
A comprehensive, battle-tested guide to choosing between MongoDB and PostgreSQL: scalability, performance, transactions, indexes, managed services, and real-world use cases for SaaS and eCommerce. Includes decision frameworks, diagrams, and code examples.
Choosing between MongoDB and PostgreSQL is one of the most consequential early decisions you'll make. Get it wrong, and you'll spend months migrating. Get it right, and your database becomes an invisible foundation that scales with you.
I've built production systems on both. I've migrated from MongoDB to PostgreSQL (twice). I've also kept both running in polyglot persistence architectures. This post breaks down exactly when to choose which one, based on real workloads, not marketing claims.
This isn't about "better" or "worse" β it's about what you're optimizing for:
Choose PostgreSQL when:
Choose MongoDB when:
Here's a detailed comparison across the dimensions that matter most:
PostgreSQL: Relational tables with predefined schemas. You define tables, columns, data types, and relationships upfront. Changes require ALTER TABLE migrations. JSONB support allows flexible fields within otherwise structured tables.
MongoDB: Flexible JSON documents. No enforced schema (though validation rules are recommended). Add/remove fields freely. Documents in the same collection can have different shapes. Embedding related data avoids joins.
When it matters: If your data model is stable and normalized (users β orders β line_items), PostgreSQL enforces correctness. If you're iterating rapidly on product features and the schema changes weekly, MongoDB lets you ship faster.
PostgreSQL: Full ACID from day one. Multi-table transactions are cheap and reliable. Serializab le isolation prevents anomalies. Foreign keys enforce referential integrity automatically.
MongoDB: ACID transactions supported since v4.0, but come with performance overhead. Best for single-document operations. Multi-document transactions work but are slower than PostgreSQL. Most Mongo use cases avoid transactions by denormalizing data.
When it matters: Payments, inventory, financial operations demand ACID β PostgreSQL wins. Content management, logging, caching where eventual consistency is acceptable β MongoDB is fine.
PostgreSQL: Scales vertically (bigger servers) and via read replicas. Horizontal sharding is possible but complex (Citus, pg_partman). Most teams run a primary + read replicas. Write scaling is limited to the primary.
MongoDB: Built for horizontal scaling. Sharding distributes data across multiple servers automatically. Atlas (managed) handles shard rebalancing. Write and read scaling happen together. Better for write-heavy, high-throughput workloads.
When it matters: If you're scaling reads, both work well (replicas/shards). If you need massive write scale (millions of writes/sec), MongoDB shines. For most SaaS apps (<100K users), PostgreSQL with replicas is simpler and cheaper.
PostgreSQL Strengths:
MongoDB Strengths:
Real numbers: PostgreSQL excels at transactional workloads (airline reservations, banking). MongoDB wins on document-heavy reads and write-intensive workloads. For hybrid workloads, PostgreSQL with JSONB columns often provides the best balance.
PostgreSQL Indexes:
MongoDB Indexes:
Key insight: Both databases require thoughtful indexing. PostgreSQL indexes are more mature and predictable. MongoDB indexes on nested documents can be tricky. Always profile with EXPLAIN (PostgreSQL) or .explain() (MongoDB).
Self-hosting is viable, but managed services remove operational overhead.
Recommendation: For serverless Next.js, use Neon (Postgres). For traditional Node.js apps, Supabase or AWS RDS. For MongoDB, Atlas is the default choice β self-hosting MongoDB in 2026 is rarely worth it unless you have dedicated ops capacity.
PostgreSQL is the safe default for most SaaS:
Use MongoDB for specific features within SaaS:
Hybrid approach works best:
Example: Otto (Europe's #2 ecommerce, 30M shoppers) uses MongoDB for 2M+ products. Shopify uses PostgreSQL for transactional data + Redis for caching. Stripe uses PostgreSQL exclusively. The pattern: transactional core on Postgres, flexible content on Mongo.
Scenario: Building a blog platform with users, posts, comments, and tags.
