MongoDB vs MySQL: When to Use Each

MongoDB vs MySQL isn't really a comparison of two databases. It's a comparison of two philosophies about how data should be organized, queried, and evolved. Choosing between them isn't about benchmarks or feature lists. It's about understanding which model fits how you think about your data.

I've shipped production systems with both, sometimes in the same application. The right choice depends on questions that have nothing to do with either database's marketing materials.

Two Different Worlds

MySQL has been around since 1995. It emerged from the Swedish company MySQL AB, created by Michael Widenius, David Axmark, and Allan Larsson. The name comes from Widenius's daughter, My. For nearly three decades, MySQL has been the default database for web applications, powering everything from WordPress blogs to Facebook's infrastructure.

MySQL embodies the relational model that Edgar Codd formalized in 1970. Data lives in tables with rows and columns. Relationships between tables are explicit. A query language (SQL) lets you combine, filter, and aggregate data from multiple tables in a single request. The database enforces rules about your data (types, constraints, foreign keys) and guarantees consistency.

MongoDB arrived in 2009 with a different vision. The founders at 10gen had built systems at DoubleClick, processing billions of events per day, and they were frustrated with the mismatch between relational schemas and the JSON objects their applications actually used. Why transform data into tables and back again? Why not just store documents?

MongoDB stores data as BSON (Binary JSON) documents. Each document is self-contained. You can nest objects, include arrays, and vary the structure between documents. There's no upfront schema definition. The database stores whatever you give it and lets you query it flexibly.

The Schema Question

The most important difference between MongoDB and MySQL isn't performance or scale. It's how you think about schema.

In MySQL, you define your schema before you write data:

CREATE TABLE products (
  id INT PRIMARY KEY AUTO_INCREMENT,
  name VARCHAR(255) NOT NULL,
  price DECIMAL(10, 2) NOT NULL,
  category_id INT REFERENCES categories(id),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE product_attributes (
  product_id INT REFERENCES products(id),
  attribute_name VARCHAR(100) NOT NULL,
  attribute_value VARCHAR(255),
  PRIMARY KEY (product_id, attribute_name)
);

This schema is a contract. Every product has a name and price. Every product belongs to a category that must exist. Attributes are stored in a separate table because products have different attributes, and relational databases handle this through normalization.

In MongoDB, you just start storing documents:

db.products.insertOne({
  name: "Wireless Headphones",
  price: 79.99,
  category: "Electronics",
  attributes: {
    brand: "Sony",
    bluetooth: true,
    batteryLife: "30 hours",
    noiseCancelling: true
  }
});

db.products.insertOne({
  name: "Cotton T-Shirt",
  price: 24.99,
  category: "Clothing",
  attributes: {
    size: "L",
    color: "Navy Blue",
    material: "100% Cotton"
  }
});

Each product has whatever attributes make sense for it. No separate tables, no joins, no schema migration when you add a new attribute. The flexibility is real.

But so is the trade-off. MySQL's schema is documentation. You can look at the table definitions and understand the data. MongoDB's schema is implicit. You have to examine actual documents to understand the structure. MySQL enforces rules; MongoDB trusts your application.

When Relationships Matter

The relational model exists because most real-world data has relationships. Users place orders. Orders contain items. Items reference products. Products belong to categories. This web of connections is the norm, not the exception.

MySQL handles relationships elegantly:

-- Get order details with customer info and product names
SELECT
  o.id AS order_id,
  o.created_at,
  c.name AS customer_name,
  c.email,
  p.name AS product_name,
  oi.quantity,
  oi.unit_price
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
WHERE o.id = 12345;

One query, multiple tables, all the data you need. The database ensures referential integrity. You can't create an order for a customer that doesn't exist, and you can't delete a customer who has orders (unless you explicitly handle it).

MongoDB handles this differently. You can either embed related data (denormalization) or reference it (requiring multiple queries).

Embedding works when data is accessed together:

{
  _id: ObjectId("..."),
  customer: {
    name: "Alice Chen",
    email: "alice@example.com"
  },
  items: [
    { product: "Wireless Headphones", quantity: 1, unitPrice: 79.99 },
    { product: "USB-C Cable", quantity: 2, unitPrice: 12.99 }
  ],
  createdAt: ISODate("2024-01-15T10:30:00Z")
}

This document is self-contained. One read gets everything. But what if you need to update the customer's email? You'd have to update it in every order. What if you want to see all orders for a product? You have to scan every document.

Referencing preserves normalization but requires application-level joins:

// Order document
{ _id: ObjectId("..."), customerId: ObjectId("..."), items: [...] }

// Fetch order
const order = await orders.findOne({ _id: orderId });

// Fetch customer separately
const customer = await customers.findOne({ _id: order.customerId });

// Fetch products for items (if you stored product IDs instead of names)
const productIds = order.items.map(i => i.productId);
const products = await products.find({ _id: { $in: productIds } }).toArray();

This pattern is common in MongoDB applications, and it works. But if you find yourself constantly doing application-level joins, you might be fighting the data model.

