❌ When NOT to Use MongoDB

❌ Scenario 1: Complex Multi-Table Joins

The Problem:

// SQL: Easy JOIN across multiple tables
SELECT o.order_id, c.name, p.product_name, s.status
FROM orders o
JOIN customers c ON o.customer_id = c.id
JOIN order_items oi ON o.id = oi.order_id
JOIN products p ON oi.product_id = p.id
JOIN shipments s ON o.id = s.order_id
WHERE o.date > '2024-01-01'

// MongoDB: Painful $lookup chain
db.orders.aggregate([
  { $match: { date: { $gt: ISODate("2024-01-01") } } },
  { $lookup: { from: "customers", localField: "customer_id", foreignField: "_id", as: "customer" } },
  { $unwind: "$customer" },
  { $lookup: { from: "order_items", localField: "_id", foreignField: "order_id", as: "items" } },
  { $unwind: "$items" },
  { $lookup: { from: "products", localField: "items.product_id", foreignField: "_id", as: "product" } },
  { $unwind: "$product" },
  { $lookup: { from: "shipments", localField: "_id", foreignField: "order_id", as: "shipment" } },
  { $unwind: "$shipment" }
])

Why it's bad:

• $lookup is slow (not indexed like SQL joins)
• Multiple $unwind operations explode document size
• Query becomes unreadable and hard to maintain
• Performance degrades significantly with data growth

❌ Scenario 2: Complex ACID Transactions

Example: Banking System

// Transfer $100 from Account A to Account B
// In PostgreSQL: Simple transaction
BEGIN;
UPDATE accounts SET balance = balance - 100 WHERE id = 'A';
UPDATE accounts SET balance = balance + 100 WHERE id = 'B';
COMMIT;  // Either both happen or neither

// In MongoDB: Multi-document transaction (slow!)
session = client.startSession();
session.startTransaction();
try {
  db.accounts.updateOne({ _id: 'A' }, { $inc: { balance: -100 } }, { session });
  db.accounts.updateOne({ _id: 'B' }, { $inc: { balance: 100 } }, { session });
  session.commitTransaction();
} catch (error) {
  session.abortTransaction();
}

Why MongoDB struggles:

• Multi-document transactions added late (MongoDB 4.0+)
• 10-100x slower than single-document operations
• Limited to 1000 operations per transaction
• Increased chance of write conflicts in high concurrency
• More complex error handling and retry logic needed

❌ Scenario 3: Heavy Analytics & Business Intelligence

The Problem: Complex aggregations across millions of records

// SQL: Optimized for analytics
SELECT 
  DATE_TRUNC('month', order_date) as month,
  category,
  region,
  COUNT(*) as orders,
  SUM(revenue) as total_revenue,
  AVG(revenue) as avg_order_value,
  PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY revenue) as median
FROM sales
JOIN products USING (product_id)
JOIN regions USING (region_id)
WHERE order_date >= '2023-01-01'
GROUP BY ROLLUP(month, category, region)
HAVING SUM(revenue) > 10000

// MongoDB: Aggregation pipeline becomes complex and slow
db.sales.aggregate([
  { $match: { order_date: { $gte: ISODate("2023-01-01") } } },
  { $lookup: { from: "products", ... } },
  { $lookup: { from: "regions", ... } },
  { $group: { ... } },
  { $facet: { ... } },  // Complex multi-level grouping
  // Missing: ROLLUP, PERCENTILE_CONT, windowing functions
])

MongoDB's analytics weaknesses:

• No window functions, ROLLUP, CUBE, GROUPING SETS
• Limited statistical functions (no percentiles, stddev, variance)
• Aggregation pipeline harder to optimize than SQL
• No materialized views for pre-computed results
• BI tools have better SQL support than MongoDB connectors

❌ Scenario 4: Small, Simple Applications

Example: Simple blog with posts, comments, users

Why MongoDB is overkill:

• Setup complexity: Replica sets, sharding config, authentication
• Higher memory requirements (min 1GB RAM)
• Larger disk footprint
• More moving parts = more things to break
• Schema flexibility not needed for simple, stable data

❌ Scenario 5: Applications Requiring Strict Schema Enforcement

Example: Medical records, government systems, financial records

The Problem:

// SQL: Schema enforced at database level
CREATE TABLE patients (
  id SERIAL PRIMARY KEY,
  ssn VARCHAR(11) NOT NULL UNIQUE,
  dob DATE NOT NULL,
  blood_type ENUM('A+','A-','B+','B-','AB+','AB-','O+','O-') NOT NULL
);
// DB rejects invalid data automatically

// MongoDB: Schema validation is opt-in and limited
db.createCollection("patients", {
  validator: {
    $jsonSchema: {
      required: ["ssn", "dob", "blood_type"],
      properties: {
        blood_type: { enum: ["A+","A-","B+","B-","AB+","AB-","O+","O-"] }
      }
    }
  }
})
// But easier to bypass, less rigid enforcement

Issues with MongoDB's flexible schema:

• Schema validation is optional (can be disabled)
• No foreign key constraints
• Easier to have inconsistent data across documents
• Regulatory/compliance systems need guaranteed data integrity
• Harder to enforce business rules at DB level

❌ Scenario 6: Financial Systems & Mission-Critical Apps

Examples: Banking, payment processing, stock trading, accounting

Why MongoDB is risky:

• Data consistency: Eventually consistent by default (replica sets)
• Transaction complexity: Multi-doc transactions slower & more error-prone
• Compliance: Harder to prove ACID compliance for audits
• Mature tooling: SQL databases have 40+ years of financial app tools
• Disaster recovery: Point-in-time recovery less mature
• Regulatory requirements: Many require ACID guarantees that SQL provides natively