1-- PostgreSQL schema with relationships
2CREATE TABLE users (
3 id SERIAL PRIMARY KEY,
4 email VARCHAR(255) UNIQUE NOT NULL,
5 name VARCHAR(100) NOT NULL,
6 created_at TIMESTAMPTZ DEFAULT NOW()
7);
8
9CREATE TABLE posts (
10 id SERIAL PRIMARY KEY,
11 user_id INTEGER REFERENCES users(id) ON DELETE CASCADE,
12 title VARCHAR(200) NOT NULL,
13 content TEXT NOT NULL,
14 published_at TIMESTAMPTZ,
15 created_at TIMESTAMPTZ DEFAULT NOW(),
16 CONSTRAINT valid_title CHECK (length(title) > 0)
17);
18
19CREATE TABLE tags (
20 id SERIAL PRIMARY KEY,
21 name VARCHAR(50) UNIQUE NOT NULL
22);
23
24CREATE TABLE post_tags (
25 post_id INTEGER REFERENCES posts(id) ON DELETE CASCADE,
26 tag_id INTEGER REFERENCES tags(id) ON DELETE CASCADE,
27 PRIMARY KEY (post_id, tag_id)
28);
29
30CREATE TABLE comments (
31 id SERIAL PRIMARY KEY,
32 post_id INTEGER REFERENCES posts(id) ON DELETE CASCADE,
33 user_id INTEGER REFERENCES users(id) ON DELETE CASCADE,
34 content TEXT NOT NULL,
35 created_at TIMESTAMPTZ DEFAULT NOW()
36);
37
38-- Indexes for performance
39CREATE INDEX idx_posts_user_id ON posts(user_id);
40CREATE INDEX idx_posts_published_at ON posts(published_at DESC);
41CREATE INDEX idx_comments_post_id ON comments(post_id);
42CREATE INDEX idx_post_tags_tag_id ON post_tags(tag_id);1-- Get post with author, comments, and tags (single query with JOINs)
2SELECT
3 p.id,
4 p.title,
5 p.content,
6 p.published_at,
7 u.name as author_name,
8 u.email as author_email,
9 json_agg(DISTINCT jsonb_build_object(
10 'id', c.id,
11 'content', c.content,
12 'author', c_user.name,
13 'created_at', c.created_at
14 )) FILTER (WHERE c.id IS NOT NULL) as comments,
15 json_agg(DISTINCT t.name) FILTER (WHERE t.name IS NOT NULL) as tags
16FROM posts p
17JOIN users u ON u.id = p.user_id
18LEFT JOIN comments c ON c.post_id = p.id
19LEFT JOIN users c_user ON c_user.id = c.user_id
20LEFT JOIN post_tags pt ON pt.post_id = p.id
21LEFT JOIN tags t ON t.id = pt.tag_id
22WHERE p.id = $1
23GROUP BY p.id, u.id;
24
25-- TypeScript with pg or Neon
26import { neon } from '@neondatabase/serverless';
27
28const sql = neon(process.env.DATABASE_URL!);
29
30export async function getPost(postId: number) {
31 const result = await sql`
32 SELECT
33 p.id,
34 p.title,
35 p.content,
36 json_build_object('name', u.name, 'email', u.email) as author,
37 (
38 SELECT json_agg(json_build_object('id', c.id, 'content', c.content))
39 FROM comments c
40 WHERE c.post_id = p.id
41 ) as comments
42 FROM posts p
43 JOIN users u ON u.id = p.user_id
44 WHERE p.id = ${postId}
45 `;
46 return result[0];
47}1// MongoDB schema with validation (using Mongoose)
2import { Schema, model } from 'mongoose';
3
4const UserSchema = new Schema({
5 email: { type: String, required: true, unique: true },
6 name: { type: String, required: true },
7 createdAt: { type: Date, default: Date.now },
8});
9
10const PostSchema = new Schema({
11 author: {
12 _id: { type: Schema.Types.ObjectId, ref: 'User', required: true },
13 name: String, // Denormalized for performance
14 email: String, // Denormalized
15 },
16 title: { type: String, required: true, maxlength: 200 },
17 content: { type: String, required: true },
18 tags: [{ type: String }], // Array of strings
19 comments: [
20 {
21 author: {
22 _id: { type: Schema.Types.ObjectId, ref: 'User' },
23 name: String,
24 },
25 content: { type: String, required: true },
26 createdAt: { type: Date, default: Date.now },
27 },
28 ],
29 publishedAt: Date,
30 createdAt: { type: Date, default: Date.now },
31});
32
33// Indexes
34PostSchema.index({ 'author._id': 1 });
35PostSchema.index({ publishedAt: -1 });
36PostSchema.index({ tags: 1 }); // Multikey index for arrays
37PostSchema.index({ title: 'text', content: 'text' }); // Full-text search
38
39export const User = model('User', UserSchema);
40export const Post = model('Post', PostSchema);1// Get post with embedded author and comments (single query, no joins)
2import { Post } from './mongodb-schemas';
3
4export async function getPost(postId: string) {
5 // β
Single query - all data embedded in document
6 const post = await Post.findById(postId).lean();
7 return post;
8}
9
10// Add a comment (update nested array)
11export async function addComment(
12 postId: string,
13 userId: string,
14 userName: string,
15 content: string
16) {
17 // β
Atomic update to nested array
18 const result = await Post.findByIdAndUpdate(
19 postId,
20 {
21 $push: {
22 comments: {
23 author: { _id: userId, name: userName },
24 content,
25 createdAt: new Date(),
26 },
27 },
28 },
29 { new: true }
30 );
31 return result;
32}
33
34// Find posts by tag (using array index)
35export async function getPostsByTag(tag: string) {
36 // β
Efficient query with multikey index on tags array
37 const posts = await Post.find({ tags: tag })
38 .sort({ publishedAt: -1 })
39 .limit(20)
40 .lean();
41 return posts;
42}Key differences:
The schema debate is the most misunderstood part of this choice.
Reality: MongoDB supports schema validation (since v3.6). You should always define validation rules. The difference is enforcement: PostgreSQL rejects invalid data at write time. MongoDB can be configured to do the same or allow flexible writes with validation warnings.
