The Dark Side of MongoDB: What They Don't Tell You

MongoDB is often praised as the go-to NoSQL database, but after years of production experience, here are the critical issues developers should know before choosing it.

The Marketing vs Reality

What They Promise

// Simple and flexible schema
const user = {
  name: "John",
  preferences: { /* anything goes! */ }
};
db.users.insert(user);

What You Get

// Schema chaos in production
const user1 = {
  name: "John",
  preferences: { theme: "dark" }
};

const user2 = {
  name: "Jane",
  preferences: "dark" // Oops, string instead of object
};

const user3 = {
  name: "Bob",
  prefs: { theme: "light" } // Different field name
};

Real Production Problems

1. Memory Usage

MongoDB's Working Set Must Fit in RAM:
- 100GB dataset
- Needs ~120GB RAM for good performance
- Otherwise, dramatic slowdown

2. Unexpected Query Behavior

// Looks simple, right?
db.users.find({ "preferences.theme": "dark" });

// But fails to find records where:
// - preferences is null
// - preferences is a string
// - field is named differently
// - nested object has different structure

3. Index Limitations

// Each index needs RAM
// Common production scenario:
{
  users: 10 million documents
  indexes: 5 compound indexes
  size: ~2GB per index
  total: 10GB just for indexes
}

The Cost of Flexibility

1. Data Integrity Issues

// No schema enforcement means:
const payments = [
  { amount: 100.00 },
  { amount: "100.00" }, // String instead of number
  { ammount: 100.00 },  // Typo in field name
  { amount: null }      // Missing value
];

2. Query Performance

// What seems simple...
db.orders.find({
  status: "pending",
  created_at: { $gt: lastWeek }
}).sort({ amount: -1 });

// Can cause full collection scan if:
// - Wrong index
// - Too many documents
// - Working set exceeds RAM

Better Practices

1. Schema Validation

// Use schema validation
db.createCollection("users", {
  validator: {
    $jsonSchema: {
      required: ["name", "email"],
      properties: {
        name: { type: "string" },
        email: { type: "string" },
        preferences: { 
          type: "object",
          required: ["theme"],
          properties: {
            theme: { enum: ["light", "dark"] }
          }
        }
      }
    }
  }
});

2. Proper Indexing Strategy

// Instead of many indexes
db.users.createIndex({ field1: 1 });
db.users.createIndex({ field2: 1 });
db.users.createIndex({ field3: 1 });

// Create compound indexes based on queries
db.users.createIndex({ 
  field1: 1, 
  field2: 1, 
  field3: 1 
});

When to Use PostgreSQL Instead

1. Complex Data Relationships

-- PostgreSQL handles relations better
SELECT users.*, orders.total 
FROM users 
JOIN orders ON users.id = orders.user_id 
WHERE orders.status = 'completed';

2. Data Integrity Needs

-- PostgreSQL enforces types and constraints
CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  preferences JSONB DEFAULT '{"theme": "light"}'::jsonb
);

Real World Performance Comparison

Query Performance

Complex Query with Joins:
- PostgreSQL: 100ms
- MongoDB: 300ms (with multiple queries)

Aggregate Operations:
- PostgreSQL: Built-in functions
- MongoDB: Complex MapReduce or Aggregate

Memory Usage

100GB Dataset:
PostgreSQL:
- Works well with 32GB RAM
- Smart buffer management

MongoDB:
- Needs ~120GB RAM
- Significant slowdown if not in RAM

When MongoDB Makes Sense

1. Document-Centric Applications
2. Rapid Prototyping
3. Simple CRUD Operations
4. No Complex Transactions
5. Sufficient RAM Available

Conclusion

MongoDB isn't bad—it's just frequently misused. Consider your use case carefully:

• Need ACID transactions? → PostgreSQL
• Complex relations? → PostgreSQL
• Simple document store? → MongoDB
• Limited RAM? → PostgreSQL

Remember: The best database is the one that matches your actual needs, not the trending choice.