• Instagram (at massive scale with sharding)
• Uber (transactions and core data)
• Robinhood (financial transactions)
• GitLab (entire application)

Companies Using MongoDB:

• Forbes (content management)
• Adobe (product catalog)
• Bosch (IoT data)
• SEGA (game data)

Testing Both

Before deciding, run a spike:

// Week 1: Build simple feature with PostgreSQL
// Week 2: Build same feature with MongoDB
// Compare:
- Development speed
- Code clarity
- Performance
- Operational complexity
- Team comfort

Final Recommendation

The Pragmatic Choice:

1. Start with PostgreSQL unless you have a specific reason not to
2. Use JSONB columns for flexibility where needed
3. Scale vertically as far as you can (further than you think)
4. Add read replicas for scale
5. Only shard if you truly need distributed writes
6. Consider MongoDB if your use case genuinely fits better

Remember: Database choice rarely kills companies. Poor data modeling and lack of understanding do.

Choose based on your team, use case, and operational capabilities—not hype.

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What's your experience with PostgreSQL vs MongoDB? What factors influenced your choice?