1// MongoDB schema validation (enforced at database level)
2db.createCollection('products', {
3 validator: {
4 $jsonSchema: {
5 bsonType: 'object',
6 required: ['name', 'sku', 'price', 'category'],
7 properties: {
8 name: {
9 bsonType: 'string',
10 description: 'Product name is required',
11 },
12 sku: {
13 bsonType: 'string',
14 pattern: '^[A-Z]{3}-[0-9]{6}$',
15 description: 'SKU must match format: ABC-123456',
16 },
17 price: {
18 bsonType: 'number',
19 minimum: 0,
20 description: 'Price must be positive',
21 },
22 category: {
23 enum: ['electronics', 'clothing', 'books', 'home'],
24 description: 'Category must be one of the enum values',
25 },
26 attributes: {
27 bsonType: 'object',
28 // Flexible nested attributes - no fixed schema
29 description: 'Product-specific attributes (flexible)',
30 },
31 },
32 },
33 },
34 validationAction: 'error', // Reject invalid documents
35});
36
37// Example documents with different attributes
38db.products.insertMany([
39 {
40 name: 'iPhone 16 Pro',
41 sku: 'APL-000001',
42 price: 999,
43 category: 'electronics',
44 attributes: {
45 // Electronics-specific
46 screen_size: '6.7"',
47 processor: 'A18 Pro',
48 storage: '256GB',
49 color: 'Titanium',
50 },
51 },
52 {
53 name: 'Cotton T-Shirt',
54 sku: 'CLO-000123',
55 price: 29.99,
56 category: 'clothing',
57 attributes: {
58 // Clothing-specific
59 size: 'M',
60 material: '100% Cotton',
61 color: 'Navy Blue',
62 fit: 'Regular',
63 },
64 },
65]);1-- PostgreSQL schema with JSONB for flexible fields
2CREATE TABLE products (
3 id SERIAL PRIMARY KEY,
4 name VARCHAR(200) NOT NULL,
5 sku VARCHAR(20) UNIQUE NOT NULL,
6 price DECIMAL(10, 2) NOT NULL CHECK (price >= 0),
7 category VARCHAR(50) NOT NULL,
8 attributes JSONB NOT NULL DEFAULT '{}', -- Flexible attributes
9 created_at TIMESTAMPTZ DEFAULT NOW()
10);
11
12-- GIN index for fast JSONB queries
13CREATE INDEX idx_products_attributes ON products USING GIN (attributes);
14
15-- Insert products with different attributes
16INSERT INTO products (name, sku, price, category, attributes) VALUES
17(
18 'iPhone 16 Pro',
19 'APL-000001',
20 999.00,
21 'electronics',
22 '{
23 "screen_size": "6.7\"",
24 "processor": "A18 Pro",
25 "storage": "256GB",
26 "color": "Titanium"
27 }'::jsonb
28),
29(
30 'Cotton T-Shirt',
31 'CLO-000123',
32 29.99,
33 'clothing',
34 '{
35 "size": "M",
36 "material": "100% Cotton",
37 "color": "Navy Blue",
38 "fit": "Regular"
39 }'::jsonb
40);
41
42-- Query products by JSONB attribute
43SELECT * FROM products
44WHERE attributes->>'color' = 'Navy Blue';
45
46-- Query with JSONB path
47SELECT name, attributes->'storage' as storage
48FROM products
49WHERE category = 'electronics'
50 AND (attributes->>'storage')::text LIKE '%256GB%';The reality: PostgreSQL with JSONB gives you 80% of MongoDB's flexibility while keeping ACID guarantees. Use it when you need both structured core data (users, orders) and flexible attributes (product metadata, user preferences). MongoDB still wins when the entire data model is fluid or you need WiredTiger's update performance on nested documents.
1// PostgreSQL: Multi-table transaction (e-commerce checkout)
2import { Pool } from 'pg';
3
4const pool = new Pool({ connectionString: process.env.DATABASE_URL });
5
6export async function processCheckout({
7 userId,
8 cartItems,
9 paymentInfo,
10}: CheckoutInput) {
11 const client = await pool.connect();
12
13 try {
14 // β
Start transaction
15 await client.query('BEGIN');
16
17 // 1. Create order
18 const orderResult = await client.query(
19 `INSERT INTO orders (user_id, total, status)
20 VALUES ($1, $2, 'pending')
21 RETURNING id`,
22 [userId, calculateTotal(cartItems)]
23 );
24 const orderId = orderResult.rows[0].id;
25
26 // 2. Insert order items
27 for (const item of cartItems) {
28 await client.query(
29 `INSERT INTO order_items (order_id, product_id, quantity, price)
30 VALUES ($1, $2, $3, $4)`,
31 [orderId, item.productId, item.quantity, item.price]
32 );
33
34 // 3. Decrement inventory (with check constraint)
35 const inventoryResult = await client.query(
36 `UPDATE inventory
37 SET quantity = quantity - $1
38 WHERE product_id = $2 AND quantity >= $1
39 RETURNING quantity`,
40 [item.quantity, item.productId]
41 );
42
43 if (inventoryResult.rows.length === 0) {
44 // Not enough inventory - ROLLBACK entire transaction
45 throw new Error(`Insufficient inventory for product ${item.productId}`);
46 }
47 }
48
49 // 4. Process payment
50 await client.query(
51 `INSERT INTO payments (order_id, amount, status, payment_method)
52 VALUES ($1, $2, 'completed', $3)`,
53 [orderId, calculateTotal(cartItems), paymentInfo.method]
54 );
55
56 // 5. Update order status
57 await client.query(
58 `UPDATE orders SET status = 'confirmed' WHERE id = $1`,
59 [orderId]
60 );
61
62 // β
Commit transaction - all or nothing
63 await client.query('COMMIT');
64
65 return { success: true, orderId };
66
67 } catch (error) {
68 // β Rollback on any error - database stays consistent
69 await client.query('ROLLBACK');
70 console.error('Checkout failed:', error);
71 throw error;
72 } finally {
73 client.release();
74 }
75}1// MongoDB: Denormalized approach (no transaction needed)
2import { ObjectId } from 'mongodb';
3import { Product, Order } from './mongodb-schemas';
4
5export async function processCheckout({
6 userId,
7 cartItems,
8 paymentInfo,
9}: CheckoutInput) {
10 // β
Single document write - atomic by default
11 const order = await Order.create({
12 user: {
13 _id: userId,
14 // Embed user details for fast access
15 name: user.name,
16 email: user.email,
17 },
18 items: cartItems.map((item) => ({
19 product: {
20 _id: item.productId,
21 name: item.name, // Denormalized
22 sku: item.sku, // Denormalized
23 price: item.price, // Snapshot price at time of order
24 },
25 quantity: item.quantity,
26 })),
27 total: calculateTotal(cartItems),
28 payment: {
29 method: paymentInfo.method,
30 status: 'completed',
31 processedAt: new Date(),
32 },
33 status: 'confirmed',
34 createdAt: new Date(),
35 });
36
37 // Inventory updated separately (eventual consistency OK)
38 // Use MongoDB Change Streams or background job
39 await Promise.all(
40 cartItems.map((item) =>
41 Product.findByIdAndUpdate(item.productId, {
42 $inc: { inventory: -item.quantity },
43 })
44 )
45 );
46
47 return { success: true, orderId: order._id };
48}
49
50// β Trade-off: Order is created immediately, inventory updated after.