The Query Power Gap

SQL has been refined for 50 years. It's a declarative language designed specifically for querying relational data, and it's remarkably expressive:

-- Find customers whose spending increased month-over-month for 3+ months
WITH monthly_spending AS (
  SELECT
    customer_id,
    DATE_FORMAT(created_at, '%Y-%m') AS month,
    SUM(total) AS spent
  FROM orders
  GROUP BY customer_id, DATE_FORMAT(created_at, '%Y-%m')
),
spending_with_prev AS (
  SELECT
    customer_id,
    month,
    spent,
    LAG(spent) OVER (PARTITION BY customer_id ORDER BY month) AS prev_spent
  FROM monthly_spending
)
SELECT DISTINCT customer_id
FROM spending_with_prev
WHERE spent > COALESCE(prev_spent, 0)
GROUP BY customer_id
HAVING COUNT(*) >= 3;

Window functions, CTEs, subqueries. SQL can express complex analytical queries concisely. This same query in MongoDB would be significantly more verbose:

db.orders.aggregate([
  { $group: {
      _id: { customerId: "$customerId", month: { $dateToString: { format: "%Y-%m", date: "$createdAt" } } },
      spent: { $sum: "$total" }
  }},
  { $sort: { "_id.customerId": 1, "_id.month": 1 } },
  { $group: {
      _id: "$_id.customerId",
      months: { $push: { month: "$_id.month", spent: "$spent" } }
  }},
  // ... additional stages to compute the same result
  // This gets complex quickly
]);

MongoDB's aggregation pipeline is capable, but SQL is more natural for complex analytical queries. Many MongoDB deployments export data to a SQL data warehouse for serious analysis.

However, MongoDB excels at document-shaped queries:

// Find products matching complex criteria
db.products.find({
  "attributes.brand": "Sony",
  "attributes.noiseCancelling": true,
  price: { $lt: 200 }
});

// Full-text search
db.products.find({ $text: { $search: "wireless bluetooth" } });

// Query nested arrays
db.orders.find({ "items.product": "Wireless Headphones" });

These queries are natural in MongoDB and awkward in MySQL (especially the nested array query, which would require a JOIN and potentially multiple rows per result).

Transactions and Consistency

MySQL's transaction model is mature and battle-tested:

START TRANSACTION;

UPDATE accounts SET balance = balance - 100 WHERE id = 1;
UPDATE accounts SET balance = balance + 100 WHERE id = 2;
INSERT INTO transfers (from_id, to_id, amount) VALUES (1, 2, 100);

COMMIT;

Either all three statements succeed or none do. This atomicity is fundamental to financial systems, inventory management, and any domain where partial updates cause problems.

MongoDB added multi-document transactions in version 4.0 (2018), and they work:

const session = client.startSession();

await session.withTransaction(async () => {
  await accounts.updateOne(
    { _id: 1 },
    { $inc: { balance: -100 } },
    { session }
  );
  await accounts.updateOne(
    { _id: 2 },
    { $inc: { balance: 100 } },
    { session }
  );
  await transfers.insertOne(
    { from: 1, to: 2, amount: 100 },
    { session }
  );
});

But MongoDB's transactions are an addition to a database designed around single-document atomicity. The documentation recommends designing schemas to minimize transaction needs. The performance overhead is higher than MySQL's. They work, but they're not the natural way to use MongoDB.

If transactions are central to your application, MySQL is more comfortable. If you can design your documents so that related data lives together, MongoDB's single-document atomicity might be sufficient.

Performance Reality

"Which is faster?" is the wrong question. Both databases can be fast or slow depending on how you use them.

MongoDB is faster when:

• You're reading complete documents that would require JOINs in MySQL
• You're writing large volumes of varied data
• Your queries match the document structure you designed

MySQL is faster when:

• You're doing complex JOINs with proper indexes
• You're running analytical queries across normalized data
• Your query patterns are well-served by relational indexes

Both databases benefit enormously from proper indexing. Both can handle millions of records. Both can be made slow by poor schema design or missing indexes.

The performance question that matters is: "Which database matches how I'll actually access my data?"

When to Choose MySQL

Your data has clear relationships. If you're modeling users, orders, products, inventory, entities that reference each other in complex ways, the relational model exists for this.

Data integrity matters. If you need the database to enforce that foreign keys are valid, that values are within ranges, that certain combinations don't exist. MySQL constraints catch bugs before they become corrupted data.

Complex reporting. If your application includes analytics, dashboards, or reports that aggregate data across multiple entity types, SQL makes this tractable.

Your team knows SQL. SQL is a universal skill. ORMs exist for every language. Hiring is easier. Knowledge transfers between projects.

Long-term maintainability. A MySQL schema is documentation. New developers can understand the data model from the table definitions. The structure is explicit.

When to Choose MongoDB

Your documents vary in structure. Product catalogs, content management, event logs. When items have different attributes, MongoDB's flexible schema is a genuine advantage.

Rapid iteration. In early product development, when the data model is changing weekly, MongoDB lets you evolve without migrations.

Hierarchical data. If your data is naturally nested (documents with embedded documents, arrays of objects), MongoDB represents this directly.

Read-heavy workloads on document-shaped data. If you're always reading "a user with their settings and recent activity," embedding that data and reading it in one query is efficient.

Horizontal scaling requirements. MongoDB's sharding is more mature and easier to configure than MySQL's. If you genuinely need to distribute writes across machines, MongoDB has an advantage.

The Hybrid Reality

Many mature applications use both:

MySQL: Users, accounts, orders, payments
  - Relational data that needs constraints and transactions
  - Data that gets analyzed and reported on

MongoDB: Product catalogs, content, logs, sessions
  - Documents with varied structures
  - High-write workloads
  - Data that's read as complete units

This adds operational complexity but lets each database do what it's best at. The boundaries between "relational" and "document" data often become clear once you start building.

The Honest Advice

If you're starting a new project and unsure, MySQL is the safer default. The relational model is well-understood, SQL is a transferable skill, and you can always add a document store later if you need one.

Choose MongoDB when you have a specific reason: your data is genuinely document-shaped, you need the flexible schema, or your team is already productive with it.

What's not a good reason: "MongoDB is more modern" or "NoSQL is the future." These aren't technical arguments. Most web applications have relational data, and pretending otherwise leads to awkward workarounds.

The database is the foundation. Changing it later is expensive. Make the choice based on how your data actually looks, not on trends or theoretical scale you might never need.