51// If inventory update fails, you need compensating logic.
52// For mission-critical consistency, use PostgreSQL or MongoDB transactions.When to use transactions:
When you can skip transactions:
Here's my actual decision process when starting a new project:
Draw your domain model. Identify:
Q1: Do operations span multiple entities?
Q2: Will the schema change frequently?
Q3: Do you need complex analytics or reporting?
Q4: What's your expected write volume?
Don't try to solve everything with one database. My typical approach:
Polyglot persistence (using both) is common in mature systems. Stripe uses Postgres for everything. Shopify uses Postgres + Redis. MongoDB's own website uses both MongoDB and Postgres. Pick the right tool for each workload.
A quick reference for key dimensions:
Dimension | PostgreSQL | MongoDB
Data Model | Relational tables, normalized | JSON documents, denormalized
Schema | Predefined, enforced by DDL | Flexible, optional validation
ACID Transactions | Full support, low overhead | Supported (v4.0+), higher overhead
Scaling Writes | Vertical (primary bottleneck) | Horizontal (sharding)
Scaling Reads | Read replicas (horizontal) | Sharding + replicas (horizontal)
JOIN Performance | Excellent (3+ tables) | Poor (use $lookup, limited)
Document Reads | Good (requires JOINs) | Excellent (single query)
Nested Updates | Slow (JSON reconstruction) | Fast (WiredTiger)
Full-Text Search | GIN indexes, pg_trgm | Text indexes, Atlas Search
Geospatial | PostGIS extension | Built-in 2dsphere
Best Managed Service | Neon, Supabase, AWS RDS | MongoDB Atlas
Learning Curve | SQL (universal skill) | MongoDB Query Language (MQL)
Ecosystem Maturity | 35+ years, massive ecosystem | 15+ years, growing ecosystem
Cost (Managed) | $20-50/mo (starter) | $0-50/mo (shared to dedicated)
Choice: PostgreSQL
Why:
Choice: MongoDB for content + PostgreSQL for users/auth
Why:
Choice: Both (Polyglot Persistence)
Why:
Keep product IDs synchronized between databases. Use event streams (Kafka, Redis Pub/Sub) to keep inventory counts updated.
Schema flexibility sounds great until you have 10 different document shapes in one collection. You still need migrations β they're just in application code instead of SQL. And they're harder to test.
Fix: Always use schema validation in MongoDB. Version your documents. Write migration scripts for breaking changes.
I've seen teams use MongoDB for billing and payments because "we're already using Mongo". Then they spend months debugging race conditions and building transaction retry logic that PostgreSQL gives you for free.
Fix: Financial data belongs in PostgreSQL, period. Use MongoDB for what it's good at (flexible content, high writes).
Embedding everything sounds efficient until you update a user's name and need to update 10,000 documents. Denormalization is a trade-off, not a default.
Fix: Denormalize data that's read frequently and changes rarely. For frequently-updated data (user names, prices), use references and $lookup or keep a sync pattern.
Default to PostgreSQL. It's the safer choice for 80% of applications. The ecosystem is mature, SQL is a universal skill, and ACID transactions save you from subtle bugs.
Add MongoDB when: You have a specific use case that benefits from flexibility (CMS, catalogs, logs) or you need massive write scale. Don't start with MongoDB unless you're building something document-shaped from day one.
Use both strategically: Transactional core in Postgres, flexible content in Mongo. This is what mature companies do.
Consider Postgres + JSONB: If you need flexibility but don't want to manage two databases, PostgreSQL with JSONB columns is incredibly powerful. You get relational guarantees + document flexibility in one system.
The database decision should happen in the architecture phase, not after you've built half the app. Here's my systematic approach for new products:
Before choosing a database, draw your entity-relationship diagram. Count the relationships:
1// Access Pattern Analysis Template
2
3interface AccessPatternAnalysis {
4 entity: string;
5 operations: {
6 type: 'read' | 'write' | 'update' | 'delete';
7 frequency: 'high' | 'medium' | 'low'; // per second
8 complexity: 'simple' | 'join' | 'aggregate';
9 example: string;
10 }[];
11}
12
13// Example: E-commerce Product Analysis
14const productPatterns: AccessPatternAnalysis = {
15 entity: 'Product',
16 operations: [
17 {
18 type: 'read',
19 frequency: 'high', // 1000+ reads/sec
20 complexity: 'simple',
21 example: 'Get product by ID/slug',
22 // β MongoDB: Single doc read, excellent
23 // β PostgreSQL: Single row, excellent
24 },
25 {
26 type: 'read',
27 frequency: 'medium', // 100 reads/sec
28 complexity: 'join',
29 example: 'Get product with reviews, ratings, seller info',
30 // β MongoDB: Embedded data or $lookup (slower)
31 // β PostgreSQL: JOINs (excellent)
32 },
33 {
34 type: 'read',
35 frequency: 'medium',
36 complexity: 'aggregate',
37 example: 'Products by category with avg rating, price range',
38 // β MongoDB: Aggregation pipeline (good)
39 // β PostgreSQL: GROUP BY, window functions (excellent)
40 },
41 {
42 type: 'write',
43 frequency: 'low', // 10 writes/sec
44 complexity: 'simple',
45 example: 'Create/update product',
46 // β Both: Excellent
47 },
48 {
49 type: 'update',
50 frequency: 'high', // 500 updates/sec
51 complexity: 'simple',
52 example: 'Update inventory count',
53 // β MongoDB: Atomic $inc, excellent
54 // β PostgreSQL: UPDATE with row lock, good
55 },
56 ],
57};
58
59// Decision: High reads + JOINs + aggregations β PostgreSQL
60// But: High update frequency on nested fields β Consider MongoDBLet's look at actual performance numbers. These benchmarks use a 1M row/document dataset on equivalent hardware (8 vCPU, 32GB RAM).
1// Index Creation Time (1M documents/rows)
2// Hardware: 8 vCPU, 32GB RAM, SSD storage
3
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6β INDEX CREATION TIME β
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8β Index Type β PostgreSQL β MongoDB β Winner β
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10β Single field (B-tree)β 2.3s β 1.8s β MongoDB β
11β Compound (2 fields) β 3.1s β 2.4s β MongoDB β
12β Text/Full-text β 8.7s β 12.3s β PostgreSQL β
13β GIN (JSONB/Array) β 5.2s β 3.1s (multikey)β MongoDB β
14β Unique constraint β 2.5s β 2.0s β MongoDB β
15β Partial index β 1.8s β 2.2s (sparse)β PostgreSQL β
16βββββββββββββββββββββββ΄βββββββββββββββ΄βββββββββββββββ΄ββββββββββββββ
17
18βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
19β QUERY PERFORMANCE (indexed) β
20βββββββββββββββββββββββ¬βββββββββββββββ¬βββββββββββββββ¬ββββββββββββββ€
21β Query Type β PostgreSQL β MongoDB β Winner β
22βββββββββββββββββββββββΌβββββββββββββββΌβββββββββββββββΌββββββββββββββ€
23β Point lookup (=) β 0.2ms β 0.3ms β PostgreSQL β
24β Range query (<, >) β 1.5ms β 2.1ms β PostgreSQL β
25β Text search β 3.2ms β 8.5ms β PostgreSQL β
26β Array contains β 2.8ms β 1.2ms β MongoDB β
27β Nested field access β 4.1ms (JSONB)β 0.8ms β MongoDB β
28β Regex search β 45ms β 52ms β PostgreSQL β
29βββββββββββββββββββββββ΄βββββββββββββββ΄βββββββββββββββ΄ββββββββββββββ
30*/
31
32// PostgreSQL: Create indexes
33CREATE INDEX idx_users_email ON users(email); -- 2.3s
34CREATE INDEX idx_products_category_price ON products(category, price); -- 3.1s
35CREATE INDEX idx_products_search ON products USING GIN(to_tsvector('english', name || ' ' || description)); -- 8.7s
36CREATE INDEX idx_products_attributes ON products USING GIN(attributes); -- 5.2s
37
38// MongoDB: Create indexes
39db.users.createIndex({ email: 1 }); // 1.8s
40db.products.createIndex({ category: 1, price: 1 }); // 2.4s
41db.products.createIndex({ name: 'text', description: 'text' }); // 12.3s
42db.products.createIndex({ tags: 1 }); // 3.1s (multikey)This is where the architectural differences really show. PostgreSQL JOINs are native and optimized. MongoDB's populate() is application-level and requires multiple round trips.
1/*
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3β JOIN (PostgreSQL) vs POPULATE (MongoDB) β
4β 1M orders, 100K users, 500K products β
5βββββββββββββββββββββββ¬βββββββββββββββ¬βββββββββββββββ¬ββββββββββββββ€
6β Operation β PostgreSQL β MongoDB β Winner β
7βββββββββββββββββββββββΌβββββββββββββββΌβββββββββββββββΌββββββββββββββ€
8β 2-table JOIN β 1.2ms β 8.5ms β PostgreSQL β
9β 3-table JOIN β 2.8ms β 24ms β PostgreSQL β
10β 4-table JOIN β 4.5ms β 65ms β PostgreSQL β
11β Embedded doc read β 3.2ms (JSONB)β 0.4ms β MongoDB β
12β Aggregation (GROUP) β 12ms β 18ms β PostgreSQL β
13β $lookup (MongoDB) β N/A β 15ms β N/A β
14βββββββββββββββββββββββ΄βββββββββββββββ΄βββββββββββββββ΄ββββββββββββββ
15*/
16
17// PostgreSQL: Native JOIN (optimized, single round trip)
18const pgQuery = `
19 SELECT
20 o.id, o.total, o.created_at,
21 u.name as user_name, u.email,
22 json_agg(json_build_object(
23 'product_name', p.name,
24 'quantity', oi.quantity,
25 'price', oi.price
26 )) as items
27 FROM orders o
28 JOIN users u ON u.id = o.user_id
29 JOIN order_items oi ON oi.order_id = o.id
30 JOIN products p ON p.id = oi.product_id
31 WHERE o.id = $1
32 GROUP BY o.id, u.id
33`;
34// Execution time: 2.8ms (3-table JOIN)
35// Network round trips: 1
36
37// MongoDB: populate() - Multiple queries under the hood
38const order = await Order.findById(orderId)
39 .populate('user', 'name email') // Query 2
40 .populate('items.product', 'name price'); // Query 3
41// Execution time: 24ms (3 separate queries)
42// Network round trips: 3
43
44// MongoDB: $lookup (server-side, but limited)
45const orderWithLookup = await Order.aggregate([
46 { $match: { _id: new ObjectId(orderId) } },
47 {
48 $lookup: {
49 from: 'users',
50 localField: 'user',
51 foreignField: '_id',
52 as: 'userDetails',
53 },
54 },
55 { $unwind: '$userDetails' },
56]);
57// Execution time: 15ms (single round trip, but limited to simple joins)
58// Limitation: No support for nested $lookup in older versionsUnderstanding execution plans is critical for optimizing queries. Both databases have powerful tools, but PostgreSQL's is more detailed.
1// ==========================================
2// PostgreSQL: EXPLAIN ANALYZE
3// ==========================================
4
5-- Detailed execution plan with actual timing
6EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
7SELECT p.*, c.name as category_name
8FROM products p
9JOIN categories c ON c.id = p.category_id
10WHERE p.price BETWEEN 50 AND 200
11 AND p.is_active = true
12ORDER BY p.created_at DESC
13LIMIT 20;
14
15/*
16Limit (cost=0.85..45.23 rows=20 width=312) (actual time=0.125..0.892 ms)
17 -> Nested Loop (cost=0.85..12456.78 rows=5623 width=312) (actual time=0.123..0.878 ms)
18 -> Index Scan Backward using idx_products_created_at on products p
19 (cost=0.42..8234.56 rows=5623 width=280) (actual time=0.089..0.456 ms)
20 Index Cond: (price >= 50 AND price <= 200)
21 Filter: is_active
22 Rows Removed by Filter: 234
23 Buffers: shared hit=45
24 -> Index Scan using categories_pkey on categories c
25 (cost=0.43..0.75 rows=1 width=32) (actual time=0.012..0.012 ms)
26 Index Cond: (id = p.category_id)
27 Buffers: shared hit=3
28Planning Time: 0.234 ms
29Execution Time: 0.952 ms
30*/
31
32-- Key insights from PostgreSQL EXPLAIN:
33-- 1. "Index Scan" = Good, index is being used
34-- 2. "Seq Scan" = Bad for large tables, needs index
35-- 3. "Buffers: shared hit" = Data from cache (fast)
36-- 4. "Rows Removed by Filter" = Index not selective enough
37-- 5. "Nested Loop" vs "Hash Join" = Join strategy
38
39// ==========================================
40// MongoDB: .explain()
41// ==========================================
42
43db.products.find({
44 price: { $gte: 50, $lte: 200 },
45 isActive: true
46})
47.sort({ createdAt: -1 })
48.limit(20)
49.explain('executionStats');
50
51/*
52{
53 "queryPlanner": {
54 "winningPlan": {
55 "stage": "LIMIT",
56 "limitAmount": 20,
57 "inputStage": {
58 "stage": "FETCH",
59 "filter": { "isActive": { "$eq": true } },
60 "inputStage": {
61 "stage": "IXSCAN",
62 "keyPattern": { "createdAt": -1 },
63 "indexBounds": {
64 "createdAt": ["[MaxKey, MinKey]"]
65 }
66 }
67 }
68 }
69 },
70 "executionStats": {
71 "executionSuccess": true,
72 "nReturned": 20,
73 "executionTimeMillis": 2,
74 "totalKeysExamined": 156,
75 "totalDocsExamined": 156,
76 "executionStages": {
77 "stage": "LIMIT",
78 "nReturned": 20,
79 "works": 157,
80 "docsExamined": 156
81 }
82 }
83}
84*/
85
86// Key insights from MongoDB explain:
87// 1. "IXSCAN" = Good, using index
88// 2. "COLLSCAN" = Bad, full collection scan
89// 3. "totalDocsExamined" vs "nReturned" = Efficiency ratio
90// 4. "executionTimeMillis" = Actual query time
91// 5. "rejectedPlans" = Alternative plans MongoDB consideredThe ORM choice significantly impacts developer experience and performance. Here's how the top options compare:
1/*
2βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
3β ORM/ODM COMPARISON (2026) β
4βββββββββββββββββββ¬ββββββββββββββββ¬ββββββββββββββββ¬ββββββββββββββββββββββββββββ€
5β Feature β Prisma (PG) β Drizzle (PG) β Mongoose (MongoDB) β
6βββββββββββββββββββΌββββββββββββββββΌββββββββββββββββΌββββββββββββββββββββββββββββ€
7β Type Safety β βββββ β βββββ β βββ (with TS) β
8β Query Perf β ββββ β βββββ β ββββ β
9β Bundle Size β 2.1MB β 45KB β 1.8MB β
10β Cold Start β 280ms β 12ms β 150ms β
11β Learning Curve β Low β Medium β Low β
12β Migrations β Built-in β Built-in β Manual/mongoose-migrate β
13β Raw SQL/Query β Supported β Native β Native MongoDB β
14β Edge/Serverless β βββ (Accelerate)β βββββ β βββ (serverless pkg) β
15β Relations β Excellent β Excellent β populate() (slow) β
16β Validation β Schema-level β Zod integrate β Built-in validators β
17βββββββββββββββββββ΄ββββββββββββββββ΄ββββββββββββββββ΄ββββββββββββββββββββββββββββ
18*/1// ==========================================
2// PRISMA (PostgreSQL) - Best DX, Good Performance
3// ==========================================
4
5// schema.prisma
6model User {
7 id Int @id @default(autoincrement())
8 email String @unique
9 name String
10 orders Order[]
11 createdAt DateTime @default(now())
12
13 @@index([email])
14}
15
16model Order {
17 id Int @id @default(autoincrement())
18 user User @relation(fields: [userId], references: [id])
19 userId Int
20 items OrderItem[]
21 total Decimal @db.Decimal(10, 2)
22 status OrderStatus @default(PENDING)
23 createdAt DateTime @default(now())
24
25 @@index([userId])
26 @@index([status, createdAt])
27}
28
29enum OrderStatus {
30 PENDING
31 CONFIRMED
32 SHIPPED
33 DELIVERED
34}
35
36// Usage - Type-safe, auto-complete
37import { PrismaClient } from '@prisma/client';
38const prisma = new PrismaClient();
39
40// Query with relations (generates optimized JOIN)
41const orderWithDetails = await prisma.order.findUnique({
42 where: { id: orderId },
43 include: {
44 user: { select: { name: true, email: true } },
45 items: {
46 include: {
47 product: { select: { name: true, price: true } },
48 },
49 },
50 },
51});
52// Execution: ~3.2ms (single optimized query)
53// Generated SQL uses JOINs under the hood
54
55// Transaction (ACID guaranteed)
56const result = await prisma.$transaction(async (tx) => {
57 const order = await tx.order.create({
58 data: { userId, total, status: 'PENDING' },
59 });
60
61 await tx.orderItem.createMany({
62 data: items.map((item) => ({
63 orderId: order.id,
64 productId: item.productId,
65 quantity: item.quantity,
66 price: item.price,
67 })),
68 });
69
70 await tx.inventory.updateMany({
71 where: { productId: { in: items.map((i) => i.productId) } },
72 data: { quantity: { decrement: 1 } },
73 });
74
75 return order;
76});
77// Full ACID transaction, auto-rollback on error1// ==========================================
2// DRIZZLE (PostgreSQL) - Best Performance, SQL-like
3// ==========================================
4
5import { pgTable, serial, varchar, decimal, timestamp, integer } from 'drizzle-orm/pg-core';
6import { relations } from 'drizzle-orm';
7import { drizzle } from 'drizzle-orm/neon-http';
8import { neon } from '@neondatabase/serverless';
9
10// Schema definition (TypeScript-native)
11export const users = pgTable('users', {
12 id: serial('id').primaryKey(),
13 email: varchar('email', { length: 255 }).notNull().unique(),
14 name: varchar('name', { length: 100 }).notNull(),
15 createdAt: timestamp('created_at').defaultNow(),
16});
17
18export const orders = pgTable('orders', {
19 id: serial('id').primaryKey(),
20 userId: integer('user_id').references(() => users.id),
21 total: decimal('total', { precision: 10, scale: 2 }).notNull(),
22 status: varchar('status', { length: 20 }).default('pending'),
23 createdAt: timestamp('created_at').defaultNow(),
24});
25
26export const orderItems = pgTable('order_items', {
27 id: serial('id').primaryKey(),
28 orderId: integer('order_id').references(() => orders.id),
29 productId: integer('product_id').references(() => products.id),
30 quantity: integer('quantity').notNull(),
31 price: decimal('price', { precision: 10, scale: 2 }).notNull(),
32});
33
34// Relations
35export const usersRelations = relations(users, ({ many }) => ({
36 orders: many(orders),
37}));
38
39export const ordersRelations = relations(orders, ({ one, many }) => ({
40 user: one(users, { fields: [orders.userId], references: [users.id] }),
41 items: many(orderItems),
42}));
43
44// Setup
45const sql = neon(process.env.DATABASE_URL!);
46const db = drizzle(sql, { schema: { users, orders, orderItems } });
47
48// Query with relations (SQL-like, maximum performance)
49const orderWithDetails = await db.query.orders.findFirst({
50 where: eq(orders.id, orderId),
51 with: {
52 user: { columns: { name: true, email: true } },
53 items: {
54 with: {
55 product: { columns: { name: true, price: true } },
56 },
57 },
58 },
59});
60// Execution: ~2.1ms (generates optimal JOIN)
61// 35% faster than Prisma for same query
62
63// Raw SQL when needed (no overhead)
64const complexQuery = await db.execute(sql`
65 SELECT
66 p.category,
67 COUNT(*) as count,
68 AVG(p.price) as avg_price,
69 SUM(oi.quantity) as total_sold
70 FROM products p
71 JOIN order_items oi ON oi.product_id = p.id
72 WHERE p.created_at > NOW() - INTERVAL '30 days'
73 GROUP BY p.category
74 HAVING SUM(oi.quantity) > 100
75 ORDER BY total_sold DESC
76`);
77// Direct SQL execution, zero abstraction overhead1// ==========================================
2// MONGOOSE (MongoDB) - Flexible, Document-oriented
3// ==========================================
4
5import mongoose, { Schema, model, Types } from 'mongoose';
6
7// Schema with validation
8const userSchema = new Schema({
9 email: {
10 type: String,
11 required: [true, 'Email is required'],
12 unique: true,
13 lowercase: true,
14 validate: {
15 validator: (v: string) => /^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(v),
16 message: 'Invalid email format',
17 },
18 },
19 name: { type: String, required: true, maxlength: 100 },
20 createdAt: { type: Date, default: Date.now },
21});
22
23const orderSchema = new Schema({
24 user: {
25 type: Schema.Types.ObjectId,
26 ref: 'User',
27 required: true,
28 index: true,
29 },
30 items: [
31 {
32 product: { type: Schema.Types.ObjectId, ref: 'Product', required: true },
33 quantity: { type: Number, required: true, min: 1 },
34 price: { type: Number, required: true, min: 0 },
35 },
36 ],
37 total: { type: Number, required: true },
38 status: {
39 type: String,
40 enum: ['pending', 'confirmed', 'shipped', 'delivered'],
41 default: 'pending',
42 },
43 createdAt: { type: Date, default: Date.now },
44});
45
46// Compound index for common queries
47orderSchema.index({ status: 1, createdAt: -1 });
48
49export const User = model('User', userSchema);
50export const Order = model('Order', orderSchema);
51
52// Query with populate (multiple round trips!)
53const orderWithDetails = await Order.findById(orderId)
54 .populate('user', 'name email') // Query 2
55 .populate('items.product', 'name price') // Query 3
56 .lean(); // Return plain JS object
57// Execution: ~18ms (3 separate database queries)
58// β οΈ N+1 problem: Each populate is a separate query
59
60// Better: Use aggregation with $lookup (single query)
61const orderAggregation = await Order.aggregate([
62 { $match: { _id: new Types.ObjectId(orderId) } },
63 {
64 $lookup: {
65 from: 'users',
66 localField: 'user',
67 foreignField: '_id',
68 as: 'userDetails',
69 },
70 },
71 { $unwind: '$userDetails' },
72 {
73 $lookup: {
74 from: 'products',
75 localField: 'items.product',
76 foreignField: '_id',
77 as: 'productDetails',
78 },
79 },
80]);
81// Execution: ~8ms (single query, but complex pipeline)
82
83// Best: Embed data when possible (fastest)
84const orderEmbedded = await Order.findById(orderId).lean();
85// Execution: ~0.5ms (single document read)
86// But: Requires denormalized schema with embedded user/product info1/*
2βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
3β ORM PERFORMANCE BENCHMARKS β
4β (1M records, same hardware) β
5βββββββββββββββββββββββββββββββββββ¬ββββββββββ¬ββββββββββ¬ββββββββββ¬ββββββββββββββ€
6β Operation β Prisma β Drizzle β Mongooseβ Raw SQL/MQL β
7βββββββββββββββββββββββββββββββββββΌββββββββββΌββββββββββΌββββββββββΌββββββββββββββ€
8β Single row/doc read β 0.8ms β 0.4ms β 0.5ms β 0.2ms β
9β Insert 1 record β 1.2ms β 0.6ms β 0.8ms β 0.4ms β
10β Insert 1000 records (batch) β 45ms β 28ms β 35ms β 18ms β
11β Update single record β 1.0ms β 0.5ms β 0.7ms β 0.3ms β
12β 2-relation include/populate β 3.2ms β 2.1ms β 18ms β 1.2ms β
13β 3-relation include/populate β 4.8ms β 3.2ms β 35ms β 2.8ms β
14β Aggregation (GROUP BY) β 12ms β 10ms β 15ms β 8ms β
15β Complex filter (5 conditions) β 2.5ms β 1.8ms β 2.2ms β 1.5ms β
16β Cold start (serverless) β 280ms β 12ms β 150ms β 5ms β
17βββββββββββββββββββββββββββββββββββΌββββββββββΌββββββββββΌββββββββββΌββββββββββββββ€
18β TOTAL (typical request) β ~8ms β ~5ms β ~25ms β ~3ms β
19βββββββββββββββββββββββββββββββββββ΄ββββββββββ΄ββββββββββ΄ββββββββββ΄ββββββββββββββ
20
21Key Insights:
221. Drizzle is 35-60% faster than Prisma for most operations
232. Mongoose populate() is 5-10x slower than PostgreSQL JOINs
243. For serverless/edge, Drizzle's 12ms cold start beats Prisma's 280ms
254. Raw queries are always fastest, but lose type safety
265. Mongoose with embedded data matches Prisma/Drizzle performance
27*/
28
29// Recommendation by use case:
30
31// π Serverless/Edge (Next.js App Router, Vercel Edge)
32// β Drizzle + Neon (smallest bundle, fastest cold start)
33
34// π οΈ Developer Experience First (Rapid prototyping)
35// β Prisma (best DX, auto-migrations, studio)
36
37// π¦ Document-heavy with embedded data
38// β Mongoose with lean() (avoid populate when possible)
39
40// π° Enterprise with complex transactions
41// β Drizzle or Prisma with PostgreSQL
42
43// π Hybrid architecture
44// β Drizzle (PostgreSQL) + Mongoose (MongoDB)
A practical checklist for speeding up Postgres-backed apps: query plans, indexes, caching with Redis, and what to watch for when you run on serverless Postgres like Neon.

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